Notes on software development

Who even uses jemalloc in 2026 anyway? (many major projects)

Thu, 16 Apr 2026 00:00:00 +0000

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How jobs, events, and funding work on The Consensus

Wed, 08 Apr 2026 00:00:00 +0000

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Who contributed to DataFusion 53

Fri, 03 Apr 2026 00:00:00 +0000

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Taking multi-master MariaDB for a spin, reproducing a Jepsen report

Sun, 29 Mar 2026 00:00:00 +0000

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Developer Spotlight: Somtochi Onyekwere from Fly.io

Thu, 19 Mar 2026 00:00:00 +0000

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A static site generator and website transferring under 20kB

Thu, 19 Mar 2026 00:00:00 +0000

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Mojo's not (yet) Python

Thu, 12 Mar 2026 00:00:00 +0000

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Source-available projects and their AI contribution policies

Mon, 02 Mar 2026 00:00:00 +0000

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Open Source Contributor Spotlight: Kosta Tarasov and DataFusion

Thu, 26 Feb 2026 00:00:00 +0000

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I started a software research company

Wed, 25 Feb 2026 00:00:00 +0000

I quit my job at EnterpriseDB hacking on PostgreSQL products last month to start a company researching and writing about software infrastructure. I believe there is space for analysis that is more focused on code than TechCrunch or The Register, more open to covering corporate software development than LWN.net, and (as much as I love some of these folks) less biased than VCs writing about their own investments.

I believe that more than ever there is a need for authentic and trustworthy analysis and coverage of the software we depend on.

This company, The Consensus, will talk about databases and programming languages and web servers and everything else that is important for experienced developers to understand and think about. It is independent of any software vendor and independent of any particular technology.

Some people were surprised (in a positive way) to see me cover MySQL already, for example. But that is exactly the point.

I don't want The Consensus to be just "Phil's thoughts". I have already started working with a number of experienced developers who will be writing, and paid to write, for The Consensus.

I also hope that this is another way, beyond the many communities I already run, to give back to the community such as in highlighting the work of open-source developers (the first interview with a DataFusion developer is coming soon), and highlighting compelling events and jobs in the software infrastructure world.

The Consensus is entirely bootstrapped and will depend on the support of subscribers and, potentially, sponsors. The first few subscribers signed up just this past week.

You can read more about the background and goals here, you can read about how contributors will work with The Consensus here, and you can get a sense for where this is going by browsing the homepage of The Consensus already.

Thank you for your support in advance! Thank you to the folks who have subscribed already despite very little fanfare.

Feedback is very welcome.

I'm very excited and having quite a bit of fun already. We're all going to learn a lot.

I started a software research company pic.twitter.com/PY3L0yhJlW
— Phil Eaton (@eatonphil) February 25, 2026

We have pgvector at home

Sun, 22 Feb 2026 00:00:00 +0000

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Generating vector embeddings for semantic search locally

Sun, 15 Feb 2026 00:00:00 +0000

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Paths of MySQL, vector search edition

Sun, 08 Feb 2026 00:00:00 +0000

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Companies behind Postgres 18 development

Mon, 02 Feb 2026 00:00:00 +0000

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LLMs and your career

Mon, 19 Jan 2026 00:00:00 +0000

The most conservative way to build a career as a software developer is 1) to be practical and effective at problem solving but 2) not to treat all existing code as a black box. 1 means that as a conservative developer you should generally use PostgreSQL or MySQL (or whatever existing database), Rails or .NET (or whatever existing framework), and adapt code from Stack Overflow or LLMs. 2 means that you're curious and work over time to better understand how web servers and databases and operating systems and the browser actually work so that you can make better decisions for your own problems as you adapt other people's code and ideas.

Zooming out, coding via LLM is not fundamentally different from coding with Rails or coding by perusing Stack Overflow. It's faster and more direct but it's still potentially just a human mindlessly adapting existing code.

The people who were only willing to look at existing frameworks and libraries and applications as black boxes were already not the most competitive when it came to finding and retaining work. And on the other hand, the most technically interesting companies always wanted to hire developers who understood fundamentals because they're 1) operating at such a scale that the way the application is written matters or they're 2) building PostgreSQL or MySQL or Rails or .NET or Stack Overflow or LLMs, etc.

The march of software has always been to reduce the need for (ever larger sizes of) SMBs (and teams within non-SMBs) to hire developers to solve problems or increase productivity. LLMs are part of that march. That doesn't change that at some point companies (or teams) need to hire developers because the business or its customer base has become too complex or too large.

The jobs that were dependent on fundamentals of software aren't going to stop being dependent on fundamentals of software. And if more non-developers are using LLMs it's going to mean all the more stress on tools and applications and systems that rely on fundamentals of software.

All of this is to say that if you like doing software development, I don't think interesting software development jobs are going to go away. So keep learning and keep building compilers and databases and operating systems and keep looking for companies that have compiler and database and operating system products, or companies with other sorts of interesting problems where fundamentals matter due to their scale.

LLMs and your career pic.twitter.com/lxu1HLF2LC
— Phil Eaton (@eatonphil) January 19, 2026

Distinguishing yourself early in your career as a developer

Sat, 03 Jan 2026 00:00:00 +0000

Sometimes I get asked questions that would be more fun to answer in public. All letters are treated as anonymous unless permission is otherwise granted.

Hello [Redacted]! You asked about a friend's son who is a recent graduate looking for work as a software developer and having a hard time.

I like to think of three key aspects of getting a job: 1) knowing where to apply, 2) getting an interview at all, and 3) passing the interview.

Knowing where to apply

There are three different types of companies I think about: 1) local (non-VC backed, non-finance) and public non-tech companies, 2) VC-backed startups and public tech companies, and 3) Big Tech and finance companies.

Category 3 companies pay the most (guesstimating the top 95th percentile) and receive the most applications. Everyone wants those jobs. They are surprisingly egalitarian about interviewing qualified applicants. They have the means and need to interview a large number of people. However, preparing for the interviews is very time-consuming.

Category 2 companies pay very well (guesstimating the top 80th percentile). They get fewer applicants than Category 3 companies. The interviews are not horribly difficult but it is hard to distinguish yourself. There is not always as much capacity to interview every qualified applicant.

Category 1 companies pay at the 50th percentile. They get very few qualified applicants. It is not difficult to distinguish yourself.

Category 3 companies are where Computer Science departments attempt to funnel their students to as they graduate. A portion of them will get in.

I think of Category 1 and Category 2 companies each making up 45% of all jobs while Category 3 companies make up the remaining 10% of jobs. Maybe that's wrong.

My path

My career started with Category 1 companies that I found via Craigslist or Built In (NYC) or the monthly Hacker News Who's Hiring thread. The interview lasted a few hours at most and involved fairly trivial demonstrations of knowledge or me sharing code snippets.

The local company aspect of Category 1 seems seriously undervalued. How many web or application development companies are in your nearest small- or medium-sized city? I started interning for these types of companies in high school. And these were a repeat source of fallback contract work in my early years when I was in a pinch.

I eventually moved on to Category 2 companies and that's where I'm most comfortable. I used to learn about them through the Hacker News Who's Hiring thread. There are a few levels of interviews but they aren't incredibly intense. You don't particularly need to do Leetcode to pass them. These days I prefer to track interesting companies on my own by observing what companies talk about in their blog and Twitter and who they employ.

I never had the patience to stick with the long interviews at Category 3 companies, nor the stamina to keep up Leetcode, but I understand that some people work for FANG companies. Hedge funds (a component of Category 3 companies) don't seem to accept people without a college degree unless they are truly exceptional (and I am not) so I never bothered with those either.

Starting somewhere

You want to be a software developer. But tech companies are also always looking for qualified technical support and QA. For many people it is (well for me it was anyway) most important early on to just have an income. And once you're there you can be pickier and think longer term. I consistently see companies move technical support and QA employees into development roles after a year or few years with the company. This path seems underrated. Or at least something to consider.

Getting an interview at all

Early in your career getting an interview is more of a numbers game. In Category 1 companies if you can show basic competence (such as an actually-used Github or code snippets if you're self-taught, or a Computer Science degree) you will likely get an interview. In Category 2 companies you will need to deal in more volume. Category 3 companies oddly enough seem a bit more egalitarian about interviewing everyone. It's quite a slog though. So you need to be prepared for the process at a single company to take months, if you keep passing rounds (which require significant preparation themselves).

In general I would estimate that even an experienced and competent developer should prepare for an average worst-case of 6-12 months to find a new job.

So again in Category 1 companies it's more a matter of demonstrating competence and in Category 3 companies every qualified person gets an interview. The companies you more so need to stand out in from the get-go are the Category 2 companies. So let's talk about things that might stick out.

You write blog posts

I think writing is best done for the sake of the act itself (it helps you build confidence about concepts, and helps you develop your communication skills). In a conversation about interviews, I have to say it's not very common in my experience to write even a popular blog post and get asked to interview.

However, once you start writing about niche concepts (for example, how Postgres development works) you may start getting invited to interview more often. But I think that's relatively rare and usually only happens to people who are already in the industry for a while.

But having an active blog certainly shows that you're passionate. When there are a bunch of competent candidates and one has an active blog, that might stand out.

You did some side projects

Doing side projects definitely helps build your skill. And I think it goes in a similar category as writing where it's a great thing to do for its own sake. I don't think it happens very often that you do your own side projects and get asked to interview because of them.

However, just like writing, having side projects might be a differentiator between otherwise similarly competent developers because it demonstrates your passion.

You contribute to OSS

If it is a major OSS that the industry largely uses such as Linux, Postgres, Kubernetes, LLVM, etc. I think you can stand out if your contributions are consistent, even if they are not large. If they are too small, for example small docs changes, to one or even a few projects I don't think this is going to make a big difference as a differentiator nor a reason for companies to invite you to interview.

Behind almost every open-source project is one or more vendors who really want developers who know how the project works. And if you have a pattern of contributing to some open-source project it is likely that you will get invited to interview at one of the vendors who rely on or sell support for it.

But because it's a matter of there being a pattern this is something that you'd have to be doing for on the order of years before a company notices.

You run a community

If you run a book club or meetup group, I think this is a pretty decent way to stand out because it shows organization skills, which is an important component of leadership in general. But like with OSS contributions it is more meaningful if you can show the pattern of organizing over the years.

You participate in a community

I think this is a decent way to stand out because showing up consistently in a community is a way to demonstrate your passion. It's not a good idea to go to any single event and hope to get hired from it. It's a chance for you to build relationships that may help you in the long-term. It's best to build relationships when you need nothing from them.

You read books, watch lectures, get a certificate

I don't think this helps you stand out very much. It's too difficult to really understand what this means or what you got out of it. I think everyone should read for its own sake. And it may help you build your own confidence, which is great. But I don't think it's going to make a difference between similarly competent candidates.

You acquire a Master's degree

I don't particularly think this helps you stand out. But it does give you more time to hunt for internships. More industry experience usually helps the entire process of staying in industry.

Passing an interview

I don't really have anything to add here beyond the numerous books and apps that help you study common interview questions and patterns.

That said, the way I preferred to prepare for interviews was not through the interview books or apps but to always be coding and always be building my confidence in all aspects of the technology stack. Do whatever you like.

Recapping

It helps your job prospects over the years to be building relationships with the community in general. And there are different tiers of companies to think about and different positions to consider in the short-term as you're getting started. It is usually easier to transfer to a different job within a company rather than interview and get that new job while also switching companies.

There are a lot of activities including side projects and writing that can immediately start to build your confidence. It's important to be knowledgeable and confident once you do get interviews. Some of these activities may distinguish you in small ways immediately. But more so there are many things that you can start doing consistently in the meantime to become more hireable over the years.

This response is generalizations based on my experience. You will find exceptions to everything I said but hopefully the generalizations are still broadly useful.

Good luck to your friend's son!

Phil

I wrote a blog post in response to a friend asking how to distinguish oneself as a developer early in your career.

Curious for your thoughts. pic.twitter.com/RV2jTkdbcX
— Phil Eaton (@eatonphil) January 4, 2026

Year in community

Wed, 31 Dec 2025 00:00:00 +0000

This year I ran three book club readings over email with 1,230 unique attendees. I ran 12 coffee club meetups in midtown Manhattan with 170 unique attendees. Angelo and I ran 6 NYC Systems meetups with 12 different speakers and 281 unique attendees. I took 3 visiting PhD students out for Banh Mi.

I raised $6,915 for educational non-profits, offering chats in return. I got coffee, lunch, or took 30 minute calls with 55 people I'd never spoken to before in person or on video. (Most, but not all, were in return for fundraising receipts.) This list included women and men based in the USA, Germany, Canada, Nigeria, Nepal, India, the United Kingdom, Brazil, New Zealand, Israel, and Australia. (I think I'm forgetting one or two.)

Thank you to every person who has been a part of these efforts, making them so special and so valuable. See you in the new year!

Year in community pic.twitter.com/n7jrmsZiKN
— Phil Eaton (@eatonphil) December 31, 2025

Year in books

Tue, 23 Dec 2025 00:00:00 +0000

Among the 50 books I read in 2025, I recommend the following 11 non-fiction and 7 fiction works (complete list here). These were the 18 books that I rated a four or five out of five stars.

Non-fiction

On Writing Well by William Zinsser

This is the third or fourth time I've read this book and it has stood the test of time. It's been a few years since I last read it so it was a good reminder that a lot of the things I believe and tell people about writing actually just came from this book. The last 25% is a bit of a slog but nonetheless it remains one of the single books I think every professional should read. 5/5

I. Asimov by Isaac Asimov

I really like reading about how writers make their living. I've also been a modest fan of Asimov's works (I've loved what I've read I just haven't read that much). I also love to hear stories of first-generation immigrants to the US and also he lived in New York his whole life so it was quite enjoyable. 4/5

Hollywood by Jeanine Basinger and Sam Wasson

This was 100 years of the evolution of the film industry told basically entirely in disparate interviews edited together. 5/5

The Chief by David Nasaw

I love the history and business of newspapers and media. Moreover it's the guy that Citizen Kane was based on. 4/5

Brazil by Lilia Moritz Schwarcz and Heloisa Murgel Starling

I have never learned about the history of Brazil and I found this introduction enjoyable. 4/5

The Silk Roads by Peter Frankopan

I loved the retelling of human history focused on Persia (and later Iran). 4/5

Louis D. Brandeis by Melvin I. Urofsky

I have never read about a supreme court justice before. This was a well-written biography and introduction to the history of law and law education. 4/5

Personal History by Katharine Graham

Once again I love reading about newspapers and media and the business and history. This was told by the publisher of The Washington Post. 5/5

The Snowball by Alice Schroeder

I've had this book about Warren Buffett on my shelf for nearly 10 years and finally went through it this year. A delightful and easy read despite the bulk. I only am unhappy that it focused more on family drama than on business decisions. Par for the course with biographies unfortunately. 4/5

The Cartiers by Francesca Cartier Brickell

This story spanned three or four major wars and a couple of continents. I didn't think I'd be interested in the history of luxury businesses but it has a lot in common with certain modern industries in tech too. You put premiums on relationships and building good faith and so on. 4/5

Secret Formula by Frederick Allen

A history of Coca-Cola over the last 100 years or so. Lessons on how they dealt with competition (Pepsi and Keurig Dr. Pepper) and product revitalization (New Coke, Diet Coke, etc.). Quite an interesting read. 5/5

Fiction

The Buffalo Hunter Hunter by Stephen Graham Jones

I got into horror fiction last year (not so much slashers but more just one of the better written categories of genre fiction). This book was one of my two favorite novels of the year.

It's a fictional retelling of American history where a Native American becomes a vampire and takes revenge on American colonizers in the American West. 5/5

The Handmaid's Tale by Margaret Atwood

This was my other favorite novel of the year. I am embarrassed not to have read it before. It's a dystopian story about the USA if all women were required to give birth to deal with a fertility crisis. 5/5

The Invisible Life of Addie Larue by V.E. Schwab

A French woman gets to live forever but everyone she meets forgets her after leaving her presence. An easy and enjoyable read. 4/5

Vampires of El Norte by Isabel Cañas

I love a good vampire story, and I love fictional retellings using fantastical horror elements to emphasize atrocities. Vampires employed by the US military help the US in the 1840s take Texas from Mexico. 4/5

The Very Secret Society of Irregular Witches by Sangu Mandanna

This was a cute cozy mystery about British witches forced to hide from society, learning how to accept themselves and develop trust in their community. 4/5

Pride and Prejudice by Jane Austen

I am likewise embarrassed I have not read this before, nor anything else by Austen. I'm told I haven't rated it highly enough. I will undoubtedly reread it. I loved the wit. It required closer reading than I expected. 4/5

James by Percival Everett

This was a retelling of Adventures of Huckleberry Finn as told by the slave, Jim. If you saw the movie American Fiction a few years ago, it's the same author (of the original book). Everett has very interesting ideas and I look forward to reading more by him. 4/5

My 2025 year in books. 18 to recommend among the 50 I read. pic.twitter.com/mIcbPk7e5x
— Phil Eaton (@eatonphil) December 24, 2025

Is psql's scripting language Turing complete? Or: fibonacci in psql

Wed, 26 Nov 2025 00:00:00 +0000

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An individual can change an organization

Mon, 03 Nov 2025 00:00:00 +0000

One of the biggest lessons I learned early in my career was from Drew DeVault at Linode, 10 years ago. He was one of the youngest developers in the company (only I was younger, at 20, at the time) but he cared really strongly about thinking through architecture and code decisions when the culture at the time was, and I love those guys, a little haphazard.

Drew had no special position. We all had the same title, "Developer". But he argued so persuasively and so doggedly even when the entire organization seemed against him and somehow he eventually transformed the entire engineering organization.

That's supposed to be impossible! It was entirely new to me. That you don't need to wait behind people with more experience to make the right decision. That you can be part of making the right decision if you can find the logic and the will to do it.

It isn't that simple of course. Politics is politics. But there are plenty of companies with people who will make a good faith effort to do what makes sense but might, without someone's unasked-for effort, do not what makes sense but what is popular because what's popular just kinda seems easiest. And I always like working for these companies, and for the most part have been able to identify them during the interview process.

I learned from Drew to put limited value in seniority. I learned that it's ok to debate. I learned to be prepared and to try to present the facts. I learned to be persistent when I wanted change. I learned that with these skills, it's possible for an individual to redirect the path of an organization.

It took a while longer (and me driving one or two people on my team to quit, to my great regret) to learn when to do these things and when to let things go. Still, this lesson from Drew on what's possible always stands out in my memory. Thank you, Drew.

Drew DeVault taught me a really important lesson early in my career, that it's possible for a person to change the direction of an organization. pic.twitter.com/EGBRdbIjVO
— Phil Eaton (@eatonphil) November 3, 2025

Transaction pooling for Postgres with pgcat

Fri, 24 Oct 2025 00:00:00 +0000

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In response to a developer asking about systems

Mon, 15 Sep 2025 00:00:00 +0000

Sometimes I get asked questions that would be more fun to answer in public. All letters are treated as anonymous unless permission is otherwise granted.

Hey [Redacted]! It's great to hear from you. I'm very glad you joined the coffee club and met some good folks. :)

You asked how to learn about systems. A great question! I think I need to start first with what I mean when I say systems.

My definition of systems is all of the underlying software we developers use but are taught not to think about because they are so solid: our compilers and interpreters, our databases, our operating system, our browser, and so on. We think of them as basically not having bugs, we just count on them to be correct and fast enough so we can build the applications that really matter to users.

But 1) some developers do actually have to work on these fundamental blocks (compilers, databases, operating systems, browsers, etc.) and 2) it's not thaaaat hard to get into this development professionally and 3) even if you don't get into it professionally, having a better understanding of these fundamental blocks will make you a better application developer. At least I think so.

To get into systems I think it starts by you just questioning how each layer you build on works. Try building that layer yourself. For example you've probably used a web framework like Rails or Next.js. But you can just go and write that layer yourself too (for education).

And you've probably used Postgres or SQLite or DynamoDB. But you can also just go and write that layer yourself (for education). It's this habit of thinking and digging into the next lower layer that will get you into systems. Basically, not being satisfied with the black box.

I do not think there are many good books on programming in general, and very very few must-read ones, but one that I recommend to everybody is Designing Data Intensive Applications. I think it's best if you read it with a group of people. (My book club will read it in December when the 2nd edition comes out, you should join.) But this book is specific to data obviously and not interested in the fundamentals of other systems things like compilers or operating systems or browsers or so on.

Also, I see getting into this as a long-term thing. Throughout my whole career (almost 11 years now) I definitely always tried to dig into compilers and interpreters, I wrote and blogged about toy implementations a lot. And then 5 years ago I started digging into databases and saw that there was more career potential there. But it still took 4 years until I got my first job as a developer working on a database (the job I currently have).

Things take time to learn and that's ok! You have a long career to look forward to. And if you end up not wanting to dig into this stuff that's totally fine too. I think very few developers actually do. And they still have fine careers.

Anyway, I hope this is at least mildly useful. I hope you join the Software Internals Discord and nycsystems.xyz as well and look forward to seeing you at future coffee clubs!

Cheers,
Phil

I wrote a letter in response to a developer asking about how to learn systems. pic.twitter.com/2ILNpzl662
— Phil Eaton (@eatonphil) September 15, 2025

A simple clustering and replication solution for Postgres

Wed, 10 Sep 2025 00:00:00 +0000

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Analytics query goes 6x faster with EDB Postgres Distributed's new analytics engine

Thu, 04 Sep 2025 00:00:00 +0000

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Set up a single-node EDB Postgres Distributed cluster on Ubuntu

Thu, 14 Aug 2025 00:00:00 +0000

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What even is distributed systems

Sat, 09 Aug 2025 00:00:00 +0000

Distributed systems is simply the study of interactions between processes. Every two interacting processes form a distributed system, whether they are on the same host or not. Distributed systems create new challenges (compared to single-process systems) in terms of correctness (i.e. consistency), reliability, and performance (i.e. latency and throughput).

The best way to learn about the principles and fundamentals of distributed systems is to 1) read Designing Data Intensive Applications and 2) read through the papers and follow the notes in the MIT Distributed Systems course.

For Designing Data Intensive Applications (DDIA), I strongly encourage you to find buddies at work or online who will read it through with you. You can also always join the Software Internals Discord's #distsys channel to ask questions as you go. But it's still best if you have some partners to go through the book with, even if they are as new to it as you.

I also used to think that you might want to wait a few years into your career before reading DDIA but when you have friends to read it with I think you need not wait.

If you have only skimmed the book you should definitely go back and give it a thorough read. I have read it three times already and I will read it again as part of the Software Internals Book Club next year after the 2nd Edition is published.

Keep in mind that every chapter of DDIA provides references to papers you can keep reading should you end up memorizing DDIA itself.

When you've read parts of DDIA or the MIT Distributed Systems course and you want practice, the Fly.io x Jepsen Distributed Systems Challenge is one guided option. Other options might include simply implementing (getting progressively more complex down the list):

two-phase commit
three-phase commit
single-decree Paxos
highly available key-value store on top of a 3rd-party consensus library
chain replication (or CRAQ), using a 3rd-party consensus library
Raft
epaxos

And if you get bored there you can see Alex Miller's Data Replication Design Spectrum for more ideas and variants.

And if you want more people to follow, check out the Distributed Systems section of my favorite blogs page.

If these projects and papers sound arcane or intimidating, know that you will see the problems these projects/papers solve whether or not you know and understand these solutions. Developers often end up reinventing hacky versions of these which are more likely to have subtle bugs.

While instead you can recognize and use one of these well-known building blocks. Or at least have the background to better reason about correctness should you be in a situation where you must work with a novel distributed system or you end up designing a new one yourself.

And again, if you want folks to bounce ideas off of or ask questions to, I strongly encourage you to join the Software Internals Discord and ask there!

I wrote a short post on learning the fundamentals of distributed systems, with a few suggested resources to read and a few suggested projects to try. pic.twitter.com/b0EhDP8K0t
— Phil Eaton (@eatonphil) August 9, 2025

Stack traces for Postgres errors with backtrace_functions

Thu, 31 Jul 2025 00:00:00 +0000

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Want to meet people, try charging them for it?

Sat, 28 Jun 2025 00:00:00 +0000

I have been blogging consistently since 2017. And one of my goals in speaking publicly was always to connect with like-minded people. I always left my email and hoped people would get in touch. Even while my blog and twitter became popular, passing 1M views and 20k followers, I basically never had people get in touch to chat or meet up.

So it felt kind of ridiculous when last November I started charging people $100 to chat. I mean, who am I? But people started showing up fairly immediately. Now granted the money did not go to me. It went to an education non-profit and I merely received the receipt.

And at this point I've met a number of interesting people, from VCs to business professors to undergraduate students to founders and everyone in between. People wanting to talk about trends in databases, about how to succeed as a programmer, about marketing for developers, and so on. Women and men thoughout North America, Europe, Africa, New Zealand, India, Nepal, and so on. And I've raised nearly $6000 for educational non-profits.

How is it that you go from giving away your time for free and getting no hits to charging and almost immediately getting results? For one, every person responded very positively to it being a fundraiser. It also helps me be entirely shameless about sharing on social media every single time someone donates; because it's such a positive thing.

But also I think that in "charging" for my time it helps people feel more comfortable about actually taking my time, especially when we have never met. It gives you a reasonable excuse to take time from an internet rando.

On the other hand, a lot of people come for advice and I think giving advice is pretty dangerous, especially since my background is not super conventional. I try to always frame things as just sharing my opinion and my perspective and that they should talk with many others and not take my suggestions without consideration.

And there's also the problem that by charging everyone for my time now, I'm no longer available to people who could maybe use it the most. I do mention on my page that I will still take calls from people who don't donate, as my schedule allows. But to be honest I feel less incentivized to spend time when people do not donate. So I guess this is an issue with the program.

But I mitigated even this slightly, and significantly jump-started the program, during my 30th birthday when I took calls with any person who donated at least $30.

Anyway, I picked this path because I have wanted to get involved with helping students figure out their lives and careers. But without a degree I am literally unqualified for many volunteering programs. And I always found the time commitments for non-profits painful.

So until starting this I figured it wouldn't be until I retire that I find some way to make a difference. But ultimately I kept meeting people who were starting their own non-profits now or donated significantly to help students. Peer pressure. I wanted to do my part now. And 30 minutes of my time in return for a donation receipt has been an easy trade.

While only raising a humble $6,000 to date, the Chat for Education program has been more successful than I imagined. I've met many amazing people through it. And it's something that should be easy to keep up indefinitely.

I hope to meet you through it too!

I wrote about trying to meet like-minded people and fundraising for educational non-profits. pic.twitter.com/UJ9U6DIHGU
— Phil Eaton (@eatonphil) June 28, 2025

Debugging memory leaks in Postgres, jemalloc edition

Sat, 21 Jun 2025 00:00:00 +0000

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Cheerleading

Mon, 26 May 2025 00:00:00 +0000

At work we're so absorbed in the difficulties we face that it becomes easy to forget what we appreciate and what we value in our coworkers. On social media we can be so focused on our own work that we forget to interact with others'.

I have heard so many times from founders that they switch between heads down building, disappearing from the community, and then pop up to talk about what they've built. Engineers often say the exact same thing. This kills me. Though I probably still do it to a degree too.

The easiest way for no one to give a shit about what you've done is for you to only interact at periodic intervals that are only convenient for you.

With networking in general the most important time to interact and build relationships is when you do not need anything. By the time you want someone's support or you want someone to care about what you've done it is way too late to start building a relationship.

One of the cheapest and most effective avenues to build genuine relationships is to be the biggest cheerleader you can be.

At work this means when someone writes a fantastic blog post you call it out in a public channel. It means when a coworker's work is featured somewhere you call this out in a public channel. At least, this is what I do at work.

I don't care that I have no leadership role or haven't met these people before. If I see something amazing that I'd like to see more of, I mention it in public and praise the person.

Or when someone shares work they've done in a public channel, you hit all the emojis and you reply "that's awesome".

On social media this means engaging seriously and (more) deeply with what people post. If the default behavior on social media is to passively observe, engaging more deeply can be liking a post. If you already show support through liking, engaging more deeply can be commenting or asking questions or (kindly) pointing out mistakes.

Engagement is a spectrum. Genuine engagement consistently over time builds genuine relationships.

I would not suggest that you do this without feeling it, or I'd rather only encourage you to respond to the degree that you feel. But I personally do feel so strongly when I cheer people on.

So much of life and work is drudgery such that when you see something positive, someone taking initiative, someone with talent or potential doing something with their skills, how can you not feel an overwhelming urge to cheer them on and hope to see more of it? Hope to see it develop?

What's more, I want to be around people who are trying new things and improving themselves. I want to be around people who celebrate. So I in turn try new things and work to improve myself and I celebrate the people around me.

This energy is infectious. And I genuinely think even a single person in a group celebrating publicly changes the group dynamic.

And I don't expect people to reciprocate in the same way or to the same degree that I show support. I'm particularly weird and confident and vocal. But if my cheerleading for some person seems like a complete sink then I'll not continue it and invest my time and energy elsewhere.

Not everybody needs my support and that's ok! It is better that it's spent where it's most needed. There were people who used to cheer me on who don't much anymore and that's totally fine, I still have positive feelings for them for the time they did cheer me on.

Cheerleading electrifies the work atmosphere. It is the proper use of social media. Cheerleading is the imperative of caring individuals as they become more experienced, gain confidence, command a wider audience, and want to continue their own growth through the development of genuine and supportive networks.

And an ambitious, high-achieving, celebratory culture is simply the most fun to be a part of.

I wrote a post on being a cheerleader. pic.twitter.com/mESNjSjQ0n
— Phil Eaton (@eatonphil) May 26, 2025

Debugging memory leaks in Postgres, heaptrack edition

Thu, 22 May 2025 00:00:00 +0000

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Burn your title

Tue, 22 Apr 2025 00:00:00 +0000

I've been a developer, a manager, a cofounder, and now I'm a developer again. I ran away from each position until being a founder because I felt like I was limited by what I was allowed to do.

But I reached an enlightenment of sorts during my career progression: that everyone around me was dying for someone to pick things up, for employees to show engagement and agency.

We think of our titles as our limits. We're quick to say and believe, "that isn't my job". While in reality titles reflect the minimum expected of us, not the maximum that is open to us.

Moving your career forward

Trying to figure out what (new minimum) you must do to get promoted seems kind of backwards to me, reinforcing our sense of our own limits. Instead, at every stage in your career, focus on doing the intersection of:

what you see needs to be done (that isn't being done)
what you are capable of doing
what you have the desire/energy (or would find fulfillment) doing

And this is the path to promotion and a successful and interesting career.

Burn your title. Burn your job description. I mean, keep your boss happy for sure. Keep your teammates happy by supporting them and building them up and communicating well. But don't wait to be officially made a lead or given a new title to do what otherwise fits into that intersection above.

And if after doing this for some time, demonstrating this level of agency, you are not promoted, it just means you're not at the right company or right organization within your company and you should look elsewhere.

What's more, this work you did (at a company that doesn't appreciate your agency, if that happens to be the case) merely makes the case stronger for your successful interview at the next company. There's no downside.

The cynical, and perhaps realistic, alternative to this is to do politics to get promoted. Or to not do politics but to do things that don't align with your long-term goals. I'm not personally interested in either path so I'm not covering them here. I'm interested in the intersection of things that move me in the direction I want, things that are useful to the company, and things that I am capable of doing (in addition to whatever minimum work I must actually do).

Examples

Here's a peek at what this looks like for me as an individual contributor, a programmer, at EnterpriseDB.

I started the EDB Engineering Newsletter because it seemed like we needed to do a better job telling the world the awesome things our engineering team is doing. (You know we're one of the biggest contributors to Postgres? Bruce Momjian, Robert Haas, Peter Eisentraut, etc. work here? The guy who implemented the WAL and MVCC in Postgres is my teammate?) Nobody asked me to do that.

I started publishing blog views for the entire company once a month internally. Nobody asked me to do that.

I wrote a number of internal docs and tutorials on the product because we were just obviously missing them. Nobody asked me to do that.

I started a fortnightly incident review meeting for my team because it seemed like we were missing chances to update docs and teach each other. Nobody asked me to do that.

I write a odd posts for the company blog on what I've learned. Nobody asked me to do that.

These are just a few of the random things that seemed like a good idea for me to do on top of my Actual Work as a developer, which I think I do a decent job of on its own.

In closing

Don't burn out. Don't do things you aren't asked for and don't find rewarding. Or that won't pave the way toward the career you want. I'm trying to be very careful not to advocate anything along those lines.

But also don't wait to be asked to do something. Do what is interesting and obvious and rewarding to you. Interesting opportunities seem to come most reliably when you make them for yourself.

Burn your title pic.twitter.com/4bQRPMX4EZ
— Phil Eaton (@eatonphil) April 22, 2025

Transactions are a protocol

Sun, 20 Apr 2025 00:00:00 +0000

Transactions are not an intrinsic part of a storage system. Any storage system can be made transactional: Redis, S3, the filesystem, etc. Delta Lake and Orleans demonstrated techniques to make S3 (or cloud storage in general) transactional. Epoxy demonstrated techniques to make Redis (and any other system) transactional. And of course there's always good old Two-Phase Commit.

If you don't want to read those papers, I wrote about a simplified implementation of Delta Lake and also wrote about a simplified MVCC implementation over a generic key-value storage layer.

It is both the beauty and the burden of transactions that they are not intrinsic to a storage system. Postgres and MySQL and SQLite have transactions. But you don't need to use them. It isn't possible to require you to use transactions. Many developers, myself a few years ago included, do not know why you should use them. (Hint: read Designing Data Intensive Applications.)

And you can take it even further by ignoring the transaction layer of an existing transactional database and implement your own transaction layer as Convex has done (the Epoxy paper above also does this). It isn't entirely clear that you have a lot to lose by implementing your own transaction layer since the indexes you'd want on the version field of a value would only be as expensive or slow as any other secondary index in a transactional database. Though why you'd do this isn't entirely clear (I will like to read about this from Convex some time).

It's useful to see transaction protocols as another tool in your system design tool chest when you care about consistency, atomicity, and isolation. Especially as you build systems that span data systems. Maybe, as Ben Hindman hinted at the last NYC Systems, even proprietary APIs will eventually provide something like two-phase commit so physical systems outside our control can become transactional too.

Transactions are a protocol

short new post pic.twitter.com/nTj5LZUpUr
— Phil Eaton (@eatonphil) April 20, 2025

Things that go wrong with disk IO

Thu, 27 Mar 2025 00:00:00 +0000

There are a few interesting scenarios to keep in mind when writing applications (not just databases!) that read and write files, particularly in transactional contexts where you actually care about the integrity of the data and when you are editing data in place (versus copy-on-write for example).

We'll go into a few scenarios where the following can happen:

Data you write never actually makes it to disk
Data you write get sent to the wrong location on disk
Data you read is read from the wrong location on disk
Data gets corrupted on disk

And how real-world data systems think about these scenarios. (They don't always think of them at all!)

If I don't say otherwise I'm talking about behavior on Linux.

The post is largely a review of two papers: Parity Lost and Parity Regained and Characteristics, Impact, and Tolerance of Partial Disk Failures. These two papers also go into the frequency of some of the issues discussed here. These behaviors actually happen in real life!

Thank you to Alex Miller and George Xanthakis for reviewing a draft of this post.

Terminology

Some of these terms are reused in different contexts, and sometimes they are reused because they effectively mean the same thing in a certain configuration. But I'll try to be explicit to avoid confusion.

Sector

The smallest amount of data that can be read and written atomically by hardware. It used to be 512 bytes, but on modern disks it is often 4KiB. There doesn't seem to be any safe assumption you can make about sector size, despite file system defaults (see below). You must check your disks to know.

Block (filesystem/kernel view)

Typically set to the sector size since only this block size is atomic. The default in ext4 is 4KiB.

Page (kernel view)

A disk block that is in memory. Any reads/writes less than the size of a block will read the entire block into kernel memory even if less than that amount is sent back to userland.

Page (database/application view)

The smallest amount of data the system (database, application, etc.) chooses to act on, when it's read or written or held in memory. The page size is some multiple of the filesystem/kernel block size (including the multiple being 1). SQLite's default page size is 4KiB. MySQL's default page size is 16KiB. Postgres's default page size is 8KiB.

Things that go wrong

The data didn't reach disk

By default, file writes succeed when the data is copied into kernel memory (buffered IO). The man page for write(2) says:

A successful return from write() does not make any guarantee that data has been committed to disk. On some filesystems, including NFS, it does not even guarantee that space has successfully been reserved for the data. In this case, some errors might be delayed until a future write(), fsync(2), or even close(2). The only way to be sure is to call fsync(2) after you are done writing all your data.

If you don't call fsync on Linux the data isn't necessarily durably on disk, and if the system crashes or restarts before the disk writes the data to non-volatile storage, you may lose data.

With O_DIRECT, file writes succeed when the data is copied to at least the disk cache. Alternatively you could open the file with O_DIRECT|O_SYNC (or O_DIRECT|O_DSYNC) and forgo fsync calls.

fsync on macOS is a no-op.

If you're confused, read Userland Disk I/O.

Postgres, SQLite, MongoDB, MySQL fsync data before considering a transaction successful by default. RocksDB does not.

The data was fsynced but fsync failed

fsync isn't guaranteed to succeed. And when it fails you can't tell which write failed. It may not even be a failure of a write to a file that your process opened:

Ideally, the kernel would report errors only on file descriptions on which writes were done that subsequently failed to be written back. The generic pagecache infrastructure does not track the file descriptions that have dirtied each individual page however, so determining which file descriptors should get back an error is not possible.

Instead, the generic writeback error tracking infrastructure in the kernel settles for reporting errors to fsync on all file descriptions that were open at the time that the error occurred. In a situation with multiple writers, all of them will get back an error on a subsequent fsync, even if all of the writes done through that particular file descriptor succeeded (or even if there were no writes on that file descriptor at all).

Don't be 2018-era Postgres.

The only way to have known which exact write failed would be to open the file with O_DIRECT|O_SYNC (or O_DIRECT|O_DSYNC), though this is not the only way to handle fsync failures.

The data was corrupted

If you don't checksum your data on write and check the checksum on read (as well as periodic scrubbing a la ZFS) you will never be aware if and when the data gets corrupted and you will have to restore (who knows how far back in time) from backups if and when you notice.

ZFS, MongoDB (WiredTiger), MySQL (InnoDB), and RocksDB checksum data by default. Postgres and SQLite do not (though databases created from Postgres 18+ will).

You should probably turn on checksums on any system that supports it, regardless of the default.

The data was partially written

Only when the page size you write = block size of your filesystem = sector size of your disk is a write guaranteed to be atomic. If you need to write multiple sectors of data atomically there is the risk that some sectors are written and then the system crashes or restarts. This behavior is called torn writes or torn pages.

Postgres, SQLite, and MySQL (InnoDB) handle torn writes. Torn writes are by definition not relevant to immutable storage systems like RocksDB (and other LSM Tree or Copy-on-Write systems like MongoDB (WiredTiger)) unless writes (that update metadata) span sectors.

If your file system duplicates all writes like MySQL (InnoDB) does (like you can with data=journal in ext4) you may also not have to worry about torn writes. On the other hand, this amplifies writes 2x.

The data didn't reach disk, part 2

Sometimes fsync succeeds but the data isn't actually on disk because the disk is lying. This behavior is called lost writes or phantom writes. You can be resilient to phantom writes by always reading back what you wrote (expensive) or versioning what you wrote.

Databases and file systems generally do not seem to handle this situation.

The data was written to the wrong place, read from the wrong place

If you aren't including where data is supposed to be on disk as part of the checksum or page itself, you risk being unaware that you wrote data to the wrong place or that you read from the wrong place. This is called misdirected writes/reads.

Databases and file systems generally do not seem to handle this situation.

Phil Eaton on Technical Blogging

Tue, 25 Mar 2025 00:00:00 +0000

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Minimal downtime Postgres major version upgrades with EDB Postgres Distributed

Fri, 28 Feb 2025 00:00:00 +0000

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From web developer to database developer in 10 years

Sat, 15 Feb 2025 00:00:00 +0000

Last month I completed my first year at EnterpriseDB. I'm on the team that built and maintains pglogical and who, over the years, contributed a good chunk of the logical replication functionality that exists in community Postgres. Most of my work, our work, is in C and Rust with tests in Perl and Python. Our focus these days is a descendant of pglogical called Postgres Distributed which supports replicating DDL, tunable consistency across the cluster, etc.

This post is about how I got here.

Black boxes

I was a web developer from 2014-2021†. I wrote JavaScript and HTML and CSS and whatever server-side language: Python or Go or PHP. I was a hands-on engineering manager from 2017-2021. I was pretty clueless about databases and indeed database knowledge was not a serious part of any interview I did.

Throughout that time (2014-2021) I wanted to move my career forward as quickly as possible so I spent much of my free time doing educational projects and writing about them on this blog (or previous incarnations of it). I learned how to write primitive HTTP servers, how to write little parsers and interpreters and compilers. It was a virtuous cycle because the internet (Hacker News anyway) liked reading these posts and I wanted to learn how the black boxes worked.

But I shied away from data structures and algorithms (DSA) because they seemed complicated and useless to the work that I did. That is, until 2020 when an inbox page I built started loading more and more slowly as the inbox grew. My coworker pointed me at Use The Index, Luke and the DSA scales fell from my eyes. I wanted to understand this new black box so I built a little in-memory SQL database with support for indexes.

I'm a college dropout so even while I was interested in compilers and interpreters earlier in my career I never dreamed I could get a job working on them. Only geniuses and PhDs did that work and I was neither. The idea of working on a database felt the same. However, I could work on little database side projects like I had done before on other topics, so I did. Or a series of explorations of Raft implementations, others' and my own.

Startups

From 2021-2023 I tried to start a company and when that didn't pan out I joined TigerBeetle as a cofounder to work on marketing and community. It was during this time I started the Software Internals Discord and /r/databasedevelopment which have since kind of exploded in popularity among professionals and academics in database and distributed systems.

TigerBeetle was my first job at a database company, and while I contributed bits of code I was not a developer there. It was a way into the space. And indeed it was an incredible learning experience both on the cofounder side and on the database side. I wrote articles with King and Joran that helped teach and affirm for myself the basics of databases and consensus-based distributed systems.

Holding out

When I left TigerBeetle in 2023 I was still not sure if I could get a job as an actual database developer. My network had exploded since 2021 (when I started my own company that didn't pan out) so I had no trouble getting referrals at database companies.

But my background kept leading hiring managers to suggest putting me on cloud teams doing orchestration in Go around a database rather than working on the database itself.

I was unhappy with this type-casting so I held out while unemployed and continued to write posts and host virtual hackweeks messing with Postgres and MySQL. I started the first incarnation of the Software Internals Book Club during this time, reading Designing Data Intensive Applications with 5-10 other developers in Bryant Park. During this time I also started the NYC Systems Coffee Club.

Postgres

After about four months of searching I ended up with three good offers, all to do C and Rust development on Postgres (extensions) as an individual contributor. Working on extensions might sound like the definition of not-sexy, but Postgres APIs are so loosely abstracted it's really as if you're working on Postgres itself.

You can mess with almost anything in Postgres so you have to be very aware of what you're doing. And when you can't mess with something in Postgres because an API doesn't yet exist, companies have the tendency to just fork Postgres so they can. (This tendency isn't specific to Postgres, almost every open-source database company seems to have a long-running internal fork or two of the database.)

EnterpriseDB

Two of the three offers were from early-stage startups and after more than 3 years being part of the earliest stages of startups I was happy for a break. But the third offer was from one of the biggest contributors to Postgres, a 20-year old company called EnterpriseDB. (You can probably come up with different rankings of companies using different metrics so I'm only saying EnterpriseDB is one of the biggest contributors.)

It seemed like the best place to be to learn a lot and contribute something meaningful.

My coworkers are a mix of Postgres veterans (people who contributed the WAL to Postgres, who contributed MVCC to Postgres, who contributed logical decoding and logical replication, who contributed parallel queries; the list goes on and on) but also my developer-coworkers are people who started at EnterpriseDB on technical support, or who were previously Postgres administrators.

It's quite a mix. Relatively few geniuses or PhDs, despite what I used to think, but they certainly work hard and have hard-earned experience.

Anyway, I've now been working at EnterpriseDB for over a year so I wanted to share this retrospective. I also wanted to cover what it's like coming from engineering management and founding companies to going back to being an individual contributor. (Spoiler: incredibly enjoyable.) But it has been hard enough to make myself write this much so I'm calling it a day. :)

I wrote a post about the winding path I took from web developer to database developer over 10 years. pic.twitter.com/tf8bUDRzjV
— Phil Eaton (@eatonphil) February 15, 2025

† From 2011-2014 I also did contract web development but this was part-time while I was in school.

Edit for clarity

Wed, 29 Jan 2025 00:00:00 +0000

I have the fortune to review a few important blog posts every year and the biggest value I add is to call out sentences or sections that make no sense. It is quite simple and you can do it too.

Without clarity only those at your company in marketing and sales (whose job it is to work with what they get) will give you the courtesy of a cursory read and a like on LinkedIn. This is all that most corporate writing achieves. It is the norm and it is understandable.

But if you want to reach an audience beyond those folks, you have to make sure you're not writing nonsense. And you, as reviewer and editor, have the chance to call out nonsense if you can get yourself to recognize it.

Immune to nonsense

But especially when editing blog posts at work, it is easy to gloss over things that make no sense because we are so constantly bombarded by things that make no sense. Maybe it's buzzwords or cliches, or simply lack of rapport. We become immune to nonsense.

And even worse, without care, as we become more experienced, we become more fearful to say "I have no idea what you are talking about". We're afraid to look incompetent by admitting our confusion. This fear is understandable, but is itself stupid. And I will trust you to deal with this on your own.

Read it out loud

So as you review a post, read it out loud to yourself. And if you find yourself saying "what on earth are you talking about", add that as a comment as gently as you feel you should. It is not offensive to say this (depending on how you say it). It is surely the case that the author did not know they were making no sense. It is worse to not mention your confusion and allow the author to look like an idiot or a bore.

Once you can call out what does not make sense to you, then read the post again and consider what would not make sense to someone without the context you have. Someone outside your company. Of course you need to make assumptions about the audience to a degree. It is likely your customers or prospects you have in mind. Not your friends or family.

With the audience you have in mind, would what you're reading make any sense? Has the author given sufficient background or introduced relevant concepts before bringing up something new?

Again this is a second step though. The first step is to make sure that the post makes sense to you. In almost every draft I read, at my company or not, there is something that does not make sense to me.

Do two paragraphs need to be reordered because the first one accidentally depended on information mentioned in the second? Are you making ambiguous use of pronouns? And so on.

In closing

Clarity on its own will put you in the 99th percentile of writing. Beyond that it definitely still matters if you are compelling and original and whatnot. But too often it seems we focus on being exciting rather than being clear. But it doesn't matter if you've got something exciting if it makes no sense to your reader.

This sounds like mundane guidance, but I have reviewed many posts that were reviewed by other people and no one else called out nonsense. I feel compelled to mention how important it is.

Wrote a new post on the most important, and perhaps least done, thing you can do while reviewing a blog post: edit for clarity. pic.twitter.com/ODblOUzB3g
— Phil Eaton (@eatonphil) January 29, 2025

An explosion of transitive dependencies

Sat, 25 Jan 2025 00:00:00 +0000

A small standard library means an explosion in transitive dependencies. A more comprehensive standard library helps you minimize dependencies. Don't misunderstand me: in a real-world project, it is practically impossible to have zero dependencies.

Armin Ronacher called for a vibe shift among programmers and I think that this actually exists already. Everyone I speak to on this topic has agreed that minimizing dependencies is ideal.

Rust and JavaScript, with their incredibly minimal standard libraries, work against this ideal. Go, Python, Java, and C# in contrast have a decent standard library, which helps minimize the explosion of transitive dependencies.

Examples

I think the standard library should reasonably include:

JSON, CSV, and Parquet support
HTTP/2 support (which includes TLS, compression, random number generation, etc.)
Support for asynchronous IO
A logging abstraction
A SQL client abstraction
Key abstract data types (BTrees, hashmaps, sets, and growable arrays)
Utilities for working with Unicode, time and timezones

But I don't think it needs to include:

Excel support
PostgreSQL or Oracle clients
Flatbuffers support
Niche data structures

Neither of these are intended to be complete lists, just examples.

Walled gardens

Minimal standard libraries force growing companies to build out their own internal collection of "standard libraries". As one example, Bloomberg did this with C++. And I've heard of companies doing this already with Rust. This allows larger companies to manage and minimize the explosion of transitive dependencies over time.

All growing companies likely do something like this eventually. But again, smaller standard libraries incentivize companies to build this internal standard library earlier on. And the community benefits relatively little from these internal standard libraries. The community would benefit more if large organizations contributed back to an actual standard library.

Smaller organizations do not have the capacity to build these internal standard libraries.

Maybe the situation will lead to libraries like Boost for JavaScript and Rust programmers. That could be fine.

Versioning

A comprehensive standard library does not prevent the language developers from releasing new versions of the standard library. It is trivial to do this with naming like Go has done with the v2 pattern. math/rand/v2 is an example.

Conclusion

I'm primarily thinking about maintainability, not security. You can read about the security risks of using a language with an ecosystem like Rust from someone who is an expert on the matter.

My concern about the standard library does not stop me from using Rust and JavaScript. They could choose to invest in the standard library at any time. We have already begun to see Bun and Deno to do exactly this. But it is clearly an area for improvement in Rust and JavaScript. And a mistake for other languages to avoid repeating.

While zero dependencies is practically impossible, everyone I've spoken to agrees that minimizing dependencies is ideal. Rust and JavaScript work against this ideal. But they could change at any time. And Bun and Deno are already examples of this.https://t.co/qkSh6oW1Yd pic.twitter.com/mY1MNErZG7
— Phil Eaton (@eatonphil) January 25, 2025

Embedding Python in Rust (for tests)

Wed, 22 Jan 2025 00:00:00 +0000

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Logical replication in Postgres: Basics

Fri, 17 Jan 2025 00:00:00 +0000

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How I run a coffee club

Tue, 31 Dec 2024 00:00:00 +0000

I started the NYC Systems Coffee Club in December of 2023. It's gone pretty well! I regularly get around 20 people each month. You bring a drink if you feel like it and you hang out with people for an hour or two.

There is no agenda, there is no speaker, there is no structure. The only "structure" is that when the circle of people talking to each other seems gets too big, I break the circle up into two smaller circles so we can get more conversations going.

People tend to talk in a little circle and then move around over time. It's basically no different than a happy hour except it is over a non-alcoholic drink and it's in the morning.

All I have to do as the organizer is periodically tell people about the Google Form to fill out. I got people to sign up to the list by posting about this on Twitter and LinkedIn. And then once a month I send an email bcc-ing everyone on the list and ask them to respond for an invite.

The first 20 people to respond get a calendar invite.

I mention all of this because people ask how they can start a coffee club in their city. They ask how it works. But it's very simple! One of the least-effortful ways to bring together people in your city.

If your city does not have indoor public spaces, you could use a food court, or a cafe, or a park during months where it is warm.

For example, the Cobble Hill Computer Coffee Club is one that meets outdoors at a park.

Good luck! :)

How I run a coffee club, a short guide for others who might be interested in running one. It's very simple!https://t.co/UgRWDQOA3v pic.twitter.com/5wYrLW7u6D
— Phil Eaton (@eatonphil) December 31, 2024

Picking up volleyball in NYC with Goodrec and New York Urban

Thu, 26 Dec 2024 00:00:00 +0000

I was so intimidated to go at first, but it is in fact easy and fun to start playing beginner volleyball in New York. The people are so friendly and welcoming that it has been easy to keep playing consistently every week since I started for the first time this August. It's been a great workout and a great way to make friends!

The two platforms I've used to find volleyball games are Goodrec and New York Urban. While these platforms may also offer classes and leagues, I mostly use them to play "pickup" games. Pickup games are where you show up and join (or get assigned to) a team to play for an hour or two. Easy to go on your own or with friends.

I'm not an expert! My only hope with this post is that maybe it makes trying out volleyball in New York feel a little less intimidating for you!

Goodrec

With Goodrec you have to use their mobile app. Beginner tier is called "social" on Goodrec. So browse available games until you find one at the level you want to play. You enroll in (buy a place in) sessions individually.

Sessions are between 90-120 minutes long.

They ask you not to arrive more than 10 minutes early at the gym. When you arrive you tell the gym managers (usually in a desk up front somewhere) you're there for Goodrec and the tier (in case the gym has multiple level games going on at the same time). Then you wait until the Goodrec "host" arrives and they will organize everyone into teams.

Goodrec hosts are players who volunteer to organize the games. They'll explain the rules of the game (makes Goodrec very good for beginners) and otherwise help you out.

Always say thank you to your host!

New York Urban

With New York Urban, pickup sessions are called "open play".

There is no mobile app, you just use the website to purchase a spot in a session. The sessions are longer and cheaper than Goodrec. But there is no host; players self-organize.

The options are more limited too. You play at one of four high schools on either a Friday night or on Sunday. And session slots tend to sell out much more quickly than with Goodrec.

Big City Volleyball

You can also check out Big City Volleyball but I haven't used it yet.

Volo

I haven't ever done Volo but I think I've heard it described as "beer league". That even some of the beginner tier sessions with Goodrec and New York Urban are more competitive.

But also, Volo is built around leagues so you have to get the timing right. Goodrec's and New York Urban's pickup games make it easy to get started playing any time of year.

Making friends

It was super awkward to go at first! I went by myself. I didn't know what I was doing. I couldn't remember, and didn't know, many rules. I didn't have court shoes or knee pads.

But the Goodrec host system is particularly great for bringing beginners in and making them feel welcome. You have a great time even if you're terrible.

The first game I went to, I tried to hang out afterward to meet people. But people either came with their SO or with their friends or by themselves so they all just left immediately or hung out in their group.

So you can't just go once and expect to make friends immediately. But if you keep going at the same place and time regularly week over week, you'll see familiar faces. Maybe half the people I play with each week are regulars. If you're friendly you'll start making friends with these people and eventually start going out to bars with them after the games.

Improving

Even if you find yourself embarrassingly bad at first, just keep going! I'm 29, 6'1, 190lbs and from observation the past 5 months, age, height, and weight have a very indirect relation to playing ability.

Most of the people who play are self-taught, especially at the lower tiers I've played at. But some people played for the school team in high school or college. These people are fun to play with and you can learn a lot from them.

Most people who are self-taught seem to watch YouTube videos like Coach Donny, helpful for learning how to serve, set, block, etc. Or they take "clinics" (classes) with Goodrec or other platforms. (I have no idea about these, I've never done them before.)

At first I played 2 hours a week and I was completely exhausted after the session. Over time it got easier so I started playing 2-3 sessions a week (6-9-ish hours). With practice and consistency (after about 3-4 months), I started playing Intermediate tier with Goodrec and New York Urban. And I don't think I'll play Beginner/Social at all anymore.

I still primarily play for fun and for the workout and to meet people. But it's also fun to get better!

I played with one person much better than myself in an Intermediate session one time and he mentioned he will probably stop playing Intermediate and only play High Intermediate. He mentioned you get better when you keep pushing yourself to play with better and better players. Good advice!

I wrote a little post on picking up volleyball in new york.

It's fun, and a great workout, and you meet interesting people!https://t.co/jEWHbRWF6C pic.twitter.com/ipuIUB1ZnM
— Phil Eaton (@eatonphil) December 26, 2024

1 million page views

Thu, 28 Nov 2024 00:00:00 +0000

I was delighted to notice this morning that this site has recently passed 1M page views. And since Murat wrote about his 1M page view accomplishment at the time, I felt compelled to now too.

I started regularly blogging in 2018. For some reason I decided to write a blog post every month. And while I have definitely skipped a month or two here or there, on average I've written 2 posts per month.

Tooling

Since at least 2018 this site has been built with a static site generator. I might have used a 3rd-party generator at one point, but for as long as I can remember most of this site has been built with a little Python script I wrote.

I used to get so pissed when static site generators would pointlessly change their APIs and I'd have to make pointless changes. I have not had to make any significant changes to my build code in many years.

I hosted the site itself on GitHub Pages for many years. But I wanted more flexibility with subdomains (ultimately not something I liked) and the ability to view server-side logs (ultimately not something I ever do).

I think this site is hosted on an OVH machine now. But at this point it is inertia keeping me there. If you have no strong feelings otherwise, GitHub Pages is perfect.

I used to use Google Analytics but then they shut down the old version. The new version was incredibly confusing to use. I could not find some very basic information. So I moved to Fathom which has been great.

I used to track all subscribers in a Google Form and bcc them but this became untenable eventually after 1000 subscribers due to GMail rate limits. I currently use MailerLite for subscriptions and sending email about new posts. But this is an absolutely terrible service. They proxy all links behind a domain that adblockers hate and they also visually shorten the URL so you can't copy the text of the URL.

I just want a service that has a hosted form for collecting subscribers and a <textarea> that lets me dump raw HTML and send that as an email to my subscribers. No branding, no watermarks, no link proxying. This apparently doesn't exist. I am too lazy to figure out Amazon SES so I stick with MailerLite for now.

Evolution

In the beginning I talked about little interpreters in JavaScript, about programming languages, about Scheme. I was into functional programming. Over time I moved into little emulators and bytecode VMs. And for the last four years I became obsessed with databases and distributed systems.

I have almost always written about little projects to teach myself a concept. Writing a bytecode VM in Rust, emulating a subset of x86 in Go, implementing Raft in Go, implementing MVCC isolation levels in Go, and so on.

So many times when I tried to learn a concept I would find blog posts with only partial code. The post would link to a GitHub repo that, by the time I got to the post, had evolved significantly beyond what was described in the post. The repo code had by then become too complex for me to follow. So I was motivated to write minimal implementations and walk through the code in its entirety.

Even today there is not a single post on implementing TCP/IP from scratch that walks through entirely working code. (Please, someone write this.)

I have also had a blast writing survey posts such as how various databases execute expressions, analyzing non-V8 JavaScript implementations, how various programming language implementations parse code, and how various database systems build on top of key-value databases.

The last two posts have even each been cited in a research paper (here and here).

Editing

In terms of quality, my single greatest trick is to read the post out loud. Multiple times. Notice parts that are awkward or unclear and rewrite them.

My second greatest trick is to ask friends for review. Some posts like an intuition for distributed consensus and a write-ahead log is not a universal part of durability would simply not have been correct or credible without my fantastic reviewers. And I'm proud to have played that part a few times in turn.

We also have a fantastic #writing-and-drafts channel on the Software Internals Discord where folks (myself occasionally included) come for post review.

Context

I've lost count of the total number of times that these posts have been on the front page of Hacker News or that a tweet announcing a post has reached triple digits likes. I think I've had 9 posts on the front of HN this year. I do know that my single best year for HN was 12 months between 2022-2023 where 20 of my posts or projects were on the front page.

Every time a post does well there's a part of me that worries that I've peaked. But the way to deal with this has been to ignore that little voice and to just keep learning new things. I haven't stopped finding things confusing yet, and confusion is a phenomenal muse.

And also to, like, go out and meet friends for dinner, run meetups, run book clubs, chat with you fascinating internet strangers, play volleyball, and so on.

It's always been about cultivating healthy obsessions.

Benediction

In parting, I'll remind you:

It is definitely worth writing about, whatever "it" is
You're not writing enough
And some ideas for posts I want to hear about if you write about them

I wrote a little reflection on writing after noticing I passed 1M page views this morning.https://t.co/eIlMDVHNht pic.twitter.com/EKSiiDUz5G
— Phil Eaton (@eatonphil) November 28, 2024

Exploring Postgres's arena allocator by writing an HTTP server from scratch

Wed, 06 Nov 2024 00:00:00 +0000

This is an external post of mine. Click here if you are not redirected.

Effective unemployment and social media

Tue, 05 Nov 2024 00:00:00 +0000

Being unemployed can be incredibly depressing. So much rejection. Everything seems to be out of your control. Everything except for one thing: what you produce.

You might know that repeatedly posting on social media that you are looking for work is ineffective. That it looks (or at least feels) worse each time you say so. But there is at least one major caveat to this.

Every single time you create something and share it publicly is a chance to also reiterate that you are looking for work. And people actually appreciate and value this!

Whether you write a blog post or build some project, you are seen as working on yourself and contributing to the community. Positive things! And it is no problem at all to learn with each new post you write and each new project you publish that you are also looking for work.

Moreover, dynamics of the internet and social media basically require that you be regularly producing something new. Either regularly producing a new version of some existing project or regularly producing new projects (or blog posts) entirely.

What you did a week ago is old news on social media. What will you do next week?

This could itself feel depressing except for that it's probably actually a fairly healthy thing for yourself anyway! It is a motivation to keep your skills sharp as time goes on.

So while you're unemployed and able to muster the motivation, write about things that are interesting to you! Build projects that intrigue you. Leave a little note on every post and project that you are looking for work. And share every post and project on social media.

You'll expose yourself to opportunities and referrals. And even if no post or project "takes off" you will still be working on yourself and contributing back knowledge to the community.

I wrote a short post on some ideas for effective unemployment and social media.https://t.co/jmiJCOe2Nk pic.twitter.com/pK9AySNdHR
— Phil Eaton (@eatonphil) November 5, 2024

Checking linearizability in Go

Thu, 31 Oct 2024 00:00:00 +0000

You want to check for strict consistency (linearizability) for your project but you don't want to have to deal with the JVM. Porcupine, used by a number of real-world systems like etcd and TiDB, has you covered!

Importantly, neither Jepsen projects nor Porcupine can prove linearizability. They can only help you build confidence that you aren't obviously violating linearizability.

The Porcupine README is pretty good but doesn't give complete working code, so I'm going to walk through checking linearizability of a distributed register. And then we'll tweak things a bit by checking linearizability for a distributed key-value store.

But rather than implementing a distributed register and implementing a distributed key-value store, to keep this post concise, we're just going to imagine that they exist and we'll come up with some example histories we might see.

Code for this post can be found on GitHub.

Boilerplate

Create a new directory and go mod init lintest. Let's add the imports we need and a helper function for generating a visualization of a history, in main.go:

package main

import "os"
import "log"
import "github.com/anishathalye/porcupine"

func visualizeTempFile(model porcupine.Model, info porcupine.LinearizationInfo) {
    file, err := os.CreateTemp("", "*.html")
    if err != nil {
        panic("failed to create temp file")
    }
    err = porcupine.Visualize(model, info, file)
    if err != nil {
        panic("visualization failed")
    }
    log.Printf("wrote visualization to %s", file.Name())
}

A distributed register

A distributed register is like a distributed key-value store but there's only a single key.

We need to tell Porcupine what the inputs and outputs for this system are. And we'll later describe for it how an idealized version of this system should behave as it receives each input; what output the idealized version should produce.

Each time we send a command to the distributed register it will include an operation (to get or to set the register). And if it is a set command it will include a value.

type registerInput struct {
    operation string // "get" and "set"
    value int
}

The register is an integer register.

Now we will define a model for Porcupine which, again, is the idealized version of this system.

func main() {
    registerModel := porcupine.Model{
        Init: func() any {
            return 0
        },
        Step: func(stateAny, inputAny, outputAny any) (bool, any) {
            input := inputAny.(registerInput)
            output := outputAny.(int)
            state := stateAny.(int)
            if input.operation == "set" {
                return true, input.value
            } else if input.operation == "get" {
                readCorrectValue := output == state
                return readCorrectValue, state
            }

            panic("Unexpected operation")
        },
    }

The step function accepts anything because it has to be able to model any sort of system with its different inputs and outputs and current state. So we have to handle casting from the any type to what we know are the inputs and outputs and state. And finally we actually do the state change and return the new state as well as if the given output matches what we know it should be.

An invalid history

Now we've only defined the idealized version of this system. Let's pretend we have some real-world implementation of this. We might have two clients and they might issue concurrent get and set requests.

Every time we stimulate the system we will generate a new history that we can validate with Porcupine against our model to see if the history is linearizable.

Let's imagine these two clients concurrently set the register to some value. Both sets succeed. Then both clients read the register. And they get different values. Here's what that history would look like modeled for Porcupine.

    ops := []porcupine.Operation{
        // Client 3 sets the register to 100. The request starts at t0 and ends at t2.
        {3, registerInput{"set", 100}, 0, 100 /* end state at t2 is 100 */, 2},
        // Client 5 sets the register to 200. The request starts at t3 and ends at t4.
        {5, registerInput{"set", 200}, 3, 200/* end state at t3 is 200 */, 4},
        // Client 3 reads the register. The request starts at t5 and ends at t6.
        {3, registerInput{"get", 0 /* doesn't matter */ }, 5, 200, 6},
        // Client 5 reads the register. The request starts at t7 and ends at t8. Reads a stale value!
        {5, registerInput{"get", 0 /* doesn't matter */}, 7, 100, 8},
    }
    res, info := porcupine.CheckOperationsVerbose(registerModel, ops, 0)
    visualizeTempFile(registerModel, info)

    if res != porcupine.Ok {
        panic("expected operations to be linearizable")
    }
}

If we build and run this code:

$ go mod tidy
go: finding module for package github.com/anishathalye/porcupine
go: found github.com/anishathalye/porcupine in github.com/anishathalye/porcupine v0.1.6
$ go build
$ ./lintest
2024/10/31 19:54:08 wrote visualization to /var/folders/cb/v27m749d0sj89h9ydfq0f0940000gn/T/463308000.html
panic: expected operations to be linearizable

goroutine 1 [running]:
main.main()
        /Users/phil/tmp/lintest/main.go:59 +0x394

Porcupine caught the stale value. Open that HTML file to see the visualization.

A valid history

Let's say we fix the bug so now there's no stale read. The new history would look like this:

    ops := []porcupine.Operation{
        // Client 3 sets the register to 100. The request starts at t0 and ends at t2.
        {3, registerInput{"set", 100}, 0, 100 /* end state at t2 is 100 */, 2},
        // Client 5 sets the register to 200. The request starts at t3 and ends at t4.
        {5, registerInput{"set", 200}, 3, 200/* end state at t3 is 200 */, 4},
        // Client 3 reads the register. The request starts at t5 and ends at t6.
        {3, registerInput{"get", 0 /* doesn't matter */ }, 5, 200, 6},
        // Client 5 reads the register. The request starts at t7 and ends at t8.
        {5, registerInput{"get", 0 /* doesn't matter */}, 7, 200, 8},
    }

Rebuild, rerun lintest (it should exit successfully now), and open the visualization.

Great! Now let's make things a little more complicated by modeling a distributed key-value store rather than a distributed register.

Distributed key-value

The inputs of this system will be slightly more complex. They will take a key along with the operation and value.

type kvInput struct {
    operation string // "get" and "set"
    key string
    value int
}

And when we model the distributed key-value store with the state and output at each step being a map[string]int.

    kvModel := porcupine.Model{
        Init: func() any {
            return map[string]int{}
        },
        Step: func(stateAny, inputAny, outputAny any) (bool, any) {
            input := inputAny.(kvInput)
            output := outputAny.(map[string]int)
            state := stateAny.(map[string]int)
            if input.operation == "set" {
                newState := map[string]int{}
                for k, v := range state {
                    newState[k] = v
                }
                newState[input.key] = input.value
                return true, newState
            } else if input.operation == "get" {
                readCorrectValue := output[input.key] == state[input.key]
                return readCorrectValue, state
            }

            panic("Unexpected operation")
        },
    }

And now the history gets slightly more complex because we are now working with some specific key. But we'll otherwise use the same history as before.

    ops := []porcupine.Operation{
        // Client 3 set key `a` to 100. The request starts at t0 and ends at t2.
        {3, kvInput{"set", "a", 100}, 0, map[string]int{"a": 100}, 2},
        // Client 5 set key `a` to 200. The request starts at t3 and ends at t4.
        {5, kvInput{"set", "a", 200}, 3, map[string]int{"a": 200}, 4},
        // Client 3 read key `a`. The request starts at t5 and ends at t6.
        {3, kvInput{"get", "a", 0 /* doesn't matter */ }, 5, map[string]int{"a": 200}, 6},
        // Client 5 read key `a`. The request starts at t7 and ends at t8.
        {5, kvInput{"get", "a", 0 /* doesn't matter */}, 7, map[string]int{"a": 200}, 8},
    }

Build and run. Open the visualization.

And there we go!

What's next

These are just a few simple examples that are not hooked up to a real system. But it still seemed useful to show how you model one or two simple different systems and check a history with Porcupine.

Another aspect of Porcupine I did not cover is partitioning the state space. The docs say:

Implementing the partition functions can greatly improve performance. If you're implementing the partition function, the model Init and Step functions can be per-partition. For example, if your specification is for a key-value store and you partition by key, then the per-partition state representation can just be a single value rather than a map.

Perhaps that, and hooking this up to some "real" system, would be a good next step.

I wrote a short tutorial on using Porcupine to check for linearizability (without needing to deal with the JVM).https://t.co/kqeBz2jX76 pic.twitter.com/teXvlp2zcv
— Phil Eaton (@eatonphil) November 1, 2024

Intuitions for Distributed Consensus by Phil Eaton

Tue, 08 Oct 2024 00:00:00 +0000

This is an external talk of mine. Click here if you are not redirected.

Build a serverless ACID database with this one neat trick (atomic PutIfAbsent)

Sun, 29 Sep 2024 00:00:00 +0000

Delta Lake is an open protocol for serverless ACID databases. Due to its simplicity, scalability, and the number of open-source implementations, it's quickly becoming the DuckDB of serverless transactional databases for analytics workloads. Iceberg is a contender too, and is similar in many ways. But since Delta Lake is simpler (simple != better) that's where we'll focus in this post.

Delta Lake has one of the most accessible database papers I've read (link). It's kind of like the movfuscator of databases.

Thanks to its simplicity, in this post we'll implement a Delta Lake-inspired serverless ACID database in 500 lines of Go code with zero dependencies. It will support creating tables, inserting rows into a table, and scanning all rows in a table. All while allowing concurrent readers and writers and achieving snapshot isolation.

There are other critical parts of Delta Lake we'll ignore: updating rows, deleting rows, checkpointing the transaction metadata log, compaction, and probably much more I'm not aware of. We must start somewhere.

All code for this post is available on GitHub.

Delta Lake basics

Delta Lake writes immutable data files to blob storage. It stores the names of new data files for a transaction in a metadata file. It handles concurrency (i.e. achieves snapshot isolation) with an atomic PutIfAbsent operation on the metadata file for the transaction.

This method of concurrency control works because the metadata files follow a naming scheme that includes the transaction id in the file name. When a new transaction starts, it finds all existing metadata files and picks its own transaction id by adding 1 to the largest transaction id it sees.

When a transaction goes to commit, writing the metadata file will fail if another transaction has already picked the same transaction id.

If a transaction does no writes and creates no tables, the transaction does not attempt to write any metadata file. Snapshot isolation!

Let's dig into the implementation.

Boilerplate

Let's give ourselves some nice assertion methods, a debug method, and a uuid generator. In main.go:

package main

import (
    "encoding/json"
    "fmt"
    "io"
    "os"
    "path"
    "slices"
    "strings"
)

func assert(b bool, msg string) {
    if !b {
        panic(msg)
    }
}

func assertEq[C comparable](a C, b C, prefix string) {
    if a != b {
        panic(fmt.Sprintf("%s '%v' != '%v'", prefix, a, b))
    }
}

var DEBUG = slices.Contains(os.Args, "--debug")

func debug(a ...any) {
    if !DEBUG {
        return
    }

    args := append([]any{"[DEBUG]"}, a...)
    fmt.Println(args...)
}

// https://datatracker.ietf.org/doc/html/rfc4122#section-4.4
func uuidv4() string {
    f, err := os.Open("/dev/random")
    assert(err == nil, fmt.Sprintf("could not open /dev/random: %s", err))
    defer f.Close()

    buf := make([]byte, 16)
    n, err := f.Read(buf)
    assert(err == nil, fmt.Sprintf("could not read 16 bytes from /dev/random: %s", err))
    assert(n == len(buf), "expected 16 bytes from /dev/random")

    // Set bit 6 to 0
    buf[8] &= ^(byte(1) << 6)
    // Set bit 7 to 1
    buf[8] |= 1 << 7

    // Set version
    buf[6] &= ^(byte(1) << 4)
    buf[6] &= ^(byte(1) << 5)
    buf[6] |= 1 << 6
    buf[6] &= ^(byte(1) << 7)

    return fmt.Sprintf("%x-%x-%x-%x-%x",
        buf[:4],
        buf[4:6],
        buf[6:8],
        buf[8:10],
        buf[10:16])
}

Is that uuid method correct? Hopefully. Efficient? No. But it's preferable to avoid dependencies in pedagogical projects.

Moving on.

Blob storage requirements

As mentioned above, the basic requirement is that we support atomically writing some bytes to a location if the location doesn't already exist.

On top of that we also need the ability to list locations by prefix, and the ability to read the bytes at some location.

We'll diverge from Delta Lake in how we name files on disk. For one, we'll keep all files in the same directory with a fixed prefix for metadata and another table name prefix for each data file. This simplifies the implementation of listPrefix a bit.

However, this also diverges from Delta Lake in that transactions will represent all tables. In Delta Lake that is not so. Delta Lake has a per-table transaction log. Only transactions that read and write the same table in Delta Lake achieve snapshot isolation.

So let's set up an interface to describe these requirements:

type objectStorage interface {
    // Must be atomic.
    putIfAbsent(name string, bytes []byte) error
    listPrefix(prefix string) ([]string, error)
    read(name string) ([]byte, error)
}

And this is literally all we need to get ACID transactions. That's crazy!

Atomic Put and cloud blob storage

We could implement the atomic putIfAbsent part of this interface in 2024 using conditional writes on S3. Or we could implement this interface with the If-None-Match header on Azure Cloud Storage. Or we could implement this interface with the x-goog-if-generation-match header on Google Cloud Storage.

Indeed a good exercise for the reader would be to implement this interface for other blob storage providers and see your serverless cloud database in action!

But the simplest method of all is to implement it on the filesystem, which is what we'll do next.

A filesystem blob store

If we had a server we could implement atomic putIfAbsent with a mutex. But we're serverless baby. Thankfully, POSIX supports atomic link which will fail if the new name is already a file.

So we'll just create a temporary file and write out all bytes. Finally, we link the temporary file to the permanent name we intended. For cleanliness (not correctness), if there is an error at any point, we'll remove the temporary file.

type fileObjectStorage struct {
    basedir string
}

func newFileObjectStorage(basedir string) *fileObjectStorage {
    return &fileObjectStorage{basedir}
}

func (fos *fileObjectStorage) putIfAbsent(name string, bytes []byte) error {
    tmpfilename := path.Join(fos.basedir, uuidv4())
    f, err := os.OpenFile(tmpfilename, os.O_WRONLY|os.O_CREATE, 0644)
    if err != nil {
        return err
    }

    written := 0
    bufSize := 1024 * 16
    for written < len(bytes) {
        toWrite := min(written+bufSize, len(bytes))
        n, err := f.Write(bytes[written:toWrite])
        if err != nil {
            removeErr := os.Remove(tmpfilename)
            assert(removeErr == nil, "could not remove")
            return err
        }

        written += n
    }

    err = f.Sync()
    if err != nil {
        removeErr := os.Remove(tmpfilename)
        assert(removeErr == nil, "could not remove")
        return err
    }

    err = f.Close()
    if err != nil {
        removeErr := os.Remove(tmpfilename)
        assert(removeErr == nil, "could not remove")
        return err
    }

    filename := path.Join(fos.basedir, name)
    err = os.Link(tmpfilename, filename)
    if err != nil {
        removeErr := os.Remove(tmpfilename)
        assert(removeErr == nil, "could not remove")
        return err
    }

    return nil
}

yencabulator on HN pointed out that an earlier version of this post had a buggy implementation of putIfAbsent (that attempted to manage atomicity solely via O_EXCL | O_CREAT) would leave around potentially bad metadata files if the os.Remove call ever failed.

The link approach works around that because the file is already fully and correctly written by the time we do the link.

listPrefix and read are minimal wrappers around filesystem APIs:

func (fos *fileObjectStorage) listPrefix(prefix string) ([]string, error) {
    dir := path.Join(fos.basedir)
    f, err := os.Open(dir)
    if err != nil {
        return nil, err
    }

    var files []string
    for err != io.EOF {
        var names []string
        names, err = f.Readdirnames(100)
        if err != nil && err != io.EOF {
            return nil, err
        }

        for _, n := range names {
            if prefix == "" || strings.HasPrefix(n, prefix) {
                files = append(files, n)
            }
        }
    }
    err = f.Close()
    return files, err
}

func (fos *fileObjectStorage) read(name string) ([]byte, error) {
    filename := path.Join(fos.basedir, name)
    return os.ReadFile(filename)
}

It is worth talking a bit about reading a directory though. Go doesn't provide a nice iterator API for us and I didn't want to implement this as callbacks with path/filepath.WalkDir.

We could use os.File.ReadDir but it allocates for all files in the directory. Sure, in a pedagogical project we don't worry about millions of files. But the ReadDir API, the error cases in particular, also isn't much simpler than Readdirnames.

What's more, even though we iterated through batches of directory entries, and did prefix filtering before accumulating, we still could have considered returning an iterator here ourselves. It seems possible and likely that the number of data files grows quite large in a production system. But I was lazy.

It would be nice if Go introduced an actual iterator API for reading a directory. :)

Delta Lake and stale reads

In any case the ACID properties of Delta Lake (and Iceberg) don't depend on being able to read up-to-date data.

This is because concurrent (or stale) transactions that write will fail on commit. And also because all files written (even metadata files) are immutable.

Since all data is immutable, we will always be able to read at least a consistent snapshot of data. But we will never be able to get SERIALIZABLE read-only transactions. This is just how Delta Lake and Iceberg work. And it is a similar or better consistency level to what any major SQL database gives you by default.

You'll see what I mean later on when we implement transaction commits.

Transaction boilerplate

Now that we've got a blob storage abstraction and a filesystem implementation of it, let's start sketching out what a client and what a transaction looks like.

In Delta Lake, a transaction consists of a list of actions. An action might be to define a table's schema, or to add a data file, or to remove a data file, etc. In this post we'll only implement the first two actions.

type DataobjectAction struct {
    Name  string
    Table string
}

type ChangeMetadataAction struct {
    Table   string
    Columns []string
}

// an enum, only one field will be non-nil
type Action struct {
    AddDataobject  *DataobjectAction
    ChangeMetadata *ChangeMetadataAction
    // TODO: Support object removal.
    // DeleteDataobject *DataobjectAction
}

These fields are all exported (i.e. capitalized, if you're not familiar with Go) because we will be writing them to disk when the transaction commits as the transaction's metadata.

In fact Actions and the transaction's id will be the only parts of the transaction we write to disk. Everything else will be in-memory state.

For our convenience we will track in memory a history of all previous actions, a mapping of table columns, and a mapping of unflushed data by table.

type transaction struct {
    Id int

    // Both are mapping table name to a list of actions on the table.
    previousActions map[string][]Action
    Actions         map[string][]Action

    // Mapping tables to column names.
    tables map[string][]string

    // Mapping table name to unflushed/in-memory rows. When rows
    // are flushed, the dataobject that contains them is added to
    // `tx.actions` above and `tx.unflushedDataPointer[table]` is
    // reset to `0`.
    unflushedData        map[string]*[DATAOBJECT_SIZE][]any
    unflushedDataPointer map[string]int
}

Only the current transaction will ever have transaction.previousActions filled out. transaction.tables will be populated when the transaction starts by reading through transaction.previousActions for ChangeMetadataActions, and we will also add onto it when we create a table in the current transaction.

We will append to transaction.Actions every time we write a new data file and every time we create a new table.

We will add rows to transaction.unflushedData for a table until transaction.unflushedDataPointer for that table reaches DATAOBJECT_SIZE upon which time we will write that data to disk and add a DataobjectAction entry to transaction.Actions.

Client boilerplate

A client will consist of an objectStorage implementation and a possibly empty *transaction. Empty meaning there is no current transaction.

type client struct {
    os objectStorage
    // Current transaction, if any. Only one transaction per
    // client at a time. All reads and writes must be within a
    // transaction.
    tx *transaction
}

func newClient(os objectStorage) client {
    return client{os, nil}
}

var (
    errExistingTx  = fmt.Errorf("Existing Transaction")
    errNoTx        = fmt.Errorf("No Transaction")
    errTableExists = fmt.Errorf("Table Exists")
    errNoTable     = fmt.Errorf("No Such Table")
)

Client or database?

In a previous version of my code I named this client struct database. But that's misleading. There is no central database. There is just the client and the blob storage.

Clients work with transactions directly and only when attempting to commit does the blob storage abstraction let the client know if the transaction succeeded or not.

Starting a transaction

When we start a transaction, we will first read all existing transactions from disk and accumulate the actions from each prior transaction.

We will interpret ChangeMetadataActions and materialize them into a current view of all tables.

And we will assign a transaction ID to this transaction to be 1 greater than the largest existing transaction ID we see.

Again it doesn't matter if the listPrefix call we use returns an up-to-date list. Notably on blob storage there are few guarantees about LIST operations recency. The Delta Lake paper mentions this too.

Out-of-date transactions attempting to write will be caught when we go to commit the transaction. Out-of-date transactions attempting only to read will still read a consistent snapshot.

func (d *client) newTx() error {
    if d.tx != nil {
        return errExistingTx
    }

    logPrefix := "_log_"
    txLogFilenames, err := d.os.listPrefix(logPrefix)
    if err != nil {
        return err
    }

    tx := &transaction{}
    tx.previousActions = map[string][]Action{}
    tx.Actions = map[string][]Action{}
    tx.tables = map[string][]string{}
    tx.unflushedData = map[string]*[DATAOBJECT_SIZE][]any{}
    tx.unflushedDataPointer = map[string]int{}

    for _, txLogFilename := range txLogFilenames {
        bytes, err := d.os.read(txLogFilename)
        if err != nil {
            return err
        }

        var oldTx transaction
        err = json.Unmarshal(bytes, &oldTx)
        if err != nil {
            return err
        }
        // Transaction metadata files are sorted
        // lexicographically so that the most recent
        // transaction (i.e. the one with the largest
        // transaction id) will be last and tx.Id will end up
        // 1 greater than the most recent transaction ID we
        // see on disk.
        tx.Id = oldTx.Id + 1

        for table, actions := range oldTx.Actions {
            for _, action := range actions {
                if action.AddDataobject != nil {
                    tx.previousActions[table] = append(tx.previousActions[table], action)
                } else if action.ChangeMetadata != nil {
                    // Store the latest version of
                    // each table in memory for
                    // easy lookup.
                    mtd := action.ChangeMetadata
                    tx.tables[table] = mtd.Columns
                } else {
                    panic(fmt.Sprintf("unsupported action: %v", action))
                }
            }
        }
    }

    d.tx = tx
    return nil
}

And we're set.

Creating a table

When we create a table, we need to add a ChangeMetadataAction to the transactions Actions. And we also want to add the table info to the in-memory transaction.tables field.

We don't do any of this durably. The change here will be written to disk on commit (if the transaction succeeds).

func (d *client) createTable(table string, columns []string) error {
    if d.tx == nil {
        return errNoTx
    }

    if _, exists := d.tx.tables[table]; exists {
        return errTableExists
    }

    // Store it in the in-memory mapping.
    d.tx.tables[table] = columns

    // And also add it to the action history for future transactions.
    d.tx.Actions[table] = append(d.tx.Actions[table], Action{
        ChangeMetadata: &ChangeMetadataAction{
            Table:   table,
            Columns: columns,
        },
    })

    return nil
}

Easy peasy. Now for the fun part, writing data!

Writing a row

This is the next area where we'll diverge from Delta Lake. For the sake of zero dependencies we are going to store data in-memory as an array of array of any. And when we later write rows to disk we'll write them as JSON. A real Delta Lake implementation would store data in-memory in Apache Arrow format, and write to disk as Parquet.

In line with Delta Lake though we will buffer data in memory until we get 64K rows. When we get 64K rows for a particular table we will flush all those rows to disk. (When we go to commit a transaction we will flush any outstanding rows.)

func (d *client) writeRow(table string, row []any) error {
    if d.tx == nil {
        return errNoTx
    }

    if _, ok := d.tx.tables[table]; !ok {
        return errNoTable
    }

    // Try to find an unflushed/in-memory dataobject for this table
    pointer, ok := d.tx.unflushedDataPointer[table]
    if !ok {
        d.tx.unflushedDataPointer[table] = 0
        d.tx.unflushedData[table] = &[DATAOBJECT_SIZE][]any{}
    }

    if pointer == DATAOBJECT_SIZE {
        d.flushRows(table)
        pointer = 0
    }

    d.tx.unflushedData[table][pointer] = row
    d.tx.unflushedDataPointer[table]++
    return nil
}

Now let's implement flushing.

Flushing a data object

Recall that data objects in Delta Lake (and Iceberg) are immutable. Once we've got enough data to write a data object, we give it a unique name, write it to disk, and add a AddObjectAction to the transaction's list of Actions.

type dataobject struct {
    Table string
    Name  string
    Data  [DATAOBJECT_SIZE][]any
    Len   int
}

func (d *client) flushRows(table string) error {
    if d.tx == nil {
        return errNoTx
    }

    // First write out dataobject if there is anything to write out.
    pointer, exists := d.tx.unflushedDataPointer[table]
    if !exists || pointer == 0 {
        return nil
    }

    df := dataobject{
        Table: table,
        Name:  uuidv4(),
        Data:  *d.tx.unflushedData[table],
        Len:   pointer,
    }
    bytes, err := json.Marshal(df)
    if err != nil {
        return err
    }

    err = d.os.putIfAbsent(fmt.Sprintf("_table_%s_%s", table, df.Name), bytes)
    if err != nil {
        return err
    }

    // Then record the newly written data file.
    d.tx.Actions[table] = append(d.tx.Actions[table], Action{
        AddDataobject: &DataobjectAction{
            Table: table,
            Name:  df.Name,
        },
    })

    // Reset in-memory pointer.
    d.tx.unflushedDataPointer[table] = 0
    return nil
}

That's it for writing data! Let's now look at reading data.

Scanning a table

We're going to make scanning mildly more complicated than it needed to be in pedagogical code because we'll have client.scan() return an iterator rather than an array with all rows.

The scanIterator will first read from in-memory (unflushed) data. And then it will read through every data object for the table that is still a part of this transaction. We will know which data objects are still a part of this transaction by reading through all AddDataobject actions. A future version of this project would also eliminate data object files from the list by observing DeleteDataobject actions. But we don't do that in this post.

func (d *client) scan(table string) (*scanIterator, error) {
    if d.tx == nil {
        return nil, errNoTx
    }

    var dataobjects []string
    allActions := append(d.tx.previousActions[table], d.tx.Actions[table]...)
    for _, action := range allActions {
        if action.AddDataobject != nil {
            dataobjects = append(dataobjects, action.AddDataobject.Name)
        }
    }

    var unflushedRows [DATAOBJECT_SIZE][]any
    if data, ok := d.tx.unflushedData[table]; ok {
        unflushedRows = *data
    }

    return &scanIterator{
        unflushedRows:    unflushedRows,
        unflushedRowsLen: d.tx.unflushedDataPointer[table],
        d:                d,
        table:            table,
        dataobjects:      dataobjects,
    }, nil
}

The scanIterator needs to track where we are in in-memory rows, in data objects, and within a particular data object.

type scanIterator struct {
    d     *client
    table string

    // First we iterate through unflushed rows.
    unflushedRows       [DATAOBJECT_SIZE][]any
    unflushedRowsLen    int
    unflushedRowPointer int

    // Then we move through each dataobject.
    dataobjects        []string
    dataobjectsPointer int

    // And within each dataobject we iterate through rows.
    dataobject           *dataobject
    dataobjectRowPointer int
}

And the scanIterator will be driven by a next() method that goes through in-memory data first and then through what's on disk.

func (d *client) readDataobject(table, name string) (*dataobject, error) {
    bytes, err := d.os.read(fmt.Sprintf("_table_%s_%s", table, name))
    if err != nil {
        return nil, err
    }

    var do dataobject
    err = json.Unmarshal(bytes, &do)
    return &do, err
}

// returns (nil, nil) when done
func (si *scanIterator) next() ([]any, error) {
    // Iterate through in-memory rows first.
    if si.unflushedRowPointer < si.unflushedRowsLen {
        row := si.unflushedRows[si.unflushedRowPointer]
        si.unflushedRowPointer++
        return row, nil
    }

    // If we've gotten through all dataobjects on disk we're done.
    if si.dataobjectsPointer == len(si.dataobjects) {
        return nil, nil
    }

    if si.dataobject == nil {
        name := si.dataobjects[si.dataobjectsPointer]
        o, err := si.d.readDataobject(si.table, name)
        if err != nil {
            return nil, err
        }

        si.dataobject = o
    }

    if si.dataobjectRowPointer > si.dataobject.Len {
        si.dataobjectsPointer++
        si.dataobject = nil
        si.dataobjectRowPointer = 0
        return si.next()
    }

    row := si.dataobject.Data[si.dataobjectRowPointer]
    si.dataobjectRowPointer++
    return row, nil
}

That's it for scanning a table! The final piece of the puzzle is committing a transaction.

Committing a transaction

When we commit a transaction we must flush any remaining data. A read-only transaction (one which has no Actions) is immediately done. There is no concurrency check.

Otherwise we will serialize transaction state and attempt to atomically putIfAbsent.

The only way this will fail is if there is another concurrent writer.

func (d *client) commitTx() error {
    if d.tx == nil {
        return errNoTx
    }

    // Flush any outstanding data
    for table := range d.tx.tables {
        err := d.flushRows(table)
        if err != nil {
            d.tx = nil
            return err
        }
    }

    wrote := false
    for _, actions := range d.tx.Actions {
        if len(actions) > 0 {
            wrote = true
            break
        }
    }
    // Read-only transaction, no need to do a concurrency check.
    if !wrote {
        d.tx = nil
        return nil
    }

    filename := fmt.Sprintf("_log_%020d", d.tx.Id)
    // We won't store previous actions, they will be recovered on
    // new transactions. So unset them. Honestly not totally
    // clear why.
    d.tx.previousActions = nil
    bytes, err := json.Marshal(d.tx)
    if err != nil {
        d.tx = nil
        return err
    }

    err = d.os.putIfAbsent(filename, bytes)
    d.tx = nil
    return err
}

func main() {
    panic("unimplemented")
}

This is the crux of Delta Lake. It's simple. And honestly it's a bit shocking. Real Delta Lake does support automatic retries in some cases. But primarily you are limited to a single writer per table, even if the writers are writing non-conflicting rows. Iceberg is basically the same here, it's just how metadata is tracked that differs.

As mentioned in another note above, our implementation is actually stricter than Delta Lake since it manages all table transaction logs together. This means you can get snapshot isolation across all tables (which Delta Lake doesn't support) but it will mean significantly more contention and failed write transactions.

The Delta Lake and Iceberg folks apparently wanted to avoid FoundationDB (i.e. the Snowflake architecture, which is mentioned in the Delta Lake paper) so much that they'd give up row-level concurrency to be mostly serverless.

Is it worth it? Dunno. Delta Lake and Iceberg are getting massive adoption. Many very smart people have worked, and continue to work, on both. Moreover it is apparently what the market wants. Every database-like product is implementing, or is planning to implement, Delta Lake or Iceberg.

Trying it out

Let's add a test in main_test.go to see what happens with concurrent writers. Follow the comments and debug logs for details:

package main

import (
    "os"
    "testing"
)

func TestConcurrentTableWriters(t *testing.T) {
    dir, err := os.MkdirTemp("", "test-database")

    if err != nil {
        panic(err)
    }

    defer os.Remove(dir)

    fos := newFileObjectStorage(dir)
    c1Writer := newClient(fos)
    c2Writer := newClient(fos)

    // Have c2Writer start up a transaction.
    err = c2Writer.newTx()
    assertEq(err, nil, "could not start first c2 tx")
    debug("[c2] new tx")

    // But then have c1Writer start a transaction and commit it first.
    err = c1Writer.newTx()
    assertEq(err, nil, "could not start first c1 tx")
    debug("[c1] new tx")
    err = c1Writer.createTable("x", []string{"a", "b"})
    assertEq(err, nil, "could not create x")
    debug("[c1] Created table")
    err = c1Writer.writeRow("x", []any{"Joey", 1})
    assertEq(err, nil, "could not write first row")
    debug("[c1] Wrote row")
    err = c1Writer.writeRow("x", []any{"Yue", 2})
    assertEq(err, nil, "could not write second row")
    debug("[c1] Wrote row")
    err = c1Writer.commitTx()
    assertEq(err, nil, "could not commit tx")
    debug("[c1] Committed tx")

    // Now go back to c2 and write data.
    err = c2Writer.createTable("x", []string{"a", "b"})
    assertEq(err, nil, "could not create x")
    debug("[c2] Created table")
    err = c2Writer.writeRow("x", []any{"Holly", 1})
    assertEq(err, nil, "could not write first row")
    debug("[c2] Wrote row")

    err = c2Writer.commitTx()
    assert(err != nil, "concurrent commit must fail")
    debug("[c2] tx not committed")
}

Try it out:

$ go mod init otf
$ go mod tidy
$ go test -run TestConcurrentTableWriters -- --debug
[DEBUG] [c2] new tx
[DEBUG] [c1] new tx
[DEBUG] [c1] Created table
[DEBUG] [c1] Wrote row
[DEBUG] [c1] Wrote row
[DEBUG] [c1] Committed tx
[DEBUG] [c2] Created table
[DEBUG] [c2] Wrote row
[DEBUG] [c2] tx not committed
PASS
ok      otf 0.311s

That's pretty cool.

And what about a reader and concurrent writer? Observe that the reader always reads a snapshot. Follow the comments again for detail:

func TestConcurrentReaderWithWriterReadsSnapshot(t *testing.T) {
    dir, err := os.MkdirTemp("", "test-database")

    if err != nil {
        panic(err)
    }

    defer os.Remove(dir)

    fos := newFileObjectStorage(dir)
    c1Writer := newClient(fos)
    c2Reader := newClient(fos)

    // First create some data and commit the transaction.
    err = c1Writer.newTx()
    assertEq(err, nil, "could not start first c1 tx")
    debug("[c1Writer] Started tx")
    err = c1Writer.createTable("x", []string{"a", "b"})
    assertEq(err, nil, "could not create x")
    debug("[c1Writer] Created table")
    err = c1Writer.writeRow("x", []any{"Joey", 1})
    assertEq(err, nil, "could not write first row")
    debug("[c1Writer] Wrote row")
    err = c1Writer.writeRow("x", []any{"Yue", 2})
    assertEq(err, nil, "could not write second row")
    debug("[c1Writer] Wrote row")
    err = c1Writer.commitTx()
    assertEq(err, nil, "could not commit tx")
    debug("[c1Writer] Committed tx")

    // Now start a new transaction for more edits.
    err = c1Writer.newTx()
    assertEq(err, nil, "could not start second c1 tx")
    debug("[c1Writer] Starting new write tx")

    // Before we commit this second write-transaction, start a
    // read transaction.
    err = c2Reader.newTx()
    assertEq(err, nil, "could not start c2 tx")
    debug("[c2Reader] Started tx")

    // Write and commit rows in c1.
    err = c1Writer.writeRow("x", []any{"Ada", 3})
    assertEq(err, nil, "could not write third row")
    debug("[c1Writer] Wrote third row")

    // Scan x in read-only transaction
    it, err := c2Reader.scan("x")
    assertEq(err, nil, "could not scan x")
    debug("[c2Reader] Started scanning")
    seen := 0
    for {
        row, err := it.next()
        assertEq(err, nil, "could not iterate x scan")

        if row == nil {
            debug("[c2Reader] Done scanning")
            break
        }

        debug("[c2Reader] Got row in reader tx", row)
        if seen == 0 {
            assertEq(row[0], "Joey", "row mismatch in c1")
            assertEq(row[1], 1.0, "row mismatch in c1")
        } else {
            assertEq(row[0], "Yue", "row mismatch in c1")
            assertEq(row[1], 2.0, "row mismatch in c1")
        }

        seen++
    }
    assertEq(seen, 2, "expected two rows")

    // Scan x in c1 write transaction
    it, err = c1Writer.scan("x")
    assertEq(err, nil, "could not scan x in c1")
    debug("[c1Writer] Started scanning")
    seen = 0
    for {
        row, err := it.next()
        assertEq(err, nil, "could not iterate x scan in c1")

        if row == nil {
            debug("[c1Writer] Done scanning")
            break
        }

        debug("[c1Writer] Got row in tx", row)

        if seen == 0 {
            assertEq(row[0], "Ada", "row mismatch in c1")
            // Since this hasn't been serialized to JSON, it's still an int not a float.
            assertEq(row[1], 3, "row mismatch in c1")
        } else if seen == 1 {
            assertEq(row[0], "Joey", "row mismatch in c1")
            assertEq(row[1], 1.0, "row mismatch in c1")
        } else {
            assertEq(row[0], "Yue", "row mismatch in c1")
            assertEq(row[1], 2.0, "row mismatch in c1")
        }

        seen++
    }
    assertEq(seen, 3, "expected three rows")

    // Writer committing should succeed.
    err = c1Writer.commitTx()
    assertEq(err, nil, "could not commit second tx")
    debug("[c1Writer] Committed tx")

    // Reader committing should succeed.
    err = c2Reader.commitTx()
    assertEq(err, nil, "could not commit read-only tx")
    debug("[c2Reader] Committed tx")
}

Run it:

$ go test -run TestConcurrentReaderWithWriterReadsSnapshot -- --debug
[DEBUG] [c1Writer] Started tx
[DEBUG] [c1Writer] Created table
[DEBUG] [c1Writer] Wrote row
[DEBUG] [c1Writer] Wrote row
[DEBUG] [c1Writer] Committed tx
[DEBUG] [c1Writer] Starting new write tx
[DEBUG] [c2Reader] Started tx
[DEBUG] [c1Writer] Wrote third row
[DEBUG] [c2Reader] Started scanning
[DEBUG] [c2Reader] Got row in reader tx [Joey 1]
[DEBUG] [c2Reader] Got row in reader tx [Yue 2]
[DEBUG] [c2Reader] Done scanning
[DEBUG] [c1Writer] Started scanning
[DEBUG] [c1Writer] Got row in tx [Ada 3]
[DEBUG] [c1Writer] Got row in tx [Joey 1]
[DEBUG] [c1Writer] Got row in tx [Yue 2]
[DEBUG] [c1Writer] Done scanning
[DEBUG] [c1Writer] Committed tx
[DEBUG] [c2Reader] Committed tx
PASS
ok      otf 0.252s

Sweet.

What's next?

As mentioned, we didn't touch a lot of things. Handling updates and deletes, transaction log checkpoints, data object compaction, etc.

Take a close look at the Delta Lake paper and the Delta Lake Spec and see what you can do!

Build a serverless ACID database with this one neat trick.

(New blog post)https://t.co/rHgfKSPY6q pic.twitter.com/1hmjsxIk6w
— Phil Eaton (@eatonphil) September 29, 2024

Be someone who does things

Mon, 23 Sep 2024 00:00:00 +0000

I wrote last month that what you want to do is one of the most useful motivations in life. I want to follow that up by saying that the only thing more important than wanting to do something is to actually do something.

The most valuable trait you can develop for yourself is to be consistent. It is absolutely something you can develop. And moreover it's kind of hard to believe that for anyone it is innate.

I meet so many people who say they want to do things. And I ask them what they're doing to get there and they get flustered. This is completely understandable.

I meet so many students who feel overwhelmed by what everyone else is doing. This is also understandable.

But it doesn't matter what anyone else is doing. It doesn't matter where anyone else is at. It matters where you are at. Compete with yourself before you compete with anyone else. What matters is that you get into a habit of consistently working on little goals.

If you pick something that is too complex, break it down. Keep on breaking problems or ideas down until you find a problem or idea you can solve.

Then keep on finding new problems to solve. Move on in complexity over time as you can and want to.

Don't worry about getting things perfect. Who can discredit you for doing your best? What shame is there when you're being earnest? The only thing that makes sense to feel bad about is not trying to do what you genuinely wanted to do.

And this doesn't have to be about projects or ideas outside of work. There may be things you want to do at work like improving documentation or writing better tests or adding new checks to code or blogging or interviewing customers or working with another team.

Like I said in Obsession, don't worry about what you do daily. That is too frequent to think about. Instead think about what you're doing once a month.

Make time once a month to publish a post or complete a small project. Whatever you want to do, I am confident you can find some small version of it that you could commit to doing once a month. Be consistent!

If a month is too often, pick a longer freqency. Find whatever cadence and whatever size of project that allows you to be consistent.

When you're consistent over the course of months I think you'll be astounded at what you accomplish in a year.

Shorter post tonight, may add to this later on.

Be someone who does things. And do these (little) things consistently.https://t.co/oVb6Sz8eEK pic.twitter.com/kNrZQ4pvTN
— Phil Eaton (@eatonphil) September 24, 2024

Obsession

Sat, 24 Aug 2024 00:00:00 +0000

In your professional and personal life, I don't believe there is a stronger motivation than having something in mind and the desire to do it. Yet the natural way to deal with a desire to do something is to justify why it's not possible.

"I want to read more books but nobody reads books these days so how could I."

"I want to write for a magazine but I have no experience writing professionally."

"I want to build a company someday but how could someone of my background."

Our official mentors, our managers, through a combination of well-intentioned defeatism and well-intentioned lack of accomplishment themselves, among other things, are often unable to process big goals or guide you toward them.

I've been one of these managers myself. In fact I have, to my immense regret, tried too often to convince people to do what is practical rather than what they want to do. Or to do what I judged they were capable of doing rather than what they wanted to do.

In the best cases, my listener had the self-confidence to ignore me. They did what they wanted to do anyway. In the worst case, again to my deep regret, I've been a well-intentioned part of derailing someone's career for years.

So I don't want to convince anyone of anything anymore. If I start trying to convince someone by accident, I try to catch myself. I try to avoid sentences like "I think you should …". Instead "Here is something that's worked for me: …" or "Here is what I've heard works well for other people: …".

Nobody wants to be convinced. But intelligent people will change their mind when exposed to new facts or different ideas. Being convinced is a battle of will. Changing one's mind is a purely personal decision.

There are certainly people with discipline who can grind on things they hate doing and eventually become experts at it. But more often I see people grind on things they hate only to become depressed and give up.

For most of us, our best hope is (healthy) obsession. And obsession, in the sense I'm talking about, does not come from something you are ambivalent about or hate. Obsession can only come when you're doing something you actually want to do.

For big goals or big changes, you need regular commitment weekly, monthly, yearly. Over the course of years. And only obsession makes that work not actually feel like work. Obsession is the only thing that makes discipline not feel like discipline.

That big goals take years to accomplish need not be scary. Obsession doesn't mean you can't pivot. There is quite a lot to gain by committing to something regularly over the course of years even if you decide to stop and commit from then on to something else. You will learn a good deal.

And healthy obsession to me is more specifically measurable on the order of weeks, not hours or days. Healthy obsession means you're still building healthy personal and professional relationships. You're still taking care of yourself, emotionally and physically.

I do not have high expectations for people in general. This seems healthy and reasonable. But as I meet more people and observe them over the years, I am only more convinced of the vast potential of individuals. Individuals are almost universally underestimated.

I think you can do almost anything you want to do. If you commit to do doing it.

I'll end this with a personal story.

Until 11th grade, I hated school. I hated the rigidity. Being forced to be somewhere for hours and to follow so many rules. I skipped so many days of school I'm embarrassed by it. I'd never do homework at home. I never studied for tests. I got Bs and Cs in the second-tier classes. I was in the orchestra for 6 years and never practiced at home. I was not cool enough to be a "bad kid" but I did not understand the system and had no discipline whatsoever.

I found out at the end of 10th grade that I could actually afford college if I got into a good enough school that paid full needs-based tuition. It sounded significantly better than the only other option that seemed obvious, joining the military as a recruit. I realized and decided that if I wanted to get into a good school I needed to not half-ass things.

Somehow, I decided to only do things I could become obsessed with. And I decided to be obsessed in the way that I wanted, not to do what everyone else did (which I basically could not do since I had no discipline). If we covered a topic in class, I'd read news about it or watch movies about it. I'd get myself excited about the topic in every way I could.

It basically worked out. I ended high school in the top 10% of the class (up from top 40% or something). I got into a good liberal arts college that paid the entirety of my tuition. But I remained a basically lazy and undisciplined person. I never stayed up late studying for a test. I dropped out after a year and a half for family reasons.

But I've now spent the last 10 years in my spare time working on compiler projects, interpreter projects, parser projects, database projects, distributed systems projects. I've spent the last 6 years consistently publishing at least one blog post per month.

I didn't want to work the way everyone else worked. I wanted to be obsessed about what I worked on.

Obsession has made all of this into something I now barely register as doing. It's allowed me to continue adding activities like organizing book clubs and meetups to the list of things I'm up to. Up until basically this year I could have in good faith said I am a very lazy and undisciplined person. But obsession turned me into someone with discipline.

Obsession became about more than just the tech. It meant trying to fully understand the product, the users, the market. It meant thinking more carefully about product documentation, user interfaces, company messaging. Obsession meant reflecting on how I treat my coworkers, and how my coworkers feel treated by others in general. Obsession meant wanting an equitable and encouraging work environment for everyone.

And, as I said, it's about healthy obsession. I didn't really understand the "healthy" part until a few years ago. But I'm now convinced that the "healthy" part is as important as the "obsession" part. To go to the gym regularly. To play pickup volleyball. To cook excellent food. To read fiction and poetry and play music. To serve the community. To be friendly and encouraging to all people. To meet new people and build better genuine friendships.

And in the context of work, "healthy obsession" means understanding you can't do everything, even while you care about everything. It means accepting that you make mistakes and that you do your best; that you try to do better and learn from mistakes the next time.

It's got to be sustainable. And we can develop a healthy obsession while we have quite a bit of fun too. :)

I wrote an essay on my mistakes trying to convince people to do something, on doing what you want to do, and on obsession.

Ended with a personal note on developing healthy discipline, and having fun. :)https://t.co/4WWdtU6AhL pic.twitter.com/lBw7zlqWeq
— Phil Eaton (@eatonphil) August 24, 2024

What's the big deal about Deterministic Simulation Testing?

Tue, 20 Aug 2024 00:00:00 +0000

Bugs in distributed systems are hard to find, largely because systems interact in chaotic ways. And even once you've found a bug, it can be anywhere from simple to impossible to reproduce it. It's about as far away as you can get from the ideal test environment: property testing a pure function.

But what if we could write our code in a way that we can isolate the chaotic aspects of our distributed system during testing: run multiple systems communicating with each other on a single thread and control all randomness in each system? And property test this single-threaded version of the distributed system with controlled randomness, all the while injecting faults (fancy term for unhappy path behavior like errors and latency) we might see in the real-world?

Crazy as it sounds, people actually do this. It's called Deterministic Simulation Testing (DST). And it's become more and more popular with startups like FoundationDB, Antithesis, TigerBeetle, Polar Signals, and WarpStream; as well as folks like Tyler Neely and Pekka Enberg, talking about and making use of this technique.

It has become so popular to talk about DST in my corner of the world that I worry it risks coming off sounding too magical and maybe a little hyped. It's worth getting a better understanding of both the benefits and the limitations.

Thank you to Alex Miller and Will Wilson for reviewing a version of this post.

Randomness and time

A big source of non-determinism in business logic is the use of random numbers—in your code or your transitive dependencies or your language runtime or your operating system.

Crucially, DST does not imply you can't have randomness! DST merely assumes that you have a global seed for all randomness in your program and that the simulator controls the seed. The seed may change across runs of the simulator.

Once you observe a bad state as a result of running the simulation on a random seed, you allow the user to enter the same seed again. This allows the user to recreate the entire program run that led to that observed bad state. Allows the user to debug the program trivially.

Another big source of non-determinism is being dependent on time. As with randomness, DST does not mean you can't depend on time. DST means you must be able to control the clock during the simulation.

To "control" randomness or time basically means you support dependency injection, or the old-school alternative to dependency injection called passing the dependency as an explicit parameter. Rather than referring to a global clock or a global seed, you need to be able to receive a clock or a seed from someone.

For example we might separate the operation of an application into the language's main() entrypoint and an actual application start() entrypoint.

# app.pseudocode

def start(clock, seed):
  # lots of business logic that might depend on time or do random things

def main:
  clock = time.clock()
  seed = time.now()
  app.start(clock, seed)

The application entrypoint is where we must be able to swap out a real clock or real random seed for one controlled by our simulator:

# sim.pseudocode

import "app.pseudocode"

def main:
  sim_clock = make_sim_clock()
  sim_seed = os.env.DST_SEED or time.now()
  try:
    app.start(sim_clock, sim_seed)
  catch(e):
    print("Bad execution at seed: %s", sim_seed)
    throw e

Let's look at another example.

Converting an existing function

Let's say that we had a helper method that kept calling a function until it succeeded, with backoff.

# retry.pseudocode
class Backoff:
  def init:
    this.rnd = rnd.new(seed = time.now())
    this.tries = 0

  async def retry_backoff(f):
    while this.tries < 3:
      if f():
        return

      await time.sleep(this.rnd.gen())
      this.tries++

There is a single source of nondeterminism here and it's where we generate a seed. We could parameterize the seed, but since we want to call time.sleep() and since in DST we control the time, we can just parameterize time.

# retry.psuedocode
class Backoff:
  def init(this, time):
    this.time = time
    this.rnd = rnd.new(seed = this.time.now())
    this.tries = 0

  async def retry_backoff(this, f):
    while this.tries < 3:
      if f():
        return

      await this.time.sleep(this.rnd.gen())
      this.tries++

Now we can write a little simulator to test this:

# sim.psuedocode
import "retry.pseudocode"

sim_time = {
  now: 0
  sleep: (ms) => {
    await future.wait(ms)
  }
  tick: (ms) => now += ms
}

backoff = Backoff(sim_time)

while true:
  failures = 0
  f = () => {
    if rnd.rand() > 0.5:
      failures++
      return false

    return true
  }
  try:
    while sim_time.now < 60min:
      promise = backoff.retry_backoff(f)
      sim_time.tick(1ms)
      if promise.read():
        break

    assert_expect_failure_and_expected_time_elapse(sim_time, failures)
  catch(e):
    print("Found logical error with seed: %d", seed)
    throw e

This demonstrates a few critical aspects of DST. First, the simulator itself depends on randomness. But allows the user to provide a seed so they can replay a simulation that discovers a bug. The controlled randomness in the simulator is what lets us do property testing.

Second, the simulation workload must be written by the user. Even when you've got a platform like Antithesis that gives you an environment for DST, it's up to you to exercise the application.

Now let's get a little more complex.

A single thread and asynchronous IO

The determinism of multiple threads can only be controlled at the operating system or emulator or hypervisor layer. Realistically, that would require third-party systems like Antithesis or Hermit (which, don't get excited, is not actively developed and hasn't worked on any interesting program of mine) or rr.

These systems transparently transform multi-threaded code into single threaded code. But also note that Hermit and rr have only limited ability to do fault injection which, in addition to deterministic execution, is a goal of ours. And you can't run them on a mac. And can't run them on ARM.

But we can, and would like, to write a simulator without writing a new operating system or emulator or hypervisor, and without a third-party system. So we must limit ourselves to writing code that can be collapsed into a single thread. Significantly, since using blocking IO would mean an entire class of concurrency bugs could not be discovered while running the simulator in a single thread, we must limit ourselves to asynchronous IO.

Single threaded and asynchronous IO. These are already two big limitations.

Some languages like Go are entirely built around transparent multi-threading and blocking IO. Polar Signals solved this for DST by compiling their application to WASM where it would run on a single thread. But that wasn't enough. Even on a single thread, the Go runtime intentionally schedules goroutines randomly. So Polar Signals forked the Go runtime to control this randomness with an environment variable. That's kind of crazy. Resonate took another approach that also looks cumbersome. I'm not going to attempt to describe it. Go seems like a difficult choice of a language if you want to do DST.

Like Go, Rust has no builtin async IO. The most mature async IO library is tokio. The tokio folks attempted to provide a tokio-compatible simulator implementation with all sources of nondeterminism removed. From what I can tell, they did not at any point fully succeed. That repo has now been replaced with a "this is very experimental" tokio-rs project called turmoil that provides deterministic execution plus network fault injection. (But not disk fault injection. More on that later.) It isn't surprising that it is difficult to provide deterministic execution for an IO library that was not designed for it. tokio is a large project with many transitive dependencies. They must all be combed for non-determinism.

On the other hand, Pekka has already demonstrated for us how we might build a simpler Rust async IO library that is designed to be simulation tested. He modeled this on the TigerBeetle design King and I wrote about two years ago.

So let's sketch out a program that does buggy IO and let's look at how we can apply DST to it.

# readfile.pseudocode
def read_file(io, name, into_buffer):
  f = await io.open(name)
  read_buffer = [4096]u8{}
  while true:
    err, n_read = await f.read(&read_buffer)
    if err == io.EOF:
      into_buffer.copy_maybe_allocate(read_buffer[0:sizeof(read_buffer)])
      return

    if err:
      throw err

    into_buffer.copy_maybe_allocate(read_buffer[0:sizeof(read_buffer)])

In our simulator, we will provide a mocked out IO system and we will randomly inject various errors while asserting pre- and post-conditions.

# sim.psuedocode
import "readfile.pseudocode"

seed = if os.env.DST_SEED ? int(os.env.DST_SEED) : time.now()
rnd = rnd.new(seed)

while true:
  sim_disk_data = rnd.rand_bytes(10MB)
  sim_fd = {
    pos: 0
    EOF: Error("eof")
    read: (fd, buf) => {
      partial_read = rnd.rand_in_range_inclusive(0, sizeof(buf))
      memcpy(sim_disk_data, buf, fd.pos, partial_read)
      fd.pos += partial_read
      if fd.pos == sizeof(sim_disk_data):
        return io.EOF, partial_read
      return partial_read 
    }
  }
  sim_io = {
    open: (filename) => sim_fd
  }

  out_buf = Vector<u8>.new()
  try:
    read_file(sim_io, "somefile", out_buf)
    assert_bytes_equal(out_buf.data, sim_disk_data)
  catch (e):
    print("Found logical error with seed: %d", seed)
    throw e

And with this simulator we would have eventually caught our partial read bug! In our original program when we wrote:

      into_buffer.copy_maybe_allocate(read_buffer[0:sizeof(read_buffer)])

We should have written:

      into_buffer.copy_maybe_allocate(read_buffer[0:n_read])

Great! Let's get a little more complex.

A distributed system

I already mentioned in the beginning that the gist of deterministic simulation testing a distributed system is that you get all of the nodes in the system to run in the same process. This would be basically impossible if you wanted to test a system that involved your application plus Kafka plus Postgres plus Redis. But if your system is a self-contained distributed system, such as one that embeds a Raft library for high availability of your application, you can actually run multiple nodes into the same process!

For a system like this, our simulator might look like:

# sim.pseudocode
import "distsys-node.pseudocode"

seed = if os.env.DST_SEED ? int(os.env.DST_SEED) : time.now()
rnd = rnd.new(seed)

while true:
  sim_fd = {
    send(fd, buf) => {
      # Inject random failure.
      if rnd.rand() > .5:
         throw Error('bad write')

      # Inject random latency.
      if rnd.rand() > .5:
        await time.sleep(rnd.rand())

      n_written = assert_ok(os.fd.write(buf))
      return n_written
    },
    recv(fd, buf) => {
      # Inject random failure.
      if rnd.rand() > .5:
         throw Error('bad read')

      # Inject random latency.
      if rnd.rand() > .5:
        await time.sleep(rnd.rand())

      return os.fd.read(buf)
    }
  }
  sim_io = {
    open: (filename) => {
      # Inject random failure.
      if rnd.rand() > .5:
        throw Error('bad open')

      # Inject random latency.
      if rnd.rand() > .5:
        await time.sleep(rnd.rand())

      return sim_fd
    }
  }

  all_ports = [6000, 6001, 6002]
  nodes = [
    await distsys-node.start(sim_io, all_ports[0], all_ports),
    await distsys-node.start(sim_io, all_ports[1], all_ports),
    await distsys-node.start(sim_io, all_ports[2], all_ports),
  ]
  history = []
  try:
    key = rnd.rand_bytes(10)
    value = rnd.rand_bytes(10)
    nodes[rnd.rand_in_range_inclusive(0, len(nodes)].insert(key, value)
    history.add((key, value))
    assert_valid_history(nodes, history)

    # Crash a process every so often
    if rnd.rand() > 0.75:
      node = nodes[rnd.rand_in_range_inclusive(0, 3)]
      node.restart()
  catch (e):
    print("Found logical error with seed: %d", seed)
    throw e

I'm completely hand waving here to demonstrate the broader point and not any specific testing strategy for a specific distributed system. The important points are that these three nodes run in the same process, on different ports.

We control disk IO. We control network IO. We control how time elapses. We run a deterministic simulated workload against the three node system while injecting disk, network, and process faults.

And we are constantly checking for an invalid state. When we get the invalid state, we can be sure the user can easily recreate this invalid state.

Other sources of non-determinism

Within some error margin, most CPU instructions and most CPU behavior are considered to be deterministic. There are, however, certain CPU instructions that are definitely not. Unfortunately that might include system calls. It might also include malloc. There is very little to trust.

If we ignore Antithesis, people doing DST seem not to worry about these smaller bits of nondeterminism. Yet it's generally agreed that DST is still worthwhile anyway. The intuition here is that every bit of non-determinism you can eliminate makes it that much easier to reproduce bugs when you find them.

Put another way: determinism, even among DST practitioners, remains a spectrum.

Considerations

As you may have noticed already from some of the pseudocode, DST is not a panacea.

Consideration 1: Edges

First, because you must swap out non-deterministic parts of your code, you are not actually testing the entirety of your code. You are certainly encouraged to keep the deterministic kernel large. But there will always be the non-deterministic edges.

Without a system like Antithesis which gives you an entire deterministic machine, you can't test your whole program.

But even with Antithesis you cannot test the integration between your system and external systems. You must mock out the external systems.

It's also worth noting that there are many areas where you could inject simulation. You could do it at a high-level RPC and storage layer. This would be simpler and easier to understand. But then you'd be omitting testing and error-handling of lower-level errors.

Consideration 2: Your workload(s)

DST is dependent on your creativity and thoroughness of your workload as much as any other type of test or benchmark.

Just as you wouldn't depend on one single benchmark to qualify your application, you may not want to depend on a single simulated workload.

Or as Will Wilson put it for me:

The biggest challenge of DST in my experience is that tuning all the random distributions, the parameters of your system, the workload, the fault injection, etc. so that it produces interesting behavior is very challenging and very labor intensive. As with fuzzing or PBT, it's terrifyingly easy to build a DST system that appears to be doing a ton of testing, but actually never explores very much of the state space of your system. At FoundationDB, the vast majority of the work we put into the simulator was an iterative process of hunting for what wasn't being covered by our tests and then figuring out how to make the tests better. This process often resembles science more than it does engineering.

Unfortunately, unlike with fuzzing, mere branch coverage in your code is usually a pretty poor signal for the kinds of systems you want to test with DST. At Antithesis we handle this with Sometimes assertions, at FDB we did something pretty similar, and I assume TigerBeetle and others have their own version of this. But of course the ultimate figure of merit is whether your DST system is finding 100% of your bugs. It's quite difficult to get to the point that it does. The truly ambitious part of Antithesis isn't the hypervisor, but the fact that we also aim to solve the much harder "is my DST working?" problem with minimal human guidance or supervision.

Consideration 3: Your knowledge of what you mocked

When you mock out the behavior of disk or network IO, the benefits of DST are tied to your understanding of the spectrum of behavior that may happen in the real world.

What are all possible error conditions? What are the extreme latency bounds of the original method? What about corruption or misdirected IO?

The flipside here is that only in deterministic simulation testing can you configure these crazy scenarios to happen at a configurable regularity. You can kick off a set of runs that have especially high IO latency or especially high corrupt reads/writes. Joran and I wrote a year ago about how the TigerBeetle simulator does exactly this.

Consideration 4: Non-reproducible seeds as code changes

Critically, the reproducibility of DST only helps so long as your code doesn't change. As soon as your code changes, the seed may no longer even get you to the state where the bug was exhibited. So the reproducibility of DST means more that it may help you convert the seed simulation run into an integration test that describes the precise scenario even as the code changes.

Consideration 5: Time and compute

Because of Consideration 4, you need to keep rerunning the simulator not just to keep finding new seeds and new histories but because the new seeds and new histories may change every time you make changes to code.

What about Jepsen?

Jepsen does limited process and network fault injection while testing for linearizability. It's a fantastic project.

However, it represents only a subset of what is possible with Deterministic Simulation Testing (if you actually put in the effort described above to get there).

But even more importantly, Jepsen has nothing to do with deterministic execution. If Jepsen finds a bug and your system can't do deterministic execution, you may or may not be able to reproduce that Jepsen bug.

Here's another Will Wilson quote for you on Jepsen and FoundationDB:

Anyway, we did [Deterministic Simulation Testing] for a while and found all of the bugs in the database. I know, I know, that’s an insane thing to say. It’s kind of true though. In the entire history of the company, I think we only ever had one or two bugs reported by a customer. Ever. Kyle Kingsbury aka “aphyr” didn’t even bother testing it with Jepsen, because he didn’t think he’d find anything.

Conclusion

The degree to which you can place faith in DST alone, and not time spent in production, has limits. However, it certainly does no harm to employ DST. And, barring the considerations described above, will likely make the kernel of your product significantly more stable. Furthermore, everyone who uses DST knows about these considerations. But I think it's worthwhile to list them out to help folks who do not know DST to build an intuition for what it's excellent at.

Delightful, production-grade replication for Postgres

Tue, 30 Jul 2024 00:00:00 +0000

This is an external post of mine. Click here if you are not redirected.

A reawakening of systems programming meetups

Sun, 07 Jul 2024 00:00:00 +0000

This year has seen a resurgence in really high quality systems programming meetups. Munich Database Meetup, Berlin Systems Group, SF Distributed Systems Meetup, NYC Systems, Bengaluru Systems, to name a few.

This post summarizes a bit of disappointing recent tech meetup history, the new trend of excellent systems programming meetups, and ends with some encouragement and guidance for running your own systems programming events.

I will be a little critical in this post but I want to preface by saying: organizing meetups is really tough! It takes a lot of work and I have a huge amount of respect for meetup organizers even when their meetup style did not resonate with me.

Although much of this post talks about NYC Systems, the reason I think this post is worth writing is because so many other meetups in a similar vein popped up. I hope to encourage these other meetups and to encourage folks in other major metros (London, for example) to start similar meetups.

Meetups

I used to attend a bunch of meetups before the pandemic. But I quickly got disillusioned. Almost every meetup was varying degrees of startups pitching their product. The last straw for me was sitting through a talk at a JavaScript meetup that was by a devrel employee of a startup who literally gave a tutorial for their product.

There were also some pretty intelligent meetups like the New York Haskell Users Group and the New York Emacs Meetup. But not being an expert in either domain, and the attendees almost solely appearing to be experts, I didn't particularly enjoy going.

There were a couple of meetups that felt inclusive for various skill-levels of attendees yet still went into interesting depth. Specifically, New York Linux User Group and Papers We Love NYC.

These meetups were exceptional because they were language- and framework-agnostic, they would start broad to give you background, but then go deep into a topic. Maybe you only understood 50% of what was covered. But you get exposed to something new from an expert in that domain.

Unfortunately, the pandemic happened and these two excellent meetups basically have not come back.

A couple of students in Munich

The pandemic ended and I tried a couple of meetups I thought might be better quality. Rust and Go. But they weren't much better than I remembered. People would give a high level talk and brush over all the interesting concepts.

I had been thinking of doing an in-person talk series since 2022.

If I put together a systems/databases/distributed systems meetup in NYC (a physical meetup, not Zoom), who'd be interested (in attending, or presenting, or helping me organize, or donating space)?

No promises!
— Phil Eaton (@eatonphil) September 27, 2022

But I was busy with TigerBeetle until December of 2023 when I was messaged on LinkedIn by Georg Kreuzmayr, a graduate student at Technical University of Munich (TUM).

Georg and his friends, fellow graduate students at TUM, started a database club: TUMuchData. We got to talking about opportunities for collaboration and I started feeling a bit embarrassed that a graduate student had more guts than I had to get back onto the meetup organizer wagon.

A week later, with assurance from Justin Jaffray that at least he would show up with me if no one else did, I started the NYC Systems Coffee Club to bring together folks in NYC interested in any topic of systems programming (e.g. compilers, databases, web browser internals, distributed systems, formal methods, etc.). To bring them together in a completely informal setting for coffee at 9am in the morning in a public space in midtown Manhattan.

Trying something new! If you're a dev in NYC working
on (or interested in) systems programming, grab a coffee and come hang out at 1 Bryant Park (indoor space) this Thursday 9AM - 9:30AM.

See post for details and fill out the Google Form or DM me!https://t.co/A4bzcPGy6x pic.twitter.com/n1ECMd59ev
— Phil Eaton (@eatonphil) December 11, 2023

I set up that linked web page and started collecting subscribers to the club via Google Form. Once a month I'd send an email out to the list asking for RSVPs to this month's coffee club. The first 20 to respond would get a calendar invite.

And about the same time I started asking around on Twitter/LinkedIn if someone would be interested in co-organizing a new systems programming meetup in NYC. Angelo Saraceno immediately took me up on the idea and we met up.

NYC Systems

We agreed on the premise: this would be a language- and framework-agnostic meetup that was focused on engineering challenges, not product pitches. It would be 100% for the sake of corporate marketing, but corporate marketing of the engineering team, not the product.

NYC Systems was born!

We'd find speakers who could start broad and dive deep into some interesting aspect of databases, programming languages, distributed systems, and so on. Product pitches were necessary to establish a context, but the focus of the talk would be about some interesting recent technical challenge and how they dealt with it.

We'd schedule talks only every other month to ease our own burden in organizing and finding great speakers.

Once Angelo and I had decided to go forward, the next two challenges were finding speakers and finding a venue. Thanks to Twitter and LinkedIn, finding speakers turned out to be the easy part.

It was harder to find a venue. It was surprisingly challenging to find a company in NYC with a shared vision that the important thing about being associated with a meetup like this is to be associated with the quality of speakers and audience we can bring in by not allowing transparent product pitches.

Almost every company in Manhattan with space we spoke with had a requirement that they have their own speaker each night. That seemed like a bad idea.

I think it was especially challenging to find a company willing to relax about branding requirements like this because we were a new meetup.

It was pretty frustrating not to find a sympathetic company with space in Manhattan. And the only reason we didn't give up was because Angelo was so adament that this kind of meetup actually happen. It's always best to start something new with someone else for this exact reason. You can keep each other going.

In the end we went with the company that did not insist on their own speaker or their own branding. A Brooklyn-based company whose CEO immediately got in touch with me that they wanted to host us, Trail of Bits.

How it works

To keep things easy, I set up a web page on my personal site with information about the meetup. (Eventually we moved this to nycsystems.xyz.) I set up a Google Form to collect emails for a mailing list. And we started posting about the group on Twitter and LinkedIn.

Very pleased to share the first NYC Systems Talks are taking place next Thursday Feb 22nd 6PM. Hosted by @trailofbits, with @paulgb and @StefanKarpinski speaking.

Space is not infinite, fill out the Google Form if you can attend and would like an invite!https://t.co/jNssr5v1kJ
— Phil Eaton (@eatonphil) February 15, 2024

We published the event calendar in advance (an HTML table on the website) and announced each event's speakers a week in advance of the event. I'd send another Google Form to the mailing list taking RSVPs for the night. The first 60 people to respond got a Google Calendar invite.

It's a bit of work, sure, but I'd do anything to avoid Meetup.com.

It is interesting to see every new systems programming meetup also not pick Meetup.com. The only one that went with it, Munich Database Meetup, is a revival of an existing group, the Munich NoSQL Meetup and presumably they didn't want to give up their subscribers. Though most others use lu.ma.

The mailing list is now about 400+ people. And in each event RSVP we have a wait list of 20-30 people. Of course although 60 people say Yes initially, by the time of the event we have typically gotten about 50 people in attendance.

At each event, Trail of Bits provided screens, chairs, food, and drink. Angelo had recording equipment so he took over audio/video capturing (and later editing and publishing).

After each event we'd publish talk videos to our @NYCSystems Youtube.

Network effects

In March 2024, the TUMuchData folks joined Alex Petrov's Munich NoSQL Meetup to form the Munich Database Meetup. In May, Kaivalya Apte and Manish Gill started the Berlin Systems Group, inspired by Alex and the Munich Database Meetup.

I want to start a Berlin Database/Storage systems group, where we have regular meetups, discussions and talks.

WDYT? @mgill25 @mehd_io @ClickHouseDB @SnowflakeDB @awscloud @GoogleDE @TUBerlin

Can I get some support? Who else would be interested? #Databases

Thanks…
— Kaivalya Apte - The Geek Narrator (@thegeeknarrator) May 15, 2024

In May 2024, two PhD students in the San Francisco Bay Area, Shadaj Laddad and Conor Power, started the SF Distributed Systems meetup.

We’re super excited to be organizing a new SF Distributed Systems meetup NEXT WEEK! Our first meetup features @julianhyde and @conor_power23 presenting work on extending SQL and applying algebraic properties, sign up at https://t.co/d2lLDaQ5iJ
— Shadaj Laddad (@ShadajL) May 15, 2024

And in July 2024, Shraddha Agrawal, Anirudh Rowjee and friends kicked off the first Bengaluru Systems Meetup.

Are you ready, Systems Enthusiasts of Bengaluru?

Speaking at our first-ever meetup on 6th July, we have:@simsimsandy with "Learn about the systems that power GenAI applications" and @vivekgalatage with "The Browser Backstage: Performance vs Security"
(talks linked below!)
— Bengaluru Systems Meetup (@BengaluruSys) July 4, 2024

Suggestions

First off, don't pay for anything yourself. Find a company who will host. At the same time, don't feel the need to give in too much to the demands of the company. I'd be happy to help you think through how to talk about the event with companies. It is mutually beneficial for them to get to give a 5-minute hiring/product pitch and not need to do extensive branding nor to give a 30-minute product tutorial.

Second, keep a bit of pressure on speakers to not do an overview talk and not to do a product pitch. Suggest that they tell the story of some interesting recent bug or interesting recent feature. What happened? Why was it hard? What did you learn?

Focusing on these types of talks will help you get a really interesting audience.

I have been continuously surprised and impressed at the folks who show up for NYC Systems. It's a mix of technical founders in the systems space, pretty experienced developers in the systems space, graduate students, and developers of all sorts.

I am certain we can only get these kinds of folks to show up because we avoid product pitch-type talks.

Third, finding speakers is still hard! The best approach so far has been to individually message folks in industry and academia who hang out on Twitter. Sending out a public call is easy but doesn't often pan out. So keep an eye on interesting companies in the area.

Another avenue I've been thinking about is messaging VC connections to ask them if they know any engineers/technical founders/CTOs in the area who could give an interesting technical talk.

Fourth, speak with other organizers! I finally met Alex Petrov in person last month and we had a great time talking about the challenges and joys of organizing really high quality meetups.

I'm always happy to chat, DMs are open.

New post telling a bit of the history behind https://t.co/NEh1tm8v3Q; why it only exists due to folks like @georg_kreuzmayr and @ngeloxyz; the explosion of systems meetups around the world; and encouragement and suggestions for future organizers!https://t.co/dwe4TtmXKK pic.twitter.com/ZMLkVYdZDJ
— Phil Eaton (@eatonphil) July 7, 2024

A write-ahead log is not a universal part of durability

Mon, 01 Jul 2024 00:00:00 +0000

A database does not need a write-ahead log (WAL) to achieve durability. A database can write its long-term data structure durably to disk before returning to a client. Granted, this is a bad idea! And granted, a WAL is critical for durability by design in most databases. But I think it's helpful to understand WALs by understanding what you could do without them.

So let's look at what terrible design we can make for a durable database that has no write-ahead log. To motivate the idea of, and build an intuition for, a write-ahead log.

Thank you to Alex Miller for reviewing a version of this post.

But first, what is durability?

Durability

Durability happens in the context of a request a client makes to a data system (either an embedded system like SQLite or RocksDB or a standalone system like Postgres). Durability is a spectrum of guarantees the server provides when a client requests to write some data: that either the request succeeds and the data is safely written to disk, or the request fails and the client must retry or decide to do something else.

It can be difficult to set an absolute definition for durability since different databases have different concepts of what can go wrong with disks (also called a "storage fault model"), or they have no concept at all.

Let's start from the beginning.

An in-memory database

An in-memory database has no durability at all. Here is pseudo-code for an in-memory database service.

db = btree()

def handle_write(req):
  db.update(req.key, req.value)
  return 200, {}

def handle_read(req):
  value = db.read(req.key)
  return 200, {"value": value}

Throughout this post, for the sake of code brevity, imagine that the environment is concurrent and that data races around shared mutable values like db are protected somehow.

Writing to disk

If we want to achieve the most basic level of durability, we can write this database to a file.

f = open("kv.db")
db = btree.init_from_disk(f)

def handle_write(req):
  db.update(req.key, req.value)
  db.write_to_disk(f)
  return 200, {}

def handle_read(req):
  value = db.read(req.key)
  return 200, {"value": value}

btree.write_to_disk will call pwrite(2) under the hood. And we'll assume it does copy-on-write for only changed pages. So imagine we have a large database represented by a btree that takes up 10GiB on disk. With the btree algorithm, if we write a single entry to the btree, often only a single (often 4Kib) page will get written rather than all pages (holding all values) in the tree. At the same time, in the worst case, the entire tree (all 10GiB of data) may need to get rewritten.

But this code isn't crash-safe. If the virtual or physical machine this code is running on reboots, the data we wrote to the file may not actually be on disk.

fsync

File data is buffered by the operating system by default. By general consensus, writing data without flushing the operating system buffer is not considered durable. Every so often a new database will show up on Hacker News claiming to beat all other databases on insert speed until a commenter points out the new database doesn't actually flush data to disk.

In other words, the commonly accepted requirement for durability is that not only do you write data to a file on disk but you fsync(2) the file you wrote. This forces the operating system to flush to disk any data it has buffered.

f = open("kv.db")
db = btree.init_from_disk(f)

def handle_write(req):
  db.update(req.key, req.value)
  db.write_to_disk(f)
  f.fsync() # Force a flush
  return 200, {}

def handle_read(req):
  value = db.read(req.key)
  return 200, {"value": value}

Furthermore you must not ignore fsync failure. How you deal with fsync failure is up to you, but exiting immediately with a message that the user should restore from a backup is sometimes considered acceptable.

Databases don't like to fsync because it's slow. Many major databases offer modes where they do not fsync data files before returning a success to a client. Postgres offers this unsafe mode, though does not default to it and warns against it. MongoDB offers this unsafe mode but does not default to it.

An earlier version of this post said that MongoDB would unsafely flush on an interval. Daniel Gomez Ferro from MongoDB messaged me that while the docs are confusing, the default write concern "majority" does actually imply "j: true" which means data is synchronized (i.e. fsync-ed) before returning a success to a client.

Almost every database trades safety for performance in some regard. For example, few databases but SQLite and Cockroach default to Serializable Isolation. While it is commonly agreed that basically no level below Serializable Isolation (that all other databases default to) can be reasoned about. Other databases offer Serializable Isolation, they just don't default to it. Because it can be slow.

Group commit

But let's get back to fsync. One way to amortize the cost of fsync is to delay requests so that you write data from each of them and then fsync the data from all requests. This is sometimes called group commit.

For example, we could update the database in-memory but have a background thread serialize to disk and call fsync only every 5ms.

f = open("kv.db")
db = btree.init_from_disk(f)

group_commit_sems = []

@background_worker()
def group_commit():
  for:
    if clock() % 5ms == 0:
      db.write_to_disk(f)
      f.fsync() # Durably flush for the group
      for sem in group_commit_sems:
        sem.signal()

def handle_write(req):
  db.update(req.key, req.value)
  sem = semaphore()
  group_commit_sems.push(sem)
  sem.wait()
  return 200, {}

def handle_read(req):
  value = db.read(req.key)
  return 200, {"value": value}

It is critical that handle_write waits to return a success until the write is durable via fsync.

So to reiterate, the key idea for durability of a client request is that you have some version of the client message stored on disk durably with fsync before returning a success to a client.

From now on in this post, when you see "durable" or "durability", it means that the data has been written and fsync-ed to disk.

Optimizing durable writes

A key insight is that it's silly to serialize the entire permanent structure of the database to disk every time a user writes.

We could just write the user's message itself to an append-only log. And then only periodically write the entire btree to disk. So long as we have fsync-ed the append-only log file, we can safely return to the user even if the btree itself has not yet been written to disk.

The additional logic this requires is that on startup we must read the btree from disk and then replay the log on top of the btree.

f = open("kv.db", "rw")
db = btree.init_from_disk(f)

log_f = open("kv.log", "rw")
l = log.init_from_disk()
for log in l.read_logs_from(db.last_log_index):
  db.update(log.key, log.value)

group_commit_sems = []

@background_worker()
def group_commit():
  for:
    log_accumulator = log_page()
    if clock() % 5ms == 0:
      for (log, _) in group_commit_sems:
        log_accumulator.add(log)

      log_f.write(log_accumulator.page()) # Write out all log entries at once
      log_f.fsync() # Durably flush wal data
      for (_, sem) in group_commit_sems:
        sem.signal()

    if clock() % 1m == 0:
      db.write_to_disk(f)
      f.fsync() # Durably flush db data

def handle_write(req):
  db.update(req.key, req.value)
  sem = semaphore()
  log = req
  group_commit_sems.push((log, sem))
  sem.wait() # This time waiting for only the log to be written and flushed, not the btree.
  return 200, {}

def handle_read(req):
  value = db.read(req.key)
  return 200, {"value": value}

This is a write-ahead log!

Consider a few scenarios. One request writes the smallest key ever seen. And one request within the same millisecond writes the largest key ever seen. Writing these to disk on the btree means modifying at least two pages spread out in space on disk.

But if we only have to durably write these two messages to a log, they can likely both be included in the same log page. ("Likely" so long as key and values are small enough that multiple can fit into the same page.)

That is, it's cheaper to write only these small messages representing the client request to disk. And we save the structured btree persistence for a less frequent durable write.

Filesystem and disk bugs

Sometimes filesystems will write data to the wrong place. Sometimes disks corrupt data. A solution to both of these is to checksum the data on write, store the checksum on disk, and confirm the checksum on read. This combined with a background process called scrubbing to validate unread data can help you learn quickly when your data has been corrupted and you must recover from backup.

MongoDB's default storage engine WiredTiger does checksum data by default.

But some databases famous for integrity do not. Postgres does no data checksumming by default:

By default, data pages are not protected by checksums, but this can optionally be enabled for a cluster. When enabled, each data page includes a checksum that is updated when the page is written and verified each time the page is read. Only data pages are protected by checksums; internal data structures and temporary files are not.

SQLite likewise does no checksumming by default. Checksumming is an optional extension:

The checksum VFS extension is a VFS shim that adds an 8-byte checksum to the end of every page in an SQLite database. The checksum is added as each page is written and verified as each page is read. The checksum is intended to help detect database corruption caused by random bit-flips in the mass storage device.

But even this isn't perfect. Disks and nodes can fail completely. At that point you can only improve durability by introducing redundancy across disks (and/or nodes), for example, via distributed consensus.

Other reasons you need a WAL?

Some databases (like SQLite) require a write-ahead log to implement aspects of ACID transactions. But this need not be a requirement for ACID transactions if you do MVCC (SQLite does not). See my previous post on implementing MVCC for details.

Logical replication (also called change data capture (CDC)) is another interesting feature that requires a write-ahead log. The idea is that the log already preserves the exact order and changes that affect the database's "state machine". So we could copy these changes out of the system by tracking the write-ahead log, preserving change order, and apply these changes to a foreign system.

But again, just CDC is not about durability. It's an ancillary feature that write-ahead logs make simple.

Conclusion

A few key points. One, durability primarily matters if it is established before returning a success to the client. Second, a write-ahead log is a cheap way to get durability.

And finally, durability is a spectrum. You need to read the docs for your database to understand what it does and does not.

Here's a new post about durability and write-ahead logs. Write-ahead logs are used almost everywhere. But to build an intuition for why, it is helpful to imagine what you would do without a WAL. And to explore the meaning of durability.https://t.co/nzS2pMz22z pic.twitter.com/m1n9x8CNcp
— Phil Eaton (@eatonphil) July 1, 2024

The limitations of LLMs, or why are we doing RAG?

Mon, 17 Jun 2024 00:00:00 +0000

This is an external post of mine. Click here if you are not redirected.

Confusion is a muse

Fri, 14 Jun 2024 00:00:00 +0000

Some of the most interesting technical blog posts I read come from, and a common reason for posts I write is, confusion. You're at work and you start asking questions that are difficult to answer. You spend a few hours or a day trying to get to the bottom of things.

If you ask a question to very experienced and successful developers at work, they have a tendency not to give context and to simplify things down to a single answer. This may be a good way to make business decisions. (One can't afford to waste an eternity considering everything indefinitely.) But accepting an answer you don't understand is actively harmful for building intuition.

Certainly, sometimes not accepting an answer can be irritating. You'll have to figure that out.

But beyond "go along to get along", another reason we don't pursue what we're confused about is because we're embarrassed that we're confused in the first place. What's worse, the embarrassment we feel naturally grows the more experienced we get. "I've got this job title, I don't want to seem like I don't know what you mean."

But if you fight the embarrassment and pursue your confusion regardless, you'll likely figure something very interesting out. Moreover, you will probably not have been the only person who was confused. At least personally it is quite rare that I am confused about something and no one else is.

So pay attention when you get confused, and consider why it happened. What did you expect to be the case, and how did reality differ? Explore the angles and the options. When you finally understand, think about what led you to that understanding.

Write it down. Put it into an internal Markdown doc, an internal Atlassian doc, an internal Google Slides page, whatever. The medium doesn't matter.

This entire process doesn't come easily. We feel embarrassed. We aren't used to lingering on something we're confused by. We aren't used to writing things down.

But if you can make yourself pause every once in a while and think about what you (or someone around you) got confused by, and if you can force yourself to stop getting embarrassed by what you got confused by, and if you can write down the background and the reasoning that led to your ultimate understanding, you're going to have something pretty interesting to talk about.

You'll contribute to the growth and intuition of your colleagues. And you'll never run out of things to write about.

Confusion is embarrassing. But fight that feeling, and dig into why you're confused. And write it down.

You won't be the only one who was confused. And you'll tend to have something pretty interesting to talk about.https://t.co/IdX1nGBheR pic.twitter.com/KzTjqMxw6u
— Phil Eaton (@eatonphil) June 14, 2024

How I run a software book club

Thu, 30 May 2024 00:00:00 +0000

I've been running software book clubs almost continuously since last summer, about 12 months ago. We read through Designing Data-Intensive Applications, Database Internals, Systems Performance, and we just started Understanding Software Dynamics.

The DDIA discussions were in-person in NYC with about 5-8 consistent attendees. The rest have been over email with 300, 500, and 600 attendees.

This post is for folks who are interested in running their own book club. None of these ideas are novel. I co-opted the best parts I saw from other people running similar things. And hopefully you'll improve on my experience too, should you try.

Despite the length of this post running a book club takes almost no noticeable effort, other than when I need to select and confirm discussion leaders. It is the limited-effort-required to thank that I've kept up the book clubs so consistently.

Google Groups

I run the virtual book clubs over email. I create a Google Group and tell people to send me their email for an invite. I use a Google Form to collect emails since I get many. If you're doing a small group book club you can just collect member emails directly.

In the Google Form I ask people to volunteer to lead discussion for a chapter (or chapters). And I ask for a Twitter/GitHub/LinkedIn account.

When I've gotten enough responses I go through the list and check Twitter/GitHub/LinkedIn info to find people who might have a particularly interesting perspective to lead a discussion.

"Lead a discussion" sounds formal but I mean anything but. All I am looking for is someone to start a new Google Group thread each week and for them to share their thoughts.

For example a discussion leader might share:

What they liked about the chapter
Something new they learned from the chapter
A story about their work that the chapter reminded them of
A little project they hacked on, inspired by reading the chapter
A paper or YouTube video this chapter reminded them of
Something from the chapter that was confusing
Etc.

The "discussion leader" has no responsibility for remaining in the discussion after posting the thread. There just isn't an easy way to say "person who kicks off discussion" than to call them a "discussion leader".

By the way, I didn't do discussion leaders for the first book club, reading DDIA. And that book club took noticeably more effort. Because I organized it, I was effectively the discussion leader every time. Having discussion leaders disperses the effort of the book club. And I think it makes the club much more interesting.

SparkNotes-ification

One thing I noticed happening often was that the discussion leader might do a large summary of the chapter. I greatly appreciate and respect that effort, but I think this is not the ideal thing to happen. Of course you can't control what people do and maybe they really wanted to write a summary. But since noticing this happen I now try to discourage the discussion leader from summarizing since 1) it must be quite time-consuming and 2) it isn't as interesting as some of the above bullet points.

Confirming with leaders

When I have picked out folks who seem like they'd be fun discussion leaders, I bcc email them all asking them to confirm. At the same time I explain what being a discussion leader means. As I just explained it here above.

Each week's discussion gets a new Google Group thread. Discussion happens in responses to the thread.

I ask the discussion leaders to create the new discussion thread between Friday and Saturday their local time.

For folks who don't confirm, I email them one last time and then if they still haven't confirmed I find someone new.

I always lead the first week's discussion so that the discussion leaders can see what I do and so that I can establish the pattern.

Managing leaders

It takes a while to read a book. Sometimes the leaders forget to do their part. If it gets to be Sunday and the discussion leader for the week hasn't started discussion, I email them to gently ask if they are still available to kick off discussion. And if they are not, no worries, I can step in.

I have had to step in a few times to start discussion and it's no problem.

Managing non-leaders

Just as you need to clarify and set expectations for discussion leaders, you need to clarify and set expectations for everyone else.

When I invite people to the Google Group I typically also create an Intro thread where I explain the discussion format.

An annoying aspect of Google Groups is that I cannot limit who can create a thread without limiting who can respond to a thread.

It would simplify things for me if I could limit thread creation to discussion leaders. But since I cannot, I try to repeatedly and explicitly mention in the Intro thread that no one should start a new discussion thread unless they are a discussion leader. And that new threads will come out each weekend to discuss the previous chapter.

Setting the tone

One of the most important things to do in the Intro email is to set the tone. I try to clarify this is a friendly and encouraging group focused on learning and improving ourselves. We have experts in the group and we have noobs in the group and they are all welcome and will all come away with different things.

Why email?

Email seems to be the most time-friendly and demographic-friendly medium. Doing live discussion sounds stressful and difficult to schedule, although I believe Alex Petrov runs live discussions. Email forces you to slow down and think things through. And email is built around an inbox. If you didn't get to read some discussion, you can mark it unread. You can't do that in Discord or Slack.

Avoiding long-term commitments

When I pick a book, aside from picking books I think are likely to be exceptionally well-written, I try to avoid books that we could not finish within 3 months. It concerns me to try to get people to commit to something longer than that.

This has led to some distortion though. Systems Performance has only 16 chapters. One chapter a week means about 3 months in total. But each chapter is 100 pages long.

I was hesitant to do a reading of Understanding Software Dynamics because it has 28 chapters. But each chapter is only 10-15 pages long. So when I decided to go with it, I decided we'd read 2 chapters a week. Each discussion leader is responsible for 2 chapters at a time. That means we can finish within 3 months. And each week we read only 20-30 pages, which is still much more doable than 100 pages of Systems Performance.

On the other hand, we did make it through Systems Performance! Which gives me confidence to pick other books that are physically daunting, should they otherwise seem like a good idea.

A book ends

Many public book clubs go through a book a month and have no ending. That is totally fair. But what I love about the way I organize book clubs is that each reading is unrelated to the next. It's an entirely new signup for each book. You need only "commit" (I mean, you can drop off whenever and definitely people do) to a 3-month reading and then you can justly feel good about yourself and join again in the future or not.

In contrast a paper reading club has no obvious ending, unless you pick all the papers in advance and organize them around a school year or something. This has made running a paper reading club feel more concerning to me. Though I greatly appreciate folks like Aleksey Charapko and Murat Demirbas who do.

Most people don't actively contribute, but they still value it

In a group of 500 people, maybe 1-2% of those people actively contribute to discussion. 5-10 people. But I often hear from people who didn't participate that they still highly valued the group. And this high percentage of non-active-participants is part of why I keep allowing the group size to grow. There's little work I have to do and a bunch of people benefit.

Partipation dies off

People start out excited and chime in to introduce themselves but by the last chapter you're lucky if there are any responses to the discussion leader at all.

This is just how it goes. I don't really see anything wrong about this.

The only way I've tried to combat this is by reassuring everyone that they should keep up with discussion even if it means they must skim the chapter instead of really read it.

I also commonly hear from people that they stop reading because they got behind. So to combat that I also stress in the beginning that technical books are often forgiving and that you should just pick up with whatever chapter we're currently on even if it means skipping some chapters. You can always go back.

Doing it at your company likely won't go well

I wrote about this before. For some reason it's hard to get people who would otherwise join an external reading club to join a company-internal reading club.

Though perhaps I'm just doing it wrong because I hear of others like Elizabeth Garrett Christensen who run an internal software book club successfully.

Good luck, have fun!

That's all I've got. Send me questions if you've got any. But mostly, just give it a shot if you want to and you'll learn!

And if you still don't get it, you can of course just join one of my book clubs. :)

Since folks have asked, here's how I run a software book club.

But also, you could just join and see. :)https://t.co/tXBrLFYbvC pic.twitter.com/4iW8EfZCeY
— Phil Eaton (@eatonphil) May 30, 2024

Implementing MVCC and major SQL transaction isolation levels

Thu, 16 May 2024 00:00:00 +0000

In this post we'll build a database in 400 lines of code with basic support for five standard SQL transaction levels: Read Uncommitted, Read Committed, Repeatable Read, Snapshot Isolation and Serializable. We'll use multi-version concurrency control (MVCC) and optimistic concurrency control (OCC) to accomplish this. The goal isn't to be perfect but to explain the basics in a minimal way.

You don't need to know what these terms mean in advance. I did not understand them before doing this project. But if you've ever dealt with SQL databases, transaction isolation levels are likely one of the dark corners you either 1) weren't aware of or 2) wanted not to think about. At least, this is how I felt.

While there are many blog posts that list out isolation levels, I haven't been able to internalize their lessons. So I built this little database to demonstrate the common isolation levels for myself. It turned out to be simpler than I expected, and made the isolation levels much easier to reason about.

Thank you to Justin Jaffray, Alex Miller, Sujay Jayakar, Peter Veentjer, and Michael Gasch for providing feedback and suggestions.

All code is available on GitHub.

Why do we need transaction isolation?

If you already know the answer, feel free to skip this section.

When I first started working with databases in CRUD applications, I did not understand the point of transactions. I was fairly certain that transactions are locks. I was wrong about that, but more on that later.

I can't remember exact code I wrote, but here's something I could have written:

with database.transaction() as t:
  users = t.query("SELECT * FROM users WHERE group = 'admin';")
  ids = []
  for user in users:
    if some_complex_logic(user):
      ids.append(user.id)

  t.query("UPDATE users SET metadata = 'some value' WHERE id IN ($1);", ids)

I would have thought that all users that were seen from the initial SELECT who matched the some_complex_logic filter would be exactly the same that are updated in my second SQL statement.

And if I were using SQLite, my guess would have been correct. But if I were using MySQL or Postgres or Oracle or SQL Server, and hadn't made any changes to defaults, that wouldn't necessarily be true! We'll discover exactly why throughout this post.

For example, some other connection and transaction could have set a user's group to admin after the initial SELECT was executed. It would then be missed from the some_complex_logic check and from the subsequent UPDATE.

Or, again after our initial SELECT, some other connection could have modified the group for some user that previously was admin. It would then be incorrectly part of the second UPDATE statement.

These are just a few examples of what could go wrong.

This is the realm of transaction isolation. How do multiple transactions running at the same time, interacting with the same data, interact with each other?

The answer is: it depends. The SQL standard itself loosely prescribes four isolation levels. But every database implements these four levels slightly differently. Sometimes using entirely different algorithms. And even among the standard levels, the default isolation level for each database differs.

Funky bugs that can show up across databases and across isolation levels, often dependent on particular details of common ways of implementing isolation levels, create what are called "anomalies". Examples include intimidating terms like "dirty reads" and "write cycles" and G2-Item.

The topic is so complex that we've got decades of research papers critiquing SQL isolation levels, categorization of common isolation anomalies, walkthroughs of anomalies by Martin Kleppmann in Designing Data-Intensive Applications, Martin Kleppman's Hermitage project documenting common anomalies across isolation levels in major databases, and the ACIDRain paper showing isolation-related bugs in major open-source ecommerce projects.

These aren't just random links. They're each quite interesting. And particularly for practitioners who don't know why they should care, check out Designing Data-Intensive Applications and the last link on ACIDRain.

And this is only a small list of some of the most interesting research and writing on the topic.

So there's a wide variety of things to consider:

Not every database implements transaction isolation levels identically, resulting in different behavior
Not all researchers agree, and not all database developers agree, on what any given isolation level means
Not every database has the same default isolation level, and most developers tend not to change the default
Not every developer is correctly using the isolation level they pick (default or not)

Transaction isolation levels are basically vibes. The only truth for real projects is Martin Kleppmann's Hermitage project that catalogs behavior across databases. And a truth some people align with is Generalized Isolation Level Definitions.

So while all these linked works above are authoritative, and even though we can see that there might be some anomalies we have to worry about, the research can still be difficult to internalize. And many developers, my recent self included, do not have a great understanding of isolation levels.

Throughout this post we'll stick to informal definitions of isolation levels to keep things simple.

Let's dig in.

Locks? MVCC?

Historically, databases implemented isolation with locking algorithms such as Two-Phase Locking (not the same thing as Two-Phase Commit). Multi-version concurrency control (MVCC) is an approach that lets us completely avoid locks.

It's worthwhile to note that while we will validly not use locks (implementing what is called optimistic concurrency control or OCC), most MVCC databases do still use locks for certain things (implementing what is called pessimistic concurrency control).

But this is the story of databases in general. There are numerous ways to implement things.

We will take the simpler lockless route.

Consider a key-value database. With MVCC, rather than storing only the value for a key, we would store versions of the value. The version includes the transaction id (a monotonic incrementing integer) wherein the version was created, and the transaction id wherein the version was deleted.

With MVCC, it is possible to express transaction isolation levels almost solely as a set of different visibility rules for a version of a value; rules that vary by isolation level.

So we will build up a general framework first and discuss and implement each isolation level last.

Scaffolding

We'll build an in-memory key-value system that acts on transactions. I usually try to stick with only the standard library for projects like this but I really wanted a sorted data structure and Go doesn't implement one.

In main.go, let's set up basic helpers for assertions and debugging:

package main

import (
        "fmt"
        "os"
        "slices"

        "github.com/tidwall/btree"
)

func assert(b bool, msg string) {
        if !b {
                panic(msg)
        }
}

func assertEq[C comparable](a C, b C, prefix string) {
        if a != b {
                panic(fmt.Sprintf("%s '%v' != '%v'", prefix, a, b))
        }
}

var DEBUG = slices.Contains(os.Args, "--debug")

func debug(a ...any) {
        if !DEBUG {
                return
        }

        args := append([]any{"[DEBUG]"}, a...)
        fmt.Println(args...)
}

As mentioned previously, a value in the database will be defined with start and end transaction ids.

type Value struct {
        txStartId uint64
        txEndId   uint64
        value     string
}

Every transaction will be in an in-progress, aborted, or committed state.

type TransactionState uint8
const (
        InProgressTransaction TransactionState = iota
        AbortedTransaction
        CommittedTransaction
)

And we'll support a few major isolation levels.

// Loosest isolation at the top, strictest isolation at the bottom.
type IsolationLevel uint8
const (
        ReadUncommittedIsolation IsolationLevel = iota
        ReadCommittedIsolation
        RepeatableReadIsolation
        SnapshotIsolation
        SerializableIsolation
)

We'll get into detail about the meaning of the levels later.

A transaction has an isolation level, an id (monotonic increasing integer), and a current state. And although we won't make use of this data yet, transactions at stricter isolation levels will need some extra info. Specifically, stricter isolation levels need to know about other transactions that were in-progress when this one started. And stricter isolation levels need to know about all keys read and written by a transaction.

type Transaction struct {
        isolation  IsolationLevel
        id         uint64
        state      TransactionState

        // Used only by Repeatable Read and stricter.
        inprogress btree.Set[uint64]

        // Used only by Snapshot Isolation and stricter.
        writeset   btree.Set[string]
        readset    btree.Set[string]
}

We'll discuss why later.

Finally, the database itself will have a default isolation level that each transaction will inherit (for our own convenience in tests).

The database will have a mapping of keys to an array of value versions. Later elements in the array will represent newer versions of a value.

The database will also store the next free transaction id it will use to assign ids to new transactions.

type Database struct {
        defaultIsolation  IsolationLevel
        store             map[string][]Value
        transactions      btree.Map[uint64, Transaction]
        nextTransactionId uint64
}

func newDatabase() Database {
        return Database{
                defaultIsolation:  ReadCommittedIsolation,
                store:             map[string][]Value{},
                // The `0` transaction id will be used to mean that
                // the id was not set. So all valid transaction ids
                // must start at 1.
                nextTransactionId: 1,
        }
}

To be thread-safe: store, transactions, and nextTransactionId should be guarded by a mutex. But to keep the code small, this post will not use goroutines and thus does not need mutexes.

There's a bit of book-keeping when creating a transaction, so we'll make a dedicated method for this. We must give the new transaction an id, store all in-progress transactions, and add it to database transaction history.

func (d *Database) inprogress() btree.Set[uint64] {
        var ids btree.Set[uint64]
        iter := d.transactions.Iter()
        for ok := iter.First(); ok; ok = iter.Next() {
                if iter.Value().state == InProgressTransaction {
                        ids.Insert(iter.Key())
                }
        }
        return ids
}

func (d *Database) newTransaction() *Transaction {
        t := Transaction{}
        t.isolation = d.defaultIsolation
        t.state = InProgressTransaction

        // Assign and increment transaction id.
        t.id = d.nextTransactionId
        d.nextTransactionId++

        // Store all inprogress transaction ids.
        t.inprogress = d.inprogress()

        // Add this transaction to history.
        d.transactions.Set(t.id, t)

        debug("starting transaction", t.id)

        return &t
}

And we'll add a few more helpers for completing a transaction, for fetching a transaction by id, and for validating a transaction.

func (d *Database) completeTransaction(t *Transaction, state TransactionState) error {
        debug("completing transaction ", t.id)

        // Update transactions.
        t.state = state
        d.transactions.Set(t.id, *t)

        return nil
}

func (d *Database) transactionState(txId uint64) Transaction {
        t, ok := d.transactions.Get(txId)
        assert(ok, "valid transaction")
        return t
}

func (d *Database) assertValidTransaction(t *Transaction) {
        assert(t.id > 0, "valid id")
        assert(d.transactionState(t.id).state == InProgressTransaction, "in progress")
}

The final bit of scaffolding we'll set up is an abstraction for database connections. A connection will have at most associated one transaction. Users must ask the database for a new connection. Then within the connection they can manage a transaction.

type Connection struct {
        tx *Transaction
        db *Database
}

func (c *Connection) execCommand(command string, args []string) (string, error) {
        debug(command, args)

        // TODO
        return "", fmt.Errorf("unimplemented")
}

func (c *Connection) mustExecCommand(cmd string, args []string) string {
        res, err := c.execCommand(cmd, args)
        assertEq(err, nil, "unexpected error")
        return res
}

func (d *Database) newConnection() *Connection {
        return &Connection{
                db: d,
                tx: nil,
        }
}

func main() {
        panic("unimplemented")
}

And that's it for scaffolding. Now set up the go module and make sure this builds.

$ go mod init gomvcc
go: creating new go.mod: module gomvcc
go: to add module requirements and sums:
        go mod tidy
$ go mod tidy
go: finding module for package github.com/tidwall/btree
go: found github.com/tidwall/btree in github.com/tidwall/btree v1.7.0
$ go build
$ ./gomvcc
panic: unimplemented

goroutine 1 [running]:
main.main()
        /Users/phil/tmp/main.go:166 +0x2c

Great!

Transaction management

When the user asks to begin a transaction, we ask the database for a new transaction and assign it to the current connection.

 func (c *Connection) execCommand(command string, args []string) (string, error) {
         debug(command, args)

+       if command == "begin" {
+               assertEq(c.tx, nil, "no running transactions")
+               c.tx = c.db.newTransaction()
+               c.db.assertValidTransaction(c.tx)
+               return fmt.Sprintf("%d", c.tx.id), nil
+       }
+
         // TODO
         return "", fmt.Errorf("unimplemented")
 }

To abort a transaction, we call the completeTransaction method (which makes sure the database transaction history gets updated) with the AbortedTransaction state.

                return fmt.Sprintf("%d", c.tx.id), nil
        }

+       if command == "abort" {
+               c.db.assertValidTransaction(c.tx)
+               err := c.db.completeTransaction(c.tx, AbortedTransaction)
+               c.tx = nil
+               return "", err
+       }
+
         // TODO
         return "", fmt.Errorf("unimplemented")
 }

And to commit a transaction is similar.

                return "", err
        }

+       if command == "commit" {
+               c.db.assertValidTransaction(c.tx)
+               err := c.db.completeTransaction(c.tx, CommittedTransaction)
+               c.tx = nil
+               return "", err
+       }
+
         // TODO
         return "", fmt.Errorf("unimplemented")
 }

The neat thing about MVCC is that beginning, committing, and aborting a transaction is metadata work. Committing a transaction will get a bit more complex when we add support for Snapshot Isolation and Serializable Isolation, but we'll get to that later. Even then, it will not involve modifying any values we get, set, or delete.

Get, set, delete

Here is where things get fun. As mentioned earlier, the key-value store is actually map[string][]Value. With the more recent versions of a value at the end of the list of values for the key.

For get support, we'll iterate the list of value versions backwards for the key. And we'll call a special new isvisible method to determine if this transaction can see this value. The first value that passes the isvisible test is the correct value for the transaction.

                return "", err
        }

+       if command == "get" {
+               c.db.assertValidTransaction(c.tx)
+
+               key := args[0]
+
+               c.tx.readset.Insert(key)
+
+               for i := len(c.db.store[key]) - 1; i >= 0; i-- {
+                       value := c.db.store[key][i]
+                       debug(value, c.tx, c.db.isvisible(c.tx, value))
+                       if c.db.isvisible(c.tx, value) {
+                               return value.value, nil
+                       }
+               }
+
+               return "", fmt.Errorf("cannot get key that does not exist")
+       }
+
         // TODO
         return "", fmt.Errorf("unimplemented")
 }

I snuck in tracking which keys are read, and we'll also soon sneak in tracking which keys are written. This is necessary in stricter isolation levels. More on that later.

set and delete are similar to get. But this time when we walk the list of value versions, we will set the txEndId for the value to the current transaction id if the value version is visible to this transaction.

Then, for set, we'll append to the value version list with the new version of the value that starts at this current transaction.

                return "", err
        }

+       if command == "set" || command == "delete" {
+               c.db.assertValidTransaction(c.tx)
+
+               key := args[0]
+
+               // Mark all visible versions as now invalid.
+               found := false
+               for i := len(c.db.store[key]) - 1; i >= 0; i-- {
+                       value := &c.db.store[key][i]
+                       debug(value, c.tx, c.db.isvisible(c.tx, *value))
+                       if c.db.isvisible(c.tx, *value) {
+                               value.txEndId = c.tx.id
+                               found = true
+                       }
+               }
+               if command == "delete" && !found {
+                       return "", fmt.Errorf("cannot delete key that does not exist")
+               }
+
+               c.tx.writeset.Insert(key)
+
+               // And add a new version if it's a set command.
+               if command == "set" {
+                       value := args[1]
+                       c.db.store[key] = append(c.db.store[key], Value{
+                               txStartId: c.tx.id,
+                               txEndId:   0,
+                               value:     value,
+                       })
+
+                       return value, nil
+               }
+
+               // Delete ok.
+               return "", nil
+       }
+
        if command == "get" {
                c.db.assertValidTransaction(c.tx)

This time rather than modifying the readset we modify the writeset for the transaction.

And that is how commands get executed!

Let's zoom in to the core of the problem we have mentioned but not implemented: MVCC visibility rules and how they differ by isolation levels.

Isolation levels and MVCC visibility rules

To varying degrees, transaction isolation levels prevent concurrent transactions from messing with each other. The looser isolation levels prevent this almost not at all.

Here is what the 1999 ANSI SQL standard (page 84) has to say.

But as I mentioned in the beginning of the post, we're going to be a bit informal. And we'll mostly refer to Jepsen summaries of each isolation levels.

Read Uncommitted

According to Jepsen, the loosest isolation level, Read Uncommitted, has almost no restrictions. We can merely read the most recent (non-deleted) version of a value, regardless of if the transaction that set it has committed or aborted or not.

func (d *Database) isvisible(t *Transaction, value Value) bool {
        // Read Uncommitted means we simply read the last value
        // written. Even if the transaction that wrote this value has
        // not committed, and even if it has aborted.
        if t.isolation == ReadUncommittedIsolation {
                // We must merely make sure the value has not been
                // deleted.
                return value.txEndId == 0
        }

       assert(false, "unsupported isolation level")
       return false
}

Let's write a test that demonstrates this. We create two transactions, c1 and c2, and set a key in c1. The value set for the key in c1 should be immediately visible if c2 asks for that key. In main_test.go:

package main

import (
        "testing"
)

func TestReadUncommitted(t *testing.T) {
        database := newDatabase()
        database.defaultIsolation = ReadUncommittedIsolation

        c1 := database.newConnection()
        c1.mustExecCommand("begin", nil)

        c2 := database.newConnection()
        c2.mustExecCommand("begin", nil)

        c1.mustExecCommand("set", []string{"x", "hey"})

        // Update is visible to self.
        res := c1.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c1 get x")

        // But since read uncommitted, also available to everyone else.
        res = c2.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c2 get x")

        // And if we delete, that should be respected.
        res = c1.mustExecCommand("delete", []string{"x"})
        assertEq(res, "", "c1 delete x")

        res, err := c1.execCommand("get", []string{"x"})
        assertEq(res, "", "c1 sees no x")
        assertEq(err.Error(), "cannot get key that does not exist", "c1 sees no x")

        res, err = c2.execCommand("get", []string{"x"})
        assertEq(res, "", "c2 sees no x")
        assertEq(err.Error(), "cannot get key that does not exist", "c2 sees no x")
}

Thank you to @glaebhoerl for pointing out that in an earlier version of this post, Read Uncommitted incorrectly made deleted values visible.

That's pretty simple! But also pretty useless if your workload has conflicts. If you can arrange your workload in a way where you know no concurrent transactions will ever read or write conflicting keys though, this could be pretty efficient! The rules will only get more complex (and thus potentially more of a bottleneck) from here on.

But for the most part, people don't use this isolation level. SQLite, Yugabyte, Cockroach, and Postgres don't even implement it. It is also not the default for any major database that does implement it.

Let's get a little stricter.

Read Committed

We'll pull again from Jepsen:

Read committed is a consistency model which strengthens read uncommitted by preventing dirty reads: transactions are not allowed to observe writes from transactions which do not commit.

This sounds pretty simple. In isvisible we'll make sure that the value has a txStartId that is either this transaction or a transaction that has committed. Moreover we will now begin checking against txEndId to make sure the value wasn't deleted by any relevant transaction.

                return value.txEndId == 0
        }

+       // Read Committed means we are allowed to read any values that
+       // are committed at the point in time where we read.
+       if t.isolation == ReadCommittedIsolation {
+               // If the value was created by a transaction that is
+               // not committed, and not this current transaction,
+               // it's no good.
+               if value.txStartId != t.id &&
+                       d.transactionState(value.txStartId).state != CommittedTransaction {
+                       return false
+               }
+
+               // If the value was deleted in this transaction, it's no good.
+               if value.txEndId == t.id {
+                       return false
+               }
+
+               // Or if the value was deleted in some other committed
+               // transaction, it's no good.
+               if value.txEndId > 0 &&
+                       d.transactionState(value.txEndId).state == CommittedTransaction {
+                       return false
+               }
+
+               // Otherwise the value is good.
+               return true
+       }
+
        assert(false, "unsupported isolation level")
        return false
 }

This begins to look useful! We will never read a value that isn't part of a committed transaction (or isn't part of our own transaction). Indeed this is the default isolation level for many databases including Postgres, Yugabyte, Oracle, and SQL Server.

Let's add a test to main_test.go. This is a bit long, but give it a slow read. It is thoroughly commented.

func TestReadCommitted(t *testing.T) {
        database := newDatabase()
        database.defaultIsolation = ReadCommittedIsolation

        c1 := database.newConnection()
        c1.mustExecCommand("begin", nil)

        c2 := database.newConnection()
        c2.mustExecCommand("begin", nil)

        // Local change is visible locally.
        c1.mustExecCommand("set", []string{"x", "hey"})

        res := c1.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c1 get x")

        // Update not available to this transaction since this is not
        // committed.
        res, err := c2.execCommand("get", []string{"x"})
        assertEq(res, "", "c2 get x")
        assertEq(err.Error(), "cannot get key that does not exist", "c2 get x")

        c1.mustExecCommand("commit", nil)

        // Now that it's been committed, it's visible in c2.
        res = c2.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c2 get x")

        c3 := database.newConnection()
        c3.mustExecCommand("begin", nil)

        // Local change is visible locally.
        c3.mustExecCommand("set", []string{"x", "yall"})

        res = c3.mustExecCommand("get", []string{"x"})
        assertEq(res, "yall", "c3 get x")

        // But not on the other commit, again.
        res = c2.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c2 get x")

        c3.mustExecCommand("abort", nil)

        // And still not, if the other transaction aborted.
        res = c2.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c2 get x")

        // And if we delete it, it should show up deleted locally.
        c2.mustExecCommand("delete", []string{"x"})

        res, err = c2.execCommand("get", []string{"x"})
        assertEq(res, "", "c2 get x")
        assertEq(err.Error(), "cannot get key that does not exist", "c2 get x")

        c2.mustExecCommand("commit", nil)

        // It should also show up as deleted in new transactions now
        // that it has been committed.
        c4 := database.newConnection()
        c4.mustExecCommand("begin", nil)

        res, err = c4.execCommand("get", []string{"x"})
        assertEq(res, "", "c4 get x")
        assertEq(err.Error(), "cannot get key that does not exist", "c4 get x")
}

Again this seems great. However! You can easily get inconsistent data within a transaction at this isolation level. If the transaction A has multiple statements it can see different results per statement, even if the transaction A did not modify data. Another transaction B may have committed changes between two statements in this transaction A.

Let's get a little stricter.

Repeatable Read

Again as Jepsen says, Repeatable Read is the same as Read Committed but with the following anomaly not allowed (quoting from the ANSI SQL 1999 standard):

P2 (“Non-repeatable read”): SQL-transaction T1 reads a row. SQL-transaction T2 then modifies or deletes that row and performs a COMMIT. If T1 then attempts to reread the row, it may receive the modified value or discover that the row has been deleted.

To support this, we will add additional checks for the Read Committed logic that make sure the value was not created and not deleted within a transaction that started before this transaction started.

As it happens, this is the same logic that will be necessary for Snapshot Isolation and Serializable Isolation. The additional logic (that makes Snapshot Isolation and Serializable Isolation different) happens at commit time.

                return true
        }

-       assert(false, "unsupported isolation level")
-       return false
+       // Repeatable Read, Snapshot Isolation, and Serializable
+       // further restricts Read Committed so only versions from
+       // transactions that completed before this one started are
+       // visible.
+
+       // Snapshot Isolation and Serializable will do additional
+       // checks at commit time.
+       assert(t.isolation == RepeatableReadIsolation ||
+               t.isolation == SnapshotIsolation ||
+               t.isolation == SerializableIsolation, "invalid isolation level")
+       // Ignore values from transactions started after this one.
+       if value.txStartId > t.id {
+               return false
+       }
+
+       // Ignore values created from transactions in progress when
+       // this one started.
+       if t.inprogress.Contains(value.txStartId) {
+               return false
+       }
+
+       // If the value was created by a transaction that is not
+       // committed, and not this current transaction, it's no good.
+       if d.transactionState(value.txStartId).state != CommittedTransaction &&
+               value.txStartId != t.id {
+               return false
+       }
+
+       // If the value was deleted in this transaction, it's no good.
+       if value.txEndId == t.id {
+               return false
+       }
+
+       // Or if the value was deleted in some other committed
+       // transaction that started before this one, it's no good.
+       if value.txEndId < t.id &&
+               value.txEndId > 0 &&
+               d.transactionState(value.txEndId).state == CommittedTransaction &&
+               !t.inprogress.Contains(value.txEndId) {
+               return false
+       }
+
+       return true
 }

 type Connection struct {

How do I derive these rules? Mostly by writing tests that should pass or fail and seeing what doesn't make sense. I tried to steal from existing projects but these rules were not so simple to discover. Which is part of what I hope makes this project particularly useful to look at.

Let's write a test for Repeatable Read. Again, the test is long but well commented.

func TestRepeatableRead(t *testing.T) {
        database := newDatabase()
        database.defaultIsolation = RepeatableReadIsolation

        c1 := database.newConnection()
        c1.mustExecCommand("begin", nil)

        c2 := database.newConnection()
        c2.mustExecCommand("begin", nil)

        // Local change is visible locally.
        c1.mustExecCommand("set", []string{"x", "hey"})
        res := c1.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c1 get x")

        // Update not available to this transaction since this is not
        // committed.
        res, err := c2.execCommand("get", []string{"x"})
        assertEq(res, "", "c2 get x")
        assertEq(err.Error(), "cannot get key that does not exist", "c2 get x")

        c1.mustExecCommand("commit", nil)

        // Even after committing, it's not visible in an existing
        // transaction.
        res, err = c2.execCommand("get", []string{"x"})
        assertEq(res, "", "c2 get x")
        assertEq(err.Error(), "cannot get key that does not exist", "c2 get x")

        // But is available in a new transaction.
        c3 := database.newConnection()
        c3.mustExecCommand("begin", nil)

        res = c3.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c3 get x")

        // Local change is visible locally.
        c3.mustExecCommand("set", []string{"x", "yall"})
        res = c3.mustExecCommand("get", []string{"x"})
        assertEq(res, "yall", "c3 get x")

        // But not on the other commit, again.
        res, err = c2.execCommand("get", []string{"x"})
        assertEq(res, "", "c2 get x")
        assertEq(err.Error(), "cannot get key that does not exist", "c2 get x")

        c3.mustExecCommand("abort", nil)

        // And still not, regardless of abort, because it's an older
        // transaction.
        res, err = c2.execCommand("get", []string{"x"})
        assertEq(res, "", "c2 get x")
        assertEq(err.Error(), "cannot get key that does not exist", "c2 get x")

        // And again still the aborted set is still not on a new
        // transaction.
        c4 := database.newConnection()
        res = c4.mustExecCommand("begin", nil)

        res = c4.mustExecCommand("get", []string{"x"})
        assertEq(res, "hey", "c4 get x")

        c4.mustExecCommand("delete", []string{"x"})
        c4.mustExecCommand("commit", nil)

        // But the delete is visible to new transactions now that this
        // has been committed.
        c5 := database.newConnection()
        res = c5.mustExecCommand("begin", nil)

        res, err = c5.execCommand("get", []string{"x"})
        assertEq(res, "", "c5 get x")
        assertEq(err.Error(), "cannot get key that does not exist", "c5 get x")
}

Let's get stricter!

Snapshot Isolation

Back to Jepsen for a definition:

In a snapshot isolated system, each transaction appears to operate on an independent, consistent snapshot of the database. Its changes are visible only to that transaction until commit time, when all changes become visible atomically to any transaction which begins at a later time. If transaction T1 has modified an object x, and another transaction T2 committed a write to x after T1’s snapshot began, and before T1’s commit, then T1 must abort.

So Snapshot Isolation is the same as Repeatable Read but with one additional rule: the keys written by any two concurrent committed transactions must not overlap.

This is why we tracked writeset. Every time a transaction modified or deleted a key, we added it to the transaction's writeset. To make sure we abort correctly, we'll add a conflict check to the commit step. (This idea is also well documented in A critique of snapshot isolation. This paper can be hard to find. Email me if you want a copy.)

When a transaction A goes to commit, it will run a conflict test for any transaction B that has committed since this transaction A started.

Serializable Isolation is going to have a similar check. So we'll add a helper for iterating through all relevant transactions, running a check function for any transaction that has committed.

func (d *Database) hasConflict(t1 *Transaction, conflictFn func(*Transaction, *Transaction) bool) bool {
        iter := d.transactions.Iter()

        // First see if there is any conflict with transactions that
        // were in progress when this one started.
        inprogressIter := t1.inprogress.Iter()
        for ok := inprogressIter.First(); ok; ok = inprogressIter.Next() {
                id := inprogressIter.Key()
                found := iter.Seek(id)
                if !found {
                        continue
                }
                t2 := iter.Value()
                if t2.state == CommittedTransaction {
                        if conflictFn(t1, &t2) {
                                return true
                        }
                }
        }

        // Then see if there is any conflict with transactions that
        // started and committed after this one started.
        for id := t1.id; id < d.nextTransactionId; id++ {
                found := iter.Seek(id)
                if !found {
                        continue
                }

                t2 := iter.Value()
                if t2.state == CommittedTransaction {
                        if conflictFn(t1, &t2) {
                                return true
                        }
                }
        }

        return false
}

It was around this point that I decided I did really need a B-Tree implementation and could not just stick to vanilla Go data structures.

Now we can modify completeTransaction to do this check if the transaction intends to commit. If the current transaction A's write set intersects with any other transaction B committed since transaction A started, we must abort.

 func (d *Database) completeTransaction(t *Transaction, state TransactionState) error {
         debug("completing transaction ", t.id)

+
+       if state == CommittedTransaction {
+               // Snapshot Isolation imposes the additional constraint that
+               // no transaction A may commit after writing any of the same
+               // keys as transaction B has written and committed during
+               // transaction A's life.
+               if t.isolation == SnapshotIsolation && d.hasConflict(t, func(t1 *Transaction, t2 *Transaction) bool {
+                       return setsShareItem(t1.writeset, t2.writeset)
+               }) {
+                       d.completeTransaction(t, AbortedTransaction)
+                       return fmt.Errorf("write-write conflict")
+               }
+       }
+
         // Update transactions.
         t.state = state
         d.transactions.Set(t.id, *t)

Lastly, the definition of setsShareItem.

func setsShareItem(s1 btree.Set[string], s2 btree.Set[string]) bool {
        s1Iter := s1.Iter()
        s2Iter := s2.Iter()
        for ok := s1Iter.First(); ok; ok = s1Iter.Next() {
                s1Key := s1Iter.Key()
                found := s2Iter.Seek(s1Key)
                if found {
                        return true
                }
        }

        return false
}

Since Snapshot Isolation shares all the same visibility rules as Repeatable Read, the tests get to be a little simpler! We'll simply test that two transactions attempting to commit a write to the same key fail. Or specifically: that the second transaction cannot commit.

func TestSnapshotIsolation_writewrite_conflict(t *testing.T) {
        database := newDatabase()
        database.defaultIsolation = SnapshotIsolation

        c1 := database.newConnection()
        c1.mustExecCommand("begin", nil)

        c2 := database.newConnection()
        c2.mustExecCommand("begin", nil)

        c3 := database.newConnection()
        c3.mustExecCommand("begin", nil)

        c1.mustExecCommand("set", []string{"x", "hey"})
        c1.mustExecCommand("commit", nil)

        c2.mustExecCommand("set", []string{"x", "hey"})

        res, err := c2.execCommand("commit", nil)
        assertEq(res, "", "c2 commit")
        assertEq(err.Error(), "write-write conflict", "c2 commit")

        // But unrelated keys cause no conflict.
        c3.mustExecCommand("set", []string{"y", "no conflict"})
        c3.mustExecCommand("commit", nil)
}

Not bad! But let's get stricter.

Upon further discussion with Alex Miller, and after reviewing A Critique of ANSI SQL Isolation Levels, the difference I am trying to suggest (between Repeatable Read and Snapshot Isolation) likely does not exist. A Critique of ANSI SQL Isolation Levels mentions Repeatable Read must not exhibit P4 (Lost Update) anomalies. And it mentions that you must check for read-write conflicts to avoid these. Therefore it seems likely that you can't easily separate Repeatable Read from Snapshot Isolation when implemented using MVCC. The differences between Repeatable Read and Snapshot Isolation may more readily show up when implementing transactions the classical way with Two-Phase Locking.

To reiterate, with MVCC and optimistic concurrency control, correct implementations of Repeatable Read and Snapshot Isolation do not seem to be distinguishable. Both require write-write conflict detection.

Serializable Isolation

In terms of end-result, this is the simplest isolation level to reason about. Serializable Isolation must appear as if only a single transaction were executing at a time. Some systems, like SQLite and TigerBeetle, do Actually Serial Execution where only one transaction runs at a time. But few databases implement Serializable like this because it removes a number of fair concurrent execution histories. For example, two concurrent read-only transactions.

Postgres implements serializability via Serializable Snapshot Isolation. MySQL implements serializability via Two-Phase Locking. FoundationDB implements serializability via sequential timestamp assignment and conflict detection.

But the paper, A critique of snapshot isolation, provides a simple (though not necessarily efficient; I have no clue) approach via what they call Write Snapshot Isolation. In their algorithm, if any two transactions read and write set intersect (but not write and write set intersect), the transaction should be aborted. And this (plus Repeatable Read rules) is sufficient for Serializability.

I leave it to that paper for the proof of correctness. In terms of implementing it though it's quite simple and very similar to the Snapshot Isolation we already mentioned.

Inside of completeTransaction add:

                }) {
                        d.completeTransaction(t, AbortedTransaction)
                        return fmt.Errorf("write-write conflict")
+               }
+
+               // Serializable Isolation imposes the additional constraint that
+               // no transaction A may commit after reading any of the same
+               // keys as transaction B has written and committed during
+               // transaction A's life, or vice-versa.
+               if t.isolation == SerializableIsolation && d.hasConflict(t, func(t1 *Transaction, t2 *Transaction) bool {
+                       return setsShareItem(t1.readset, t2.writeset) ||
+                               setsShareItem(t1.writeset, t2.readset)
+               }) {
+                       d.completeTransaction(t, AbortedTransaction)
+                       return fmt.Errorf("read-write conflict")
                }
        }

And if we add a test for read-write conflicts:

func TestSerializableIsolation_readwrite_conflict(t *testing.T) {
        database := newDatabase()
        database.defaultIsolation = SerializableIsolation

        c1 := database.newConnection()
        c1.mustExecCommand("begin", nil)

        c2 := database.newConnection()
        c2.mustExecCommand("begin", nil)

        c3 := database.newConnection()
        c3.mustExecCommand("begin", nil)

        c1.mustExecCommand("set", []string{"x", "hey"})
        c1.mustExecCommand("commit", nil)

        _, err := c2.execCommand("get", []string{"x"})
        assertEq(err.Error(), "cannot get key that does not exist", "c2 get x")

        res, err := c2.execCommand("commit", nil)
        assertEq(res, "", "c2 commit")
        assertEq(err.Error(), "read-write conflict", "c2 commit")

        // But unrelated keys cause no conflict.
        c3.mustExecCommand("set", []string{"y", "no conflict"})
        c3.mustExecCommand("commit", nil)
}

We see it work! And that's it for a basic implementation of MVCC and major transaction isolation levels.

Production-quality testing

There are two major projects I'm aware of that help you test transaction implementations: Elle and Hermitage. These are probably where I'd go looking if I were implementing this for real.

This project took me long enough on its own and I felt reasonably comfortable with my tests that the gist of my logic was right that I did not test further. For that reason it surely has bugs.

Vacuuming and cleanup

One of the major things this implementation does not do is cleaning up old data. Eventually, older versions of values will be required by no transactions. They should be removed from the value version array. Similarly, eventually older transactions will be required by no transactions. They should be removed from the database transaction history list.

Even if we had the vacuuming process in place though, what about some extreme use patterns. What if a key's value was always going to be 1GB long. And what if multiple transactions made only small changes to the 1GB data. We'd be duplicating a lot of the value across versions.

It sounds less extreme when thinking about storing rows of data rather than key-value data. If a user has 100 columns and only updates one column a number of times, in our scheme we'd end up storing a ton of duplicate cell data for a row.

This is a real-world issue in Postgres that was called out by Andy Pavlo and the Ottertune folks. It turns out that Postgres alone among major databases stores the entire value for every version. In contrast, other major databases like MySQL store a diff.

Conclusion

This post only begins to demonstrate that database behavior differs quite a bit both in terms of results and in terms of optimizations. Everyone implements the ideas differently and to varying degrees.

Moreover, we have only begun to implement the behavior a real SQL database supports. For example, how do visibility rules and conflict detection work with range queries? What about sub-transactions, and save points? These will have to be covered another time.

Hopefully seeing this simple implementation of MVCC and visibility rules helps to clarify at least some of the basics.

Here's a new post walking through an implementation of MVCC and major SQL transaction isolation levels, in 400 lines of Go code.

These ideas might sound esoteric, but they impact almost every developer using any database.https://t.co/crFKM74R5h pic.twitter.com/o9awTPpvvx
— Phil Eaton (@eatonphil) May 16, 2024

What makes a great technical blog

Wed, 10 Apr 2024 00:00:00 +0000

I want to explain why the blogs in My favorite technical blogs are my favorite. That page is solely about non-corporate tech blogs. So this post is too. I'll have to make another list for favorite corporate tech blogs.

In short, they:

Tackle hard and confusing topics
Show working code
Make things simpler
Write regularly
Talk about tradeoffs and downsides
Avoid internet slang, memes, swearing, sarcasm, and ranting

Tackle hard and confusing topics

There are a number of problems in programming and computer science where otherwise knowledgeable programmers have to start mumbling about, or revert to cliches or group-think, because they aren't sure.

These are the best topics you can possibly dive deep into. And my favorite writers do exactly this.

They write about durability guarantees of disks and filesystems. They write about common pitfalls in benchmarking. They write about database consistency anomalies. They write about threading and IO models.

And they write about it by showing concrete examples and concrete logic so you can learn how to stop handwaving on the topic.

Their writing helps you come out with a useful mental model you can apply to your own problems.

And you know, sometimes it's not about the topic being obscure. Good writers have the ability to tackle a boring topic in an interesting light. Maybe by digging deeper into a root cause. Or showing you the history behind the scenes.

Moreover, my favorite writers don't know everything. But they also don't pretend to know everything. They're quick to admit they don't understand something and ask for help from their readers.

Show working code

I love to see complete working code in a post. In contrast there are many projects that start out simple and people write an article that covers the project at a high level. But they keep working on the project and it becomes more complex.

It's not always easy to follow commits over time.

Eli Bendersky and Serge Zaitsev are particularly great at developing small but meaningful projects in a single post or short series.

On the other hand, if people only did this, we wouldn't hear about the development of long-running projects like V8 or Postgres. So I guess this style has limits. And I don't penalize people talking about long-running projects for not showing working code.

Make things simpler

One of the marks of a good writer is that you can make complex topics simple. And not just by being reductive. Though sometimes even being reductive is useful for education.

In contrast I sometimes see articles by less experienced writers and I marvel how they make a simple topic so complex. I recognize this because I was absolutely like that 10 years ago, if not 5 years ago.

Write regularly

My favorite blogs typically get a new post at least once a month. Some people, like Murat, write once a week.

I think the practice probably does improve your writing but mostly it's that they keep my attention by publishing regularly!

Talk about tradeoffs and downsides

Nothing builds trust like talking about the issues with something you built. No project is perfect. And to ignore the downsides risks seeming like you don't know or understand them.

So the writers I like the most talk about decisions in context. They talk about the good and the bad.

Avoid internet slang, memes, swearing, sarcasm, and ranting

There's no way I can think of talking about this without sounding super lame.

One thing I've noticed, particularly among younger colleagues, is the use of memes or swearing or using 4chan slang or using sarcasm. I used to write like this 10 years ago too.

There is a chunk of your audience who won't care. The problem is that there's also a chunk of your (potential) audience who definitely does care. There's even a chunk of your audience who may not care but just won't understand (i.e. non-native English speakers).

I have friends and folks I respect who write very well. But that also are also overly, unnecessarily edgy when they write. I don't like sharing these posts because I don't want to unnecessarily offend or turn off people either.

Closing thoughts

It would be boring if everyone wrote the same way. I'm glad the internet is fun and weird. But I wanted to share a few things that go into my favorite technical blogs that I'm always happy to refer people to.

I wrote a short post on what I think makes a great technical blog.https://t.co/QRFtQyQyU5 pic.twitter.com/QpsQC90EX5
— Phil Eaton (@eatonphil) April 10, 2024

A paper reading club at work; databases and distributed systems research

Fri, 05 Apr 2024 00:00:00 +0000

I started a paper reading club this week at work, focused on databases and distributed systems research. I posted in a general channel about the premise and asked if anyone was interested. I clarified the intended topic and that discussions would be asynchronous over email, run fortnightly.

Weekly seemed way too often. A month seemed way too infrequent. Fortnightly seemed decent.

I was nervous to do this because I've been here about 2 months. In the past I would have waited 6 months or a year to do this. But I don't know. If you see something you think should exist, why wait?

The only other consideration was past experiences I've written about having difficulty getting engagement with clubs at work. But EDB has near 1,000 employees. I figured there might at least be a couple interested.

Furthermore I figured if I only got a few people this entire idea would at least benefit myself, since I have been wanting to force myself to build a paper reading habit. And if no one responded, it would be only mildly embarassing and I'd not pursue it further.

But after a day, about 6 people showed interest. Which was better than I hoped! Folks from product management, support, development, and beyond.

So I opened a dedicated channel and asked people to start submitting papers and voting on them. One of my teammates started submitting some great papers on caches and reference counting.

I picked a first one, the Redshift paper, to get us started. Demonstrating the process to avoid confusion. And I made a calendar invite for everyone in the channel, the paper linked in the invite. I clarified in the invite that it was just a reminder and that the real discussion would still be async over email. (I've found it's best to repeatedly clarify process stuff.)

Once I had these first few folks interested I was able to post again in a broader company channel that a couple of us were starting this paper club. By the end of the day the dedicated channel was 29 folks. All in about 2 days.

Mailing lists are nicer than Slack or Discord in my opinion because they sort of force you to slow down, they are harder to miss (if someone starts a thread after you've seen a message in Slack or Discord, you tend to miss it), and easier to manage (read/unread).

Engineers often seem to get overwhelmed by a mass of Slack messages. Whereas they seem to be a bit more comfortable with email threads.

All of this is all the more important when you're running a global group. EDB has people everywhere.

Why do this?

Before I dropped out of college I did a research internship with a VLSI group at Harvard SEAS. And my favorite part was that they had a weekly (or biweekly?) Wednesday paper reading session where 15 people from the lab and adjacent labs would eat pizza after hours and discuss a paper.

I've been dying to recreate this at a company ever since. Since EDB is so distributed, we won't be discussing over pizza. But I'm still excited.

And I hope my experience serves as a blueprint for others.

I started a paper reading club at work, wrote about it as a possible blueprint for others.

I'm excited! I've wanted to have a gang at work with whom to read papers for a long time.https://t.co/vpwERj8pHe
— Phil Eaton (@eatonphil) April 6, 2024

Finding memory leaks in Postgres C code

Wed, 27 Mar 2024 00:00:00 +0000

This is an external post of mine. Click here if you are not redirected.

Zig, Rust, and other languages

Fri, 15 Mar 2024 00:00:00 +0000

Having worked a bit in Zig, Rust, Go and now C, I think there are a few common topics worth having a fresh conversation on: automatic memory management, the standard library, and explicit allocation.

Zig is not a mature language. But it has made enough useful choices for a number of companies to invest in it and run it in production. The useful choices make Zig worth talking about.

Go and Rust are mature languages. But they have both made questionable choices that seem worth talking about.

All of these languages are developed by highly intelligent folks I personally look up to. And your choice to use any one of these is certainly fine, whichever it is.

The positive and negative choices particular languages made, though, are worth talking about as we consider what a systems programming language 10 years from now would look like. Or how these languages themselves might evolve in the next 10 years.

My perspective is mostly building distributed databases. So the points that I bring up may have no relevance to the kind of work you do, and that's alright. Moreover, I'm already aware most of these opinions are not shared by the language maintainers, and that's ok too. I am not writing to convince anyone.

Automatic memory management

One of my bigger issues with Zig is that it doesn't support RAII. You can defer cleanup to the end of a block; and this is half of the problem. But only RAII will allow for smart pointers and automatic (not manual) reference counting. RAII is an excellent option to default to, but in Zig you aren't allowed to. In contrast, even C "supports" automatic cleanup (via compiler extensions).

But most of the time, arenas are fine. Postgres is written in C and memory is almost entirely managed through nested arenas (called "memory contexts") that get cleaned up when some subset of a task finishes, recursively. Zig has builtin support for arenas, which is great.

Standard library

It seems regrettable that some languages have been shipping smaller standard libraries. Smaller standard libraries seem to encourage users of the language to install more transitively-unvetted third-party libraries, which increases build time and build flakiness, and which increases bitrot over time as unnecessary breaking changes occur.

People have been making jokes about node_modules for a decade now, but this problem is just as bad in Rust codebases I've seen. And to a degree it happens in Java and Go as well, though their larger standard libraries allow you to get further without dependencies.

Zig has a good standard library, which may be Go and Java tier in a few years. But one goal of their package manager seemed to be to allow the standard library to be broken up; made smaller. For example, JSON support moving out of the standard library into a package. I don't know if that is actually the planned direction. I hope not.

Having a large standard library doesn't mean that the programmer shouldn't be able to swap out implementations easily as needed. But all that is required is for the standard library to define an interface along with the standard library implementation.

The small size of the standard library doesn't just affect developers using the language, it even encourages developers of the language itself to depend on libraries owned by individuals.

Take a look at the transitive dependencies of an official Node.js package like node-gyp. Is it really the ideal outcome of a small standard library to encourage dependence in official libraries on libraries owned by individuals, like env-paths, that haven't been modified in 3 years? 68 lines of code. Is it not safer at this point to vendor that code? i.e. copy the env-paths code into node-gyp.

Similarly, if you go looking for compression support in Rust, there's none in the standard library. But you may notice the flate2-rs repo under the official rust-lang GitHub namespace. If you look at its transitive dependencies: flate2-rs depends on (an individual's) miniz_oxide which depends on (an individual's) adler that hasn't been updated in 4 years. 300 lines of code including tests. Why not vendor this code? It's the habits a small standard library builds that seem to encourage everyone not to.

I don't mean these necessarily constitute a supply-chain risk. I'm not talking about left-pad. But the pattern is sort of clear. Even official packages may end up depending on external party packages, because the commitment to a small standard library meant omitting stuff like compression, checksums, and common OS paths.

It's a tradeoff and maybe makes the job of the standard library maintainer easier. But I don't think this is the ideal situation. Dependencies are useful but should be kept to a reasonable minimum.

Hopefully languages end up more like Go than like Rust in this regard.

Explicit allocation

When folk discuss the Zig standard library's pattern of requiring an allocator argument for every method that allocates, they often talk about the benefit of swapping out allocators or the benefit of being able to handle OOM failures.

Both of these seem pretty niche to me. For example, in Zig tests you are encouraged to pass around a debug allocator that tells you about memory leaks. But this doesn't seem too different from compiling a C project with a debug allocator or compiling with different sanitizers on and running tests against the binary produced. In both cases you mostly deal with allocators at a global level depending on the environment you're running the code in (production or tests).

The real benefit of explicit allocations to me is much more trivial. You basically can't code a method in Zig without acknowledging allocations.

This is particularly useful for hotpath code. Take an iterator for example. It has a new() method, a next() method, and a done() method. In most languages, it's basically impossible at the syntax or compiler-level to know if you are allocating in the next() method. You may know because you know the behavior of all the code in next() by heart. But that won't happen all the time.

Zig is practically alone in that if you write the next() method and and don't pass an allocator to any method in the next() body, nothing in that next() method will allocate.

In any other language it might not be until you run a profiler that you notice an allocation that should have been done once in new() accidentally ended up in next() instead.

On the other hand, for all the same reasons, writing Zig is kind of a pain because everything takes an allocator!

Explicit allocation is not intrinsic to Zig, the language. It is a convention that is prevalent in the standard library. There is still a global allocator and any user of Zig could decide to use the global allocator. At which point you've got implicit allocation. So explicit allocation as a convention isn't a perfect solution.

But it, by default, gives you a level of awareness of allocations you just can't get from typical Go or Rust or C code, depending on the project's practices. Perhaps it's possible to switch off the Go, Rust and C standard library and use one where all functions that allocate do require an allocator.

But explicitly passing allocators is still sort of a visual hack.

I think the ideal situation in the future will be that every language supports annotating blocks of code as must-not-allocate or something along those lines. Either the compiler will enforce this and fail if you seem to allocate in a block marked must-not-allocate, or it will panic during runtime so you can catch this in tests.

This would be useful beyond static programming languages. It would be as interesting to annotate blocks in JavaScript or Python as must-not-allocate too.

Otherwise the current state of things is that you'd normally configure this sort of thing at the global level. Saying "there must not be any allocations in this entire program" just doesn't seem as useful in general as being able to say "there must not be any allocations in this one block".

Optional, not required, allocator arguments

Rust has nascent support for passing an allocator to methods that allocate. But it's optional. From what I understand, C++ STL is like this too.

These are both super useful for programming extensions. And it's one of the reasons I think Zig makes a ton of sense for Postgres extensions specifically. Because it was only and always ever built for running in an environment with someone else's allocator.

Praise for Zig, Rust, and Go tooling

All three of these have really great first-party tooling including build system, package management, test runners and formatters. The idea that the language should provide a great environment to code in (end-to-end) makes things simpler and nicer for programmers.

Meandering non-conclusion

Use the language you want to use. Zig and Rust are both nice alternatives to writing vanilla C.

On the other hand, I've been pleasantly surprised writing Postgres C. How high level it is. It's almost a separate language since you're often dealing with user-facing constructs, like Postgres's Datum objects which represent what you might think of as a cell in a Postgres database. And you can use all the same functions provided for Postgres SQL for working with Datums, but from C.

I've also been able work a bit on Postgres extensions in Rust with pgrx lately, which I hope to write about soon. And when I saw pgzx for writing Postgres extensions in Zig I was excited to spend some time with that too.

Wrote a post on my wishlist for Zig and Rust. Focused on automatic memory management, the standard library, and explicit allocation.https://t.co/dvynizU9V2 pic.twitter.com/iTXp5QVxj0
— Phil Eaton (@eatonphil) March 15, 2024

First month on a database team

Mon, 11 Mar 2024 00:00:00 +0000

A little over a month ago, I joined EnterpriseDB on a distributed Postgres product (PGD). The process of onboarding myself has been pretty similar at each company in the last decade, though I think I've gotten better at it. The process is of course influenced by the team, and my coworkers have been excellent. Still, I wanted to share my thought process and personal strategies.

Avoid, at first, what is always challenging

Trickier things at companies are the people, organization, and processes. What code exists? How does it work together? Who owns what? How can I find easy code issues to tackle? How do I know what's important (so I can avoid picking it up and becoming a bottleneck).

But also, in the first few days or weeks you aren't exactly expected to contribute meaningfully to features or bugs. Your sprint contributions are not tracked too closely.

The combination of 1) what to avoid and 2) the sprint-freedom-you-have leads to a few interesting and valuable areas to work on on your own: the build process, tests, running the software, and docs.

But code need not be ignored either. Some frequent areas to get your first code contributions in include user configuration code, error messages, and stale code comments.

What follows are some little 1st day, 1st week, 1st month projects I went through to bootstrap my understanding of the system.

Build process

First off, where is the code and how do you build it? This requires you to have all the relevant dependencies. Much of my work is on a Postgres extension. This meant having a local Postgres development environment, having gcc, gmake (on mac), Perl, and so on. And furthermore, PGD is a pretty mature product so it supports building against multiple Postgres distributions. Can I build against all of them?

The easiest situation is when there are instructions for all of this, linked directly from your main repo. When I started, the instructions did exist but in a variety of places. So over the first week I started collecting all of what I had learned about building the system, with dependencies, across distributions, and with various important flags (debug mode on, asserts enabled, etc.). I finished the first week by writing a little internal blog post called "Hacking on PGD".

I hadn't yet figured out the team processes so I didn't want to bother anyone by trying to get this "blog post" committed anywhere yet as official internal documentation. Maybe there already was a good doc, I just hadn't noticed it yet. So I just published it in a private Confluence page and shared it in the private team slack. If anyone else benefited from it, great! Otherwise, I knew I'd want to refer back to it.

This is an important attitude I think. It can be hard to tell what others will benefit from. If you get into the habit of writing things down internally for your own sake, but making it available internally, it is likely others will benefit from it too. This is something I've learned from years of blogging publicly outside of work.

Moreover, the simple act of writing a good post creates yourself as something of an authority. This is useful for yourself if no one else.

Writing a good post

Let's get distracted here for a second. One of the most important things I think in documentation is documenting not just what does exist but what doesn't. If you had to take a path to get something to work, did you try other paths that didn't work? It can be extremely useful to figure out what exactly is required for something.

Was there a flag that you tried to build with but you didn't try building without it? Well try again without it and make sure it was necessary. Was there some process you executed where the build succeeded but you can't remember if it was actually necessary for the build to succeed?

It's difficult to explain why I think this sort of precision is useful but I'm pretty sure it is. Maybe because it builds the habit of not treating things as magic when you can avoid it. It builds the habit of asking questions (if only to yourself) to understand and not just to get by.

Static analysis? Dynamic analysis?

Going back to builds, another aspect to consider is static and dynamic analysis. Are there special steps to using gdb or valgrind or other analyzers? Are you using them already? Can you get them running locally? Has any of this been documented?

Maybe the answer to all of those is yes, or maybe none of those are relevant but there are likely similar tools for your ecosystem. If analysis tools are relevant and no one has yet explored them, that's another very useful area to explore as a newcomer.

Testing

After I got the builds working, I felt the obvious next step was to run tests. But what tests exist? Are there unit tests? Integration tests? Anything else? Moreover, is there test coverage? I was certain I'd be able to find some low-hanging contributions to make if I could find some files with low test coverage.

Alas, my certainty hit the wall in that there were in fact too many types of integration tests that all do provide coverage already. They just don't all report coverage.

The easiest ways to report coverage (with gcov) were only reporting coverage for certain integration tests that we run locally. There are more integration tests run in cloud environments and getting coverage reports there to merge with my local coverage files would have required more knowledge of people and processes, areas that I wanted not to be forced to think about too quickly.

So coverage wasn't a good route to go. But around this time, I noticed a ticket that asked for a simple change to user configuration code. I was able to make the change pretty quickly and wanted to add tests. We have our own test framework built on top of Postgres's powerful Perl test framework. But it was a little difficult to figure out how to use either of them.

So I copied code from other tests and pared it down until I got the smallest version of test code I could get. This took maybe a day or two of tweaking lines and rerunning tests since I didn't understand everything that was/wasn't required. Also it's Perl and I've never written Perl before so that took a bit of time and ChatGPT. (Arrays, man.)

In the end though I was able to collect my learnings into another internal confluence post just about how to write tests, how to debug tests, how to do common things within tests (for example, ensuring a Postgres log line was outputted), etc. I published this post as well and shared it in the team Slack.

Running

I had PGD built locally and was able to run integration tests locally, but I still hadn't gotten a cluster running. Nor played with the eventual consistency demos I knew we supported. We had a great quickstart that ran through all the manual steps of getting a two-node cluster up. This was a distillation, for devs, of a more elaborate process we give to customers in a production-quality script.

But I was looking for something in between a production-quality script and manually initializing a local cluster. And I also wanted to practice my understanding of our test process. So I ported our quickstart to our integration test framework and made a PR with this new test, eventually merging this into the repo. And I wrote a minimal Python script for bringing up a local cluster. I've got an open PR to add this script to the repo. Maybe I'll learn though that a simple script such as this does already exist, and that's fine!

Docs

The entire time, as I'd been trying to build and test and run PGD, I was trying to understand our terminology and architecture by going through our public docs. I had a lot of questions coming out of this I'd ask in the team channel.

Not to toot my horn but I think it's somewhat of a superpower to be able/willing to ask "dumb questions" in a group setting. That's how I frame it anyway. "Dumb question: what does X mean in this paragraph?" Or, "dumb question: when we say performance improvement because of Y, what is the intuition here?" Because of the time spent here, I was able to make a few more docs contributions as I read through the docs as well.

You have to balance where you ask your dumb questions though. Asking dumb questions to one person doesn't benefit the team. But asking dumb questions in too wide a group is sometimes bad politics. Asking "dumb questions" in front of your team seems to have the best bang for buck.

But maybe the more important contributions were, when I got more comfortable with the team, proposing to merge my personal, internal Confluence blog posts into the repo as docs. I think in a number of cases, what I wrote about indeed hadn't been concisely collected before and thus was useful to have as team documentation.

Even more challenging was trying to distill (a chunk of) the internal architecture. Only after following many varied internal docs and videos, and following through numerous code paths, was I able to propose an architecture diagram outlining major components and communication between them, with their differing formats (WAL records, internal enums, etc.) and means of communication (RPC, shared memory, etc.). This architecture diagram is still in review and may be totally off. But it's already helped at least me think about the system.

In most cases this was all information that the team had already written or explained but just bringing it together and summarizing provided a different useful perspective I think. Even if none of the docs got merged it still helped to build my own understanding.

Beyond the repo

Learning the project is just one aspect of onboarding. Beyond that I join the #cats channel, the #dogs channel, found some fellow New Yorkers and opened a NYC channel, and tried to find Zoom-time with the various people I'd see hanging around common team Slack channels. Trying to meet not just devs but support folk, product managers, marketing folk, sales folk, and anyone else!

Walking the line between scouring our docs and GitHub and Confluence and Jira on my own, and bugging people with my incessant questions.

I've enjoyed my time at startups. I've been a dev, a manager, a founder, a cofounder. But I'm incredibly excited to be back, at a bigger company, full-time as a developer hacking on a database!

And what about you? What do you do to onboard yourself at a new company or new project?

I've been having an absolute blast in my first month at EDB and I wanted to share a few of my strategies for onboarding myself on a database team. Strategies broadly applicable for devs on a new team/project.https://t.co/TS5qRLysuA pic.twitter.com/lvuxDBQJwx
— Phil Eaton (@eatonphil) March 12, 2024

An intuition for distributed consensus in OLTP systems

Thu, 08 Feb 2024 00:00:00 +0000

Distributed consensus in transactional databases (e.g. etcd or Cockroach) is a big deal these days. Most often under the hood are variations of log-based Paxos-like algorithms such as MultiPaxos, Viewstamped Replication, or Raft. While there are new variations that come out each year, optimizing for various workloads, these algorithms are fairly standard and well-understood.

In fact they are used in so many places, Kubernetes for example, that even if you don't decide to implement Raft (which is fun and I encourage it), it seems worth building an intuition for distributed consensus.

What happens as you tweak a configuration. What happens as the production environment changes. Or what to reach for as product requirements change.

I've been thinking about the basics of distributed consensus recently. There has been a lot to digest and characterize. And I'm only beginning to get an understanding.

This post is an attempt to share some of the intuition built up reading about and working in this space. Originally this post was also going to end with a walkthrough of my most recent Raft implementation in Rust. But I'm going to hold off on that for another time.

I was fortunate to have a few excellent reviewers looking at versions of this post: Paul Nowoczynski, Alex Miller, Jack Vanlightly, Daniel Chia, and Alex Petrov. Thank you!

Let's start with Raft.

Raft

Raft is a distributed consensus algorithm that allows you to build a replicated state machine on top of a replicated log.

A Raft library handles replicating and durably persisting a sequence (or log) of commands to at least a majority of nodes in a cluster. You provide the library a state machine that interprets the replicated commands. From the perspective of the Raft library, commands are just opaque byte strings.

For example, you could build a replicated key-value store out of SET and GET commands that are passed in by a client. You provide a Raft library state machine code that interprets the Raft log of SET and GET commands to modify or read from an in-memory hashtable. You can find concrete examples of exactly this replicated key-value store modeling in previous Raft posts I've written.

All nodes in the cluster run the same Raft code (including the state machine code you provide); communicating among themselves. Nodes elect a semi-permanent leader that accepts all reads and writes from clients. (Again, reads and writes are modeled as commands).

To commit a new command to the cluster, clients send the command to all nodes in the cluster. Only the leader accepts this command, if there is currently a leader. Clients retry until there is a leader that accepts the command.

The leader appends the command to its log and makes sure to replicate all commands in its log to followers in the same order. The leader sends periodic heartbeat messages to all followers to prolong its term as leader. If a follower hasn't heard from the leader within a period of time, it becomes a candidate and requests votes from the cluster.

When a follower is asked to accept a new command from a leader, it checks if its history is up-to-date with the leader. If it is not, the follower rejects the request and asks the leader to send previous commands to bring it up-to-date. It does this ultimately, in the worst case of a follower that has lost all history, by going all the way back to the very first command ever sent.

When a quorum (typically a majority) of nodes has accepted a command, the leader marks the command as committed and applies the command to its own state machine. When followers learn about newly committed commands, they also apply committed commands to their own state machine.

For the most part, these details are graphically summarized in Figure 2 of the Raft paper.

Availability and linearizability

Taking a step back, distributed consensus helps a group of nodes, a cluster, agree on a value. A client of the cluster can treat a value from the cluster as if the value was atomically written to and read from a single thread. This property is called linearizability.

However, with distributed consensus, the client of the cluster has better availability guarantees from the cluster than if the client atomically wrote to or read from a single thread. A single thread that crashes becomes unavailable. But some number f nodes can crash in a cluster implementing distributed consensus and still 1) be available and 2) provide linearizable reads and writes.

That is: distributed consensus solves the problem of high availability for a system while remaining linearizable.

Without distributed consensus you can still achieve high availability. For example, a database might have two read replicas. But a client reading from a read replica might get stale data. Thus, this system (a database with two read replicas) is not linearizable.

Without distributed consensus you can also try synchronous replication. It would be very simple to do. Have a fixed leader and require all nodes to acknowledge before committing, But the value here is extremely limited. If a single node in the cluster goes down the entire cluster is down.

You might think I'm proposing a strawman. We could simply designate a permanent leader that handles all reads and writes; and require a majority of nodes to commit a command before the leader responds to a client. But in that case, what's the process for getting a lagging follower up-to-date? And what happens if it is the leader who goes down?

Well, these are not trivial problems! And, beyond linearizability that we already mentioned, these problems are exactly what distributed consensus solves.

Why does linearizability matter?

It's very nice, and often even critical, to have a highly available system that will never give you stale data. And regardless, it's convenient to have a term for what we might naively think of as the "correct" way you'd always want to set and get a value.

So linearizability is a convenient way of thinking about complex systems, if you can use or build a system that supports it. But it's not the only consistency approach you'll see in the wild.

As you increase the guarantees of your consistency model, you tend to sacrifice performance. Going the opposite direction, some production systems sacrifice consistency to improve performance. For example, you might allow stale reads from any node, reading only from local state and avoiding consensus, so that you can reduce load on a leader and avoid the overhead of consensus.

There are formal definitions for lower consistency models, including sequential and read-your-writes. You can read the Jepsen page for more detail.

Best and worst case scenarios

A distributed system relies on communicating over the network. The worse the network, whether in terms of latency or reliability, the longer it will take for communication to happen.

Aside from the network, disks can misdirect writes or corrupt data. Or you could be mounted on a network filesystem such as EBS.

And processes themselves can crash due to low disk space or the OOM killer.

It will take longer to achieve consensus to commit messages these scenarios. If messages take longer to reach nodes, or if nodes are constantly crashing, followers will timeout more often, triggering leader election. And the leader election itself (which also requires consensus) will also take longer.

The best case scenario for distributed consensus is where the network is reliable and low-latency. Where disks are reliable and fast. And where processes don't often crash.

TigerBeetle has an incredible visual simulator that demonstrates what happens across ever-worsening environments. While TigerBeetle and this simulator use Viewstamped Replication, the demonstrated principles apply to Raft as well.

What happens when you add nodes?

Distributed consensus algorithms make sure that some minimum number of nodes in a cluster agree before continuing. The minimum number is proportional to the total number of nodes in the cluster.

A typical implementation of Raft for example will require 3 nodes in a 5-node cluster to agree before continuing. 4 nodes in a 7-node cluster. And so on.

Recall that the p99 latency for a service is at least as bad as the slowest external request the service must make. As you increase the number of nodes you must talk to in a consensus cluster, you increase the chance of a slow request.

Consider the extreme case of a 101-node cluster requiring 51 nodes to respond before returning to the client. That's 51 chances for a slower request. Compared to 4 chances in a 7-node cluster. The 101-node cluster is certainly more highly available though! It can tolerate 49 nodes going down. The 7-node cluster can only tolerate 3 nodes going down. The scenario where 49 nodes go down (assuming they're in different availability zones) seems pretty unlikely!

Horizontal scaling with distributed consensus? Not exactly

All of this is to say that the most popular algorithms for distributed consensus, on their own, have nothing to do with horizontal scaling.

The way that horizontally scaling databases like Cockroach or Yugabyte or Spanner work is by sharding the data, transparent to the client. Within each shard data is replicated with a dedicated distributed consensus cluster.

So, yes, distributed consensus can be a part of horizontal scaling. But again what distributed consensus primarily solves is high availability via replication while remaining linearizable.

This is not a trivial point to make. etcd, consul, and rqlite are examples of databases that do not do sharding, only replication, via a single Raft cluster that replicates all data for the entire system.

For these databases there is no horizontal scaling. If they support "horizontal scaling", they support this by doing non-linearizable (stale) reads. Writes remain a challenge.

This doesn't mean these databases are bad. They are not. One obvious advantage they have over Cockroach or Spanner is that they are conceptually simpler. Conceptually simpler often equates to easier to operate. That's a big deal.

Optimizations

We've covered the basics of operation, but real-world implementations get more complex.

Snapshots

Rather than letting the log grow indefinitely, most libraries implement snapshotting. The user of the library provides a state machine and also provides a method for serializing the state machine to disk. The Raft library periodically serializes the state machine to disk and truncates the log.

When a follower is so far behind that the leader no longer has a log entry (because it has been truncated), the leader transfers an entire snapshot to the follower. Then once the follower is caught up on snapshots, the leader can transfer normal log entries again.

This technique is described in the Raft paper. While it isn't necessary for Raft to work, it's so important that it is hardly an optimization and more a required part of a production Raft system.

Batching

Rather than limiting clients of the cluster to submitting only one command at a time, it's common for the cluster to accept many commands at a time. Similarly, many commands at a time are submitted to followers. When any node needs to write commands to disk, it can batch commands to disk as well.

But you can go a step beyond this in a way that is completely opaque to the Raft library. Each opaque command the client submits can also contain a batch of messages. In this scenario, only the user-provided state machine needs to be aware that each command it receives is actually a batch of messages that it should pull apart and interpret separately.

This latter techinque is a fairly trivial way to increase throughput by an order of magnitude or two.

Disk and network

In terms of how data is stored on disk and how data is sent over the network there is obvious room for optimization.

A naive implementation might store JSON on disk and send JSON over the network. A slightly more optimized implementation might store binary data on disk and send binary data over the network.

Similarly you can swap out your RPC for gRPC or introduce zlib for compression to network or disk.

You can swap out synchronous IO for libaio or io_uring or SPDK/DPDK.

A little tweak I made in my latest Raft implementation was to index log entries so searching the log was not a linear operation. Another little tweak was to introduce a page cache to eliminate unnecessary disk reads. This increased throughput for by an order of magnitude.

Flexible quorums

This brilliant optimization by Heidi Howard and co. shows you can relax the quorum required for committing new commands so long as you increase the quorum required for electing a leader.

In an environment where leader election doesn't happen often, flexible quorums can increase throughput and decrease latency. And it's a pretty easy change to make!

These are just a couple common optimizations. You can also read about parallel state machine apply, parallel append to disk, witnesses, compartmentalization, and leader leases. TiKV, Scylla, RedPanda, and Cockroach tend to have public material talking about this stuff.

There are also a few people I follow who are often reviewing relevant papers, if they are not producing their own. I encourage you to follow them too if this is interesting to you:

Safety and testing

The other aspect to consider is safety. For example, checksums for everything written to disk and passed over the network; or being able to recover from corruption in the log.

Testing is also a big deal. There are prominent tools like Jepsen that check for consistency in the face of fault injection (process failure, network failure, etc.). But even Jepsen has its limits. For example, it doesn't test disk failure.

FoundationDB made popular a number of testing techniques. And the people behind this testing went on to build a product, Antithesis, around deterministic testing of non-deterministic code while injecting faults.

And on that topic there's Facebook Experimental's Hermit deterministic Linux hypervisor that may help to test complex distributed systems. However, my experience with it has not been great and the maintainers do not seem very engaged with other people who have reported bugs. I'm hopeful for it but we'll see.

Antithesis and Hermit seem like a boon when half the trouble of working on distributed consensus implementations is avoiding flakey tests.

Another promising avenue is emitting logs during the Raft lifecycle and validating the logs against a TLA+ spec. Microsoft has such a project that has begun to see adoption among open-source Raft implementations.

Conclusion

Everything aside, consensus is expensive. There is overhead to the entire consensus process. So if you do not need this level of availability and can settle for some process via backups, it's going to have lower latency and higher throughput than if it had to go through distributed consensus.

If you do need high availability, distributed consensus can be a great choice. But consider the environment and what you want from your consensus algorithm.

Also, while MultiPaxos, Raft, and Viewstamped Replication are some of the most popular algorithms for distributed consensus, there is a world beyond. Two-phase commit, ZooKeeper Atomic Broadcast, PigPaxos, EPaxos, Accord by Cassandra. The world of distributed consensus also gets especially weird and interesting outside of OLTP systems.

But that's enough for one post.

Writing a minimal in-memory storage engine for MySQL/MariaDB

Tue, 09 Jan 2024 00:00:00 +0000

I spent a week looking at MySQL/MariaDB internals along with ~80 other devs. Although MySQL and MariaDB are mostly the same (more on that later), I focused on MariaDB specifically this week.

Before last week I had never built MySQL/MariaDB before. The first day of this hack week, I got MariaDB building locally and made a code tweak so that SELECT 23 returned 213, and another tweak so that SELECT 80 + 20 returned 60. The second day I got a basic UDF in C working so that SELECT mysum(20, 30) returned 50.

The rest of the week I spent figuring out how to build a minimal in-memory storage engine, which I'll walk through in this post. 218 lines of C++.

It supports CREATE, DROP, INSERT, and SELECT for tables that only have INTEGER fields. It is explicitly not thread-safe because I didn't have time to understand MariaDB's lock primitives.

In this post I'll also talk about how the MariaDB custom storage API compares to the Postgres one, based on a previous hack week project I did.

All code for this post can be found in my fork on GitHub.

MySQL and MariaDB

Before we go further though, why do I keep saying MySQL/MariaDB?

MySQL is GPL licensed (let's completely ignore the commercial variations of MySQL that Oracle offers). The code is open-source. However, the development is done behind closed doors. There is a code dump every month or so.

MariaDB is a fork of MySQL by the creator of MySQL (who is no longer involved, as it happens). It is also GPL licensed (let's completely ignore the commercial variations of MariaDB that MariaDB Corporation offers). The code is open-source. The development is also open-source.

When you install "MySQL" in your Linux distro you are often actually installing MariaDB.

The two are mostly compatible. During this week, I stumbled onto that they evolved support for SELECT .. FROM VALUES .. differently. Some differences are documented on the MariaDB KB. But this KB is painful to browse. Which leads me to my next point.

The MySQL docs are excellent. Easy to read, browse; and they are thorough. The MariaDB docs are a work in progress. I'm sorry I can't be stoic: in just a week I've come to really hate using this KB. Thankfully, in some twisted way, it also doesn't seem to be very thorough either. It isn't completely avoidable though since there is no guarantee MySQL and MariaDB do the same thing.

Ultimately, I spent the week using MariaDB because I'm biased toward fully open projects. But I kept having to look at MySQL docs, hoping they were relevant.

Now that you understand the state of things, let's move on to fun stuff!

Storage engines

Mature databases often support swapping out the storage layer. Maybe you want an in-memory storage layer so that you can quickly run integration tests. Maybe you want to switch between B-Trees (read-optimized) and LSM Trees (write-optimized) and unordered heaps (write-optimized) depending on your workload. Or maybe you just want to try a third-party storage library (e.g. RocksDB or Sled or TiKV).

The benefit of swapping out only the storage engine is that, from a user's perspective, the semantics and features of the database stay mostly the same. But the database is magically faster for a workload.

You keep powerful user management, extension support, SQL support, and a well-known wire protocol. You modify only the method of storing the actual data.

Existing storage engines

MySQL/MariaDB is particularly well known for its custom storage engine support. The MySQL docs for alternate storage engines are great.

While the docs do warn that you should probably stick with the default storage engine, that warning didn't quite feel strong enough because nothing else seemed to indicate the state of other engines.

Specifically, in the past I was always interested in the CSV storage engine. But when you look at the actual code for the CSV engine there is a pretty strong warning:

First off, this is a play thing for me, there are a number of things
wrong with it:
  *) It was designed for csv and therefore its performance is highly
     questionable.
  *) Indexes have not been implemented. This is because the files can
     be traded in and out of the table directory without having to worry
     about rebuilding anything.
  *) NULLs and "" are treated equally (like a spreadsheet).
  *) There was in the beginning no point to anyone seeing this other
     then me, so there is a good chance that I haven't quite documented
     it well.
  *) Less design, more "make it work"

Now there are a few cool things with it:
  *) Errors can result in corrupted data files.
  *) Data files can be read by spreadsheets directly.

TODO:
 *) Move to a block system for larger files
 *) Error recovery, its all there, just need to finish it
 *) Document how the chains work.

-Brian

The difference between the seeming confidence of the docs and seeming confidence of the contributor made me chuckle.

The benefit of these diverse storage engines for me was that they give examples of how to implement the storage engine API. The csv, blackhole, example, and heap storage engines were particularly helpful to read.

The heap engine is a complete in-memory storage engine. Complete means complex though. So there seemed to be room for a stripped down version of an in-memory engine.

And that's we'll cover in this post! First though I want to talk a little bit about the limitations of custom storage engines.

Limitations

While being able to tailor a storage engine to a workload is powerful, there are limits to the benefits based on the design of the storage API.

Both Postgres and MySQL/MariaDB currently have a custom storage API built around individual rows.

Column-wise execution

I have previously written that custom storage engines allows you to switch between column- and row-oriented data storage. Two big reasons to do column-wise storage are 1) opportunity for compression, and 2) fast operations on a single column.

The opportunity for 1) compression on disk would still exist even if you needed to deal with individual rows at the storage API layer since the compression could happen on disk. However any benefits of passing around compressed columns in memory disappear if you must convert to rows for the storage API.

You'd also lose the advantage for 2) fast operations on a single column if the column must be converted into a row at the storage API whereupon it's passed to higher levels that perform execution. The execution would happen row-wise, not column-wise.

All of this is to say that while column-wise storage is possible, the benefit of doing so is not obvious with the current API design for both MySQL/MariaDB and Postgres.

Vectorization

An API built around individual rows also sets limits on the amount of vectorization you can do. A custom storage engine could still do some vectorization under the hood: always filling a buffer with N rows and returning a row from the buffer when the storage API requests a single row. But there is likely some degree of performance left on the table with an API that deals with individual rows.

Remember though: if you did batched reads and writes of rows in the custom storage layer, there isn't necessarily any vectorization happening at the execution layer. From a previous study I did, neither MySQL/MariaDB nor Postgres do vectorized query execution. This paragraph isn't a critique of the storage API, it's just something to keep in mind.

Storage versus execution

The general point I'm making here is that unless both the execution and storage APIs are designed in a certain way, you may attempt optimizations in the storage layer that are ineffective or even harmfull because the execution layer doesn't or can't take advantage of them.

Nothing permanent

The current limitations of the storage API are not intrinsic aspects of MySQL/MariaDB or Postgres's design. For both project there used to be no pluggable storage at all. We can imagine a future patch to either project that allows support for batched row reads and writes that together could make column-wise storage and vectorized execution more feasible.

Even today there have been invasive attempts to fully support column-wise storage and execution in Postgres. And there have also been projects to bring vectorized execution to Postgres.

I'm not as familiar with the MySQL landscape to comment about efforts at the moment their.

Debug build of MariaDB running locally

Now that you've got some background, let's get a debug build of MariaDB!

$ git clone https://github.com/MariaDB/server mariadb
$ cd mariadb
$ mkdir build
$ cd build
$ cmake -DCMAKE_BUILD_TYPE=Debug ..
$ make -j8

This takes a while. When I was hacking on Postgres (a C project), it took 1 minute on my beefy Linux server to build. It took 20-30 minutes to build MySQL/MariaDB from scratch. That's C++ for you!

Thankfully incremental builds of MySQL/MariaDB for a tweak after the initial build take roughly the same time as incremental builds of Postgres after a tweak.

Once the build is done, create a database.

$ ./build/scripts/mariadb-install-db --srcdir=$(pwd) --datadir=$(pwd)/db

And create a config for the database.

$ echo "[client]
socket=$(pwd)/mariadb.sock

[mariadb]
socket=$(pwd)/mariadb.sock

basedir=$(pwd)
datadir=$(pwd)/db
pid-file=$(pwd)/db.pid" > my.cnf

Start up the server.

$ ./build/sql/mariadbd --defaults-extra-file=$(pwd)/my.cnf --debug:d:o,$(pwd)/db.debug
./build/sql/mariadbd: Can't create file '/var/log/mariadb/mariadb.log' (errno: 13 "Permission denied")
2024-01-03 17:10:15 0 [Note] Starting MariaDB 11.4.0-MariaDB-debug source revision 3fad2b115569864d8c1b7ea90ce92aa895cfef08 as process 185550
2024-01-03 17:10:15 0 [Note] InnoDB: !!!!!!!! UNIV_DEBUG switched on !!!!!!!!!
2024-01-03 17:10:15 0 [Note] InnoDB: Compressed tables use zlib 1.2.13
2024-01-03 17:10:15 0 [Note] InnoDB: Number of transaction pools: 1
2024-01-03 17:10:15 0 [Note] InnoDB: Using crc32 + pclmulqdq instructions
2024-01-03 17:10:15 0 [Note] InnoDB: Initializing buffer pool, total size = 128.000MiB, chunk size = 2.000MiB
2024-01-03 17:10:15 0 [Note] InnoDB: Completed initialization of buffer pool
2024-01-03 17:10:15 0 [Note] InnoDB: Buffered log writes (block size=512 bytes)
2024-01-03 17:10:15 0 [Note] InnoDB: End of log at LSN=57155
2024-01-03 17:10:15 0 [Note] InnoDB: Opened 3 undo tablespaces
2024-01-03 17:10:15 0 [Note] InnoDB: 128 rollback segments in 3 undo tablespaces are active.
2024-01-03 17:10:15 0 [Note] InnoDB: Setting file './ibtmp1' size to 12.000MiB. Physically writing the file full; Please wait ...
2024-01-03 17:10:15 0 [Note] InnoDB: File './ibtmp1' size is now 12.000MiB.
2024-01-03 17:10:15 0 [Note] InnoDB: log sequence number 57155; transaction id 16
2024-01-03 17:10:15 0 [Note] InnoDB: Loading buffer pool(s) from ./db/ib_buffer_pool
2024-01-03 17:10:15 0 [Note] Plugin 'FEEDBACK' is disabled.
2024-01-03 17:10:15 0 [Note] Plugin 'wsrep-provider' is disabled.
2024-01-03 17:10:15 0 [Note] InnoDB: Buffer pool(s) load completed at 240103 17:10:15
2024-01-03 17:10:15 0 [Note] Server socket created on IP: '0.0.0.0'.
2024-01-03 17:10:15 0 [Note] Server socket created on IP: '::'.
2024-01-03 17:10:15 0 [Note] mariadbd: Event Scheduler: Loaded 0 events
2024-01-03 17:10:15 0 [Note] ./build/sql/mariadbd: ready for connections.
Version: '11.4.0-MariaDB-debug'  socket: './mariadb.sock'  port: 3306  Source distribution

With that --debug flag, debug logs will show up in $(pwd)/db.debug. It's unclear why debug logs are treated separately from the console logs shown here. I'd rather them all be in one place.

In another terminal, run a client and make a request!

$ ./build/client/mariadb --defaults-extra-file=$(pwd)/my.cnf --database=test
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A

Welcome to the MariaDB monitor.  Commands end with ; or \g.
Your MariaDB connection id is 3
Server version: 11.4.0-MariaDB-debug Source distribution

Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

MariaDB [test]> SELECT 1;
+---+
| 1 |
+---+
| 1 |
+---+
1 row in set (0.001 sec)

Huzzah! Let's write a custom storage engine!

Where does the code go?

When writing an extension for some project, I usually expect to have the extension exist in its own repo. I was able to do this with the Postgres in-memory storage engine I wrote. And in general, Postgres extensions exist as their own repos.

I was able to create and build a UDF plugin outside the MariaDB source tree. But when it came to getting a storage engine to build and load successfully, I wasted almost an entire day (a large amount of time in a single hack week) getting nowhere.

Extensions for MySQL/MariaDB are most easily built via the CMake infrastructure within the repo. Surely there's some way to replicate that infrastructure from outside the repo but I wasn't able to figure it out within a day and didn't want to spend more time on it.

Apparently the normal thing to do in MySQL/MariaDB is to keep extensions within a fork of MySQL/MariaDB.

When I switched to this method I was able to very quickly get the storage engine building and loaded. So that's what we'll do.

Boilerplate

Within the MariaDB source tree, create a new folder in the storage subdirectory.

$ mkdir storage/memem

Within storage/memem/CMakeLists.txt add the following.

# Copyright (c) 2006, 2010, Oracle and/or its affiliates. All rights reserved.
# 
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; version 2 of the License.
# 
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
# 
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1335 USA

SET(MEMEM_SOURCES  ha_memem.cc ha_memem.h)
MYSQL_ADD_PLUGIN(memem ${MEMEM_SOURCES} STORAGE_ENGINE)

This hooks into MySQL/MariaDB build infrastructure. So next time you run make within the build directory we created above, it will also build this project.

The storage engine class

It would be nice to see a way to extend MySQL in C (for one, because it would then be easier to port to other languages). But all of the builtin storage methods use classes. So we'll do that too.

The class we must implement is an instance of handler. There is a single handler instance per thread, corresponding to a single running query. (Postgres gives each query its own process, MySQL gives each query its own thread.) However, handler instances are reused across different queries.

There are a number of virtual methods on handler we must implement in our subclass. For most of them we'll do nothing: simply returning immediately. These simple methods will be implemented in ha_memem.h. The methods with more complex logic will be implemented in ha_memem.cc.

Let's set up includes in ha_memem.h.

/* Copyright (c) 2005, 2010, Oracle and/or its affiliates. All rights reserved.

  This program is free software; you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation; version 2 of the License.

  This program is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with this program; if not, write to the Free Software
  Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1335  USA */

#ifdef USE_PRAGMA_INTERFACE
#pragma interface /* gcc class implementation */
#endif

#include "thr_lock.h"
#include "handler.h"
#include "table.h"
#include "sql_const.h"

#include <vector>
#include <memory>

Next we'll define structs for our in-memory storage.

typedef std::vector<uchar> MememRow;

struct MememTable
{
  std::vector<std::shared_ptr<MememRow>> rows;
  std::shared_ptr<std::string> name;
};

struct MememDatabase
{
  std::vector<std::shared_ptr<MememTable>> tables;
};

Within ha_memem.cc we'll implement a global (not thread-safe) static MememDatabase* that all handler instances will query when requested. We need the definitions in the header file though because we'll store the table currently being queried in the handler subclass.

This is so that every call to write_row to write a single row or call to rnd_next to read a single row does not need to look up the in-memory table object N times within the same query.

And finally we'll define the subclass of handler and implementations of trivial methods.

class ha_memem final : public handler
{
  uint current_position= 0;
  std::shared_ptr<MememTable> memem_table= 0;

public:
  ha_memem(handlerton *hton, TABLE_SHARE *table_arg) : handler(hton, table_arg)
  {
  }
  ~ha_memem()= default;
  const char *index_type(uint key_number) { return ""; }
  ulonglong table_flags() const { return 0; }
  ulong index_flags(uint inx, uint part, bool all_parts) const { return 0; }
  /* The following defines can be increased if necessary */
#define MEMEM_MAX_KEY MAX_KEY     /* Max allowed keys */
#define MEMEM_MAX_KEY_SEG 16      /* Max segments for key */
#define MEMEM_MAX_KEY_LENGTH 3500 /* Like in InnoDB */
  uint max_supported_keys() const { return MEMEM_MAX_KEY; }
  uint max_supported_key_length() const { return MEMEM_MAX_KEY_LENGTH; }
  uint max_supported_key_part_length() const { return MEMEM_MAX_KEY_LENGTH; }
  int open(const char *name, int mode, uint test_if_locked) { return 0; }
  int close(void) { return 0; }
  int truncate() { return 0; }
  int rnd_init(bool scan);
  int rnd_next(uchar *buf);
  int rnd_pos(uchar *buf, uchar *pos) { return 0; }
  int index_read_map(uchar *buf, const uchar *key, key_part_map keypart_map,
                     enum ha_rkey_function find_flag)
  {
    return HA_ERR_END_OF_FILE;
  }
  int index_read_idx_map(uchar *buf, uint idx, const uchar *key,
                         key_part_map keypart_map,
                         enum ha_rkey_function find_flag)
  {
    return HA_ERR_END_OF_FILE;
  }
  int index_read_last_map(uchar *buf, const uchar *key,
                          key_part_map keypart_map)
  {
    return HA_ERR_END_OF_FILE;
  }
  int index_next(uchar *buf) { return HA_ERR_END_OF_FILE; }
  int index_prev(uchar *buf) { return HA_ERR_END_OF_FILE; }
  int index_first(uchar *buf) { return HA_ERR_END_OF_FILE; }
  int index_last(uchar *buf) { return HA_ERR_END_OF_FILE; }
  void position(const uchar *record) { return; }
  int info(uint flag) { return 0; }
  int external_lock(THD *thd, int lock_type) { return 0; }
  int create(const char *name, TABLE *table_arg, HA_CREATE_INFO *create_info);
  THR_LOCK_DATA **store_lock(THD *thd, THR_LOCK_DATA **to,
                             enum thr_lock_type lock_type)
  {
    return to;
  }
  int delete_table(const char *name) { return 0; }

private:
  void reset_memem_table();
  virtual int write_row(const uchar *buf);
  int update_row(const uchar *old_data, const uchar *new_data)
  {
    return HA_ERR_WRONG_COMMAND;
  };
  int delete_row(const uchar *buf) { return HA_ERR_WRONG_COMMAND; }
};

A complete storage engine might seriously implement all of these methods. But we'll only seriously implement 7 of them.

To finish up the boilerplate, we'll switch over to ha_memem.cc and set up the includes.

/* Copyright (c) 2005, 2012, Oracle and/or its affiliates. All rights reserved.

  This program is free software; you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation; version 2 of the License.

  This program is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with this program; if not, write to the Free Software
  Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1335  USA */

#ifdef USE_PRAGMA_IMPLEMENTATION
#pragma implementation // gcc: Class implementation
#endif

#define MYSQL_SERVER 1
#include <my_global.h>
#include "sql_priv.h"
#include "unireg.h"
#include "sql_class.h"

#include "ha_memem.h"

Ok! Let's dig into the implementation.

Implementation

The global database

First up, we need to declare a global MememDatabase* instance. We'll also implement a helper function for finding the index of a table by name within the database.

// WARNING! All accesses of `database` in this code are thread
// unsafe. Since this was written during a hack week, I didn't have
// time to figure out MySQL/MariaDB's runtime well enough to do the
// thread-safe version of this.
static MememDatabase *database;

static int memem_table_index(const char *name)
{
  int i;
  assert(database->tables.size() < INT_MAX);
  for (i= 0; i < (int) database->tables.size(); i++)
  {
    if (strcmp(database->tables[i]->name->c_str(), name) == 0)
    {
      return i;
    }
  }

  return -1;
}

As I wrote this post I noticed that this code also assumes there's only a single database. That isn't how MySQL works. Everytime you call USE ... in MySQL you are switching between databases. You can query tables across databases. A real in-memory backend would need to be aware of the different databases, not just different tables. But to keep the code succinct we won't implement that in this post.

Next we'll implement plugin initialization and cleanup.

Plugin lifecycle

Before we register the plugin with MariaDB, we need to set up initialization and cleanup methods for it.

The initialization method will take care of initializing the global MememDatabase* database object. It will set up a handler for creating new instances of our handler subclass. And it will set up a handler for deleting tables.

static handler *memem_create_handler(handlerton *hton, TABLE_SHARE *table,
                                     MEM_ROOT *mem_root)
{
  return new (mem_root) ha_memem(hton, table);
}

static int memem_init(void *p)
{
  handlerton *memem_hton;

  memem_hton= (handlerton *) p;
  memem_hton->db_type= DB_TYPE_AUTOASSIGN;
  memem_hton->create= memem_create_handler;
  memem_hton->drop_table= [](handlerton *, const char *name) {
    int index= memem_table_index(name);
    if (index == -1)
    {
      return HA_ERR_NO_SUCH_TABLE;
    }

    database->tables.erase(database->tables.begin() + index);
    DBUG_PRINT("info", ("[MEMEM] Deleted table '%s'.", name));

    return 0;
  };
  memem_hton->flags= HTON_CAN_RECREATE;

  // Initialize global in-memory database.
  database= new MememDatabase;

  return 0;
}

The DBUG_PRINT macro is a debug helper MySQL/MariaDB gives us. As noted above, the output is directed to a file specified by the --debug flag. Unfortunately I couldn't figure out how to flush the stream this macro writes to. It seemed like occasionally when there was a segfault logs I expected to be there weren't there. And the file would often contain what looked like partially written logs. Anyway, as long as there wasn't a segfault the debug file will eventually contain the DBUG_PRINT logs.

The only thing the plugin cleanup function must do is delete the global database.

static int memem_fini(void *p)
{
  delete database;
  return 0;
}

Now we can register the plugin!

Plugin registration

The maria_declare_plugin and maria_declare_plugin_end register the plugin's metadata (name, version, etc.) and callbacks.

struct st_mysql_storage_engine memem_storage_engine= {
    MYSQL_HANDLERTON_INTERFACE_VERSION};

maria_declare_plugin(memem){
    MYSQL_STORAGE_ENGINE_PLUGIN,
    &memem_storage_engine,
    "MEMEM",
    "MySQL AB",
    "In-memory database.",
    PLUGIN_LICENSE_GPL,
    memem_init, /* Plugin Init */
    memem_fini, /* Plugin Deinit */
    0x0100 /* 1.0 */,
    NULL,                          /* status variables                */
    NULL,                          /* system variables                */
    "1.0",                         /* string version */
    MariaDB_PLUGIN_MATURITY_STABLE /* maturity */
} maria_declare_plugin_end;

That's it! Now we need to implement methods for writing rows, reading rows, and creating a new table.

Create table

To create a table, we make sure one by this name doesn't already exist, make sure it only has INTEGER fields, allocate memory for the table, and append it to the global database.

int ha_memem::create(const char *name, TABLE *table_arg,
                     HA_CREATE_INFO *create_info)
{
  assert(memem_table_index(name) == -1);

  // We only support INTEGER fields for now.
  uint i = 0;
  while (table_arg->field[i]) {
    if (table_arg->field[i]->type() != MYSQL_TYPE_LONG)
      {
    DBUG_PRINT("info", ("Unsupported field type."));
    return 1;
      }

    i++;
  }

  auto t= std::make_shared<MememTable>();
  t->name= std::make_shared<std::string>(name);
  database->tables.push_back(t);
  DBUG_PRINT("info", ("[MEMEM] Created table '%s'.", name));

  return 0;
}

Not very complicated. Let's handle INSERT-ing rows next.

Insert row

There is no method called when an INSERT starts. There is a table field on the handler parent class that is updated though when a SELECT or INSERT is going. So we can fetch the current table from that field.

Since we have a slot for a std::shared_ptr<MememTable> memem_table on the ha_memem class, we can check if it is NULL when we insert a row. If it is, we look up the current table and set this->memem_table to its MememTable.

But there's a bit more to it than just the table name. The const char* name passed to the create() method above seems to be a sort of fully qualified name for the table. By observation, when creating a table y in a database test, the const char* name value is ./test/y. The . prefix probably means that the database is local, but I'm not sure.

So we'll write a helper method that will reconstruct the fully qualified table name before looking up that fully qualified table name in the global database.

void ha_memem::reset_memem_table()
{
  // Reset table cursor.
  current_position= 0;

  std::string full_name= "./" + std::string(table->s->db.str) + "/" +
                         std::string(table->s->table_name.str);
  DBUG_PRINT("info", ("[MEMEM] Resetting to '%s'.", full_name.c_str()));
  assert(database->tables.size() > 0);
  int index= memem_table_index(full_name.c_str());
  assert(index >= 0);
  assert(index < (int) database->tables.size());

  memem_table= database->tables[index];
}

Then we can use this within write_row to figure out the current MememTable being queried.

But first, let's digress into how MySQL stores rows.

The MySQL row API

When you write a Postgres custom storage API, you are expected to basically read from or write to an array of Datum.

Totally sensible.

In MySQL, you read from and write to an array of bytes. That's pretty weird to me. Of course you can build your own higher level serialization/deserialization on top of it. But it's just strange to me everyone has to know this basically opaque API.

Certainly it's documented.

The handler class is the interface for dynamically loadable
storage engines. Do not add ifdefs and take care when adding or
changing virtual functions to avoid vtable confusion

Functions in this class accept and return table columns data. Two data
representation formats are used:
1. TableRecordFormat - Used to pass [partial] table records to/from
   storage engine

2. KeyTupleFormat - used to pass index search tuples (aka "keys") to
   storage engine. See opt_range.cc for description of this format.

TableRecordFormat
=================
[Warning: this description is work in progress and may be incomplete]
The table record is stored in a fixed-size buffer:

  record: null_bytes, column1_data, column2_data, ...

The offsets of the parts of the buffer are also fixed: every column has 
an offset to its column{i}_data, and if it is nullable it also has its own
bit in null_bytes.

In our implementation, we'll skip the support for NULL values. We'll only support INTEGER fields. But we still need to be aware that the first byte will be taken up. We'll also assume there won't be more than one byte of a NULL bitmap.

It is this opaque byte array that we'll read from in write_row(const uchar* buf) and write to in read_row(uchar* buf).

Insert row (take two)

To keep things simple we're going to store the row in MememTable the same way MySQL passes it around.

int ha_memem::write_row(const uchar *buf)
{
  if (memem_table == NULL)
  {
    reset_memem_table();
  }

  // Assume there are no NULLs.
  buf++;

  uint field_count = 0;
  while (table->field[field_count]) field_count++;

  // Store the row in the same format MariaDB gives us.
  auto row= std::make_shared<std::vector<uchar>>(
      buf, buf + sizeof(int) * field_count);
  memem_table->rows.push_back(row);

  return 0;
}

Which makes reading the row quite simple too!

Read row

The only slight difference between reading and writing a row is that MySQL/MariaDB will tell us when the SELECT scan for a table starts.

We'll use that opportunity to reset the current_row cursor and reset the memem_table field. Since, again, handler classes are only used once per query but they are reused for queries running at other times.

int ha_memem::rnd_init(bool scan)
{
  reset_memem_table();
  return 0;
}

int ha_memem::rnd_next(uchar *buf)
{
  if (current_position == memem_table->rows.size())
  {
    // Reset the in-memory table to make logic errors more obvious.
    memem_table= NULL;
    return HA_ERR_END_OF_FILE;
  }
  assert(current_position < memem_table->rows.size());

  uchar *ptr= buf;
  *ptr= 0;
  ptr++;

  // Rows internally are stored in the same format that MariaDB
  // wants. So we can just copy them over.
  std::shared_ptr<std::vector<uchar>> row= memem_table->rows[current_position];
  std::copy(row->begin(), row->end(), ptr);

  current_position++;
  return 0;
}

And we're done!

Build and test

Go back into the build directory we created within the source tree root and rerun make -j8.

Kill the server (you'll need to do something like killall mariadbd since the server doesn't respond to Ctrl-c). And restart it.

For some reason this plugin doesn't need to be loaded. We can run SHOW PLUGINS; in the MariaDB CLI and we'll see it.

$ ./build/client/mariadb --defaults-extra-file=/home/phil/vendor/mariadb/my.cnf --database=test
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A

Welcome to the MariaDB monitor.  Commands end with ; or \g.
Your MariaDB connection id is 5
Server version: 11.4.0-MariaDB-debug Source distribution

Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

MariaDB [test]> SHOW PLUGINS;
+-------------------------------+----------+--------------------+-----------------+---------+
| Name                          | Status   | Type               | Library         | License |
+-------------------------------+----------+--------------------+-----------------+---------+
| binlog                        | ACTIVE   | STORAGE ENGINE     | NULL            | GPL     |
...
| MEMEM                         | ACTIVE   | STORAGE ENGINE     | NULL            | GPL     |
...
| BLACKHOLE                     | ACTIVE   | STORAGE ENGINE     | ha_blackhole.so | GPL     |
+-------------------------------+----------+--------------------+-----------------+---------+
73 rows in set (0.012 sec)

There we go! To create a table with it we need to set ENGINE = MEMEM. For example, CREATE TABLE x (i INT) ENGINE = MEMEM.

Let's create a script to try out the memem engine, in storage/memem/test.sql.

drop table if exists y;
drop table if exists z;

create table y(i int, j int) engine = MEMEM;
insert into y values (2, 1029);
insert into y values (92, 8);
select * from y where i + 8 = 10;

create table z(a int) engine = MEMEM;
insert into z values (322);
insert into z values (8);
select * from z where a > 20;

And run it.

$ ./build/client/mariadb --defaults-extra-file=$(pwd)/my.cnf --database=test --table --verbose < storage/memem/test.sql
--------------
drop table if exists y
--------------

--------------
drop table if exists z
--------------

--------------
create table y(i int, j int) engine = MEMEM
--------------

--------------
insert into y values (2, 1029)
--------------

--------------
insert into y values (92, 8)
--------------

--------------
select * from y where i + 8 = 10
--------------

+------+------+
| i    | j    |
+------+------+
|    2 | 1029 |
+------+------+
--------------
create table z(a int) engine = MEMEM
--------------

--------------
insert into z values (322)
--------------

--------------
insert into z values (8)
--------------

--------------
select * from z where a > 20
--------------

+------+
| a    |
+------+
|  322 |
+------+

What you see there is the power of storage engines! It supports the full SQL language even while we implemented storage somewhere completely different than the default.

In-memory is boring

Certainly, I'm getting bored doing the same project over and over again on different databases. However, it's minimal projects like this that make it super easy to then go and port the storage engine to something else.

The goal here is to be minimal but meaningful. And I've accomplished that for myself at least!

On ChatGPT

As I've written before, this sort of exploration wouldn't be possible within the time frame I gave myself if it weren't for ChatGPT. Specifically, the paid tier GPT4.

Neither the MySQL nor the MariaDB docs were so helpful that I could immediately figure out things like how to get the current table name within a scan (the table member of the handler class).

With ChatGPT you can ask questions like: "In a MySQL C++ plugin, how do I get the name of the table from a handler class as a C string?". Sometimes it's right and sometime's it's not. But you can try out the code and if it builds it is at least somewhat correct!

Wrote a post walking you through building a super minimal in-memory storage engine for MySQL/MariaDB in 218 lines of C++.

And took time again to reflect on the limitations of custom storage engines and how MySQL compares to Postgres internally here.https://t.co/nImUC36DPs pic.twitter.com/1Oj2Lcua8O
— Phil Eaton (@eatonphil) January 9, 2024

Make your own way

Wed, 27 Dec 2023 00:00:00 +0000

Over the years, I have repeatedly felt like I missed the timing for a meetup or an IRC group or social media in general. I'd go to a meetup every so often but I'd never make a meaningful connection with people, whereas everyone else knew each other. I'd join an IRC group and have difficulty catching up with what seemed to be the flow of conversation.

I hadn't thought much about this until the pandemic when I started a Discord group for software internals and a virtual tech talk series called Hacker Nights. Since 2021 the Discord reached around 1,500 members and ~20 fairly active members. And the Meetup peaked at about 300 members with about 10-20 showing up each Meetup.

After the pandemic receded I started an NYC-based book club over 2 months with about 5-8 active attendees. I ran a virtual hack week on Discord where I got ~100 devs into a temporary Discord server and we talked about Postgres internals and shared resources. Ultimately around 5 of us wrote blog posts and built new projects to explore Postgres.

I started a virtual, async email book club (that is still ongoing) with 300 devs from November 2023 to Feb 2024. There have been around 20 active members of the club. And each week the discussion is kicked off by one of the members, not myself.

And I felt like there wasn't enough community opportunity for folks in systems programming in NYC so I started an Manhattan-based Systems Coffee Club. Around 15 people showed up to the first meeting and seemed even more excited about it than I was. (And I was excited!) We'll see where it goes from here.

Organizing people to do this stuff doesn't come easy to me. I enjoy doing it to a degree, but every night before an event I have trouble sleeping. Worried about embarrassing myself. When the event happens though, and people are happy to be there to chat with everyone else, as they invariably have been, it makes it worthwhile.

Everyone want community

Something I realized along the way is that people (maybe devs especially, I don't know) are looking for community. And when I have noticed there seems to be a missing flashpoint (a topic, a career focus, a book, etc.) for community, it's been pretty easy to get people together around it.

The lifecycle of groups

Groups, meetups, naturally live and die. Organizers get burnt out. I don't see this as a problem. It's just the way it is.

At some point I'll get burnt out too. Or I'll get pickier. For example, I've been avoiding starting a systems programming meetup in NYC because I know it will be a big effort. So I've done lower effort groups like book clubs and coffee clubs.

Don't worry about signing yourself up for indefinite work. Just do whatever you'd like to and don't feel bad if you have to stop. Someone else will eventually start the next great group, even if it comes in a different medium or flavor.

Community is contagious

There are great communities out there that have inspired me.

Aleksey Charapko's and Murat Demirbas's virtual Distributed Systems Reading Group
Alex Petrov's database paper reading group
Andy Pavlo's database interview series
Paul Butler's BrowserTech meetup
Eric Zhang's New York Systems Reading Group

And this year I've been hearing about more.

TU Munich students started a Student Database Group
A group of developers starting a Türkiye-language CS reading group

There are yet a few more systems programming groups I've heard rumors about being started on the US West Coast and Stockholm.

Do whatever you want!

If you feel like you can't find the right group or that you don't fit in with existing groups or that you're missing a moment, there are surely other folks in the same boat. Waiting for a new group to join. You may be the catalyst.

There's enormous potential for getting people together and doing something interesting and there isn't necessarily anyone telling you you should. Things you try may work and they may not. The more you try the more you'll learn what works and what doesn't. I've had a few years of making mistakes organizing to hone the sense.

The only boring thing to do is to necessarily limit yourself to the sort of thing others have done before! Run a browser meetup instead of a React meetup. Interview hardware developers to teach software developers something. Get software developers with 20 years of experience in niche fields to teach the rest of us something. Read books beyond SICP or Clean Code. Try difficult programming projects.

Whatever you want though, don't let me deter you. If you think something should exist, give it a shot!

I used to struggle to get much out of meetups, couldn't pick up the flow of IRC. Some point I stopped trying solely to fit in. Instead to do what I thought was interesting. And to my surprise, folks were interested in coming along too!

Make your own wayhttps://t.co/tVEa2ndiZm pic.twitter.com/piWSsv14lj
— Phil Eaton (@eatonphil) December 27, 2023

Exploring a Postgres query plan

Sun, 19 Nov 2023 00:00:00 +0000

I learned this week that you can intercept and redirect Postgres query execution. You can hook into the execution layer so you're given a query plan and you get to decide what to do with it. What rows to return, if any, and where they come from.

That's very interesting. So I started writing code to explore execution hooks. However, I got stuck interpreting the query plan. Either there's no query plan walking infrastructure or I just didn't find it.

So this post is a digression into walking a Postgres query plan. By the end we'll be able to run psql -c 'SELECT a FROM x WHERE a > 1' and reconstruct the entire SQL string from a Postgres QueryDesc object, the query plan object Postgres builds.

With that query plan walking infrastructure in place, we'll be in a good state to not just print out the query plan while walking it but instead to translate the query plan or evaluate it in our own way (e.g. over column-wise data, or vectorized execution over row-wise data).

Code for this project is available on Github.

What is a query plan?

If you're familiar with parsers and compilers, a query plan is like an intermediate representation (IR) of a program. It is not as raw as an abstract syntax tree (AST); it has already been optimized.

If that doesn't mean anything to you, think of a query plan as a structured and optimized version of the SQL query you submit to your database. It isn't text anymore. It is probably a tree.

Check out another Justin Jaffray article on the subject for more detail.

Development environment

Before we get to walking the query plan, let's set up the infrastructure to intercept query execution where we can eventually add in our print debugging of the query plan reconstructed as a SQL string.

Once you've got Postgres build dependencies, build and install a debug version of Postgres:

$ git clone https://github.com/postgres/postgres && cd postgres
$ # Make sure you're on the same commit I'm on, just to be safe.
$ git checkout b218fbb7a35fcf31539bfad12732038fe082a2eb
$ ./configure --enable-cassert --enable-debug CFLAGS="-ggdb -Og -g3 -fno-omit-frame-pointer"
$ make -j8
$ # Installs to to /usr/local/pgsql/bin.
$ sudo make install

I'm not going to cover Postgres extension infrastructure in detail. I wrote a bit about it in my last post. You need only read the first half, if at all; not the actual Table Access Method implementation.

It will be even simpler in this post because Postgres hooks are extensions but not extensions you install with CREATE EXTENSION. If you want to read about the different kinds of Postgres extensions, check out this article by Steven Miller.

The minimum we need, aside from the hook code itself, is a Makefile that uses PGXS:

MODULES = pgexec

PG_CONFIG = /usr/local/pgsql/bin/pg_config
PGXS := $(shell $(PG_CONFIG) --pgxs)
include $(PGXS)

The MODULES value there corresponds to the C file we'll create shortly, pgexec.c.

This pg_config binary path is important because you might have different versions of Postgres installed, for example by your package manager. It is important that the extension is built against the same version of Postgres which will load the extension.

Now we're ready for some hook code.

Intercepting query execution

You can find the basic structure of a hook (and which hooks are available) in Tamika Nomara's unofficial Postgres hooks docs.

There is no official central place describing all hooks I could find in Postgres docs. Some hooks are described in various places throughout the docs though.

Based on that page, we can write a bare minimum hook that will intercept queries, log when we've done so, and pass control back to the standard execution path for the actual query. In pgexec.c:

#include "postgres.h"
#include "fmgr.h"
#include "executor/executor.h"

PG_MODULE_MAGIC;

static ExecutorRun_hook_type prev_executor_run_hook = NULL;

static void print_plan(QueryDesc* queryDesc) {
  elog(LOG, "[pgexec] HOOKED SUCCESSFULLY!");
}

static void pgexec_run_hook(
  QueryDesc* queryDesc,
  ScanDirection direction,
  uint64 count,
  bool execute_once
) {
  print_plan(queryDesc);
  return prev_executor_run_hook(queryDesc, direction, count, execute_once);
}

void _PG_init(void) {
  prev_executor_run_hook = ExecutorRun_hook;
  if (prev_executor_run_hook == NULL) {
    prev_executor_run_hook = standard_ExecutorRun;
  }
  ExecutorRun_hook = pgexec_run_hook;
}

void _PG_fini(void) {
  ExecutorRun_hook = prev_executor_run_hook;
}

You can discover the standard_ExectutorRun function from a quick git grep ExecutorRun_hook in the Postgres source which leads to src/backend/executor/execMain.c#L306:

void
ExecutorRun(QueryDesc *queryDesc,
            ScanDirection direction, uint64 count,
            bool execute_once)
{
    if (ExecutorRun_hook)
        (*ExecutorRun_hook) (queryDesc, direction, count, execute_once);
    else
        standard_ExecutorRun(queryDesc, direction, count, execute_once);
}

So our hook will just log and pass back execution to the existing execution hook. Let's build and install the extension.

$ make
$ sudo make install

Now we'll create a new database and tell it to load the extension.

$ /usr/local/pgsql/bin/initdb test-db
$ echo "shared_preload_libraries = 'pgexec'" > test-db/postgresql.conf

Remember, hooks are not CREATE EXTENSION extensions. As far as I can tell they can't be dynamically loaded (without some additional dynamic loading infrastructure one could potentially write). So every time you make a change you need to rebuild the extension, reinstall it, and restart the Postgres server.

And start the server in the foreground:

$ /usr/local/pgsql/bin/postgres \
  --config-file=$(pwd)/test-db/postgresql.conf \
  -D $(pwd)/test-db
  -k $(pwd)/test-db
2023-11-18 19:35:16.680 GMT [3215547] LOG:  starting PostgreSQL 17devel on x86_64-pc-linux-gnu, compiled by gcc (GCC) 13.2.1 20230728 (Red Hat 13.2.1-1), 64-bit
2023-11-18 19:35:16.681 GMT [3215547] LOG:  listening on IPv6 address "::1", port 5432
2023-11-18 19:35:16.681 GMT [3215547] LOG:  listening on IPv4 address "127.0.0.1", port 5432
2023-11-18 19:35:16.681 GMT [3215547] LOG:  listening on Unix socket "/tmp/.s.PGSQL.5432"
2023-11-18 19:35:16.682 GMT [3215550] LOG:  database system was shut down at 2023-11-18 19:20:16 GMT
2023-11-18 19:35:16.684 GMT [3215547] LOG:  database system is ready to accept connections

Keep an eye on this foreground process since this is where elog(LOG, ...) calls will show up.

Now in a new window, create a test.sql script that we can use to exercise the hook:

DROP TABLE IF EXISTS x;
CREATE TABLE x (a INT);
INSERT INTO x VALUES (309);
SELECT a FROM x WHERE a > 1;

Run psql so we can trigger the hook:

$ /usr/local/pgsql/bin/psql -h localhost postgres -f test.sql
DROP TABLE
CREATE TABLE
INSERT 0 1
  a
-----
 309
(1 row)

And in the postgres foreground process you should see a log:

2023-11-19 17:42:03.045 GMT [3242321] LOG:  [pgexec] HOOKED SUCCESSFULLY!
2023-11-19 17:42:03.045 GMT [3242321] STATEMENT:  INSERT INTO x VALUES (309);
2023-11-19 17:42:03.045 GMT [3242321] LOG:  [pgexec] HOOKED SUCCESSFULLY!
2023-11-19 17:42:03.045 GMT [3242321] STATEMENT:  SELECT a FROM x WHERE a > 1;

That's our hook! Interestingly only the INSERT and SELECT statements show up, not the DROP and CREATE.

Now let's see if we can reconstruct the query text from that first argument, the QueryDesc* that pgexec_run_hook receives. And let's simplify things for ourselves and only worry about reconstructing a SELECT query.

`Node`s and `Datum`s

But first, let's talk about two fundemental ways data in Postgres (code) is organized.

Postgres code is extremely dynamic and, maybe relatedly, fairly object-oriented. Almost every entity in Postgres is a Node. While values in Postgres that are exposed to users of Postgres are Datums.

Each node has a type, NodeTag, that we can switch on to decide what to do. In contrast, Datum has no type. The type of the Datum must be known by context before using one of the transform functions like DatumGetBool to retrieve a C value from a Datum.

A table is a Node. A query plan is a Node. A sequential scan is a Node. A join is a Node. A literal in a query is a Node. The value for the literal in a query is a Datum.

Here is how The Internals of PostgreSQL book visualizes a query plan for example:

Every box in that image is a Node.

And all Nodes in code I've seen share a common definition prefix like this:

typedef struct SomeThing {
  pg_node_attr(abstract) // If the node is indeed abstract in the OOP sense.
  NodeTag type;
}

Many Nodes you'll see are abstract, like Plan. But by printing out NodeTag and checking the value printed in src/include/nodes/nodetags.h, you can find the concrete type of the Node.

src/include/nodes/nodetags.h is generated during a preprocessing step. (Don't look if regex in Perl worries you).

We'll get back to Nodes later.

What's in a `QueryDesc`?

Let's take a look at the QueryDesc struct:

typedef struct QueryDesc
{
    /* These fields are provided by CreateQueryDesc */
    CmdType     operation;      /* CMD_SELECT, CMD_UPDATE, etc. */
    PlannedStmt *plannedstmt;   /* planner's output (could be utility, too) */
    const char *sourceText;     /* source text of the query */
    Snapshot    snapshot;       /* snapshot to use for query */
    Snapshot    crosscheck_snapshot;    /* crosscheck for RI update/delete */
    DestReceiver *dest;         /* the destination for tuple output */
    ParamListInfo params;       /* param values being passed in */
    QueryEnvironment *queryEnv; /* query environment passed in */
    int         instrument_options; /* OR of InstrumentOption flags */

    /* These fields are set by ExecutorStart */
    TupleDesc   tupDesc;        /* descriptor for result tuples */
    EState     *estate;         /* executor's query-wide state */
    PlanState  *planstate;      /* tree of per-plan-node state */

    /* This field is set by ExecutorRun */
    bool        already_executed;   /* true if previously executed */

    /* This is always set NULL by the core system, but plugins can change it */
    struct Instrumentation *totaltime;  /* total time spent in ExecutorRun */
} QueryDesc;

The PlannedStmt field looks interesting. Let's take a look:

typedef struct PlannedStmt
{
    pg_node_attr(no_equal, no_query_jumble)

    NodeTag     type;

    CmdType     commandType;    /* select|insert|update|delete|merge|utility */

    uint64      queryId;        /* query identifier (copied from Query) */

    bool        hasReturning;   /* is it insert|update|delete RETURNING? */

    bool        hasModifyingCTE;    /* has insert|update|delete in WITH? */

    bool        canSetTag;      /* do I set the command result tag? */

    bool        transientPlan;  /* redo plan when TransactionXmin changes? */

    bool        dependsOnRole;  /* is plan specific to current role? */

    bool        parallelModeNeeded; /* parallel mode required to execute? */

    int         jitFlags;       /* which forms of JIT should be performed */

    struct Plan *planTree;      /* tree of Plan nodes */

    List       *rtable;         /* list of RangeTblEntry nodes */

    List       *permInfos;      /* list of RTEPermissionInfo nodes for rtable
                                 * entries needing one */

    /* rtable indexes of target relations for INSERT/UPDATE/DELETE/MERGE */
    List       *resultRelations;    /* integer list of RT indexes, or NIL */

    List       *appendRelations;    /* list of AppendRelInfo nodes */

    List       *subplans;       /* Plan trees for SubPlan expressions; note
                                 * that some could be NULL */

    Bitmapset  *rewindPlanIDs;  /* indices of subplans that require REWIND */

    List       *rowMarks;       /* a list of PlanRowMark's */

    List       *relationOids;   /* OIDs of relations the plan depends on */

    List       *invalItems;     /* other dependencies, as PlanInvalItems */

    List       *paramExecTypes; /* type OIDs for PARAM_EXEC Params */

    Node       *utilityStmt;    /* non-null if this is utility stmt */

    /* statement location in source string (copied from Query) */
    int         stmt_location;  /* start location, or -1 if unknown */
    int         stmt_len;       /* length in bytes; 0 means "rest of string" */
} PlannedStmt;

The struct Plan* planTree field in there looks like what we'd want. But Plan is abstract:

typedef struct Plan
{
    pg_node_attr(abstract, no_equal, no_query_jumble)

    NodeTag     type;

So let's try printing out the planTree->type field and find the Node it is concretely. In pgexec.c change the definition of print_plan:

static void print_plan(QueryDesc* queryDesc) {
  elog(LOG, "[pgexec] HOOKED SUCCESSFULLY! %d", queryDesc->plannedstmt->planTree->type);
}

Rebuild and reinstall the extension, and restart Postgres:

$ make
$ sudo make install
$ /usr/local/pgsql/bin/postgres \
  --config-file=$(pwd)/test-db/postgresql.conf \
  -D $(pwd)/test-db
  -k $(pwd)/test-db
2023-11-18 19:35:16.680 GMT [3215547] LOG:  starting PostgreSQL 17devel on x86_64-pc-linux-gnu, compiled by gcc (GCC) 13.2.1 20230728 (Red Hat 13.2.1-1), 64-bit
2023-11-18 19:35:16.681 GMT [3215547] LOG:  listening on IPv6 address "::1", port 5432
2023-11-18 19:35:16.681 GMT [3215547] LOG:  listening on IPv4 address "127.0.0.1", port 5432
2023-11-18 19:35:16.681 GMT [3215547] LOG:  listening on Unix socket "/tmp/.s.PGSQL.5432"
2023-11-18 19:35:16.682 GMT [3215550] LOG:  database system was shut down at 2023-11-18 19:20:16 GMT
2023-11-18 19:35:16.684 GMT [3215547] LOG:  database system is ready to accept connections

And in another window run psql:

$ /usr/local/pgsql/bin/psql -h localhost postgres -f test.sql

And check the logs from the postgres process we just started and you should notice:

2023-11-19 17:46:18.834 GMT [3242495] LOG:  [pgexec] HOOKED SUCCESSFULLY! 322
2023-11-19 17:46:18.834 GMT [3242495] STATEMENT:  SELECT a FROM x WHERE a > 1;

So 322 is the NodeTag for the Plan. If we look that up in Postgres's src/include/nodes/nodetags.h (remember, this is generated after ./configure && make so I can't link you to it):

$ grep ' = 322' src/include/nodes/nodetags.h
        T_SeqScan = 322,

Hey, that makes sense! A SELECT without any indexes definitely sounds like a sequential scan!

Walking a sequential scan

Let's take a look at the SeqScan struct:

typedef struct SeqScan
{
    Scan        scan;
} SeqScan;

Ok, that's not very interesting. Let's look at Scan then:

typedef struct Scan
{
    pg_node_attr(abstract)

    Plan        plan;
    Index       scanrelid;      /* relid is index into the range table */
} Scan;

That's interesting! scanrelid represents the table we're scanning. I don't know what "range table" means exactly. But there was a field on the PlannedStmt called rtable that seems relevant.

rtable was described as a List of RangeTblEntry nodes. And browsing around the file where List is defined we can see some nice methods for working with Lists, like list_length().

Let's print out the scanrelid and let's check out the length of the rtable and see if it's filled out. Let's also restrict our print_plan code to only look at SeqScan nodes. In pgexec.c:

static void print_plan(QueryDesc* queryDesc) {
  SeqScan* scan = NULL;
  Plan* plan = queryDesc->plannedstmt->planTree;
  if (plan->type != T_SeqScan) {
    elog(LOG, "[pgexec] Unsupported plan type.");
    return;
  }

  scan = (SeqScan*)plan;

  elog(LOG, "[pgexec] relid: %d, rtable length: %d", scan->scan.scanrelid, list_length(queryDesc->plannedstmt->rtable));
}

Rebuild and reinstall the extension, and restart Postgres. (You can find the instructions for this above if you've forgotten.) Re-run the test.sql script. And check the Postgres server logs. You should see:

2023-11-19 18:00:34.184 GMT [3244438] LOG:  [pgexec] relid: 1, rtable length: 1
2023-11-19 18:00:34.184 GMT [3244438] STATEMENT:  SELECT a FROM x WHERE a > 1;

Awesome! So rtable does have data in it. There's only one table in this query so its length makes sense to be 1. The scanrelid being 1 also though is weird. Let's fetch the nth value from the rtable list using scanrelid-1 as the index.

For the RangeTblEntry itself, let's take a look:

typedef enum RTEKind
{
    RTE_RELATION,               /* ordinary relation reference */
    RTE_SUBQUERY,               /* subquery in FROM */
    RTE_JOIN,                   /* join */
    RTE_FUNCTION,               /* function in FROM */
    RTE_TABLEFUNC,              /* TableFunc(.., column list) */
    RTE_VALUES,                 /* VALUES (<exprlist>), (<exprlist>), ... */
    RTE_CTE,                    /* common table expr (WITH list element) */
    RTE_NAMEDTUPLESTORE,        /* tuplestore, e.g. for AFTER triggers */
    RTE_RESULT,                 /* RTE represents an empty FROM clause; such
                                 * RTEs are added by the planner, they're not
                                 * present during parsing or rewriting */
} RTEKind;

typedef struct RangeTblEntry
{
    pg_node_attr(custom_read_write, custom_query_jumble)

    NodeTag     type;

    RTEKind     rtekind;        /* see above */

    /*
     * XXX the fields applicable to only some rte kinds should be merged into
     * a union.  I didn't do this yet because the diffs would impact a lot of
     * code that is being actively worked on.  FIXME someday.
     */

    /*
     * Fields valid for a plain relation RTE (else zero):
     *
     * rellockmode is really LOCKMODE, but it's declared int to avoid having
     * to include lock-related headers here.  It must be RowExclusiveLock if
     * the RTE is an INSERT/UPDATE/DELETE/MERGE target, else RowShareLock if
     * the RTE is a SELECT FOR UPDATE/FOR SHARE target, else AccessShareLock.
     *
     * Note: in some cases, rule expansion may result in RTEs that are marked
     * with RowExclusiveLock even though they are not the target of the
     * current query; this happens if a DO ALSO rule simply scans the original
     * target table.  We leave such RTEs with their original lockmode so as to
     * avoid getting an additional, lesser lock.
     *
     * perminfoindex is 1-based index of the RTEPermissionInfo belonging to
     * this RTE in the containing struct's list of same; 0 if permissions need
     * not be checked for this RTE.
     *
     * As a special case, relid, relkind, rellockmode, and perminfoindex can
     * also be set (nonzero) in an RTE_SUBQUERY RTE.  This occurs when we
     * convert an RTE_RELATION RTE naming a view into an RTE_SUBQUERY
     * containing the view's query.  We still need to perform run-time locking
     * and permission checks on the view, even though it's not directly used
     * in the query anymore, and the most expedient way to do that is to
     * retain these fields from the old state of the RTE.
     *
     * As a special case, RTE_NAMEDTUPLESTORE can also set relid to indicate
     * that the tuple format of the tuplestore is the same as the referenced
     * relation.  This allows plans referencing AFTER trigger transition
     * tables to be invalidated if the underlying table is altered.
     */
    Oid         relid;          /* OID of the relation */
    char        relkind;        /* relation kind (see pg_class.relkind) */
    int         rellockmode;    /* lock level that query requires on the rel */
    struct TableSampleClause *tablesample;  /* sampling info, or NULL */
    Index       perminfoindex;

In SELECT a FROM x, x should be a plain relation RTE (to use the terminology there). So we can add a guard that validates that. But we don't get a Relation. (You might remember from my previous post that Relation is where we can finally see the table name.)

We get an Oid for the Relation. So we need to find a way to lookup a Relation from an Oid. And by grepping around in Postgres (or via judicious use of ChatGPT, I confess), we can notice RelationIdGetRelation that takes an Oid and returns a Relation. Notice also that the comment says we should close the relation when we're done with RelationClose.

So putting it altogether (and again, reusing some code from that previous post), we can print out the table name.

static void print_plan(QueryDesc* queryDesc) {
  SeqScan* scan = NULL;
  RangeTblEntry* rte = NULL;
  Relation relation = {};
  char* tablename = NULL;
  Plan* plan = queryDesc->plannedstmt->planTree;
  if (plan->type != T_SeqScan) {
    elog(LOG, "[pgexec] Unsupported plan type.");
    return;
  }

  scan = (SeqScan*)plan;
  rte = list_nth(queryDesc->plannedstmt->rtable, scan->scan.scanrelid-1);
  if (rte->rtekind != RTE_RELATION) {
    elog(LOG, "[pgexec] Unsupported FROM type: %d.", rte->rtekind);
    return;
  }

  relation = RelationIdGetRelation(rte->relid);
  tablename = NameStr(relation->rd_rel->relname);

  elog(LOG, "[pgexec] SELECT [todo] FROM %s", tablename);

  RelationClose(relation);
}

You'll also need to add a new #include for utils/rel.h.

Rebuild and reinstall the extension, and restart Postgres. Re-run the test.sql script. Check the Postgres server logs and you should see:

2023-11-19 18:36:03.986 GMT [3246777] LOG:  [pgexec] SELECT [todo] FROM x
2023-11-19 18:36:03.986 GMT [3246777] STATEMENT:  SELECT a FROM x WHERE a > 1;

Fantastic! Before we get into walking the SELECT columns and the (optional) WHERE clause, let's do some quick refactoring.

A string builder

Let's add a little string builder library so we can emit a single string we build up to a single elog() call.

I wrote this ahead of time and won't explain it here since the details aren't relevant.

Just copy this and paste near the top of pgexec.c:

typedef struct {
  char* mem;
  size_t len;
  size_t offset;
} PGExec_Buffer;

static void buffer_init(PGExec_Buffer* buf) {
  buf->offset = 0;
  buf->len = 8;
  buf->mem = (char*)malloc(sizeof(char) * buf->len);
}

static void buffer_resize_to_fit_additional(PGExec_Buffer* buf, size_t additional) {
  char* new = {};
  size_t newsize = 0;

  Assert(additional >= 0);

  if (buf->offset + additional < buf->len) {
    return;
  }

  newsize = (buf->offset + additional) * 2;
  new = (char*)malloc(sizeof(char) * newsize);
  Assert(new != NULL);
  memcpy(new, buf->mem, buf->len * sizeof(char));
  free(buf->mem);
  buf->len = newsize;
  buf->mem = new;
}

static void buffer_append(PGExec_Buffer*, char*, size_t);

static void buffer_appendz(PGExec_Buffer* buf, char* c) {
  buffer_append(buf, c, strlen(c));
}

static void buffer_append(PGExec_Buffer* buf, char* c, size_t chars) {
  buffer_resize_to_fit_additional(buf, chars);
  memcpy(buf->mem + buf->offset, c, chars);
  buf->offset += chars;
}

static void buffer_appendf(
  PGExec_Buffer *,
  const char* restrict,
  ...
) __attribute__ ((format (gnu_printf, 2, 3)));

static void buffer_appendf(PGExec_Buffer *buf, const char* restrict fmt, ...) {
  // First figure out how long the result will be.
  size_t chars = 0;
  va_list arglist;
  va_start(arglist, fmt);
  chars = vsnprintf(0, 0, fmt, arglist);
  Assert(chars >= 0); // TODO: error handling.

  // Resize to fit result.
  buffer_resize_to_fit_additional(buf, chars);

  // Actually do the printf into buf.
  va_end(arglist);
  va_start(arglist, fmt);
  chars = vsprintf(buf->mem + buf->offset, fmt, arglist);
  Assert(chars >= 0); // TODO: error handling.
  buf->offset += chars;
  va_end(arglist);
}

static char* buffer_cstring(PGExec_Buffer* buf) {
  char zero = 0;
  const size_t prev_offset = buf->offset;

  if (buf->offset == buf->len) {
    buffer_append(buf, &zero, 1);
    buf->offset--;
  } else {
    buf->mem[buf->offset] = 0;
  }

  // Offset should stay the same. This is a fake NULL.
  Assert(buf->offset == prev_offset);

  return buf->mem;
}

static void buffer_free(PGExec_Buffer* buf) {
  free(buf->mem);
}

Next we'll modify print_plan() in pgexec.c to use it, and add stubs for printing the SELECT columns and WHERE clauses.

static void buffer_print_where(PGExec_Buffer* buf, QueryDesc* queryDesc, Plan* plan) {
  buffer_appendz(buf, " [where todo]");
}

static void buffer_print_select_columns(PGExec_Buffer* buf, QueryDesc* queryDesc, Plan* plan) {
  buffer_appendz(buf, "[columns todo]");
}

static void print_plan(QueryDesc* queryDesc) {
  SeqScan* scan = NULL;
  RangeTblEntry* rte = NULL;
  Relation relation = {};
  char* tablename = NULL;
  Plan* plan = queryDesc->plannedstmt->planTree;
  PGExec_Buffer buf = {};

  if (plan->type != T_SeqScan) {
    elog(LOG, "[pgexec] Unsupported plan type.");
    return;
  }

  scan = (SeqScan*)plan;
  rte = list_nth(queryDesc->plannedstmt->rtable, scan->scan.scanrelid-1);
  if (rte->rtekind != RTE_RELATION) {
    elog(LOG, "[pgexec] Unsupported FROM type: %d.", rte->rtekind);
    return;
  }

  buffer_init(&buf);

  relation = RelationIdGetRelation(rte->relid);
  tablename = NameStr(relation->rd_rel->relname);

  buffer_appendz(&buf, "SELECT ");
  buffer_print_select_columns(&buf, queryDesc, plan);
  buffer_appendf(&buf, " FROM %s", tablename);
  buffer_print_where(&buf, queryDesc, plan);

  elog(LOG, "[pgexec] %s", buffer_cstring(&buf));

  RelationClose(relation);
  buffer_free(&buf);
}

Now we just need to implement the buffer_print_where and buffer_print_select_columns functions and our walking infrastructure will be done! For now. :)

Walking the `WHERE` clause

If you remember back to the SeqScan and Scan nodes, they were both basically empty. They had a Plan and a scanrelid. So the rest of the SELECT info must be in the Plan since it wasn't in the Scan.

Let's look at Plan again. One part that stands out is:

    /*
     * Common structural data for all Plan types.
     */
    int         plan_node_id;   /* unique across entire final plan tree */
    List       *targetlist;     /* target list to be computed at this node */
    List       *qual;           /* implicitly-ANDed qual conditions */
    struct Plan *lefttree;      /* input plan tree(s) */
    struct Plan *righttree;
    List       *initPlan;       /* Init Plan nodes (un-correlated expr
                                 * subselects) */

qual kinda looks like a WHERE clause. (And targetlist kinda looks like the columns the SELECT pulls).

Lists just contain void pointers, so we can't tell what the type of qual or targetlist children are. But I'm going to make a wild guess they are Nodes.

There's even a nice helper that casts void pointers to Node* and pulls out the type, nodeTag().

And reading around pg_list.h shows some interesting helper utilities like foreach that we can use to iterate the list.

Let's try printing out the type of qual's members.

static void buffer_print_where(PGExec_Buffer* buf, QueryDesc* queryDesc, Plan* plan) {
  ListCell* cell = NULL;
  bool first = true;
  if (plan->qual == NULL) {
    return;
  }

  buffer_appendz(buf, " WHERE ");
  foreach(cell, plan->qual) {
    if (!first) {
      buffer_appendz(buf, " AND ");
    }

    first = false;
    buffer_appendf(buf, "[node: %d]", nodeTag(lfirst(cell)));
  }
}

Notice any vestiges of LISP?

Rebuild and reinstall the extension, and restart Postgres. Re-run the test.sql script. Check the Postgres server logs and you should see:

2023-11-19 19:17:00.879 GMT [3250850] LOG:  [pgexec] SELECT [columns todo] FROM x WHERE [node: 15]
2023-11-19 19:17:00.879 GMT [3250850] STATEMENT:  SELECT a FROM x WHERE a > 1;

Well, our code didn't crash! So the guess about qual List entries being Nodes seems right. Let's look up that node type in the Postgres repo:

$ grep ' = 15,' src/include/nodes/nodetags.h
        T_OpExpr = 15,

Woot! That is exactly what I'd expect the WHERE clause here to be.

Now that we know qual is a List of Nodes, let's do a bit of refactoring since targetlist will probably also be a List of Nodes. Back in pgexec.c:

static void buffer_print_expr(PGExec_Buffer*, Node*);
static void buffer_print_list(PGExec_Buffer*, List*, char*);

static void buffer_print_opexpr(PGExec_Buffer* buf, OpExpr* op) {
  buffer_appendf(buf, "[opexpr: todo]");
}

static void buffer_print_expr(PGExec_Buffer* buf, Node* expr) {
  switch (nodeTag(expr)) {
  case T_OpExpr:
    buffer_print_opexpr(buf, (OpExpr*)expr);
    break;

  default:
    buffer_appendf(buf, "[Unknown: %d]", nodeTag(expr));
  }
}

static void buffer_print_list(PGExec_Buffer* buf, List* list, char* sep) {
  ListCell* cell = NULL;
  bool first = true;

  foreach(cell, list) {
    if (!first) {
      buffer_appendz(buf, sep);
    }

    first = false;
    buffer_print_expr(buf, (Node*)lfirst(cell));
  }
}

static void buffer_print_where(PGExec_Buffer* buf, QueryDesc* queryDesc, Plan* plan) {
  if (plan->qual == NULL) {
    return;
  }

  buffer_appendz(buf, " WHERE ");
  buffer_print_list(buf, plan->qual, " AND ");
}

And let's check out OpExpr!

Walking `OpExpr`

Take a look at the definition of OpExpr:

typedef struct OpExpr
{
    Expr        xpr;

    /* PG_OPERATOR OID of the operator */
    Oid         opno;

    /* PG_PROC OID of underlying function */
    Oid         opfuncid pg_node_attr(equal_ignore_if_zero, query_jumble_ignore);

    /* PG_TYPE OID of result value */
    Oid         opresulttype pg_node_attr(query_jumble_ignore);

    /* true if operator returns set */
    bool        opretset pg_node_attr(query_jumble_ignore);

    /* OID of collation of result */
    Oid         opcollid pg_node_attr(query_jumble_ignore);

    /* OID of collation that operator should use */
    Oid         inputcollid pg_node_attr(query_jumble_ignore);

    /* arguments to the operator (1 or 2) */
    List       *args;

    /* token location, or -1 if unknown */
    int         location;
} OpExpr;

The important fields are opno, the Oid of the operator, and args. args looks like another List of Nodes so we already know how to handle that.

But how do we find the string name of the operator? Presumably there's infrastructure like RelationIdGetRelation that takes an Oid and gets us an operator object.

Well I got stuck here as well. Again, thankfully, ChatGPT gave me some suggestions. There's no great story for how I got it working. So here's buffer_print_opexpr.

static void buffer_print_op(PGExec_Buffer* buf, OpExpr* op) {
  HeapTuple opertup = SearchSysCache1(OPEROID, ObjectIdGetDatum(op->opno));

  buffer_print_expr(buf, lfirst(list_nth_cell(op->args, 0)));

  if (HeapTupleIsValid(opertup)) {
    Form_pg_operator operator = (Form_pg_operator)GETSTRUCT(opertup);
    buffer_appendf(buf, " %s ", NameStr(operator->oprname));
    ReleaseSysCache(opertup);
  } else {
    buffer_appendf(buf, "[Unknown operation: %d]", op->opno);
  }

  // TODO: Support single operand operations.
  buffer_print_expr(buf, lfirst(list_nth_cell(op->args, 1)));
}

And add the following two includes to the top of pgexec.c:

#include "catalog/pg_operator.h"
#include "utils/syscache.h"

Rebuild and reinstall the extension, and restart Postgres. Re-run the test.sql script. Check the Postgres server logs and you should see:

2023-11-19 19:42:52.916 GMT [3252974] LOG:  [pgexec] SELECT [columns todo] FROM x WHERE [Unknown: 6] > [Unknown: 7]
2023-11-19 19:42:52.916 GMT [3252974] STATEMENT:  SELECT a FROM x WHERE a > 1;

And we continue to make progress! Let's look up the type of these two unknown nodes.

$ grep ' = 6,' src/include/nodes/nodetags.h
        T_Var = 6,
$ grep ' = 7,' src/include/nodes/nodetags.h
        T_Const = 7,

Let's deal with Const first.

Walking `Const`

If we take a look at the Const definition:

typedef struct Const
{
    pg_node_attr(custom_copy_equal, custom_read_write)

    Expr        xpr;
    /* pg_type OID of the constant's datatype */
    Oid         consttype;
    /* typmod value, if any */
    int32       consttypmod pg_node_attr(query_jumble_ignore);
    /* OID of collation, or InvalidOid if none */
    Oid         constcollid pg_node_attr(query_jumble_ignore);
    /* typlen of the constant's datatype */
    int         constlen pg_node_attr(query_jumble_ignore);
    /* the constant's value */
    Datum       constvalue pg_node_attr(query_jumble_ignore);
    /* whether the constant is null (if true, constvalue is undefined) */
    bool        constisnull pg_node_attr(query_jumble_ignore);

    /*
     * Whether this datatype is passed by value.  If true, then all the
     * information is stored in the Datum.  If false, then the Datum contains
     * a pointer to the information.
     */
    bool        constbyval pg_node_attr(query_jumble_ignore);

    /*
     * token location, or -1 if unknown.  All constants are tracked as
     * locations in query jumbling, to be marked as parameters.
     */
    int         location pg_node_attr(query_jumble_location);
} Const;

It looks like we need to switch on the consttype (an Oid) to figure out how to interpret the constvalue (a Datum). Remember I mentioned earlier that how to interpret a Datum is dependent on context. consttype is the context here.

In this case, although consttype is an Oid and we had to use Postgres infrastructure to look up the Oid's corresponding object, there are some builtin types and the literals we've queried with are among them.

We can simply check if consttype == INT4OID and the interpret the Datum as an int32 if so. DatumGetInt32 will get us that int32 in that case.

To support the new Const type, we'll add a case in buffer_print_expr to look for a T_Const.

static void buffer_print_expr(PGExec_Buffer* buf, Node* expr) {
  switch (nodeTag(expr)) {
  case T_Const:
    buffer_print_const(buf, (Const*)expr);
    break;

  case T_OpExpr:
    buffer_print_opexpr(buf, (OpExpr*)expr);
    break;

  default:
    buffer_appendf(buf, "[Unknown: %d]", nodeTag(expr));
  }
}

And add a new function, buffer_print_const:

static void buffer_print_const(PGExec_Buffer* buf, Const* cnst) {
  switch (cnst->consttype) {
  case INT4OID:
    int32 val = DatumGetInt32(cnst->constvalue);
    buffer_appendf(buf, "%d", val);
    break;

  default:
    buffer_appendf(buf, "[Unknown consttype oid: %d]", cnst->consttype);
  }
}

Rebuild and reinstall the extension, and restart Postgres. Re-run the test.sql script. Check the Postgres server logs and you should see:

2023-11-19 19:53:47.922 GMT [3253746] LOG:  [pgexec] SELECT [columns todo] FROM x WHERE [Unknown: 6] > 1
2023-11-19 19:53:47.922 GMT [3253746] STATEMENT:  SELECT a FROM x WHERE a > 1;

Great! Now we just have to tackle T_Var.

Walking `Var`

Let's take a look at the definition of Var:

typedef struct Var
{
    Expr        xpr;

    /*
     * index of this var's relation in the range table, or
     * INNER_VAR/OUTER_VAR/etc
     */
    int         varno;

    /*
     * attribute number of this var, or zero for all attrs ("whole-row Var")
     */
    AttrNumber  varattno;

    /* pg_type OID for the type of this var */
    Oid         vartype pg_node_attr(query_jumble_ignore);
    /* pg_attribute typmod value */
    int32       vartypmod pg_node_attr(query_jumble_ignore);
    /* OID of collation, or InvalidOid if none */
    Oid         varcollid pg_node_attr(query_jumble_ignore);

    /*
     * RT indexes of outer joins that can replace the Var's value with null.
     * We can omit varnullingrels in the query jumble, because it's fully
     * determined by varno/varlevelsup plus the Var's query location.
     */
    Bitmapset  *varnullingrels pg_node_attr(query_jumble_ignore);

    /*
     * for subquery variables referencing outer relations; 0 in a normal var,
     * >0 means N levels up
     */
    Index       varlevelsup;

    /*
     * varnosyn/varattnosyn are ignored for equality, because Vars with
     * different syntactic identifiers are semantically the same as long as
     * their varno/varattno match.
     */
    /* syntactic relation index (0 if unknown) */
    Index       varnosyn pg_node_attr(equal_ignore, query_jumble_ignore);
    /* syntactic attribute number */
    AttrNumber  varattnosyn pg_node_attr(equal_ignore, query_jumble_ignore);

    /* token location, or -1 if unknown */
    int         location;
} Var;

It looks like this refers to a relation in the range table list again. So this means we need to have access to the full PlannedStmt so we can read its rtable field again to find the table. Then we need to look up the Relation for the table and then we can use the Var's varattno field to pick the nth attribute from the relation and get its string representation.

However, ChatGPT found a slightly higher-level function: get_attname() that takes a relation Oid and an attribute index and returns the string name of the column.

So here's what buffer_print_var looks like:

static void buffer_print_var(PGExec_Buffer* buf, PlannedStmt* stmt, Var* var) {
  char* name = NULL;
  RangeTblEntry* rte = list_nth(stmt->rtable, var->varno-1);
  if (rte->rtekind != RTE_RELATION) {
    elog(LOG, "[Unsupported relation type for var: %d].", rte->rtekind);
    return;
  }

  name = get_attname(rte->relid, var->varattno, false);
  buffer_appendz(buf, name);
  pfree(name);
}

You'll also need to add another #include for utils/lsyscache.h.

Let's add the case T_Var: check in buffer_print_expr, and also feed the PlannedStmt* through all the necessary buffer_print_X functions:

static void buffer_print_expr(PGExec_Buffer*, PlannedStmt*, Node*);
static void buffer_print_list(PGExec_Buffer*, PlannedStmt*, List*, char*);

static void buffer_print_opexpr(PGExec_Buffer* buf, PlannedStmt* stmt, OpExpr* op) {
  HeapTuple opertup = SearchSysCache1(OPEROID, ObjectIdGetDatum(op->opno));

  buffer_print_expr(buf, stmt, lfirst(list_nth_cell(op->args, 0)));

  if (HeapTupleIsValid(opertup)) {
    Form_pg_operator operator = (Form_pg_operator)GETSTRUCT(opertup);
    buffer_appendf(buf, " %s ", NameStr(operator->oprname));
    ReleaseSysCache(opertup);
  } else {
    buffer_appendf(buf, "[Unknown operation: %d]", op->opno);
  }

  // TODO: Support single operand operations.
  buffer_print_expr(buf, stmt, lfirst(list_nth_cell(op->args, 1)));
}

static void buffer_print_const(PGExec_Buffer* buf, Const* cnst) {
  switch (cnst->consttype) {
  case INT4OID:
    int32 val = DatumGetInt32(cnst->constvalue);
    buffer_appendf(buf, "%d", val);
    break;

  default:
    buffer_appendf(buf, "[Unknown consttype oid: %d]", cnst->consttype);
  }
}

static void buffer_print_var(PGExec_Buffer* buf, PlannedStmt* stmt, Var* var) {
  char* name = NULL;
  RangeTblEntry* rte = list_nth(stmt->rtable, var->varno-1);
  if (rte->rtekind != RTE_RELATION) {
    elog(LOG, "[Unsupported relation type for var: %d].", rte->rtekind);
    return;
  }

  name = get_attname(rte->relid, var->varattno, false);
  buffer_appendz(buf, name);
  pfree(name);
}

static void buffer_print_expr(PGExec_Buffer* buf, PlannedStmt* stmt, Node* expr) {
  switch (nodeTag(expr)) {
  case T_Const:
    buffer_print_const(buf, (Const*)expr);
    break;

  case T_Var:
    buffer_print_var(buf, stmt, (Var*)expr);
    break;

  case T_OpExpr:
    buffer_print_opexpr(buf, stmt, (OpExpr*)expr);
    break;

  default:
    buffer_appendf(buf, "[Unknown: %d]", nodeTag(expr));
  }
}

static void buffer_print_list(PGExec_Buffer* buf, PlannedStmt* stmt, List* list, char* sep) {
  ListCell* cell = NULL;
  bool first = true;

  foreach(cell, list) {
    if (!first) {
      buffer_appendz(buf, sep);
    }

    first = false;
    buffer_print_expr(buf, stmt, (Node*)lfirst(cell));
  }
}

static void buffer_print_where(PGExec_Buffer* buf, QueryDesc* queryDesc, Plan* plan) {
  if (plan->qual == NULL) {
    return;
  }

  buffer_appendz(buf, " WHERE ");
  buffer_print_list(buf, queryDesc->plannedstmt, plan->qual, " AND ");
}

Rebuild and reinstall the extension, and restart Postgres. Re-run the test.sql script. Check the Postgres server logs and you should see:

2023-11-19 20:03:14.351 GMT [3254458] LOG:  [pgexec] SELECT [columns todo] FROM x WHERE a > 1
2023-11-19 20:03:14.351 GMT [3254458] STATEMENT:  SELECT a FROM x WHERE a > 1;

Huzzah!

Walking the column list

Let's get rid of [columns todo]. We already had the idea that List* targetlist on the Plan struct was a list of expression Nodes. Let's try it.

static void buffer_print_select_columns(PGExec_Buffer* buf, QueryDesc* queryDesc, Plan* plan) {
  if (plan->targetlist == NULL) {
    return;
  }

  buffer_print_list(buf, queryDesc->plannedstmt, plan->targetlist, ", ");
}

Rebuild and reinstall the extension, and restart Postgres. Re-run the test.sql script. Check the Postgres server logs and you should see:

2023-11-19 20:12:48.091 GMT [3255398] LOG:  [pgexec] SELECT [Unknown: 53] FROM x WHERE a > 1
2023-11-19 20:12:48.091 GMT [3255398] STATEMENT:  SELECT a FROM x WHERE a > 1;

Hmm. Let's look up Node 53 in Postgres:

$ grep ' = 53,' src/include/nodes/nodetags.h
        T_TargetEntry = 53,

Based on the definition of TargetEntry, it looks like we can ignore most of the fields (because we don't need to handle SELECT a AS b yet) and just proxy the child expr field.

Let's add a case T_TargetEntry to buffer_print_expr:

static void buffer_print_expr(PGExec_Buffer* buf, PlannedStmt* stmt, Node* expr) {
  switch (nodeTag(expr)) {
  case T_Const:
    buffer_print_const(buf, (Const*)expr);
    break;

  case T_Var:
    buffer_print_var(buf, stmt, (Var*)expr);
    break;

  case T_TargetEntry:
    buffer_print_expr(buf, stmt, (Node*)((TargetEntry*)expr)->expr);
    break;

  case T_OpExpr:
    buffer_print_opexpr(buf, stmt, (OpExpr*)expr);
    break;

  default:
    buffer_appendf(buf, "[Unknown: %d]", nodeTag(expr));
  }
}

Rebuild and reinstall the extension, and restart Postgres. Re-run the test.sql script. Check the Postgres server logs and:

2023-11-19 20:17:51.114 GMT [3257827] LOG:  [pgexec] SELECT a FROM x WHERE a > 1
2023-11-19 20:17:51.114 GMT [3257827] STATEMENT:  SELECT a FROM x WHERE a > 1;

We did it!

Variations

Let's try out some other queries to make sure this wasn't just luck.

$ /usr/local/pgsql/bin/psql -h localhost postgres -c 'SELECT a + 1 FROM x'
 ?column?
----------
      310
(1 row)

$ /usr/local/pgsql/bin/psql -h localhost postgres -c 'SELECT a + 1 FROM x WHERE 2 > a'
 ?column?
----------
(0 rows)

And back in the Postgres server logs:

2023-11-19 20:19:28.057 GMT [3257874] LOG:  [pgexec] SELECT a + 1 FROM x
2023-11-19 20:19:28.057 GMT [3257874] STATEMENT:  SELECT a + 1 FROM x
2023-11-19 20:19:30.474 GMT [3257878] LOG:  [pgexec] SELECT a + 1 FROM x WHERE 2 > a
2023-11-19 20:19:30.474 GMT [3257878] STATEMENT:  SELECT a + 1 FROM x WHERE 2 > a

Not bad!

Next steps

Printing out the statement here isn't incredibly useful. But it establishes a basis for future work that might avoid Postgres's query execution engine and do the execution ourselves, or to proxy execution to another system.

Postscript: On ChatGPT

My recent Postgres explorations would have been basically impossible if it weren't for being able to ask ChatGPT simple, stupid questions like "How do I get from a Postgres Var to a column name".

It isn't always right. It doesn't always give great code. Actually, it normally gives pretty weird code. But it's been extremely useful for quick iteration when I get stuck.

The only other place the information exists is in small blog posts around the internet, the Postgres mailing lists (that so far for me hasn't been super responsive), and the code itself.

I've been on a Postgres roll. Let's dig into interpreting a Postgres query plan in preparation for future work on completely diverting the flow of Postgres query execution using execution hooks!https://t.co/EZrgoIiTuX pic.twitter.com/7S6d6olPX8
— Phil Eaton (@eatonphil) November 19, 2023

Writing a storage engine for Postgres: an in-memory Table Access Method

Wed, 01 Nov 2023 00:00:00 +0000

With Postgres 12, released in 2019, it became possible to swap out Postgres's storage engine.

This is a feature MySQL has supported for a long time. There are at least 8 different built-in engines you can pick from. MyRocks, MySQL on RocksDB, is another popular third-party distribution.

I assume there will be a renaissance of Postgres storage engines. To date, the efforts are nascent. OrioleDB and Citus Columnar are two promising third-party table access methods being actively developed.

Why alternative storage engines?

The ability to swap storage engines is useful because different workloads sometimes benefit from different storage approaches. Analytics workloads and columnar storage layouts go well together. Write-heavy workloads and LSM trees go well together. And some people like in-memory storage for running integration tests.

By swapping out only the storage engine, you get the benefit of the rest of the Postgres or MySQL infrastructure. The query language, the wire protocol, the ecosystem, etc.

Why not foreign data wrappers?

Very little has been written about the difference between foreign data wrappers (FDWs) and table access methods. Table access methods seems to be the lower-level layer where presumably you get better performance and cleaner integration. But there is clearly overlap between these two extension options.

For example there is a FDW for ClickHouse so when you create tables and rows and query the tables you are really creating and querying rows in a ClickHouse server. Similarly there's a FDW for RocksDB. And Citus's columnar engine works either as a foreign data wrapper or a table access method.

The Citus page draws the clearest distinction between FDWs and table access methods, but even that page is vague. Performance doesn't seem to be the main difference. Closer integration, and thus the ability to look more like vanilla Postgres from the outside, seems to be the gist.

In any case, I wanted to explore the table access method API.

Digging in

I haven't written Postgres extensions before and I've never written C professionally. If you're familiar with Postgres internals or C and notice something funky, please let me know!

It turns out that almost no one has written how to implement the minimal table access methods for various storage engine operations. So after quite a bit of stumbling to get the basics of an in-memory storage engine working, I'm going to walk you through my approach.

This is prototype-quality code which hopefully will be a useful base for further exploration.

All code for this post is available on GitHub.

A debug Postgres build

First off, let's make a debug build of Postgres.

$ git clone https://github.com/postgres/postgres
$ # An arbitrary commit from `master` after Postgres 16 I am on
$ git checkout 849172ff4883d44168f96f39d3fde96d0aa34c99
$ cd postgres
$ ./configure --enable-cassert --enable-debug CFLAGS="-ggdb -Og -g3 -fno-omit-frame-pointer"
$ make -j8
$ sudo make install

This will install Postgres binaries (e.g. psql, pg_ctl, initdb, pg_config) into /usr/local/pgsql/bin.

I'm going to reference those absolute paths throughout this post because you might have a system (package manager) install of Postgres already.

Let's create a database and start up this debug build:

$ /usr/local/pgsql/bin/initdb test-db
$ /usr/local/pgsql/bin/pg_ctl -D test-db -l logfile start

Extension infrastructure

Since we installed Postgres from scratch, /usr/local/pgsql/bin/pg_config will supply all of the infrastructure we need.

The "infrastructure" is basically just PGXS: Postgres Makefile utilities.

It's convention-heavy. So in a new Makefile for this project we'll specify:

MODULES: Any C sources to build, without the .c file extension
EXTENSION: Extension metadata file, without the .control file extension
DATA: A SQL file that is executed when the extension is loaded, this time with the .sql extension

MODULES = pgtam
EXTENSION = pgtam
DATA = pgtam--0.0.1.sql

PG_CONFIG = /usr/local/pgsql/bin/pg_config
PGXS := $(shell $(PG_CONFIG) --pgxs)
include $(PGXS)

The final three lines set up the PGXS Makefile library based on the particular installed Postgres build we want to build the extension against and install the extension to.

PGXS gives us a few important targets like make distclean, make, and make install we'll use later on.

`pgtam.c`

A minimal C file that registers a function capable of serving as a table access method is:

#include "postgres.h"
#include "fmgr.h"
#include "access/tableam.h"

PG_MODULE_MAGIC;

const TableAmRoutine memam_methods = {
  .type = T_TableAmRoutine,
};

PG_FUNCTION_INFO_V1(mem_tableam_handler);
Datum mem_tableam_handler(PG_FUNCTION_ARGS) {
  PG_RETURN_POINTER(&memam_methods);
}

If you want to read about extension basics without the complexity of table access methods, you can find a complete, minimal Postgres extension I wrote to validate the infrastructure here. Or you can follow a larger tutorial.

The workflow for registering a table access method is to first run CREATE EXTENSION pgtam. This assumes pgtam is an extension that has a function that returns a TableAmRoutine struct instance, a table of table access methods.

Then you must run CREATE ACCESS METHOD mem TYPE TABLE HANDLER mem_tableam_handler. And finally you can use the access method when creating a table with USING mem: CREATE TABLE x(a INT) USING mem.

`pgtam.control`

This file contains extension metadata. At a minimum, the version of the extension and the filename for the extension where it should be installed.

default_version = '0.0.1'
module_pathname = '$libdir/pgtam'

`pgtam--0.0.1.sql`

Finally, in pgtam--0.0.1.sql (which is executed when we call CREATE EXTENSION pgtam), we register the handler function as a foreign function, and then we register the function as an access method.

CREATE OR REPLACE FUNCTION mem_tableam_handler(internal)
RETURNS table_am_handler AS 'pgtam', 'mem_tableam_handler'
LANGUAGE C STRICT;

CREATE ACCESS METHOD mem TYPE TABLE HANDLER mem_tableam_handler;

Build

Now that we've got all the pieces in place, we can build and install the extension.

$ make
$ sudo make install

Let's add a test.sql script to exercise the extension:

DROP EXTENSION IF EXISTS pgtam CASCADE;
CREATE EXTENSION pgtam;
CREATE TABLE x(a INT) USING mem;

And run it:

$ /usr/local/pgsql/bin/psql postgres -f test.sql
DROP EXTENSION
CREATE EXTENSION
psql:test.sql:3: server closed the connection unexpectedly
        This probably means the server terminated abnormally
        before or while processing the request.
psql:test.sql:3: error: connection to server was lost

Ok, so psql crashed! Let's look at the server logs. When we started Postgres with pg_ctl we specified the log file as logfile in the directory where we ran pg_ctl.

If we look through it we'll spot an assertion failure:

$ grep Assert logfile
TRAP: failed Assert("routine->scan_begin != NULL"), File: "tableamapi.c", Line: 52, PID: 2906922

That's a great sign! This is Postgres's debug infrastructure helping to make sure the table access method is correctly implemented.

Table access method stubs

The next step is to add function stubs for all the non-optional methods of the TableAmRoutine struct.

I've done all the work for you already so you can just copy this over the existing pgtam.c. It's a big file, but don't worry. There's nothing to explain. Just a bunch of blank functions returning default values when required.

#include "postgres.h"
#include "fmgr.h"
#include "access/tableam.h"
#include "access/heapam.h"
#include "nodes/execnodes.h"
#include "catalog/index.h"
#include "commands/vacuum.h"
#include "utils/builtins.h"
#include "executor/tuptable.h"

PG_MODULE_MAGIC;

const TableAmRoutine memam_methods;

static const TupleTableSlotOps* memam_slot_callbacks(
  Relation relation
) {
  return NULL;
}

static TableScanDesc memam_beginscan(
  Relation relation,
  Snapshot snapshot,
  int nkeys,
  struct ScanKeyData *key,
  ParallelTableScanDesc parallel_scan,
  uint32 flags
) {
  return NULL;
}

static void memam_rescan(
  TableScanDesc sscan,
  struct ScanKeyData *key,
  bool set_params,
  bool allow_strat,
  bool allow_sync,
  bool allow_pagemode
) {
}

static void memam_endscan(TableScanDesc sscan) {
}

static bool memam_getnextslot(
  TableScanDesc sscan,
  ScanDirection direction,
  TupleTableSlot *slot
) {
  return false;
}

static IndexFetchTableData* memam_index_fetch_begin(Relation rel) {
  return NULL;
}

static void memam_index_fetch_reset(IndexFetchTableData *scan) {
}

static void memam_index_fetch_end(IndexFetchTableData *scan) {
}

static bool memam_index_fetch_tuple(
  struct IndexFetchTableData *scan,
  ItemPointer tid,
  Snapshot snapshot,
  TupleTableSlot *slot,
  bool *call_again,
  bool *all_dead
) {
  return false;
}

static void memam_tuple_insert(
  Relation relation,
  TupleTableSlot *slot,
  CommandId cid,
  int options,
  BulkInsertState bistate
) {
}

static void memam_tuple_insert_speculative(
  Relation relation,
  TupleTableSlot *slot,
  CommandId cid,
  int options,
  BulkInsertState bistate,
  uint32 specToken) {
}

static void memam_tuple_complete_speculative(
  Relation relation,
  TupleTableSlot *slot,
  uint32 specToken,
  bool succeeded) {
}

static void memam_multi_insert(
  Relation relation,
  TupleTableSlot **slots,
  int ntuples,
  CommandId cid,
  int options,
  BulkInsertState bistate
) {
}

static TM_Result memam_tuple_delete(
  Relation relation,
  ItemPointer tid,
  CommandId cid,
  Snapshot snapshot,
  Snapshot crosscheck,
  bool wait,
  TM_FailureData *tmfd,
  bool changingPart
) {
  TM_Result result = {};
  return result;
}

static TM_Result memam_tuple_update(
  Relation relation,
  ItemPointer otid,
  TupleTableSlot *slot,
  CommandId cid,
  Snapshot snapshot,
  Snapshot crosscheck,
  bool wait,
  TM_FailureData *tmfd,
  LockTupleMode *lockmode,
  TU_UpdateIndexes *update_indexes
) {
  TM_Result result = {};
  return result;
}

static TM_Result memam_tuple_lock(
  Relation relation,
  ItemPointer tid,
  Snapshot snapshot,
  TupleTableSlot *slot,
  CommandId cid,
  LockTupleMode mode,
  LockWaitPolicy wait_policy,
  uint8 flags,
  TM_FailureData *tmfd)
{
  TM_Result result = {};
  return result;
}

static bool memam_fetch_row_version(
  Relation relation,
  ItemPointer tid,
  Snapshot snapshot,
  TupleTableSlot *slot
) {
  return false;
}

static void memam_get_latest_tid(
  TableScanDesc sscan,
  ItemPointer tid
) {
}

static bool memam_tuple_tid_valid(TableScanDesc scan, ItemPointer tid) {
  return false;
}

static bool memam_tuple_satisfies_snapshot(
  Relation rel,
  TupleTableSlot *slot,
  Snapshot snapshot
) {
  return false;
}

static TransactionId memam_index_delete_tuples(
  Relation rel,
  TM_IndexDeleteOp *delstate
) {
  TransactionId id = {};
  return id;
}

static void memam_relation_set_new_filelocator(
  Relation rel,
  const RelFileLocator *newrlocator,
  char persistence,
  TransactionId *freezeXid,
  MultiXactId *minmulti
) {
}

static void memam_relation_nontransactional_truncate(
  Relation rel
) {
}

static void memam_relation_copy_data(
  Relation rel,
  const RelFileLocator *newrlocator
) {
}

static void memam_relation_copy_for_cluster(
  Relation OldHeap,
  Relation NewHeap,
  Relation OldIndex,
  bool use_sort,
  TransactionId OldestXmin,
  TransactionId *xid_cutoff,
  MultiXactId *multi_cutoff,
  double *num_tuples,
  double *tups_vacuumed,
  double *tups_recently_dead
) {
}

static void memam_vacuum_rel(
  Relation rel,
  VacuumParams *params,
  BufferAccessStrategy bstrategy
) {
}

static bool memam_scan_analyze_next_block(
  TableScanDesc scan,
  BlockNumber blockno,
  BufferAccessStrategy bstrategy
) {
  return false;
}

static bool memam_scan_analyze_next_tuple(
  TableScanDesc scan,
  TransactionId OldestXmin,
  double *liverows,
  double *deadrows,
  TupleTableSlot *slot
) {
  return false;
}

static double memam_index_build_range_scan(
  Relation heapRelation,
  Relation indexRelation,
  IndexInfo *indexInfo,
  bool allow_sync,
  bool anyvisible,
  bool progress,
  BlockNumber start_blockno,
  BlockNumber numblocks,
  IndexBuildCallback callback,
  void *callback_state,
  TableScanDesc scan
) {
  return 0;
}

static void memam_index_validate_scan(
  Relation heapRelation,
  Relation indexRelation,
  IndexInfo *indexInfo,
  Snapshot snapshot,
  ValidateIndexState *state
) {
}

static bool memam_relation_needs_toast_table(Relation rel) {
  return false;
}

static Oid memam_relation_toast_am(Relation rel) {
  Oid oid = {};
  return oid;
}

static void memam_fetch_toast_slice(
  Relation toastrel,
  Oid valueid,
  int32 attrsize,
  int32 sliceoffset,
  int32 slicelength,
  struct varlena *result
) {
}

static void memam_estimate_rel_size(
  Relation rel,
  int32 *attr_widths,
  BlockNumber *pages,
  double *tuples,
  double *allvisfrac
) {
}

static bool memam_scan_sample_next_block(
  TableScanDesc scan, SampleScanState *scanstate
) {
  return false;
}

static bool memam_scan_sample_next_tuple(
  TableScanDesc scan,
  SampleScanState *scanstate,
  TupleTableSlot *slot
) {
  return false;
}

const TableAmRoutine memam_methods = {
  .type = T_TableAmRoutine,

  .slot_callbacks = memam_slot_callbacks,

  .scan_begin = memam_beginscan,
  .scan_end = memam_endscan,
  .scan_rescan = memam_rescan,
  .scan_getnextslot = memam_getnextslot,

  .parallelscan_estimate = table_block_parallelscan_estimate,
  .parallelscan_initialize = table_block_parallelscan_initialize,
  .parallelscan_reinitialize = table_block_parallelscan_reinitialize,

  .index_fetch_begin = memam_index_fetch_begin,
  .index_fetch_reset = memam_index_fetch_reset,
  .index_fetch_end = memam_index_fetch_end,
  .index_fetch_tuple = memam_index_fetch_tuple,

  .tuple_insert = memam_tuple_insert,
  .tuple_insert_speculative = memam_tuple_insert_speculative,
  .tuple_complete_speculative = memam_tuple_complete_speculative,
  .multi_insert = memam_multi_insert,
  .tuple_delete = memam_tuple_delete,
  .tuple_update = memam_tuple_update,
  .tuple_lock = memam_tuple_lock,

  .tuple_fetch_row_version = memam_fetch_row_version,
  .tuple_get_latest_tid = memam_get_latest_tid,
  .tuple_tid_valid = memam_tuple_tid_valid,
  .tuple_satisfies_snapshot = memam_tuple_satisfies_snapshot,
  .index_delete_tuples = memam_index_delete_tuples,

  .relation_set_new_filelocator = memam_relation_set_new_filelocator,
  .relation_nontransactional_truncate = memam_relation_nontransactional_truncate,
  .relation_copy_data = memam_relation_copy_data,
  .relation_copy_for_cluster = memam_relation_copy_for_cluster,
  .relation_vacuum = memam_vacuum_rel,
  .scan_analyze_next_block = memam_scan_analyze_next_block,
  .scan_analyze_next_tuple = memam_scan_analyze_next_tuple,
  .index_build_range_scan = memam_index_build_range_scan,
  .index_validate_scan = memam_index_validate_scan,

  .relation_size = table_block_relation_size,
  .relation_needs_toast_table = memam_relation_needs_toast_table,
  .relation_toast_am = memam_relation_toast_am,
  .relation_fetch_toast_slice = memam_fetch_toast_slice,

  .relation_estimate_size = memam_estimate_rel_size,

  .scan_sample_next_block = memam_scan_sample_next_block,
  .scan_sample_next_tuple = memam_scan_sample_next_tuple
};

PG_FUNCTION_INFO_V1(mem_tableam_handler);

Datum mem_tableam_handler(PG_FUNCTION_ARGS) {
  PG_RETURN_POINTER(&memam_methods);
}

Let's build and test it!

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE

Hey we're getting somewhere! It successfully created the table with our custom table access method.

Querying rows

Next, let's try querying the table by adding a SELECT a FROM x to test.sql and running it:

$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
psql:test.sql:6: server closed the connection unexpectedly
        This probably means the server terminated abnormally
        before or while processing the request.
psql:test.sql:6: error: connection to server was lost

This time there's nothing in logfile that helps:

$ tail -n15 logfile
2023-11-01 18:43:32.449 UTC [2906199] LOG:  database system is ready to accept connections
2023-11-01 18:58:32.572 UTC [2907997] LOG:  checkpoint starting: time
2023-11-01 18:58:35.305 UTC [2907997] LOG:  checkpoint complete: wrote 28 buffers (0.2%); 0 WAL file(s) added, 0 removed, 0 recycled; write=2.712 s, sync=0.015 s, total=2.733 s; sync files=23, longest=0.004 s, average=0.001 s; distance=128 kB, estimate=150 kB; lsn=0/15F88E0, redo lsn=0/15F8888
2023-11-01 19:08:14.485 UTC [2906199] LOG:  server process (PID 2908242) was terminated by signal 11: Segmentation fault
2023-11-01 19:08:14.485 UTC [2906199] DETAIL:  Failed process was running: SELECT a FROM x;
2023-11-01 19:08:14.485 UTC [2906199] LOG:  terminating any other active server processes
2023-11-01 19:08:14.486 UTC [2906199] LOG:  all server processes terminated; reinitializing
2023-11-01 19:08:14.508 UTC [2908253] LOG:  database system was interrupted; last known up at 2023-11-01 18:58:35 UTC
2023-11-01 19:08:14.518 UTC [2908253] LOG:  database system was not properly shut down; automatic recovery in progress
2023-11-01 19:08:14.519 UTC [2908253] LOG:  redo starts at 0/15F8888
2023-11-01 19:08:14.520 UTC [2908253] LOG:  invalid record length at 0/161DE70: expected at least 24, got 0
2023-11-01 19:08:14.520 UTC [2908253] LOG:  redo done at 0/161DE38 system usage: CPU: user: 0.00 s, system: 0.00 s, elapsed: 0.00 s
2023-11-01 19:08:14.521 UTC [2908254] LOG:  checkpoint starting: end-of-recovery immediate wait
2023-11-01 19:08:14.532 UTC [2908254] LOG:  checkpoint complete: wrote 35 buffers (0.2%); 0 WAL file(s) added, 0 removed, 0 recycled; write=0.001 s, sync=0.010 s, total=0.012 s; sync files=27, longest=0.003 s, average=0.001 s; distance=149 kB, estimate=149 kB; lsn=0/161DE70, redo lsn=0/161DE70
2023-11-01 19:08:14.533 UTC [2906199] LOG:  database system is ready to accept connections

This was the first place I got stuck. How on earth do I figure out what methods to implement? I mean, it's clearly one or more of these methods from the struct. But there are so many methods.

I tried setting a breakpoint in gdb on the process returned by SELECT pg_backend_pid() for a psql session, but the breakpoint never seemed to be hit for any of my methods.

So I did the low-tech solution and opened a file, /tmp/pgtam.log, turned off buffering on it, and added a log to every method on the TableAmRoutine struct:

@@ -12,9 +12,13 @@

 const TableAmRoutine memam_methods;

+FILE* fd;
+#define DEBUG_FUNC() fprintf(fd, "in %s\n", __func__);
+
 static const TupleTableSlotOps* memam_slot_callbacks(
   Relation relation
 ) {
+  DEBUG_FUNC();
   return NULL;
 }

@@ -26,6 +30,7 @@
   ParallelTableScanDesc parallel_scan,
   uint32 flags
 ) {
+  DEBUG_FUNC();
   return NULL;
 }

@@ -37,9 +42,11 @@
   bool allow_sync,
   bool allow_pagemode
 ) {
+  DEBUG_FUNC();
 }

 static void memam_endscan(TableScanDesc sscan) {
+  DEBUG_FUNC();
 }

 static bool memam_getnextslot(
@@ -47,17 +54,21 @@
   ScanDirection direction,
   TupleTableSlot *slot
 ) {
+  DEBUG_FUNC();
   return false;
 }

 static IndexFetchTableData* memam_index_fetch_begin(Relation rel) {
+  DEBUG_FUNC();
   return NULL;
 }

 static void memam_index_fetch_reset(IndexFetchTableData *scan) {
+  DEBUG_FUNC();
 }

 static void memam_index_fetch_end(IndexFetchTableData *scan) {
+  DEBUG_FUNC();
 }

 static bool memam_index_fetch_tuple(
@@ -68,6 +79,7 @@
   bool *call_again,
   bool *all_dead
 ) {
+  DEBUG_FUNC();
   return false;
 }

@@ -78,6 +90,7 @@
   int options,
   BulkInsertState bistate
 ) {
+  DEBUG_FUNC();
 }

 static void memam_tuple_insert_speculative(
@@ -87,6 +100,7 @@
   int options,
   BulkInsertState bistate,
   uint32 specToken) {
+  DEBUG_FUNC();
 }

 static void memam_tuple_complete_speculative(
@@ -94,6 +108,7 @@
   TupleTableSlot *slot,
   uint32 specToken,
   bool succeeded) {
+  DEBUG_FUNC();
 }

 static void memam_multi_insert(
@@ -104,6 +119,7 @@
   int options,
   BulkInsertState bistate
 ) {
+  DEBUG_FUNC();
 }

 static TM_Result memam_tuple_delete(
@@ -117,6 +133,7 @@
   bool changingPart
 ) {
   TM_Result result = {};
+  DEBUG_FUNC();
   return result;
 }

@@ -133,6 +150,7 @@
   TU_UpdateIndexes *update_indexes
 ) {
   TM_Result result = {};
+  DEBUG_FUNC();
   return result;
 }

@@ -148,6 +166,7 @@
   TM_FailureData *tmfd)
 {
   TM_Result result = {};
+  DEBUG_FUNC();
   return result;
 }

@@ -157,6 +176,7 @@
   Snapshot snapshot,
   TupleTableSlot *slot
 ) {
+  DEBUG_FUNC();
   return false;
 }

@@ -164,9 +184,11 @@
   TableScanDesc sscan,
   ItemPointer tid
 ) {
+  DEBUG_FUNC();
 }

 static bool memam_tuple_tid_valid(TableScanDesc scan, ItemPointer tid) {
+  DEBUG_FUNC();
   return false;
 }

@@ -175,6 +197,7 @@
   TupleTableSlot *slot,
   Snapshot snapshot
 ) {
+  DEBUG_FUNC();
   return false;
 }

@@ -183,6 +206,7 @@
   TM_IndexDeleteOp *delstate
 ) {
   TransactionId id = {};
+  DEBUG_FUNC();
   return id;
 }

@@ -193,17 +217,20 @@
   TransactionId *freezeXid,
   MultiXactId *minmulti
 ) {
+  DEBUG_FUNC();
 }

 static void memam_relation_nontransactional_truncate(
   Relation rel
 ) {
+  DEBUG_FUNC();
 }

 static void memam_relation_copy_data(
   Relation rel,
   const RelFileLocator *newrlocator
 ) {
+  DEBUG_FUNC();
 }

 static void memam_relation_copy_for_cluster(
@@ -218,6 +245,7 @@
   double *tups_vacuumed,
   double *tups_recently_dead
 ) {
+  DEBUG_FUNC();
 }

 static void memam_vacuum_rel(
@@ -225,6 +253,7 @@
   VacuumParams *params,
   BufferAccessStrategy bstrategy
 ) {
+  DEBUG_FUNC();
 }

 static bool memam_scan_analyze_next_block(
@@ -232,6 +261,7 @@
   BlockNumber blockno,
   BufferAccessStrategy bstrategy
 ) {
+  DEBUG_FUNC();
   return false;
 }

@@ -242,6 +272,7 @@
   double *deadrows,
   TupleTableSlot *slot
 ) {
+  DEBUG_FUNC();
   return false;
 }

@@ -258,6 +289,7 @@
   void *callback_state,
   TableScanDesc scan
 ) {
+  DEBUG_FUNC();
   return 0;
 }

@@ -268,14 +300,17 @@
   Snapshot snapshot,
   ValidateIndexState *state
 ) {
+  DEBUG_FUNC();
 }

 static bool memam_relation_needs_toast_table(Relation rel) {
+  DEBUG_FUNC();
   return false;
 }

 static Oid memam_relation_toast_am(Relation rel) {
   Oid oid = {};
+  DEBUG_FUNC();
   return oid;
 }

@@ -287,6 +322,7 @@
   int32 slicelength,
   struct varlena *result
 ) {
+  DEBUG_FUNC();
 }

 static void memam_estimate_rel_size(
@@ -296,11 +332,13 @@
   double *tuples,
   double *allvisfrac
 ) {
+  DEBUG_FUNC();
 }

 static bool memam_scan_sample_next_block(
   TableScanDesc scan, SampleScanState *scanstate
 ) {
+  DEBUG_FUNC();
   return false;
 }

@@ -309,6 +347,7 @@
   SampleScanState *scanstate,
   TupleTableSlot *slot
 ) {
+  DEBUG_FUNC();
   return false;
 }

And then in the entrypoint, initialize the file for logging.

@@ -369,5 +408,9 @@
 PG_FUNCTION_INFO_V1(mem_tableam_handler);

 Datum mem_tableam_handler(PG_FUNCTION_ARGS) {
+  fd = fopen("/tmp/pgtam.log", "a");
+  setvbuf(fd, NULL, _IONBF, 0); // Prevent buffering
+  fprintf(fd, "\n\nmem_tableam handler loaded\n");
+
   PG_RETURN_POINTER(&memam_methods);
 }

Let's give it a shot!

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
psql:test.sql:6: server closed the connection unexpectedly
        This probably means the server terminated abnormally
        before or while processing the request.
psql:test.sql:6: error: connection to server was lost

And let's check our log file:

$ cat /tmp/pgtam.log


mem_tableam handler loaded


mem_tableam handler loaded
in memam_relation_set_new_filelocator


mem_tableam handler loaded
in memam_relation_needs_toast_table


mem_tableam handler loaded
in memam_estimate_rel_size
in memam_slot_callbacks

Now we're getting somewhere!

I later realized elog() is the way most people log within Postgres/within extensions. I didn't know that when I was getting started though. This separate logging was a simple way to get the info out.

`slot_callbacks`

Since the request crashes and the last logged function is memam_slot_callbacks, it seems like that is where we should concentrate. The table access method docs suggest looking at the default heap access method for inspiration.

Its version of slot_callbacks returns &TTSOpsBufferHeapTuple:

static const TupleTableSlotOps *
heapam_slot_callbacks(Relation relation)
{
    return &TTSOpsBufferHeapTuple;
}

I have no idea what that means, but since it is defined in src/backend/executor/execTuples.c it doesn't seem to be tied to the heap access method implementation. Let's try it.

While it works initially, I noticed later on that TTSOpsBufferHeapTuple turns out not to be the right choice here. TTSOpsVirtual seems to be the right implementation.

@@ -19,7 +19,7 @@
   Relation relation
 ) {
   DEBUG_FUNC();
-  return NULL;
+  return &TTSOpsVirtual;
 }

 static TableScanDesc memam_beginscan(

Build and run:

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
psql:test.sql:6: server closed the connection unexpectedly
        This probably means the server terminated abnormally
        before or while processing the request.
psql:test.sql:6: error: connection to server was lost

It still crashes. But this time in /tmp/pgtam.log we made it into a new method!

$ cat /tmp/pgtam.log


mem_tableam handler loaded


mem_tableam handler loaded
in memam_relation_set_new_filelocator


mem_tableam handler loaded
in memam_relation_needs_toast_table


mem_tableam handler loaded
in memam_estimate_rel_size
in memam_slot_callbacks
in memam_beginscan

`scan_begin`

The function signature is:

TableScanDesc heap_beginscan(
  Relation relation,
  Snapshot snapshot,
  int nkeys,
  ScanKey key,
  ParallelTableScanDesc parallel_scan,
  uint32 flags
);

Since we just implemented stub versions of all the methods, we've been returning NULL. Since we're failing in this function, maybe we should try returning something that isn't NULL.

By looking at the definition of TableScanDesc, we can see it is a pointer to the TableScanDescData struct defined in src/include/access/relscan.h.

Let's malloc a TableScanDescData, free it in endscan, and return the TableScanDescData instance in beginscan:

@@ -30,8 +30,12 @@
   ParallelTableScanDesc parallel_scan,
   uint32 flags
 ) {
+  TableScanDescData* scan = {};
   DEBUG_FUNC();
-  return NULL;
+
+  scan = (TableScanDescData*)malloc(sizeof(TableScanDescData));
+
+  return (TableScanDesc)scan;
 }

 static void memam_rescan(
@@ -87,6 +87,7 @@

 static void memam_endscan(TableScanDesc sscan) {
   DEBUG_FUNC();
+  free(sscan);
 }

Build and run (you can do it on your own). No difference.

I got stuck for a while here too. Clearly something must be filled out in this struct but it could be anything. Through trial and error I realized the one field that must be filled out is scan->rs_rd.

@@ -34,6 +34,7 @@
   DEBUG_FUNC();

   scan = (TableScanDescData*)malloc(sizeof(TableScanDescData));
+  scan->rs_rd = relation;

   return (TableScanDesc)scan;
 }

We build and run:

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
 a
---
(0 rows)

And it works! It doesn't return anything but that's correct. There's nothing to return.

So what if we actually want to return something? Let's check our logs in /tmp/pgtam.log.

$ cat /tmp/pgtam.log


mem_tableam handler loaded


mem_tableam handler loaded
in memam_relation_set_new_filelocator


mem_tableam handler loaded
in memam_relation_needs_toast_table


mem_tableam handler loaded
in memam_estimate_rel_size
in memam_slot_callbacks
in memam_beginscan
in memam_getnextslot
in memam_endscan

Ok, I'm getting the gist of the API. A full table scan (which this is, because there are no indexes at play) starts with an initialization for a slot, then the scan begins, then getnextslot is called for each row, and then endscan is called to allow for cleanup.

So let's try returning a row in getnextslot.

`getnextslot`

The getnextslot signature is:

bool memam_getnextslot(
  TableScanDesc sscan,
  ScanDirection direction,
  TupleTableSlot *slot
);

So the sscan should be what we returned from beginscan and the interface docs say the current row gets stored in slot.

The return value seems to indicate whether or not we've reached the end of the scan. However, the scan will still end even if you return true if the slot is not filled out correctly. If the slot is filled out correctly and you unconditionally return true, you will crash the process.

Let's take a look at the definition of TupleTableSlot:

typedef struct TupleTableSlot
{
    NodeTag     type;
#define FIELDNO_TUPLETABLESLOT_FLAGS 1
    uint16      tts_flags;      /* Boolean states */
#define FIELDNO_TUPLETABLESLOT_NVALID 2
    AttrNumber  tts_nvalid;     /* # of valid values in tts_values */
    const TupleTableSlotOps *const tts_ops; /* implementation of slot */
#define FIELDNO_TUPLETABLESLOT_TUPLEDESCRIPTOR 4
    TupleDesc   tts_tupleDescriptor;    /* slot's tuple descriptor */
#define FIELDNO_TUPLETABLESLOT_VALUES 5
    Datum      *tts_values;     /* current per-attribute values */
#define FIELDNO_TUPLETABLESLOT_ISNULL 6
    bool       *tts_isnull;     /* current per-attribute isnull flags */
    MemoryContext tts_mcxt;     /* slot itself is in this context */
    ItemPointerData tts_tid;    /* stored tuple's tid */
    Oid         tts_tableOid;   /* table oid of tuple */
} TupleTableSlot;

tts_values is an array of Datum (which is a Postgres value). So that sounds like the actual values of the row. The tts_isnull field also looks important since that seems to be whether each value in the row is null or not. And tts_nvalid sounds important too since presumably it's the length of the tts_isnull and tts_values arrays.

The rest of it may or may not be important. Let's try filling out these three fields though and see what happens.

Datum

Back in the Postgres C extension documentation, we can see some simple examples of converting between C types and Postgres's Datum type.

For example:

Datum
add_one(PG_FUNCTION_ARGS)
{
    int32   arg = PG_GETARG_INT32(0);

    PG_RETURN_INT32(arg + 1);
}

If we look at the definition of PG_RETURN_INT32 in src/include/fmgr.h, we see:

#define PG_RETURN_INT32(x)   return Int32GetDatum(x)

So Int32GetDatum() is the function we'll use to set a Datum for a cell in a row.

@@ -54,13 +54,26 @@
   DEBUG_FUNC();
 }

+static bool done = false;
 static bool memam_getnextslot(
   TableScanDesc sscan,
   ScanDirection direction,
   TupleTableSlot *slot
 ) {
   DEBUG_FUNC();
-  return false;
+
+  if (done) {
+    return false;
+  }
+
+  slot->tts_nvalid = 1;
+  slot->tts_values = (Datum*)malloc(sizeof(Datum) * slot->tts_nvalid);
+  slot->tts_values[0] = Int32GetDatum(314 /* Some unique-looking value */);
+  slot->tts_isnull = (bool*)malloc(sizeof(bool) * slot->tts_nvalid);
+  slot->tts_isnull[0] = false;
+  done = true;
+
+  return true;
 }

 static IndexFetchTableData* memam_index_fetch_begin(Relation rel) {

The goal is that we return a single row and then exit the scan. It will have one 32-bit integer cell (remember we created the table CREATE TABLE x (a INT); INT is shorthand for INT4 which is a 32-bit integer) that will have the value 314.

But if we build and run this, we get no rows.

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
 a
---
(0 rows)

I got stuck for a while here. Plugging my getnextslot code into ChatGPT helped. One thing it gave me to try was calling ExecStoreVirtualTuple on the slot. I noticed that the built-in heap access method also called a function like this in getnextslot.

And I realized that tts_nvalid is already set up and the memory for tts_values and tts_isnull is already allocated. So the code became a little simpler.

@@ -66,11 +66,9 @@
     return false;
   }

-  slot->tts_nvalid = 1;
-  slot->tts_values = (Datum*)malloc(sizeof(Datum) * slot->tts_nvalid);
   slot->tts_values[0] = Int32GetDatum(314 /* Some unique-looking value */);
-  slot->tts_isnull = (bool*)malloc(sizeof(bool) * slot->tts_nvalid);
   slot->tts_isnull[0] = false;
+  ExecStoreVirtualTuple(slot);
   done = true;

   return true;

Build and run:

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
  a
-----
 314
(1 row)

Fantastic!

Creating a table

Now that we've proven we can return random data, let's set up infrastructure for storing tables in memory.

@@ -15,6 +15,41 @@
 FILE* fd;
 #define DEBUG_FUNC() fprintf(fd, "in %s\n", __func__);

+
+struct Column {
+  int value;
+};
+
+struct Row {
+  struct Column* columns;
+  size_t ncolumns;
+};
+
+#define MAX_ROWS 100
+struct Table {
+  char* name;
+  struct Row* rows;
+  size_t nrows;
+};
+
+#define MAX_TABLES 100
+struct Database {
+  struct Table* tables;
+  size_t ntables;
+};
+
+struct Database* database;
+
+static void get_table(struct Table** table, Relation relation) {
+  char* this_name = NameStr(relation->rd_rel->relname);
+  for (size_t i = 0; i < database->ntables; i++) {
+    if (strcmp(database->tables[i].name, this_name) == 0) {
+      *table = &database->tables[i];
+      return;
+    }
+  }
+}
+
 static const TupleTableSlotOps* memam_slot_callbacks(
   Relation relation
 ) {

Based on what we logged in /tmp/pgtam.log it seems like memam_relation_set_new_filelocator is called when a new table is created. So let's handle adding a new table there.

@@ -233,7 +268,16 @@
   TransactionId *freezeXid,
   MultiXactId *minmulti
 ) {
+  struct Table table = {};
   DEBUG_FUNC();
+
+  table.name = strdup(NameStr(rel->rd_rel->relname));
+  fprintf(fd, "Created table: [%s]\n", table.name);
+  table.rows = (struct Row*)malloc(sizeof(struct Row) * MAX_ROWS);
+  table.nrows = 0;
+
+  database->tables[database->ntables] = table;
+  database->ntables++;
 }

 static void memam_relation_nontransactional_truncate(

Finally, we'll initialize the in-memory Database* when the handler is loaded.

@@ -428,5 +472,11 @@
   setvbuf(fd, NULL, _IONBF, 0); // Prevent buffering
   fprintf(fd, "\n\nmem_tableam handler loaded\n");

+  if (database == NULL) {
+    database = (struct Database*)malloc(sizeof(struct Database));
+    database->ntables = 0;
+    database->tables = (struct Table*)malloc(sizeof(struct Table) * MAX_TABLES);
+  }
+
   PG_RETURN_POINTER(&memam_methods);
 }

If we build and run, we won't notice anything new.

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
  a
-----
 314
(1 row)

But we should see a message in /tmp/pgtam.log about the new table being created.

$ cat /tmp/pgtam.log


mem_tableam handler loaded


mem_tableam handler loaded
in memam_relation_set_new_filelocator
Created table: [x]


mem_tableam handler loaded
in memam_relation_needs_toast_table


mem_tableam handler loaded
in memam_estimate_rel_size
in memam_slot_callbacks
in memam_beginscan
in memam_getnextslot
in memam_getnextslot
in memam_endscan

And there it is! Creation looks good.

Inserting rows

Let's add INSERT INTO x VALUES (23), (101); to test.sql and run the SQL script.

$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
INSERT 0 2
  a
-----
 314
(1 row)

And let's check the log to see what method is called when we try to INSERT.

$ cat /tmp/pgtam.log


mem_tableam handler loaded


mem_tableam handler loaded
in memam_relation_set_new_filelocator
Created table: [x]


mem_tableam handler loaded
in memam_relation_needs_toast_table


mem_tableam handler loaded
in memam_slot_callbacks
in memam_tuple_insert
in memam_tuple_insert
in memam_estimate_rel_size
in memam_slot_callbacks
in memam_beginscan
in memam_getnextslot
in memam_getnextslot
in memam_endscan

tuple_insert seems to be the method! Looks like it gets called once for each row to insert. Perfect.

The signature for tuple_insert is:

void memam_tuple_insert(
  Relation relation,
  TupleTableSlot *slot,
  CommandId cid,
  int options,
  BulkInsertState bistate
);

We can get the table name from relation, and instead of writing to slot we can read from slot->tts_values instead.

@@ -141,7 +141,38 @@
   int options,
   BulkInsertState bistate
 ) {
+  TupleDesc desc = RelationGetDescr(relation);
+  struct Table* table = NULL;
+  struct Column column = {};
+  struct Row row = {};
+
   DEBUG_FUNC();
+
+  get_table(&table, relation);
+  if (table == NULL) {
+    elog(ERROR, "table not found");
+    return;
+  }
+
+  if (table->nrows == MAX_ROWS) {
+    elog(ERROR, "cannot insert more rows");
+    return;
+  }
+
+  row.ncolumns = desc->natts;
+  Assert(slot->tts_nvalid == row.ncolumns);
+  Assert(row.ncolumns > 0);
+
+  row.columns = (struct Column*)malloc(sizeof(struct Column) * row.ncolumns);
+  for (size_t i = 0; i < row.ncolumns; i++) {
+    Assert(desc->attrs[i].atttypid == INT4OID);
+    column.value = DatumGetInt32(slot->tts_values[i]);
+    row.columns[i] = column;
+    fprintf(fd, "Got value: %d\n", column.value);
+  }
+
+  table->rows[table->nrows] = row;
+  table->nrows++;
 }

 static void memam_tuple_insert_speculative(

Build and run and again we won't notice anything new.

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
INSERT 0 2
  a
-----
 314
(1 row)

But if we check the logs, we should see the two column values we inserted, one for each row.

$ cat /tmp/pgtam.log


mem_tableam handler loaded


mem_tableam handler loaded
in memam_relation_set_new_filelocator
Created table: [x]


mem_tableam handler loaded
in memam_relation_needs_toast_table


mem_tableam handler loaded
in memam_slot_callbacks
in memam_tuple_insert
Got value: 23
in memam_tuple_insert
Got value: 101
in memam_estimate_rel_size
in memam_slot_callbacks
in memam_beginscan
in memam_getnextslot
in memam_getnextslot
in memam_endscan

Woohoo!

Un-hardcoding the scan

The final thing we need to do is drop the hardcoded 314 we returned from getnextslot and instead we need to look up the current table and return rows from it. This also means we need to keep track of which row we're on. So beginscan will also need to change slightly.

@@ -57,6 +56,14 @@
   return &TTSOpsVirtual;
 }

+
+struct MemScanDesc {
+  TableScanDescData rs_base; // Base class from access/relscan.h.
+
+  // Custom data.
+  uint32 cursor;
+};
+
 static TableScanDesc memam_beginscan(
   Relation relation,
   Snapshot snapshot,
@@ -65,11 +72,13 @@
   ParallelTableScanDesc parallel_scan,
   uint32 flags
 ) {
-  TableScanDescData* scan = {};
-  DEBUG_FUNC();
+  struct MemScanDesc* scan;

-  scan = (TableScanDescData*)malloc(sizeof(TableScanDescData));
-  scan->rs_rd = relation;
+  DEBUG_FUNC();
+
+  scan = (struct MemScanDesc*)malloc(sizeof(struct MemScanDesc));
+  scan->rs_base.rs_rd = relation;
+  scan->cursor = 0;

   return (TableScanDesc)scan;
 }
@@ -89,23 +97,26 @@
   DEBUG_FUNC();
 }

-static bool done = false;
 static bool memam_getnextslot(
   TableScanDesc sscan,
   ScanDirection direction,
   TupleTableSlot *slot
 ) {
+  struct MemScanDesc* mscan = (struct MemScanDesc*)sscan;
+  struct Table* table = NULL;
   DEBUG_FUNC();

-  if (done) {
+  ExecClearTuple(slot);
+
+  get_table(&table, mscan->rs_base.rs_rd);
+  if (table == NULL || mscan->cursor == table->nrows) {
     return false;
   }

-  slot->tts_values[0] = Int32GetDatum(314 /* Some unique-looking value */);
+  slot->tts_values[0] = Int32GetDatum(table->rows[mscan->cursor].columns[0].value);
   slot->tts_isnull[0] = false;
   ExecStoreVirtualTuple(slot);
-  done = true;
-
+  mscan->cursor++;
   return true;
 }

Let's try it out.

$ make && sudo make install
$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to table x
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
INSERT 0 2
  a
-----
  23
 101
(2 rows)

And there we have it. :)

Awesome SQL power

So we tried one table and we tried a SELECT without anything else.

What happens if we use more of SQL? Let's create another table and try some more complex queries. Edit test.sql:

DROP EXTENSION IF EXISTS pgtam CASCADE;
CREATE EXTENSION pgtam;
CREATE TABLE x(a INT) USING mem;
CREATE TABLE y(b INT) USING mem;
INSERT INTO x VALUES (23), (101);
SELECT a FROM x;
SELECT a + 100 FROM x WHERE a = 23;
SELECT a, COUNT(1) FROM x GROUP BY a ORDER BY COUNT(1) DESC;
SELECT b FROM y;

Run it:

$ /usr/local/pgsql/bin/psql postgres -f test.sql
psql:test.sql:1: NOTICE:  drop cascades to 2 other objects
DETAIL:  drop cascades to table x
drop cascades to table y
DROP EXTENSION
CREATE EXTENSION
CREATE TABLE
CREATE TABLE
INSERT 0 2
  a
-----
  23
 101
(2 rows)

 ?column?
----------
      123
(1 row)

  a  | count
-----+-------
  23 |     1
 101 |     1
(2 rows)

 b
---
(0 rows)

Pretty sweet!

Next steps

It would be neat to build a storage engine that reads from and writes to a CSV a la MySQL's CSV storage engine. Or a storage engine that uses RocksDB.

It would also be good to figure out how indexes work, how deletions work, how updates and DDL beyond CREATE works.

And I should probably contribute some of this to the table access method docs which are pretty sparse at the moment.

I've been working this week to understand Postgres Table Access Methods for alternative storage engines.

Especially challenging because the documentation is pretty sparse and few minimal implementations exist.

I wrote up my approach!https://t.co/LQGglRkev5 pic.twitter.com/v0MeOu4Hbr
— Phil Eaton (@eatonphil) November 2, 2023

io_uring basics: Writing a file to disk

Thu, 19 Oct 2023 00:00:00 +0000

King and I wrote a blog post about building an event-driven cross-platform IO library that used io_uring on Linux. We sketched out how it works at a high level but I hadn't yet internalized how you actually code with io_uring. So I strapped myself down this week and wrote some benchmarks to build my intuition about io_uring and other IO models.

I started with implementations in Go and ported them to Zig to make sure I had done the Go versions decently. And I got some help from King and other internetters to find some inefficiencies in my code.

This post will walk through my process, getting increasingly efficient (and a little increasingly complex) ways to write an entire file to disk with io_uring, from Go and Zig.

Notably, we're not going to fsync() and we're not going to use O_DIRECT. So we won't be testing the entire IO pipeline from userland to disk hardware but just how fast IO gets to the kernel. The focus of this post is more on IO methods and using io_uring, not absolute numbers.

All code for this post is available on GitHub.

This code is going to indirectly show some differences in timing between Go and Zig. I could care less about benchmarketing. And I hope something about Zig vs Go is not what you take away from this post either.

The goal is to build an intuition and be generally correct. Observing the same relative behavior between implementations across two languages helps me gain confidence what I'm doing is correct.

io_uring

With normal blocking syscalls you just call read() or write() and wait for the results. io_uring is one of Linux's more powerful asynchronous IO offerings. Unlike epoll, you can use io_uring with both files and network connections. And unlike epoll you can even have the syscall run in the kernel.

To interact with io_uring, you register a submission queue for syscalls and their arguments. And you register a completion queue for syscall results.

You can batch many syscalls in one single call to io_uring, effectively turning up to N (4096 at most) syscalls into just one syscall. The kernel still does all the work of the N syscalls but you avoid some overhead.

As you check the completion queue and handle completed submissions, the submission queue is also freed all or somewhat, and you can now add more submissions.

For a more complete understanding, check out the kernel document Efficient IO with io_uring.

io_uring vs liburing

io_uring is a complex, low-level interface. Shuveb Hussain has an excellent series on programming io_uring. But that was too low-level for me as I was trying to figure out how to just get something working.

Instead, most people use liburing or a ported version of it like the Zig standard library's io_uring.zig or Iceber's iouring-go.

io_uring started clicking for me when I tried out the iouring-go library. So we'll start there.

Boilerplate

First off, let's set up some boilerplate for the Go and Zig code.

In main.go add:

package main

import (
  "bytes"
  "fmt"
  "os"
  "time"
)

func assert(b bool) {
  if !b {
    panic("assert")
  }
}

const BUFFER_SIZE = 4096

func readNBytes(fn string, n int) []byte {
  f, err := os.Open(fn)
  if err != nil {
    panic(err)
  }
  defer f.Close()

  data := make([]byte, 0, n)

  var buffer = make([]byte, BUFFER_SIZE)
  for len(data) < n {
    read, err := f.Read(buffer)
    if err != nil {
      panic(err)
    }

    data = append(data, buffer[:read]...)
  }

  assert(len(data) == n)

  return data
}

func benchmark(name string, data []byte, fn func(*os.File)) {
  fmt.Printf("%s", name)
  f, err := os.OpenFile("out.bin", os.O_RDWR | os.O_CREATE | os.O_TRUNC, 0755)
  if err != nil {
    panic(err)
  }

  t1 := time.Now()

  fn(f)

  s := time.Now().Sub(t1).Seconds()
  fmt.Printf(",%f,%f\n", s, float64(len(data))/s)

  if err := f.Close(); err != nil {
    panic(err)
  }

  assert(bytes.Equal(readNBytes("out.bin", len(data)), data))
}

And in main.zig add:

const std = @import("std");

const OUT_FILE = "out.bin";
const BUFFER_SIZE: u64 = 4096;

fn readNBytes(
  allocator: *const std.mem.Allocator,
  filename: []const u8,
  n: usize,
) ![]const u8 {
  const file = try std.fs.cwd().openFile(filename, .{});
  defer file.close();

  var data = try allocator.alloc(u8, n);
  var buf = try allocator.alloc(u8, BUFFER_SIZE);

  var written: usize = 0;
  while (data.len < n) {
    var nwritten = try file.read(buf);
    @memcpy(data[written..], buf[0..nwritten]);
    written += nwritten;
  }

  std.debug.assert(data.len == n);
  return data;
}

const Benchmark = struct {
  t: std.time.Timer,
  file: std.fs.File,
  data: []const u8,
  allocator: *const std.mem.Allocator,

  fn init(
    allocator: *const std.mem.Allocator,
    name: []const u8,
    data: []const u8,
  ) !Benchmark {
    try std.io.getStdOut().writer().print("{s}", .{name});

    var file = try std.fs.cwd().createFile(OUT_FILE, .{
      .truncate = true,
    });

    return Benchmark{
      .t = try std.time.Timer.start(),
      .file = file,
      .data = data,
      .allocator = allocator,
    };
  }

  fn stop(b: *Benchmark) void {
    const s = @as(f64, @floatFromInt(b.t.read())) / std.time.ns_per_s;
    std.io.getStdOut().writer().print(
      ",{d},{d}\n",
      .{ s, @as(f64, @floatFromInt(b.data.len)) / s },
    ) catch unreachable;

    b.file.close();

    var in = readNBytes(b.allocator, OUT_FILE, b.data.len) catch unreachable;
    std.debug.assert(std.mem.eql(u8, in, b.data));
    b.allocator.free(in);
  }
};

Keep it simple: write()

Now let's add the naive version of writing bytes to disk: calling write() repeatedly until all data has been written to disk.

In main.go:

func main() {
  size := 104857600 // 100MiB
  data := readNBytes("/dev/random", size)

  const RUNS = 10
  for i := 0; i < RUNS; i++ {
    benchmark("blocking", data, func(f *os.File) {
      for i := 0; i < len(data); i += BUFFER_SIZE {
        size := min(BUFFER_SIZE, len(data)-i)
        n, err := f.Write(data[i : i+size])
        if err != nil {
          panic(err)
        }

        assert(n == BUFFER_SIZE)
      }
    })
  }
}

And in main.zig:

pub fn main() !void {
  var allocator = &std.heap.page_allocator;

  const SIZE = 104857600; // 100MiB
  var data = try readNBytes(allocator, "/dev/random", SIZE);
  defer allocator.free(data);

  const RUNS = 10;
  var run: usize = 0;
  while (run < RUNS) : (run += 1) {
    {
      var b = try Benchmark.init(allocator, "blocking", data);
      defer b.stop();

      var i: usize = 0;
      while (i < data.len) : (i += BUFFER_SIZE) {
        const size = @min(BUFFER_SIZE, data.len - i);
        const n = try b.file.write(data[i .. i + size]);
        std.debug.assert(n == size);
      }
    }
  }
}

Let's build and run these programs and store the results to CSV we can analyze with DuckDB.

Go first:

$ go build main.go -o gomain
$ ./gomain > go.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'go.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
blocking	0.07251540000000001s	1.4GB/s

And Zig:

$ zig build-exe main.zig
$ ./main > zig.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'zig.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
blocking	0.0656907669s	1.5GB/s

Alright, we've got a baseline now and both language implementations are in the same ballpark.

Let's add a simple io_uring version!

io_uring, 1 entry, Go

The iouring-go library has really excellent documentation for getting started.

To keep it simple, we'll use io_uring with only 1 entry. Add the following to func main() after the existing benchmark() call in main.go:

benchmark("io_uring", data, func(f * os.File) {
  iour, err := iouring.New(1)
  if err != nil {
    panic(err)
  }
  defer iour.Close()

  for i := 0; i < len(data); i += BUFFER_SIZE {
    size := min(BUFFER_SIZE, len(data)-i)
    prepRequest := iouring.Pwrite(int(f.Fd()), data[i : i+size], uint64(i))
    res, err := iour.SubmitRequest(prepRequest, nil)
    if err != nil {
      panic(err)
    }

    <-res.Done()
    i, err := res.ReturnInt()
    if err != nil {
      panic(err)
    }
    assert(size == i)
  }
})

Note that benchmark takes care of f.Seek(0) before each run. And it also validates that the file contents are equivalent to the input data. So it validates the benchmark for correctness.

Alright, let's run this new Go implementation with io_uring!

$ go mod init gomain
$ go mod tidy
$ go build main.go -o gomain
$ ./gomain > go.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'go.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
blocking	0.0811486s	1.3GB/s
io_uring	0.5083049999999999s	213.2MB/s

Well that looks terrible.

Let's port it to Zig to see if we notice the same behavior there.

io_uring, 1 entry, Zig

There isn't an official Zig tutorial on io_uring I'm aware of. But io_uring.zig is easy enough to browse through. And there are tests in that file that also show how to use it.

And now that we've explored a bit in Go the basic gist should be similar:

initialize io_uring
submit an entry
wait for it to finish
move on

Add the following to fn main() after the existing benchmark block in main.zig:

{
  var b = try Benchmark.init(allocator, "iouring", data);
  defer b.stop();

  const entries = 1;
  var ring = try std.os.linux.IO_Uring.init(entries, 0);
  defer ring.deinit();

  var i: usize = 0;
  while (i < data.len) : (i += BUFFER_SIZE) {
    const size = @min(BUFFER_SIZE, data.len - i);
    _ = try ring.write(0, b.file.handle, data[i .. i + size], i);

    const submitted = try ring.submit_and_wait(1);
    std.debug.assert(submitted == 1);

    const cqe = try ring.copy_cqe();
    std.debug.assert(cqe.err() == .SUCCESS);
    std.debug.assert(cqe.res >= 0);
    const n = @as(usize, @intCast(cqe.res));
    std.debug.assert(n <= BUFFER_SIZE);
  }
}

Now build and run:

$ zig build-exe main.zig
$ ./main > zig.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'zig.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
blocking	0.06650093630000001s	1.5GB/s
io_uring	0.17542890139999998s	597.7MB/s

Well it's similarly pretty bad. But our implementation ignores one major aspect of io_uring: batching requests.

Let's do some refactoring!

io_uring, N entries, Go

To support submitting N entries, we're going to have an inner loop running up to N that fills up N entries to io_uring.

Then we'll wait for the N submissions to complete and check their results.

We'll keep going until we write the entire file.

All of this can stay inside the loop in main, I'm just dropping preceding whitespace for nicer formatting here:

benchmarkIOUringNEntries := func (nEntries int) {
  benchmark(fmt.Sprintf("io_uring_%d_entries", nEntries), data, func(f * os.File) {
    iour, err := iouring.New(uint(nEntries))
    if err != nil {
      panic(err)
    }
    defer iour.Close()

    requests := make([]iouring.PrepRequest, nEntries)

    for i := 0; i < len(data); i += BUFFER_SIZE * nEntries {
      submittedEntries := 0
      for j := 0; j < nEntries; j++ {
        base := i + j * BUFFER_SIZE
        if base >= len(data) {
          break
        }
        submittedEntries++
        size := min(BUFFER_SIZE, len(data)-i)
        requests[j] = iouring.Pwrite(int(f.Fd()), data[base : base+size], uint64(base))
      }

      if submittedEntries == 0 {
        break
      }

      res, err := iour.SubmitRequests(requests[:submittedEntries], nil)
      if err != nil {
        panic(err)
      }

      <-res.Done()

      for _, result := range res.ErrResults() {
        _, err := result.ReturnInt()
        if err != nil {
          panic(err)
        }
      }
    }
  })
}
benchmarkIOUringNEntries(1)
benchmarkIOUringNEntries(128)

There are some specific things in there to notice.

First, toward the end of the file we may not have N entries to submit. We may have 1 or even 0.

If we have 0 to submit, we need to not even submit anything otherwise the Go library hangs. Similarly, if we don't slice requests to requests[:submittedEntries], the Go library will segfault if submittedEntries < N.

Other than that, let's build and run this!

$ go build -o gomain
$ ./gomain > go.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'go.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
blocking	0.0740368s	1.4GB/s
io_uring_128_entries	0.127519s	836.6MB/s
io_uring_1_entries	0.46831579999999995s	226.9MB/s

Now we're getting somewhere! Still half the throughput but a 4x improvement from using only a single entry.

Let's port the N entry code to Zig.

io_uring, N entries, Zig

Unlike Go we can't do closures, so we'll have to make benchmarkIOUringNEntries a top-level function and keep the calls to it in the loop in main:

pub fn main() !void {
    var allocator = &std.heap.page_allocator;

    const SIZE = 104857600; // 100MiB
    var data = try readNBytes(allocator, "/dev/random", SIZE);
    defer allocator.free(data);

    const RUNS = 10;
    var run: usize = 0;
    while (run < RUNS) : (run += 1) {
        {
            var b = try Benchmark.init(allocator, "blocking", data);
            defer b.stop();

            var i: usize = 0;
            while (i < data.len) : (i += BUFFER_SIZE) {
                const size = @min(BUFFER_SIZE, data.len - i);
                const n = try b.file.write(data[i .. i + size]);
                std.debug.assert(n == size);
            }
        }

        try benchmarkIOUringNEntries(allocator, data, 1);
        try benchmarkIOUringNEntries(allocator, data, 128);
    }
}

And for the implementation itself, the only two big differences from the first version are that we'll bulk-read completion events (cqes) and that we'll create and wait for many submissions at once.

fn benchmarkIOUringNEntries(
  allocator: *const std.mem.Allocator,
  data: []const u8,
  nEntries: u13,
) !void {
  const name = try std.fmt.allocPrint(allocator.*, "iouring_{}_entries", .{nEntries});
  defer allocator.free(name);

  var b = try Benchmark.init(allocator, name, data);
  defer b.stop();

  var ring = try std.os.linux.IO_Uring.init(nEntries, 0);
  defer ring.deinit();

  var cqes = try allocator.alloc(std.os.linux.io_uring_cqe, nEntries);
  defer allocator.free(cqes);

  var i: usize = 0;
  while (i < data.len) : (i += BUFFER_SIZE * nEntries) {
    var submittedEntries: u32 = 0;
    var j: usize = 0;
    while (j < nEntries) : (j += 1) {
      const base = i + j * BUFFER_SIZE;
      if (base >= data.len) {
        break;
      }
      submittedEntries += 1;
      const size = @min(BUFFER_SIZE, data.len - base);
      _ = try ring.write(0, b.file.handle, data[base .. base + size], base);
    }

    const submitted = try ring.submit_and_wait(submittedEntries);
    std.debug.assert(submitted == submittedEntries);

    const waited = try ring.copy_cqes(cqes[0..submitted], submitted);
    std.debug.assert(waited == submitted);

    for (cqes[0..submitted]) |*cqe| {
      std.debug.assert(cqe.err() == .SUCCESS);
      std.debug.assert(cqe.res >= 0);
      const n = @as(usize, @intCast(cqe.res));
      std.debug.assert(n <= BUFFER_SIZE);
    }
  }
}

Let's build and run:

$ zig build-exe main.zig
$ ./main > zig.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'zig.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
blocking	0.0674331114s	1.5GB/s
iouring_128_entries	0.06773539590000001s	1.5GB/s
iouring_1_entries	0.1855542556s	569.9MB/s

Huh, that's surprising! We caught up to blocking writes with io_uring in Zig, but not in Go, even though we made good progress in Go.

Ring buffers

But we can do a bit better. We're doing batching, but the API is called "io_uring" not "io_batch". We're not even making use of the ring buffer behavior io_uring gives us!

We are waiting for all submitted results complete. But there's no reason to do that. Instead we should submit as much as we can. But we should not block waiting for completions. We should handle completions when they happen. And we should retry submissions until we're done reading. Retrying if there's no space for the moment.

Unfortunately the Go library doesn't seem to expose this ring behavior of io_uring. Or I've missed it.

But we can do it in Zig. Let's go.

io_uring, ring buffer, Zig

We need to change the way we track which offsets we need to submit so far. We also need to keep the loop going until we are sure we have written all data. And we need to stop blocking on the number we submitted; never blocking at all.

fn benchmarkIOUringNEntries(
  allocator: *const std.mem.Allocator,
  data: []const u8,
  nEntries: u13,
) !void {
  const name = try std.fmt.allocPrint(allocator.*, "iouring_{}_entries", .{nEntries});
  defer allocator.free(name);

  var b = try Benchmark.init(allocator, name, data);
  defer b.stop();

  var ring = try std.os.linux.IO_Uring.init(nEntries, 0);
  defer ring.deinit();

  var cqes = try allocator.alloc(std.os.linux.io_uring_cqe, nEntries);
  defer allocator.free(cqes);

  var written: usize = 0;
  var i: usize = 0;
  while (i < data.len or written < data.len) {
    var submittedEntries: u32 = 0;
    var j: usize = 0;
    while (true) {
      const base = i + j * BUFFER_SIZE;
      if (base >= data.len) {
        break;
      }
      const size = @min(BUFFER_SIZE, data.len - base);
      _ = ring.write(0, b.file.handle, data[base .. base + size], base) catch |e| switch (e) {
        error.SubmissionQueueFull => break,
        else => unreachable,
      };
      submittedEntries += 1;
      i += size;
    }

    _ = try ring.submit_and_wait(0);
    const cqesDone = try ring.copy_cqes(cqes, 0);

    for (cqes[0..cqesDone]) |*cqe| {
      std.debug.assert(cqe.err() == .SUCCESS);
      std.debug.assert(cqe.res >= 0);
      const n = @as(usize, @intCast(cqe.res));
      std.debug.assert(n <= BUFFER_SIZE);
      written += n;
    }
  }
}

The code got a bit simpler! Granted, we're omitting error handling.

Build and run:

$ zig build-exe main.zig
$ ./main > zig.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'zig.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
iouring_128_entries	0.06035423609999999s	1.7GB/s
iouring_1_entries	0.0610197624s	1.7GB/s
blocking	0.0671628515s	1.5GB/s

Not bad!

Crank it up

We've been inserting 100MiB of data. Let's go up to 1GiB to see how that affects things. Ideally the more data we write the more we reflect realistic long-term results.

In main.zig just change SIZE to 1073741824. Rebuild and run:

$ zig build-exe main.zig
$ ./main > zig.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'out.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
iouring_128_entries	0.6063814535s	1.7GB/s
iouring_1_entries	0.6167537295000001s	1.7GB/s
blocking	0.6831747749s	1.5GB/s

No real difference, perfect!

Let's make one more change though. Let's up the BUFFER_SIZE from 4KiB to 1MiB.

$ zig build-exe main.zig
$ ./main > zig.csv
$ duckdb -c "select column0 as method, avg(cast(column1 as double)) || 's' avg_time, format_bytes(avg(column2::double)::bigint) || '/s' as avg_throughput from 'out.csv' group by column0 order by avg(cast(column1 as double)) asc"

method	avg_time	avg_throughput
iouring_128_entries	0.2756831357s	3.8GB/s
iouring_1_entries	0.27575404880000004s	3.8GB/s
blocking	0.2833337046s	3.7GB/s

Hey that's an improvement!

Control

All these numbers are machine-specific obviously. So what does an existing tool like fio say? (Assuming I'm using it correctly. I await your corrections!)

With a 4KiB buffer size:

$ fio --name=fiotest --rw=write --size=1G --bs=4k --group_reporting --ioengine=sync
fiotest: (g=0): rw=write, bs=(R) 4096B-4096B, (W) 4096B-4096B, (T) 4096B-4096B, ioengine=sync, iodepth=1
fio-3.33
Starting 1 process
Jobs: 1 (f=1)
fiotest: (groupid=0, jobs=1): err= 0: pid=2437359: Thu Oct 19 23:33:42 2023
  write: IOPS=282k, BW=1102MiB/s (1156MB/s)(1024MiB/929msec); 0 zone resets
    clat (nsec): min=2349, max=54099, avg=2709.48, stdev=1325.83
     lat (nsec): min=2390, max=54139, avg=2752.89, stdev=1334.62
    clat percentiles (nsec):
     |  1.00th=[ 2416],  5.00th=[ 2416], 10.00th=[ 2416], 20.00th=[ 2448],
     | 30.00th=[ 2448], 40.00th=[ 2448], 50.00th=[ 2448], 60.00th=[ 2480],
     | 70.00th=[ 2512], 80.00th=[ 2544], 90.00th=[ 2832], 95.00th=[ 3504],
     | 99.00th=[ 5792], 99.50th=[15296], 99.90th=[19584], 99.95th=[20096],
     | 99.99th=[22656]
   bw (  KiB/s): min=940856, max=940856, per=83.36%, avg=940856.00, stdev= 0.00, samples=1
   iops        : min=235214, max=235214, avg=235214.00, stdev= 0.00, samples=1
  lat (usec)   : 4=97.22%, 10=2.03%, 20=0.71%, 50=0.04%, 100=0.01%
  cpu          : usr=17.35%, sys=82.11%, ctx=26, majf=0, minf=11
  IO depths    : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued rwts: total=0,262144,0,0 short=0,0,0,0 dropped=0,0,0,0
     latency   : target=0, window=0, percentile=100.00%, depth=1

Run status group 0 (all jobs):
  WRITE: bw=1102MiB/s (1156MB/s), 1102MiB/s-1102MiB/s (1156MB/s-1156MB/s), io=1024MiB (1074MB), run=929-929msec

1.2GB/s is about in the ballpark of what we got.

And with a 1MiB buffer size?

$ fio --name=fiotest --rw=write --size=1G --bs=1M --group_reporting --ioengine=sync
fiotest: (g=0): rw=write, bs=(R) 1024KiB-1024KiB, (W) 1024KiB-1024KiB, (T) 1024KiB-1024KiB, ioengine=sync, iodepth=1
fio-3.33
Starting 1 process
fiotest: Laying out IO file (1 file / 1024MiB)

fiotest: (groupid=0, jobs=1): err= 0: pid=2437239: Thu Oct 19 23:32:09 2023
  write: IOPS=3953, BW=3954MiB/s (4146MB/s)(1024MiB/259msec); 0 zone resets
    clat (usec): min=221, max=1205, avg=241.83, stdev=43.93
     lat (usec): min=228, max=1250, avg=251.68, stdev=45.80
    clat percentiles (usec):
     |  1.00th=[  225],  5.00th=[  225], 10.00th=[  227], 20.00th=[  227],
     | 30.00th=[  231], 40.00th=[  233], 50.00th=[  235], 60.00th=[  239],
     | 70.00th=[  243], 80.00th=[  249], 90.00th=[  262], 95.00th=[  269],
     | 99.00th=[  302], 99.50th=[  318], 99.90th=[ 1074], 99.95th=[ 1205],
     | 99.99th=[ 1205]
  lat (usec)   : 250=80.96%, 500=18.85%
  lat (msec)   : 2=0.20%
  cpu          : usr=4.26%, sys=94.96%, ctx=3, majf=0, minf=10
  IO depths    : 1=100.0%, 2=0.0%, 4=0.0%, 8=0.0%, 16=0.0%, 32=0.0%, >=64=0.0%
     submit    : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     complete  : 0=0.0%, 4=100.0%, 8=0.0%, 16=0.0%, 32=0.0%, 64=0.0%, >=64=0.0%
     issued rwts: total=0,1024,0,0 short=0,0,0,0 dropped=0,0,0,0
     latency   : target=0, window=0, percentile=100.00%, depth=1

Run status group 0 (all jobs):
  WRITE: bw=3954MiB/s (4146MB/s), 3954MiB/s-3954MiB/s (4146MB/s-4146MB/s), io=1024MiB (1074MB), run=259-259msec

3.9GB/s is also roughly in the same ballpark we got.

Our code seems reasonable!

What's next?

None of this is original. fio is a similar tool, written in C, with many different IO engines including libaio and writev support. And it has many different IO workloads.

But it's been enjoyable to learn more about these APIs. How to program them and how they compare to eachother.

So next steps could include adding additional IO engines or IO workloads.

Also, either I need to understand Iceber's Go library better or its API needs to be loosened up a little bit so we can get that awesome ring buffer behavior we could use from Zig.

Keep an eye out here and on my io-playground repo!

Selected responses after publication

wizeman on lobsters suggests measuring at least 30 seconds worth of writing data and fsync()-ing if you want to test the entire IO subsystem and not just hitting the kernel cache.

Digging into io_uring has been on my list for a long time now! This week I finally made made some progress.

Let's go on a little journey through a few increasingly complex (and useful) implementations of writing a file to disk with io_uring.https://t.co/gR9K2OQs2R pic.twitter.com/TMaC8QYL6k
— Phil Eaton (@eatonphil) October 19, 2023

Go database driver overhead on insert-heavy workloads

Thu, 05 Oct 2023 00:00:00 +0000

The most popular SQLite and PostgreSQL database drivers in Go are (roughly) 20-76% slower than alternative Go drivers on insert-heavy benchmarks of mine. So if you are bulk-inserting data with Go (and potentially also bulk-retrieving data with Go), you may want to consider the driver carefully. And you may want to consider avoiding database/sql.

Some driver authors have noted and benchmarked issues with database/sql.

So it may be the case that database/sql is responsible for some of this overhead. And indeed the variations between drivers in this post will be demonstrated by using database/sql and avoiding it. This post won't specifically prove that the variation is due to the database/sql interface. But that doesn't change the premise.

Not covered in this post but something to consider: JetBrains has suggested that other frontends like sqlc, sqlx, and GORM do worse than database/sql.

This post is built on the workload, environment, libraries, and methodology in my databases-intuition repo on GitHub. See the repo for details that will help you reproduce or correct me.

INSERT workload

In this workload, the data is random and there are no indexes. Neither of these aspects matter for this post though because we're comparing behavior within the same database among different drivers. This was just a workload I already had.

Two different data sizes are tested:

10M rows with 16 columns, each column is 32 bytes
10M rows with 3 columns, each column is 8 bytes

Each test is run 10 times and we record median, standard deviation, min, max and throughput.

SQLite

Both variations presented here load 10M rows using a single prepared statement called for each row within a single transaction.

The most popular driver is mattn/go-sqlite3.

It is roughly 20-40% slower than another driver that avoids database/sql.

10M Rows, 16 columns, each column 32 bytes:

Timing: 56.53 ± 1.26s, Min: 55.05s, Max: 59.62s
Throughput: 176,893.65 ± 3,853.90 rows/s, Min: 167,719.97 rows/s, Max: 181,646.02 rows/s

10M Rows, 3 columns, each column 8 bytes:

Timing: 15.92 ± 0.25s, Min: 15.69s, Max: 16.67s
Throughput: 628,044.37 ± 9,703.92 rows/s, Min: 599,852.91 rows/s, Max: 637,435.60 rows/s

The other driver I tested is my own fork of bvinc/go-sqlite-lite called eatonphil/gosqlite. I forked it because it is unmaintained and I wanted to bring it up-to-date for tests like this.

10M Rows, 16 columns, each column 32 bytes:

Timing: 45.51 ± 0.70s, Min: 43.72s, Max: 45.93s
Throughput: 219,729.65 ± 3,447.56 rows/s, Min: 217,742.98 rows/s, Max: 228,711.51 rows/s

10M Rows, 3 columns, each column 8 bytes:

Timing: 10.44 ± 0.20s, Min: 10.02s, Max: 10.68s
Throughput: 957,939.60 ± 18,879.43 rows/s, Min: 936,114.60 rows/s, Max: 998,426.62 rows/s

PostgreSQL

Both variations presented use PostgreSQL's COPY FROM support. This is significantly faster for PostgreSQL than doing the prepared statement we do in SQLite. (Here are my results for doing prepared statement INSERTs in PostgreSQL if you are curious.)

The most popular PostgreSQL driver is lib/pq. The performance issues with lib/pq are well-known, and the repo itself is marked as no longer developed.

It is roughly 44-76% slower than an alternative driver that avoids database/sql.

10M Rows, 16 columns, each column 32 bytes:

Timing: 104.53 ± 2.40s, Min: 102.57s, Max: 110.08s
Throughput: 95,665.37 ± 2,129.25 rows/s, Min: 90,847.08 rows/s, Max: 97,490.96 rows/s

10M Rows, 3 columns, each column 8 bytes:

Timing: 8.16 ± 0.43s, Min: 7.44s, Max: 8.80s
Throughput: 1,225,986.47 ± 66,631.53 rows/s, Min: 1,136,581.82 rows/s, Max: 1,343,441.37 rows

The other driver I tested is jackc/pgx, without database/sql.

10M Rows, 16 columns, each column 32 bytes:

Timing: 46.54 ± 1.60s, Min: 44.09s, Max: 49.51s
Throughput: 214,869.42 ± 7,265.10 rows/s, Min: 201,991.37 rows/s, Max: 226,801.07 rows/s

10M Rows, 3 columns, each column 8 bytes:

Timing: 5.20 ± 0.44s, Min: 4.71s, Max: 5.96s
Throughput: 1,923,722.79 ± 156,820.46 rows/s, Min: 1,676,894.32 rows/s, Max: 2,124,966.60 rows/

The discrepancies here are even greater than with the different SQLite drivers.

Workloads with small resultset

I won't go into as much detail but if you're doing queries that don't return many rows, the difference between drivers is negligible.

See here for details.

Conclusion

If you are doing INSERT-heavy workloads, or you are processing large number of rows returned from your SQL database, you might want to try benchmarking the same workload with different drivers.

And specifically, there is likely no good reason to use lib/pq anymore for accessing PostgreSQL from Go. Just use jackc/pgx.

For INSERT-heavy workloads in Go, you may want to switch database drivers. For PostgreSQL and SQLite, the popular drivers are 20-76% slower for this workload in my tests.

Some driver developers have reported issues with database/sql as an interface.https://t.co/NLVp0P2uiV pic.twitter.com/RxTbgMZ1MG
— Phil Eaton (@eatonphil) October 6, 2023

Intercepting and modifying Linux system calls with ptrace

Sun, 01 Oct 2023 00:00:00 +0000

How software fails is interesting. But real-world errors can be infrequent to manifest. Fault injection is a formal-sounding term that just means: trying to explicitly trigger errors in the hopes of discovering bad logic, typically during automated tests.

Jepsen and ChaosMonkey are two famous examples that help to trigger process and network failure. But what about disk and filesystem errors?

A few avenues seem worth investigating:

A custom FUSE filesystem
An LD_PRELOAD interception layer
A ptrace system call interception layer
A SECCOMP_RET_TRAP interception layer
Or, symbolic analysis a la Alice from University of Wisconsin-Madison

I would like to try out FUSE sometime. But LD_PRELOAD layer only works if IO goes through libc, which won't be the case for all programs. ptrace is something I've wanted to dig into for years since learning about gvisor.

SECCOMP_RET_TRAP doesn't have the same high-level guides that ptrace does so maybe I'll dig into it later. And symbolic analysis might be able to detect bad workloads but it also isn't fault injection. Maybe it's the better idea but fault injection just sounds more fun.

So this particular post will cover intercepting system calls (syscalls) using ptrace with code written in Zig. Not because readers will likely write their own code in Zig but because hopefully the Zig code will be easier for you to read and adapt to your language compared to if we had to deal with the verbosity and inconvenience of C.

In the end, we'll be able to intercept and force short (incomplete) writes in a Go, Python, and C program. Emulating a disk that is having an issue completing the write. This is a case that isn't common, but should probably be handled with retries in production code.

This post corresponds roughly to this commit on GitHub.

A bad program

First off, let's write some code for a program that would exhibit a short write. Basically, we write to a file and don't check how many bytes we wrote. This is extremely common code; or at least I've written it often.

$ cat test/main.go
package main

import (
        "os"
)

func main() {
        f, err := os.OpenFile("test.txt", os.O_RDWR|os.O_CREATE|os.O_TRUNC, 0755)
        if err != nil {
                panic(err)
        }

        text := "some great stuff"
        _, _ = f.Write([]byte(text))

        _ = f.Close()
}

With this code, if the Write() call doesn't actually succeed in writing everything, we won't know that. And the file will contain less than all of some great stuff.

This logical mistake will happen rarely, if ever, on a normal disk. But it is possible.

Now that we've got an example program in mind, let's see if we can trigger the logic error.

ptrace

ptrace is a somewhat cross-platform layer that allows you to intercept syscalls in a process. You can read and modify memory and registers in the process, when the syscalls starts and before it finishes.

gdb and strace both use ptrace for their magic.

Google's gvisor that powers various serverless runtimes in Google Cloud was also historically based on ptrace (PTRACE_SYSEMU specifically, which we won't explore much in this post).

Interestingly though, gvisor switched only this year (2023) to a different default backend for trapping system calls. Based on SECCOMP_RET_TRAP.

You can get similar vibes from this Brendan Gregg post on the dangers of using strace (that is based on ptrace) in production.

Although ptrace is cross-platform, actually writing cross-platform-aware code with ptrace can be complex. So this post assumes amd64/linux.

Protocol

The ptrace protocol is described in the ptrace manpage, but Chris Wellons and a University of Alberta group also wrote nice introductions. I referenced these three pages heavily.

Here's what the UAlberta page has to say:

We fork and have the child call PTRACE_TRACEME. Then we handle each syscall entrance by calling PTRACE_SYSCALL and waiting with wait until the child has entered the syscall. It is in this moment we can mess with things.

Implementation

Let's turn that graphic into Zig code.

const std = @import("std");
const c = @cImport({
    @cInclude("sys/ptrace.h");
    @cInclude("sys/user.h");
    @cInclude("sys/wait.h");
    @cInclude("errno.h");
});

const cNullPtr: ?*anyopaque = null;

// TODO //

pub fn main() !void {
    var arena = std.heap.ArenaAllocator.init(std.heap.page_allocator);
    defer arena.deinit();

    var args = try std.process.argsAlloc(arena.allocator());
    std.debug.assert(args.len >= 2);

    const pid = try std.os.fork();

    if (pid < 0) {
        std.debug.print("Fork failed!\n", .{});
        return;
    } else if (pid == 0) {
        // Child process
        _ = c.ptrace(c.PTRACE_TRACEME, pid, cNullPtr, cNullPtr);
        return std.process.execv(
            arena.allocator(),
            args[1..],
        );
    } else {
        // Parent process
        const childPid = pid;
        _ = c.waitpid(childPid, 0, 0);
        var cm = ChildManager{ .arena = &arena, .childPid = childPid };
        try cm.childInterceptSyscalls();
    }
}

So like the graphic suggested, we fork and start a child process. That means this Zig program should be called like:

$ zig build-exe --library c main.zig
$ ./main /actual/program/to/intercept --and --its args

Presumably, as with strace or gdb, we could instead attach to an already running process with PTRACE_ATTACH or PTRACE_SEIZE (based on the ptrace manpage) rather than going the PTRACE_TRACEME route. But I haven't tried that out yet.

With the child ready to be intercepted, we can implement the ChildManager that actually does the interception.

ChildManager

The core of the ChildManager is an infinite loop (at least as long as the child process lives) that waits for the next syscall and then calls a hook for the sytem call if it exists.

const ChildManager = struct {
    arena: *std.heap.ArenaAllocator,
    childPid: std.os.pid_t,

    // TODO //

    fn childInterceptSyscalls(
        cm: *ChildManager,
    ) !void {
        while (true) {
            // Handle syscall entrance
            const status = cm.childWaitForSyscall();
            if (std.os.W.IFEXITED(status)) {
                break;
            }

            var args: ABIArguments = cm.getABIArguments();
            const syscall = args.syscall();

            for (hooks) |hook| {
                if (syscall == hook.syscall) {
                    try hook.hook(cm.*, &args);
                }
            }
        }
    }
};

Later we'll write a hook for the sys_write syscall that will force an incomplete write.

Back to the protocol, childWaitForSyscall will call PTRACE_SYSCALL to allow the child process to start up again and continue until the next syscall. We'll follow that by wait-ing for the child process to be stopped again so we can handle the syscall entrance.

    fn childWaitForSyscall(cm: ChildManager) u32 {
        var status: i32 = 0;
        _ = c.ptrace(c.PTRACE_SYSCALL, cm.childPid, cNullPtr, cNullPtr);
        _ = c.waitpid(cm.childPid, &status, 0);
        return @bitCast(status);
    }

Now that we've intercepted a syscall (after waitpid finishes blocking), we need to figure out what syscall it was. We do this by calling PTRACE_GETREGS and reading the rax register which according to amd64/linux calling convention is the syscall called.

Registers

PTRACE_GETREGS fills out the following struct:

struct user_regs_struct
{
  unsigned long r15;
  unsigned long r14;
  unsigned long r13;
  unsigned long r12;
  unsigned long rbp;
  unsigned long rbx;
  unsigned long r11;
  unsigned long r10;
  unsigned long r9;
  unsigned long r8;
  unsigned long rax;
  unsigned long rcx;
  unsigned long rdx;
  unsigned long rsi;
  unsigned long rdi;
  unsigned long orig_rax;
  unsigned long rip;
  unsigned long cs;
  unsigned long eflags;
  unsigned long rsp;
  unsigned long ss;
  unsigned long fs_base;
  unsigned long gs_base;
  unsigned long ds;
  unsigned long es;
  unsigned long fs;
  unsigned long gs;
};

Let's write a little amd64/linux-specific wrapper for accessing meaningful fields.

const ABIArguments = struct {
    regs: c.user_regs_struct,

    fn nth(aa: ABIArguments, i: u8) c_ulong {
        std.debug.assert(i < 4);

        return switch (i) {
            0 => aa.regs.rdi,
            1 => aa.regs.rsi,
            2 => aa.regs.rdx,
            else => unreachable,
        };
    }

    fn setNth(aa: *ABIArguments, i: u8, value: c_ulong) void {
        std.debug.assert(i < 4);

        switch (i) {
            0 => { aa.regs.rdi = value; },
            1 => { aa.regs.rsi = value; },
            2 => { aa.regs.rdx = value; },
            else => unreachable,
        }
    }

    fn result(aa: ABIArguments) c_ulong { return aa.regs.rax; }

    fn setResult(aa: *ABIArguments, value: c_ulong) void {
        aa.regs.rax = value;
    }

    fn syscall(aa: ABIArguments) c_ulong { return aa.regs.orig_rax; }
};

One thing to note is that the field we read to get rax is not aa.regs.rax but aa.regs.orig_rax. This is because rax is also the return value and PTRACE_SYSCALL gets called twice for some syscalls on entrance and exit. The orig_rax field preserves the original rax value on syscall entrance. You can read more about this here.

Getting and setting registers

Now let's write the ChildManager code that actually calls PTRACE_GETREGS to fill out one of these structs.

    fn getABIArguments(cm: ChildManager) ABIArguments {
        var args = ABIArguments{ .regs = undefined };
        _ = c.ptrace(c.PTRACE_GETREGS, cm.childPid, cNullPtr, &args.regs);
        return args;
    }

Setting registers is similar, we just pass the struct back and call PTRACE_SETREGS instead:

    fn setABIArguments(cm: ChildManager, args: *ABIArguments) void {
        _ = c.ptrace(c.PTRACE_SETREGS, cm.childPid, cNullPtr, &args.regs);
    }

A hook

Now we finally have enough code to write a hook that can get and set registers; i.e. manipulate a system call!

We'll start by registering a sys_write hook in the hooks field we check in childInterceptSyscalls above.

    const hooks = &[_]struct {
        syscall: c_ulong,
        hook: *const fn (ChildManager, *ABIArguments) anyerror!void,
    }{.{
        .syscall = @intFromEnum(std.os.linux.syscalls.X64.write),
        .hook = writeHandler,
    }};

If we look at the manpage for write we see it takes three arguments

The file descriptor (fd) to write to
The address to start writing data from
And the number of bytes to write

Going back to the calling convention that means the fd will be in rdi, the data address in rsi, and the data length in rdx.

So if we shorten the data length, we should be creating a short (incomplete) write.

    fn writeHandler(cm: ChildManager, entryArgs: *ABIArguments) anyerror!void {
        const fd = entryArgs.nth(0);
        const dataAddress = entryArgs.nth(1);
        var dataLength = entryArgs.nth(2);

        // Truncate some bytes
        if (dataLength > 2) {
            dataLength -= 2;
            entryArgs.setNth(2, dataLength);
            cm.setABIArguments(entryArgs);
        }
    }

In a more sophisticated version of this program, we could randomly decide when to truncate data and randomly decide how much data to truncate. However, for our purposes this is sufficient.

But there are some real problems with this code. When I ran this program against a basic Go program, I saw duplicate requests.

Ah ok, PTRACE_SYSCALL gets hit when you both enter and exit a syscall.

So each time you call PTRACE_SYSCALL and you do stuff, you just call it again afterwards to handle/wait for the exit. pic.twitter.com/PjmNwcMepG
— Phil Eaton (@eatonphil) September 29, 2023

So the deal with PTRACE_SYSCALL is that for (most?) syscalls, you get to modify data before the data actually is handled by the kernel. And you get to modify data after the kernel has finished the syscall too.

This makes sense because PTRACE_SYSCALL (unlike PTRACE_SYSEMU) allows the syscall to actually happen. And if we wanted to, for example, modify the syscall exit code, we'd have to do that after the syscall was done not before it started. We are modifying registers directly after all.

All this means for our Zig code is that when we handle sys_write, we need to call PTRACE_SYSCALL again to process the syscall exit. Otherwise we'd reach this writeHandler for both entries and exits, which would require some additional way of disambiguating entrances from exits.

    fn writeHandler(cm: ChildManager, entryArgs: *ABIArguments) anyerror!void {
        const fd = entryArgs.nth(0);
        const dataAddress = entryArgs.nth(1);
        var dataLength = entryArgs.nth(2);

        // Truncate some bytes
        if (dataLength > 2) {
            dataLength -= 2;
            entryArgs.setNth(2, dataLength);
            cm.setABIArguments(entryArgs);
        }

        const data = try cm.childReadData(dataAddress, dataLength);
        defer data.deinit();
        std.debug.print("Got a write on {}: {s}\n", .{ fd, data.items });

        // Handle syscall exit
        _ = cm.childWaitForSyscall();
    }

We could put the cm.childWaitForSyscall() waiting for the syscall exit in the main loop and I did try that at first. However, not all syscalls seemed to have the same entry and exit hook and this resulted in the hooks sometimes starting with a syscall exit rather than a syscall entry. So rather than making the code more complicated, I decided to only wait for the exit on syscalls I knew had an exit (by observation at least), like sys_write.

Multiple writes? No bad logic?

So I had this code as is, correctly handling syscall entrances and exits, but I was seeing multiple write calls. And the text file I was writing to had the complete text I wanted to write. There was no short write even though I truncated the data length.

Ok so what happens in this Go program if I truncate the amount of data?

I assumed Go would do nothing since all I did was call `f.Write()` once and `f.Write()` returns a number of bytes written.

But actually, it still writes everything! pic.twitter.com/OSalKEbERM
— Phil Eaton (@eatonphil) September 29, 2023

This took some digging into Go source code to understand. If you trace what os.File.Write() does on Linux you eventually get to src/internal/poll/fd_unix.go:

// Write implements io.Writer.
func (fd *FD) Write(p []byte) (int, error) {
        if err := fd.writeLock(); err != nil {
                return 0, err
        }
        defer fd.writeUnlock()
        if err := fd.pd.prepareWrite(fd.isFile); err != nil {
                return 0, err
        }
        var nn int
        for {
                max := len(p)
                if fd.IsStream && max-nn > maxRW {
                        max = nn + maxRW
                }
                n, err := ignoringEINTRIO(syscall.Write, fd.Sysfd, p[nn:max])
                if n > 0 {
                        nn += n
                }
                if nn == len(p) {
                        return nn, err
                }
                if err == syscall.EAGAIN && fd.pd.pollable() {
                        if err = fd.pd.waitWrite(fd.isFile); err == nil {
                                continue
                        }
                }
                if err != nil {
                        return nn, err
                }
                if n == 0 {
                        return nn, io.ErrUnexpectedEOF
                }
        }
}

This might be common knowledge but I didn't realize Go did this. And when I tried out the same basic program in Python and even C, the behavior was the same. The builtin write() behavior on a file (in many languages apparantly) is to retry until all data is written, with some exceptions.

This makes sense since files on disk, unlike file descriptors backed by network sockets, are generally always available. Compared to a network connection, disks are physically close and almost always stay connected. (With some obvious exceptions like network-attached storage and thumb drives.)

So to trigger the short write, the easiest way seems to have the sys_write call return an error that is NOT EAGAIN since the code will retry if that is the error.

After looking through the list of errors that sys_write can return, EIO seems like a nice one.

So let's do our final version of writeHandler and on the syscall exit, we'll modify the return value (rax in amd64/linux) to be EIO.

    fn writeHandler(cm: ChildManager, entryArgs: *ABIArguments) anyerror!void {
        const fd = entryArgs.nth(0);
        const dataAddress = entryArgs.nth(1);
        var dataLength = entryArgs.nth(2);

        // Truncate some bytes
        if (dataLength > 2) {
            dataLength -= 2;
            entryArgs.setNth(2, dataLength);
            cm.setABIArguments(entryArgs);
        }

        // Handle syscall exit
        _ = cm.childWaitForSyscall();

        var exitArgs = cm.getABIArguments();
        dataLength = exitArgs.nth(2);
        if (dataLength > 2) {
            // Force the writes to stop after the first one by returning EIO.
            var result: c_ulong = 0;
            result = result -% c.EIO;
            exitArgs.setResult(result);
            cm.setABIArguments(&exitArgs);
        }
    }

Let's give it a whirl!

All together

Build the Zig fault injector and the Go test code:

$ zig build-exe --library c main.zig
$ ( cd test && go build main.go )

And run:

$ ./main test/main

And check test.txt:

$ cat test.txt
some great stu

Hey, that's a short write! :)

Sidenote: Reading data from the child

We accomplished everything we set out to, but there's one other useful thing we can do: reading the actual data passed to the write syscall.

Just like how we can get the child process registers with PTRACE_GETREGS, we can read child memory with PTRACE_PEEKDATA. PTRACE_PEEKDATA takes the child process id and the memory address in the child to read from. It returns a word of data (which on amd64/linux is 8 bytes).

We can use the syscall arguments (data address and length) to keep calling PTRACE_PEEKDATA on the child until we've read all bytes of the data the child process wanted to write:

    fn childReadData(
        cm: ChildManager,
        address: c_ulong,
        length: c_ulong,
    ) !std.ArrayList(u8) {
        var data = std.ArrayList(u8).init(cm.arena.allocator());
        while (data.items.len < length) {
            var word = c.ptrace(
                c.PTRACE_PEEKDATA,
                cm.childPid,
                address + data.items.len,
                cNullPtr,
            );

            for (std.mem.asBytes(&word)) |byte| {
                if (data.items.len == length) {
                    break;
                }
                try data.append(byte);
            }
        }
        return data;
    }

And we could modify writeHandler to print out the entirety of the write message each time (for debugging):

    fn writeHandler(cm: ChildManager, entryArgs: *ABIArguments) anyerror!void {
        const fd = entryArgs.nth(0);
        const dataAddress = entryArgs.nth(1);
        var dataLength = entryArgs.nth(2);

        // Truncate some bytes
        if (dataLength > 2) {
            dataLength -= 2;
            entryArgs.setNth(2, dataLength);
            cm.setABIArguments(entryArgs);
        }

        const data = try cm.childReadData(dataAddress, dataLength);
        defer data.deinit();
        std.debug.print("Got a write on {}: {s}\n", .{ fd, data.items });

        // Handle syscall exit
        _ = cm.childWaitForSyscall();

        var exitArgs = cm.getABIArguments();
        dataLength = exitArgs.nth(2);
        if (dataLength > 2) {
            // Force the writes to stop after the first one by returning EIO.
            var result: c_ulong = 0;
            result = result -% c.EIO;
            exitArgs.setResult(result);
            cm.setABIArguments(&exitArgs);
        }
    }

That's pretty neat!

Next steps

Short writes are just one of many bad IO interactions. Another fun one would be to completely buffer all writes on a file descriptor (not allowing anything to be written to disk at all) until fsync is called on the file descriptor. Or forcing fsyncs to fail.

An interesting optimization would be to apply seccomp filters so that rather than paying a penalty for watching every syscall, I only get notified about the ones I have hooks for like sys_write. Here's another post that explores ptrace with seccomp filters.

Credits: Thank you Charlie Cummings and Paul Khuong for reviewing a draft of this post!

Selected responses after publication

oscooter on Reddit gave some tips on using ptrace, including using process_vm_readv instead of PTRACE_PEEKDATA to read memory from the tracee process.

Fault injection is a scary-sounding term. Intercepting and modifying Linux system calls sounds scary too.

But it's a neat way to trigger logical errors in programs, to build confidence we wrote code correctly.

Let's trigger short writes to disk in Zig!https://t.co/0C3tWt3vtT pic.twitter.com/OS7auDe8jR
— Phil Eaton (@eatonphil) October 1, 2023

How do databases execute expressions?

Thu, 21 Sep 2023 00:00:00 +0000

Databases are fun. They sit at the confluence of Computer Science topics that might otherwise not seem practical in life as a developer. For example, every database with a query language is also a programming language implementation of some caliber. That doesn't include all databases though of course; see: RocksDB, FoundationDB, TigerBeetle, etc.

This post looks at how various databases execute expressions in their query language.

tldr; Most surveyed databases use a tree-walking interpreter. A few use stack- or register-based virtual machines. A couple have just-in-time compilers. And, tangentially, a few do vectorized interpretation.

Throughout this post I'll use "virtual machine" as a shorthand for stack- or register-based loops that process a linearized set of instructions. I say this since it is sometimes fair to call a tree-walking interpreter a virtual machine. But that is not what I mean when I say virtual machine in this post.

Stepping back

Programming languages are typically implemented by turning an Abstract Syntax Tree (AST) into a linear set of instructions for a virtual machine (e.g. CPython, Java, C#) or native code (e.g. GCC's C compiler, Go, Rust). Some of the former implementations also generate and run Just-In-Time (JIT) compiled native code (e.g. Java and C#).

Less commonly these days in programming languages does the implementation interpret off the AST or some other tree-like intermediate representation. This style is often called tree-walking.

Shell languages sometimes do tree-walking. Otherwise, implementations that interpret directly off of a tree normally do so as a short-term measure before switching to compiled virtual machine code or JIT-ed native code (e.g. some JavaScript implementations, GraalVM, RPython, etc.)

That is, while some major programming language implementations started out with tree-walking interpreters, they mostly moved away from solely tree-walking over a decade ago. See JSC in 2008, Ruby in 2007, etc.

My intuition is that tree-walking takes up more memory and is less cache-friendly than the linear instructions you give to a virtual machine or to your CPU. There are some folks who disagree, but they mostly talk about tree-walking when you've also got a JIT compiler hooked up. Which isn't quite the same thing. There has also been some early exploration and improvements reported when tree-walking with a tree organized as an array.

And databases?

Databases often interpret directly off a tree. (It isn't, generally speaking, fair to say they are AST-walking interpreters because databases typically transform and optimize beyond just an AST as parsed from user code.)

But not all databases interpret a tree. Some have a virtual machine. And some generate and run JIT-ed native code.

Methodology

If a core function (in the query execution path that does something like arithmetic or comparison) returns a value, that's a sign it's a tree-walking interpreter. Or, if you see code that is evaluating its arguments during execution, that's also a sign of a tree-walking interpreter.

On the other hand, if the function mutates internal state such as by assigning a value to a context or pushing to a stack, that's a sign it's a stack- or register-based virtual machine. If a function pulls its arguments from memory and doesn't evaluate the arguments, that's also an indication it's a stack- or register-based virtual machine.

This approach can result in false-positives depending on the architecture of the interpreter. User-defined functions (UDFs) would probably accept evaluated arguments and return a value regardless of how the interpreter is implemented. So it's important to find not just functions that could be implemented like UDFs, but core builtin behavior. Control flow implementations of functions like if or case can be great places to look.

Note: In talking about a broad swath of projects, maybe I've misunderstood one or some. If you've got a correction, let me know! If there's a proprietary database you work on where you can link to the (publicly described) execution strategy, feel free to pass it along! Or if I'm missing your public-source database in this list, send me a message!

Survey

Cockroach (Ruling: Tree Walker)

Judging by functions like func (e *evaluator) EvalBinaryExpr that evaluates the left-hand side and then evaluates the right-hand side and returns a value, Cockroach looks like a tree walking interpreter.

It gets a little more interesting though, since Cockroach also supports vectorized expression execution. Vectorizing is a fancy term for acting on many pieces of data at once rather than one at a time. It doesn't necessarily imply SIMD. Here is an example of a vectorized addition of two int64 columns.

ClickHouse (Ruling: Tree Walker + JIT)

The ClickHouse architecture is a little unique and difficult for me to read through – likely due to it being fairly mature, with serious optimization. But they tend to document their header files well. So files like src/Functions/IFunction.h and src/Interpreters/ExpressionActions.h were helpful.

They have also spoken publicly about their pipeline execution model; e.g. this presentation and this roadmap issue. But it isn't completely clear how much pipeline execution (which is broader than just expression evaluation) connects to expression evaluation.

Moreover, they have publicly spoken about their support for JIT compilation for query execution. But let's look at how execution works when the JIT is not enabled. For example, If we take a look at how if is implemented, we know that the then and else rows must be conditionally evaluated.

In the runtime entrypoint, executeImpl, we see the function call executeShortCircuitArguments which in turn calls ColumnFunction::reduce() which evaluates each column vector that is an argument to the function and then calls execute on the function.

So from this we can tell the non-JIT execution is a tree walker and that it is over chunks of columns, i.e. vectorized data, similar to Cockroach. However in ClickHouse execution is always over column vectors.

In the original version of this post, I had some confusion about the ClickHouse execution strategy. Robert Schulze from ClickHouse helped clarify things for me. Thanks Robert!

DuckDB (Ruling: Tree Walker)

If we take a look at how function expressions are executed, we can see each argument in the function being evaluated before being passed to the actual function. So that looks like a tree walking interpreter.

Like ClickHouse, DuckDB expression execution is always over column vectors. You can read more about this architecture here and here.

Influx (Ruling: Tree Walker)

Influx originally had a SQL-like query language called InfluxQL. If we look at how it evaluates a binary expression, it first evaluates the left-hand side and then the right-hand side before operating on the sides and returning a value. That's a tree-walking interpreter.

Flux was the new query language for Influx. While the Flux docs suggest they transform to an intermediate representation that is executed on a virtual machine, there's nothing I'm seeing that looks like a stack- or register-based virtual machine. All the evaluation functions evaluate their arguments and return a value. That looks like a tree-walking interpreter to me.

Today Influx announced that Flux is in maintenance mode and they are focusing on InfluxQL again.

MariaDB / MySQL (Ruling: Tree Walker)

Control flow methods are normally a good way to see how an interpreter is implemented. The implementation of COALESCE looks pretty simple. We see it call val_str() for each argument to COALESCE. But I can only seem to find implementations of val_str() on raw values and not expressions. Item_func_coalesce itself does not implement val_str() for example, which would be a strong indication of a tree walker. Maybe it does implement val_str() through inheritance.

It becomes a little clearer if we look at non-control flow methods like acos. In this method we see Item_func_acos itself implement val_real() and also call val_real() on all its arguments. In this case it's obvious how the control flow of acos(acos(.5)) would work. So that seems to indicate expressions are executed with a tree walking interpreter.

I also noticed sql/sp_instr.cc. That is scary (in terms of invalidating my analysis) since it looks like a virtual machine. But after looking through it, I think this virtual machine only corresponds to how stored procedures are executed, hence the sp_ prefix for Stored Programs. MySQL docs also explain that stored procedures are executed with a bytecode virtual machine.

I'm curious why they don't use that virtual machine for query execution.

As far as I can tell MySQL and MariaDB do not differ in this regard.

MongoDB (Ruling: Virtual Machine)

Mongo recently introduced a virtual machine for executing queries, called Slot Based Execution (SBE). We can find the SBE code in src/mongo/db/exec/sbe/vm/vm.cpp and the main virtual machine entrypoint here. Looks like a classic stack-based virtual machine!

It isn't completely clear to me if the SBE path is always used or if there are still cases where it falls back to their old execution model. You can read more about Mongo execution here and here.

PostgreSQL (Ruling: Virtual Machine + JIT)

The top of PostgreSQL's src/backend/executor/execExprInterp.c clearly explains that expression execution uses a virtual machine. You see all the hallmarks: opcodes, a loop over a giant switch, etc. And if we look at how function expressions are executed, we see another hallmark which is that the function expression code doesn't evaluate its arguments. They've already been evaluated. And function expression code just acts on the results of its arguments.

PostgreSQL also supports JIT-ing expression execution. And we can find the switch between interpreting and JIT-compiling an expression here.

QuestDB (Ruling: Tree Walker + JIT)

QuestDB wrote about their execution engine recently. When the conditions are right, they'll switch over to a JIT compiler and run native code.

But let's look at the default path. For example, how AND is implemented. AndBooleanFunction implements BooleanFunction which implements Function. An expression can be evaluated by calling a getX() method on the expression type that implements Function. AndBooleanFunction calls getBool() on its left and right hand sides. And if we look at the partial implementation of BooleanFunction we'll also see it doing getX() specific conversions during the call of getX(). So that's a tree-walking interpreter.

Scylla (Ruling: Tree Walker)

If we take a look at how functions are evaluated in Scylla, we see function evaluation first evaluating all of its arguments. And the function evaluation function itself returns a cql3::raw_value. So that's a tree-walking interpreter.

SQLite (Ruling: Virtual Machine)

SQLite's virtual machine is comprehensive and well-documented. It encompasses more than just expression evaluation but the entirety of query execution.

We can find the massive virtual machine switch in src/vdbe.c.

And if we look, for example, at how AND is implemented, we see it pulling its arguments out of memory (already evaluated) and assigning the result back to a designated point in memory.

SingleStore (Ruling: Virtual Machine + JIT)

While there's no source code to link to, SingleStore gave a talk at CMU that broke down their query execution pipeline. Their docs also cover the topic.

TiDB (Ruling: Tree Walker)

Similar to DuckDB and ClickHouse, TiDB implements vectorized interpretation. They've written publicly about their switch to this method.

Let's take a look at how if is implemented in TiDB. There is a vectorized and non-vectorized version of if (in expression/control_builtin.go and expression/control_builtin_generated.go respectively). So maybe they haven't completely switched over to vectorized execution or maybe it can only be used in some conditions.

If we look at the non-vectorized version of if, we see the condition evaluated. And then the then or else is evaluated depending on the result of the condition. That's a tree-walking interpreter.

Conclusion

As the DuckDB team points out, vectorized interpretation or JIT compilation seem like the future for database expression execution. These strategies seem particularly important for analytics or time-series workloads. But vectorized interpretation seems to make the most sense for column-wise storage engines. And column-wise storage normally only makes sense for analytics workloads. Still, TiDB and Cockroach are transactional databases that also vectorize execution.

And while SQLite and PostgreSQL use the virtual machine model, it's possible databases with tree-walking interpreters like Scylla and MySQL/MariaDB have decided there is not significant enough gains to be had (for transactional workloads) to justify the complexity of moving to a compiler + virtual machine architecture.

Tree-walking interpreters and virtual machines are also independent from whether or not execution is vectorized. So that will be another interesting dimension to watch: if more databases move toward vectorized execution even if they don't adapt JIT compilation.

Yet another alternative is that maybe as databases mature we'll see compilation tiers similar to what browsers do with JavaScript.

Credits: Thanks Max Bernstein, Alex Miller, and Justin Jaffray for reviewing a draft version of this! And thanks to the #dbs channel on Discord for instigating this post!

I spent some time looking into how various databases execute expressions in their query language.

Most of them have a tree-walking interpreter, some have a virtual machine, and some do just-in-time compilation.

Let's dig into some database code to see!https://t.co/BIGtHKh1X4 pic.twitter.com/nmhe9HmYw7
— Phil Eaton (@eatonphil) September 21, 2023

Eight years of organizing tech meetups

Mon, 04 Sep 2023 00:00:00 +0000

This is a collection of random personal experiences. So if you don't want to read everything, feel free to skip to the end for takeaways.

I write because I'd like to see more high-quality meetups. And maybe my little bit of experience will help someone out.

2015: Philadelphia

I first tried to organize a meetup in Philly in 2015. I was contracting at the time and I figured a meetup might be a good way to source contracts or just meet interesting people. I created the "Philadelphia Software in Business" (or some other similarly vaguely named) group on Meetup.com.

I didn't have any network; the first companies I worked for were not in Philly. But Meetup.com got me a few tens of people joining the group.

My first challenge was finding a place to meet. I didn't know what I was doing so I looked at restaurants, bars, and cafes for dedicated event space. Needless to say, renting space was expensive on its own. And there was always an additional required minimum dollar spent per attendee.

I ultimately found a place near the Schuylkill River. Maybe it was a community event space. Maybe I paid for it. I can't remember.

The first and only time I hosted an event for the group, I got a surprising number of people for such a vague topic. There were maybe 6 of us. I was the youngest by far (I was 20), they were middle age. Excel users and one visionary type.

There was no real point to the meetup and I didn't continue doing it.

2016 - 2017: Linode

While I was at Linode, I organized "hack nights". I didn't ask for anyone's approval before starting it. I just said I'd be ordering pizza for anyone interested in staying after work to hack on Linode-related projects. I was willing to pay for the pizza, in part because I didn't want to risk being shut down by asking. But caker paid for it each time.

I was nervous because people would show up and ask for pizza and not want to hack. It was company-provided under the aspiration of doing Linode-related work. Maybe I mentioned this or not. I can't remember. I'm pretty sure they got their pizza.

Aside from myself, developers at Linode didn't really attend. The folks who attended were support staff or folks from the technical writing team who wanted more experience coding.

I ran this for maybe 3 to 5 Wednesdays before not continuing. It was pretty fun! But staying after work for a few hours each Wednesday lost its charm.

Book Club

Another time at Linode I started a book club. I was very torn about attempting to make the book club open to anyone in the area or just to Linode employees.

I knew I'd probably get more people to attend if I made it public. But I wasn't sure if Linode would be cool with having external folks in the office. Before they moved to the Old City office, visitors weren't really a thing.

So I made it private to Linode. And I started with the most obvious book for your average developer: Practical Common Lisp.

I am pretty sure I learned one big trick by this time though. When I announced I'd be starting the book club I said something like this:

Hey folks! I'm thinking of starting a book club. A book I have in mind to start with is Practical Common Lisp. If I get at least one other person to join in then I'll move forward!

I ended up getting two folks: one developer and one support staff member. We held the book club for 30 minutes once a week, covering one chapter each week. I was the only one who read anything I think, but the other two guys faithfully showed up for discussion.

I didn't ask for permission to do this either. And this time we met during company time. I think it was 2-2:30PM.

It was fun. We finished the book. But Practical Common Lisp probably wasn't a good choice. And I don't think I started a second book.

2017 - 2020: False starts

I moved to NYC and joined a small startup (~20 employees). Linode was 100+ employees.

We were in a WeWork so I considered starting a book club that was public to the WeWork. I had learned by then the law of numbers: I probably wouldn't get anyone from my company to join.

I considered putting up posters around the WeWork to advertise. But in the end, I didn't end up going through with anything.

I did present at a few meetups in NYC during this time. But I didn't organize anything.

And then the pandemic hit and everything disappeared.

2021 - 2022: Virtual

In 2021 I started contracting again, thinking about starting a company. I wanted a community to be at the center.

So I started a Discord focused on software internals.

I had a bit more of a network at this point so I posted about the Discord on Twitter and got 100 likes or something and slowly started gaining folks in the Discord.

I knew it was going to do better if I was pretty active in it so I made sure to post interesting blog posts at some regular interval. About compilers or databases or something.

The Discord didn't turn out to help me out much in the starting-a-company front. Or I didn't use it effectively for that.

I wanted more of an independent Discord of cool people who like to learn about systems internals. And that's what I got.

This turned out to be ok though because I stopped working on that company and the Discord is still around and I still get to hang out with cool people.

This Discord is still around and hit 1,700 members recently. Among other things, it has developers from many different database companies in it these days. They hang out and help out the noobs like me learn about database internals.

I culled inactive members recently, so today the total is around 1,100.

Hacker Nights

During the pandemic I became frustrated that all the good meetups disappeared so I decided to start an online one that would be somewhat tied to the Discord and be about software internals.

I would find 2 or 3 people to present for 10-20 minutes each on anything to do with software internals. We'd meet once a month at 8PM NY time I think.

To get speakers I'd mostly DM people who I saw do interesting things on Twitter or Hacker News. I was lucky to have Philip O'Toole (author of rqlite), Simon Eskildsen (author of the Napkin Math blog), Rasmus Andersson, and many other excellent folks speak.

You can find videos of these talks on YouTube.

The events were organized on Meetup.com. The group grew quickly and I'd have about 100 people RSVP to each event. 10-20 normally showed up.

I'd post a Zoom link on Meetup.com. Sometimes Meetup.com crashed right as the meetup started, so no one could get a link. That was fun.

On two different nights I had Zoom bombers show up and play crazy music or impersonate other members of the call and act weirdly (Zoom lets you change your name after you've joined the call).

I learned a little bit about how to administrate a Zoom meeting.

I ran Hacker Nights for 5 months. It was tiring to find speakers, tiring to deal with Zoom bombers. It was thankless and I wasn't really enjoying it.

I was proud though that I was offering a channel for developers to learn about software internals of compilers, databases, etc. And it was great to meet many interesting speakers and attendees.

2023: Designing Data Intensive Applications

A month ago I put out a call on Twitter for folks in NYC interested in reading through the book Designing Data Intensive Applications.

I'd read the book before and while it was challenging, I knew it was immensely useful to any developer who works with data or an API.

By this time I'd learned my second trick: not asking for public responses.

I said something like:

Hey folks! I'm thinking of starting a book club meeting in Midtown NYC reading through Designing Data Intensive Applications. DM me if you'd be interested! If I get 2 other interested folks this will be on!

I got maybe 40 DMs and 20 of them were based in NYC. Attendence thus would have been higher if I made the book club virtual. But virtual events take about as much effort as in-person events and somehow feel less rewarding. So I went through with the NYC group.

I'm sure I could have gotten some company to provide us space, but this would just mean more negotation for me and tedium for everyone involved (bring your ID to be checked in, make sure you're registered, etc.).

The group would meet every 2 weeks and cover 2 chapters at a time. We'd meet for 30 minutes. To avoid needing to find a place to meet, we'd meet in public at Bryant Park. (There turns out to be plenty of available seating on Fridays at 9AM in Bryant Park. When it rains we meet online.)

I wanted to keep the overhead minimal and the timeline slightly aggressive. We'd be through the book in only 3 months. No crazy commitment.

We've meet twice now and are 25% done the book. Attendance has been around 7 to 9 people each time so far, or a little less than 50%.

They're almost all software developers, with one manager I think, who work for a variety of large and small tech companies.

I'm loving it so far. And if it continues to go well, I'll probably continue running in-person book clubs.

But it would only meet a few months a year, giving me a few month breaks from running it.

Takeaways: The meh

Organizing any event takes effort. Meetups are especially hard because you need to find a place to run the meetup, you probably want to provide food, and you need to find speakers.

Often you can find a single place to host the meetup, but you have to constantly search for new speakers. Even one of the greatest meetups in NYC, Papers We Love, seems to be struggling to find speakers.

The CMU Database Group and the Distributed Systems Reading Group seem to have the right idea though. They only run sessions part of the year, and they plan out all sessions in advance (including speakers).

However, they are both virtual. And I'm not so interested in running virtual events anymore.

Takeaways: The good

For one, meetups are an awesome way to meet random people and expand your network.

Two, they're educational. Even beyond the content you are meeting about, there's the discussion alongside it you wouldn't get by yourself. And you, as organizer, get to pick the topic.

These work out great for me. I love to meet people, and I love to learn.

Tricks

Starting something new is embarassing because you're putting yourself out there. Maybe no one in your network shares your interests (to the degree or in the direction you do).

My tricks are:

Most importantly: keep things low key! Don't stress people out. Before learning and networking, the point of meetups is (or should be) fun.
Saying you are "thinking about X" is a lightweight way to gauge interest. As compared to just saying you're "starting X", which gives you less room to back out if there turns out not to be interest.
Asking people to DM you with interest is less embarrassing than asking for people to respond in public. Not everyone would want to respond in public. If there's interest in private, you can share the interest in public later on. But if you only ask for responses in public and there are no responses, that can feel embarassing.
Indicating success criteria can help people understand how big you're thinking of. I'm normally fine with doing something as small as only two other people, so I say that. It's kind of like how Kickstarters work with minimum funding levels.

These ideas apply to corporate planning too. I think about them when I'm sharing some new idea in company Slack as much as when I share on Twitter.

A note on attendance rates: 10-20% actual attendance versus RSVP seems normal. If you get a higher percentage of people actually attending versus RSVP-ing you're doing pretty well!

Finding sponsors

One final idea is about paying for space or paying for food. Companies with space and money for food are often willing to partner with folks willing to do the work to run an event.

Running your own event in a company's space is advertising for them. They get to be associated with cool tech. It's a chance for them to pitch their open positions.

Obviously this happens often when you start a meetup hosted by your own company. But you can also find other companies to host space.

The kind of people to find to make this happen are senior developers or engineering managers, often on Twitter and sometimes on LinkedIn.

I haven't done this myself yet because I'm not ready to commit to running a meetup. But I see it happen. And it's the approach I'd take if I were to run a real meetup again.

Though now that I've got some time off there are a few talks I'd like to do myself.

Wrote a post on my experience organizing tech meetups of various stripes over the years. And a few things I've learned.

"meetups" taken pretty broadly to include online communities, book clubs, and actual speaker events.https://t.co/xnd0LTneup pic.twitter.com/w1oEaSNDHb
— Phil Eaton (@eatonphil) September 4, 2023

Thinking about functional programming

Tue, 15 Aug 2023 00:00:00 +0000

Someone on Discord asked about how to learn functional programming.

The question and my initial tweet on the subject prompted an interesting discussion with Shriram Krishnamurthi and other folks.

So here's a slightly more thought out exploration.

And just for backstory sake: I spent a few years a while ago programming in Standard ML and I wrote a chunk of a Scheme implementation. I'm not an expert, but I have a bit of background.

Hey, this is a free opinion.

Concepts from functional programming

When people talk about functional programming, I think of a few key choices you can make while programming:

Immutability by default
(Tail) recursion by default
First-class functions (and the suite of tools that go along with it. e.g. map, reduce/fold)

And if you have experience as a programmer, you either get the basic gist of these tenets or you can easily read about the basics.

That is, while most programmers I've met understood the basics, most programmers I've met were not particularly comfortable or fluent expressing programs with these ideas.

For myself, the only way I got comfortable expressing code with these ideas was lots of practice (as I mentioned above). And yet, even after I did a bunch of programming in Standard ML and Scheme, I really didn't see a particular benefit to practicing in a language other than one with which I wa already generally comfortable.

You have to learn a lot of other random things when you pick up Scheme or Standard ML that aren't just: practice immutability by default, recursion, and first-class functions.

So I think it's kind of misguided when person A asks how to learn functional programming and person B responds that they should learn Haskell or OCaml or whatever. I see this happen pretty often online.

Beyond any "language for functional programming" as a recommendation in general, Haskell is a particularly egregious suggestion to make in my opinion because not only are you trying to practice functional programming tenets but you're also dealing with a complex type system and lazy evaluation.

Instead, practice immutability, recursion, map/reduce in whatever language you like.

Programming languages

If you want to study programming languages, that's awesome. However, functional programming doesn't really have any direct connection to studying programming languages.

Languages are all over the place. Scheme, Standard ML, and Haskell are worlds apart, even within the functional programming family.

And modern languages have mostly adapted the aspects of functional programming that used to be unique 20 years ago.

Moreover, there are many other worthwhile families of languages to learn about:

Imperative/C-like (ok, you probably already know these)
Stack-based (JVM, x86 assembly sort of, Forth)
Array-oriented (APL, J)
Declarative (CSS, SQL, TLA+, Prolog)
Data (HTML, JSON, YAML)
Proof assistants (Isabelle/HOL, Coq)

The list isn't exhaustive, and the variations within families can be massive. But the point is that functional programming doesn't mean crazy programming languages or crazy programming ideas. Functional programming is a subset of crazy programming languages and crazy programming ideas.

If you want to learn about crazy programming languages and crazy programming ideas, you should! Go for it!

Introduction to Computer Science

SICP is famous as the (former) introductory textbook for computer science at MIT, and for its use of Scheme and the Metacircular Evaluator.

I don't have any experience teaching beginners how to program so I don't have thoughts on if this made sense. That's for folks like Shriram to think about.

However, I'm a half-decent programmer and I can't make it through this book. If you liked the book or want to read it, that's great! But I don't recommend it to anyone.

And many introductory Computer Science textbooks just don't make much sense to give to experienced programmers. For an experienced programmer, they can be quite slow!

Most of the folks I see asking about how to learn functional programming are experienced programmers.

Do whatever you feel like doing

I don't mean to overanalyze things, or get you overanalyzing things. If you want to learn functional programming by writing Haskell, that's awesome, you should go for it.

Wanting to do something is basically the best motivation there is.

The only reason I write this sort of post is so that folks who think that using Haskell or Standard ML or Scheme or reading SICP is the only way to learn functional programming see those ideas aren't necessarily true.

Write a Scheme!

Finally, for folks with time and motivation wanting to seriously work out their functional programming muscles, writing a Scheme implementation with a decent chunk of the standard library can be an immensely enjoyable project.

You'll learn a lot about languages and compilers and algorithms and data structures. It's leetcode with meaning.

Wrote a short post on this idea about different things to think about when talking about learning functional programming

1. Core concepts (immutability, first-class functions, recursion)

2. Exploring programming languages

3. Teaching CS to studentshttps://t.co/k4LzvnHbNs
— Phil Eaton (@eatonphil) August 16, 2023

We put a distributed database in the browser – and made a game of it

Tue, 11 Jul 2023 00:00:00 +0000

This is an external post of mine. Click here if you are not redirected.

Metaprogramming in Zig and parsing CSS

Mon, 19 Jun 2023 00:00:00 +0000

I knew Zig supported some sort of reflection on types. But I had been confused about how to use it. What's the difference between @typeInfo and @TypeOf? I ignored this aspect of Zig until a problem came up at work where reflection made sense.

The situation was parsing and storing parsed fields in a struct. Each field name that is parsed should match up to a struct field.

This is a fairly common problem. So this post walks through how to use Zig's metaprogramming features in a simpler but related domain: parsing CSS into typed objects, and pretty-printing these typed CSS objects.

I live-streamed the implementation of this project yesterday on Twitch. The video is available on YouTube. And the source is available on GitHub.

If you want to skip the parsing steps and just see the metaprogramming, jump to the implementation of match_property.

Parsing CSS

Let's imagine a CSS that only has alphabetical selectors, property names and values.

The following would be valid:

div {
  background: black;
  color: white;
}

a {
  color: blue;
}

Thinking about the structure of this stripped down CSS we've got:

CSS properties that consist of property names and values (in our case the property names are limited to background and color)
CSS rules that have a selector and a list of rules
CSS sheets that have a list of rules

Turning that into Zig in main.zig:

const std = @import("std");

const CSSProperty = union(enum) {
    unknown: void,
    color: []const u8,
    background: []const u8,
};

const CSSRule = struct {
    selector: []const u8,
    properties: []CSSProperty,
};

const CSSSheet = struct {
    rules: []CSSRule,
};

The parser is going to look for CSS rules which contain a selector and a list of CSS rules. The entrypoint is that simple:

fn parse(
    arena: *std.heap.ArenaAllocator,
    css: []const u8,
) !CSSSheet {
    var index: usize = 0;
    var rules = std.ArrayList(CSSRule).init(arena.allocator());

    // Parse rules until EOF.
    while (index < css.len) {
        var res = try parse_rule(arena, css, index);
        index = res.index;
        try rules.append(res.rule);

        // In case there is trailing whitespace before the EOF,
        // eating whitespace here makes sure we exit the loop
        // immediately before trying to parse more rules.
        index = eat_whitespace(css, index);
    }

    return CSSSheet{
        .rules = rules.items,
    };
}

Let's implement the eat_whitespace helper we've referenced. It increments a cursor into the css file while it sees whitespace.

fn eat_whitespace(
    css: []const u8,
    initial_index: usize,
) usize {
    var index = initial_index;
    while (index < css.len and std.ascii.isWhitespace(css[index])) {
        index += 1;
    }

    return index;
}

In our stripped-down version of CSS all we have to think about is ASCII. So the builtin std.ascii.isWhitespace() function is perfect.

Next, parsing CSS rules.

`parse_rule()`

A rule consists of a selector, opening curly braces, any number of properties, and closing curly braces. We need to remember to eat whitespace between each piece of syntax.

And we'll reference a few more parsing helpers we'll talk about next for the selector, braces, and properties.

const ParseRuleResult = struct {
    rule: CSSRule,
    index: usize,
};
fn parse_rule(
    arena: *std.heap.ArenaAllocator,
    css: []const u8,
    initial_index: usize,
) !ParseRuleResult {
    var index = eat_whitespace(css, initial_index);

    // First parse selector(s).
    var selector_res = try parse_identifier(css, index);
    index = selector_res.index;

    index = eat_whitespace(css, index);

    // Then parse opening curly brace: {.
    index = try parse_syntax(css, index, '{');

    index = eat_whitespace(css, index);

    var properties = std.ArrayList(CSSProperty).init(arena.allocator());
    // Then parse any number of properties.
    while (index < css.len) {
        index = eat_whitespace(css, index);
        if (index < css.len and css[index] == '}') {
            break;
        }

        var attr_res = try parse_property(css, index);
        index = attr_res.index;

        try properties.append(attr_res.property);
    }

    index = eat_whitespace(css, index);

    // Then parse closing curly brace: }.
    index = try parse_syntax(css, index, '}');

    return ParseRuleResult{
        .rule = CSSRule{
            .selector = selector_res.identifier,
            .properties = properties.items,
        },
        .index = index,
    };
}

The parse_syntax helper is pretty simple, it does a bounds check and increments the cursor if the current character matches the one you pass in.

fn parse_syntax(
    css: []const u8,
    initial_index: usize,
    syntax: u8,
) !usize {
    if (initial_index < css.len and css[initial_index] == syntax) {
        return initial_index + 1;
    }

    debug_at(css, initial_index, "Expected syntax: '{c}'.", .{syntax});
    return error.NoSuchSyntax;
}

This calls attention to debugging messages on failure. When we fail to parse a syntax, we want to give a useful error message and point at the exact line and column of code where the error happens.

So let's implement debug_at.

`debug_at`

First, we iterate over the entire CSS source code until we find the entire line that contains the index where the parser failed. We also want to identify the exact line and column corresponding to that index.

fn debug_at(
    css: []const u8,
    index: usize,
    comptime msg: []const u8,
    args: anytype,
) void {
    var line_no: usize = 1;
    var col_no: usize = 0;

    var i: usize = 0;
    var line_beginning: usize = 0;
    var found_line = false;
    while (i < css.len) : (i += 1) {
        if (css[i] == '\n') {
            if (!found_line) {
                col_no = 0;
                line_beginning = i;
                line_no += 1;
                continue;
            } else {
                break;
            }
        }

        if (i == index) {
            found_line = true;
        }

        if (!found_line) {
            col_no += 1;
        }
    }

Then we print it all out in a nice format for users (which will likely just be ourselves).

    std.debug.print("Error at line {}, column {}. ", .{ line_no, col_no });
    std.debug.print(msg ++ "\n\n", args);
    std.debug.print("{s}\n", .{css[line_beginning..i]});
    while (col_no > 0) : (col_no -= 1) {
        std.debug.print(" ", .{});
    }
    std.debug.print("^ Near here.\n", .{});
}

Ok, popping our mental stack, if we look back at parse_rule we still need to implement parse_identifier and parse_property.

`parse_identifier`

An "identifier" for us here is just going to be an ASCII alphabetical string (i.e. [a-zA-Z]+). We're going to really simplify CSS because we're going to use this method for parsing not just selectors but property names and even property values.

Zig again has a nice builtin std.ascii.isAlphabetical we can use.

const ParseIdentifierResult = struct {
    identifier: []const u8,
    index: usize,
};
fn parse_identifier(
    css: []const u8,
    initial_index: usize,
) !ParseIdentifierResult {
    var index = initial_index;
    while (index < css.len and std.ascii.isAlphabetic(css[index])) {
        index += 1;
    }

    if (index == initial_index) {
        debug_at(css, initial_index, "Expected valid identifier.", .{});
        return error.InvalidIdentifier;
    }

    return ParseIdentifierResult{
        .identifier = css[initial_index..index],
        .index = index,
    };
}

In reality, CSS properties are highly complex. Parsing CSS correctly isn't the main aim of this post though. :)

`parse_property`

The final piece of CSS we need to parse is properties. These consist of a property name, then a colon, then a property value, and finally a semicolon. And within each piece we eat whitespace.

const ParsePropertyResult = struct {
    property: CSSProperty,
    index: usize,
};
fn parse_property(
    css: []const u8,
    initial_index: usize,
) !ParsePropertyResult {
    var index = eat_whitespace(css, initial_index);

    // First parse property name.
    var name_res = parse_identifier(css, index) catch |e| {
        std.debug.print("Could not parse property name.\n", .{});
        return e;
    };
    index = name_res.index;

    index = eat_whitespace(css, index);

    // Then parse colon: :.
    index = try parse_syntax(css, index, ':');

    index = eat_whitespace(css, index);

    // Then parse property value.
    var value_res = parse_identifier(css, index) catch |e| {
        std.debug.print("Could not parse property value.\n", .{});
        return e;
    };
    index = value_res.index;

    // Finally parse semi-colon: ;.
    index = try parse_syntax(css, index, ';');

    var property = match_property(name_res.identifier, value_res.identifier) catch |e| {
        debug_at(css, initial_index, "Unknown property: '{s}'.", .{name_res.identifier});
        return e;
    };

    return ParsePropertyResult{
        .property = property,
        .index = index,
    };
}

Finally we get to the first bit of metaprogramming. Once we have a property name and value, we need to turn that into a Zig union.

That's what match_property() is going to be responsible for doing.

`match_property`

This function needs to take a property name and value and return a CSSProperty with the correct field (matching up to the property name passed in) and assigned to the value passed in.

If we didn't have metaprogramming or reflection, the implementation might look like this:

fn match_property(
    name: []const u8,
    value: []const u8,
) !CSSProperty {
    if (std.mem.eql(u8, name, "color")) {
        return CSSProperty{.color = value};
    } else if (std.mem.eql(u8, name, "background")) {
        return CSSProperty{.background = value};
    }

    return error.UnknownProperty;
}

And that is not necessarily bad. In fact it may be how a lot of production code looks over time as product needs evolve. You can keep the internal field name unrelated to the external field name.

However for the sake of learning, we'll try to implement the same thing with Zig metaprogramming.

And specifically, we can take a look at lib/std/json/static.zig to understand the reflection APIs.

Specifically, if we look at line 210-226 of that file, we can see them iterating over fields of a Union:

        .Union => |unionInfo| {
            if (comptime std.meta.trait.hasFn("jsonParse")(T)) {
                return T.jsonParse(allocator, source, options);
            }

            if (unionInfo.tag_type == null) @compileError("Unable to parse into untagged union '" ++ @typeName(T) ++ "'");

            if (.object_begin != try source.next()) return error.UnexpectedToken;

            var result: ?T = null;
            var name_token: ?Token = try source.nextAllocMax(allocator, .alloc_if_needed, options.max_value_len.?);
            const field_name = switch (name_token.?) {
                inline .string, .allocated_string => |slice| slice,
                else => return error.UnexpectedToken,
            };

            inline for (unionInfo.fields) |u_field| {

Then right after that (lines 226-243) we see them conditionally modifying the result object:

            inline for (unionInfo.fields) |u_field| {
                if (std.mem.eql(u8, u_field.name, field_name)) {
                    // Free the name token now in case we're using an allocator that optimizes freeing the last allocated object.
                    // (Recursing into parseInternal() might trigger more allocations.)
                    freeAllocated(allocator, name_token.?);
                    name_token = null;

                    if (u_field.type == void) {
                        // void isn't really a json type, but we can support void payload union tags with {} as a value.
                        if (.object_begin != try source.next()) return error.UnexpectedToken;
                        if (.object_end != try source.next()) return error.UnexpectedToken;
                        result = @unionInit(T, u_field.name, {});
                    } else {
                        // Recurse.
                        result = @unionInit(T, u_field.name, try parseInternal(u_field.type, allocator, source, options));
                    }
                    break;
                }

We can see that the .Union => |unionInfo| condition is entered by switching on @typeInfo(T) (line 149) and that T is a type (line 144).

We don't have a generic type though. We know we are working with a CSSProperty. And we know CSSProperty is a union so we don't need the switch either.

So let's apply that to our match_property implementation.

fn match_property(
    name: []const u8,
    value: []const u8,
) !CSSProperty {
    const cssPropertyInfo = @typeInfo(CSSProperty);

    for (cssPropertyInfo.Union.fields) |u_field| {
        if (std.mem.eql(u8, u_field.name, name)) {
            return @unionInit(CSSProperty, u_field.name, value);
        }
    }

    return error.UnknownProperty;
}

And if we try to build that we'll get an error like this:

main.zig:15:31: error: values of type '[]const builtin.Type.UnionField' must be comptime-known, but index value is runtime-known
    for (cssPropertyInfo.Union.fields) |u_field| {

Zig's "reflection" abilities here are comptime only. So we can't use a runtime for loop, we must use a comptime inline for loop.

fn match_property(
    name: []const u8,
    value: []const u8,
) !CSSProperty {
    const cssPropertyInfo = @typeInfo(CSSProperty);

    inline for (cssPropertyInfo.Union.fields) |u_field| {
        if (std.mem.eql(u8, u_field.name, name)) {
            return @unionInit(CSSProperty, u_field.name, value);
        }
    }

    return error.UnknownProperty;
}

As far as I understand it, this loop is basically unrolled and the generated code would look a lot like our hard-coded initial version.

i.e. it would probably look like this:

fn match_property(
    name: []const u8,
    value: []const u8,
) !CSSProperty {
    const cssPropertyInfo = @typeInfo(CSSProperty);

    if (std.mem.eql(u8, "background", name)) {
        return @unionInit(CSSProperty, "background", value);
    }

    if (std.mem.eql(u8, "color", name)) {
        return @unionInit(CSSProperty, "color", value);
    }

    if (std.mem.eql(u8, "unknown", name)) {
        return @unionInit(CSSProperty, "unknown", value);
    }

    return error.UnknownProperty;
}

Again that's just how I imagine the compiler to generate code from the Union field reflection and inline for over the fields.

Try compiling that code. I get this:

main.zig:17:58: error: expected type 'void', found '[]const u8'
            return @unionInit(CSSProperty, u_field.name, value);

Thinking about the generated code makes it especially clear what's happening. We have an unknown field in there that has a void type. You can't assign a string to void.

We know at runtime that the condition where that happens should be impossible because the user shouldn't enter unknown as a property name. (Though now that I write this, I see they actually could. But let's pretend they wouldn't.)

So the problem isn't a runtime failure but a comptime type-checking failure.

Thankfully we can work around this with comptime conditionals.

If we wrap our current condition in an additional conditional that is evaluated at comptime and filters out the unknown pass of the inline for loop, the compiler shouldn't generate any code trying to assign to the unknown field.

fn match_property(
    name: []const u8,
    value: []const u8,
) !CSSProperty {
    const cssPropertyInfo = @typeInfo(CSSProperty);

    inline for (cssPropertyInfo.Union.fields) |u_field| {
        if (comptime !std.mem.eql(u8, u_field.name, "unknown")) {
            if (std.mem.eql(u8, u_field.name, name)) {
                return @unionInit(CSSProperty, u_field.name, value);
            }
        }
    }

    return error.UnknownProperty;
}

And indeed, if you try to compile it, this works. Since the conditional is evaluated at compile time, we can imagine the code the compiler generates is this:

fn match_property(
    name: []const u8,
    value: []const u8,
) !CSSProperty {
    const cssPropertyInfo = @typeInfo(CSSProperty);

    if (std.mem.eql(u8, "background", name)) {
        return @unionInit(CSSProperty, "background", value);
    }

    if (std.mem.eql(u8, "color", name)) {
        return @unionInit(CSSProperty, "color", value);
    }

    return error.UnknownProperty;
}

The unknown field has been skipped.

In retrospect, I realize that the unknown field probably isn't even needed. We could eliminate it from the CSSProperty union and get rid of that comptime conditional. However, sometimes there are in fact private fields you want to skip. And I wanted to show how to deal with that case.

For the last bit of metaprogramming, let's talk about displaying the resulting CSSSheet we'd get after parsing.

`sheet.display()`

If we didn't have metaprogramming and wanted to display the sheet, we'd have to switch on every possible union field.

Like so (I've modified the CSSSheet struct definition so it includes this method):

    fn display(sheet: *CSSSheet) void {
        for (sheet.rules) |rule| {
            std.debug.print("selector: {s}\n", .{rule.selector});
            for (rule.properties) |property| {
                switch (property) {
                    .unknown => unreachable,
                    .color => |color_value| std.debug.print("  color: {s}\n", .{color_value}),
                    .background => |background_value| std.debug.print("  background: {s}\n", .{background_value}),
                };
            }
            std.debug.print("\n", .{});
        }
    }

This is already a little annoying and could get unwieldy as we add fields to the CSSProperty union.

Instead we can use the inline for (@typeInfo(CSSProperty).Union.fields) |u_field| method to iterate over all fields, skip the unknown field at comptime, and print out the field name and value by matching on the current value of the property enum by using the @tagName builtin.

    fn display(sheet: *CSSSheet) void {
        for (sheet.rules) |rule| {
            std.debug.print("selector: {s}\n", .{rule.selector});
            for (rule.properties) |property| {
                inline for (@typeInfo(CSSProperty).Union.fields) |u_field| {
                    if (comptime !std.mem.eql(u8, u_field.name, "unknown")) {
                        if (std.mem.eql(u8, u_field.name, @tagName(property))) {
                            std.debug.print("  {s}: {s}\n", .{
                                @tagName(property),
                                @field(property, u_field.name),
                            });
                        }
                    }
                }
            }
            std.debug.print("\n", .{});
        }
    }

`main`

Finally, we pull it all together with a little main function.

pub fn main() !void {
    var arena = std.heap.ArenaAllocator.init(std.heap.page_allocator);
    defer arena.deinit();

    const allocator = arena.allocator();

    // Let's read in a CSS file.
    var args = std.process.args();

    // Skips the program name.
    _ = args.next();

    var file_name: []const u8 = "";
    if (args.next()) |f| {
        file_name = f;
    }

    const file = try std.fs.cwd().openFile(file_name, .{});
    defer file.close();

    const file_size = try file.getEndPos();
    var css_file = try allocator.alloc(u8, file_size);
    _ = try file.read(css_file);

    var sheet = parse(&arena, css_file) catch return;
    sheet.display();
}

And try it against some tests.

$ zig build-exe main.zig
$ cat tests/basic.css
div {
    background: white;
}
$ ./main tests/basic.css
selector: div
  background: white

Nice! Let's try it against a more complex test.

$ cat tests/multiple-blocks.css
div {
    background: black;
    color: white;
}

a {
    color: blue;
}
$ ./main tests/multiple-blocks.css
selector: div
  background: black
  color: white

selector: a
  color: blue

Awesome. And against a bad CSS sheet:

$ cat tests/bad-property.css
a {
    big: pink;
}
$ ./main cat tests/bad-property.css
Error at line 2, column 4. Unknown property: 'big'.


    big: pink;
    ^ Near here.

We've got it!

Addendum: `@field`

The docs were quite clear about using @field(object, fieldName) to access the value of an object of type @TypeOf(object) at field fieldName.

And the docs do mention @field() can be used as LHS but that only really struct me when I was browsing the Zig JSON code like at line 307.

I didn't use that in this little project but I've used it elsewhere, so it I wanted to call this LHS behavior out.

Wrote a short post on parsing CSS as a way to motivate some basic exploration of metaprogramming in Zig.

I heavily referenced Zig's builtin JSON parser when learning this. And it is referenced multiple times in the post as well.https://t.co/CX6jXSLGiR pic.twitter.com/jAJJZ0pONQ
— Phil Eaton (@eatonphil) June 19, 2023

Implementing the Raft distributed consensus protocol in Go

Thu, 25 May 2023 00:00:00 +0000

As part of bringing myself up-to-speed after joining TigerBeetle, I wanted some background on how distributed consensus and replicated state machines protocols work. TigerBeetle uses Viewstamped Replication. But I wanted to understand all popular protocols and I decided to start with Raft.

We'll implement two key components of Raft in this post (leader election and log replication). Around 1k lines of Go. It took me around 7 months of sporadic studying to come to (what I hope is) an understanding of the basics.

Disclaimer: I'm not an expert. My implementation isn't yet hooked up to Jepsen. I've run it through a mix of manual and automated tests and it seems generally correct. This is not intended to be used in production. It's just for my education.

All code for this project is available on GitHub.

Let's dig in!

The algorithm

The Raft paper itself is quite readable. Give it a read and you'll get the basic idea.

The gist is that nodes in a cluster conduct elections to pick a leader. Users of the Raft cluster send messages to the leader. The leader passes the message to followers and waits for a majority to store the message. Once the message is committed (majority consensus has been reached), the message is applied to a state machine the user supplies. Followers learn about the latest committed message from the leader and apply each new committed message to their local user-supplied state machine.

There's more to it including reconfiguration and snapshotting, which I won't get into in this post. But you can get the gist of Raft by thinking about 1) leader election and 2) replicated logs powering replicated state machines.

Modeling with state machines and key-value stores

I've written before about how you can build a key-value store on top of Raft. How you can build a SQL database on top of a key-value store. And how you can build a distributed SQL database on top of Raft.

This post will start quite similarly to that first post except for that we won't stop at the Raft layer.

A distributed key-value store

To build on top of the Raft library we'll build, we need to create a state machine and commands that are sent to the state machine.

Our state machine will have two operations: get a value from a key, and set a key to a value.

This will go in cmd/kvapi/main.go.

package main

import (
    "bytes"
    crypto "crypto/rand"
    "encoding/binary"
    "fmt"
    "log"
    "math/rand"
    "net/http"
    "os"
    "strconv"
    "strings"
    "sync"

    "github.com/eatonphil/goraft"
)

type statemachine struct {
    db     *sync.Map
    server int
}

type commandKind uint8

const (
    setCommand commandKind = iota
    getCommand
)

type command struct {
    kind  commandKind
    key   string
    value string
}

func (s *statemachine) Apply(cmd []byte) ([]byte, error) {
    c := decodeCommand(cmd)

    switch c.kind {
    case setCommand:
        s.db.Store(c.key, c.value)
    case getCommand:
        value, ok := s.db.Load(c.key)
        if !ok {
            return nil, fmt.Errorf("Key not found")
        }
        return []byte(value.(string)), nil
    default:
        return nil, fmt.Errorf("Unknown command: %x", cmd)
    }

    return nil, nil
}

But the Raft library we'll build needs to deal with various state machines. So commands passed from the user into the Raft cluster must be serialized to bytes.

func encodeCommand(c command) []byte {
    msg := bytes.NewBuffer(nil)
    err := msg.WriteByte(uint8(c.kind))
    if err != nil {
        panic(err)
    }

    err = binary.Write(msg, binary.LittleEndian, uint64(len(c.key)))
    if err != nil {
        panic(err)
    }

    msg.WriteString(c.key)

    err = binary.Write(msg, binary.LittleEndian, uint64(len(c.value)))
    if err != nil {
        panic(err)
    }

    msg.WriteString(c.value)

    return msg.Bytes()
}

And the Apply() function from above needs to be able to decode the bytes:

func decodeCommand(msg []byte) command {
    var c command
    c.kind = commandKind(msg[0])

    keyLen := binary.LittleEndian.Uint64(msg[1:9])
    c.key = string(msg[9 : 9+keyLen])

    if c.kind == setCommand {
        valLen := binary.LittleEndian.Uint64(msg[9+keyLen : 9+keyLen+8])
        c.value = string(msg[9+keyLen+8 : 9+keyLen+8+valLen])
    }

    return c
}

HTTP API

Now that we've modeled the key-value store as a state machine. Let's build the HTTP endpoints that allow the user to operate the state machine through the Raft cluster.

First, let's implement the set operation. We need to grab the key and value the user passes in and call Apply() on the Raft cluster. Calling Apply() on the Raft cluster will eventually call the Apply() function we just wrote, but not until the message sent to the Raft cluster is actually replicated.

type httpServer struct {
    raft *goraft.Server
    db   *sync.Map
}

// Example:
//
//  curl http://localhost:2020/set?key=x&value=1
func (hs httpServer) setHandler(w http.ResponseWriter, r *http.Request) {
    var c command
    c.kind = setCommand
    c.key = r.URL.Query().Get("key")
    c.value = r.URL.Query().Get("value")

    _, err := hs.raft.Apply([][]byte{encodeCommand(c)})
    if err != nil {
        log.Printf("Could not write key-value: %s", err)
        http.Error(w, http.StatusText(http.StatusBadRequest), http.StatusBadRequest)
        return
    }
}

To reiterate, we tell the Raft cluster we want this message replicated. The message contains the operation type (set) and the operation details (key and value). These messages are custom to the state machine we wrote. And they will be interpreted by the state machine we wrote, on each node in the cluster.

Next we handle get-ing values from the cluster. There are two ways to do this. We already embed a local copy of the distributed key-value map. We could just read from that map in the current process. But it might not be up-to-date or correct. It would be fast to read though. And convenient for debugging.

But the only correct way to read from a Raft cluster is to pass the read through the log replication too.

So we'll support both.

// Example:
//
//  curl http://localhost:2020/get?key=x
//  1
//  curl http://localhost:2020/get?key=x&relaxed=true # Skips consensus for the read.
//  1
func (hs httpServer) getHandler(w http.ResponseWriter, r *http.Request) {
    var c command
    c.kind = getCommand
    c.key = r.URL.Query().Get("key")

    var value []byte
    var err error
    if r.URL.Query().Get("relaxed") == "true" {
        v, ok := hs.db.Load(c.key)
        if !ok {
            err = fmt.Errorf("Key not found")
        } else {
            value = []byte(v.(string))
        }
    } else {
        var results []goraft.ApplyResult
        results, err = hs.raft.Apply([][]byte{encodeCommand(c)})
        if err == nil {
            if len(results) != 1 {
                err = fmt.Errorf("Expected single response from Raft, got: %d.", len(results))
            } else if results[0].Error != nil {
                err = results[0].Error
            } else {
                value = results[0].Result
            }

        }
    }

    if err != nil {
        log.Printf("Could not encode key-value in http response: %s", err)
        http.Error(w, http.StatusText(http.StatusInternalServerError), http.StatusInternalServerError)
        return
    }

    written := 0
    for written < len(value) {
        n, err := w.Write(value[written:])
        if err != nil {
            log.Printf("Could not encode key-value in http response: %s", err)
            http.Error(w, http.StatusText(http.StatusInternalServerError), http.StatusInternalServerError)
            return
        }

        written += n
    }
}

Main

Now that we've set up our custom state machine and our HTTP API for interacting with the Raft cluster, we'll tie it together with reading configuration from the command-line and actually starting the Raft node and the HTTP API.

type config struct {
    cluster []goraft.ClusterMember
    index   int
    id      string
    address string
    http    string
}

func getConfig() config {
    cfg := config{}
    var node string
    for i, arg := range os.Args[1:] {
        if arg == "--node" {
            var err error
            node = os.Args[i+2]
            cfg.index, err = strconv.Atoi(node)
            if err != nil {
                log.Fatal("Expected $value to be a valid integer in `--node $value`, got: %s", node)
            }
            i++
            continue
        }

        if arg == "--http" {
            cfg.http = os.Args[i+2]
            i++
            continue
        }

        if arg == "--cluster" {
            cluster := os.Args[i+2]
            var clusterEntry goraft.ClusterMember
            for _, part := range strings.Split(cluster, ";") {
                idAddress := strings.Split(part, ",")
                var err error
                clusterEntry.Id, err = strconv.ParseUint(idAddress[0], 10, 64)
                if err != nil {
                    log.Fatal("Expected $id to be a valid integer in `--cluster $id,$ip`, got: %s", idAddress[0])
                }

                clusterEntry.Address = idAddress[1]
                cfg.cluster = append(cfg.cluster, clusterEntry)
            }

            i++
            continue
        }
    }

    if node == "" {
        log.Fatal("Missing required parameter: --node $index")
    }

    if cfg.http == "" {
        log.Fatal("Missing required parameter: --http $address")
    }

    if len(cfg.cluster) == 0 {
        log.Fatal("Missing required parameter: --cluster $node1Id,$node1Address;...;$nodeNId,$nodeNAddress")
    }

    return cfg
}

func main() {
    var b [8]byte
    _, err := crypto.Read(b[:])
    if err != nil {
        panic("cannot seed math/rand package with cryptographically secure random number generator")
    }
    rand.Seed(int64(binary.LittleEndian.Uint64(b[:])))

    cfg := getConfig()

    var db sync.Map

    var sm statemachine
    sm.db = &db
    sm.server = cfg.index

    s := goraft.NewServer(cfg.cluster, &sm, ".", cfg.index)
    go s.Start()

    hs := httpServer{s, &db}

    http.HandleFunc("/set", hs.setHandler)
    http.HandleFunc("/get", hs.getHandler)
    err = http.ListenAndServe(cfg.http, nil)
    if err != nil {
        panic(err)
    }
}

And that's it for the easy part: a distributed key-value store on top of a Raft cluster.

Next we need to implement Raft.

A Raft server

If we take a look at Figure 2 in the Raft paper, we get an idea for all the state we need to model.

We'll dig into the details as we go. But for now let's turn that model into a few Go types. This goes in raft.go in the base directory, not cmd/kvapi.

package goraft

import (
    "bufio"
        "context"
    "encoding/binary"
    "errors"
    "fmt"
    "io"
    "math/rand"
    "net"
    "net/http"
    "net/rpc"
    "os"
    "path"
    "sync"
    "time"
)

type StateMachine interface {
    Apply(cmd []byte) ([]byte, error)
}

type ApplyResult struct {
    Result []byte
    Error  error
}

type Entry struct {
    Command []byte
    Term    uint64

    // Set by the primary so it can learn about the result of
    // applying this command to the state machine
    result  chan ApplyResult
}

type ClusterMember struct {
    Id      uint64
    Address string

    // Index of the next log entry to send
    nextIndex uint64
    // Highest log entry known to be replicated
    matchIndex uint64

    // Who was voted for in the most recent term
    votedFor uint64

    // TCP connection
    rpcClient *rpc.Client
}

type ServerState string

const (
    leaderState    ServerState = "leader"
    followerState              = "follower"
    candidateState             = "candidate"
)

type Server struct {
    // These variables for shutting down.
    done bool
        server *http.Server

    Debug bool

    mu sync.Mutex
    // ----------- PERSISTENT STATE -----------

    // The current term
    currentTerm uint64

    log []Entry

    // votedFor is stored in `cluster []ClusterMember` below,
    // mapped by `clusterIndex` below

    // ----------- READONLY STATE -----------

    // Unique identifier for this Server
    id uint64

    // The TCP address for RPC
    address string

    // When to start elections after no append entry messages
    electionTimeout time.Time

    // How often to send empty messages
    heartbeatMs int

    // When to next send empty message
    heartbeatTimeout time.Time

    // User-provided state machine
    statemachine StateMachine

    // Metadata directory
    metadataDir string

    // Metadata store
    fd *os.File

    // ----------- VOLATILE STATE -----------

    // Index of highest log entry known to be committed
    commitIndex uint64

    // Index of highest log entry applied to state machine
    lastApplied uint64

    // Candidate, follower, or leader
    state ServerState

    // Servers in the cluster, including this one
    cluster []ClusterMember

    // Index of this server
    clusterIndex int
}

And let's build a constructor to initialize the state for all servers in the cluster, as well as local server state.

func NewServer(
    clusterConfig []ClusterMember,
    statemachine StateMachine,
    metadataDir string,
    clusterIndex int,
) *Server {
    // Explicitly make a copy of the cluster because we'll be
    // modifying it in this server.
    var cluster []ClusterMember
    for _, c := range clusterConfig {
        if c.Id == 0 {
            panic("Id must not be 0.")
        }
        cluster = append(cluster, c)
    }

    return &Server{
        id:           cluster[clusterIndex].Id,
        address:      cluster[clusterIndex].Address,
        cluster:      cluster,
        statemachine: statemachine,
        metadataDir:  metadataDir,
        clusterIndex: clusterIndex,
        heartbeatMs:  300,
        mu:           sync.Mutex{},
    }
}

And add a few debugging and assertion helpers.

func (s *Server) debugmsg(msg string) string {
    return fmt.Sprintf("%s [Id: %d, Term: %d] %s", time.Now().Format(time.RFC3339Nano), s.id, s.currentTerm, msg)
}

func (s *Server) debug(msg string) {
    if !s.Debug {
        return
    }
    fmt.Println(s.debugmsg(msg))
}

func (s *Server) debugf(msg string, args ...any) {
    if !s.Debug {
        return
    }

    s.debug(fmt.Sprintf(msg, args...))
}

func (s *Server) warn(msg string) {
    fmt.Println("[WARN] " + s.debugmsg(msg))
}

func (s *Server) warnf(msg string, args ...any) {
    fmt.Println(fmt.Sprintf(msg, args...))
}

func Assert[T comparable](msg string, a, b T) {
    if a != b {
        panic(fmt.Sprintf("%s. Got a = %#v, b = %#v", msg, a, b))
    }
}

func Server_assert[T comparable](s *Server, msg string, a, b T) {
    Assert(s.debugmsg(msg), a, b)
}

Persistent state

As Figure 2 says, currentTerm, log, and votedFor must be persisted to disk as they're edited.

I like to initially doing the stupidest thing possible. So in the first version of this project I used encoding/gob to write these three fields to disk every time s.persist() was called.

Here is what this first version looked like:

func (s *Server) persist() {
    s.mu.Lock()
    defer s.mu.Unlock()

    s.fd.Truncate(0)
    s.fd.Seek(0, 0)
    enc := gob.NewEncoder(s.fd)
    err := enc.Encode(PersistentState{
        CurrentTerm: s.currentTerm,
        Log:         s.log,
        VotedFor:    s.votedFor,
    })
    if err != nil {
        panic(err)
    }
    if err = s.fd.Sync(); err != nil {
        panic(err)
    }
    s.debug(fmt.Sprintf("Persisted. Term: %d. Log Len: %d. Voted For: %s.", s.currentTerm, len(s.log), s.votedFor))
}

But doing so means this implementation is a function of the size of the log. And that was horrible for throughput.

I also noticed that encoding/gob is pretty inefficient.

For a simple struct like:

type X struct {
    A uint64
    B []uint64
    C bool
}

encoding/gob uses 68 bytes to store that data for when B has two entries. If we wrote the encoder/decoder ourselves we could store that struct in 33 bytes (8 (sizeof(A)) + 8 (sizeof(len(B))) + 16 (len(B) * sizeof(B)) + 1 (sizeof(C))).

It's not that encoding/gob is bad. It just likely has different constraints than we are party to.

So I decided to swap out encoding/gob for simply binary encoding the fields and also, importantly, keeping track of exactly how many entries in the log must be written and only writing that many.

`s.persist()`

Here's what that looks like.

const PAGE_SIZE = 4096
const ENTRY_HEADER = 16
const ENTRY_SIZE = 128

// Must be called within s.mu.Lock()
func (s *Server) persist(writeLog bool, nNewEntries int) {
    t := time.Now()

    if nNewEntries == 0 && writeLog {
        nNewEntries = len(s.log)
    }

    s.fd.Seek(0, 0)

    var page [PAGE_SIZE]byte
    // Bytes 0  - 8:   Current term
    // Bytes 8  - 16:  Voted for
    // Bytes 16 - 24:  Log length
    // Bytes 4096 - N: Log

    binary.LittleEndian.PutUint64(page[:8], s.currentTerm)
    binary.LittleEndian.PutUint64(page[8:16], s.getVotedFor())
    binary.LittleEndian.PutUint64(page[16:24], uint64(len(s.log)))
    n, err := s.fd.Write(page[:])
    if err != nil {
        panic(err)
    }
    Server_assert(s, "Wrote full page", n, PAGE_SIZE)

    if writeLog && nNewEntries > 0 {
        newLogOffset := max(len(s.log)-nNewEntries, 0)

        s.fd.Seek(int64(PAGE_SIZE+ENTRY_SIZE*newLogOffset), 0)
        bw := bufio.NewWriter(s.fd)

        var entryBytes [ENTRY_SIZE]byte
        for i := newLogOffset; i < len(s.log); i++ {
            // Bytes 0 - 8:    Entry term
            // Bytes 8 - 16:   Entry command length
            // Bytes 16 - ENTRY_SIZE: Entry command

            if len(s.log[i].Command) > ENTRY_SIZE-ENTRY_HEADER {
                panic(fmt.Sprintf("Command is too large (%d). Must be at most %d bytes.", len(s.log[i].Command), ENTRY_SIZE-ENTRY_HEADER))
            }

            binary.LittleEndian.PutUint64(entryBytes[:8], s.log[i].Term)
            binary.LittleEndian.PutUint64(entryBytes[8:16], uint64(len(s.log[i].Command)))
            copy(entryBytes[16:], []byte(s.log[i].Command))

            n, err := bw.Write(entryBytes[:])
            if err != nil {
                panic(err)
            }
            Server_assert(s, "Wrote full page", n, ENTRY_SIZE)
        }

        err = bw.Flush()
        if err != nil {
            panic(err)
        }
    }

    if err = s.fd.Sync(); err != nil {
        panic(err)
    }
    s.debugf("Persisted in %s. Term: %d. Log Len: %d (%d new). Voted For: %d.", time.Now().Sub(t), s.currentTerm, len(s.log), nNewEntries, s.getVotedFor())
}

Again the important thing is that only the entries that need to be written are written. We do that by seek-ing to the offset of the first entry that needs to be written.

And we collect writes of entries in a bufio.Writer so we don't waste write syscalls. Don't forget to flush the buffered writer!

And don't forget to flush all writes to disk with fd.Sync().

ENTRY_SIZE is something that I could see being configurable based on the workload. Some workloads truly need only 128 bytes. But a key-value store probably wants much more than that. This implementation doesn't try to handle the case of completely arbitrary sized keys and values.

Lastly, a few helpers used in there:

func min[T ~int | ~uint64](a, b T) T {
    if a < b {
        return a
    }

    return b
}

func max[T ~int | ~uint64](a, b T) T {
    if a > b {
        return a
    }

    return b
}

// Must be called within s.mu.Lock()
func (s *Server) getVotedFor() uint64 {
    for i := range s.cluster {
        if i == s.clusterIndex {
            return s.cluster[i].votedFor
        }
    }

    Server_assert(s, "Invalid cluster", true, false)
    return 0
}

`s.restore()`

Now let's do the reverse operation, restoring from disk. This will only be called once on startup.

func (s *Server) restore() {
    s.mu.Lock()
    defer s.mu.Unlock()

    if s.fd == nil {
        var err error
        s.fd, err = os.OpenFile(
            path.Join(s.metadataDir, fmt.Sprintf("md_%d.dat", s.id)),
            os.O_SYNC|os.O_CREATE|os.O_RDWR,
            0755)
        if err != nil {
            panic(err)
        }
    }

    s.fd.Seek(0, 0)

    // Bytes 0  - 8:   Current term
    // Bytes 8  - 16:  Voted for
    // Bytes 16 - 24:  Log length
    // Bytes 4096 - N: Log
    var page [PAGE_SIZE]byte
    n, err := s.fd.Read(page[:])
    if err == io.EOF {
        s.ensureLog()
        return
    } else if err != nil {
        panic(err)
    }
    Server_assert(s, "Read full page", n, PAGE_SIZE)

    s.currentTerm = binary.LittleEndian.Uint64(page[:8])
    s.setVotedFor(binary.LittleEndian.Uint64(page[8:16]))
    lenLog := binary.LittleEndian.Uint64(page[16:24])
    s.log = nil

    if lenLog > 0 {
        s.fd.Seek(int64(PAGE_SIZE), 0)

        var e Entry
        for i := 0; uint64(i) < lenLog; i++ {
            var entryBytes [ENTRY_SIZE]byte
            n, err := s.fd.Read(entryBytes[:])
            if err != nil {
                panic(err)
            }
            Server_assert(s, "Read full entry", n, ENTRY_SIZE)

            // Bytes 0 - 8:    Entry term
            // Bytes 8 - 16:   Entry command length
            // Bytes 16 - ENTRY_SIZE: Entry command
            e.Term = binary.LittleEndian.Uint64(entryBytes[:8])
            lenValue := binary.LittleEndian.Uint64(entryBytes[8:16])
            e.Command = entryBytes[16 : 16+lenValue]
            s.log = append(s.log, e)
        }
    }

    s.ensureLog()
}

And a few helpers it calls:

func (s *Server) ensureLog() {
    if len(s.log) == 0 {
        // Always has at least one log entry.
        s.log = append(s.log, Entry{})
    }
}

// Must be called within s.mu.Lock()
func (s *Server) setVotedFor(id uint64) {
    for i := range s.cluster {
        if i == s.clusterIndex {
            s.cluster[i].votedFor = id
            return
        }
    }

    Server_assert(s, "Invalid cluster", true, false)
}

The main loop

Now let's think about the main loop. Before starting the loop we need to 1) restore persistent state from disk and 2) kick off an RPC server so servers in the cluster can send and receive messages to and from eachother.

// Make sure rand is seeded
func (s *Server) Start() {
    s.mu.Lock()
    s.state = followerState
    s.done = false
    s.mu.Unlock()

    s.restore()

    rpcServer := rpc.NewServer()
    rpcServer.Register(s)
    l, err := net.Listen("tcp", s.address)
    if err != nil {
        panic(err)
    }
    mux := http.NewServeMux()
    mux.Handle(rpc.DefaultRPCPath, rpcServer)

    s.server = &http.Server{Handler: mux}
    go s.server.Serve(l)

    go func() {
        s.mu.Lock()
        s.resetElectionTimeout()
        s.mu.Unlock()

        for {
            s.mu.Lock()
            if s.done {
                s.mu.Unlock()
                return
            }
            state := s.state
            s.mu.Unlock()

In the main loop we are either in the leader state, follower state or candidate state.

All states will potentially receive RPC messages from other servers in the cluster but that won't be modeled in this main loop.

The only thing going on in the main loop is that:

We send heartbeat RPCs (leader state)
We try to advance the commit index (leader state only) and apply commands to the state machine (leader and follower states)
We trigger a new election if we haven't received a message in some time (candidate and follower states)
Or we become the leader (candidate state)

            switch state {
            case leaderState:
                s.heartbeat()
                s.advanceCommitIndex()
            case followerState:
                s.timeout()
                s.advanceCommitIndex()
            case candidateState:
                s.timeout()
                s.becomeLeader()
            }
        }
    }()
}

Let's deal with leader election first.

Leader election

Leader election happens every time nodes haven't received a message from a valid leader in some time.

I'll break this up into four major pieces:

Timing out and becoming a candidate after a random (but bounded) period of time of not hearing a message from a valid leader: s.timeout().
The candidate requests votes from all other servers: s.requestVote().
All servers handle vote requests: s.HandleRequestVoteRequest().
A candidate with a quorum of vote requests becomes the leader: s.becomeLeader().

You increment currentTerm, vote for yourself and send RPC vote requests to other nodes in the server.

func (s *Server) resetElectionTimeout() {
    interval := time.Duration(rand.Intn(s.heartbeatMs*2) + s.heartbeatMs*2)
    s.debugf("New interval: %s.", interval*time.Millisecond)
    s.electionTimeout = time.Now().Add(interval * time.Millisecond)
}

func (s *Server) timeout() {
    s.mu.Lock()
    defer s.mu.Unlock()

    hasTimedOut := time.Now().After(s.electionTimeout)
    if hasTimedOut {
        s.debug("Timed out, starting new election.")
        s.state = candidateState
        s.currentTerm++
        for i := range s.cluster {
            if i == s.clusterIndex {
                s.cluster[i].votedFor = s.id
            } else {
                s.cluster[i].votedFor = 0
            }
        }

        s.resetElectionTimeout()
        s.persist(false, 0)
        s.requestVote()
    }
}

Everything in there is implemented already except for s.requestVote(). Let's dig into that.

`s.requestVote()`

By referring back to Figure 2 from the Raft paper we can see how to model the request vote request and response. Let's turn that into some Go types.

type RPCMessage struct {
    Term uint64
}

type RequestVoteRequest struct {
    RPCMessage

    // Candidate requesting vote
    CandidateId uint64

    // Index of candidate's last log entry
    LastLogIndex uint64

    // Term of candidate's last log entry
    LastLogTerm uint64
}

type RequestVoteResponse struct {
    RPCMessage

    // True means candidate received vote
    VoteGranted bool
}

Now we just need to fill the RequestVoteRequest struct out and send it to each other node in the cluster in parallel. As we iterate through nodes in the cluster, we skip ourselves (we always immediately vote for ourselves).

func (s *Server) requestVote() {
    for i := range s.cluster {
        if i == s.clusterIndex {
            continue
        }

        go func(i int) {
            s.mu.Lock()

            s.debugf("Requesting vote from %d.", s.cluster[i].Id)

            lastLogIndex := uint64(len(s.log) - 1)
            lastLogTerm := s.log[len(s.log)-1].Term

            req := RequestVoteRequest{
                RPCMessage: RPCMessage{
                    Term: s.currentTerm,
                },
                CandidateId:  s.id,
                LastLogIndex: lastLogIndex,
                LastLogTerm:  lastLogTerm,
            }
            s.mu.Unlock()

            var rsp RequestVoteResponse
            ok := s.rpcCall(i, "Server.HandleRequestVoteRequest", req, &rsp)
            if !ok {
                // Will retry later
                return
            }

Now remember from Figure 2 in the Raft paper that we must always check that the RPC request and response is still valid. If the term of the response is greater than our own term, we must immediately stop processing and revert to follower state.

Otherwise only if the response is still relevant to us at the moment (the response term is the same as the request term) and the request has succeeded do we count the vote.

            s.mu.Lock()
            defer s.mu.Unlock()

            if s.updateTerm(rsp.RPCMessage) {
                return
            }

            dropStaleResponse := rsp.Term != req.Term
            if dropStaleResponse {
                return
            }

            if rsp.VoteGranted {
                s.debugf("Vote granted by %d.", s.cluster[i].Id)
                s.cluster[i].votedFor = s.id
            }
        }(i)
    }
}

And that's it for the candidate side of requesting a vote.

The implementation of s.updateTerm() is simple. It just takes care of transitioning to follower state if the term of an RPC message is greater than the node's current term.

// Must be called within a s.mu.Lock()
func (s *Server) updateTerm(msg RPCMessage) bool {
    transitioned := false
    if msg.Term > s.currentTerm {
        s.currentTerm = msg.Term
        s.state = followerState
        s.setVotedFor(0)
        transitioned = true
        s.debug("Transitioned to follower")
        s.resetElectionTimeout()
        s.persist(false, 0)
    }
    return transitioned
}

And the implementation of s.rpcCall() is a wrapper around net/rpc to lazily connect.

func (s *Server) rpcCall(i int, name string, req, rsp any) bool {
    s.mu.Lock()
    c := s.cluster[i]
    var err error
    var rpcClient *rpc.Client = c.rpcClient
    if c.rpcClient == nil {
        c.rpcClient, err = rpc.DialHTTP("tcp", c.Address)
        rpcClient = c.rpcClient
    }
    s.mu.Unlock()

    // TODO: where/how to reconnect if the connection must be reestablished?

    if err == nil {
        err = rpcClient.Call(name, req, rsp)
    }

    if err != nil {
        s.warnf("Error calling %s on %d: %s.", name, c.Id, err)
    }

    return err == nil
}

Let's dig into the other side of request vote, what happens when a node receives a vote request?

`s.HandleVoteRequest()`

First off, as discussed above, we must always check the RPC term versus our own and revert to follower if the term is greater than our own. (Remember that since this is an RPC request it could come to a server in any state: leader, candidate, or follower.)

func (s *Server) HandleRequestVoteRequest(req RequestVoteRequest, rsp *RequestVoteResponse) error {
    s.mu.Lock()
    defer s.mu.Unlock()

    s.updateTerm(req.RPCMessage)

    s.debugf("Received vote request from %d.", req.CandidateId)

Then we can return immediately if the request term is lower than our own (that means it's an old request).

    rsp.VoteGranted = false
    rsp.Term = s.currentTerm

    if req.Term < s.currentTerm {
        s.debugf("Not granting vote request from %d.", req.CandidateId)
        Server_assert(s, "VoteGranted = false", rsp.VoteGranted, false)
        return nil
    }

And finally, we check to make sure the requester's log is at least as up-to-date as our own and that we haven't already voted for ourselves.

The first condition (up-to-date log) was not described in the Raft paper that I could find. But the author of the paper published a Raft TLA+ spec that does have it defined.

And the second condition you might think could never happen since we already wrote the code that said when we trigger an election we vote for ourselves. But since each server has a random election timeout, the one who starts the election will differ in timing sufficiently enough to catch other servers and allow them to vote for it.

    lastLogTerm := s.log[len(s.log)-1].Term
    logLen := uint64(len(s.log) - 1)
    logOk := req.LastLogTerm > lastLogTerm ||
        (req.LastLogTerm == lastLogTerm && req.LastLogIndex >= logLen)
    grant := req.Term == s.currentTerm &&
        logOk &&
        (s.getVotedFor() == 0 || s.getVotedFor() == req.CandidateId)
    if grant {
        s.debugf("Voted for %d.", req.CandidateId)
        s.setVotedFor(req.CandidateId)
        rsp.VoteGranted = true
        s.resetElectionTimeout()
        s.persist(false, 0)
    } else {
        s.debugf("Not granting vote request from %d.", +req.CandidateId)
    }

    return nil
}

Lastly, we need to address how the candidate who sent out vote requests actually becomes the leader.

`s.becomeLeader()`

This is a relatively simple method. If we have a quorum of votes, we become the leader!

func (s *Server) becomeLeader() {
    s.mu.Lock()
    defer s.mu.Unlock()

    quorum := len(s.cluster)/2 + 1
    for i := range s.cluster {
        if s.cluster[i].votedFor == s.id && quorum > 0 {
            quorum--
        }
    }

There is a bit of bookkeeping we need to do like resetting nextIndex and matchIndex for each server (noted in Figure 2). And we also need to append a blank entry for the new term.

Despite the section quoted below in code, I still don't understand why this blank entry is necessary.

    if quorum == 0 {
        // Reset all cluster state
        for i := range s.cluster {
            s.cluster[i].nextIndex = uint64(len(s.log) + 1)
            // Yes, even matchIndex is reset. Figure 2
            // from Raft shows both nextIndex and
            // matchIndex are reset after every election.
            s.cluster[i].matchIndex = 0
        }

        s.debug("New leader.")
        s.state = leaderState

        // From Section 8 Client Interaction:
        // > First, a leader must have the latest information on
        // > which entries are committed. The Leader
        // > Completeness Property guarantees that a leader has
        // > all committed entries, but at the start of its
        // > term, it may not know which those are. To find out,
        // > it needs to commit an entry from its term. Raft
        // > handles this by having each leader commit a blank
        // > no-op entry into the log at the start of its term.
        s.log = append(s.log, Entry{Term: s.currentTerm, Command: nil})
        s.persist(true, 1)

        // Triggers s.appendEntries() in the next tick of the
        // main state loop.
        s.heartbeatTimeout = time.Now()
    }
}

And we're done with elections!

When I was working on this for the first time, I just stopped here and made sure I could get to a stable leader quickly. If it takes more than 1 term to establish a leader when you run three servers in the cluster on localhost, you've probably got a bug.

In an ideal environment (which three processes on one machine most likely is), leadership should be established quite quickly and without many term changes. As the environment gets more adversarial (e.g. processes crash frequently or network latency is high and variable), leadership (and log replication) will take longer.

But just because we have leader election working when there are no logs does not mean we'll have it working when we introduce log replication since parts of voting depend on log analysis.
I had leader election working at one time but then it broke when I got log replication working until I found some more bugs in leader election and fixed them. Of course, there may still be bugs even now.

Log replication

I'll break up log replication into four major pieces:

User submits a message to the leader to be replicated: s.Apply().
The leader sends uncommitted messages (messages from nextIndex) to all followers: s.appendEntries().
A follower receives a AppendEntriesRequest and stores new messages if appropriate, letting the leader know when it does store the messages: s.HandleAppendEntriesRequest().
The leader tries to update commitIndex for the last uncommitted message by seeing if it's been replicated on a quorum of servers: s.advanceCommitIndex().

Let's dig in in that order.

`s.Apply()`

This is the entry point for a user of the cluster to attempt to get messages replicated into the cluster.

It must be called on the current leader of the cluster. In the future the failure response might include the current leader. Or the user could submit messages in parallel to all nodes in the cluster and ignore ErrApplyToLeader. In the meantime we just assume the user can figure out which server in the cluster is the leader.

var ErrApplyToLeader = errors.New("Cannot apply message to follower, apply to leader.")

func (s *Server) Apply(commands [][]byte) ([]ApplyResult, error) {
    s.mu.Lock()
    if s.state != leaderState {
        s.mu.Unlock()
        return nil, ErrApplyToLeader
    }
    s.debugf("Processing %d new entry!", len(commands))

Next we'll store the message in the leader's log along with a Go channel that we must block on for the result of applying the message in the state machine after the message has been committed to the cluster.

    resultChans := make([]chan ApplyResult, len(commands))
    for i, command := range commands {
        resultChans[i] = make(chan ApplyResult)
        s.log = append(s.log, Entry{
            Term:    s.currentTerm,
            Command: command,
            result:  resultChans[i],
        })
    }

    s.persist(true, len(commands))

Then we kick off the replication process (this will not block).

    s.debug("Waiting to be applied!")
    s.mu.Unlock()

    s.appendEntries()

And then we block until we receive results from each of the channels we created.

    // TODO: What happens if this takes too long?
    results := make([]ApplyResult, len(commands))
    var wg sync.WaitGroup
    wg.Add(len(commands))
    for i, ch := range resultChans {
        go func(i int, c chan ApplyResult) {
            results[i] = <-c
            wg.Done()
        }(i, ch)
    }

    wg.Wait()

    return results, nil
}

The interesting thing here is that appending entries is detached from the messages we just received. s.appendEntries() will probably include at least the messages we just appended to our log, but it might include more too if some servers are not very up-to-date. It may even include less than the messages we append to our log since we'll restrict the number of entries to send at one time so we keep latency down.

`s.appendEntries()`

This is the meat of log replication on the leader side. We send unreplicated messages to each other server in the cluster.

By again referring back to Figure 2 from the Raft paper we can see how to model the request vote request and response. Let's turn that into some Go types too.

type AppendEntriesRequest struct {
    RPCMessage

    // So follower can redirect clients
    LeaderId uint64

    // Index of log entry immediately preceding new ones
    PrevLogIndex uint64

    // Term of prevLogIndex entry
    PrevLogTerm uint64

    // Log entries to store. Empty for heartbeat.
    Entries []Entry

    // Leader's commitIndex
    LeaderCommit uint64
}

type AppendEntriesResponse struct {
    RPCMessage

    // true if follower contained entry matching prevLogIndex and
    // prevLogTerm
    Success bool
}

For the method itself, we start optimistically sending no entries and decrement nextIndex for each server as the server fails to replicate messages. This means that we might eventually end up sending the entire log to one or all servers.

We'll set a max number of entries to send per request so we avoid unbounded latency as followers store entries to disk. But we still want to send a large batch so that we amortize the cost of fsync.

const MAX_APPEND_ENTRIES_BATCH = 8_000

func (s *Server) appendEntries() {
    for i := range s.cluster {
        // Don't need to send message to self
        if i == s.clusterIndex {
            continue
        }

        go func(i int) {
            s.mu.Lock()

            next := s.cluster[i].nextIndex
            prevLogIndex := next - 1
            prevLogTerm := s.log[prevLogIndex].Term

            var entries []Entry
            if uint64(len(s.log)-1) >= s.cluster[i].nextIndex {
                s.debugf("len: %d, next: %d, server: %d", len(s.log), next, s.cluster[i].Id)
                entries = s.log[next:]
            }

            // Keep latency down by only applying N at a time.
            if len(entries) > MAX_APPEND_ENTRIES_BATCH {
                entries = entries[:MAX_APPEND_ENTRIES_BATCH]
            }

            lenEntries := uint64(len(entries))
            req := AppendEntriesRequest{
                RPCMessage: RPCMessage{
                    Term: s.currentTerm,
                },
                LeaderId:     s.cluster[s.clusterIndex].Id,
                PrevLogIndex: prevLogIndex,
                PrevLogTerm:  prevLogTerm,
                Entries:      entries,
                LeaderCommit: s.commitIndex,
            }

            s.mu.Unlock()

            var rsp AppendEntriesResponse
            s.debugf("Sending %d entries to %d for term %d.", len(entries), s.cluster[i].Id, req.Term)
            ok := s.rpcCall(i, "Server.HandleAppendEntriesRequest", req, &rsp)
            if !ok {
                // Will retry next tick
                return
            }

Now, as with every RPC request and response, we must check terms and potentially drop the message if it's outdated.

            s.mu.Lock()
            defer s.mu.Unlock()
            if s.updateTerm(rsp.RPCMessage) {
                return
            }

            dropStaleResponse := rsp.Term != req.Term && s.state == leaderState
            if dropStaleResponse {
                return
            }

Otherwise, if the message was successful, we'll update matchIndex (the last confirmed message stored on the follower) and nextIndex (the next likely message to send to the follower).

If the message was not successful, we decrement nextIndex. Next time s.appendEntries() is called it will include one more previous message for this replica.

            if rsp.Success {
                prev := s.cluster[i].nextIndex
                s.cluster[i].nextIndex = max(req.PrevLogIndex+lenEntries+1, 1)
                s.cluster[i].matchIndex = s.cluster[i].nextIndex - 1
                s.debugf("Message accepted for %d. Prev Index: %d, Next Index: %d, Match Index: %d.", s.cluster[i].Id, prev, s.cluster[i].nextIndex, s.cluster[i].matchIndex)
            } else {
                s.cluster[i].nextIndex = max(s.cluster[i].nextIndex-1, 1)
                s.debugf("Forced to go back to %d for: %d.", s.cluster[i].nextIndex, s.cluster[i].Id)
            }
        }(i)
    }
}

And we're done the leader side of append entries!

`s.HandleAppendEntriesRequest()`

Now for the follower side of log replication. This is, again, an RPC handler that could be called at any moment. So we need to potentially update the term (and transition to follower).

func (s *Server) HandleAppendEntriesRequest(req AppendEntriesRequest, rsp *AppendEntriesResponse) error {
    s.mu.Lock()
    defer s.mu.Unlock()

    s.updateTerm(req.RPCMessage)

"Hidden" in the "Candidates (§5.2):" section of Figure 2 is an additional rule about:

If AppendEntries RPC received from new leader: convert to follower

So we also need to handle that here. And if we're still not a follower, we'll return immediately.

    // From Candidates (§5.2) in Figure 2
    // If AppendEntries RPC received from new leader: convert to follower
    if req.Term == s.currentTerm && s.state == candidateState {
        s.state = followerState
    }

    rsp.Term = s.currentTerm
    rsp.Success = false

    if s.state != followerState {
        s.debugf("Non-follower cannot append entries.")
        return nil
    }

Next, we also return early if the request term is less than our own. This would represent an old request.

    if req.Term < s.currentTerm {
        s.debugf("Dropping request from old leader %d: term %d.", req.LeaderId, req.Term)
        // Not a valid leader.
        return nil
    }

Now, finally, we know we're receiving a request from a valid leader. So we need to immediately bump the election timeout.

    // Valid leader so reset election.
    s.resetElectionTimeout()

Then we do the log comparison to see if we can add the entries sent from this request. Specifically, we make sure that our log at req.PrevLogIndex exists and has the same term as req.PrevLogTerm.

    logLen := uint64(len(s.log))
    validPreviousLog := req.PrevLogIndex == 0 /* This is the induction step */ ||
        (req.PrevLogIndex < logLen &&
            s.log[req.PrevLogIndex].Term == req.PrevLogTerm)
    if !validPreviousLog {
        s.debug("Not a valid log.")
        return nil
    }

Next, we've got valid entries that we need to add to our log. This implementation is a little more complex because we'll make use of Go slice capacity so that append() never allocates.

Importantly, we must truncate the log if a new entry ever conflicts with an existing one:

If an existing entry conflicts with a new one (same index but different terms), delete the existing entry and all that follow it (§5.3)

    next := req.PrevLogIndex + 1
    nNewEntries := 0

    for i := next; i < next+uint64(len(req.Entries)); i++ {
        e := req.Entries[i-next]
        if i >= uint64(cap(s.log)) {
            newTotal := next + uint64(len(req.Entries))
            // Second argument must actually be `i`
            // not `0` otherwise the copy after this
            // doesn't work.
            // Only copy until `i`, not `newTotal` since
            // we'll continue appending after this.
            newLog := make([]Entry, i, newTotal*2)
            copy(newLog, s.log)
            s.log = newLog
        }

        if i < uint64(len(s.log)) && s.log[i].Term != e.Term {
            prevCap := cap(s.log)
            // If an existing entry conflicts with a new
            // one (same index but different terms),
            // delete the existing entry and all that
            // follow it (§5.3)
            s.log = s.log[:i]
            Server_assert(s, "Capacity remains the same while we truncated.", cap(s.log), prevCap)
        }

        s.debugf("Appending entry: %s. At index: %d.", string(e.Command), len(s.log))

        if i < uint64(len(s.log)) {
            Server_assert(s, "Existing log is the same as new log", s.log[i].Term, e.Term)
        } else {
            s.log = append(s.log, e)
            Server_assert(s, "Length is directly related to the index.", uint64(len(s.log)), i+1)
            nNewEntries++
        }
    }

Finally, we update the server's local commitIndex to the min of req.LeaderCommit and our own log length.

And finally we persist all these changes and mark the response as successful.

    if req.LeaderCommit > s.commitIndex {
        s.commitIndex = min(req.LeaderCommit, uint64(len(s.log)-1))
    }

    s.persist(nNewEntries != 0, nNewEntries)

    rsp.Success = true
    return nil
}

So the combined behavior of the leader and follower when replicating is that a follower not in sync with the leader may eventually go down to the beginning of the log so the leader and follower have some first N messages of the log that match.

`s.advanceCommitIndex()`

Now when not just one follower but a quorum of followers all have a matching first N messages, the leader can advance the cluster's commitIndex.

func (s *Server) advanceCommitIndex() {
    s.mu.Lock()
    defer s.mu.Unlock()

    // Leader can update commitIndex on quorum.
    if s.state == leaderState {
        lastLogIndex := uint64(len(s.log) - 1)

        for i := lastLogIndex; i > s.commitIndex; i-- {
            quorum := len(s.cluster) / 2 + 1

            for j := range s.cluster {
                if quorum == 0 {
                    break
                }

                isLeader := j == s.clusterIndex
                if s.cluster[j].matchIndex >= i || isLeader {
                    quorum--
                }
            }

            if quorum == 0 {
                s.commitIndex = i
                s.debugf("New commit index: %d.", i)
                break
            }
        }
    }

And for every state a server might be in, if there are messages committed but not applied, we'll apply one here. And importantly, we'll pass the result back to the message's result channel if it exists, so that s.Apply() can learn about the result.

    if s.lastApplied <= s.commitIndex {
        log := s.log[s.lastApplied]

        // len(log.Command) == 0 is a noop committed by the leader.
        if len(log.Command) != 0 {
            s.debugf("Entry applied: %d.", s.lastApplied)
            // TODO: what if Apply() takes too long?
            res, err := s.statemachine.Apply(log.Command)

            // Will be nil for follower entries and for no-op entries.
            // Not nil for all user submitted messages.
            if log.result != nil {
                log.result <- ApplyResult{
                    Result: res,
                    Error:  err,
                }
            }
        }

        s.lastApplied++
    }
}

Heartbeats

Heartbeats combine log replication and leader election. Heartbeats stave off leader election (follower timeouts). And heartbeats also bring followers up-to-date if they are behind.

And it's a simple method. If it's time to heartbeat, we call s.appendEntries(). That's it.

func (s *Server) heartbeat() {
    s.mu.Lock()
    defer s.mu.Unlock()

    timeForHeartbeat := time.Now().After(s.heartbeatTimeout)
    if timeForHeartbeat {
        s.heartbeatTimeout = time.Now().Add(time.Duration(s.heartbeatMs) * time.Millisecond)
        s.debug("Sending heartbeat")
        s.appendEntries()
    }
}

The reason this staves off leader election is because any number of entries (0 or N) will come from a valid leader and will thus cause the followers to reset their election timeout.

And that's the entirety of (the basics of) Raft.

There are probably bugs.

Running kvapi

Now let's run the key-value API.

$ cd cmd/kvapi && go build
$ rm *.dat

Terminal 1

$ ./kvapi --node 0 --http :2020 --cluster "0,:3030;1,:3031;2,:3032"

Terminal 2

$ ./kvapi --node 1 --http :2021 --cluster "0,:3030;1,:3031;2,:3032"

Terminal 3

$ ./kvapi --node 2 --http :2022 --cluster "0,:3030;1,:3031;2,:3032"

Terminal 4

Remember that requests will go through the leader (except for if we turn that off in the /get request). So you'll have to try sending a message to each server until you find the leader.

To set a key:

$ curl http://localhost:2020/set?key=y&value=hello

To get a key:

$ curl http://localhost:2020/get\?key\=y

And that's that! Try killing a server and restarting it. A new leader will be elected so you'll need to find the right one to send requests to again. But all existing entries should still be there.

A test rig

I won't cover the implementation of my test rig in this post but I will describe it.

It's nowhere near Jepsen but it does have a specific focus:

Can the cluster elect a leader?
Can the cluster store logs correctly?
Can the cluster of three nodes tolerate one node down?
How fast can it store N messages?
Are messages recovered correctly when the nodes shut down and start back up?
If a node's logs are deleted, is the log for that node recovered after it is restarted?

This implementation passes these tests and handles around 20k-40k entries/second.

Considerations

This was quite a challenging project. Normally when I hack on stuff like this I have TV (The Simpsons) on in the background. It's sort of dumb but this was the first project where I absolutely could not focus with that background noise.

There are a tedious number of conditions and I am not sure I got them all (right). Numerous ways for subtle bugs.

Race conditions and deadlocks

It's very easy to program in race conditions. Thankfully Go has the -race flag that detects this. This makes sure that you are locking read and write access to shared variables when necessary.

On the other side of race conditions, Go does not help you out with: deadlocks. Once you've got locks in place for shared variables, you need to make sure you're releasing the locks appropriately too.

Thankfully someone wrote a swap-in replacement for the Go sync package called go-deadlock. When you import this package instead of the default sync package, it will panic and give you a stacktrace when it thinks you hit a deadlock.

Sometimes it thinks you hit a deadlock because a method that needs a lock takes too long. Sometimes that time it takes is legitimate (or something you haven't optimized yet). But actually its default of 30s is not really aggressive at all.

So I normally set the deadlock timeout to 2s and eventually would like to make that more like 100ms:

sync.Opts.DeadlockTimeout = 2000 * time.Millisecond

It's mostly the persist() function that causes go-deadlock to think there's a deadlock because it tries to synchronously write a bunch of data to disk.

`go-deadlock` is slow

The go-deadlock package is incredibly useful. But don't forget to turn it off for benchmarks. With it on I get around 4-8k entries/second. With it off I get around 20k-40k entries/second.

Unbounded memory

Another issue in this implementation is that the log keeps growing indefinitely and the entire log is duplicated in memory.

There are two ways to deal with that:

Implement Raft snapshotting so the log can be truncated safely.
Keep only some number of entries in memory (say, 1 million) and read from disk as needed when logs need to be verified. In ideal operation this would never happen since ideally all servers are always on, never miss entries, and just keep appending. But "ideal" won't always happen.

Similarly, there is unbounded and unreused channel creation for notifying s.Apply() when the user-submitted message(s) finish.

net/rpc and encoding/gob

In the persist() section above I already mentioned how I prototyped this using Go's builtin gob encoding. And I mentioned how inefficient it was. It's also pretty slow and I learned that because net/rpc uses it and after everything I did net/rpc started to be the bottleneck in my benchmarks. This isn't incredibly surprising.

So a future version of this code might implement its own protocol and own encoding (like we did for disk) on top of TCP rather than use net/rpc.

Jepsen

Everyone wants to know how a distributed algorithm does against Jepsen, which tests linearizability of distributed systems in the face of network and process faults.

But the setup is not trivial so I haven't hooked it up to this project yet. This would be a good area for future work.

Election timeout and the environment

One thing I noticed as I was trying out alternatives to net/rpc (alternatives that injected latency to simulate a bad environment) is that election timeouts should probably be tuned with latency of the cluster in mind.

If the election timeout is every 300ms but the latency of the cluster is near 1s, you're going to have non-stop leader election.

When I adjusted the election timeout to be every 2s when the latency of the cluster is near 1s, everything was fine. Maybe this means there's a bug in my code but I don't think so.

Client request serial identifier

One major part of the Raft protocol I did not cover is that the client is supposed to send a serial identifier for each message sent to the cluster. This is to ensure that messages are not accidentally duplicated at any level of the entire software stack.

Diego Ongaro's thesis goes into more detail about this than the Raft paper. Search in that PDF for "session".

Again I just completely ignored the possibility of duplicate messages in this implementation so far.

References

Finally, I could not have done this without a bunch of internet help. This project took me about 7 months in total. The first 5 months I was trying to figure it out mostly on my own, just looking at the Raft paper.

The biggest breakthrough came from discovering the author of Raft's TLA+ spec for Raft. Formal methods sound scary but it was truly not too bad! This was the first "implementation" of Raft that was in a single file of code. And under 500 lines.

Jack Vanlightly's guide to reading TLA+ helped a bunch.

Finally, I had to peer at other implementations, especially to figure out locking and avoiding deadlocks.

Here's everything that helped me out.

In Search of an Understandable Consensus Algorithm: The Raft paper.
raft.tla: Diego Ongaro's TLA+ spec for Raft.
Jon Gjengset's Students' Guide to Raft
Jack Vanlightly's Detecting Bugs in Data Infrastructure using Formal Methods (TLA+ Series Part 1): An intro to TLA+.

And useful implementations I looked at for inspiration and clarity.

Hashicorp's Raft implementation in Go: Although it's often quite complicated to learn from since it actually is intended for production.
Eli Bendersky's Raft implementation in Go: Although I got confused following it since it used signed integers and -1 to represent base cases. Signed integers is a fair choice as far as I can tell, I just wanted to only use unsigned integers.
Jing Yang's Raft implementation in Rust: Although I find Rust hard to read.

And I haven't tried these but they look cool:

Cheers!

I wrote about implementing Raft in Go. By far the most challenging project I've worked on in spare time. About 7 months sporadically.

I'm not an expert, and this is not intended to be used in production. I wanted a better background on the subject!https://t.co/EhyBuQ4pD3 pic.twitter.com/vGhBbV1shf
— Phil Eaton (@eatonphil) May 25, 2023

Two books I recommend to developers

Tue, 16 May 2023 00:00:00 +0000

Originally published on February 1, 2021. The original version included two books I don't think are actually so worthwhile. This list is down to two. I think that's a good thing actually.

These are the books I recommend to developers wanting to improve their skills as professional programmers because of high information density, believable premises/examples, and being well edited.

You don't need to read books to improve as a developer but they are unparalleled in quickly helping you gain depth in a subject.

High Performance Browser Networking

If you deal with networks, you would probably benefit from this book. It is a thorough high level introduction to mobile networks, browser network protocols, and fundamentals of networking.

Designing Data-Intensive Applications

If you use a database (including an in-memory array of items you search periodically) or if you build APIs, you would probably benefit from this book. A solid introduction to distributed computing, data transfer, indexing, etc.

That's it!

Generic software books conspicuously not on this list for me:

Clean Code
JavaScript the Good Parts
Design Patterns/Gang of Four
Structure and Interpretation of Computer Programs
A Philosophy of Software Design

They're not all bad but give nowhere near as much return for the investment of your time.

Four books I recommend to professional developers wanting to improve their craft, and a few I'd nothttps://t.co/1aTrfqZ9bd
— Phil Eaton (@phil_eaton) February 2, 2021

My favorite software subreddits

Tue, 16 May 2023 00:00:00 +0000

Originally published on December 5, 2021.

If you are an experienced software developer whose only exposure to reddit is dank memes, proggit or even language-specific subreddits like /r/python, you're missing out.

What follows are my favorite subreddits in tech. My criteria is that:

The subreddit topic is relevant to advancing as a programmer
Posts generally go into good depth
The comments stay on topic
And the shit-posting is minimal

This list isn't hard to guess at if you consider advanced topics in software. But I wanted to share because I think it's worth explicitly supporting high-quality subreddits.

/r/EmuDev
- My favorite sub of all. Also has a phenomenal Discord group.
/r/ProgrammingLanguages
- Focuses a little more on PLT topics (parsing techniques, syntax, type systems) than on compiling and interpreting techniques, but still good.
/r/DatabaseDevelopment
- All about database internals, which ends up involving a bunch of correctness and distributed systems stuff as well.
- Disclosure: I run this sub. It's at 2.7k+ members at time of publishing.
/r/ReverseEngineering
- The largest subreddit on this list but still has pretty good posts.
/r/EsoLangs
- One of the best/most fun intros to programming languages/compilers/interpreters is through languages like Brainfuck. This sub does a good job of keeping the fun going.
/r/Compilers
/r/GraphicsProgramming

While some language subreddits are pretty good, they are more so a mixed bag than some of the topic-specific subreddits here. So they don't make my list, more on principle than anything else.

If there is a good one already, send me it!

What am I missing?

Am I missing other amazing subreddits? Just don't say language-specific ones. :)

It's an incorrect meme IMO that tech Reddit is low-quality. You just have to find the interesting subreddits.

I've updated my list for 2023.https://t.co/OtM2tk8HOn pic.twitter.com/ymyzChp0SO
— Phil Eaton (@eatonphil) May 16, 2023

Errors and Zig

Tue, 21 Mar 2023 00:00:00 +0000

At TigerBeetle these last few weeks I've been doing a mix of documenting client libraries, writing sample code for client libraries, and writing integration tests against the sample code.

The client library documentation is generated with a Zig script. The sample code is integration tested with a Zig script. A bunch of Zig scripts.

It's not the same rigorous sort of Zig as the main database. (We're generally more lax about scripts and test code.)

And I'm specifically writing this post on my personal blog since my script code is not under incredible scrutiny.

Furthermore, I'm still new to Zig. Since I'm still learning, there have been a few things that tripped me up.

And now that I've written this out, I realize most of my stumbling was related to errors.

Failure

Lots of things in programs allocate memory. This sounds dumb and obvious but before programming Zig I did not appreciate how many operations I'm used to allocate memory. I've previously only programmed in GC languages that do the allocations behind the scenes.

Furthermore, memory allocation can fail. Zig makes allocation failures explicit. So lots of things in Zig code need to handle failure.

Selectively omitting error handling is not allowed:

const std = @import("std");

fn thing(a: std.mem.Allocator) !void {
    std.fmt.allocPrint(a, "", .{});
}

pub fn main() !void {
    var arena = std.heap.ArenaAllocator.init(std.heap.page_allocator);
    defer arena.deinit();

    const allocator = arena.allocator();
    try thing(allocator);
}

Run zig run test.zig:

test.zig:4:23: error: error is ignored
    std.fmt.allocPrint(a, "", .{});
    ~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~
test.zig:4:23: note: consider using 'try', 'catch', or 'if'
referenced by:
    main: test.zig:12:9
    callMain: /home/phil/vendor/zig-linux-x86_64-0.11.0-dev.2213+515e1c93e/lib/std/start.zig:617:32
    remaining reference traces hidden; use '-freference-trace' to see all reference traces

This ends up meaning lots of code like:

fn do_stuff(
  alloc: std.mem.Allocator, // Let's assume this is an arena allocator so I don't care about freeing.
  stuff: Stuff,
) !void {
  var x = std.ArrayList([]const u8).init(alloc);
  try x.appendSlice(&[_][]const u8{
    "first of something",
    "one more",
  });
  try x.append(stuff.thing);
  try x.append(try std.fmt.allocPrint(alloc, "build some string {s}.\n", .{stuff.athing}));

  var other_stuff = try std.fmt.allocPrint(alloc, "things... {s}", .{blah});

  try do_other_stuff(x.items, other_stuff);
}

You have try-es all over the place.

Limits of `try`

Now I don't have a problem with acknowledging that allocations can fail. At least outside of scripts. In scripts like I've been writing though I don't really care.

Having all of those try-es is just extra typing all over the place.

It would be nice if I could have instead done:

fn do_stuff(
  alloc: std.mem.Allocator, // Let's assume this is an arena allocator so I don't care about freeing.
  stuff: Stuff,
) !void {
  var x = std.ArrayList([]const u8).init(alloc);
  try {
      x.appendSlice(&[_][]const u8{
      "first of something",
      "one more",
    });
    x.append(stuff.thing);
    x.append(std.fmt.allocPrint(alloc, "build some string {s}.\n", .{stuff.athing}));

    var other_stuff = std.fmt.allocPrint(alloc, "things... {s}", .{blah});

    do_other_stuff(x.items, other_stuff);
  }
}

But Zig's try doesn't work like that. I'm not sure why not. The Zig developers are sensible so I'm sure there's a good reason.

Still, are there other options?

`catch unreachable`

So the problem isn't just that you have to acknowledge memory allocation failures but that these failures within every helper function need to be acknowledged by the caller of the helper function. Failures infiltrate the entire call tree.

Now of course these potential failures would exist whether or not Zig exposed them. So I don't mean to say it's Zig's fault for exposing them.

But you can avoid failure handling by instead of try-ing everything, mark error conditions as unreachable.

fn do_stuff(
  alloc: std.mem.Allocator, // Let's assume this is an arena allocator so I don't care about freeing.
  stuff: Stuff,
) void {
  var x = std.ArrayList([]const u8).init(alloc);
  x.appendSlice(&[_][]const u8{
    "first of something",
    "one more",
  }) catch unreachable;
  x.append(stuff.thing) catch unreachable;
  x.append(std.fmt.allocPrint(alloc, "build some string {s}.\n", .{stuff.athing}) catch unreachable) catch unreachable;

  var other_stuff = std.fmt.allocPrint(alloc, "things... {s}", .{blah}) catch unreachable;

  do_other_stuff(x.items, other_stuff) catch unreachable;
}

As you can see from the function signature, this function no longer returns any error at all. But it could possibly panic.

Now in scripts, for things like memory allocations that can fail, I actually think it's reasonable to mark allocation failures as unreachable.

But I took it a bit further. Using @panic or unreachable in general failure conditions.

fn run(alloc: std.mem.Allocator, cmds: []const u8) void {
  var res = try std.ChildProcess.exec(.{
    .allocator = self.allocator,
    .argv = cmd,
  });
  switch (res.term) {
    .Exited => |code| {
      if (code != 0) {
        @panic("Expected command to succeed.");
      }
    },

    else => unreachable,
  }
}

Handling panics

But there are some things that will fail quite frequently (like running subprocesses or interacting with the filesystem in general).

Panicing (like what happens if @panic() ~~or `unreachable`~~ is hit) in these situations is all good until you have things that you want to get cleaned up.

My coworker points out I'm wrongly conflating unreachable and @panic() since depending on the release mode, hitting unreachable is actually undefined behavior whereas @panic() is always a panic.

Panics don't trigger defer or errdefer statements. So if you have a script that starts a background process or creates a temporary directory, and if you panic in that script, the script won't be able to run defer steps to stop the background process or delete the temporary directory.

There are panic handlers in Zig (not yet documented, Ctrl-f for "TODO: pub fn panic" in the Zig docs. But I'd just be getting further from what seems sensible if I went in that direction.

Zig errors

So I stopped panic-ing everywhere and switched to using real Zig errors, like:

fn run(alloc: std.mem.Allocator, cmds: []const u8) !void {
  var res = try std.ChildProcess.exec(.{
    .allocator = self.allocator,
    .argv = cmd,
  });
  switch (res.term) {
    .Exited => |code| {
      if (code != 0) {
        std.debug.print("Expected command to succeed.\n", .{});
        return error.RunCommandFailed;
      }
    },

    else => unreachable,
  }
}

It's pretty sweet. You get to make up a new error enum wherever you'd like.

It is unfortunate you can't (currently) include a payload with the error return value. There's an active issue discussing it.

But so far I've been able to work around that, as seen in that example above, by logging before returning an error. Since most of the time the payload you want to return is detailed information to provide context.

This logging is fine in a CLI application but probably not everything you'd want in a library. I'm not sure.

And now without panics, functions that deal with error enums and try work with defer and errdefer again! Cleanup of my background processes and temporary directories happens like I want.

Handling errors with `if`

Ok so now that I'm fully bought into Zig errors there were still a few more things that tripped me up.

First is that you can handle errors a few ways. You already saw the first one with try.

  var x = try thingThatCouldFail();

This will cause the function the statement is inside to short-circuit, returning immediately, if thingThatCouldFail has an error result.

But then I wanted to retry a function that could fail in a loop after handling the error.

  var x: SomeType = somedefault;
  while (tries > 0) {
    if (thingThatCouldFail()) |good_value| {
      x = good_value;
      break;
    }

    // do something that should fix it for the next time
    tries -= 1;
  }

But that isn't a real syntax. The Zig docs show an example of how you can use if with an error function:

fn doAThing(str: []u8) void {
  if (parseU64(str, 10)) |number| {
    doSomethingWithNumber(number);
  } else |err| switch (err) {
    error.Overflow => {
      // handle overflow...
    },
    // we promise that InvalidChar won't happen (or crash in debug mode if it does)
    error.InvalidChar => unreachable,
  }
}

But I don't care about the error at this moment (maybe I should, but I don't right now).

So I tried:

  var x: SomeType = somedefault;
  while (tries > 0) {
    if (thingThatCouldFail()) |good_value| {
      x = good_value;
      break;
    } else {
      // do something that should fix it for the next time
      tries -= 1;
    }
  }

But that gives me an obscure type error.

I was stumped here for a while until I decided to try the whole syntax in that example. And it turns out that at least the capture part is necessary at the parser layer:

  var x: SomeType = somedefault;
  while (tries > 0) {
    if (thingThatCouldFail()) |good_value| {
      x = good_value;
      break;
    } else |err| switch (err) {
      else => {
        // do something that should fix it for the next time
        tries -= 1;
      },
    }
  }

And eventually I guessed an unnamed error variable might also work without the switch, and that was correct:

  var x: SomeType = somedefault;
  while (tries > 0) {
    if (thingThatCouldFail()) |good_value| {
      x = good_value;
      break;
    } else |_| {
      // do something that should fix it for the next time
      tries -= 1;
    }
  }

Nice!

`catch` blocks

One last thing that I was stumbling around with was that when you use catch with a function that returns an error or some non-void value, the catch must "return" a value of the same type as the function.

The Zig docs show a simple example:

const number = parseU64(str, 10) catch 13;

But I also use catch with blocks sometimes:

const number = parseU64(str, 10) catch {
  // do some more complex stuff, maybe log, who knows
};

But that won't compile. So the "trick" is to combine Zig's named blocks with catch.

const number = parseU64(str, 10) catch blk: {
  // do some more complex stuff, maybe log, who knows

  // and then "return" a result
  break :blk 13;
};

Contributing to Zig docs

I didn't want to write this post without offering some of my examples to the docs. While there's a dedicated effort around autodoc, the tool that builds docs for the standard library, I haven't yet stumbled on docs for contributing the main Zig docs.

So I grepped in the Zig repo git grep 'Blocks are expressions.', a phrase that showed up in the HTML docs, and found doc/langref.html.in.

Then someone on the Zig Programming Language Discord pointed me at running zig build docs in the repo root to generate the HTML.

And now I've got a PR up! We'll see what folks think.

I wrote a new post about error-handling and Zig, as I've been doing a bunch of scripting with Zig recently.

I stumbled a few times so maybe that will be useful to you. And I was able to turn parts of my stumbling into a potential PR to the Zig docs. 🎉https://t.co/00RVWpodmd pic.twitter.com/wENSEpj63A
— Phil Eaton (@eatonphil) March 22, 2023

Notes from Neal Gabler's Walt Disney

Sat, 18 Feb 2023 00:00:00 +0000

Disney was a celebrity by his mid-30s, Disney the company was famous by 1930s.

Even though politically the 1930s was considered the decade of Roosevelt (elected President in 1933), culturally the 1930s was considered the decade of Mickey Mouse.

Almost every new animation/filmmaking technique they tried, they would experiment with it in shorts (Silly Symphonies) before applying to big films like Snow White. Examples of this include:

Multiple layers of animation moving independently to create depth in The Old Mill
The first Disney animations with humans (not flora/fauna) like The Cookie Carnival

Nobody took animation seriously, didn't think there was much possibility for it in film. Disney kept pushing the envelope. Some examples include:

Not including the hand inside the drawing (though early Disney ones did)
Eventually focusing on actual stories, not just gags/jokes
Sound (the famous Mickey Steamboat Willie animation, read more)
Merchandise, not just the art
Feature films (i.e. Snow White in 1937, the first animated feature film), not just shorts
Brought Hollywood to Television
- "Walt Disney signed an exclusive long-term contract today with the American Broadcasting Company to become the first leading Hollywood producer to enter into formal alliance with television. NY Times
Added color to TV shows

Snow White

Disney hired fine arts teachers to come and teach employees. From time to time he forced the artists to take night classes.

They trained for years(?) before starting the animation of Snow White and did almost all the animation in the last 10 months or so before the release in December 1937.

They had to do 24-hour animation in 8 hour shifts to get up to speed. They had to hire 100s of animators to do fill in work so the “master” animators could focus on “drawing the extremes”.

The average age at Disney was 25. These days of the 1930s really felt quite similar to what a Silicon Valley startup is thought to be.

Disney preferred to hire recent art school students so they could train them in the Disney style.

They could not animate humans during Snow White well enough so they ended up just tracing them, called rotoscoping.

The Snow White voice cast were quite famous at the time. We wouldn't know it now but it was basically an ensemble cast.

World War 2

Ran low on money so they produced films for the US Government. Propaganda, basically. But also instructional videos.

Disney workers began striking (1941) and established unions. If Disney was a dick before this, he became a much bigger dick after this.

Post War

Got into television with ABC initially. First Hollywood company to do so. Arrangement with ABC was in part to finance Disneyland. (Not covered in the book but Disney eventually took over ABC, not before eventually splitting ABC and working with NBC though.)

Disney stopped caring about films and moved to mostly thinking about Disneyland, this under WED (what is now Walt Disney Imagineering).

After Disneyland launched he moved on to world fairs and eventually Disneyworld. He died of lung cancer before completing Disneyworld.

Tidbits

The Reluctant Dragon, a throwaway film because they needed money when they went public. It is the story of a children's book author trying to get Disney to make a film out of his book. He stumbles around the new Disney Burbank Studio through art classes and musicians practicing, uncovering how Disney films are made in the process.

Questions

What were the other major animation studies? Even if Snow White was the first animated feature film, surely others must have rushed to copy the success. Who were they?
- UPA (Mr Magoo) was one. Also Warner Brothers

Conclusion

Basically after every turn he'd get tired of the stuff he had already done (and killed at doing) to do something new. From animated shorts to feature films to television to Disneyland to Disneyworld and EPCOT.

To his employees he was a huge dick. They'd be in constant fear of upsetting him and getting fired. And he admitted that he would basically fire people randomly. He'd fire anyone important enough to get their name on a door (i.e. establish their own fiefdom within the company). But it seems more like Disney the company worked in spite of this rather than because of this.

After Mary Poppins (1964, two years before he died): "I'm on the spot. I have to keep trying to keep up to that same level. And the way to do it is not to worry, not to get tense. Not to think, 'I got to beat Mary Poppins', 'I got to beat Mary Poppins'. The way to do it is just to go off and get interested in some little thing, some little idea that interests me. Some little idea that looks like fun."

Finished Neal Gabler's Walt Disney (5/5) and here are my raw notes. (If I had to polish the notes I wouldn't have the will to publish.) Hopefully a few interesting bits and links in there though.

In particularly this quote (2nd pic) really struck me.https://t.co/P9astFZ6Ts pic.twitter.com/wKPd6zjLau
— Phil Eaton (@eatonphil) February 18, 2023

Lessons learned streaming building a Scheme-like interpreter in Go

Mon, 30 Jan 2023 00:00:00 +0000

I wanted to practice making coding videos so I did a four-part series on writing a basic Scheme-like language (minus macros and arrays and tons of stuff).

I picked this simple topic because I wanted a low-stakes way to learn what I did not know about making videos.

Here was the end result (nothing crazy):

$ go build
$ cat examples/fib.scm
(func fib (a)
      (if (< a 2)
          a
          (+ (fib (- a 1)) (fib (- a 2)))))

(fib 11)
$ ./livescheme examples/fib.scm
89

The code for the project is here.

Video archives

Here are the four episodes! Each about an hour long. One per week for four weeks.

Live live

The videos were streamed to Twitch live.

I didn't prep for them because I wanted to show warts and all. The thought process.

But some things turned out to be tricky to explain without preparation (function calling conventions, mostly).

Overall hopefully the series was somewhat useful.

Full screen windows

The first episode I did I didn't make sure that the terminal window was captured full screen. So some of my code went off the bottom of the video. That was dumb.

I even have a tmux mode-line at the bottom of the terminal app that I could have looked for to notice it didn't exist in the OBS view.

So I made sure to have the full window in view after the first episode.

Twitch moderation

Twitch Shield Mode is great. But the default setting prevents folks from commenting live until they've followed you for 2 weeks or something.

For someone starting a channel that doesn't make much sense. So in my first video I disabled it so folks could chat. And then some crypto scammer came in. Go figure.

After the first video I turned Shield Mode back on but set the minimum follow time to 10 minutes I think.

OBS Studio

I used OBS Studio to record. I was frustrated with it for a while because the video would lag so much when I tested out streaming. After playing around with Twitch Studio and giving up on it for being too simple, I messed with OBS video settings enough to get my video to not lag. Unfortunately I can't remember what settings I used.

Noise Gate / pop filter

The Noise Gate Filter is awesome. My mechanical keyboard sounded obnoxious before I turned it on. I was considering getting a pop filter but then discovered that the Noise Gate Filter is built in, you just have to turn it on.

Scenes

It also took me a while to understand OBS Scenes but then I realized I can use them to have an intro graphic (without the mic on!), a main coding scene (focused on my terminal and with my webcam overlayed), and a "back soon" graphic if I needed it.

To get the mic off you have to disable the mic globally (it's on globally by default) and then add it as an input only to the scenes you want.

Storage and export to YouTube

Twitch doesn't store streams by default. You have to turn on Video on Demand.

Even when it's turned on the videos only seem to be stored for 1 week. Maybe that's configurable but I didn't see it.

In any case it's not a problem because you can set up a YouTube connection. Then after a stream is complete you find the stream video and click Export. It takes about a minute to upload the hour long videos I did. Though YouTube post-processing took a while longer after that.

Next?

I'm forced to take a break from recording these videos for the next two weeks since I'll be in Cape Town.

I haven't decided yet if I'll continue this series (not something I'm extremely excited about since everyone builds a Scheme-like language).

I'd like to have a project that I can keep contributing to over time but I don't see very much value in doing that based on a Scheme or any lisp-like.

Maybe I'll do a basic JavaScript implementation next. Or another basic SQL database. Dunno.

Now that I'm done that series on the Scheme-like interpreter in Go (at least for a few weeks), I wrote down a few thoughts about the experience and the Twitch and OBS Studio setup.

Up next after Cape Town? Not totally sure yet!https://t.co/bgdO1ZI5Ow pic.twitter.com/E1kwMRcCWY
— Phil Eaton (@eatonphil) January 31, 2023

An effective product manager

Mon, 23 Jan 2023 00:00:00 +0000

There are three specific activities I have loved in some product managers I've worked with (and missed in others).

tldr;

Talk with customers and prospects
Develop and share a vision
Evangelize

Talk with customers and prospects

As a product manager, your superpower over engineering is to have spent time with customers and prospects. You should have (or develop) a good understanding of the market and your product's potential.

The only way you can do this is by spending time, over time, with customers and prospects. Understanding their workflows and their issues.

Cynical folks will cringe at the word "vision" but it is a serious and necessary part of a successful organization.

As a product manager, you should establish and share a path for engineering to follow based on your understanding of customers, prospects, the market, and the company.

This is the "roadmap" and "prioritization". But prioritization is useless without a long-term vision.

The roadmap should represent (and broadly demonstrate) a concrete and meaningful goal. A goal that you can and should adjust over time as the company and market changes.

Evangelize

In bigger organizations there might be dedicated evangelism teams. But product managers must drive this work.

Evangelism should fit the vision you've developed.

And in the absense of dedicated evangelism teams, product managers should be creating demos, writing blog posts, and testing the solution with customers and prospects.

Again, it's fine for dedicated teams outside of product management to do bits of that work. But it must be driven and led by the product manager.

It's hard

Observed as I have from outside, being an effective product manager feels like a massively challenging task.

It's so easy to go without talking to customers, to get sucked into day-to-day issues and not create a vision, and to allow evangelism to happen ad-hoc.

Then there's the fact you don't live in a vacuum. You may have a boss in product management. Your engineering peers may have competing priorities. You may have a hard time understanding the founders or CEO. In a large company, you may not even have a CEO.

My ideas, your ideas

These are my ideas based on my experience. You may have your own ideas. If mine help you, great! If they don't, great!

I've been considering recently what makes an effective product manager. So I wrote down a few of my thoughts.

What I've loved the most in some PMs and missed the most in others.

I'd likewise love to hear what you think!https://t.co/5vTWTNhs68 pic.twitter.com/vXjPY9fiVT
— Phil Eaton (@eatonphil) January 23, 2023

The year in books: 2022

Thu, 12 Jan 2023 00:00:00 +0000

In 2022 I finished 20 books spanning 15,801 pages. 3 more than I read in 2021, but about twice the number of pages. 3 fiction and 17 non-fiction. Another ~30 started but not finished.

I had a hard time reading books while I was trying to start my own company. But I also discovered audiobooks. I would put on a book and listen while I did my chores. Only 5 of the 20 books I finished were physical (or kindle) books. The other 15 were audiobooks.

After I started read Robert Caro's Master of the Senate I got hooked on history and felt less daunted about larger books.

The only non-fiction I read in 2022 was US and UK history.

Here were my favorites:

Master of the Senate by Robert Caro: Covering more than just Lyndon B. Johnson but the history of the Senate and the Civil Rights movements in the US. This book is now on my list of best books.
The Last Lion: Winston Spencer Churchill: Visions of Glory, 1874-1932 by William Manchester: First in a three-volume series about Churchill. He's an especially interesting guy to read about because he served in UK politics 1901 to his retirement (for the second time) as UK Prime Minister in 1955. He was First Lord of the Admiralty in World War 1 before he, more famously, become Prime Minister during World War 2. This entire series is on my list of best books.
The Autobiography of Martin Luther King, Jr.: Sad and revealing. Though it doesn't talk much about his legacy since it only includes his writings.
Passage of Power by Robert Caro: Covering LBJ's pathetic failed attempts at the presidency before becoming JFK's Vice President, up to JFK's assassination. Still a very good book. I can't wait for Caro's final book to come out.
Truman by David McCullough: I always thought Truman was a lame nerd but he actually had a very interesting life (and as I'd later discover, is far from the lamest president. Wilson hands down takes that place.) And unlike most other famous politicians I read about, he had a great relationship with his wife. He was honest and respectable and was the first US president to normalize relations with Mexico since the Mexican-American War (that U.S. Grant and Robert E. Lee fought in the 1840s).
The Last Lion: Winston Spencer Churchill: Alone, 1932-40 by William Manchester: The second book in the series. Pretty depressing because it's a decade of Churchill noticing Nazi German behavior and stressing UK preparedness and the UK ignoring him and Nazi Germany.
The Last Lion: Winston Spencer Churchill: Defender of the Realm, 1940-1965 by William Manchester: The final book in the series, covering his Prime Ministership.
Eleanor Roosevelt, Volume 1: The Early Years, 1884-1933 by Blanche Wiesen Cook: Her background and many problems, as the daughter of Theodore Roosevelt's brother and later husband of their distant cousin, is pretty hard to relate to. Still it was quite interesting to hear about her life and early activities how she became such an outspoken progressive activist from being quite conservative.
Abraham Lincoln: A Life, Volume One by Michael Burlingame
Abraham Lincoln: A Life, Volume Two by Michael Burlingame
Grant by Ron Chernow: Among famous generals of the Civil War, somehow Robert E. Lee and Stonewall Jackson came to mind to me more readily than Grant. I'm glad I read this book because the popularity of Southern generals today seems like revisionism. This book makes strong arguments that while Lee was a great officer, he could only think in terms of short-term tactics and the Virginia region. Whereas Grant was the first (US, anyway) officer to consider and command (via telegraph) all theaters of war at once, every day. And this book redeems his presidency somewhat. His progressive adoption of freed Black people and work to make them equal citizens is highly commendable. Even with the horror of what happened in the South after the war ended.
The Rise of Theodore Roosevelt by Edmund Morris: First in a three-volume series about the 26th President. I read somewhere that it can feel impossible to read a bad biography of Roosevelt because he was such an interesting human. That may be true. This book didn't disappoint. Roosevelt growing up in a townhouse in Manhattan, going to Harvard, buying a farm on Long Island is all hard to relate to. His Puritanical morals and machismo were also difficult to get past. But he was a very interesting guy.
Theodore Rex by Edmund Morris: Second in the series, covering the entirety of Roosevelt's presidency. Like the first volume, a great read. I always used to think Roosevelt was a pure war-monger. But he helped avert war with the UK and Germany over Venezuelan debt-default. And he later received the Nobel Peace Prize for mediating peace between Japan and Russia in 1905.

Of the three I read last year, I really enjoyed one:

The Leopard by Giuseppe Tomasi di Lampedusa: A gentle piece of historical fiction set during the 1860s in Sicily during and after the unification of Italy. I learned about this book from a Rick Stein episode in the Mediterranean Escapes series.

Favorite compiler and interpreter resources

Thu, 05 Jan 2023 00:00:00 +0000

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General book recommendations

Wed, 04 Jan 2023 00:00:00 +0000

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In response to a frontend developer asking about database development

Sun, 01 Jan 2023 00:00:00 +0000

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Is it worth writing about?

Thu, 01 Dec 2022 00:00:00 +0000

You acquire a skill or experience through time and effort, then downplay the impact of writing and sharing the learning process.

Professionals seem naturally to imagine a high bar for what is worth writing about.

I think that's misguided. This article is not criticism of folks with these beliefs, but rather encouragement for folks looking for a reason to write.

There are (at least) a few concrete reasons to write about what you've learned, even when you don't think it's novel.

To practice writing

This is the easiest reason. While practice does not imply improvement, you cannot improve without practice.

Every time you learn something is a chance to write down both what you've learned and also how you learned it.

For professional developers this chance happens all the time. Daily, really. But most developers, even those who want to write more, let the opportunity slip.

Providing variety

When I learn a topic I normally go through dozens of posts, papers, docs, videos or books to find a version that clicks. If I can. I prefer to start with blog posts and often there are not blog posts on the subject. Books, docs, videos, and academic papers aren't often as accessible.

Even if you're writing about a popular topic, there's still a chance your post gets through to someone in a way other posts do not.

For programmers there are notorious topics you can avoid if you'd like ("What is a monad", "Why is lisp interesting", "Kubernetes sucks"). Or not. I've fallen into those traps.

Additionally, as you gain experience as a programmer (or product manager, or whatever), your perspective and approach becomes both more interesting and more valuable.

I don't recall ever thinking: "I wish they'd write less". But I'm always wishing some folks wrote more, or at all.

Some folks with experience, writing about widely varied topics in software include:

But experience need not be a prerequisite. Experts (who don't practice explaining) easily forget how they came to their current understanding. A beginner's experience is valuable for everyone who is not a beginner, sometimes also for beginners.

To cement understanding

Finally, honest writing forces you to either understand the dark corners of what you've learned or to ask for help in these dark corners.

I have repeatedly wrestled with topics in software only to be further forced to explain why (or how) when I write.

And it has often forced me to restructure code or ideas in ways that are easier to explain. I think that's a pretty valuable act for the long-term.

Bad faith

There's a bad faith argument that you sometimes see. Here's a variation that comes to mind.

The internet is already full of crap. People who aren't experts are just making it worse.

I hope you ignore these comments. :) If there's a quality problem that is genuinely causing harm, that's for search engines and trade organizations to deal with.

In the extreme

Simon Willison's TIL site is the most prolific version of this I've ever seen. I don't know if I personally aspire to Simon's level, but I think it's worth seeing.

Topics

Some topics I think are always worth writing about and sharing:

Your process, failures and successes, to figuring something out
How to hack on some major open source project
In-depth comparison of projects or approaches, down to source code, benchmarks, and architecture when relevant
Building minimal versions of some production system
How some major systems works under the hood, down to the code
Mistakes you made in structuring organizations, or production architecture, or testing, etc.
How to get the dang configuration right for testing Electron apps in Github Actions

For programming posts specifically: I strongly encourage you to include or walk through working code. Have tests. And have the code build process hooked up to GitHub Actions or SourceHut CI or whatever. This helps ensure your work is still relevant over time.

When you write

Write to explain and teach. When you don't understand something, call out that you don't understand it. That's not a bad thing, and the internet is normally happy to help.

Don't shy away from showing code, showing things that broke, showing the ugly process. It's encouraging for others to see.

End goal

Well, ideally we have fewer clickbait "5 best React alternatives" articles and more thoughtful pieces intended to teach and educate with a bit of rigor.

It's better for individuals and for companies. It's better for the internet.

Community

If you want a community of folks where you can find encouragement to write and eyes to review drafts, check out the #writing-and-drafts channel on the Software Internals Discord.

Is it worth writing about?

Well if you come to me I'm almost surely going to say yes. Poor Betteridge.

Wrote a short post as a bit of encouragement to folks who want to write more but imagine a high bar for what's worthwhile.

tldr; if you ask me it's almost always going to be a yes. And I think there's a path toward a higher-quality internet.https://t.co/Nn6BvXhNdZ pic.twitter.com/KELvsxnr2w
— Phil Eaton (@phil_eaton) December 1, 2022

A Programmer-Friendly I/O Abstraction Over io_uring and kqueue

Wed, 23 Nov 2022 00:00:00 +0000

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Writing a SQL database, take two: Zig and RocksDB

Sun, 13 Nov 2022 00:00:00 +0000

For my second project while learning Zig, I decided to port an old, minimal SQL database project from Go to Zig.

In this post, in ~1700 lines of code (yes, I'm sorry it's bigger than my usual), we'll create a basic embedded SQL database in Zig on top of RocksDB. Other than the RocksDB layer it will not use third-party libraries.

The code for this project is available on GitHub.

Here are a few example interactions we'll support:

$ ./main --database data --script <(echo "CREATE TABLE y (year int, age int, name text)")
echo "CREATE TABLE y (year int, age int, name text)"
ok
$ ./main --database data --script <(echo "INSERT INTO y VALUES (2010, 38, 'Gary')")
echo "INSERT INTO y VALUES (2010, 38, 'Gary')"
ok
$ ./main --database data --script <(echo "INSERT INTO y VALUES (2021, 92, 'Teej')")
echo "INSERT INTO y VALUES (2021, 92, 'Teej')"
ok
$ ./main --database data --script <(echo "INSERT INTO y VALUES (1994, 18, 'Mel')")
echo "INSERT INTO y VALUES (1994, 18, 'Mel')"
ok

# Basic query
$ ./main --database data --script <(echo "SELECT name, age, year FROM y")
echo "SELECT name, age, year FROM y"
| name          |age            |year           |
+ ====          +===            +====           +
| Mel           |18             |1994           |
| Gary          |38             |2010           |
| Teej          |92             |2021           |

# With WHERE
$ ./main --database data --script <(echo "SELECT name, year, age FROM y WHERE age < 40")
echo "SELECT name, year, age FROM y WHERE age < 40"
| name          |year           |age            |
+ ====          +====           +===            +
| Mel           |1994           |18             |
| Gary          |2010           |38             |

# With operations
$ ./main --database data --script <(echo "SELECT 'Name: ' || name, year + 30, age FROM y WHERE age < 40")
echo "SELECT 'Name: ' || name, year + 30, age FROM y WHERE age < 40"
| unknown               |unknown                |age            |
+ =======               +=======                +===            +
| Name: Mel             |2024           |18             |
| Name: Gary            |2040           |38             |

This post is standalone (except for the RocksDB layer which I wrote about here) but it builds on a number of ideas I've explored that you may be interested in:

This project is mostly a port of my SQL database from scratch in Go project, but unlike that series this project will have persistent storage via RocksDB.

And unlike that post, this project is written in Zig!

Let's get started. :)

Components

We're going to split up the project into the following major components:

Lexing
Parsing
Storage
- RocksDB
Execution
Entrypoint (main)

Lexing takes a query and breaks it into an array of tokens.

Parsing takes the lexed array of tokens and pattern matches into a syntax tree (AST).

Storage maps high-level SQL entities like tables and rows into bytes that can be easily stored on disk. And it handles recovering high-level tables and rows from bytes on disk.

Invisible to users of the Storage component is RocksDB, which is how the bytes are actually stored on disk. RocksDB is a persistent store that maps arbitary byte keys to arbitrary byte values. We'll use it for storing and recovering both table metadata and actual row data.

Execution takes a query AST and executes it against Storage, potentially returning result rows.

These terms are a vast simplification of real-world database design. But they are helpful structure to have even in a project this small.

Memory Management

Zig doesn't have a garbage collector. Mitchell Hashimoto wrote bindings to Boehm GC. But Zig also has a builtin Arena allocator which is perfect for this simple project.

The main function will create the arena and pass it to each component, where they can do allocations as they please. At the end of main, the entire arena will be freed at once.

The only other place where we must do manual memory management is in the RocksDB wrapper. But I've already covered that in a separate post.

Zig Specifics

I'm not going to cover the basics of Zig syntax. If you are new to Zig, read this first! (It's short.)

Now that we've got the basic idea, we can start coding!

Types (`types.zig`, 10 LoC)

Let's create a few helper types that we'll use in the rest of the code.

pub const String = []const u8;

pub const Error = String;

pub fn Result(comptime T: type) type {
    return union(enum) {
        val: T,
        err: Error,
    };
}

That's it. :) Makes things a little more readable.

Lexing (`lex.zig`, 308 LoC)

Lexing turns a query string into an array of tokens.

There are a few kinds of tokens we'll define:

Keywords (like CREATE, true, false, null)
- Syntax (commas, parentheses, operators, and all other builtin symbols)
Strings
Integers
Identifiers

And not listed there but important to skip past is whitespace.

Let's turn this into a Zig struct!

const std = @import("std");

const Error = @import("types.zig").Error;
const String = @import("types.zig").String;

pub const Token = struct {
    start: u64,
    end: u64,
    kind: Kind,
    source: String,

    pub const Kind = enum {
        // Keywords
        select_keyword,
        create_table_keyword,
        insert_keyword,
        values_keyword,
        from_keyword,
        where_keyword,

        // Operators
        plus_operator,
        equal_operator,
        lt_operator,
        concat_operator,

        // Other syntax
        left_paren_syntax,
        right_paren_syntax,
        comma_syntax,

        // Literals
        identifier,
        integer,
        string,
    };

    pub fn string(self: Token) String {
        return self.source[self.start..self.end];
    }

Using an enum helps us with type safety. And since we're storing location in the token, we can build a nice debug function for when lexing or parsing fails.

    fn debug(self: Token, msg: String) void {
        var line: usize = 0;
        var column: usize = 0;
        var lineStartIndex: usize = 0;
        var lineEndIndex: usize = 0;
        var i: usize = 0;
        var source = self.source;
        while (i < source.len) {
            if (source[i] == '\n') {
                line = line + 1;
                column = 0;
                lineStartIndex = i;
            } else {
                column = column + 1;
            }

            if (i == self.start) {
                // Find the end of the line
                lineEndIndex = i;
                while (source[lineEndIndex] != '\n') {
                    lineEndIndex = lineEndIndex + 1;
                }
                break;
            }

            i = i + 1;
        }

        std.debug.print(
            "{s}\nNear line {}, column {}.\n{s}\n",
            .{ msg, line + 1, column, source[lineStartIndex..lineEndIndex] },
        );
        while (column - 1 > 0) {
            std.debug.print(" ", .{});
            column = column - 1;
        }
        std.debug.print("^ Near here\n\n", .{});
    }
};

And similarly, let's add a debug helper for when we're dealing with an array of tokens.

pub fn debug(tokens: []Token, preferredIndex: usize, msg: String) void {
    var i = preferredIndex;
    while (i >= tokens.len) {
        i = i - 1;
    }

    tokens[i].debug(msg);
}

Token <> String Mapping

Before we get too far from Token definition, let's define a mapping from the Token.kind enum to strings we can see in a query.

const Builtin = struct {
    name: String,
    kind: Token.Kind,
};

// These must be sorted by length of the name text, descending, for lexKeyword.
var BUILTINS = [_]Builtin{
    .{ .name = "CREATE TABLE", .kind = Token.Kind.create_table_keyword },
    .{ .name = "INSERT INTO", .kind = Token.Kind.insert_keyword },
    .{ .name = "SELECT", .kind = Token.Kind.select_keyword },
    .{ .name = "VALUES", .kind = Token.Kind.values_keyword },
    .{ .name = "WHERE", .kind = Token.Kind.where_keyword },
    .{ .name = "FROM", .kind = Token.Kind.from_keyword },
    .{ .name = "||", .kind = Token.Kind.concat_operator },
    .{ .name = "=", .kind = Token.Kind.equal_operator },
    .{ .name = "+", .kind = Token.Kind.plus_operator },
    .{ .name = "<", .kind = Token.Kind.lt_operator },
    .{ .name = "(", .kind = Token.Kind.left_paren_syntax },
    .{ .name = ")", .kind = Token.Kind.right_paren_syntax },
    .{ .name = ",", .kind = Token.Kind.comma_syntax },
};

We'll use this in a few lexing functions below.

Whitespace

Outside of tokens, we need to be able to skip past whitespace.

fn eatWhitespace(source: String, index: usize) usize {
    var res = index;
    while (source[res] == ' ' or
        source[res] == '\n' or
        source[res] == '\t' or
        source[res] == '\r')
    {
        res = res + 1;
        if (res == source.len) {
            break;
        }
    }

    return res;
}

All lexing functions will look like this. They'll take the source as one argument and a cursor to the current index in the source as another.

Keywords

Let's handle lexing keyword tokens next. Keywords are case insensitive. I don't think there's a builtin case insensitive string comparison function in Zig. So let's write that first.

fn asciiCaseInsensitiveEqual(left: String, right: String) bool {
    var min = left;
    if (right.len < left.len) {
        min = right;
    }

    for (min) |_, i| {
        var l = left[i];
        if (l >= 97 and l <= 122) {
            l = l - 32;
        }

        var r = right[i];
        if (r >= 97 and r <= 122) {
            r = r - 32;
        }

        if (l != r) {
            return false;
        }
    }

    return true;
}

Unfortunately it only supports ASCII for now.

Now we can write a simple longest-matching-substring function. It is simple because the keyword mapping we set up above is already ordered by length descending.

fn lexKeyword(source: String, index: usize) struct { nextPosition: usize, token: ?Token } {
    var longestLen: usize = 0;
    var kind = Token.Kind.select_keyword;
    for (BUILTINS) |builtin| {
        if (index + builtin.name.len >= source.len) {
            continue;
        }

        if (asciiCaseInsensitiveEqual(source[index .. index + builtin.name.len], builtin.name)) {
            longestLen = builtin.name.len;
            kind = builtin.kind;
            // First match is the longest match
            break;
        }
    }

    if (longestLen == 0) {
        return .{ .nextPosition = 0, .token = null };
    }

    return .{
        .nextPosition = index + longestLen,
        .token = Token{
            .source = source,
            .start = index,
            .end = index + longestLen,
            .kind = kind,
        },
    };
}

That's it!

Integers

For integers we read through the source until we stop seeing decimal digits. Obviously this is a subset of what people consider integers, but it will do for now!

fn lexInteger(source: String, index: usize) struct { nextPosition: usize, token: ?Token } {
    var start = index;
    var end = index;
    var i = index;
    while (source[i] >= '0' and source[i] <= '9') {
        end = end + 1;
        i = i + 1;
    }

    if (start == end) {
        return .{ .nextPosition = 0, .token = null };
    }

    return .{
        .nextPosition = end,
        .token = Token{
            .source = source,
            .start = start,
            .end = end,
            .kind = Token.Kind.integer,
        },
    };
}

Strings

Strings are enclosed in single quotes.

fn lexString(source: String, index: usize) struct { nextPosition: usize, token: ?Token } {
    var i = index;
    if (source[i] != '\'') {
        return .{ .nextPosition = 0, .token = null };
    }
    i = i + 1;

    var start = i;
    var end = i;
    while (source[i] != '\'') {
        end = end + 1;
        i = i + 1;
    }

    if (source[i] == '\'') {
        i = i + 1;
    }

    if (start == end) {
        return .{ .nextPosition = 0, .token = null };
    }

    return .{
        .nextPosition = i,
        .token = Token{
            .source = source,
            .start = start,
            .end = end,
            .kind = Token.Kind.string,
        },
    };
}

Identifiers

Identifiers for this project are alphanumeric characters. We could support more by optionally checking for double quote enclosed strings. But I'll leave that as an exercise for the reader.

fn lexIdentifier(source: String, index: usize) struct { nextPosition: usize, token: ?Token } {
    var start = index;
    var end = index;
    var i = index;
    while ((source[i] >= 'a' and source[i] <= 'z') or
        (source[i] >= 'A' and source[i] <= 'Z') or
        (source[i] == '*'))
    {
        end = end + 1;
        i = i + 1;
    }

    if (start == end) {
        return .{ .nextPosition = 0, .token = null };
    }

    return .{
        .nextPosition = end,
        .token = Token{
            .source = source,
            .start = start,
            .end = end,
            .kind = Token.Kind.identifier,
        },
    };
}

`lex`

Now we can pull together all these helper functions in a public entrypoint for lexing.

It will loop through a query string, eating whitespace and checking for tokens. It will continue until it hits the end of the query string. If it ever can't continue it fails.

pub fn lex(source: String, tokens: *std.ArrayList(Token)) ?Error {
    var i: usize = 0;
    while (true) {
        i = eatWhitespace(source, i);
        if (i >= source.len) {
            break;
        }

        const keywordRes = lexKeyword(source, i);
        if (keywordRes.token) |token| {
            tokens.append(token) catch return "Failed to allocate space for keyword token";
            i = keywordRes.nextPosition;
            continue;
        }

        const integerRes = lexInteger(source, i);
        if (integerRes.token) |token| {
            tokens.append(token) catch return "Failed to allocate space for integer token";
            i = integerRes.nextPosition;
            continue;
        }

        const stringRes = lexString(source, i);
        if (stringRes.token) |token| {
            tokens.append(token) catch return "Failed to allocate space for string token";
            i = stringRes.nextPosition;
            continue;
        }

        const identifierRes = lexIdentifier(source, i);
        if (identifierRes.token) |token| {
            tokens.append(token) catch return "Failed to allocate space for identifier token";
            i = identifierRes.nextPosition;
            continue;
        }

        if (tokens.items.len > 0) {
            debug(tokens.items, tokens.items.len - 1, "Last good token.\n");
        }
        return "Bad token";
    }

    return null;
}

That's it for lexing! Now we can do parsing.

Parsing (`parse.zig`, 407 LoC)

Parsing takes an array of tokens from the lexing stage and discovers the tree structure in them that maps to a predefined syntax tree (AST).

If it can't discover a valid tree from the array of tokens, it fails.

Let's set up the basics of the Parser struct:

const std = @import("std");

const lex = @import("lex.zig");
const Result = @import("types.zig").Result;

const Token = lex.Token;

pub const Parser = struct {
    allocator: std.mem.Allocator,

    pub fn init(allocator: std.mem.Allocator) Parser {
        return Parser{ .allocator = allocator };
    }

    fn expectTokenKind(tokens: []Token, index: usize, kind: Token.Kind) bool {
        if (index >= tokens.len) {
            return false;
        }

        return tokens[index].kind == kind;
    }

Expressions

Expressions are at the bottom of the syntax tree.

They can be:

Literals (like strings, integers, booleans, etc.)
Or binary operations

Let's define these in Zig:

    pub const BinaryOperationAST = struct {
        operator: Token,
        left: *ExpressionAST,
        right: *ExpressionAST,

        fn print(self: BinaryOperationAST) void {
            self.left.print();
            std.debug.print(" {s} ", .{self.operator.string()});
            self.right.print();
        }
    };

    pub const ExpressionAST = union(enum) {
        literal: Token,
        binary_operation: BinaryOperationAST,

        fn print(self: ExpressionAST) void {
            switch (self) {
                .literal => |literal| switch (literal.kind) {
                    .string => std.debug.print("'{s}'", .{literal.string()}),
                    else => std.debug.print("{s}", .{literal.string()}),
                },
                .binary_operation => self.binary_operation.print(),
            }
        }
    };

Now we can attempt to parse either of these from an array of tokens.

    fn parseExpression(self: Parser, tokens: []Token, index: usize) Result(struct {
        ast: ExpressionAST,
        nextPosition: usize,
    }) {
        var i = index;

        var e: ExpressionAST = undefined;

        if (expectTokenKind(tokens, i, Token.Kind.integer) or
                expectTokenKind(tokens, i, Token.Kind.identifier) or
                expectTokenKind(tokens, i, Token.Kind.string))
            {
                e = ExpressionAST{ .literal = tokens[i] };
                i = i + 1;
        } else {
                return .{ .err = "No expression" };
        }

        if (expectTokenKind(tokens, i, Token.Kind.equal_operator) or
                expectTokenKind(tokens, i, Token.Kind.lt_operator) or
                expectTokenKind(tokens, i, Token.Kind.plus_operator) or
                expectTokenKind(tokens, i, Token.Kind.concat_operator))
            {
                var newE = ExpressionAST{
                    .binary_operation = BinaryOperationAST{
                        .operator = tokens[i],
                        .left = self.allocator.create(ExpressionAST) catch return .{
                            .err = "Could not allocate for left expression.",
                        },
                        .right = self.allocator.create(ExpressionAST) catch return .{
                            .err = "Could not allocate for right expression.",
                        },
                    },
                };
                newE.binary_operation.left.* = e;
                e = newE;

                switch (self.parseExpression(tokens, i + 1)) {
                    .err => |err| return .{ .err = err },
                    .val => |val| {
                        e.binary_operation.right.* = val.ast;
                        i = val.nextPosition;
                    },
                }
        }

        return .{ .val = .{ .ast = e, .nextPosition = i } };
    }

Basically, we assume it's a literal expression unless we see an operator after it. If there's an operator after it we call parseExpression recursively and return a binary expression.

Important to note: this skips both implicit operator precedence and explicit precedence via parenthesis.

`SELECT`

A SELECT query's structure has a FROM table name, a comma-separated list of expressions, and an optional WHERE section with another expression for the where.

    pub const SelectAST = struct {
        columns: []ExpressionAST,
        from: Token,
        where: ?ExpressionAST,

        fn print(self: SelectAST) void {
            std.debug.print("SELECT\n", .{});
            for (self.columns) |column, i| {
                std.debug.print("  ", .{});
                column.print();
                if (i < self.columns.len - 1) {
                    std.debug.print(",", .{});
                }
                std.debug.print("\n", .{});
            }
            std.debug.print("FROM\n  {s}", .{self.from.string()});

            if (self.where) |where| {
                std.debug.print("\nWHERE\n  ", .{});
                where.print();
            }

            std.debug.print("\n", .{});
        }
    };

To parse it we look for:

SELECT
Then a comma separated list of ExpressionASTs
Then a FROM
Then optionally a WHERE
- And then another ExpressionAST

With the help of expectTokenKind and parseExpression it is not too difficult, but a little verbose, to write.

    fn parseSelect(self: Parser, tokens: []Token) Result(AST) {
        var i: usize = 0;
        if (!expectTokenKind(tokens, i, Token.Kind.select_keyword)) {
            return .{ .err = "Expected SELECT keyword" };
        }
        i = i + 1;

        var columns = std.ArrayList(ExpressionAST).init(self.allocator);
        var select = SelectAST{
            .columns = undefined,
            .from = undefined,
            .where = null,
        };

        // Parse columns
        while (!expectTokenKind(tokens, i, Token.Kind.from_keyword)) {
            if (columns.items.len > 0) {
                if (!expectTokenKind(tokens, i, Token.Kind.comma_syntax)) {
                    lex.debug(tokens, i, "Expected comma.\n");
                    return .{ .err = "Expected comma." };
                }

                i = i + 1;
            }

            switch (self.parseExpression(tokens, i)) {
                .err => |err| return .{ .err = err },
                .val => |val| {
                    i = val.nextPosition;

                    columns.append(val.ast) catch return .{
                        .err = "Could not allocate for token.",
                    };
                },
            }
        }

        if (!expectTokenKind(tokens, i, Token.Kind.from_keyword)) {
            lex.debug(tokens, i, "Expected FROM keyword after this.\n");
            return .{ .err = "Expected FROM keyword" };
        }
        i = i + 1;

        if (!expectTokenKind(tokens, i, Token.Kind.identifier)) {
            lex.debug(tokens, i, "Expected FROM table name after this.\n");
            return .{ .err = "Expected FROM keyword" };
        }
        select.from = tokens[i];
        i = i + 1;

        if (expectTokenKind(tokens, i, Token.Kind.where_keyword)) {
            // i + 1, skip past the where
            switch (self.parseExpression(tokens, i + 1)) {
                .err => |err| return .{ .err = err },
                .val => |val| {
                    select.where = val.ast;
                    i = val.nextPosition;
                },
            }
        }

        if (i < tokens.len) {
            lex.debug(tokens, i, "Unexpected token.");
            return .{ .err = "Did not complete parsing SELECT" };
        }

        select.columns = columns.items;
        return .{ .val = AST{ .select = select } };
    }

That's it!

`CREATE TABLE`

A CREATE TABLE query's structure has a table name and a list of comma separated identifier pairs for column name and kind.

    const CreateTableColumnAST = struct {
        name: Token,
        kind: Token,
    };

    pub const CreateTableAST = struct {
        table: Token,
        columns: []CreateTableColumnAST,

        fn print(self: CreateTableAST) void {
            std.debug.print("CREATE TABLE {s} (\n", .{self.table.string()});
            for (self.columns) |column, i| {
                std.debug.print(
                    "  {s} {s}",
                    .{ column.name.string(), column.kind.string() },
                );
                if (i < self.columns.len - 1) {
                    std.debug.print(",", .{});
                }
                std.debug.print("\n", .{});
            }
            std.debug.print(")\n", .{});
        }
    };

To parse it we look for:

CREATE TABLE
Followed by an identifier (the table name)
Followed by open parenthesis
Followed by a comma separated list of identifier pairs
Followed by close parenthesis

    fn parseCreateTable(self: Parser, tokens: []Token) Result(AST) {
        var i: usize = 0;
        if (!expectTokenKind(tokens, i, Token.Kind.create_table_keyword)) {
            return .{ .err = "Expected CREATE TABLE keyword" };
        }
        i = i + 1;

        if (!expectTokenKind(tokens, i, Token.Kind.identifier)) {
            lex.debug(tokens, i, "Expected table name after CREATE TABLE keyword.\n");
            return .{ .err = "Expected CREATE TABLE name" };
        }

        var columns = std.ArrayList(CreateTableColumnAST).init(self.allocator);
        var create_table = CreateTableAST{
            .columns = undefined,
            .table = tokens[i],
        };
        i = i + 1;

        if (!expectTokenKind(tokens, i, Token.Kind.left_paren_syntax)) {
            lex.debug(tokens, i, "Expected opening paren after CREATE TABLE name.\n");
            return .{ .err = "Expected opening paren" };
        }
        i = i + 1;

        while (!expectTokenKind(tokens, i, Token.Kind.right_paren_syntax)) {
            if (columns.items.len > 0) {
                if (!expectTokenKind(tokens, i, Token.Kind.comma_syntax)) {
                    lex.debug(tokens, i, "Expected comma.\n");
                    return .{ .err = "Expected comma." };
                }

                i = i + 1;
            }

            var column = CreateTableColumnAST{ .name = undefined, .kind = undefined };
            if (!expectTokenKind(tokens, i, Token.Kind.identifier)) {
                lex.debug(tokens, i, "Expected column name after comma.\n");
                return .{ .err = "Expected identifier." };
            }

            column.name = tokens[i];
            i = i + 1;

            if (!expectTokenKind(tokens, i, Token.Kind.identifier)) {
                lex.debug(tokens, i, "Expected column type after column name.\n");
                return .{ .err = "Expected identifier." };
            }

            column.kind = tokens[i];
            i = i + 1;

            columns.append(column) catch return .{
                .err = "Could not allocate for column.",
            };
        }

        // Skip past final paren.
        i = i + 1;

        if (i < tokens.len) {
            lex.debug(tokens, i, "Unexpected token.");
            return .{ .err = "Did not complete parsing CREATE TABLE" };
        }

        create_table.columns = columns.items;
        return .{ .val = AST{ .create_table = create_table } };
    }

`INSERT INTO`

And last we've got INSERT INTO. This tree has table name and a list of expressions to insert into the table.

    pub const InsertAST = struct {
        table: Token,
        values: []ExpressionAST,

        fn print(self: InsertAST) void {
            std.debug.print("INSERT INTO {s} VALUES (", .{self.table.string()});
            for (self.values) |value, i| {
                value.print();
                if (i < self.values.len - 1) {
                    std.debug.print(", ", .{});
                }
            }
            std.debug.print(")\n", .{});
        }
    };

We parse it by looking for:

INSERT INTO
Followed by a table name
Followed by VALUES
Followed by open parenthesis
Followed by a comma-separated list of expressions
Followed by a close parenthesis

    fn parseInsert(self: Parser, tokens: []Token) Result(AST) {
        var i: usize = 0;
        if (!expectTokenKind(tokens, i, Token.Kind.insert_keyword)) {
            return .{ .err = "Expected INSERT INTO keyword" };
        }
        i = i + 1;

        if (!expectTokenKind(tokens, i, Token.Kind.identifier)) {
            lex.debug(tokens, i, "Expected table name after INSERT INTO keyword.\n");
            return .{ .err = "Expected INSERT INTO table name" };
        }

        var values = std.ArrayList(ExpressionAST).init(self.allocator);
        var insert = InsertAST{
            .values = undefined,
            .table = tokens[i],
        };
        i = i + 1;

        if (!expectTokenKind(tokens, i, Token.Kind.values_keyword)) {
            lex.debug(tokens, i, "Expected VALUES keyword.\n");
            return .{ .err = "Expected VALUES keyword" };
        }
        i = i + 1;

        if (!expectTokenKind(tokens, i, Token.Kind.left_paren_syntax)) {
            lex.debug(tokens, i, "Expected opening paren after CREATE TABLE name.\n");
            return .{ .err = "Expected opening paren" };
        }
        i = i + 1;

        while (!expectTokenKind(tokens, i, Token.Kind.right_paren_syntax)) {
            if (values.items.len > 0) {
                if (!expectTokenKind(tokens, i, Token.Kind.comma_syntax)) {
                    lex.debug(tokens, i, "Expected comma.\n");
                    return .{ .err = "Expected comma." };
                }

                i = i + 1;
            }

            switch (self.parseExpression(tokens, i)) {
                .err => |err| return .{ .err = err },
                .val => |val| {
                    values.append(val.ast) catch return .{
                        .err = "Could not allocate for expression.",
                    };
                    i = val.nextPosition;
                },
            }
        }

        // Skip past final paren.
        i = i + 1;

        if (i < tokens.len) {
            lex.debug(tokens, i, "Unexpected token.");
            return .{ .err = "Did not complete parsing INSERT INTO" };
        }

        insert.values = values.items;
        return .{ .val = AST{ .insert = insert } };
    }

`AST`

Finally we can define the top-level SQL AST as being the union of the above three query types.

    pub const AST = union(enum) {
        select: SelectAST,
        insert: InsertAST,
        create_table: CreateTableAST,

        pub fn print(self: AST) void {
            switch (self) {
                .select => |select| select.print(),
                .insert => |insert| insert.print(),
                .create_table => |create_table| create_table.print(),
            }
        }
    };

And we can implement parse by switching on the current token.

    pub fn parse(self: Parser, tokens: []Token) Result(AST) {
        if (expectTokenKind(tokens, 0, Token.Kind.select_keyword)) {
            return switch (self.parseSelect(tokens)) {
                .err => |err| .{ .err = err },
                .val => |val| .{ .val = val },
            };
        }

        if (expectTokenKind(tokens, 0, Token.Kind.create_table_keyword)) {
            return switch (self.parseCreateTable(tokens)) {
                .err => |err| .{ .err = err },
                .val => |val| .{ .val = val },
            };
        }

        if (expectTokenKind(tokens, 0, Token.Kind.insert_keyword)) {
            return switch (self.parseInsert(tokens)) {
                .err => |err| .{ .err = err },
                .val => |val| .{ .val = val },
            };
        }

        return .{ .err = "Unknown statement" };
    }
};

Perfect. For today. :)

Storage (`storage.zig`, 338 LoC)

Next we're going to switch contexts completely and think about how tables and rows will get serialized into bytes that can be stored on disk.

The storage layer will define a few general helpers for correctly serializing and deserializing strings and numbers:

const std = @import("std");

const RocksDB = @import("rocksdb.zig").RocksDB;
const Error = @import("types.zig").Error;
const Result = @import("types.zig").Result;
const String = @import("types.zig").String;

pub fn serializeInteger(comptime T: type, buf: *std.ArrayList(u8), i: T) !void {
    var length: [@sizeOf(T)]u8 = undefined;
    std.mem.writeIntBig(T, &length, i);
    try buf.appendSlice(length[0..8]);
}

pub fn deserializeInteger(comptime T: type, buf: String) T {
    return std.mem.readIntBig(T, buf[0..@sizeOf(T)]);
}

pub fn serializeBytes(buf: *std.ArrayList(u8), bytes: String) !void {
    try serializeInteger(u64, buf, bytes.len);
    try buf.appendSlice(bytes);
}

pub fn deserializeBytes(bytes: String) struct {
    offset: usize,
    bytes: String,
} {
    var length = deserializeInteger(u64, bytes);
    var offset = length + 8;
    return .{ .offset = offset, .bytes = bytes[8..offset] };
}

Then we'll define the Storage struct itself. Under the hood it will use RocksDB to store and recover data on disk.

pub const Storage = struct {
    db: RocksDB,
    allocator: std.mem.Allocator,

    pub fn init(allocator: std.mem.Allocator, db: RocksDB) Storage {
        return Storage{
            .db = db,
            .allocator = allocator,
        };
    }

Now let's think about storage entities.

Values

The fundamental unit in the database is a value, or cell. It can be either a boolean, an integer, a string, or null.

    pub const Value = union(enum) {
        bool_value: bool,
        null_value: bool,
        string_value: String,
        integer_value: i64,

        pub const TRUE = Value{ .bool_value = true };
        pub const FALSE = Value{ .bool_value = false };
        pub const NULL = Value{ .null_value = true };

Since all values are strings in the original query, we'll provide a fromIntegerString that we can use to convert.

        pub fn fromIntegerString(iBytes: String) Value {
            const i = std.fmt.parseInt(i64, iBytes, 10) catch return Value{
                .integer_value = 0,
            };
            return Value{ .integer_value = i };
        }

Next we'll define functions to cast values to boolean.

        pub fn asBool(self: Value) bool {
            return switch (self) {
                .null_value => false,
                .bool_value => |value| value,
                .string_value => |value| value.len > 0,
                .integer_value => |value| value != 0,
            };
        }

To strings.

        pub fn asString(self: Value, buf: *std.ArrayList(u8)) !void {
            try switch (self) {
                .null_value => _ = 1, // Do nothing
                .bool_value => |value| buf.appendSlice(if (value) "true" else "false"),
                .string_value => |value| buf.appendSlice(value),
                .integer_value => |value| buf.writer().print("{d}", .{value}),
            };
        }

And to integers.

        pub fn asInteger(self: Value) i64 {
            return switch (self) {
                .null_value => 0,
                .bool_value => |value| if (value) 1 else 0,
                .string_value => |value| fromIntegerString(value).integer_value,
                .integer_value => |value| value,
            };
        }

And finally the storage layer's core concern: serialization...

        pub fn serialize(self: Value, buf: *std.ArrayList(u8)) String {
            switch (self) {
                .null_value => buf.append('0') catch return "",

                .bool_value => |value| {
                    buf.append('1') catch return "";
                    buf.append(if (value) '1' else '0') catch return "";
                },

                .string_value => |value| {
                    buf.append('2') catch return "";
                    buf.appendSlice(value) catch return "";
                },

                .integer_value => |value| {
                    buf.append('3') catch return "";
                    serializeInteger(i64, buf, value) catch return "";
                },
            }

            return buf.items;
        }

And deserialization.

        pub fn deserialize(data: String) Value {
            return switch (data[0]) {
                '0' => Value.NULL,
                '1' => Value{ .bool_value = data[1] == '1' },
                '2' => Value{ .string_value = data[1..] },
                '3' => Value{ .integer_value = deserializeInteger(i64, data[1..]) },
                else => unreachable,
            };
        }
    };

We use a simple, space-inefficient scheme for encoding/decoding to bytes that can be written to disk.

Rows

Now that we've got values, we can define rows in terms of values. And we can provide a few helper functions for getting cells by field name.

    pub const Row = struct {
        allocator: std.mem.Allocator,
        cells: std.ArrayList(String),
        fields: []String,

        pub fn init(allocator: std.mem.Allocator, fields: []String) Row {
            return Row{
                .allocator = allocator,
                .cells = std.ArrayList(String).init(allocator),
                .fields = fields,
            };
        }

        pub fn append(self: *Row, cell: Value) !void {
            var cellBuffer = std.ArrayList(u8).init(self.allocator);
            try self.cells.append(cell.serialize(&cellBuffer));
        }

        pub fn appendBytes(self: *Row, cell: String) !void {
            try self.cells.append(cell);
        }

        pub fn get(self: Row, field: String) Value {
            for (self.fields) |f, i| {
                if (std.mem.eql(u8, field, f)) {
                    // Results are internal buffer views. So make a copy.
                    var copy = std.ArrayList(u8).init(self.allocator);
                    copy.appendSlice(self.cells.items[i]) catch return Storage.Value.NULL;
                    return Storage.Value.deserialize(copy.items);
                }
            }

            return Value.NULL;
        }

        pub fn items(self: Row) []String {
            return self.cells.items;
        }

        fn reset(self: *Row) void {
            self.cells.clearRetainingCapacity();
        }
    };

Since values are serialized with length prefixes, we can serialize a row by concatenating all the values together.

Since we must map to keys and values for RocksDB, we give each row a key prefix that is the table name. And then we give it a random suffix to distinguish it from other rows in the table. A more intelligent design would use the table's primary key as the suffix but we don't support primary keys yet. (See also, the section on "Mapping SQL to key-value storage" in What's the big deal about key-value databases like FoundationDB and RocksDB?.)

    fn generateId() ![]u8 {
        const file = try std.fs.cwd().openFileZ("/dev/random", .{});
        defer file.close();

        var buf: [16]u8 = .{};
        _ = try file.read(&buf);
        return buf[0..];
    }

    pub fn writeRow(self: Storage, table: String, row: Row) ?Error {
        // Table name prefix
        var key = std.ArrayList(u8).init(self.allocator);
        key.writer().print("row_{s}_", .{table}) catch return "Could not allocate row key";

        // Unique row id
        var id = generateId() catch return "Could not generate id";
        key.appendSlice(id) catch return "Could not allocate for id";

        var value = std.ArrayList(u8).init(self.allocator);
        for (row.cells.items) |cell| {
            serializeBytes(&value, cell) catch return "Could not allocate for cell";
        }

        return self.db.set(key.items, value.items);
    }

RowIter

Reading rows will be slightly different from writing rows since reading rows will use an iterator. We will wrap the RocksDB iterator so the consumer of Storage only needs to deal with Rows and Values.

    pub const RowIter = struct {
        row: Row,
        iter: RocksDB.Iter,

        fn init(allocator: std.mem.Allocator, iter: RocksDB.Iter, fields: []String) RowIter {
            return RowIter{
                .iter = iter,
                .row = Row.init(allocator, fields),
            };
        }

        pub fn next(self: *RowIter) ?Row {
            var rowBytes: String = undefined;
            if (self.iter.next()) |b| {
                rowBytes = b.value;
            } else {
                return null;
            }

            self.row.reset();
            var offset: usize = 0;
            while (offset < rowBytes.len) {
                var d = deserializeBytes(rowBytes[offset..]);
                offset += d.offset;
                self.row.appendBytes(d.bytes) catch return null;
            }

            return self.row;
        }

        pub fn close(self: RowIter) void {
            self.iter.close();
        }
    };

It does the opposite of what writeRow did in terms of deserializing cells one after another. Again, this works because each cell is length-prefixed.

Next we must provide the interface for actually getting a RowIter. The only condition for the RowIter at the moment is that it contains all rows in the table.

Since we wrote each row with a table name prefix, we can recover it by iterating over all rows with that prefix.

    pub fn getRowIter(self: Storage, table: String) Result(RowIter) {
        var rowPrefix = std.ArrayList(u8).init(self.allocator);
        rowPrefix.writer().print("row_{s}_", .{table}) catch return .{
            .err = "Could not allocate for row prefix",
        };

        var iter = switch (self.db.iter(rowPrefix.items)) {
            .err => |err| return .{ .err = err },
            .val => |it| it,
        };

        var tableInfo = switch (self.getTable(table)) {
            .err => |err| return .{ .err = err },
            .val => |t| t,
        };

        return .{
            .val = RowIter.init(self.allocator, iter, tableInfo.columns),
        };
    }

Tables

Finally we've got tables. We must store table metadata: its name, columns and column types.

    pub const Table = struct {
        name: String,
        columns: []String,
        types: []String,
    };

We will use a tbl_ prefix instead of row_ prefix for table metadata. But we'll otherwise encode with the same length-prefixed concatentations.

    pub fn writeTable(self: Storage, table: Table) ?Error {
        // Table name prefix
        var key = std.ArrayList(u8).init(self.allocator);
        key.writer().print("tbl_{s}_", .{table.name}) catch return "Could not allocate key for table";

        var value = std.ArrayList(u8).init(self.allocator);
        for (table.columns) |column, i| {
            serializeBytes(&value, column) catch return "Could not allocate for column";
            serializeBytes(&value, table.types[i]) catch return "Could not allocate for column type";
        }

        return self.db.set(key.items, value.items);
    }

And the opposite for decoding.

    pub fn getTable(self: Storage, name: String) Result(Table) {
        var tableKey = std.ArrayList(u8).init(self.allocator);
        tableKey.writer().print("tbl_{s}_", .{name}) catch return .{
            .err = "Could not allocate for table prefix",
        };

        var columns = std.ArrayList(String).init(self.allocator);
        var types = std.ArrayList(String).init(self.allocator);
        var table = Table{
            .name = name,
            .columns = undefined,
            .types = undefined,
        };
        // First grab table info
        var columnInfo = switch (self.db.get(tableKey.items)) {
            .err => |err| return .{ .err = err },
            .val => |val| val,
            .not_found => return .{ .err = "No such table" },
        };

        var columnOffset: usize = 0;
        while (columnOffset < columnInfo.len) {
            var column = deserializeBytes(columnInfo[columnOffset..]);
            columnOffset += column.offset;
            columns.append(column.bytes) catch return .{
                .err = "Could not allocate for column name.",
            };

            var kind = deserializeBytes(columnInfo[columnOffset..]);
            columnOffset += kind.offset;
            types.append(kind.bytes) catch return .{
                .err = "Could not allocate for column kind.",
            };
        }

        table.columns = columns.items;
        table.types = types.items;

        return .{ .val = table };
    }
};

And that's it for storage! Again, we're building on top of the RocksDB layer I already wrote about. If you want to see how that works, go for it!

If you just want the rocksdb.zig file, grab it from here.

Execute (`execute.zig`, 210 LoC)

Now that we've got a storage layer and an AST from our parser, we can execute the query on top of the storage!

A better implementation might translate the AST to bytecode and implement a bytecode interpreter for expression evaluation. But we'll build a tree-walking interpreter instead.

const std = @import("std");

const Parser = @import("parse.zig").Parser;
const RocksDB = @import("rocksdb.zig").RocksDB;
const Storage = @import("storage.zig").Storage;
const Result = @import("types.zig").Result;
const String = @import("types.zig").String;

pub const Executor = struct {
    allocator: std.mem.Allocator,
    storage: Storage,

    pub fn init(allocator: std.mem.Allocator, storage: Storage) Executor {
        return Executor{ .allocator = allocator, .storage = storage };
    }

In general we'll make query responses optional. They can be empty or they can be an array of an array of strings (rows and cells) and an array of strings (column names).

    const QueryResponse = struct {
        fields: []String,
        // Array of cells (which is an array of serde (which is an array of u8))
        rows: [][]String,
        empty: bool,
    };
    const QueryResponseResult = Result(QueryResponse);

Expressions

For execution we start again at the bottom with expressions. There are literals.

    fn executeExpression(self: Executor, e: Parser.ExpressionAST, row: Storage.Row) Storage.Value {
        return switch (e) {
            .literal => |lit| switch (lit.kind) {
                .string => Storage.Value{ .string_value = lit.string() },
                .integer => Storage.Value.fromIntegerString(lit.string()),
                .identifier => row.get(lit.string()),
                else => unreachable,
            },

And there are a handful of binary operations.

            .binary_operation => |bin_op| {
                var left = self.executeExpression(bin_op.left.*, row);
                var right = self.executeExpression(bin_op.right.*, row);

                if (bin_op.operator.kind == .equal_operator) {
                    // Cast dissimilar types to serde
                    if (@enumToInt(left) != @enumToInt(right)) {
                        var leftBuf = std.ArrayList(u8).init(self.allocator);
                        left.asString(&leftBuf) catch unreachable;
                        left = Storage.Value{ .string_value = leftBuf.items };

                        var rightBuf = std.ArrayList(u8).init(self.allocator);
                        right.asString(&rightBuf) catch unreachable;
                        right = Storage.Value{ .string_value = rightBuf.items };
                    }

                    return Storage.Value{
                        .bool_value = switch (left) {
                            .null_value => true,
                            .bool_value => |v| v == right.asBool(),
                            .string_value => blk: {
                                var leftBuf = std.ArrayList(u8).init(self.allocator);
                                left.asString(&leftBuf) catch unreachable;

                                var rightBuf = std.ArrayList(u8).init(self.allocator);
                                right.asString(&rightBuf) catch unreachable;

                                break :blk std.mem.eql(u8, leftBuf.items, rightBuf.items);
                            },
                            .integer_value => left.asInteger() == right.asInteger(),
                        },
                    };
                }

                if (bin_op.operator.kind == .concat_operator) {
                    var copy = std.ArrayList(u8).init(self.allocator);
                    left.asString(&copy) catch unreachable;
                    right.asString(&copy) catch unreachable;
                    return Storage.Value{ .string_value = copy.items };
                }

                return switch (bin_op.operator.kind) {
                    .lt_operator => if (left.asInteger() < right.asInteger()) Storage.Value.TRUE else Storage.Value.FALSE,
                    .plus_operator => Storage.Value{ .integer_value = left.asInteger() + right.asInteger() },
                    else => Storage.Value.NULL,
                };
            },
        };
    }

`SELECT`

To execute a SELECT query we first validate the requested table and requested fields.

    fn executeSelect(self: Executor, s: Parser.SelectAST) QueryResponseResult {
        switch (self.storage.getTable(s.from.string())) {
            .err => |err| return .{ .err = err },
            else => _ = 1,
        }

        // Now validate and store requested fields
        var requestedFields = std.ArrayList(String).init(self.allocator);
        for (s.columns) |requestedColumn| {
            var fieldName = switch (requestedColumn) {
                .literal => |lit| switch (lit.kind) {
                    .identifier => lit.string(),
                    // TODO: give reasonable names
                    else => "unknown",
                },
                // TODO: give reasonable names
                else => "unknown",
            };
            requestedFields.append(fieldName) catch return .{
                .err = "Could not allocate for requested field.",
            };
        }

Then grab an iterator for rows in the table.

        var rows = std.ArrayList([]String).init(self.allocator);
        var response = QueryResponse{
            .fields = requestedFields.items,
            .rows = undefined,
            .empty = false,
        };

        var iter = switch (self.storage.getRowIter(s.from.string())) {
            .err => |err| return .{ .err = err },
            .val => |it| it,
        };
        defer iter.close();

And finally we iterate through all rows and add rows to the response if there is no WHERE condition or if we evaluate the WHERE condition successfully.

When we add rows to the response, we need to actually evaluate the expression for each column in the SELECT AST.

        while (iter.next()) |row| {
            var add = false;
            if (s.where) |where| {
                if (self.executeExpression(where, row).asBool()) {
                    add = true;
                }
            } else {
                add = true;
            }

            if (add) {
                var requested = std.ArrayList(String).init(self.allocator);
                for (s.columns) |exp| {
                    var val = self.executeExpression(exp, row);
                    var valBuf = std.ArrayList(u8).init(self.allocator);
                    val.asString(&valBuf) catch unreachable;
                    requested.append(valBuf.items) catch return .{
                        .err = "Could not allocate for requested cell",
                    };
                }
                rows.append(requested.items) catch return .{
                    .err = "Could not allocate for row",
                };
            }
        }

        response.rows = rows.items;
        return .{ .val = response };
    }

`INSERT INTO`

Inserting is pretty simple, we just evaluate the VALUES passed and write them to storage.

    fn executeInsert(self: Executor, i: Parser.InsertAST) QueryResponseResult {
        var emptyRow = Storage.Row.init(self.allocator, undefined);
        var row = Storage.Row.init(self.allocator, undefined);
        for (i.values) |v| {
            var exp = self.executeExpression(v, emptyRow);
            row.append(exp) catch return .{ .err = "Could not allocate for cell" };
        }

        if (self.storage.writeRow(i.table.string(), row)) |err| {
            return .{ .err = err };
        }

        return .{
            .val = .{ .fields = undefined, .rows = undefined, .empty = true },
        };
    }

`CREATE TABLE`

Similarly to INSERT INTO, but without any expression evaluation, we map the CreateTableAST to Storage entities and write them to storage.

    fn executeCreateTable(self: Executor, c: Parser.CreateTableAST) QueryResponseResult {
        var columns = std.ArrayList(String).init(self.allocator);
        var types = std.ArrayList(String).init(self.allocator);

        for (c.columns) |column| {
            columns.append(column.name.string()) catch return .{
                .err = "Could not allocate for column name",
            };
            types.append(column.kind.string()) catch return .{
                .err = "Could not allocate for column kind",
            };
        }

        var table = Storage.Table{
            .name = c.table.string(),
            .columns = columns.items,
            .types = types.items,
        };

        if (self.storage.writeTable(table)) |err| {
            return .{ .err = err };
        }
        return .{
            .val = .{ .fields = undefined, .rows = undefined, .empty = true },
        };
    }

For both CREATE TABLE and INSERT INTO there is more validation we could do. Exercise for the reader and whatnot. :)

`execute`

Finally we can switch on the AST and call the appropriate execution function.

    pub fn execute(self: Executor, ast: Parser.AST) QueryResponseResult {
        return switch (ast) {
            .select => |select| switch (self.executeSelect(select)) {
                .val => |val| .{ .val = val },
                .err => |err| .{ .err = err },
            },
            .insert => |insert| switch (self.executeInsert(insert)) {
                .val => |val| .{ .val = val },
                .err => |err| .{ .err = err },
            },
            .create_table => |createTable| switch (self.executeCreateTable(createTable)) {
                .val => |val| .{ .val = val },
                .err => |err| .{ .err = err },
            },
        };
    }
};

And now we're ready to put it all together in main!

`main` (`main.zig`, 144 LoC)

First we set up our arena allocator.

const std = @import("std");

const RocksDB = @import("rocksdb.zig").RocksDB;
const lex = @import("lex.zig");
const parse = @import("parse.zig");
const execute = @import("execute.zig");
const Storage = @import("storage.zig").Storage;

pub fn main() !void {
    var arena = std.heap.ArenaAllocator.init(std.heap.page_allocator);
    defer arena.deinit();
    const allocator = arena.allocator();

Then we parse CLI arguments. Importantly we need to grab a location on disk for RocksDB to store data. And we need a query to execute.

    var debugTokens = false;
    var debugAST = false;
    var args = std.process.args();
    var scriptArg: usize = 0;
    var databaseArg: usize = 0;
    var i: usize = 0;
    while (args.next()) |arg| {
        if (std.mem.eql(u8, arg, "--debug-tokens")) {
            debugTokens = true;
        }

        if (std.mem.eql(u8, arg, "--debug-ast")) {
            debugAST = true;
        }

        if (std.mem.eql(u8, arg, "--database")) {
            databaseArg = i + 1;
            i += 1;
            _ = args.next();
        }

        if (std.mem.eql(u8, arg, "--script")) {
            scriptArg = i + 1;
            i += 1;
            _ = args.next();
        }

        i += 1;
    }

    if (databaseArg == 0) {
        std.debug.print("--database is a required flag. Should be a directory for data.\n", .{});
        return;
    }

    if (scriptArg == 0) {
        std.debug.print("--script is a required flag. Should be a file containing SQL.\n", .{});
        return;
    }

Next we read the file passed for the query.

    const file = try std.fs.cwd().openFileZ(std.os.argv[scriptArg], .{});
    defer file.close();

    const file_size = try file.getEndPos();
    var prog = try allocator.alloc(u8, file_size);

    _ = try file.read(prog);

And pass the query to the lexer.

    var tokens = std.ArrayList(lex.Token).init(allocator);
    const lexErr = lex.lex(prog, &tokens);
    if (lexErr) |err| {
        std.debug.print("Failed to lex: {s}", .{err});
        return;
    }

    if (debugTokens) {
        for (tokens.items) |token| {
            std.debug.print("Token: {s}\n", .{token.string()});
        }
    }

    if (tokens.items.len == 0) {
        std.debug.print("Program is empty", .{});
        return;
    }

Pass the tokens to the parser.

    const parser = parse.Parser.init(allocator);
    var ast: parse.Parser.AST = undefined;
    switch (parser.parse(tokens.items)) {
        .err => |err| {
            std.debug.print("Failed to parse: {s}", .{err});
            return;
        },
        .val => |val| ast = val,
    }

    if (debugAST) {
        ast.print();
    }

Initialize storage.

    var db: RocksDB = undefined;
    var dataDirectory = std.mem.span(std.os.argv[databaseArg]);
    switch (RocksDB.open(allocator, dataDirectory)) {
        .err => |err| {
            std.debug.print("Failed to open database: {s}", .{err});
            return;
        },
        .val => |val| db = val,
    }
    defer db.close();

    const storage = Storage.init(allocator, db);

And execute and print results. :)

    const executor = execute.Executor.init(allocator, storage);
    switch (executor.execute(ast)) {
        .err => |err| {
            std.debug.print("Failed to execute: {s}", .{err});
            return;
        },
        .val => |val| {
            if (val.rows.len == 0) {
                std.debug.print("ok\n", .{});
                return;
            }

            std.debug.print("| ", .{});
            for (val.fields) |field| {
                std.debug.print("{s}\t\t|", .{field});
            }
            std.debug.print("\n", .{});
            std.debug.print("+ ", .{});
            for (val.fields) |field| {
                var fieldLen = field.len;
                while (fieldLen > 0) {
                    std.debug.print("=", .{});
                    fieldLen -= 1;
                }
                std.debug.print("\t\t+", .{});
            }
            std.debug.print("\n", .{});

            for (val.rows) |row| {
                std.debug.print("| ", .{});
                for (row) |cell| {
                    std.debug.print("{s}\t\t|", .{cell});
                }
                std.debug.print("\n", .{});
            }
        },
    }
}

build.zig

Finally, finally, tie it all together with build.zig.

const version = @import("builtin").zig_version;
const std = @import("std");

pub fn build(b: *std.build.Builder) void {
    const exe = b.addExecutable("main", "main.zig");
    exe.linkLibC();
    exe.linkSystemLibraryName("rocksdb");

    if (@hasDecl(@TypeOf(exe.*), "addLibraryPath")) {
        exe.addLibraryPath("./rocksdb");
        exe.addIncludePath("./rocksdb/include");
    } else {
        exe.addLibPath("./rocksdb");
        exe.addIncludeDir("./rocksdb/include");
    }

    exe.setOutputDir(".");

    if (exe.target.isDarwin()) {
        exe.addRPath(".");
    }

    exe.install();
}

Grab RocksDB, build it, and build our CLI.

$ git clone https://github.com/facebook/rocksdb
$ ( cd rocksdb && make shared_lib -j8 )

# ONLY IF YOU ARE ON A MAC
$ cp rocksdb/*.dylib . # ONLY IF YOU ARE ON A MAC
# DONE ONLY IF YOU ARE ON A MAC

$ zig build

And give it a go. :)

$ ./main --database data --script <(echo "CREATE TABLE y (year int, age int, name text)")
echo "CREATE TABLE y (year int, age int, name text)"
ok
$ ./main --database data --script <(echo "INSERT INTO y VALUES (2010, 38, 'Gary')")
echo "INSERT INTO y VALUES (2010, 38, 'Gary')"
ok
$ ./main --database data --script <(echo "INSERT INTO y VALUES (2021, 92, 'Teej')")
echo "INSERT INTO y VALUES (2021, 92, 'Teej')"
ok
$ ./main --database data --script <(echo "INSERT INTO y VALUES (1994, 18, 'Mel')")
echo "INSERT INTO y VALUES (1994, 18, 'Mel')"
ok

# Basic query
$ ./main --database data --script <(echo "SELECT name, age, year FROM y")
echo "SELECT name, age, year FROM y"
| name          |age            |year           |
+ ====          +===            +====           +
| Mel           |18             |1994           |
| Gary          |38             |2010           |
| Teej          |92             |2021           |

# With WHERE
$ ./main --database data --script <(echo "SELECT name, year, age FROM y WHERE age < 40")
echo "SELECT name, year, age FROM y WHERE age < 40"
| name          |year           |age            |
+ ====          +====           +===            +
| Mel           |1994           |18             |
| Gary          |2010           |38             |

# With operations
$ ./main --database data --script <(echo "SELECT 'Name: ' || name, year + 30, age FROM y WHERE age < 40")
echo "SELECT 'Name: ' || name, year + 30, age FROM y WHERE age < 40"
| unknown               |unknown                |age            |
+ =======               +=======                +===            +
| Name: Mel             |2024           |18             |
| Name: Gary            |2040           |38             |

From Here

As mentioned, this project is a vast simplification and there are plenty of bugs and subpar design choices. But hopefully it helps to make database development feel a little less intimidating!

If you liked this, here are some other things you might want to check out!

And of course, other posts on this blog. :)

Lastly, a few resources that helped me out while hacking on this:

Zig Documentation
Browsing the source code (and tests!!) of standard library data structures
Zig Programming Language Discord's #zig-help channel
- Friendly and helpful crowd :)

Spent a month hacking on it and happy to finally have this post out.

Let's build a basic SQL database in Zig on top of RocksDB. 😃https://t.co/fkSnaEKsya pic.twitter.com/adfpMvvvOn
— Phil Eaton (@phil_eaton) November 14, 2022