2025-04-22 08:00:00
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 enlightment 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.
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:
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).
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.
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
2025-04-20 08:00:00
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
— Phil Eaton (@eatonphil) April 20, 2025
short new post pic.twitter.com/nTj5LZUpUr
2025-03-27 08:00:00
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:
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.
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.
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.
Typically set to the sector size since only this block size is atomic. The default in ext4 is 4KiB.
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.
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.
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.
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.
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.
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.
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.
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.
In increasing levels of paranoia (laudatory) follow ZFS, Andrea and Remzi Arpaci-Dusseau, and TigerBeetle.
I wrote a post covering some of the scenarios you might want to be aware of, and resilient to, when you write systems that read and write files. pic.twitter.com/7FxbpMo1xm
— Phil Eaton (@eatonphil) March 27, 2025
2025-03-25 08:00:00
This is an external post of mine. Click here if you are not redirected.
2025-02-28 08:00:00
This is an external post of mine. Click here if you are not redirected.
2025-02-15 08:00:00
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.
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.
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.
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.
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.)
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.
