Data-modeling

• `updated_at` Is Not a Conflict-Resolution Strategy

  In the last post we talked about the problems with a distributed system, and touched on the fact that timestamps are not as reliable as you think they are.

  If you have two updated_at fields and you compare them, how do you decide which side is the correct one?

  The updated_at field only tells you that a write happened. It doesn’t tell you the meaning, or if it was intentional. Conflict resolution is fundamentally a question about causality. Did side A intend for this change to happen? A wall clock timestamp can’t tell you the answer to that.

  Two independent clocks can drift, and will drift. Yes, it will get corrected by NTP occasionally. But you can’t always rely on their NTP service working. Timestamps are a fine signal that something occurred, and they’re a reasonable way for a human to sort a list and answer roughly when we think a change occurred. But if you use them as a foundation to decide what data to keep, you’re gonna end up destroying and losing data.

  Things That Actually Work

  The good news is the alternatives are not exotic, and you don’t need all of them.

  Content hashes. Hash the meaningful content and compare hashes instead of times. This kills the metadata-edit problem outright: if the hash matches, nothing changed, no matter what the timestamp claims. It’s the highest-value change on this list and usually the easiest, because it’s a pure function of data you already have.

  Version counters. A monotonic integer per record, incremented on every meaningful write. Immune to clock skew entirely, because it isn’t a clock. The cost is that somebody has to own the increment, which is straightforward with a single authority and gets harder without one.

  Sync checkpoints. Record what was confirmed at the last successful sync, not just when it happened. Then the question becomes “has this changed since the last agreed state,” which is answerable, instead of “is this newer,” which is a guess.

  Operation logs. Store what happened rather than only the result. Heavier, but it’s the only option that lets you reconstruct intent after the fact, and it turns “which one wins” into a question you can actually audit.

  You can get most of the benefit from the first one. Hash the content, and let the timestamp go back to being a display field.

  When Last-Write-Wins Is Fine

  I’m not gonna lie, last write wins is often the correct engineering choice, and replacing it with something more complicated can be its own mistake. Sometimes it’s fine. If a write gets lost and the data is recoverable, that’s a trade you can live with.

  If it’s a simple tool without a ton of users, adding a lot of complexity is not the way to go.

  If the data is just a cache or a projection, then who cares? You can rebuild it from the authoritative source anyway.

  What I’d Actually Do

  Keep updated_at. It’s useful. Sort by it, display it, log it.

  Just stop letting it decide things. Add a content hash and check that first, so a no-op edit stays a no-op. If a field can be written from two sides independently, give it a version counter or an explicit authority rule, and write the rule down somewhere the next person will find it.

  I’d appreciate a follow. You can subscribe with your email below. The emails go out once a week, or you can find me on Mastodon at @[email protected].

  Aug 5, 2026 Databases Software-development Distributed-systems Local-first Data-modeling

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• Your Local File Should Not Have to Argue With Your Database

  Sync bugs usually all start the same way. Two copies of something, both of them mostly right, and no written rule about which one wins.

  The problems occur when you don’t notice The bug. When the file says one thing and the database says another. It’s not a problem until it is. And then you have to spend time figuring the why and when’s of the drift.

  So let’s talk about authority. Not storage, not sync, not “where does the data live.” Authority. Which copy is allowed to be right when two copies disagree.

  The Question Nobody Writes Down

  Most systems that hold the same data in two places never actually decide this. The decision gets made accidentally, by whichever code path happened to run last, and then it gets re-made differently by the next feature.

  The failure is duplication without a stated rule.

  Here’s a concrete version. I run a content pipeline for this blog. Posts are Markdown files with YAML frontmatter sitting in a directory. There’s also a Turso database holding metadata about those same posts. Two copies of what looks like the same information.

  Ask the naive question, “which one is the source of truth,” and you get a bad answer, because the honest answer is neither, and both, depending on the field.

  Split Authority by Field, Not by Store

  You might try to pick an authoritative source based on store. Files win, or the database wins. But the useful granularity is usually the field.

  In my pipeline it breaks down like this:

  • Post content and tags: the Markdown file wins. The frontmatter is authoritative. If the database has a different tag list, the database is wrong, and it gets rebuilt from the file.
  • Scheduling: the database wins. What time a post goes out, what slot it holds, whether it’s been claimed. The file does not get a vote.

  Those are different answers for the same post, and that’s fine, because each one is written down and each one has a reason.

  The content lives in the file because content is the thing I edit by hand, in an editor, with Git history behind it. I want git log to be the real record of what changed. Putting that in a database would mean my writing history lives somewhere that is harder to access.

  The schedule lives in the database because scheduling is a coordination problem. It needs uniqueness constraints, it needs to answer “what’s in the 10am slot on Tuesday,” and it needs to do that without me parsing 241 files. A database is genuinely better at that. It just isn’t better at holding prose.

  A Database Can Be Useful Without Being Authoritative

  I think there’s a reflex where adding a database feels like promoting the data into it. You put the posts in Postgres and now Postgres is where posts are.

  It doesn’t have to work that way. A database can be a query layer over data that lives somewhere else, and that’s a completely respectable job. Indexes, joins, counts, “show me every post tagged local-first published before June.” All of that is worth having, and none of it requires the database to be the authority.

  The test I use: if I deleted the database right now, what would I lose forever?

  For me, it would be the scheduling state because that’s what I put in the database. The important thing is I wouldn’t lose a single word that I’ve written. Every post would still be in a directory. This choice is deliberate.

  It’s easy for the database to become a Cache and not an authoritative source.

  What Should Happen When They Disagree

  If you have documented your authoritative source, then the disagreements stops becoming a crisis, and it just is a routine. Resolution event

  You should be able to rebuild it. There should be nothing to decide. The decision is documented and how you resolve conflicts. Just depends on. Which authoritative source owns Which s segment of your data?

  In my case, there’s actually a third authoritative source, and that’s the remote blog system that hands back an ID every time I schedule a new post.

  So, this is totally fine if you pick the authority at the field level and Document that decision to prevent trip-ups in the future.

  Your files and your database shouldn’t be arguing, all it requires is a bit of planning.

  I’d appreciate a follow. You can subscribe with your email below. The emails go out once a week, or you can find me on Mastodon at @[email protected].

  Aug 3, 2026 Databases Software-development Architecture Local-first Data-modeling

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