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169 points adityaathalye | 2 comments | | HN request time: 0s | source
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whalesalad ◴[] No.45119238[source]
I've been absolutely waist deep in a bitemporal system on top of PostgreSQL using tstzrange fields. We manage an enormous portfolio of hundreds of thousands of domain names. Every time our core db is modified, before/after states are emitted to a change table. We've been doing this since 2022. Those changes get lightly transformed via trigger into a time travel record, with the valid from/to range and a gist index to make asking questions about the state of the world at a particular point in time easy. For perspective our change table has 90M rows.

All of it works quite well and is decently performant. We can ask questions like, how many domains did we own on March 13th, 2024? Or look at the entire lifecycle of a domains ownership (owned, released, re-acquired, transfered, etc).

The big challenge and core issue we discovered though is that our data sucks. QAing this new capability has been a moving target. Tons of mistakes over time that were partially undone or manually undone without proper audit trail. Ghost records. Rapid changes by our bulk editor tool a->b->a->b that need to get squashed into just a->b. The schema of our database has evolved over time, too, which has made this tough to view a consistent representation of things even if the fields storing that data were renamed. When the system was first introduced, we had ~5 columns to track. Now we have over 30.

Suffice to say if I were to do things over again, I would implement a much better change tracking system that bakes in tools to clean/erase/undo/soft-delete/hard-delete mistakes so that future me (now) wouldn't have so many edge cases to deal with in this time traveling system. I'd also like to just make the change tracking capable of time travel itself, versus building that as a bolt-on side table that tracks and works from the change table. Transitioning to an EAV (entity-attr-value) approach is on my spike list, too. Makes it easier to just reduce (key,val) tuples down into an up to date representation versus looking at diffs of before/after.

Really interesting stuff. I learned a lot about this from Clojure/Datomic and think its quite neat that so many Clojurists are interested in and tackling this problem. As the author notes in this post, XTDB is another one.

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1. adityaathalye ◴[] No.45119468[source]
The XTDB people did a bunch of interviews with people doing stuff in the temporal data systems universe.

Several are out on YouTube. One with Richard Snodgrass is yet to be published... waiting for that eagerly!

ref. topics and links to videos: https://github.com/orgs/xtdb/discussions/4419

  "Decades in the making: Bitemporality in SQL, with Rob Squire"

  "As-Of joins and database architecture, with Arjun Narayan (co-founded Materialize)"

  "Building an identity service on XTDB, with Andrew Maddock (Foundry OS)"  

  "Solving FinTech pains with Suresh Nageswaran (Senior Director, Grid Dynamics)"

  "A meeting with the one and only Richard T. Snodgrass."

  "Building a payments integration system on XTDB, with Edward Hughes"
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2. refset ◴[] No.45119651[source]
> waiting for that eagerly!

Wait no longer! I just updated the page with the unlisted video link: https://youtu.be/6Q_pAI20QPA

We are hoping to record another (more polished) session with Professor Snodgrass soon :)