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Comment by lgrapenthin | Hacker News Reader

I wouldn't use it. The footgun is imaginary. I use Datomic for ten years and I can assure you that I never stepped on it. As a Datomic user you see transactions as clean small diffs, not as complicated multi step processes. This is actually much more pleasant to work with.

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This is also good to hear! I'm not sure whether I'd call it a "footgun" per se--that's really an empirical question about how Datomic's users understand its model. I can say that as someone with some database experience and a few weeks of reading the Datomic docs, this issue actually "broke" several of the tests I wrote for Datomic. It was especially tricky because the transactions mostly worked as expected, but would occasionally "lose updates" or cause updates intended for one entity to wind up assigned to another.

Things looked fine in my manual testing, but when I ran the full test suite Elle kept catching what looked like serious Serializability violations. Took me quite a while to figure out I was holding the database wrong!

Now I’m curious: what’s a useful example of a Datomic transaction that reads a value in multiple of its elements and modifies it?

You could include two transaction functions that constrain a transaction to different properties about the same fact and then alter that fact. I don't know of a practical usecase or that I ever encountered that, it would be extremely rare IME.

In traditional databases, only the database engine has a scalable view of the data - that’s why you send SQL to it and stream back the response data set. With Datomic, the peer has the same level of read access as the transactor; it’s like the database comes to you.

In this read and update scenario, the peer will, at its leisure, read existing data and put together update data; some careful use of compare and set, or a custom transaction function, can ensure that the database has not changed between read and writes in such a way that the update is improper, when that is even a possibility - a rarity.

At scale, you want to minimize the amount of work the transactor must perform, since it so aggressively single threaded. Off loading work to the peer is amazingly effective.

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