My personal experience was using Datomic backed by DynamoDB, at the second Clojure company I worked at. In particular I remember feeling like it was hard to anticipate and understand its performance characteristics in particular, and how indices can be leveraged effectively. Maybe if we had chosen Postgres as a backing store that would have been better? I dunno.
Using it was pretty nice at the scale of a small startup with a motivated team, but scaling it up organizationally-speaking was a challenge due to Datalog's relative idiosyncrasy and poor tooling around the database itself. This was compounded by the parallel challenge of keeping a Clojure codebase from going spaghetti-shaped, which happens in that language when teams scale without a lot of "convention and discipline"--it may be easier to manage otherwise. All of that said, this was years ago so maybe things have changed.
At this point I'd choose either PostgreSQL or SQLite for any project I'm getting started with, as they are both rock-solid, full-featured projects with great tooling and widespread adoption. If things need to scale a basic PostgreSQL setup can usually handle a lot until you need to move to e.g. RDS or whatever, and I'm probably biased but I think SQL is not really that much worse than Datalog for common use-cases. Datalog is nice though, don't get me wrong.
EDIT: one point I forgot to make: the killer feature of being an immutable data store that lets you go back in time is in fact super cool, and it's probably exactly what some organizations need, but it is also costly, and I suspect the number of organizations who really need that functionality is pretty small. The place I was at certainly didn't, which is probably part of the reason for the friction I experienced.
Newer releases have improved significantly in this area. It's now possible to understand perf implications with the addition of io-stats[1] and query-stats[2].
Although it is true that "time traveling" queries are relatively rare for production needs, the basic architecture supports things that many applications really need:
- It is possible to make queries against the database PLUS additional data not yet added, that is, "what if" queries
- Having a stable database-as-value is really useful for paginating results; you don't have to worry about new values being inserted into your results during execution, the way you do with traditional databases no longer how long (minutes, hours, even days) you take to traverse the data
- Reified transactions makes it possible to store extra data with each transaction, trivially, such as who made the update and why
- Immutability is amazing for caching at all layers
Using it was pretty nice at the scale of a small startup with a motivated team, but scaling it up organizationally-speaking was a challenge due to Datalog's relative idiosyncrasy and poor tooling around the database itself. This was compounded by the parallel challenge of keeping a Clojure codebase from going spaghetti-shaped, which happens in that language when teams scale without a lot of "convention and discipline"--it may be easier to manage otherwise. All of that said, this was years ago so maybe things have changed.
At this point I'd choose either PostgreSQL or SQLite for any project I'm getting started with, as they are both rock-solid, full-featured projects with great tooling and widespread adoption. If things need to scale a basic PostgreSQL setup can usually handle a lot until you need to move to e.g. RDS or whatever, and I'm probably biased but I think SQL is not really that much worse than Datalog for common use-cases. Datalog is nice though, don't get me wrong.
EDIT: one point I forgot to make: the killer feature of being an immutable data store that lets you go back in time is in fact super cool, and it's probably exactly what some organizations need, but it is also costly, and I suspect the number of organizations who really need that functionality is pretty small. The place I was at certainly didn't, which is probably part of the reason for the friction I experienced.