Posted on 2019-11-22 Edit on GitHub
I've recently begun evaluating Datomic for a project at work. Though the product has been around for over 7 years, it's the first opportunity I've had to investigate it in depth1. Having had some experience with it, I've come to really like it, and will provide some thoughts on why I think Datomic is the right database for many applications.
In Datomic, all data is stored as datoms, 5-tuples of entity-attribute-value-transaction-added. Data is immutable. Once you put (assert") a datom (a fact) into the database, it's there forever. A new fact can supersede, but not remove2, an old fact. This has amazing implications for information retention. Ever wanted to run a report against your data or experiment with a "what if" scenario from 6 months ago? With Datomic, it's as easy as requesting a snapshot of the database on that date.
The system is easy to reason about. Transactions are ACID and serial, so there's a total ordering of events, which is available via the API as data structures to easily inspect and manipulate. This is a much simpler model than the various transaction isolation levels seen in RDBMS systems. It does limit the system's write performance, but many applications are more read than write-intensive, and a single thread kept busy, not contending with others, can be highly efficient.
Read performance, which Datomic is designed for, is dependent on caching and storage. Queries can be cached, and there's never a need to evict outdated data because the they're immutable facts that are always true. As memory is getting cheaper and more plentiful very quickly, especially via cloud providers like AWS, Datomic's performance stands to benefit as many users' result sets–or even entire databases–can be held in memory. The same is true r.e. storage, which is separated from other database concerns. Datomic can make use of various storage backends, from in-memory (amazing performance for tests) to Cassandra (highly scalable & distributed).
The query language, which seems formidable at first, has a number of advantages, including composability, due to use of data structures instead of strings, and expressiveness, thanks to the power of Datalog, the subset of Prolog the query language is based on. Logic programming makes navigating relationships between entities a breeze, as it spares the user the worry of going from here to there or there to here.
Speaking of navigation, "everything is a datom" makes indexing straightforward. There are only a few to worry about, and most are automatic–no worrying about which columns in which tables to index based on usage patterns. The configuration judgment calls3 and pervasive manual tweaking typical of other databases just isn't present in Datomic. And like the transaction log, indexes are exposed via the API as data structures, in case you need to do something the query language can't do fast enough.
There's a lot more to Datomic that can be discussed, but the point is this: the good folks at Cognitect designed a database chock full of lessons learned and good ideas from the past 40 years, which translates to a downright pleasing product to work with. I highly recommend anyone starting a new project to give Datomic a try; even if you decide it's not right for you, I guarantee you'll have learned a lot about database design from the experience.
There's an excision API that serves as an escape valve for e.g. regulatory and performance needs, but in general once something is in Datomic, it's there forever.