Event Store VS datalevin

I've been doing some research and found this: https://github.com/EventStore/EventStore

C#: In Europe, Java is still strong but many trading firms use C# because of the strong Microsoft culture in Europe, as well as because of strongly supported C# libraries like say EventStore, which tends to be used for the matchmaking engines for stock exchanges (especially that exchange matchmaking problem is basically SMR). And skimming over the code, it has Paxos implemented too, making it good for dealing with partial failures (failover), essential for any HFT/trading firm. C#'s also the biggest ecosystem that many of the breakthrough java tech mentioned earlier was first ported to.

Support for eventstore.com eventstore. esdb

Where's the catch, then? Well, there's a couple of catches, in fact. First of all, in a distributed setting, appending data to a log isn't that easy. First, you need to make your log distributed. Again, Kafka/Cassandra/EventStore make this possible, however, whenever you start dealing with distributed data, you‘re introducing new operational and implementation complexity.

You can also consider other durable Datalog options like datahike or datalevin which can work either as lib (SQLite style) or in a client-server setup; if you want to play with bi-temporality XTDB is a rock solid option with very good support and documentation.

There are some Clojure-ecosystems things that are pretty cool, too, that you'd probably miss going into Haskell. lacinia is an extremely cool GraphQL library, and there are a variety of interesting datalog-based datastores which are spiritual descendents of Datomic, notably xtdb (formerly crux) and datalevin. Also as noted, you can write the front-end in ClojureScript if you want to, and there are a lot of cool libraries for that as well.

There are plenty of open source Datomic Inspired databases. Check out https://github.com/juji-io/datalevin and scroll down all the way down to “Alternatives”. There was even the beginning of a rust one by Mozilla: https://github.com/mozilla/mentat

The benchmarks in question have several implementation issues, I reported them on GitHub.

https://github.com/juji-io/datalevin/issues/created_by/caval...

Highly recommend you give https://github.com/juji-io/datalevin a chance. You can use it both as a key-value and/or relational datalog store (like datomic) but it’s very simple to set up and blazing fast!

Unlike some other commenters, I agree that graph models are usually a better fit for most data than relational models. There's been some interesting work in recent years developing this idea: in the Clojure world there's Datomic, XTDB, and a host of competitors, all of which build on work from Semantic Web/SPARQL/triplestores and logic programming. Some are even intended to be used as primary datastores: they support some amount of schema and constraints, have well-defined consistency and ACID guarantees, etc. This makes them unlike graph databases like Neo4J and others, which fill an architectural role more like Elasticsearch as a read-optimization tool. Here's an interesting talk making a case for triple-based databases.