Apache AGE VS datalevin

What about the AGE extension for Postgres? https://age.apache.org/

Waiting for Postgres to release theirs.

I think they are talking about the AGE extension https://age.apache.org

It's my understanding of the "incubation" period of Apache Software Foundation projects is to determine if they're able to actually execute the ASF process, and a bunch of other "project maturity metrics" (https://community.apache.org/apache-way/apache-project-matur...) of which AGE currently has some self-certification: https://age.apache.org/?l=maturity#

I recognize that's not exactly an answer to the question you asked, but I would be surprised if someone other than a project member knows a more forward-looking one

Postgres has a graph extension: https://age.apache.org. This means you can keep all your data in PG and use both models.

PostgreSQL with graph extension, developed by a team at Apache Software Foundation as Apache AGE. Apache AGE uses Gremlin.

The big thing that graph dbs provide is transitive traversals of join relationships.

The problem with graph dbs is trying to return something that is not a graph. Like a count. Or derived information. And which graph model do you use? There’s more than one. Lots of information is very poorly modeled in graph dbs. Temporal organization, for example.

Ultimately, graphs are a way to use relations. But relations allow you much more flexibility to associate information (subject to the issue of transitive relationship traversal).

Mixed graph-relational is perfectly reasonable. Reasonable start here: [https://age.apache.org/]

their actual landing page is actually better than the Github one. It's a translation layer(s) to allow querying Postgres using openCypher

Depending on how one views "postgres", there are at least two extensions that allegedly do it: https://age.apache.org/ and the AgensGraph from which AGE derives

There's an extension to postgresql (I haven't used it, but I am familiar with node/edge tables in MSSQL) that allows you to do this: https://age.apache.org/

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.