grakn VS datahike

TypeDB : A Strongly-typed Database

How does it compare to, say grakn (renamed https://vaticle.com/, I think?), or draph (https://dgraph.io/), or Ontotext's GraphDB (https://www.ontotext.com/products/graphdb/), or Datomic?

Edge database looks pretty freakin awesome. It basically seems to re-write SQL and Graph databases together to create some new-ish programming language. It takes care of all the problems GraphQL has, and seems to be built separately but on top of postgres. It is really something unique, beautiful, and powerful. They don't have a security layer yet or a cloud hosting environment, but both are in the works. However, postgres still suffers from the scalable problems we all know. If you like unique fetching and strong typing, also check out TypeDB. It doesn't make its own list number because there is not cloud version, middleware, etc. However, worth checking out.

If you find yourself limited by triplestores, there's also a new growing area of development in knowledge engines, which allow edges-of-edges, entailed relations, hypergraph relations, and more of the power you'd get from full logic programming. TypeDB (recently renamed from Grakn) is an example of that type of database.

In the story to follow, David presents how his team at Roche was able to identify potential novel targets that were not identified by Open Targets as highly ranked. This was made possible with TypeDB, which his team used to store the relevant data and then find underlying biological evidence for those new targets.

{ stdenv, lib, openjdk,typedbHome ? "~/.typedb_home", fetchzip}: let typedbVersion = "2.1.1"; typedbDirLinux = "typedb-all-linux-${typedbVersion}"; typedbDirMac = "typedb-all-mac-${typedbVersion}"; typedbDirWindows = "typedb-all-windows-${typedbVersion}"; typedbDir = if stdenv.hostPlatform.isWindows then typedbDirWindows else if stdenv.isDarwin then typedbDirMac else typedbDirLinux; linuxSrc = builtins.fetchTarball { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-linux-2.1.1.tar.gz"; sha256 = "15nwm2dr68p67c2xcqigs66gd679j1zr72gqv7qxgvflwyvyz8fb"; }; windowsSrc = fetchzip { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-windows-2.1.1.zip"; sha256 = "0vd66gfshkg697z07nhy957mwqzlli4r4pmn67hx58n9mkg024kq"; }; macSrc = fetchzip { url = "https://github.com/vaticle/typedb/releases/download/2.1.1/typedb-all-mac-2.1.1.zip"; sha256 = "16hlfy6kh2rnvcralz206q13mghb0rv8wazpg6q3h324p5rdys54"; }; srcFolder = if stdenv.hostPlatform.isWindows then windowsSrc else if stdenv.isDarwin then macSrc else linuxSrc ; javaPatch = '' 20c20 < JAVA_BIN=java --- > JAVA_BIN=${openjdk}/bin/java ''; in stdenv.mkDerivation rec { pname = "typedb"; version = typedbVersion; src = srcFolder; phases = [ "installPhase" ]; buildDepends = [ openjdk ]; installPhase = '' echo "here" # added for debugging ls -lah # " echo "--" # " #patch before install echo "${javaPatch}" > typedb_java.patch patch ./${typedbDir}/typedb typedb_java.patch mkdir $out cp -r ./${typedbDir} $out # add a wrapper script to $out that will move typedb to $typedb # this is necessary because typedb needs a writable environment echo " # on the first start copy everything to typedbHome if [ ! -f ${typedbHome}/typedb ]; then mkdir -p ${typedbHome}; cp -r $out/${typedbDir}/* ${typedbHome}; # correct permissions so that typedb and the user can write there chmod -R u+rw ${typedbHome} chmod u+x ${typedbHome}/typedb fi; ${typedbHome}/typedb \$@; " > $out/typedb chmod +x $out/typedb ''; doCheck = true; meta = with lib; { description = "TypeDB is a distributed knowledge graph: a logical database to organise large and complex networks of data as one body of knowledge."; longDescription = '' TypeDB is a distributed knowledge graph: a logical database to organise large and complex networks of data as one body of knowledge. TypeDB provides the knowledge engineering tools for developers to easily leverage the power of Knowledge Representation and Automated Reasoning when building complex systems. Ultimately, TypeDB serves as the knowledge-base foundation for intelligent systems. ''; homepage = "https://www.grakn.ai/"; license = licenses.gpl3Plus; platforms = platforms.all; maintainers = [ maintainers.haskie ]; }; }

grakn.ai : The Database for AI

Give the open-source Grakn a look as well (full transparency: I work there), it is an abstraction over a built in-house hypergraph storage engine and persisted layer using RocksDB. It's a logical database with a reasoning engine at the database level. https://github.com/graknlabs/grakn

Datahike [0] provides similar functionality to datomic and is open source. It lacks some features however that Datomic does have [1].

[0]: https://github.com/replikativ/datahike

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.

Check https://github.com/replikativ/datahike

Oh really interesting. I didn't know about that. I was actually going threw the old Mendat code base and was considering using that.

I would really like a pure Rust version of Datomic for embed use cases.

There is all also Datahike, that is going in that direction too. It is maintained and actively developed.

https://github.com/replikativ/datahike

Having an Datomic like store backed by something like this.

https://github.com/replikativ/datahike

Is an Open Source variant of Datomic.

Lambdaforge wants to eventually have this work with CRDTs.

Using the Matrix ecosystem for this is quite interesting as it solves many problems for you already.

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.

Have a look at https://github.com/replikativ/datahike and https://github.com/replikativ/datahike-postgres

There is now a datahike linux native image preview available: https://github.com/replikativ/datahike/releases/tag/preview

And implemented as a full-on datastore queried via Datalog: https://github.com/replikativ/datahike