Postgres: The Graph Database You Didn't Know You Had

• Apache AGE

  132 2,716 8.9 C

  Graph database optimized for fast analysis and real-time data processing. It is provided as an extension to PostgreSQL. (by apache)

  

Of course relational db can act like a graph db. It's just not as efficient due to how things are stored and queried. Would be great to have a graph db plugin (and I found one https://github.com/apache/age)

• clair

  21 10,052 9.2 Go

  Vulnerability Static Analysis for Containers

  

It scaled well compared to a naive graph abstraction implemented outside the database, but when performance wasn't great, it REALLY wasn't great. We ended up throwing it out in later versions to try and get more consistent performance.

I've since worked on SpiceDB[1] which takes the traditional design approach for graph databases and simply treating Postgres as triple-store and that scales far better. IME, if you need a graph, you probably want to use a database optimized for graph access patterns. Most general-purpose graph databases are just bags of optimizations for common traversals.

[0]: https://github.com/quay/clair

[1]: https://github.com/authzed/spicedb

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• spicedb

  38 4,565 9.7 Go

  Open Source, Google Zanzibar-inspired permissions database to enable fine-grained access control for customer applications

  

It scaled well compared to a naive graph abstraction implemented outside the database, but when performance wasn't great, it REALLY wasn't great. We ended up throwing it out in later versions to try and get more consistent performance.

I've since worked on SpiceDB[1] which takes the traditional design approach for graph databases and simply treating Postgres as triple-store and that scales far better. IME, if you need a graph, you probably want to use a database optimized for graph access patterns. Most general-purpose graph databases are just bags of optimizations for common traversals.

[0]: https://github.com/quay/clair

[1]: https://github.com/authzed/spicedb

• ldbc_snb_bi

  3 33 7.7 Python

  Reference implementations for the LDBC Social Network Benchmark's Business Intelligence (BI) workload

  

I designed and maintain several graph benchmarks in the Linked Data Benchmark Council, including workloads aimed for databases [1]. We make no restrictions on implementations, they can any query language like Cypher, SQL, etc.

In our last benchmark aimed at analytical systems [2], we found that SQL queries using WITH RECURSIVE can work for expressing reachability and even weighted shortest path queries. However, formulating an efficient algorithm yields very complex SQL queries [3] and their execution requires a system with a sophisticated optimizer such as Umbra developed at TU Munich [4]. Industry SQL systems are not yet at this level but they may attain that sometime in the future.

Another direction to include graph queries in SQL is the upcoming SQL/PGQ (Property Graph Queries) extension. I'm involved in a project at CWI Amsterdam to incorporate this language into DuckDB [5].

[1] https://ldbcouncil.org/benchmarks/snb/

[2] https://www.vldb.org/pvldb/vol16/p877-szarnyas.pdf

[3] https://github.com/ldbc/ldbc_snb_bi/blob/main/umbra/queries/...

[4] https://umbra-db.com/

[5] https://www.cidrdb.org/cidr2023/slides/p66-wolde-slides.pdf

• ldbc_snb_datagen_spark

  5 164 3.7 Java

  Synthetic graph generator for the LDBC Social Network Benchmark, running on Spark

  

I designed and maintain several graph benchmarks in the Linked Data Benchmark Council, including workloads aimed for databases [1]. We make no restrictions on implementations, they can any query language like Cypher, SQL, etc.

In our last benchmark aimed at analytical systems [2], we found that SQL queries using WITH RECURSIVE can work for expressing reachability and even weighted shortest path queries. However, formulating an efficient algorithm yields very complex SQL queries [3] and their execution requires a system with a sophisticated optimizer such as Umbra developed at TU Munich [4]. Industry SQL systems are not yet at this level but they may attain that sometime in the future.

Another direction to include graph queries in SQL is the upcoming SQL/PGQ (Property Graph Queries) extension. I'm involved in a project at CWI Amsterdam to incorporate this language into DuckDB [5].

[1] https://ldbcouncil.org/benchmarks/snb/

[2] https://www.vldb.org/pvldb/vol16/p877-szarnyas.pdf

[3] https://github.com/ldbc/ldbc_snb_bi/blob/main/umbra/queries/...

[4] https://umbra-db.com/

[5] https://www.cidrdb.org/cidr2023/slides/p66-wolde-slides.pdf

• quine

  6 282 9.4 Scala

  Quine • a streaming graph • https://quine.io • Discord: https://discord.gg/GMhd8TE4MR

  

Re [5]'s asssertion under "blunders" of the diminish usecases post sql/pgq, what do you think of sometime like Quine?

https://github.com/thatdot/quine

Their claim to fame is progressive incremental computation - each node is an actor responding to events -- and I'm not sure how a relational db could do that and match the latencies. That usecase is pretty much pattern matching and forensics and stuff like that.

https://docs.quine.io/core-concepts/architecture.html

• materialize

  117 5,585 10.0 Rust

  The data warehouse for operational workloads. (by MaterializeInc)

  

Quine seems to be an interesting project but I'm not too familiar with it. Its main feature, evaluating complex multi-way join queries on incoming streaming data, exists in the relational world as the Materialize database which leverages differential dataflow for computation.

Quine uses Cypher so expressing path queries can be done with the concise Kleene-star syntax, e.g. (p1:Person)-[:knows*]-(p2:Person).

Materalize is getting support for WITH RECURSIVE and WITH MUTUALLY RECURSIVE (their own SQL extension that fixes some issues of WITH RECURSIVE):

https://github.com/MaterializeInc/materialize/issues/11176

• SaaSHub

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  SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

  

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