quine VS ldbc_snb_datagen_spark

Quine

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

Download Quine - JAR file | Docker Image | Github

The first step to making a Standing Query is determining the graph pattern you want to watch for. You may have deployed Quine in your data pipeline to perform a series of tasks to isolate data, implement a specific feature, or monitor the stream to find a specific pattern in real time. In any case, Quine will implement your logic using Cypher. The recipe for this example is included in the Quine repo if you'd like to follow along.

Quine is open source if you want to run this analysis for yourself. Download a precompiled version or build it yourself from the codebase (Quine Github). I published the recipe that I developed at https://quine.io/recipes. The page has instructions for downloading the CSV files and running the recipe.

I welcome your feedback! Drop in to Quine Slack and let me know what you think. I'm always happy to discuss Quine or answer questions.

Because of the size, complexity, and feedback from the community, we decided to add a larger dataset. So the next dataset should be large, more complex, and recognizable. The choice was easy here; the industry-leading benchmark group Linked Data Benchmark Council (LDBC), which Memgraph is a part of, has open-sourced the datasets for benchmarking. The exact dataset is the social network dataset. It is a synthetically generated dataset representing a social network. It is being used in LDBC audited benchmarks, SNB interactive, and SNB Buissines intelligence benchmarks. Keep in mind that this is NOT an official implementation of an LDBC benchmark, the open-source dataset is being used as a basis for benchmarks, and it will be used for our in-house testing process and improving Memgraph.

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

Pull the source code from https://github.com/ldbc/ldbc_snb_datagen/tree/stable.To generate data for scale factor 1-1000, use the stable branch.