ldbc_snb_datagen_spark VS ldbc_snb_interactive_v1_impls

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.

At first, the plan was to use only the LDBC dataset and write different queries for the dataset, but LDBC has a set of well-designed queries that were specifically prepared to stress the database. Each query targets a special scenario, also called “chock point.” Not to be mistaken, they do not have deep graph traversal doing around 100 hops, but they are definitely more complex than the ones written for the Pokec dataset. There are two sets of queries for the LDBC SNB: interactive and business intelligence. LDBC provides a reference Cypher implementation for both of these queries for Neo4j. We took those queries, tweaked the data types, and made the queries work on Memgraph. Again, to be perfectly clear, this is NOT an official implementation of an LDBC Benchmark; this goes for both interactive and business intelligence queries. The queries were used as the basis for running the benchmark.