ldbc_snb_datagen_hadoop VS ldbc_snb_interactive_v1_impls

cd nebula-bench sudo yum install -y git \ make \ file \ libev \ libev-devel \ gcc \ wget \ python3 \ python3-devel \ java-1.8.0-openjdk \ maven pip3 install --user -r requirements.txt # Using `snb.interactive.1` parameter in ldbc_snb_datagen_hadoop, for more infor https://github.com/ldbc/ldbc_snb_datagen_hadoop/wiki/Configuration python3 run.py data # Date generated by mv mv target/data/test_data/ ./sf1

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.