Comprehensive database query test, with a focus on linked data and complex query.
Query on a given key, for showing baseline performance. For DBMS, use SELECT on specified key, for Graph DB, use their own methods to access a specified node.
- Sequential Reads
- Random Reads
- Sequential Writes
- Random Writes
Dataset: kv-dataset-generator.py
Query linked data, to find out the database support for relationships.
Dataset: linked-dataset-generator.py
Real World Data: Stanford Large Network Dataset Collection
Query graph connectivity.
Dataset: graph-dataset-generator.py
Query sub-graph.
Dataset: graph-dataset-generator.py
kv-query-yyyy-mm-dd-.csv ...
- Benchmarking Top NoSQL Databases, link
- Scalable SQL and NoSQL data stores, link
- Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j, link
- Survey of graph database performance on the HPC scalable graph analysis benchmark, link
- Survey of graph database models, link
- Graph Database Indexing Using Structured Graph Decomposition, link
- A comparison of a graph database and a relational database: a data provenance perspective, link
- Freebase: a collaboratively created graph database for structuring human knowledge, link
- Fast and practical indexing and querying of very large graphs, link ...