Decomposing Engine and Schema Effects in Database Performance: A Result-Equivalent E-Commerce Benchmark

Published database comparisons usually cover engine pairs, conflate engine and schema effects, and compare queries that compute different results. StreamCart, a realistic e-commerce benchmark, decomposes performance across an optimized relational baseline (PostgreSQL) and five specialized engines (MongoDB, Cassandra, Neo4j, Elasticsearch, and TimescaleDB), each at a naive and an engine-optimized level. All eleven configurations were measured in three independent runs, and the analytics results of Elasticsearch, and those of Neo4j on rolling revenue, were verified row by row. A naive port outperformed the baseline in two of 35 query cells, and no single engine dominated. Engine and implementation level explained 10.0% and 6.0% of the variance of log latency as main effects and 10.6% through their interaction. Optimized Elasticsearch answered the two revenue analytics queries 35 and 45 times faster than PostgreSQL, but load-time precomputation that PostgreSQL can adopt as well accounts for factors of 12 and 6.8. Latency grew less with dataset size for optimized Elasticsearch and Cassandra than for PostgreSQL, though their incremental growth was not distinguished, and against PostgreSQL with the same precomputation, only Elasticsearch on rolling revenue kept that advantage. PostgreSQL led in concurrent writes while acknowledging every insert after an fsync. All code, data, and timings are public.