quadstore-perf

The performance profile of quadstore is strongly influenced by its design choices in terms of atomicity. As all update operations are implemented through AbstractLevel#batch operations that atomically update all indexes, they are performed in a manner that closely approximates batch random updates.

The testing platform is a 2020 MacBook Pro (Apple Silicon M1 / arm64, 16 GB) running Node v20.19.0.

Reading quads

Sequential reads iterating through quads in any given index run at about ~1.7M quads per second.

node dist/read.js

Importing quads

Our reference benchmark for import performance is the level-bench batch-put benchmark, which scores ~1M updates per second when run as follows:

node level-bench.js run batch-put leveldown --concurrency 1 --chained true --batchSize 10 --valueSize 256

We test import performance by importing the 21million.rdf file or a subset of it.

node dist/loadfile.js /path/to/21million.rdf

With the default six indexes and the classic-level backend, import performance clocks at ~44k quads per second when importing quads one-by-one, with a density of ~6.7k quads per MB. Due to the six indexes, this translates to ~264k batched update operations per second, ~0.25 times the reference target.

Setting the batchSize parameter to 128 leads to quads being imported in groups of 128, which boosts performance up to ~65k quads per second, roughly ~0.45 times the reference target when accounting for the six indexes.