Benchmarks

Placeholder data

The numbers below are realistic placeholders establishing the methodology and chart pipeline. They will be replaced with measured bisonbench --json output; until then, treat shapes as indicative and absolute values as illustrative. The honest-numbers policy applies here hardest of all.

Methodology

• Single bisond instance, local loopback, default settings unless stated.
• Dataset: the 29,470-document ZIP-code collection used throughout these docs, plus synthetic documents for the 1M-row runs.
• Each measurement: warm-up run discarded, median of 5 reported.
• Hardware and exact versions are embedded in benchmarks.json alongside the data, so charts and provenance travel together.

Insert throughput: The price of fsync

Batched inserts amortize the fsync; per-document fsync pays the full disk-flush latency on every write. The ~25× gap is the storage layer's honesty made visible: durability is a real cost, and the append-only design makes it a single fsync per batch rather than one per touched file.

Reproduce: bisonbench insert --docs 100000 --batch 500 vs --batch 1.

The headline: indexed range vs full scan

Note the log scale. A full scan reads every document and runs the matcher on each; the indexed query seeks the B+Tree and reads only matches. The gap widens linearly with collection size; this chart is the B+Tree page in one picture.

Reproduce: bisonbench query --filter '{pop:{$gte:40000}}' --with-index --without-index.

docsExamined: The same story without a stopwatch

Latency depends on hardware; docsExamined doesn't. The planner's explain output reports it for any query; this chart is simply explain run on the ZIP dataset before and after createIndex('pop'), and you can reproduce it in the shell in four statements.

What is deliberately not benchmarked

We do not benchmark comparisons against MongoDB, SQLite, or Postgres. They would be misleading in both directions; those engines do vastly more work per operation (transactions, WAL, statistics), and benchmark theater is the opposite of this project's point. The numbers here measure BisonDB against itself: the cost of durability and the value of an index.