Performance & benchmarks

The repository ships a benchmark suite (pytest-benchmark) that compares a direct MEF read against the gRPC server, with and without prefetch, over a generated dataset. The full write-up — methodology, access patterns, and results — lives in BENCHMARKS.md.

The three access patterns

	Pattern	When the server wins
A	Interactive viewing (page around, think between reads)	Prefetch overlaps decode with the user's think-time, and the cache serves re-reads.
B	Repeated reads of overlapping ranges	The shared tile cache serves what has already been decoded.
C	Automated single-pass processing (a detector walks the whole recording once)	At high channel counts, parallel decode across worker processes overlaps decode with per-window compute.

The native reader in a loop is the baseline in all three.

Running the benchmarks

Benchmarks generate large data and run a real server, so they are excluded from normal test runs. Opt in explicitly:

pip install -e ".[dev]"
pytest -m benchmark

Useful options:

pytest -m benchmark --benchmark-only            # timing only
pytest -m benchmark --benchmark-save=baseline   # save a baseline
pytest -m benchmark --benchmark-compare         # compare to the last saved run
pytest -m benchmark -s                          # print the setup block per benchmark

Each benchmark records the file/dataset and server config it ran under (attached to the result's extra_info), so results are self-describing. Results are machine-specific — record the host when publishing numbers.