One run of scripts/bench/run.ts against the seeded account (1,266,549
measurement rows, anchor 2026-10-22), 20 measured requests per row after 3
warm-up requests, web-only production build, 792 MB heap ceiling, Postgres
16.15. The commit adds only lint rules, guards, tests and scripts on top of
v1.44.0, so the server code measured is the v1.44.0 code.
Cold (caches evicted before each request), p95 and statements per request:
/api/auth/me 17 ms / 19, dashboard summary 233 ms / 31, dashboard snapshot
2253 ms / 158, pulse series 30 d 59 ms / 8, 365 d 311 ms / 8, 3650 d
2118 ms / 8, day view 78 ms / 49, insights health status 140 ms / 68,
correlations 105 ms / 45. Warm, the snapshot is 24 ms / 13 and the 3650-day
series still 1506 ms, since that read is not cached. The 5,000-row batch
ingest took 1722 ms and 424 statements over ten requests.
The machine was shared with other work during the run (load average 8 to
20), so latency is indicative; the statement counts do not depend on it.
The results directory is written by the script and left out of prettier,
so a re-run diffs cleanly.
scripts/bench/seed-large-account.ts (moved from scripts/, extended) writes
one synthetic account of ten years: 1,265,561 measurement rows across the
main types and five sources (Apple Health dense spot rows, Withings blood
pressure and scale, a weekly manual weight, three years of Oura nightly
values, 90 days of Nightscout CGM), `stats:` and per-sample cumulative rows,
nightly sleep stages, 3,020 workouts, 1,095 mood entries and three
medications with 3,285 intake events. Everything goes in through set-based
INSERT … SELECT generate_series; values and timestamps are functions of the
row index and `--anchor`, so a run is reproducible. The rollup tier is
folded with recomputeUserRollups, as the boot backfill does, and the tables
are analysed. About 80 s on the Mac mini.
scripts/bench/run.ts boots the production build once with every role (so
the queues exist), then measures against a web-only process under the
production heap ceiling: p50, p95 and max latency and SQL statements per
request (pg_stat_statements, read once nothing is in flight and the count
holds still) for /api/auth/me, the dashboard summary and snapshot, the pulse
series over 30, 365 and 3650 days, the day view, insights health status and
correlations, each cold (caches evicted by a one-row manual-sync ingest
before every request) and warm, plus a 5,000-row batch ingest as ten
requests of 500. It writes JSON and a markdown table.
docs/contributing/benchmarks.md describes the setup against a throwaway
Postgres in Docker with the production pg_stat_statements settings.
Marc-André Bombeck