Files
Eric Allam 60d71da90e perf(webapp,run-engine): cut CPU on the engine-facing worker-action routes (#4746)
Cuts CPU on the `engine/v1/worker-actions/*` routes a managed supervisor
calls, and adds the benchmark harness the numbers come from.

Measured on a local stack: **on-CPU per completed run 9.07ms → 6.59ms
(−27%)**, busy fraction 45.6% → 33.8%, with every worker-action p50 down
23–27%. Load was 5,000 runs / 24 virtual supervisors / 90s window /
30,120 requests / 0 errors.

Query-count work from the same investigation is deliberately **not**
here — it will follow as a separate PR.

## The three changes

**1. Split the event-loop monitor in two (~14% of on-CPU, plus ~5pp of
GC).**

`eventLoopMonitor.server.ts` installs a global `async_hooks` hook:
`init` writes a `Map` entry for *every* async resource the process
creates, `before` calls `process.hrtime()` and `context.active()` on
every one. Enabling any async hook also puts V8 on the slow path for
promise instrumentation process-wide. `EVENT_LOOP_MONITOR_ENABLED`
defaulted to `"1"`, so this was the shipping configuration.

The blocked-loop detector is now opt-in (`EVENT_LOOP_MONITOR_ENABLED`,
default `0`). The event-loop *utilization* gauge — a single interval
timer with no per-request cost — moves to its own flag
(`EVENT_LOOP_UTILIZATION_MONITOR_ENABLED`, default `1`) and stays on, so
the useful half survives without the expensive half.

A/B under identical load:

| | monitor on | monitor off | change |
|---|---|---|---|
| on-CPU per run | 9.08ms | 7.25ms | −20% |
| GC self time | 9.80% | 5.05% | −4.75pp |
| dequeue p50 | 76.6ms | 62.8ms | −18% |
| attempts/start p50 | 56.3ms | 43.5ms | −23% |

**2. Bucket route matching by first static path segment (10.4% → 3.9% of
on-CPU).**

`patches/@remix-run__router@1.23.3.patch` already memoized flattened
branches and compiled path regexes. What remained was the linear scan:
`matchRouteBranch` walked the ranked branch list calling `matchPath` per
branch across 521 route files, so every worker-action request paid a
scan proportional to the whole route table.

Branches are now indexed by their lowercased leading segment, with one
always-considered list for branches whose leading segment is dynamic,
splat or optional (and for root/pathless paths). A request walks only
its own bucket merged with that list. Route-matching self time dropped
64% (3.6s → 1.3s over a 90s window).

Ordering is preserved exactly: both lists hold indexes into the already
rank-sorted branch array and are walked in ascending-index order, so the
first match found is the same branch the full scan would have found.
Bucketing lowercases on both sides, so case-insensitive matching still
resolves and `caseSensitive: true` routes are still rejected by
`matchPath` itself. A pathname whose own leading segment can't be
bucketed falls back to the full scan.

Verified equivalent to the unpatched matcher over 20,050 pathnames
(literal, dynamic, splat, optional, case variants, basenames,
percent-encoded) with zero mismatches.
`apps/webapp/test/routeMatchingPatch.test.ts` pins the matching
semantics rather than the optimisation, so it still passes without the
patch.

**3. Demote per-heartbeat and per-dequeue `info` logs to `debug`.**

These are the two highest-rate engine calls and each wrote a synchronous
structured log line on every request. Synchronous `console` writes can
block the loop when stdout backs up, which costs more than the ~1.3% CPU
share suggests.

## The harness

Two benchmarks, neither in the default suite (they run for minutes,
attach the V8 profiler, and report numbers rather than assert on them).
See `apps/webapp/test/bench/README.md`.

- `apps/webapp/test/bench/engineHttp.bench.test.ts` — spawns a real
webapp against throwaway Postgres/Redis containers, seeds a production
environment with a promoted managed deployment, and drives a closed-loop
supervisor pool through the full lifecycle. Profiling runs over CDP
rather than `--cpu-prof` so it covers only the measured window instead
of being swamped by boot, and `performance.eventLoopUtilization()` is
sampled *inside* the webapp process.
-
`internal-packages/run-engine/src/engine/bench/runEngineLifecycle.bench.test.ts`
— drives `RunEngine` directly, profiling enqueue and lifecycle
separately so engine cost isn't mixed with request-stack overhead.
- `apps/webapp/test/bench/analyzeProfile.ts` — dependency-free
`.cpuprofile` analyzer that symbolicates through the build's source maps
and ranks CPU by package, self time and total time. Percentages are
shares of on-CPU time (V8's `(idle)`/`(program)` excluded).

`startWebapp` gains `overrideEnv`, applied after the worker-disable
defaults, so the HTTP bench can re-enable the run engine worker that
drains the master queue into the worker queues a supervisor dequeues
from.

The local OTel collector gains a traces pipeline. It only defined a
metrics pipeline, so pointing `INTERNAL_OTEL_TRACE_EXPORTER_URL` at it
locally failed and the webapp silently fell back to the console span
logger.

## Configuration

For operators upgrading:

- `EVENT_LOOP_MONITOR_ENABLED` (now defaults to `0`) — the
per-async-resource blocked-loop detector. Set to `1` to restore the
previous behaviour and keep emitting `event-loop-blocked` spans.
- `EVENT_LOOP_UTILIZATION_MONITOR_ENABLED` (new, defaults to `1`) — the
`nodejs.event_loop.utilization` gauge. Unchanged in behaviour; it just
has its own flag now so it survives turning the detector off.

## Notes for review

- `pnpm-lock.yaml` changes only because the router patch content
changed, which changes its patch hash.
- One thing the profile ruled out: with a real OTLP collector receiving
spans, tracing costs ~1.7% of on-CPU at 100% sampling and ~0.8% at the
production rate. Span shipping is not a hidden cost, so nothing here
touches it.
- Caveats on the numbers: a laptop, not production hardware, so DB and
Redis *latency* are unrepresentative (client-side CPU is what's ranked);
single webapp process; throughput varies ~5% run to run, which is why
the claims rest on on-CPU per run rather than req/s.

## Verification

- 20,050-pathname router equivalence check vs the unpatched matcher,
zero mismatches
- `apps/webapp/test/routeMatchingPatch.test.ts` (12 cases) passes
- webapp e2e smoke suite (68 tests) passes through the patched router
- run-engine suites covering the snapshot/attempt paths pass
- `typecheck`, `format`, `lint`, `knip` clean
2026-08-21 11:53:16 +01:00
..

Test containers

Vitest utilities for writing tests against real Postgres, Prisma, Redis and ClickHouse - we don't mock (see the root CLAUDE.md), we boot containers. Also exposes a duration-weighted shard sequencer for splitting slow suites across CI shards.

Choosing a fixture

Most tests share one set of containers per vitest worker (booted once, reset between tests) - this is much faster than a container per test. Reach for an isolated variant only when a test needs it.

Fixture Postgres Redis ClickHouse Use for
redisTest - shared - redis-only tests
postgresTest shared (clone) - - db-only tests
containerTest shared (clone) shared shared the default - needs all three
isolatedRedisTest - per-test - background redis work (see below)
containerTestWithIsolatedRedis shared (clone) per-test shared background redis work + db/clickhouse
replicationContainerTest per-test per-test shared Postgres→ClickHouse logical replication

"shared (clone)" = one Postgres per worker with a template database; each test gets a fast CREATE DATABASE ... TEMPLATE clone, so schema isn't re-pushed per test.

The background-work gotcha

If a test spawns work that outlives the test body - a RunEngine, a redis-worker Worker, a BatchQueue - and that work isn't fully drained before the test ends, you must use an isolated redis fixture (isolatedRedisTest / containerTestWithIsolatedRedis).

On the shared fixture, the leaked background loop keeps polling the one worker-scoped redis after the test's clients close, bleeding into the next test. The symptom is an intermittent "Connection is closed" error or a test that hangs until its timeout. FLUSHALL between tests does not fix this - it clears data, not live connections/loops, so per-test key prefixes won't help either. A plain db/redis test with no lingering background work is fine on the shared fixtures.

Sharding (./sequencer)

CI splits the slow suites with vitest --shard=i/N. DurationShardingSequencer replaces vitest's default file-count split with a duration-weighted one: it reads test-timings.json at the repo root ({ "<repo-relative path>": <ms> }) and greedily bin-packs files so each shard does roughly equal work, not an equal number of files. The packing is deterministic, so every shard computes the same bins and runs each file exactly once.

Configs opt in via:

import { DurationShardingSequencer } from "@internal/testcontainers/sequencer";
// in defineConfig:
test: {
  sequence: {
    sequencer: DurationShardingSequencer,
  },
}

Adding tests - nothing to do

New test files are discovered by vitest's glob and sharded automatically. A file with no entry in test-timings.json is given the median duration as a fallback, so it's still placed on exactly one shard - correctness never depends on the timings being present or current.

What the timings affect is balance. A new heavy test estimated at the median can be under-weighted and land on an already-full shard, making that shard slower. There's headroom between the current makespan and the CI budget to absorb this, so it tolerates drift - but if a shard creeps toward the budget, refresh the timings.

Refreshing test-timings.json

Measure each shard with the JSON reporter and write per-file endTime - startTime (ms), keyed by repo-relative path, back into test-timings.json. Set GITHUB_ACTIONS=true so suites that skipIf(CI) are excluded, matching what actually runs on CI:

GITHUB_ACTIONS=true pnpm exec vitest run --reporter=json --outputFile=/tmp/run.json

Stale entries for deleted/renamed files are harmless (they're simply ignored). This is a periodic chore, not a per-PR one.