Claude
7b26ff014c
Companion benches/*.md (matching the repo convention) capturing the before/after numbers and the honest interpretation behind the read_prefetch 1->2 tuning and the runtime pool sizing. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01JG5yYZ9s868mJwqT2Qz7ez
94 lines
4.8 KiB
Markdown
94 lines
4.8 KiB
Markdown
# Tokio runtime tuning benchmark
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Measures the two things `build_runtime` (`src/main.rs`) changes versus the bare
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`#[tokio::main]` defaults, sized by `common::runtime::runtime_pool_sizes`:
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- **Worker count.** `#[tokio::main]` defaults to `available_parallelism()`, which
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honours CPU *affinity* (`sched_getaffinity`: cpuset, `taskset`) but **ignores
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the CFS bandwidth quota** (`docker --cpus`, cgroup v2 `cpu.max`, v1
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`cpu.cfs_quota_us`). On a 2-core-quota container on a many-core host it spawns
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one worker per *host* core. `effective_parallelism()` folds the quota back in.
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- **Blocking pool.** `#[tokio::main]` defaults to a flat `max_blocking_threads =
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512` — a multi-GB RSS blast radius for this heavy `spawn_blocking` user
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(thumbnails, transcode, zip, PDF/text extraction, Argon2 ≈19 MB/hash). The
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builder caps it at `max(32, 8 × workers)`.
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## Reproduce
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```bash
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cargo build --release --features bench --example bench_tokio_runtime
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# Pin to 2 cores to model a 2-core CPU quota on a bigger host:
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taskset -c 0,1 ./target/release/examples/bench_tokio_runtime
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# Part B uses a fixed glibc mmap threshold for a clean RSS read:
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MALLOC_MMAP_THRESHOLD_=131072 MALLOC_TRIM_THRESHOLD_=131072 \
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taskset -c 0,1 ./target/release/examples/bench_tokio_runtime
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# tunables: BENCH_CONCURRENCY=96 BENCH_SECONDS=4 BENCH_BURN_KB=256
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# BENCH_WORKERS_BEFORE=32 BENCH_BLOCKING_TASKS=96 BENCH_ALLOC_MB=16 BENCH_MAX_BLOCKING_AFTER=16
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```
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## Results (4-core box, pinned to 2 cores via `taskset -c 0,1`)
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### [A] Worker over-subscription under CPU contention
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96 concurrent async "requests", each an async hop + a 256 KiB BLAKE3 (models a
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handler that interleaves I/O with on-worker compute), over 4 s.
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| runtime | req/s | p50 µs | p99 µs |
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|-----------------------|-------:|-------:|-------:|
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| before: 32 workers | 46 854 | 121 | 60 360 |
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| after: 2 workers | 42 893 | 2 140 | 4 962 |
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→ **throughput −8.5 %, p99 latency −91.8 %** (after vs before)
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### [B] Blocking-pool RSS blast radius
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96 concurrent `spawn_blocking` tasks, 16 MiB resident each, held 120 ms
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(fixed glibc mmap threshold so freed allocations leave RSS promptly).
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| max_blocking_threads | peak RSS MiB | vs default |
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|-----------------------------|-------------:|-----------:|
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| before: 512 (tokio default) | 1 231 | — |
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| after: 16 (bounded) | 261 | −970 MiB |
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## Conclusions
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1. **Blocking-pool cap — clear win, no downside.** Bounding 512→16 cut peak RSS
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under a 96-task flood from **1231 MiB to 261 MiB (−970 MiB)**. The cap only
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engages under a pile-up; steady-state operation is unaffected, and the app's
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heaviest blocking consumers are already semaphore-limited (Argon2 = 2,
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thumbnail decode ≈ cores), so `max(32, 8×workers)` is generous headroom that
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simply removes the unbounded tail that can OOM-kill the process under a spike.
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2. **Worker sizing — a latency/throughput trade, favourable for a server.**
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Over-subscription (32 workers on 2 cores, what tokio's default does under a
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CFS quota) won **+8.5 % peak throughput** but at a **catastrophic p99 of
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60 ms** (12× the tuned 5 ms) with a bimodal distribution — some requests fly
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(p50 121 µs), others starve. Sizing to the quota (2 workers) gives uniform,
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predictable latency at a small throughput cost. For an interactive file
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server, p99 dominates UX (timeouts, head-of-line blocking), so this is the
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right trade.
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3. **This microbenchmark is a worst case *for* the tuned config.** It is pure
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on-worker CPU, which is exactly where over-subscription's throughput edge
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shows. Real OxiCloud handlers push CPU to `spawn_blocking` and the async
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workers mostly await I/O (DB, disk) — there the over-subscription throughput
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edge evaporates (idle workers just park) while its tail-latency penalty
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remains. Production should see the worker change as ≥ neutral on throughput
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and strictly better on tail latency.
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4. **No regression off-quota.** `effective_parallelism()` == `available_
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parallelism()` whenever there is no CFS quota (or affinity already restricts
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the process), so on bare metal / affinity-pinned deployments the worker count
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is unchanged from the old default. The change only bites under a CFS quota —
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precisely the case it fixes.
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5. **Follow-up:** the same `available_parallelism()` blind spot affects the
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image/rayon pools (`thumbnail_service.rs`, `image_transcode_service.rs`,
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`di.rs` video) — they over-spawn under a CFS quota too. Switching those to
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`common::runtime::effective_parallelism()` is the natural next step (left out
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here to keep this change focused on the runtime).
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Both knobs are env-overridable (`OXICLOUD_WORKER_THREADS` /
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`OXICLOUD_MAX_BLOCKING_THREADS`) and logged at startup ("Tokio runtime pools
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sized"), so operators can see and tune what is in effect.
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