bench/

lantern bench harness (testbed/bench/)

Reusable performance + memory-leak harness for the HA docker compose cluster (see deploy/compose/). Drives the cluster with ghz, captures Prometheus range queries + Go pprof snapshots, applies a per-scenario leak gate, and renders a Markdown report.

PR CI does not run this harness. Per the parent epic #237, the default PR pipeline runs only fast functional checks; this harness is a local / on-demand tool meant to be used by maintainers when investigating performance or leak regressions. Its outputs (under testbed/bench/out/) are git-ignored.

Release CI does run a smoke subset. On every vX.Y.Z tag push, the Release workflow runs ./testbed/bench/release.sh — a driver that sweeps the scenarios listed in release-scenarios.txt (currently the smoke_* variants) and produces a fixed-format bench-report.md that is spliced into the GitHub Release notes. The bench job is continue-on-error, so a noisy runner cannot block a release. See issue #256.

Prerequisites

• Docker + docker compose v2
• ghz ≥ 0.117
• yq v4 (Go reimplementation)
• jq, curl, bash ≥ 4
• Go (matching go.mod toolchain) — used to build the report renderer

Quick start

# Run any scenario:
./testbed/bench/run.sh write_heavy

# Keep the cluster up after the run (useful for ad-hoc poking):
KEEP_UP=1 ./testbed/bench/run.sh read_heavy

# Reuse an already-running cluster (skip compose up / down):
SKIP_UP=1 KEEP_UP=1 ./testbed/bench/run.sh mixed_rw

# Also capture a 30s CPU profile per replica after the steady phase:
PPROF_CPU=1 ./testbed/bench/run.sh addedge_contention

The exit code mirrors the leak-gate verdict (0=pass, 1=fail). Run artifacts are written under testbed/bench/out/<scenario>/<UTC-timestamp>/.

Scenarios

Each YAML declares the phases (warmup, steady, cooldown), the ghz target (call + data_template), optional subscribe and chaos blocks, and the leak-gate thresholds (goroutine_max_delta, heap_alloc_max_delta_mb). The gate evaluates against heap_alloc (post-GC live bytes), forcing a runtime.GC() via /debug/pprof/heap?gc=1 before each snapshot so the reading reflects live memory rather than span-level allocator headroom. The legacy field name heap_inuse_max_delta_mb is still accepted for backward compatibility but produced false-positive verdicts under sustained churn — see issue #248.

Output layout

testbed/bench/out/<scenario>/<ts>/
├── report.md                       # rendered summary (start here)
├── leak_gate.json                  # verdict + per-replica deltas + thresholds
├── runtime_pre.json                # per-replica go_goroutines + heap_alloc/heap_inuse/heap_objects, after warmup
├── runtime_post.json               # same, after cooldown
├── ghz_warmup_<endpoint>.json      # raw ghz results, one per invocation
├── ghz_steady_<...>.json
├── ghz_sub*_<n>.json               # if subscribers were launched
├── prom/
│   ├── _index.ndjson               # {query, file} pairs
│   └── q_NN.json                   # raw Prom /api/v1/query_range output
└── pprof/
    └── <endpoint>__{pre,post}__{heap,goroutine,allocs,mutex,block[,profile]}.pb.gz

Drill-down workflow

1. Verdict & deltas. Open report.md. The leak-gate table shows the pre/post goroutines + heap_alloc + heap_inuse + heap_objects per replica with the Δ vs the scenario threshold. The verdict is driven by heap_alloc (post-GC live bytes); heap_inuse and heap_objects are shown for context. Any cell exceeding the threshold is the first place to look.

2. Allocation source. For a leaking replica, diff its heap profiles:

   go tool pprof -http=:0 \
     -base testbed/bench/out/<sc>/<ts>/pprof/<ep>__pre__heap.pb.gz \
            testbed/bench/out/<sc>/<ts>/pprof/<ep>__post__heap.pb.gz
   

3. Stuck goroutines. Open <ep>__post__goroutine.pb.gz and look for blocked stacks (e.g. on channel send/recv, on a sync.Mutex). For addedge_contention the mutex and block profiles (<ep>__post__mutex.pb.gz, <ep>__post__block.pb.gz) are the primary signal.

4. Tail latency. Cross-reference the ghz p99 column in report.md with the matching Prom histogram queries listed under "Prometheus range queries" (raw data in prom/q_NN.json).

5. Pubsub (#240). many_subscribers and replication_soak exercise the subscription queue. Inspect the per-policy lantern_subscription_dropped_total and lantern_subscription_queue_depth_bucket queries in prom/.

Why not in CI?

This harness needs real wall-clock time (multi-minute steady phases, a 10-minute soak), a healthy Docker daemon, and stable host conditions to produce trustworthy numbers. Running it in CI would be both slow and flaky. The parent epic (#237) explicitly carves out this harness as a maintainer-driven tool; functional regressions are covered by the existing unit + integration suites that CI already runs.