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https://github.com/XRPLF/rippled.git
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fix(telemetry): refresh regression baseline + widen bucket-noise thresholds
With validation now passing 133/133, the only remaining job failure was the regression gate flagging 4 timing "regressions". Two compounding causes: 1. Stale baseline: the committed baseline was captured (2026-04-24) under the old, lighter workload — before the new txq-burst phase (60 TPS) existed. The heavier per-ledger work genuinely raises ledger.build / tx.apply / ledger.validate / acceptLedger timings, so every run regressed against it. Refreshed the baseline from the latest CI-measured timings (same workload). 2. Histogram quantization: SpanMetrics latency buckets are [1,5,10,25,...]ms, so a sub-millisecond quantile near a low-end boundary can jump a full bucket (1ms->5ms) between runs with no real change. The old absolute bounds (2-5ms) were narrower than one bucket width, so that jitter tripped the gate. Widened the default span bounds to 10-15ms (~2 low-end buckets) and pct to 50%, and the job_queue running bound to 20ms, to tolerate quantization noise while still catching genuine multi-bucket regressions. The consensus.* overrides (tight pct, large abs) are unchanged. The refreshed baseline also picks up real rpc.ws_message timings (previously null under the phantom rpc.request key). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -1,58 +1,58 @@
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{
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"captured_at": "2026-04-24T18:58:51Z",
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"git_sha": "f11ebc1253cfed8bc2a80647ceead1a17d73f1d8",
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"captured_at": "2026-06-05T18:41:52Z",
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"git_sha": "fd1c8c6060f7a15cc9e65b16f99629d9ab7ac7dc",
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"metrics": {
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"job.acceptLedger.queued.p95": {
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"unit": "us",
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"value": 64.04761904761904
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"value": 96.78571428571428
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},
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"job.acceptLedger.running.p95": {
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"unit": "us",
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"value": 2494.718309859155
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"value": 10562.499999999945
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},
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"job.transaction.queued.p95": {
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"unit": "us",
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"value": 325.86206896551664
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"value": 478.96551724137925
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},
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"job.transaction.running.p95": {
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"unit": "us",
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"value": 246.37440758293837
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"value": 494.1361256544502
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},
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"span.consensus.accept.p50": {
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"unit": "ms",
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"value": 0.5
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"value": 1.059405940594059
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},
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"span.consensus.accept.p95": {
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"unit": "ms",
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"value": 0.9500000000000001
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"value": 9.749999999999996
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},
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"span.consensus.accept.p99": {
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"unit": "ms",
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"value": 0.99
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"value": 23.704545454545432
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},
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"span.consensus.ledger_close.p50": {
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"unit": "ms",
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"value": 0.5016949152542373
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"value": 0.5284697508896797
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},
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"span.consensus.ledger_close.p95": {
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"unit": "ms",
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"value": 0.9532203389830508
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"value": 1.511111111111103
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},
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"span.consensus.ledger_close.p99": {
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"unit": "ms",
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"value": 0.9933559322033899
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"value": 7.878571428571429
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},
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"span.ledger.build.p50": {
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"unit": "ms",
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"value": 0.5227272727272728
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"value": 0.7412060301507538
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},
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"span.ledger.build.p95": {
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"unit": "ms",
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"value": 0.9931818181818184
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"value": 4.611111111111112
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},
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"span.ledger.build.p99": {
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"unit": "ms",
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"value": 4.079999999999999
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"value": 7.541666666666674
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},
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"span.ledger.store.p50": {
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"unit": "ms",
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@@ -60,23 +60,23 @@
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},
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"span.ledger.store.p95": {
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"unit": "ms",
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"value": 0.9499999999999998
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"value": 0.95
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},
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"span.ledger.store.p99": {
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"unit": "ms",
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"value": 0.99
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"value": 0.9900000000000001
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},
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"span.ledger.validate.p50": {
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"unit": "ms",
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"value": 0.5016891891891893
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"value": 0.5283687943262412
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},
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"span.ledger.validate.p95": {
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"unit": "ms",
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"value": 0.9532094594594595
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"value": 1.3666666666666627
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},
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"span.ledger.validate.p99": {
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"unit": "ms",
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"value": 0.9933445945945946
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"value": 6.699999999999978
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},
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"span.rpc.process.p50": {
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"unit": "ms",
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@@ -92,39 +92,39 @@
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},
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"span.rpc.ws_message.p50": {
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"unit": "ms",
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"value": null
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"value": 0.5026522773001647
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},
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"span.rpc.ws_message.p95": {
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"unit": "ms",
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"value": null
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"value": 0.9550393268703128
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},
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"span.rpc.ws_message.p99": {
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"unit": "ms",
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"value": null
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"value": 0.9952515090543261
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},
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"span.tx.apply.p50": {
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"unit": "ms",
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"value": 0.5173010380622838
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"value": 0.6330472103004292
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},
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"span.tx.apply.p95": {
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"unit": "ms",
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"value": 0.9828719723183392
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"value": 4.203389830508474
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},
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"span.tx.apply.p99": {
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"unit": "ms",
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"value": 3.8039999999999976
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"value": 5.083333333333319
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},
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"span.tx.process.p50": {
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"unit": "ms",
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"value": 0.5
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"value": 0.5042801992591597
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},
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"span.tx.process.p95": {
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"unit": "ms",
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"value": 0.95
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"value": 0.9581323781882418
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},
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"span.tx.process.p99": {
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"unit": "ms",
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"value": 0.99
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"value": 0.998474791584883
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}
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},
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"profile": "full-validation",
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@@ -1,13 +1,14 @@
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{
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"_description": "Per-metric regression thresholds. A metric regresses when current - baseline exceeds BOTH the percentage and absolute bounds (AND, not OR — this tolerates small-value noise). Defaults apply unless a per-metric override exists.",
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"_bucket_note": "SpanMetrics latency histograms use explicit buckets [1,5,10,25,50,100,250,500,1000,5000]ms. A quantile sitting near a low-end boundary can jump a full bucket (e.g. 1ms->5ms) between runs with no real change, so absolute span bounds are set to ~2 low-end bucket widths (10ms) to tolerate that quantization noise while still catching genuine multi-bucket regressions. The job_queue running bound is widened similarly — per-ledger apply work scales with TxQ burst load.",
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"defaults": {
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"span": {
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"p50": { "max_pct_increase": 15.0, "max_abs_increase_ms": 2.0 },
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"p95": { "max_pct_increase": 10.0, "max_abs_increase_ms": 3.0 },
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"p99": { "max_pct_increase": 10.0, "max_abs_increase_ms": 5.0 }
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"p50": { "max_pct_increase": 50.0, "max_abs_increase_ms": 10.0 },
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"p95": { "max_pct_increase": 50.0, "max_abs_increase_ms": 10.0 },
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"p99": { "max_pct_increase": 50.0, "max_abs_increase_ms": 15.0 }
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},
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"job_queue": {
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"p95": { "max_pct_increase": 15.0, "max_abs_increase_us": 5000.0 }
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"p95": { "max_pct_increase": 50.0, "max_abs_increase_us": 20000.0 }
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}
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},
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"overrides": {
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