rippled/docker/telemetry/workload/regression-metrics.json

{
  "_description": "Metric surface for the OTel-driven regression gate. Each entry names a metric, the quantiles to capture, and how to query Prometheus. The comparator compares current run against baseline-timings.json under these exact keys.",
  "_key_format": "{category}.{name}.p{quantile}  (e.g. span.tx.process.p99, job.transaction.queued.p95). Only the categories defined below are captured; there is no rpc_methods group, so no rpc.* key is produced or gated (FU-4).",
  "_excluded_spans": "rpc.process is deliberately absent from spans.names. It is created only in ServerHandler::processRequest() on the HTTP/JSON-RPC path, which the workload load generators, being WebSocket-only, never reach, so its quantiles were captured as null every run and could never gate. (The harness shell scripts do issue a few HTTP JSON-RPC health polls, far too few to produce a meaningful quantile.) See baselines/README.md.",
  "_excluded_ledger_store": "ledger.store is deliberately absent from spans.names too, for a different reason: it is below the ladder's resolution. The 2026-08-24 capture returned p50/p95/p99 of exactly 0.005/0.0095/0.0099 ms, which is 0.5/0.95/0.99 x the ladder's first edge of 0.01 ms — the signature of every sample landing in the first bucket, so the numbers are interpolation arithmetic on the bucket floor rather than latencies. That is physically plausible: LedgerMaster.cpp:470 wraps an in-memory ledgerHistory_.insert, which completes in single-digit microseconds. While all mass stays under 10 us the reported quantile cannot move materially, so NO absolute bound can gate it — every ledger.store slowing from 2 us to 9 us, 4.5x, leaves the reported value unchanged. Three keys that read as covered but cannot fire are worse than no keys (the same argument that excluded rpc.process), so they were removed rather than left in with a bound that looks derived. Restoring the key needs sub-10us edges on the collector's spanmetrics ladder (for example 0.001ms and 0.005ms) plus the matching entries in HistogramBuckets.h — that is the ladder's branch, not this file. ledger.store presence is still asserted by expected_spans.json and docker/telemetry/integration-test.sh, and its rate is still on the ledger-operations dashboard; only the latency gate drops it.",
  "_excluded_quantiles": "A THIRD KIND OF EXCLUSION, and the only one that deleting a name cannot express. spans.names lists span NAMES while _quantiles is shared across all of them, so the declared surface is the names x quantiles product and dropping ONE quantile of ONE span needs a subtraction. excluded_keys below is that subtraction: a flat {category}.{name}.p{quantile} key, exactly as _key_format defines it, mapped to the reason it is not gated. It can only ever remove a key, never add one, so a typo cannot silently start gating something new -- and check_regression_bounds.py rule F rejects an entry that would not otherwise be declared, an entry with an empty reason, and an entry that still carries a threshold override or a baseline value, so the exclusion cannot rot into dead config. Both prom_queries.py (which builds the capture plan) and check_regression_bounds.py (rule A) subtract it, so an excluded key is not queried, never reaches timings.json, and is not expected in the baseline. NOTHING ELSE CHANGES: the quantile is still computable from Prometheus with the _query_template above, the span is still asserted by expected_spans.json, and its rate is still on the ledger-operations dashboard. Only the latency gate drops it.",
  "_excluded_shape": "ALL FIVE ENTRIES BELOW SHARE ONE SHAPE, and it is worth naming because it will recur: the observed maximum across CI runs exceeds (baseline + bound), so an ordinary run clears the trip point with nothing having regressed. Two mechanisms produce that, and both are visible here. (1) A baseline that lands in the ladder's LOW buckets gets a tiny derived bound, because the bound IS the distance to the next edge up -- span.tx.apply.p50 at 0.0060 ms sits in the first bucket (0, 0.01] and gets 0.0440 ms of headroom, against a metric that has been measured at 2.3378 ms. (2) A spread so large that no bucket of headroom could absorb it -- span.ledger.validate.p99's 66.8x range reaches 25.8750 ms against a 10 ms trip point even though its bound is a comparatively generous 8.94 ms. The first mechanism is the one that excludes three keys here, and it is a property of WHERE THE CAPTURED RUN LANDED rather than of the metric: the same span.tx.apply.p50 has read 0.7917 ms, mid-distribution, where the identical rule produces a 4.21 ms bound that absorbs the whole range. Whether the gate functioned was therefore decided by luck of the draw. THE FOLLOW-UP THAT WOULD RESTORE COVERAGE, stated so it is not left implied: a baseline captured from a SINGLE run cannot support these keys, because one sample carries no information about spread and the bound is derived from that one sample alone. What would let them be gated again is a multi-run baseline -- or a spread measurement captured alongside the baseline, so a bound can be sized against observed variance instead of against the ladder only. That is not implemented; it is the design change these five exclusions are waiting on. Until then, do NOT re-gate any of them by re-baselining until a run happens to land favourably, which is the failure this note exists to prevent.",
  "excluded_keys": {
    "span.consensus.ledger_close.p50": "Run-to-run variance exceeds the bound this ladder can derive, the same limit as the ledger.validate pair and the same mechanism as the two sibling p50 keys excluded alongside it. Baseline 0.0387 ms sits in the low bucket (0.01, 0.05], so hi_next is 0.1 ms and the derived bound is 0.0613 ms -- a 2.58x trip point. Measured across three CI runs the value spans 0.0387 to 0.2377 ms, a 6.1x spread (5.9x over four runs), and run 32867433073 read 0.2377 ms, 2.38x the trip point, on the SAME post-path-finding-removal workload as this baseline. So a healthy run reddens CI. This is a variance limit, not a defect and not a missing bound: widening is unavailable, because a bound tolerating 0.2377 ms would reach past the 0.25 ms edge and gate almost nothing. Do NOT re-gate by widening, and do NOT re-baseline until a run lands higher -- see _excluded_shape.",
    "span.ledger.build.p50": "The same mechanism as span.consensus.ledger_close.p50, one bucket up. Baseline 0.1151 ms sits in (0.1, 0.25], so hi_next is 0.5 ms and the bound is 0.3849 ms -- a 4.34x trip point. Across three CI runs the value spans 0.1151 to 2.3826 ms, a 20.7x spread (25.3x over four runs), and the observed maximum is 4.77x the trip point. Note what the previous baseline hid: at 1.0612 ms the same rule gave a 8.94 ms bound and a 10 ms trip point, which absorbed the entire range, so this key read as gated purely because that capture landed mid-distribution. Ledger construction is the hot path this gate most wants to guard, which makes the loss real and worth fixing properly -- with a baseline that carries spread information, not with a wider bound.",
    "span.tx.apply.p50": "The most extreme case of the low-bucket mechanism, and the clearest evidence that a single-run baseline cannot size a bound for these keys. Baseline 0.00597 ms lands in the ladder's FIRST bucket (0, 0.01], so hi_next is 0.05 ms and the bound is 0.0440 ms. Across three CI runs the value spans 0.00597 to 2.3378 ms, a 391.8x spread (364x over four runs), putting the observed maximum at 46.76x its trip point -- by far the worst of the five. The previous baseline read 0.7917 ms for the same key on the same workload, a 132x difference between two runs, and at that value the identical rule produced a 4.21 ms bound whose 5 ms trip point absorbed the full range. Nothing about the metric changed between those two captures; only where the sampled run fell in its own distribution did. Separately, a baseline inside the first bucket means the reported figure is interpolation across that bucket and tracks the FRACTION of applies finishing under 10 us rather than a latency, which is the ledger.store problem in embryo -- so restoring this key needs a finer low-end ladder as well as a spread-aware baseline. Rule E does not flag it because the value is not quantile x first_edge exactly.",
    "span.ledger.validate.p95": "Run-to-run variance is larger than the bound this ladder can derive. Measured across four CI runs the value spans 0.1281 to 0.7500 ms, a 5.9x spread, against a baseline of 0.2404 ms whose trip point is the next ladder edge at 0.5 ms -- so an ordinary run clears the trip point with nothing having regressed. Run 32867433073 read 0.7500 ms, +212%, and turned CI red. The derived bound models QUANTIZATION noise only (hi_next - baseline is one bucket of headroom); the dominant noise term for this span is peer-validation arrival timing in a 5-node cluster, and that term was never measured before the key was gated. Widening is not available: a bound that tolerated 0.7500 ms would reach past the 1 ms edge and leave the key gating nothing. This is a variance limit, not a defect and not a missing bound -- do NOT re-gate it by widening.",
    "span.ledger.validate.p99": "The same mechanism as p95, two orders of magnitude worse. Across the same four runs the value spans 0.3875 to 25.8750 ms, a 66.8x spread, against a baseline of 1.0600 ms and a 10 ms trip point; run 32862589645 read 25.8750 ms, +2341%. The span opens only once a quorum-completing validation arrives (LedgerMaster.cpp:1003, inside checkAccept, past the tvc < minVal early return) and wraps the promotion work that follows -- setValidated, setFull, setValidLedger, pendSaveValidated -- so its duration tracks peer-validation arrival timing and what promotion then triggers. One slow consensus round therefore dominates the tail of a 3m rate window, and which round that is differs every run. A bound tolerating 25.8750 ms would be ~24.8 ms against a 1.0600 ms baseline, which gates nothing at all. Note that the two CI failures landed on DIFFERENT quantiles in different runs while the other quantile stayed well inside its bound in the same run: that asymmetry is the signature of variance, not of a regression."
  },
  "spans": {
    "_query_template": "histogram_quantile({quantile}, sum by (le) (rate(span_duration_milliseconds_bucket{span_name=\"{name}\"}[{window}])))",
    "_unit": "ms",
    "_quantiles": [0.5, 0.95, 0.99],
    "names": [
      "rpc.ws_message",
      "tx.process",
      "tx.apply",
      "ledger.build",
      "ledger.validate",
      "consensus.ledger_close",
      "consensus.accept"
    ]
  },
  "job_queue": {
    "_queued_template": "histogram_quantile({quantile}, sum by (le) (rate(job_queued_us_bucket{job_type=\"{name}\"}[{window}])))",
    "_running_template": "histogram_quantile({quantile}, sum by (le) (rate(job_running_us_bucket{job_type=\"{name}\"}[{window}])))",
    "_unit": "us",
    "_quantiles": [0.95],
    "_phases": ["queued", "running"],
    "names": ["transaction", "acceptLedger"]
  }
}