Merge branch 'pratik/otel-phase10-workload-validation' into pratik/otel-sync-diagnostics

# Conflicts:
#	src/xrpld/telemetry/MetricsRegistry.cpp
This commit is contained in:
Pratik Mankawde
2026-08-21 13:09:09 +01:00
24 changed files with 1190 additions and 123 deletions

View File

@@ -1251,7 +1251,7 @@
},
{
"title": "Job Queue Wait p95 By Type",
"description": "###### What this is:\n*95th-percentile time a job waits in the queue before a worker thread picks it up, for the sync-critical job types. This is the metric form of the 'ProcessLData wait: NNNNms' warnings in the debug log.*\n\n###### How it's computed:\n*histogram_quantile(0.95, rate(jobq_<type>_q_milliseconds_bucket[$__rate_interval])) for ledgerdata, acceptledger, fetchtxndata, transaction, advanceledger, ledgerrequest.*\n\n###### Reading it:\n*Queue wait should be single-digit to low-tens of ms. High ledgerdata/fetchtxndata wait = the node cannot process inbound ledger data fast enough.*\n\n###### Healthy range:\n*< ~50ms p95 per type on a healthy node.*\n\n###### Watch for:\n*ledgerdata or fetchtxndata q-wait spiking to seconds = worker threads are blocked (usually on NuDB reads - see the cause tier).*\n\n###### Keywords:\n- **Job queue / job type** *(per node)* \u2014 xrpld's worker-thread pool; every unit of background work is enqueued under a named job type.\n- **Deferred job** *(per node)* \u2014 a job held back because its type is already at its concurrency limit; the leading indicator of queue backpressure.\n\n###### Computation boundary:\n*Result: Per node \u2014 each series is one server's own value.*\n*Recorded in xrpld code as a native metric (beast::insight); the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[core/JobQueue.cpp](https://github.com/XRPLF/rippled/blob/develop/src/libxrpl/core/detail/JobQueue.cpp)\n\n###### Function:\n`JobQueue::getJson (per-type queue timing)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#job-queue-job-type)",
"description": "###### What this is:\n*95th-percentile time a job waits in the queue before a worker thread picks it up, for the sync-critical job types. This is the metric form of the 'ProcessLData wait: NNNNms' warnings in the debug log.*\n\n###### How it's computed:\n*histogram_quantile(0.95, rate(job_queued_us_bucket{job_type=\"<type>\"}[$__rate_interval])) for ledgerData, acceptLedger, fetchTxnData, transaction, advanceLedger, ledgerRequest. Reads the OTel-native microsecond instrument rather than the beast jobq_* pair: beast Events round every duration up to a whole millisecond, so 94-100% of their samples landed in the first bucket and every percentile was an interpolation inside it rather than a measurement.*\n\n###### Reading it:\n*Queue wait is normally tens to hundreds of microseconds. High ledgerData/fetchTxnData wait = the node cannot process inbound ledger data fast enough.*\n\n###### Healthy range:\n*< ~500us p95 per type on a healthy node; sustained milliseconds is already backpressure.*\n\n###### Watch for:\n*ledgerData or fetchTxnData q-wait spiking to seconds = worker threads are blocked (usually on NuDB reads - see the cause tier).*\n\n###### Keywords:\n- **Job queue / job type** *(per node)* \u2014 xrpld's worker-thread pool; every unit of background work is enqueued under a named job type.\n- **Deferred job** *(per node)* \u2014 a job held back because its type is already at its concurrency limit; the leading indicator of queue backpressure.\n\n###### Computation boundary:\n*Result: Per node \u2014 each series is one server's own value.*\n*Recorded in xrpld code by MetricsRegistry as an OTel-native histogram in microseconds; the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[core/JobQueue.cpp](https://github.com/XRPLF/rippled/blob/develop/src/libxrpl/core/detail/JobQueue.cpp)\n\n###### Function:\n`JobQueue::getJson (per-type queue timing)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#job-queue-job-type)",
"type": "timeseries",
"gridPos": {
"h": 10,
@@ -1272,48 +1272,48 @@
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(jobq_ledgerdata_q_milliseconds_bucket{service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"ledgerdata q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(job_queued_us_bucket{job_type=\"ledgerData\", service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"ledgerData q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(jobq_acceptledger_q_milliseconds_bucket{service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"acceptledger q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(job_queued_us_bucket{job_type=\"acceptLedger\", service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"acceptLedger q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(jobq_fetchtxndata_q_milliseconds_bucket{service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"fetchtxndata q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(job_queued_us_bucket{job_type=\"fetchTxnData\", service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"fetchTxnData q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(jobq_transaction_q_milliseconds_bucket{service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"transaction q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(job_queued_us_bucket{job_type=\"transaction\", service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"transaction q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(jobq_advanceledger_q_milliseconds_bucket{service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"advanceledger q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(job_queued_us_bucket{job_type=\"advanceLedger\", service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"advanceLedger q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
},
{
"datasource": {
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(jobq_ledgerrequest_q_milliseconds_bucket{service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"ledgerrequest q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(job_queued_us_bucket{job_type=\"ledgerRequest\", service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[$__rate_interval]))), \"series\", \"ledgerRequest q-wait p95\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
}
],
"fieldConfig": {
"defaults": {
"displayName": "${__field.labels.series} ${__field.labels.xrpl_ident}",
"unit": "ms",
"unit": "µs",
"custom": {
"axisLabel": "p95 Wait (ms)",
"spanNulls": 1800000,

View File

@@ -154,7 +154,7 @@
},
{
"title": "RPC Response Size",
"description": "\u26a0 Instrument mismatch \u2014 values unreliable. Response size is recorded through the millisecond-scaled event histogram (rpc_size_milliseconds_bucket), so byte values saturate at the top time bucket (5000) and the percentiles are not true byte sizes. A dedicated byte-unit histogram is needed to fix this; tracked separately. Treat this panel as indicative only until then.\n\n###### What this is:\n*The 95th-percentile size of RPC response payloads in bytes.*\n\n###### How it's computed:\n*95th-percentile of response payload sizes over the dashboard rate interval, per node.*\n\n###### Reading it:\n*Smaller is cheaper; large responses cost bandwidth and memory.*\n\n###### Healthy range:\n*Workload-dependent; small for status queries, large for bulk data queries.*\n\n###### Watch for:\n*Growth in large responses, consistent with expensive queries or API misuse.*\n\n###### Keywords:\n- **RPC command / method** *(per node)* \u2014 a named API request served by the node (e.g. account_info, ledger, submit), the unit RPC panels break down by.\n\n###### Computation boundary:\n*Result: Per node \u2014 each series is one server's own value.*\n*Recorded in xrpld code as a native metric (beast::insight); the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[ServerHandler.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/rpc/detail/ServerHandler.cpp)\n\n###### Function:\n`ServerHandler ctor`\n\n###### References:\n[RPC command / method](https://xrpl.org/docs/references/http-websocket-apis/public-api-methods) \u00b7 [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#rpc-command-method)",
"description": "\u26a0 Instrument mismatch \u2014 values unreliable. Response size is recorded through the millisecond-scaled event histogram (rpc_size_bytes_bucket), so byte values saturate at the top time bucket (5000) and the percentiles are not true byte sizes. A dedicated byte-unit histogram is needed to fix this; tracked separately. Treat this panel as indicative only until then.\n\n###### What this is:\n*The 95th-percentile size of RPC response payloads in bytes.*\n\n###### How it's computed:\n*95th-percentile of response payload sizes over the dashboard rate interval, per node.*\n\n###### Reading it:\n*Smaller is cheaper; large responses cost bandwidth and memory.*\n\n###### Healthy range:\n*Workload-dependent; small for status queries, large for bulk data queries.*\n\n###### Watch for:\n*Growth in large responses, consistent with expensive queries or API misuse.*\n\n###### Keywords:\n- **RPC command / method** *(per node)* \u2014 a named API request served by the node (e.g. account_info, ledger, submit), the unit RPC panels break down by.\n\n###### Computation boundary:\n*Result: Per node \u2014 each series is one server's own value.*\n*Recorded in xrpld code as a native metric (beast::insight); the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[ServerHandler.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/rpc/detail/ServerHandler.cpp)\n\n###### Function:\n`ServerHandler ctor`\n\n###### References:\n[RPC command / method](https://xrpl.org/docs/references/http-websocket-apis/public-api-methods) \u00b7 [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#rpc-command-method)",
"type": "timeseries",
"gridPos": {
"h": 10,
@@ -175,7 +175,7 @@
"type": "prometheus",
"uid": "${DS_PROMETHEUS}"
},
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(rpc_size_milliseconds_bucket{service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[5m]))), \"series\", \"P95 Response Size\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
"expr": "label_replace(label_join(label_replace(histogram_quantile(0.95, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role, xrpl_work_item) (rate(rpc_size_bytes_bucket{service_instance_id=~\"$node\", deployment_environment=~\"$deployment_environment\", xrpl_network_type=~\"$xrpl_network_type\", service_name=~\"$service_name\", xrpl_work_item=~\"$xrpl_work_item\", xrpl_branch=~\"$xrpl_branch\", xrpl_node_role=~\"$xrpl_node_role\"}[5m]))), \"series\", \"P95 Response Size\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
}
],
"fieldConfig": {

View File

@@ -480,6 +480,15 @@ groups:
# p99 time a job waits in the queue before running. A sustained p99
# above 1s means the node is saturated and work is backing up. `le` must
# stay inside the inner sum or histogram_quantile cannot interpolate.
#
# Threshold re-validated after the microsecond ladder was re-cut. Do NOT
# tune it down against a casual reading of this p99: before that change
# the ladder's first edge was 100us with 99.3% of samples beneath it, so
# p99 reported 99.7us -- the bucket edge scaled by the quantile, not a
# latency. Measured cumulative distribution: 99.26% of samples land
# within 100us, 99.969% within 5ms, 99.990% within 100ms, and only
# 0.0022% exceed 1s. So 1s sits about four orders of magnitude above the
# healthy p99 and fires only on genuine saturation, which is the intent.
- uid: xrpld-jobqueue-latency-high
title: JobQueueLatencyHigh
condition: C
@@ -544,6 +553,12 @@ groups:
# first and explains the others. Measured p99-of-p95 is 37-49ms on
# healthy nodes and 488-566ms on nodes that are actively flapping, so
# 1000ms flags genuine degradation rather than the current baseline.
#
# Still valid after the millisecond ladder was extended: that change only
# ADDED edges above 5s (2s/3s/4s/10s/30s/60s/120s) and removed none, so
# every edge bracketing this threshold -- 25/50/100/250/500/1000ms -- is
# unchanged and the measurements above still hold. ios_latency's own mean
# is 12.9ms, far below the threshold.
- uid: xrpld-nodestore-io-latency-high
title: NodeStoreIOLatencyHigh
condition: C

View File

@@ -1,23 +1,8 @@
{
"_note": "job.* entries were removed on 2026-08-21. They were captured against the old microsecond ladder whose first edge was 100us, with 99.3% of job_queued_us samples beneath it, so job.acceptLedger.queued.p95 = 96.79us was 0.95/0.9926 x 100 -- arithmetic on the bucket edge, not a latency. Recapture them on a node running the re-cut ladder (floor 1us); until then the comparer reports them as \"new metric (not in baseline)\" and gates only the span metrics, which are unaffected. Removed values, for reference: job.acceptLedger.queued.p95=96.79us, job.acceptLedger.running.p95=10562.50us, job.transaction.queued.p95=478.97us, job.transaction.running.p95=494.14us.",
"captured_at": "2026-06-05T18:41:52Z",
"git_sha": "fd1c8c6060f7a15cc9e65b16f99629d9ab7ac7dc",
"metrics": {
"job.acceptLedger.queued.p95": {
"unit": "us",
"value": 96.78571428571428
},
"job.acceptLedger.running.p95": {
"unit": "us",
"value": 10562.499999999945
},
"job.transaction.queued.p95": {
"unit": "us",
"value": 478.96551724137925
},
"job.transaction.running.p95": {
"unit": "us",
"value": 494.1361256544502
},
"span.consensus.accept.p50": {
"unit": "ms",
"value": 1.059405940594059

View File

@@ -1,6 +1,6 @@
{
"_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.",
"_bucket_note": "SpanMetrics latency histograms use explicit buckets [1,5,10,25,50,100,250,500,1000,2000,3000,4000,5000,10000,30000]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. Second-scale consensus spans now have 2s/3s/4s boundaries (previously all fell in one 1s-5s bucket); their quantiles quantize to ~1s widths there. The job_queue running bound is widened similarly — per-ledger apply work scales with TxQ burst load.",
"_bucket_note": "SpanMetrics latency histograms use explicit buckets [0.01,0.05,0.1,0.25,0.5,1,5,10,25,50,100,250,500]ms then [1,2,3,4,5,10,30]s (20 edges; docker/telemetry/otel-collector-config.yaml is the authoritative list). An earlier version of this note claimed 15 edges starting at 1ms and justified the 10ms absolute span bound as \"~2 low-end bucket widths\" — that derivation is void, because the sub-millisecond edges make the low-end bucket width 0.01ms, not 5ms. The 10ms bound is retained on its own merit: it is roughly two bucket widths in the 5-25ms band where most span quantiles actually sit, so it still absorbs single-bucket quantization jitter while catching multi-bucket regressions. Second-scale consensus spans have 2s/3s/4s boundaries, so their quantiles quantize to ~1s widths there. The job_queue running bound is widened similarly — per-ledger apply work scales with TxQ burst load. NOTE: the native job_queue histograms are microsecond-valued and their ladder was re-cut (floor 100us → 1us), so any job_queue baseline captured before that change is an interpolation artefact, not a latency.",
"defaults": {
"span": {
"p50": { "max_pct_increase": 50.0, "max_abs_increase_ms": 10.0 },