docs(telemetry): align new panels with the dashboard conventions

The eight panels added for the GetObject and job-queue metrics did not
follow the conventions the rest of the dashboards use.

- Descriptions go from seven sections to the ten used by the other 159
  panels, adding Keywords, Computation boundary and References. The
  existing diagnostic guidance is kept; only the format changed.
- Six glossary entries added for the terms those References link to
  (concurrency limit, deferred job, handler label, NodeStore lookup
  hit/miss, resource charge), so no link is dead.
- displayName becomes `${series} ${xrpl_ident}`, the form 122 of 143
  panels use. This needs the label_join wrapper that builds xrpl_ident,
  which these queries lacked, so it is added to ten targets; without it
  the legend would render an unresolved label.
- Legend blocks now match each dashboard's own convention rather than
  being split three-with and four-without across the new panels.

Job Queue Wait Time and Job Execution Time dropped job_type from their
aggregation, so every queue collapsed into one line and the legend could
not say which queue was slow. Both now split by type, capped with topk
to stay readable, matching the per-type panel already on that dashboard.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

docker/telemetry/grafana/dashboards/job-queue.json

@@ -235,7 +235,7 @@
     },
     {
       "title": "Job Queue Wait Time",
-      "description": "###### What this is:\n*Distribution of how long jobs sit in the queue before a worker picks them up (p75 typical, p99 tail).*\n\n###### How it's computed:\n*Percentiles derived from the job wait-time histogram over a 5-minute window.*\n\n###### Reading it:\n*Lower is better; a widening gap between p75 and p99 signals occasional stalls.*\n\n###### Healthy range:\n*Sub-millisecond to low-millisecond waits on an unloaded node.*\n\n###### Watch for:\n*Rising p99 wait, an early sign of worker-thread saturation.*\n\n###### Source:\n[MetricsRegistry.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/telemetry/MetricsRegistry.cpp)\n\n###### Function:\n`recordJobStarted`",
+      "description": "###### What this is:\n*How long jobs sit in the queue before a worker picks them up, split by job type (p75 typical, p99 tail).*\n\n###### How it's computed:\n*Percentiles derived from the job wait-time histogram over a 5-minute window, kept per job type. Limited to the ten types with the highest wait so the legend stays readable.*\n\n###### Reading it:\n*Lower is better. The legend names the job type, so a single slow queue is identifiable rather than hidden in an all-types average. A widening p75-to-p99 gap on one type signals occasional stalls there.*\n\n###### Healthy range:\n*Sub-millisecond to low-millisecond waits on an unloaded node.*\n\n###### Watch for:\n*Rising p99 wait on a capped type -- ledgerRequest, ledgerData and makeFetchPack have small concurrency limits, so they queue first. Cross-check the deferred gauge for that type.*\n\n###### Source:\n[MetricsRegistry.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/telemetry/MetricsRegistry.cpp)\n\n###### Function:\n`recordJobStarted`",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -255,13 +255,13 @@
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.75, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_queued_us_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=~\"$xrpl_node_role\", job_type=~\"$job_type\", handler=~\"$handler\"}[5m]))), \"series\", \"p75 Wait\", \"\", \"\")"
+          "expr": "label_replace(topk(10, histogram_quantile(0.75, sum by (le, job_type, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_queued_us_bucket{service_instance_id=~\"$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\", job_type=~\"$job_type\", handler=~\"$handler\"}[5m])))), \"series\", \"p75 Wait $1\", \"job_type\", \"(.*)\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_queued_us_bucket{service_instance_id=~\"$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\", job_type=~\"$job_type\", handler=~\"$handler\"}[5m]))), \"series\", \"p99 Wait\", \"\", \"\")"
+          "expr": "label_replace(topk(10, histogram_quantile(0.99, sum by (le, job_type, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_queued_us_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=~\"$xrpl_node_role\", job_type=~\"$job_type\", handler=~\"$handler\"}[5m])))), \"series\", \"p99 Wait $1\", \"job_type\", \"(.*)\")"
         }
       ],
       "fieldConfig": {
@@ -288,7 +288,7 @@
     },
     {
       "title": "Job Execution Time",
-      "description": "###### What this is:\n*Distribution of how long jobs run once started (p75 typical, p99 tail).*\n\n###### How it's computed:\n*Percentiles derived from the job run-time histogram over a 5-minute window.*\n\n###### Reading it:\n*Lower is better; stable p75 with a controlled p99 is healthy.*\n\n###### Healthy range:\n*Workload-dependent, but stable over time.*\n\n###### Watch for:\n*Growing execution times, which point to CPU pressure or expensive individual jobs.*\n\n###### Source:\n[MetricsRegistry.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/telemetry/MetricsRegistry.cpp)\n\n###### Function:\n`recordJobFinished`",
+      "description": "###### What this is:\n*How long jobs run once started, split by job type (p75 typical, p99 tail).*\n\n###### How it's computed:\n*Percentiles derived from the job run-time histogram over a 5-minute window, kept per job type. Limited to the ten slowest types so the legend stays readable.*\n\n###### Reading it:\n*Lower is better. The legend names the job type, so an expensive job type is identifiable rather than averaged away. Stable p75 with a controlled p99 is healthy.*\n\n###### Healthy range:\n*Workload-dependent, but stable over time.*\n\n###### Watch for:\n*Growing execution times, which point to CPU pressure or expensive individual jobs.*\n\n###### Source:\n[MetricsRegistry.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/telemetry/MetricsRegistry.cpp)\n\n###### Function:\n`recordJobFinished`",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -308,13 +308,13 @@
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.75, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_running_us_bucket{service_instance_id=~\"$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\", job_type=~\"$job_type\", handler=~\"$handler\"}[5m]))), \"series\", \"p75 Exec\", \"\", \"\")"
+          "expr": "label_replace(topk(10, histogram_quantile(0.75, sum by (le, job_type, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_running_us_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=~\"$xrpl_node_role\", job_type=~\"$job_type\", handler=~\"$handler\"}[5m])))), \"series\", \"p75 Exec $1\", \"job_type\", \"(.*)\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_running_us_bucket{service_instance_id=~\"$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\", job_type=~\"$job_type\", handler=~\"$handler\"}[5m]))), \"series\", \"p99 Exec\", \"\", \"\")"
+          "expr": "label_replace(topk(10, histogram_quantile(0.99, sum by (le, job_type, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_running_us_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=~\"$xrpl_node_role\", job_type=~\"$job_type\", handler=~\"$handler\"}[5m])))), \"series\", \"p99 Exec $1\", \"job_type\", \"(.*)\")"
         }
       ],
       "fieldConfig": {

docker/telemetry/grafana/dashboards/ledger-data-sync.json

@@ -1372,7 +1372,7 @@
     },
     {
       "title": "Job Queue Backlog and Deferred by Type",
-      "description": "###### What this is:\n*Per-job-type queue depth, two series per type. Waiting is the whole backlog: every job enqueued for that type that has not started yet. Deferred is the subset of that backlog that is blocked specifically because the type is already running at its concurrency limit. Deferred is the leading indicator of backpressure, because JobQueue::addJob never rejects a job -- it always returns success and defers instead, so a capped type under pressure produces no error and no dropped work. Without these the only evidence is latency, which appears after the harm is already done.*\n\n###### How it's computed:\n*Two targets, each the top 10 gauges by current value: jobq_<jobtype>_waiting and jobq_<jobtype>_deferred. Both are live depths published by JobQueue::collect() under the same mutex that guards the counters, so the pair is always read at the same instant and is directly comparable. Gauges exist only for non-special job types, so the 11 special types -- the ones declared with a limit of 0, which bypass the limit logic entirely and therefore never defer -- do not appear on either series.*\n\n###### Reading it:\n*Read the two together; the ratio is the diagnostic, not either value alone. Deferred is always a subset of waiting, because addRefCountedJob increments waiting for every job and deferred only for the ones that arrive while the type is at its limit. Waiting high with deferred at zero means the type has spare slots and the backlog is just arrival burstiness -- it will drain without intervention. Waiting high with deferred also high means the concurrency limit is the binding constraint, not the work. Both near zero is the normal state. These are depths, not rates: the value is how many jobs are queued right now. finishJob drains deferred one per completion, so a deferred line that stays elevated means arrivals are outpacing completions rather than one isolated burst. Only the 10 highest series per state are drawn, which on an idle node is arbitrary among the zeros and under load is exactly the types under pressure.*\n\n###### Healthy range:\n*Deferred zero on all types. Waiting near zero, with brief spikes during ledger close.*\n\n###### Watch for:\n*ledgerrequest deferred above zero: the 3-slot ledgerRequest queue is full, so TMGetLedger service to syncing peers is being delayed. Use LedgerReq Wait by Handler next to see which of its two producers is responsible. ledgerdata or fetchtxndata deferred: inbound ledger data cannot be absorbed fast enough, which is what makes validated ledger age grow. A waiting line that climbs steadily while deferred stays flat points at the worker pool or at slow jobs rather than at the limit. Note both are sampled by the collector hook once per interval, so a sub-second spike can be missed; a reading of zero is not proof that nothing was ever queued or deferred.*\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::addRefCountedJob / JobQueue::collect`",
+      "description": "###### What this is:\n*Per-job-type queue depth, two series per type. Waiting is the whole backlog: every job enqueued for that type that has not started yet. Deferred is the subset of that backlog that is blocked specifically because the type is already running at its concurrency limit. Deferred is the leading indicator of backpressure, because JobQueue::addJob never rejects a job for queue pressure -- it returns success and defers instead, so a capped type under pressure produces no error and no dropped work. Without these the only evidence is latency, which appears after the harm is already done.*\n\n###### How it's computed:\n*Two targets, each the top 10 gauges by current value: jobq_<jobtype>_waiting and jobq_<jobtype>_deferred. JobQueue::collect snapshots both counters under the one lock that guards them, so the pair is read at the same instant and is directly comparable, then publishes them on the 1-second export cycle. Gauges exist only for non-special job types, so the 11 special types -- the ones declared with a limit of 0, which bypass the limit logic entirely and therefore never defer -- do not appear on either series.*\n\n###### Reading it:\n*Read the two together; the ratio is the diagnostic, not either value alone. Deferred is always a subset of waiting, because addRefCountedJob increments waiting for every job and deferred only for the ones that arrive while the type is at its limit. Waiting high with deferred at zero means the type has spare slots and the backlog is just arrival burstiness -- it will drain without intervention. Waiting high with deferred also high means the concurrency limit is the binding constraint, not the work. Both near zero is the normal state. These are depths, not rates: the value is how many jobs are queued right now. finishJob drains deferred one per completion, so a deferred line that stays elevated means arrivals are outpacing completions rather than one isolated burst. Only the 10 highest series per state are drawn, which on an idle node is arbitrary among the zeros and under load is exactly the types under pressure.*\n\n###### Healthy range:\n*Deferred zero on all types. Waiting near zero, with brief spikes during ledger close.*\n\n###### Watch for:\n*ledgerrequest deferred above zero: the 3-slot ledgerRequest queue is full, so TMGetLedger service to syncing peers is being delayed. Use LedgerReq Wait by Handler next to see which of its two producers is responsible. ledgerdata or fetchtxndata deferred: inbound ledger data cannot be absorbed fast enough, which is what makes validated ledger age grow. A waiting line that climbs steadily while deferred stays flat points at the worker pool or at slow jobs rather than at the limit. Note both are sampled once per export cycle, so a sub-second spike can be missed; a reading of zero is not proof that nothing was ever queued or deferred.*\n\n###### Keywords:\n- **Deferred job** *(per node)* — a job held back because its type is already at its concurrency limit; the leading indicator of queue backpressure.\n- **Concurrency limit** *(per node)* — the cap on how many jobs of one type may run at once; a type at its cap cannot start more work.\n- **Job queue / job type** *(per node)* — xrpld's worker-thread pool; every unit of background work is enqueued under a named job type.\n\n###### Computation boundary:\n*Result: Per node — 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::addRefCountedJob / JobQueue::collect`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#deferred-job)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -1385,11 +1385,6 @@
           "maxHeight": 600,
           "mode": "multi",
           "sort": "desc"
-        },
-        "legend": {
-          "displayMode": "table",
-          "placement": "right",
-          "calcs": ["mean", "max"]
         }
       },
       "targets": [
@@ -1426,7 +1421,7 @@
     },
     {
       "title": "LedgerReq Wait by Handler",
-      "description": "###### What this is:\n*Queue wait for the ledgerRequest job type, split by which handler enqueued the job. The type has a concurrency limit of 3 and two producers that compete for those slots: RcvGetLedger, which serves TMGetLedger to syncing peers, and RcvGetObjByHash, which serves TMGetObjectByHash. Both report the same job_type, so without the handler split a wait spike cannot be attributed to either.*\n\n###### How it's computed:\n*p99 of job_queued_us for job_type=\"ledgerRequest\", grouped by the handler label. The handler value is the addJob name passed through a sanitiser that keeps letters-only names and folds anything else to \"other\", which bounds the label domain.*\n\n###### Reading it:\n*This is the panel that answers which producer is starving the 3-slot queue. Both lines high together means the queue is genuinely oversubscribed and both kinds of peer request are being delayed. One line high while the other is flat means that producer is arriving faster than 3 concurrent slots can absorb, and it is the one delaying the other. Wait is queue time only, so a high line here is contention, not slow work; the work itself is on the GetObject Handler Latency Breakdown panel.*\n\n###### Healthy range:\n*Single-digit to low-tens of milliseconds p99 for both handlers, matching the wider Job Queue Wait p95 By Type panel.*\n\n###### Watch for:\n*RcvGetObjByHash wait climbing: one in-bounds TMGetObjectByHash request can perform thousands of NodeStore lookups, so a few concurrent ones occupy every slot and delay TMGetLedger to peers that are themselves syncing. Cross-check Job Queue Backlog and Deferred by Type for jobq_ledgerrequest_deferred above zero to confirm the limit, not the work, is the binding constraint.*\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::processTask -> PerfLog::jobStart`",
+      "description": "###### What this is:\n*Queue wait for the ledgerRequest job type, split by which handler enqueued the job. The type has a concurrency limit of 3 and two producers that compete for those slots: RcvGetLedger, which serves TMGetLedger to syncing peers, and RcvGetObjByHash, which serves TMGetObjectByHash. Both report the same job_type, so without the handler split a wait spike cannot be attributed to either.*\n\n###### How it's computed:\n*p99 of job_queued_us for job_type=\\\"ledgerRequest\\\", grouped by the handler label. JobQueue::processTask measures the wait, then PerfLog hands it to MetricsRegistry::recordJobStarted, which is where the histogram is recorded. The handler value is the addJob name passed through a sanitizer that keeps letters-only names and folds everything else to \\\"other\\\", which bounds the label domain to 43 names plus \\\"other\\\". Both producers here are letters-only, so both appear under their own names; \\\"other\\\" is a mixed bucket and never means one specific caller.*\n\n###### Reading it:\n*This is the panel that answers which producer is starving the 3-slot queue. Both lines high together means the queue is genuinely oversubscribed and both kinds of peer request are being delayed. One line high while the other is flat means that producer is arriving faster than 3 concurrent slots can absorb, and it is the one delaying the other. Wait is queue time only, so a high line here is contention, not slow work; the work itself is on the GetObject Handler Latency Breakdown panel.*\n\n###### Healthy range:\n*Single-digit to low-tens of milliseconds p99 for both handlers, matching the wider Job Queue Wait p95 By Type panel.*\n\n###### Watch for:\n*RcvGetObjByHash wait climbing: one in-bounds TMGetObjectByHash request can perform thousands of NodeStore lookups, so a few concurrent ones occupy every slot and delay TMGetLedger to peers that are themselves syncing. Cross-check Job Queue Backlog and Deferred by Type for jobq_ledgerrequest_deferred above zero to confirm the limit, not the work, is the binding constraint.*\n\n###### Keywords:\n- **Handler label** *(per node)* — the addJob call-site name attached to job metrics, so producers sharing one job type stay separable.\n- **Concurrency limit** *(per node)* — the cap on how many jobs of one type may run at once; a type at its cap cannot start more work.\n- **Job queue / job type** *(per node)* — xrpld's worker-thread pool; every unit of background work is enqueued under a named job type.\n\n###### Computation boundary:\n*Result: Per node — each series is one server's own value.*\n*Computed in xrpld code (MetricsRegistry, OpenTelemetry SDK) and exported as a metric; the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[MetricsRegistry.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/telemetry/MetricsRegistry.cpp)\n\n###### Function:\n`MetricsRegistry::recordJobStarted`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#handler-label)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,

docker/telemetry/grafana/dashboards/node-health.json

@@ -5107,7 +5107,7 @@
     },
     {
       "title": "Job Queue Saturation (Running vs Limit)",
-      "description": "###### What this is:\n*How close each concurrency-capped job type is to its ceiling. JobQueue enforces a per-type limit on how many jobs of that type may run at once, and the tight ones carry ledger-sync traffic: makeFetchPack 1, ledgerRequest 3, ledgerData 3, updatePaths 1, fetchTxnData 5. A type at its ceiling cannot start more work no matter how many workers are idle, so this is a different kind of limit from CPU or disk.*\n\n###### How it's computed:\n*Each jobq_<jobtype>_running gauge divided by that type's own limit from JobTypes.h, so every line shares one 0-to-1 axis. 1.0 means running equals the limit. Multiply a reading by the limit shown in its legend to recover the raw job count. The gauges are published by JobQueue::collect() under the mutex that guards the counters.*\n\n###### Reading it:\n*Read the distance to 1.0, not the absolute height. Below 1.0 the type has spare slots and its queue wait is not the limit's fault. Touching 1.0 briefly is normal work. Sitting at 1.0 means the type is pinned at its ceiling and every further job of that type is being deferred rather than started, which is what turns into queue wait downstream. Because the limits differ, a raw count of 3 is saturation for ledgerRequest but only 60 percent for fetchTxnData; normalising is what makes the lines comparable.*\n\n###### Healthy range:\n*Below 1.0, with brief touches under load.*\n\n###### Watch for:\n*A line flat at 1.0: that type is the binding constraint. ledgerRequest pinned means the 3 slots shared by RcvGetLedger and RcvGetObjByHash are full, so peer ledger and object requests are queueing behind each other; the Ledger Data and Sync dashboard splits that wait by handler and shows the matching deferred depth. ledgerData or fetchTxnData pinned means inbound ledger data cannot be absorbed and validated ledger age will grow. makeFetchPack or updatePaths pinned at their limit of 1 means a single long job is blocking the whole type. Note the collector samples once per interval, so a line that never reaches 1.0 is not proof the type was never momentarily saturated.*\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::getNextJob (limit enforcement) / JobQueue::collect (publication)`",
+      "description": "###### What this is:\n*How close each concurrency-capped job type is to its ceiling. JobQueue enforces a per-type limit on how many jobs of that type may run at once, and the tight ones carry ledger-sync traffic: makeFetchPack 1, ledgerRequest 3, ledgerData 3, updatePaths 1, fetchTxnData 5. A type at its ceiling cannot start more work no matter how many workers are idle, so this is a different kind of limit from CPU or disk.*\n\n###### How it's computed:\n*Each jobq_<jobtype>_running gauge divided by that type's own limit from JobTypes.h, so every line shares one 0-to-1 axis. 1.0 means running equals the limit. Multiply a reading by the limit shown in its legend to recover the raw job count. JobQueue::collect snapshots all three per-type counters under the queue's own lock and publishes them after releasing it, on the 1-second export cycle.*\n\n###### Reading it:\n*Read the distance to 1.0, not the absolute height. Below 1.0 the type has spare slots and its queue wait is not the limit's fault. Touching 1.0 briefly is normal work. Sitting at 1.0 means the type is pinned at its ceiling and every further job of that type is being deferred rather than started, which is what turns into queue wait downstream. Because the limits differ, a raw count of 3 is saturation for ledgerRequest but only 60 percent for fetchTxnData; normalizing is what makes the lines comparable.*\n\n###### Healthy range:\n*Below 1.0, with brief touches under load.*\n\n###### Watch for:\n*A line flat at 1.0: that type is the binding constraint. ledgerRequest pinned means the 3 slots shared by RcvGetLedger and RcvGetObjByHash are full, so peer ledger and object requests are queueing behind each other; the Ledger Data and Sync dashboard splits that wait by handler and shows the matching deferred depth. ledgerData or fetchTxnData pinned means inbound ledger data cannot be absorbed and validated ledger age will grow. makeFetchPack or updatePaths pinned at their limit of 1 means a single long job is blocking the whole type. These are sampled gauges, so a line that never reaches 1.0 is not proof the type was never momentarily saturated.*\n\n###### Keywords:\n- **Job queue / job type** *(per node)* — xrpld's worker-thread pool; every unit of background work is enqueued under a named job type.\n- **Concurrency limit** *(per node)* — the cap on how many jobs of one type may run at once; a type at its cap cannot start more work.\n- **Deferred job** *(per node)* — 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 — 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::getNextJob (limit enforcement) / JobQueue::collect (publication)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#concurrency-limit)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -5122,9 +5122,12 @@
           "sort": "desc"
         },
         "legend": {
-          "displayMode": "table",
-          "placement": "right",
-          "calcs": ["mean", "max"]
+          "calcs": [],
+          "displayMode": "list",
+          "enableFacetedFilter": false,
+          "overflow": "ellipsis",
+          "placement": "bottom",
+          "showLegend": true
         }
       },
       "targets": [
@@ -5132,36 +5135,36 @@
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(jobq_makefetchpack_running{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=~\"$xrpl_node_role\"} / 1, \"series\", \"makeFetchPack (Limit 1)\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(jobq_makefetchpack_running{service_instance_id=~\"$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\"} / 1, \"series\", \"makeFetchPack (Limit 1)\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(jobq_ledgerrequest_running{service_instance_id=~\"$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\"} / 3, \"series\", \"ledgerRequest (Limit 3)\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(jobq_ledgerrequest_running{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\"} / 3, \"series\", \"ledgerRequest (Limit 3)\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(jobq_ledgerdata_running{service_instance_id=~\"$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\"} / 3, \"series\", \"ledgerData (Limit 3)\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(jobq_ledgerdata_running{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\"} / 3, \"series\", \"ledgerData (Limit 3)\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(jobq_updatepaths_running{service_instance_id=~\"$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\"} / 1, \"series\", \"updatePaths (Limit 1)\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(jobq_updatepaths_running{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\"} / 1, \"series\", \"updatePaths (Limit 1)\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(jobq_fetchtxndata_running{service_instance_id=~\"$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\"} / 5, \"series\", \"fetchTxnData (Limit 5)\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(jobq_fetchtxndata_running{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\"} / 5, \"series\", \"fetchTxnData (Limit 5)\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         }
       ],
       "fieldConfig": {
         "defaults": {
-          "displayName": "${__field.labels.series} [${__field.labels.service_instance_id} ${__field.labels.xrpl_branch} ${__field.labels.xrpl_node_role} ${__field.labels.xrpl_work_item}]",
+          "displayName": "${__field.labels.series} ${__field.labels.xrpl_ident}",
           "unit": "percentunit",
           "max": 1,
           "min": 0,

docker/telemetry/grafana/dashboards/overlay-traffic-detail.json

@@ -32,7 +32,7 @@
       }
     ]
   },
-  "description": "What this shows: Fine-grained breakdown of peer-to-peer overlay traffic beyond the main network view: squelch relay control, protocol overhead, validator-list distribution, transaction-set exchange, transaction availability, ledger-proof and replay traffic, and unclassified messages.  \u2014  Use it to: Drill into individual overlay message categories to diagnose relay efficiency, overhead, and catch-up traffic.",
+  "description": "What this shows: Fine-grained breakdown of peer-to-peer overlay traffic beyond the main network view: squelch relay control, protocol overhead, validator-list distribution, transaction-set exchange, transaction availability, ledger-proof and replay traffic, and unclassified messages.  —  Use it to: Drill into individual overlay message categories to diagnose relay efficiency, overhead, and catch-up traffic.",
   "editable": true,
   "fiscalYearStartMonth": 0,
   "graphTooltip": 1,
@@ -41,7 +41,7 @@
   "panels": [
     {
       "title": "Squelch Traffic (Messages)",
-      "description": "###### What this is:\n*Squelch relay-control messages in/out, plus messages suppressed by squelch and squelch directives that were ignored. Squelch reduces redundant message forwarding between peers.*\n\n###### How it's computed:\n*Per-second message rate for the squelch, squelch-suppressed, and squelch-ignored categories, in and out.*\n\n###### Reading it:\n*High suppressed counts mean squelch is saving bandwidth; ignored should stay low.*\n\n###### Healthy range:\n*workload-dependent; suppressed far above ignored.*\n\n###### Watch for:\n*High ignored counts (peers not honoring squelch) or squelch traffic itself dominating.*\n\n###### Keywords:\n- **Transaction suppression** *(per node)* \u2014 dropping a transaction already seen from another peer, so it is not reprocessed.\n- **Squelch** *(per node)* \u2014 control messages that tell a peer to stop forwarding a given validator's messages, cutting redundancy.\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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#transaction-suppression)",
+      "description": "###### What this is:\n*Squelch relay-control messages in/out, plus messages suppressed by squelch and squelch directives that were ignored. Squelch reduces redundant message forwarding between peers.*\n\n###### How it's computed:\n*Per-second message rate for the squelch, squelch-suppressed, and squelch-ignored categories, in and out.*\n\n###### Reading it:\n*High suppressed counts mean squelch is saving bandwidth; ignored should stay low.*\n\n###### Healthy range:\n*workload-dependent; suppressed far above ignored.*\n\n###### Watch for:\n*High ignored counts (peers not honoring squelch) or squelch traffic itself dominating.*\n\n###### Keywords:\n- **Transaction suppression** *(per node)* — dropping a transaction already seen from another peer, so it is not reprocessed.\n- **Squelch** *(per node)* — control messages that tell a peer to stop forwarding a given validator's messages, cutting redundancy.\n\n###### Computation boundary:\n*Result: Per node — 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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#transaction-suppression)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -117,7 +117,7 @@
     },
     {
       "title": "Overhead Traffic Breakdown (Bytes)",
-      "description": "###### What this is:\n*Overlay protocol overhead bytes split into base overhead, intra-cluster overhead, and validator-manifest distribution overhead.*\n\n###### How it's computed:\n*Per-second in/out byte rate for the overhead, overhead-cluster, and overhead-manifest categories.*\n\n###### Reading it:\n*Base overhead is routine; cluster and manifest rise around cluster syncs and manifest changes.*\n\n###### Healthy range:\n*workload-dependent; low and stable.*\n\n###### Watch for:\n*Sustained high cluster or manifest overhead (frequent cluster state churn or manifest reissue).*\n\n###### Keywords:\n- **Overlay** *(per node)* \u2014 the peer-to-peer network layer over which nodes exchange transactions, proposals, and validations.\n- **Manifest** *(network-wide)* \u2014 a signed record binding a validator's rotating signing key to its stable master key.\n- **Cluster** *(cluster-wide)* \u2014 a group of trusted co-operated nodes that share load information and skip some verification.\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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Overlay](https://xrpl.org/docs/concepts/networks-and-servers/peer-protocol) \u00b7 [Cluster](https://xrpl.org/docs/concepts/networks-and-servers/clustering) \u00b7 [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#overlay)",
+      "description": "###### What this is:\n*Overlay protocol overhead bytes split into base overhead, intra-cluster overhead, and validator-manifest distribution overhead.*\n\n###### How it's computed:\n*Per-second in/out byte rate for the overhead, overhead-cluster, and overhead-manifest categories.*\n\n###### Reading it:\n*Base overhead is routine; cluster and manifest rise around cluster syncs and manifest changes.*\n\n###### Healthy range:\n*workload-dependent; low and stable.*\n\n###### Watch for:\n*Sustained high cluster or manifest overhead (frequent cluster state churn or manifest reissue).*\n\n###### Keywords:\n- **Overlay** *(per node)* — the peer-to-peer network layer over which nodes exchange transactions, proposals, and validations.\n- **Manifest** *(network-wide)* — a signed record binding a validator's rotating signing key to its stable master key.\n- **Cluster** *(cluster-wide)* — a group of trusted co-operated nodes that share load information and skip some verification.\n\n###### Computation boundary:\n*Result: Per node — 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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Overlay](https://xrpl.org/docs/concepts/networks-and-servers/peer-protocol) · [Cluster](https://xrpl.org/docs/concepts/networks-and-servers/clustering) · [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#overlay)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -193,7 +193,7 @@
     },
     {
       "title": "Validator List Traffic",
-      "description": "###### What this is:\n*Bytes and messages exchanged distributing validator lists (trusted-list configuration) between peers.*\n\n###### How it's computed:\n*Per-second in/out byte and message rate for the validator-lists category.*\n\n###### Reading it:\n*Bursts when lists update or new peers connect; quiet otherwise.*\n\n###### Healthy range:\n*workload-dependent; occasional bursts.*\n\n###### Watch for:\n*Continuous high volume (repeated list re-fetching or churn).*\n\n###### Keywords:\n- **Validator list** *(network-wide)* \u2014 signed lists of recommended validators (UNLs) that peers distribute to each other.\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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Validator list](https://xrpl.org/docs/concepts/consensus-protocol/unl) \u00b7 [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#validator-list)",
+      "description": "###### What this is:\n*Bytes and messages exchanged distributing validator lists (trusted-list configuration) between peers.*\n\n###### How it's computed:\n*Per-second in/out byte and message rate for the validator-lists category.*\n\n###### Reading it:\n*Bursts when lists update or new peers connect; quiet otherwise.*\n\n###### Healthy range:\n*workload-dependent; occasional bursts.*\n\n###### Watch for:\n*Continuous high volume (repeated list re-fetching or churn).*\n\n###### Keywords:\n- **Validator list** *(network-wide)* — signed lists of recommended validators (UNLs) that peers distribute to each other.\n\n###### Computation boundary:\n*Result: Per node — 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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Validator list](https://xrpl.org/docs/concepts/consensus-protocol/unl) · [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#validator-list)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -272,7 +272,7 @@
     },
     {
       "title": "Set Get/Share Traffic (Bytes)",
-      "description": "###### What this is:\n*Transaction-set fetch (get) and share bytes exchanged during ledger close.*\n\n###### How it's computed:\n*Per-second in/out byte rate for the set-get and set-share categories.*\n\n###### Reading it:\n*Some exchange each ledger is normal as peers reconcile transaction sets.*\n\n###### Healthy range:\n*workload-dependent.*\n\n###### Watch for:\n*High set-get (peers frequently missing transaction sets: possible sync delays).*\n\n###### Keywords:\n- **Set get/share** *(per node)* \u2014 exchange of candidate transaction sets between peers as they reconcile during a ledger close.\n- **Ledger close** *(network event)* \u2014 the current open ledger is closed and a new closed ledger is built from the agreed transaction set.\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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Ledger close](https://xrpl.org/docs/concepts/ledgers/open-closed-validated-ledgers) \u00b7 [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#set-get-share)",
+      "description": "###### What this is:\n*Transaction-set fetch (get) and share bytes exchanged during ledger close.*\n\n###### How it's computed:\n*Per-second in/out byte rate for the set-get and set-share categories.*\n\n###### Reading it:\n*Some exchange each ledger is normal as peers reconcile transaction sets.*\n\n###### Healthy range:\n*workload-dependent.*\n\n###### Watch for:\n*High set-get (peers frequently missing transaction sets: possible sync delays).*\n\n###### Keywords:\n- **Set get/share** *(per node)* — exchange of candidate transaction sets between peers as they reconcile during a ledger close.\n- **Ledger close** *(network event)* — the current open ledger is closed and a new closed ledger is built from the agreed transaction set.\n\n###### Computation boundary:\n*Result: Per node — 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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Ledger close](https://xrpl.org/docs/concepts/ledgers/open-closed-validated-ledgers) · [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#set-get-share)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -334,7 +334,7 @@
     },
     {
       "title": "Have/Requested Transactions (Messages)",
-      "description": "###### What this is:\n*Transaction-availability messages: advertisements that a peer has certain transactions, and explicit requests for transaction data.*\n\n###### How it's computed:\n*Per-second in/out message rate for the have-transactions and requested-transactions categories.*\n\n###### Reading it:\n*Compare requested versus have to gauge how well transactions are propagating.*\n\n###### Healthy range:\n*workload-dependent.*\n\n###### Watch for:\n*Requested far exceeding have (peers behind on transaction propagation).*\n\n###### Keywords:\n- **Have / requested transactions** *(per node)* \u2014 advertisements that a peer holds certain transactions, and explicit requests for transaction data.\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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#have-requested-transactions)",
+      "description": "###### What this is:\n*Transaction-availability messages: advertisements that a peer has certain transactions, and explicit requests for transaction data.*\n\n###### How it's computed:\n*Per-second in/out message rate for the have-transactions and requested-transactions categories.*\n\n###### Reading it:\n*Compare requested versus have to gauge how well transactions are propagating.*\n\n###### Healthy range:\n*workload-dependent.*\n\n###### Watch for:\n*Requested far exceeding have (peers behind on transaction propagation).*\n\n###### Keywords:\n- **Have / requested transactions** *(per node)* — advertisements that a peer holds certain transactions, and explicit requests for transaction data.\n\n###### Computation boundary:\n*Result: Per node — 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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#have-requested-transactions)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -396,7 +396,7 @@
     },
     {
       "title": "Unknown / Unclassified Traffic",
-      "description": "###### What this is:\n*Overlay traffic that matches no known message category, in bytes and messages.*\n\n###### How it's computed:\n*Current in/out byte and message counts for the unknown category.*\n\n###### Reading it:\n*Should be at or near zero.*\n\n###### Healthy range:\n*zero.*\n\n###### Watch for:\n*Any sustained non-zero value (protocol version mismatch, corrupted messages, or an unclassified new message type).*\n\n###### Keywords:\n- **Overlay** *(per node)* \u2014 the peer-to-peer network layer over which nodes exchange transactions, proposals, and validations.\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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Overlay](https://xrpl.org/docs/concepts/networks-and-servers/peer-protocol) \u00b7 [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#overlay)",
+      "description": "###### What this is:\n*Overlay traffic that matches no known message category, in bytes and messages.*\n\n###### How it's computed:\n*Current in/out byte and message counts for the unknown category.*\n\n###### Reading it:\n*Should be at or near zero.*\n\n###### Healthy range:\n*zero.*\n\n###### Watch for:\n*Any sustained non-zero value (protocol version mismatch, corrupted messages, or an unclassified new message type).*\n\n###### Keywords:\n- **Overlay** *(per node)* — the peer-to-peer network layer over which nodes exchange transactions, proposals, and validations.\n\n###### Computation boundary:\n*Result: Per node — 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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Overlay](https://xrpl.org/docs/concepts/networks-and-servers/peer-protocol) · [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#overlay)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -475,7 +475,7 @@
     },
     {
       "title": "Proof Path Traffic",
-      "description": "###### What this is:\n*Proof-path request/response bytes used to verify individual ledger entries without downloading the whole ledger.*\n\n###### How it's computed:\n*Per-second in/out byte rate for the proof-path request and response categories.*\n\n###### Reading it:\n*Rises when peers verify specific state, often during catch-up.*\n\n###### Healthy range:\n*workload-dependent.*\n\n###### Watch for:\n*High sustained request volume (heavy state-verification load).*\n\n###### Keywords:\n- **Path request / discovery** *(per node)* \u2014 a client's ongoing pathfinding subscription (request) and the periodic path-refresh passes (discovery).\n- **Proof path** *(per node)* \u2014 messages that prove a single ledger entry exists without transferring the whole ledger.\n- **Back-fill / catch-up** *(per node)* \u2014 fetching missing historical ledgers from peers to fill gaps or reach the network tip.\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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Path request / discovery](https://xrpl.org/docs/references/http-websocket-apis/public-api-methods/path-and-order-book-methods/path_find) \u00b7 [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#path-request-discovery)",
+      "description": "###### What this is:\n*Proof-path request/response bytes used to verify individual ledger entries without downloading the whole ledger.*\n\n###### How it's computed:\n*Per-second in/out byte rate for the proof-path request and response categories.*\n\n###### Reading it:\n*Rises when peers verify specific state, often during catch-up.*\n\n###### Healthy range:\n*workload-dependent.*\n\n###### Watch for:\n*High sustained request volume (heavy state-verification load).*\n\n###### Keywords:\n- **Path request / discovery** *(per node)* — a client's ongoing pathfinding subscription (request) and the periodic path-refresh passes (discovery).\n- **Proof path** *(per node)* — messages that prove a single ledger entry exists without transferring the whole ledger.\n- **Back-fill / catch-up** *(per node)* — fetching missing historical ledgers from peers to fill gaps or reach the network tip.\n\n###### Computation boundary:\n*Result: Per node — 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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Path request / discovery](https://xrpl.org/docs/references/http-websocket-apis/public-api-methods/path-and-order-book-methods/path_find) · [Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#path-request-discovery)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -537,7 +537,7 @@
     },
     {
       "title": "Replay Delta Traffic",
-      "description": "###### What this is:\n*Replay-delta request/response bytes used to efficiently replay ledger state changes during catch-up.*\n\n###### How it's computed:\n*Per-second in/out byte rate for the replay-delta request and response categories.*\n\n###### Reading it:\n*Active during catch-up and replay; quiet when synced.*\n\n###### Healthy range:\n*workload-dependent; low when synced.*\n\n###### Watch for:\n*Continuous replay traffic (node repeatedly replaying rather than staying current).*\n\n###### Keywords:\n- **Replay delta** *(per node)* \u2014 messages carrying just the changes between ledgers, to replay state efficiently during catch-up.\n- **Back-fill / catch-up** *(per node)* \u2014 fetching missing historical ledgers from peers to fill gaps or reach the network tip.\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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#replay-delta)",
+      "description": "###### What this is:\n*Replay-delta request/response bytes used to efficiently replay ledger state changes during catch-up.*\n\n###### How it's computed:\n*Per-second in/out byte rate for the replay-delta request and response categories.*\n\n###### Reading it:\n*Active during catch-up and replay; quiet when synced.*\n\n###### Healthy range:\n*workload-dependent; low when synced.*\n\n###### Watch for:\n*Continuous replay traffic (node repeatedly replaying rather than staying current).*\n\n###### Keywords:\n- **Replay delta** *(per node)* — messages carrying just the changes between ledgers, to replay state efficiently during catch-up.\n- **Back-fill / catch-up** *(per node)* — fetching missing historical ledgers from peers to fill gaps or reach the network tip.\n\n###### Computation boundary:\n*Result: Per node — 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[OverlayImpl.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/OverlayImpl.cpp)\n\n###### Function:\n`OverlayImpl ctor (TrafficGauges)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#replay-delta)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -611,7 +611,7 @@
     },
     {
       "title": "GetObject Handler Latency Breakdown",
-      "description": "###### What this is:\n*The three additive parts of TMGetObjectByHash service time, drawn on one axis so the expensive part names itself. Queue Wait is how long the RcvGetObjByHash job sat queued before a worker took it; its job type, ledgerRequest, allows only 3 to run at once. Handler Total is the whole job body once running. NodeStore Lookup is only the fetch loop inside that body. End-to-end service time is Queue Wait plus Handler Total, and Handler Total itself splits into NodeStore Lookup plus everything else.*\n\n###### How it's computed:\n*p99 of job_queued_us and job_running_us, both filtered to handler=\"RcvGetObjByHash\", plus p99 of getobject_lookup_us. Each is histogram_quantile over the microsecond bucket series, summed by le so the quantile is computed across the whole bucket set. The handler label is the sanitised addJob name, so RcvGetObjByHash is separated from RcvGetLedger even though both are job type ledgerRequest.*\n\n###### Reading it:\n*Read it as a subtraction, not as three independent lines. The vertical gap between Handler Total and NodeStore Lookup is the serialization and protobuf reply-building cost, because those are the only other things the job body does. So: Queue Wait tall with Handler Total flat means queue contention and the work itself is fine. Handler Total tracking NodeStore Lookup closely means storage is the bottleneck. Handler Total well above NodeStore Lookup means the cost has moved out of the fetch loop into reply construction. All three flat means this path is not the source of the slowness.*\n\n###### Healthy range:\n*All three sub-millisecond while peers ask for the usual 8 hashes per request; workload-dependent above that.*\n\n###### Watch for:\n*Queue Wait climbing while the other two stay flat: the ledgerRequest queue is saturated, so cross-check Job Queue Backlog and Deferred by Type and LedgerReq Wait by Handler on the Ledger Data and Sync dashboard to see which producer is starving it. A widening Handler Total minus NodeStore Lookup gap: reply construction regressed. NodeStore Lookup rising on its own: check getobject_lookups_total misses and the NuDB panels.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::recordGetObjectMetrics (lookup) / MetricsRegistry::recordJobStarted, recordJobFinished (queue, total)`",
+      "description": "###### What this is:\n*The three additive parts of TMGetObjectByHash service time, drawn on one axis so the expensive part names itself. Queue Wait is how long the RcvGetObjByHash job sat queued before a worker took it; its job type, ledgerRequest, allows only 3 to run at once. Handler Total is the whole job body once running. NodeStore Lookup is only the fetch loop inside that body. End-to-end service time is Queue Wait plus Handler Total, and Handler Total itself splits into NodeStore Lookup plus everything else.*\n\n###### How it's computed:\n*p99 of job_queued_us and job_running_us, both filtered to handler=\\\"RcvGetObjByHash\\\", plus p99 of getobject_lookup_us. Each is histogram_quantile over the microsecond bucket series, summed by le so the quantile is computed across the whole bucket set. The handler label is the sanitized addJob name, so RcvGetObjByHash is separated from RcvGetLedger even though both are job type ledgerRequest.*\n\n###### Reading it:\n*Read it as a subtraction, not as three independent lines. The timed fetch loop covers both the NodeStore fetches and the copying of each returned object into the reply, so the vertical gap between Handler Total and NodeStore Lookup is what happens after the loop: serializing the reply message, plus computing the charge and recording the metrics. So: Queue Wait tall with Handler Total flat means queue contention and the work itself is fine. Handler Total tracking NodeStore Lookup closely means storage is the bottleneck. Handler Total well above NodeStore Lookup means the cost has moved out of the fetch loop into reply serialization.*\n\n###### Healthy range:\n*All three sub-millisecond while peers ask for the handful of objects the sync path produces; workload-dependent above that.*\n\n###### Watch for:\n*Queue Wait climbing while the other two stay flat: the ledgerRequest queue is saturated, so cross-check Job Queue Backlog and Deferred by Type and LedgerReq Wait by Handler on the Ledger Data and Sync dashboard to see which producer is starving it. A widening Handler Total minus NodeStore Lookup gap: reply serialization regressed. NodeStore Lookup rising on its own: check getobject_lookups_total misses and the NuDB panels.*\n\n###### Keywords:\n- **GetObject / object fetch** *(per node)* — peer requests that fetch individual pieces of ledger data by hash, such as tree nodes or transactions.\n- **Handler label** *(per node)* — the addJob call-site name attached to job metrics, so producers sharing one job type stay separable.\n- **NodeStore lookup (hit / miss)** *(per node)* — one object-store fetch by hash; a hit is usually served from cache, a miss does a disk seek.\n\n###### Computation boundary:\n*Result: Per node — each series is one server's own value.*\n*Computed in xrpld code (MetricsRegistry, OpenTelemetry SDK) and exported as a metric; the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::recordGetObjectMetrics (lookup) / MetricsRegistry::recordJobStarted, recordJobFinished (queue, total)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#getobject-object-fetch)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -624,11 +624,6 @@
           "maxHeight": 600,
           "mode": "multi",
           "sort": "desc"
-        },
-        "legend": {
-          "displayMode": "table",
-          "placement": "right",
-          "calcs": ["mean", "max"]
         }
       },
       "targets": [
@@ -636,24 +631,24 @@
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_queued_us_bucket{handler=\"RcvGetObjByHash\", 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\", \"Queue Wait p99\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_queued_us_bucket{handler=\"RcvGetObjByHash\", 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\", \"Queue Wait p99\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_running_us_bucket{handler=\"RcvGetObjByHash\", service_instance_id=~\"$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\"}[$__rate_interval]))), \"series\", \"Handler Total p99\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(job_running_us_bucket{handler=\"RcvGetObjByHash\", service_instance_id=~\"$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\", \"Handler Total p99\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_lookup_us_bucket{service_instance_id=~\"$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\"}[$__rate_interval]))), \"series\", \"NodeStore Lookup p99\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_lookup_us_bucket{service_instance_id=~\"$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\", \"NodeStore Lookup p99\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         }
       ],
       "fieldConfig": {
         "defaults": {
-          "displayName": "${__field.labels.series} [${__field.labels.service_instance_id} ${__field.labels.xrpl_branch} ${__field.labels.xrpl_node_role} ${__field.labels.xrpl_work_item}]",
+          "displayName": "${__field.labels.series} ${__field.labels.xrpl_ident}",
           "unit": "µs",
           "custom": {
             "axisLabel": "Duration (μs)",
@@ -674,7 +669,7 @@
     },
     {
       "title": "GetObject Request Size Distribution",
-      "description": "###### What this is:\n*How many objects peers ask for per TMGetObjectByHash message, as a full distribution rather than an average. This characterises the request that caused any latency seen in the breakdown panel: large batches make the work genuinely large, which is a different problem from the same work becoming slower.*\n\n###### How it's computed:\n*Counts of requests falling in each object-count band per 5-minute window, from the getobject_request_objects bucket series, drawn as colour density.*\n\n###### Reading it:\n*A tight band at the bottom is honest traffic: InboundLedger asks for at most 8 hashes per call. Bands above 64 and above 1024 are the medium and large pricing bands, so mass there means the size surcharge is being applied. A hot cell in the top row is the overflow bucket and means requests larger than the top bucket boundary.*\n\n###### Healthy range:\n*Nearly all mass in the lowest bands (8 objects or fewer per request).*\n\n###### Watch for:\n*Mass appearing in the high bands, especially a persistent hot row near the top: a peer is batching thousands of hashes per message, which is what the differential pricing exists to charge for. Confirm with GetObject Charge Distribution and GetObject Rejections. Buckets are explicit (1,2,4,8,16,64,256,1024,4096,12288) and reach the handler's hard cap, so the top row is real traffic at the cap, not a measurement ceiling.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::recordGetObjectMetrics`",
+      "description": "###### What this is:\n*How many objects peers ask for per TMGetObjectByHash message, as a full distribution rather than an average. This characterizes the request that caused any latency seen in the breakdown panel: large batches make the work genuinely large, which is a different problem from the same work becoming slower.*\n\n###### How it's computed:\n*Counts of requests falling in each object-count band per 5-minute window, from the getobject_request_objects bucket series, drawn as color density.*\n\n###### Reading it:\n*A tight band at the bottom is honest traffic: the inbound-ledger acquire path asks for at most 4 hashes of one object type per message. Bands above 64 and above 1024 are the medium and large pricing bands, so mass there means the size surcharge is being applied. A hot cell in the top row is the overflow bucket and means requests larger than the top bucket boundary.*\n\n###### Healthy range:\n*Nearly all mass in the lowest bands (8 objects or fewer per request).*\n\n###### Watch for:\n*Mass appearing in the high bands, especially a persistent hot row near the top: a peer is batching thousands of hashes per message, which is what the differential pricing exists to charge for. Confirm with GetObject Charge Distribution and GetObject Rejections. Buckets are explicit (1,2,4,8,16,64,256,1024,4096,12288) and reach the handler's hard cap, so the top row is real traffic at the cap, not a measurement ceiling.*\n\n###### Keywords:\n- **GetObject / object fetch** *(per node)* — peer requests that fetch individual pieces of ledger data by hash, such as tree nodes or transactions.\n- **Resource charge** *(per node)* — the load cost the resource manager bills a peer per request; crossing the warning then drop threshold sheds the peer.\n\n###### Computation boundary:\n*Result: Per node — each series is one server's own value.*\n*Computed in xrpld code (MetricsRegistry, OpenTelemetry SDK) and exported as a metric; the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::recordGetObjectMetrics`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#getobject-object-fetch)",
       "type": "heatmap",
       "gridPos": {
         "h": 8,
@@ -712,7 +707,7 @@
     },
     {
       "title": "GetObject Lookups by Result",
-      "description": "###### What this is:\n*NodeStore lookups performed by the handler, split into hits and misses. A miss does a node-store seek while a hit is usually served from cache, so the hit/miss mix is the reason NodeStore Lookup time moves.*\n\n###### How it's computed:\n*Per-second rate of getobject_lookups_total, grouped by the result label. The counter is advanced once per request with the batch total, not once per object, so the rate is objects per second rather than requests per second.*\n\n###### Reading it:\n*Use this to explain the NodeStore Lookup line on the breakdown panel. A miss-heavy mix makes that line rise for a real reason: seeks, not a regression. A hit-heavy mix with rising lookup time points at the storage layer instead.*\n\n###### Healthy range:\n*Hits dominating on a warm synced node; misses low and driven by genuine catch-up requests.*\n\n###### Watch for:\n*A sustained miss rate far above the hit rate: a peer is asking for hashes this node does not hold, which is either a badly out-of-sync peer or probing traffic. Cross-check GetObject Charge Distribution, since misses are billed first and at eight times the hit cost.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::recordGetObjectMetrics`",
+      "description": "###### What this is:\n*NodeStore lookups performed by the handler, split into hits and misses. A miss does a node-store seek while a hit is usually served from cache, so the hit/miss mix is the reason NodeStore Lookup time moves.*\n\n###### How it's computed:\n*Per-second rate of getobject_lookups_total, grouped by the result label. The counter is advanced once per request with the batch totals -- hits are the objects returned, misses are the rest of the request -- not once per object, so the rate is objects per second rather than requests per second.*\n\n###### Reading it:\n*Use this to explain the NodeStore Lookup line on the breakdown panel. A miss-heavy mix makes that line rise for a real reason: seeks, not a regression. A hit-heavy mix with rising lookup time points at the storage layer instead.*\n\n###### Healthy range:\n*Hits dominating on a warm synced node; misses low and driven by genuine catch-up requests.*\n\n###### Watch for:\n*A sustained miss rate far above the hit rate: a peer is asking for hashes this node does not hold, which is either a peer far out of sync or a client requesting objects this node never stored. Cross-check GetObject Charge Distribution, since misses are billed first and at eight times the hit cost.*\n\n###### Keywords:\n- **NodeStore lookup (hit / miss)** *(per node)* — one object-store fetch by hash; a hit is usually served from cache, a miss does a disk seek.\n- **NodeStore** *(per node)* — the key-value object store holding ledger data (tree nodes), backed by NuDB.\n- **Resource charge** *(per node)* — the load cost the resource manager bills a peer per request; crossing the warning then drop threshold sheds the peer.\n\n###### Computation boundary:\n*Result: Per node — each series is one server's own value.*\n*Computed in xrpld code (MetricsRegistry, OpenTelemetry SDK) and exported as a metric; the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::recordGetObjectMetrics`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#nodestore-lookup-hit-miss)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -732,12 +727,12 @@
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(sum by (result, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_lookups_total{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\", result=~\"$result\"}[$__rate_interval])), \"series\", \"$1\", \"result\", \"(.*)\")"
+          "expr": "label_replace(label_join(label_replace(sum by (result, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_lookups_total{service_instance_id=~\"$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\", result=~\"$result\"}[$__rate_interval])), \"series\", \"$1\", \"result\", \"(.*)\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         }
       ],
       "fieldConfig": {
         "defaults": {
-          "displayName": "${__field.labels.series} [${__field.labels.service_instance_id} ${__field.labels.xrpl_branch} ${__field.labels.xrpl_node_role} ${__field.labels.xrpl_work_item}]",
+          "displayName": "${__field.labels.series} ${__field.labels.xrpl_ident}",
           "unit": "cps",
           "custom": {
             "axisLabel": "Lookups / Sec",
@@ -752,7 +747,7 @@
     },
     {
       "title": "GetObject Rejections",
-      "description": "###### What this is:\n*Requests refused by the message handler before any NodeStore access, split by which gate refused them: oversize means more objects than the handler accepts, malformed_ledgerhash means the ledger hash was not 32 bytes.*\n\n###### How it's computed:\n*Per-second rate of getobject_rejected_total, grouped by the reason label. Both gates run in onMessage before the job is queued, so a rejection consumes no queue slot and no NodeStore lookup.*\n\n###### Reading it:\n*Any non-zero value is traffic that does not conform to the protocol: an honest peer asks for at most 8 hashes and always sends a full-size hash. Because the gates fire before the fetch loop, rejections explain why request volume can be high while lookups stay flat.*\n\n###### Healthy range:\n*Zero. No honest peer produces either rejection.*\n\n###### Watch for:\n*A rising oversize rate: a peer is probing the request-size limit. Confirm the pricing response on GetObject Charge Distribution, and expect the peer to be disconnected once its resource balance crosses the drop threshold. A rising malformed rate points at a broken or hostile client rather than at load.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::onMessage (TMGetObjectByHash)`",
+      "description": "###### What this is:\n*Requests refused by the message handler before any NodeStore access, split by which gate refused them: oversize means more objects than the handler accepts (its hard cap is 12288), malformed_ledgerhash means the ledger hash was not 32 bytes.*\n\n###### How it's computed:\n*Per-second rate of getobject_rejected_total, grouped by the reason label. Both gates run in onMessage on the generic query path before the job is queued, so a rejection consumes no queue slot and no NodeStore lookup. The fetch-pack and transaction sub-types return earlier and never reach either gate.*\n\n###### Reading it:\n*Any non-zero value is traffic that does not conform to the protocol: the sync path asks for a handful of hashes and always sends a full-size hash. Because the gates fire before the fetch loop, rejections explain why request volume can be high while lookups stay flat.*\n\n###### Healthy range:\n*Zero. No conforming peer produces either rejection, so a flat zero line is the expected reading and is not on its own evidence that the counter is wired -- confirm that from the other GetObject panels, which do move on a healthy node.*\n\n###### Watch for:\n*A rising oversize rate: a peer is sending requests above the accepted object count. Confirm the pricing response on GetObject Charge Distribution, and expect the peer to be shed once its resource balance crosses the drop threshold. A rising malformed rate points at a broken or non-conforming client rather than at load.*\n\n###### Keywords:\n- **GetObject / object fetch** *(per node)* — peer requests that fetch individual pieces of ledger data by hash, such as tree nodes or transactions.\n- **Resource drops / warnings** *(per node)* — the resource manager warning (then dropping/blocking) a peer or client for excessive usage.\n- **Resource charge** *(per node)* — the load cost the resource manager bills a peer per request; crossing the warning then drop threshold sheds the peer.\n\n###### Computation boundary:\n*Result: Per node — each series is one server's own value.*\n*Computed in xrpld code (MetricsRegistry, OpenTelemetry SDK) and exported as a metric; the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::onMessage (TMGetObjectByHash)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#getobject-object-fetch)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -772,12 +767,12 @@
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(sum by (reason, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_rejected_total{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\", reason=~\"$reason\"}[$__rate_interval])), \"series\", \"$1\", \"reason\", \"(.*)\")"
+          "expr": "label_replace(label_join(label_replace(sum by (reason, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_rejected_total{service_instance_id=~\"$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\", reason=~\"$reason\"}[$__rate_interval])), \"series\", \"$1\", \"reason\", \"(.*)\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         }
       ],
       "fieldConfig": {
         "defaults": {
-          "displayName": "${__field.labels.series} [${__field.labels.service_instance_id} ${__field.labels.xrpl_branch} ${__field.labels.xrpl_node_role} ${__field.labels.xrpl_work_item}]",
+          "displayName": "${__field.labels.series} ${__field.labels.xrpl_ident}",
           "unit": "cps",
           "custom": {
             "axisLabel": "Rejections / Sec",
@@ -792,7 +787,7 @@
     },
     {
       "title": "GetObject Charge Distribution",
-      "description": "###### What this is:\n*The dynamic resource charge applied per TMGetObjectByHash request, as percentiles. This is the differential-pricing component only, so it shows whether cost actually escalates with request size and miss ratio the way the pricing model intends.*\n\n###### How it's computed:\n*p50, p90 and p99 of getobject_charge over the dashboard rate interval, from its bucket series summed by le. The value recorded is the charge that was applied, computed from billable hits, billable misses and the request-size band.*\n\n###### Reading it:\n*p50 sitting at zero is the healthy shape: requests inside the free allowance cost nothing. Movement in p99 while p50 stays at zero means a small number of expensive requests, which is exactly the traffic the model is meant to price. Compare with GetObject Request Size Distribution: charge should rise in steps as requests cross the size-band edges, not smoothly.*\n\n###### Healthy range:\n*p50 at zero, p99 low. Honest requests of 16 objects or fewer are free by design.*\n\n###### Watch for:\n*p99 climbing steadily: sustained expensive traffic, and the peers producing it should be approaching the resource drop threshold. Buckets are explicit and bracket the resource thresholds (5000 warning, 25000 drop), so p99 crossing 25000 means senders are being disconnected on a single message. The axis is deliberately unscaled rather than abbreviated, so those two numbers are readable exactly rather than as 5 K and 25 K.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::computeGetObjectByHashFee`",
+      "description": "###### What this is:\n*The dynamic resource charge applied per TMGetObjectByHash request, as percentiles. This is the differential-pricing component only -- a flat base charge is applied separately when the message is admitted -- so it shows whether cost actually escalates with request size and miss ratio the way the pricing model intends.*\n\n###### How it's computed:\n*p50, p90 and p99 of getobject_charge over the dashboard rate interval, from its bucket series summed by le. The value recorded is the charge that was applied, computed from billable hits, billable misses and the request-size band.*\n\n###### Reading it:\n*p50 sitting at zero is the healthy shape: requests inside the free allowance cost nothing. Movement in p99 while p50 stays at zero means a small number of expensive requests, which is exactly the traffic the model is meant to price. Compare with GetObject Request Size Distribution: charge should rise in steps as requests cross the size-band edges at 64 and 1024, not smoothly.*\n\n###### Healthy range:\n*p50 at zero, p99 low. Requests of 16 objects or fewer carry no dynamic charge by design.*\n\n###### Watch for:\n*p99 climbing steadily: sustained expensive traffic, and the peers producing it should be approaching the resource drop threshold. Buckets are explicit and bracket the resource thresholds (5000 warning, 25000 drop), so p99 crossing 25000 means senders are being shed on a single message. The axis is deliberately unscaled rather than abbreviated, so those two numbers are readable exactly rather than as 5 K and 25 K.*\n\n###### Keywords:\n- **Resource charge** *(per node)* — the load cost the resource manager bills a peer per request; crossing the warning then drop threshold sheds the peer.\n- **Resource drops / warnings** *(per node)* — the resource manager warning (then dropping/blocking) a peer or client for excessive usage.\n- **GetObject / object fetch** *(per node)* — peer requests that fetch individual pieces of ledger data by hash, such as tree nodes or transactions.\n\n###### Computation boundary:\n*Result: Per node — each series is one server's own value.*\n*Computed in xrpld code (MetricsRegistry, OpenTelemetry SDK) and exported as a metric; the collector only forwards it; the Grafana query selects and aggregates it.*\n\n###### Source:\n[PeerImp.cpp](https://github.com/XRPLF/rippled/blob/develop/src/xrpld/overlay/detail/PeerImp.cpp)\n\n###### Function:\n`PeerImp::computeGetObjectByHashFee (charge) / PeerImp::recordGetObjectMetrics (recording)`\n\n###### References:\n[Telemetry glossary](https://github.com/XRPLF/rippled/blob/develop/docs/telemetry-glossary.md#resource-charge)",
       "type": "timeseries",
       "gridPos": {
         "h": 8,
@@ -812,24 +807,24 @@
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.5, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_charge_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\"}[$__rate_interval]))), \"series\", \"Charge p50\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(histogram_quantile(0.5, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_charge_bucket{service_instance_id=~\"$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\", \"Charge p50\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.9, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_charge_bucket{service_instance_id=~\"$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\"}[$__rate_interval]))), \"series\", \"Charge p90\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(histogram_quantile(0.9, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_charge_bucket{service_instance_id=~\"$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\", \"Charge p90\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         },
         {
           "datasource": {
             "type": "prometheus"
           },
-          "expr": "label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_charge_bucket{service_instance_id=~\"$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\"}[$__rate_interval]))), \"series\", \"Charge p99\", \"\", \"\")"
+          "expr": "label_replace(label_join(label_replace(histogram_quantile(0.99, sum by (le, service_instance_id, xrpl_branch, xrpl_node_role) (rate(getobject_charge_bucket{service_instance_id=~\"$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\", \"Charge p99\", \"\", \"\"), \"xrpl_ident\", \", \", \"service_instance_id\", \"xrpl_branch\", \"xrpl_work_item\"), \"xrpl_ident\", \"[$1]\", \"xrpl_ident\", \"(?:, )*(.*[^, ])(?:, )*\")"
         }
       ],
       "fieldConfig": {
         "defaults": {
-          "displayName": "${__field.labels.series} [${__field.labels.service_instance_id} ${__field.labels.xrpl_branch} ${__field.labels.xrpl_node_role} ${__field.labels.xrpl_work_item}]",
+          "displayName": "${__field.labels.series} ${__field.labels.xrpl_ident}",
           "unit": "none",
           "custom": {
             "axisLabel": "Charge (Cost Units)",

docs/telemetry-glossary.md

@@ -17,6 +17,7 @@ documentation.
 - [Consensus](#cat-consensus)
 - [Transaction Pipeline](#cat-transaction-pipeline)
 - [Fees & Queue](#cat-fees-queue)
+- [Job Queue](#cat-job-queue)
 - [Node State & Sync](#cat-node-state-sync)
 - [Peer & Overlay Networking](#cat-peer-overlay-networking)
 - [Storage Internals](#cat-storage-internals)
@@ -479,6 +480,42 @@ The transaction queue (TxQ) holds transactions that pay enough for local relay b
 
 **See also:** [Transaction queue (TxQ) on xrpl.org](https://xrpl.org/docs/concepts/transactions/transaction-queue)
 
+<a id="cat-job-queue"></a>
+
+## Job Queue
+
+<a id="concurrency-limit"></a>
+
+### Concurrency limit
+
+Each job type declares how many of its jobs may run at the same time. Sync-critical types are deliberately tight so one kind of work cannot monopolize the worker pool. A type sitting at its limit cannot start more work even when worker threads are idle, which makes the limit a distinct kind of bottleneck from CPU or disk.
+
+**Scope:** per node — measured on and specific to this individual server.
+
+<a id="deferred-job"></a>
+
+### Deferred job
+
+When a job arrives for a type that is already at its concurrency limit, the queue holds it back rather than refusing it. Adding a job never fails for queue pressure, so a deferred count is the earliest signal that a type is oversubscribed — latency only shows the problem afterwards. Each completing job releases one deferred job, so a count that stays high means arrivals are outpacing completions.
+
+**Scope:** per node — measured on and specific to this individual server.
+
+<a id="handler-label"></a>
+
+### Handler label
+
+Several call sites can enqueue work under the same job type, so job type alone cannot say which one caused a latency spike. The handler label carries the name of the call site that enqueued the job. Names are kept only when they are letters-only; anything else — including names that embed a ledger sequence number — folds into a shared `other` bucket, which keeps the number of series bounded. A reading under `other` therefore mixes several callers and never identifies one.
+
+**Scope:** per node — measured on and specific to this individual server.
+
+<a id="job-queue-job-type"></a>
+
+### Job queue / job type
+
+The job queue is the worker-thread pool that runs xrpld's background work. Every unit of work is enqueued under a named job type — serving a peer's ledger request, absorbing inbound ledger data, updating payment paths, and so on — and each type is accounted separately: waiting, running, and deferred. Types that carry sync-critical traffic are the ones worth watching, because a backlog there translates directly into the node falling behind.
+
+**Scope:** per node — measured on and specific to this individual server.
+
 <a id="cat-node-state-sync"></a>
 
 ## Node State & Sync
@@ -621,6 +658,14 @@ Replay-delta request/response messages transfer only the state changes between l
 
 **Scope:** per node — measured on and specific to this individual server.
 
+<a id="resource-charge"></a>
+
+### Resource charge
+
+The resource manager bills each peer a load cost per request, so expensive requests cost the sender more than cheap ones. For object fetches the charge scales with how many objects were asked for and how many of those were misses, with a surcharge once the request crosses a size band. A running balance above the warning threshold marks the peer as overactive; above the drop threshold the node sheds it. Requests inside the free allowance carry no charge beyond the flat per-message cost.
+
+**Scope:** per node — measured on and specific to this individual server.
+
 <a id="resource-disconnect"></a>
 
 ### Resource disconnect
@@ -685,6 +730,14 @@ The NodeStore is xrpld's content-addressed object database holding all ledger tr
 
 **Scope:** per node — measured on and specific to this individual server.
 
+<a id="nodestore-lookup-hit-miss"></a>
+
+### NodeStore lookup (hit / miss)
+
+A lookup is one attempt to fetch an object from the NodeStore by its hash. A hit is usually served from an in-memory cache and is cheap; a miss goes to the back-end store and costs a disk seek, so it is far more expensive. The hit/miss mix is therefore the main reason lookup time moves: a rising miss share explains slower lookups without any regression in the storage layer, while slower lookups on a hit-heavy mix point at the storage layer itself.
+
+**Scope:** per node — measured on and specific to this individual server.
+
 <a id="nudb"></a>
 
 ### NuDB