rippled/docs/telemetry-runbook.md

xrpld Telemetry Operator Runbook

Table of Contents

• Overview
• Quick Start
• Configuration Reference
• Exporting to Grafana Cloud
• Span Reference
  • RPC Spans
  • Transaction Spans
  • Transaction Queue Spans
  • PathFinding Spans
  • Consensus Spans
  • Ledger Spans
  • Peer Spans
• Insights and Sample Queries
  • Transaction Workflow Analysis
  • DEX (OfferCreate / OfferCancel)
  • Apply Pipeline by Stage
  • Transaction Queue Health
  • RPC Debugging
  • PathFinding Performance
  • Consensus Health
  • Cross-Subsystem Correlation
• Cross-Node Trace Propagation
• Prometheus Metrics (Spanmetrics)
• System Metrics (OTel native -- beast::insight)
• Grafana Dashboards
• Alerting
• Log-Trace Correlation
• Troubleshooting
• Performance Tuning
• Disabling Telemetry
• Validating Telemetry Stack
• Performance Benchmarking

Overview

xrpld supports OpenTelemetry distributed tracing to provide visibility into RPC requests, transaction processing, and consensus rounds.

This runbook covers operating a running node and querying its traces. For building xrpld with telemetry support and the internal architecture, see build/telemetry.md.

Quick Start

1. Start the observability stack

docker compose -f docker/telemetry/docker-compose.yml up -d

This starts:

• OTel Collector on ports 4317 (gRPC) and 4318 (HTTP), and 13133 (health)
• Tempo on http://localhost:3200 (trace backend)
• Prometheus on http://localhost:9090
• Loki on http://localhost:3100 (log aggregation)
• Grafana on http://localhost:3000 (Tempo pre-configured as datasource)

2. Enable telemetry in xrpld

Add to your xrpld.cfg:

[telemetry]
enabled=1
endpoint=http://localhost:4318/v1/traces

3. Build with telemetry support

conan install . --build=missing -o telemetry=True
cmake --preset default -Dtelemetry=ON
cmake --build --preset default

4. Run against a live network

Two ready-made configs connect a tracking node (no validator credentials) to a public network with all tracing and native metrics enabled:

Config	Network
docker/telemetry/xrpld-telemetry.cfg	Devnet
docker/telemetry/xrpld-telemetry-mainnet.cfg	Mainnet

.build/xrpld --conf docker/telemetry/xrpld-telemetry-mainnet.cfg

Both set [insight] server=otel (native metrics → collector → Prometheus, which drives the dashboards) and service_instance_id, exposed by Prometheus as the service_instance_id label that the $node dashboard variable filters on. The mainnet config logs to /var/log/xrpld/mainnet/debug.log — the path the collector's filelog receiver tails for log-trace correlation.

Metrics begin flowing as soon as the node connects to peers (server_state ≥ connected); full ledger and consensus panels populate after sync (server_state = full). Check progress with:

curl -s http://localhost:5005 -d '{"method":"server_info"}' |
    jq '.result.info | {server_state, peers, complete_ledgers}'

Mainnet sync is bandwidth- and disk-heavy. For a quick check use the devnet config or the standalone test in docker/telemetry/TESTING.md, which generates spans without waiting for a live sync.

Configuration Reference

Option	Default	Description
enabled	0	Master switch for telemetry
endpoint	http://localhost:4318/v1/traces	OTLP/HTTP endpoint
service_name	xrpld	OpenTelemetry service name resource attribute
service_instance_id	node public key	OpenTelemetry service instance ID resource attribute
trace_rpc	1	Enable RPC request tracing
trace_transactions	1	Enable transaction tracing
trace_consensus	1	Enable consensus tracing
trace_peer	1	Enable peer message tracing (high volume)
trace_ledger	1	Enable ledger tracing
consensus_trace_strategy	deterministic	Consensus trace ID strategy (deterministic or random)
batch_size	512	Max spans per batch export
batch_delay_ms	5000	Delay between batch exports
max_queue_size	2048	Max spans queued before dropping
use_tls	0	Use TLS for exporter connection
tls_ca_cert	(empty)	Path to CA certificate bundle
tls_client_cert	(empty)	Client cert (PEM) for mutual TLS; empty = one-way TLS
tls_client_key	(empty)	Private key (PEM) for tls_client_cert

Exporting to Grafana Cloud

The collector can ship traces, metrics, and logs to a hosted Grafana Cloud stack instead of (or alongside) the local Tempo/Prometheus/Loki backends. This is a runtime choice — no xrpld rebuild and no change to the base stack. xrpld still exports to the local collector exactly as before; the collector adds one OTLP/HTTP exporter that forwards all three signals to the Grafana Cloud OTLP gateway, which fans them out to hosted Tempo, Mimir, and Loki.

Credentials

Find these under Grafana Cloud → Connections → OpenTelemetry (OTLP):

Value	Used as	Notes
GRAFANA_CLOUD_OTLP_ENDPOINT	exporter endpoint	Full gateway URL incl. /otlp path
GRAFANA_CLOUD_INSTANCE_ID	Basic-auth user	Numeric stack/instance id
GRAFANA_CLOUD_API_TOKEN	Basic-auth pass	Access-policy token with *:write for all signals

Enable

1. Copy the template and fill in the three values:

   cp docker/telemetry/.env.grafanacloud.example docker/telemetry/.env.grafanacloud
   # edit .env.grafanacloud — this file is gitignored, never commit tokens
   

2. Bring the stack up with the base file and the Grafana Cloud override:

   docker compose -f docker/telemetry/docker-compose.yml \
       -f docker/telemetry/docker-compose.grafanacloud.yaml up -d
   

To return to local-only export, bring the stack up with just the base docker-compose.yml.

Files

File	Role
otel-collector-config.grafanacloud.yaml	Collector config: local backends plus a Grafana Cloud OTLP exporter on all three pipelines
docker-compose.grafanacloud.yaml	Override that mounts that config and injects the credentials
.env.grafanacloud.example	Credential template (copy to .env.grafanacloud)

Local + cloud vs cloud-only

The prepared config dual-exports: data goes to both the local stack and Grafana Cloud, so the on-box backends remain a fallback. For cloud-only, remove the local exporters (debug, otlp/tempo, prometheus, otlphttp/loki) from the respective pipelines in otel-collector-config.grafanacloud.yaml, leaving only otlphttp/grafanacloud.

Note

: shipping logs to Grafana Cloud requires keeping xrpld file logging on (at least warning level) so the collector's filelog receiver has a debug.log to tail. Traces and metrics are unaffected by log level.

Importing dashboards to Grafana Cloud

Shipping data (above) is independent of installing the dashboards. The local stack auto-provisions dashboards from a mounted folder (grafana/provisioning/dashboards/dashboards.yaml, type: file); Grafana Cloud cannot read your filesystem, so its dashboards must be imported over the HTTP API or the UI.

The dashboard JSON in docker/telemetry/grafana/dashboards/ references its backends through datasource template variables (${DS_PROMETHEUS}, ${DS_TEMPO}) rather than fixed UIDs. On import, Grafana binds each variable to a datasource of the matching type — auto-selecting it when only one exists (the usual case: one Mimir, one Tempo). This is what makes the same files work unchanged on both the local stack and Cloud.

Dashboards are parameterized by grafana/parameterize-datasources.py. If you add a dashboard exported with hardcoded UIDs, re-run that script (idempotent) before committing so it stays portable.

To import:

1. In Grafana Cloud, go to Dashboards → New → Import.
2. Upload a file from docker/telemetry/grafana/dashboards/ (or paste its JSON), then click Load.
3. At the datasource prompt, confirm the auto-selected Prometheus/Mimir datasource — and Tempo for dashboards that query traces — then Import.
4. Repeat per dashboard. Only consensus-health uses Tempo; the rest need only the Prometheus/Mimir datasource.

Span Reference

All spans instrumented in xrpld, grouped by subsystem:

RPC Spans

Span Name	Source File	Attributes	Description
rpc.http_request	ServerHandler.cpp	request_payload_size	Top-level HTTP RPC request
rpc.ws_upgrade	ServerHandler.cpp	—	WebSocket upgrade handshake
rpc.ws_message	ServerHandler.cpp	command	WebSocket RPC message
rpc.process	ServerHandler.cpp	is_batch, batch_size	RPC processing (child of rpc.http_request/ws_message)
rpc.command.<name>	RPCHandler.cpp	command, version, rpc_role, rpc_status, load_type	Per-command span (e.g., rpc.command.server_info)

Transaction Spans

Span Name	Source File	Attributes	Description
tx.process	NetworkOPs.cpp	tx_hash, local, path, tx_type, fee, sequence, ter_result, applied	Transaction submission and processing
tx.receive	PeerImp.cpp	peer_id, tx_hash, tx_type, peer_version, suppressed, tx_status	Transaction received from peer relay
tx.apply	BuildLedger.cpp	ledger_seq, tx_count, tx_failed	Transaction set applied per ledger
tx.preflight	applySteps.cpp	stage, tx_type, ter_result	Stateless checks stage
tx.preclaim	applySteps.cpp	stage, tx_type, ter_result	Ledger-aware checks stage
tx.transactor	Transactor.cpp	stage, tx_type, ter_result, applied	Apply stage (transactor runs)

The three apply-pipeline spans (tx.preflight, tx.preclaim, tx.transactor) share a deterministic trace_id from txID[0:16], so they group under one trace per transaction. The stage attribute (preflight / preclaim / apply) drives the collector spanmetrics stage dimension, giving per-stage RED metrics on the Transaction Overview dashboard.

Transaction Queue Spans

Span Name	Source File	Attributes	Description
txq.enqueue	TxQ.cpp	tx_hash, tx_type	Transaction enqueue decision (child of tx.process)
txq.apply_direct	TxQ.cpp	--	Direct apply attempt (bypassing queue)
txq.batch_clear	TxQ.cpp	--	Batch clear of queued transactions for an account
txq.accept	TxQ.cpp	queue_size, ledger_changed	Ledger-close accept loop over queued transactions
txq.accept_tx	TxQ.cpp	tx_hash, retries_remaining, ter_code, txq_status	Per-transaction apply during accept
txq.cleanup	TxQ.cpp	ledger_seq	Post-close cleanup of expired queue entries

PathFinding Spans

Span Name	Source File	Attributes	Description
pathfind.request	PathFind.cpp / RipplePathFind.cpp	pathfind_source_account, pathfind_dest_account	Path-find RPC entry (accounts hashed; set when present)
pathfind.compute	PathRequest.cpp	pathfind_fast, pathfind_dest_currency	Path computation for one request (doUpdate)
pathfind.discover	PathRequest.cpp	pathfind_search_level, pathfind_num_paths	Graph exploration (one per RPC call in findPaths)
pathfind.update_all	PathRequestManager.cpp	pathfind_ledger_index, pathfind_num_requests	Async recomputation of active requests on ledger close

Consensus Spans

Span Name	Source File	Attributes	Description
consensus.round	RCLConsensus.cpp	consensus_ledger_id, ledger_seq, consensus_mode, trace_strategy, consensus_round_id	Root span for a consensus round (deterministic or random trace ID)
consensus.phase.open	Consensus.h	--	Open phase duration (child of round)
consensus.proposal.send	RCLConsensus.cpp	consensus_round, is_bow_out	Consensus proposal broadcast
consensus.ledger_close	RCLConsensus.cpp	ledger_seq, consensus_mode	Ledger close event
consensus.establish	Consensus.h	converge_percent, establish_count, proposers	Establish phase duration (child of round)
consensus.update_positions	Consensus.h	converge_percent, proposers, disputes_count	Position update and dispute resolution (see Events below)
consensus.check	Consensus.h	agree_count, disagree_count, converge_percent, have_close_time_consensus, threshold_percent, proposers_finished, consensus_stalled, establish_count, consensus_result	Consensus threshold check
consensus.accept	RCLConsensus.cpp	proposers, round_time_ms, quorum, disputes_count, consensus_state	Ledger accepted by consensus
consensus.accept.apply	RCLConsensus.cpp	ledger_seq, close_time, close_time_correct, close_resolution_ms, consensus_state, proposing, round_time_ms, parent_close_time, close_time_self, close_time_vote_bins, resolution_direction, tx_count	Ledger application with close time details (see Events below)
consensus.validation.send	RCLConsensus.cpp	ledger_seq, proposing, ledger_hash, full_validation, validation_sign_time	Validation sent after accept (follows-from link)
consensus.mode_change	RCLConsensus.cpp	mode_old, mode_new	Consensus mode transition
consensus.proposal.receive	PeerImp.cpp	proposal_trusted, consensus_round	Proposal received from peer (extracts parent context from TraceContext when present; falls back to standalone span for older peers)
consensus.validation.receive	PeerImp.cpp	validation_trusted, ledger_seq	Validation received from peer (extracts parent context from TraceContext when present; falls back to standalone span for older peers)

Consensus Span Events

Parent Span	Event Name	Event Attributes	Description
consensus.update_positions	dispute.resolve	tx_id, dispute_our_vote, dispute_yays, dispute_nays	Emitted per dispute when votes are tallied
consensus.accept.apply	tx.included	tx_id	Emitted per transaction included in the accepted ledger

Close Time Queries (Tempo TraceQL)

Span attributes are filtered with span.<attr> inside {}. Combine conditions with &&.

# Find rounds where validators disagreed on close time
{name="consensus.accept.apply" && span.close_time_correct = false}

# Find consensus failures (moved_on)
{name="consensus.accept.apply" && span.consensus_state = "moved_on"}

# Find slow ledger applications (>5s)
{name="consensus.accept.apply" && duration > 5000ms}

# Find specific ledger's consensus details
{name="consensus.accept.apply" && span.ledger_seq = 92345678}

# Find all spans in a consensus round (deterministic trace strategy)
{name="consensus.round" && span.consensus_round_id = "<round_id>"}

# Find dispute resolutions
{name="consensus.update_positions"} >> {event:name="dispute.resolve"}

Ledger Spans

Span Name	Source File	Attributes	Description
ledger.build	BuildLedger.cpp:31	ledger_seq, tx_count, tx_failed	Ledger build during consensus
ledger.validate	LedgerMaster.cpp:915	ledger_seq, validations	Ledger promoted to validated
ledger.store	LedgerMaster.cpp:409	ledger_seq	Ledger stored in history

Peer Spans

Span Name	Source File	Attributes	Description
peer.proposal.receive	PeerImp.cpp:1667	peer_id, proposal_trusted	Proposal received from peer
peer.validation.receive	PeerImp.cpp:2264	peer_id, validation_trusted	Validation received from peer

---

Insights and Sample Queries

This section shows what questions you can answer using the span attributes, with example Tempo TraceQL queries.

TraceQL syntax note: span attributes must be referenced with the span. prefix inside {}. Conditions are combined with &&. The | pipeline operator is not supported on this Tempo version.

# General pattern
{name="<span-name>" && span.<attr> = <value> && span.<attr2> != <value2>}

# Duration filter (no prefix needed)
{name="<span-name>" && duration > 500ms}

# Regex match
{name="<span-name>" && span.<attr> =~ "<pattern>.*"}

# Multiple span names
{name = "<span-a>" || name = "<span-b>"}

# Name regex
{name =~ "<pattern>.*" && span.<attr> = <value>}

# Structural: find parent spans that have a matching child/event
{name="<parent>"} >> {event:name="<event-name>"}

Transaction Workflow Analysis

# Find all AMM transactions (AMMDeposit, AMMWithdraw, AMMVote)
{name="tx.process" && span.tx_type =~ "AMM.*"}

# Find a specific AMM operation
{name="tx.process" && span.tx_type = "AMMDeposit"}
{name="tx.process" && span.tx_type = "AMMWithdraw"}
{name="tx.process" && span.tx_type = "AMMVote"}

# Find Payment transactions that failed
{name="tx.process" && span.tx_type = "Payment" && span.ter_result != "tesSUCCESS"}

# Find Payment failures due to path issues
{name="tx.process" && span.tx_type = "Payment" && span.ter_result =~ "tecPATH.*"}

# Compare latency of different transaction types
{name="tx.process" && span.tx_type = "OfferCreate"}
{name="tx.process" && span.tx_type = "Payment"}

# Find high-fee transactions (fee > 1 XRP = 1000000 drops)
{name="tx.process" && span.fee > 1000000}

# Find transactions that were not applied
{name="tx.process" && span.applied = false}

# Find NFTokenMint across tx and txq spans
{name =~ "tx.*|txq.*" && span.tx_type = "NFTokenMint"}

# Find all NFT-related activity
{name =~ "tx.*|txq.*" && span.tx_type =~ "NFToken.*"}

# Find TrustSet transactions (IOU trust lines)
{name="tx.process" && span.tx_type = "TrustSet"}

# Find oracle price updates
{name="tx.process" && span.tx_type = "OracleSet"}

DEX (OfferCreate / OfferCancel)

# All DEX offer creates
{name="tx.process" && span.tx_type = "OfferCreate"}

# Offers killed (ImmediateOrCancel/FillOrKill with no fill)
{name="tx.process" && span.tx_type = "OfferCreate" && span.ter_result = "tecKILLED"}

# Offers that failed due to insufficient funds
{name="tx.process" && span.tx_type = "OfferCreate" && span.ter_result = "tecUNFUNDED_OFFER"}

# Offers failed due to insufficient reserve to place the offer
{name="tx.process" && span.tx_type = "OfferCreate" && span.ter_result = "tecINSUF_RESERVE_OFFER"}

# Offer cancellations
{name="tx.process" && span.tx_type = "OfferCancel"}

# OfferCreate transactions received from peers (cross-node relay)
{name="tx.receive" && span.tx_type = "OfferCreate"}

Apply Pipeline by Stage

# All three stages of one transaction (preflight -> preclaim -> apply)
{name=~"tx.preflight|tx.preclaim|tx.transactor"}

# Transactions that failed at the preclaim stage
{name="tx.preclaim"} | ter_result != "tesSUCCESS"

# Transactions that hard-failed preflight (never reached preclaim/apply)
{name="tx.preflight"} | ter_result != "tesSUCCESS"

PromQL on the span-derived metrics (dashboard: Transaction Overview):

# Per-stage throughput — the funnel preflight >= preclaim >= apply
sum by (stage) (rate(span_calls_total{span_name=~"tx.preflight|tx.preclaim|tx.transactor"}[5m]))

# Per-stage p95 latency
histogram_quantile(0.95, sum by (le, stage) (rate(span_duration_milliseconds_bucket{span_name=~"tx.preflight|tx.preclaim|tx.transactor"}[5m])))

# Per-stage failure rate (ter_result != tesSUCCESS; a failing ter completes the
# span normally, so filter on the attribute, not status_code which only flags exceptions)
sum by (stage) (rate(span_calls_total{span_name=~"tx.preflight|tx.preclaim|tx.transactor", ter_result!~"tesSUCCESS|"}[5m]))

Alerting: a rising tx.preflight / tx.preclaim failure rate points to malformed or stale-sequence submissions (often spam or a misbehaving client); a rising tx.transactor failure rate points to apply-time problems. Alert per stage rather than on a single aggregate so the failing stage is obvious.

Sampling caveat: these stage metrics are span-derived and inherit the tracer head-sampling ratio (sampling_ratio). At sampling_ratio < 1.0 they undercount proportionally — treat them as relative trends, not absolute transaction counts. Native StatsD metrics are unsampled.

Transaction Queue Health

# Find transactions rejected from the queue
{name="txq.accept_tx" && span.txq_status = "failed"}

# Find transactions being retried
{name="txq.accept_tx" && span.txq_status = "retried"}

# Find transactions that exhausted retries
{name="txq.accept_tx" && span.txq_status = "retried" && span.retries_remaining = 0}

# Which transaction types get queued most often?
{name="txq.enqueue" && span.tx_type = "Payment"}
{name="txq.enqueue" && span.tx_type = "OfferCreate"}
{name="txq.enqueue" && span.tx_type =~ "NFToken.*"}

# Find ledger closes that applied queued transactions
{name="txq.accept" && span.ledger_changed = true}

RPC Debugging

# Find batch RPC requests
{name="rpc.process" && span.is_batch = true}

# Find large RPC payloads (>100KB)
{name="rpc.http_request" && span.request_payload_size > 100000}

# Find resource-heavy RPC commands (by load_type)
{name =~ "rpc.command.*" && span.load_type = "exceptioned RPC"}

# Find a specific WebSocket command
{name="rpc.ws_message" && span.command = "subscribe"}

# Find server_info calls
{name="rpc.command.server_info"}

# Find slow pathfinding with many source assets
{name="pathfind.discover" && span.pathfind_num_source_assets > 10}

PathFinding Performance

# Find pathfinding for specific currencies
{name="pathfind.compute" && span.pathfind_dest_currency = "USD"}

# Find expensive pathfinding (many source assets to explore)
{name="pathfind.discover" && span.pathfind_num_source_assets > 20}

# Find slow pathfinding requests
{name="pathfind.compute" && duration > 1000ms}

Consensus Health

# Find rounds where consensus timed out (expired)
{name="consensus.accept" && span.consensus_state = "expired"}

# Find rounds where we moved on without full agreement
{name="consensus.accept" && span.consensus_state = "moved_on"}

# Find rounds with many disputes
{name="consensus.accept" && span.disputes_count > 5}

# Find slow consensus rounds (>5s)
{name="consensus.accept" && span.round_time_ms > 5000}

# Find bow-out proposals (node resigned from round)
{name="consensus.proposal.send" && span.is_bow_out = true}

# Correlate validation with its ledger
{name="consensus.validation.send" && span.ledger_hash = "<hash>"}

# Find rounds where validators disagreed on close time
{name="consensus.accept.apply" && span.close_time_correct = false}

# Find both validation send and receive (compare sender vs receiver latency)
{name = "consensus.validation.send" || name = "consensus.validation.receive"}

Cross-Subsystem Correlation

# Follow a transaction from receive through queue to ledger
{name =~ "tx.*|txq.*" && span.tx_type = "Payment" && duration > 500ms}

# Find all NFT-related activity across tx and txq spans
{name =~ "tx.*|txq.*" && span.tx_type =~ "NFToken.*"}

# Find all AMM activity across tx and txq spans
{name =~ "tx.*|txq.*" && span.tx_type =~ "AMM.*"}

# Find cross-node transaction receives (no errors)
{name="tx.receive" && status != error}

Where to Look (Quick Reference)

Question	Span	Key Attributes
"Which tx type is slowest?"	tx.process	span.tx_type + duration
"Why was my tx rejected?"	tx.process	span.ter_result, span.applied
"What AMM operations happened?"	tx.process	span.tx_type =~ "AMM.*"
"What DEX offers failed?"	tx.process	span.tx_type, span.ter_result
"What NFT activity occurred?"	tx.process, txq.enqueue	span.tx_type =~ "NFToken.*"
"Is the TxQ backing up?"	txq.accept	span.queue_size, span.ledger_changed
"Why was my tx dropped from queue?"	txq.accept_tx	span.txq_status, span.ter_code
"Are batch requests a problem?"	rpc.process	span.is_batch, span.batch_size
"Which RPC is expensive?"	rpc.command.*	span.load_type, duration
"Did consensus reach threshold?"	consensus.check	span.consensus_result
"Was consensus outcome normal?"	consensus.accept	span.consensus_state
"Did a validator bow out?"	consensus.proposal.send	span.is_bow_out
"Which ledger was validated?"	consensus.validation.send	span.ledger_hash
"Did close time agreement fail?"	consensus.accept.apply	span.close_time_correct

---

Cross-Node Trace Propagation

xrpld propagates trace context across nodes via protobuf TraceContext fields embedded in peer-to-peer messages. When Node A sends a transaction, proposal, or validation, it injects its active span's trace/span IDs into the protobuf message. Node B extracts that context on receipt and creates a child span, linking the two nodes into a single distributed trace.

How It Works

Node A (sender)                          Node B (receiver)
+-----------------------------+          +-------------------------------+
| tx.process / consensus.*    |          | PeerImp::onMessage()          |
|   |                         |          |   |                           |
|   v                         |          |   v                           |
| SpanGuard::getTraceBytes()  |          | extract TraceContext from      |
|   |                         |          | protobuf message               |
|   v                         |   send   |   |                           |
| injectSpanContext() --------|--------->|   v                           |
| sets TraceContext fields    |  proto   | txReceiveSpan()               |
| (trace_id, span_id, flags) |  msg     | proposalReceiveSpan()         |
+-----------------------------+          | validationReceiveSpan()       |
                                         |   |                           |
                                         |   v                           |
                                         | child span with parent link   |
                                         +-------------------------------+

Send-Side Injection

Message Type	Injection Point	Mechanism
TMTransaction	NetworkOPs::apply()	Injects tx.process span into relay msg
TMProposeSet	RCLConsensus::propose()	Injects active context into proposal msg
TMValidation	RCLConsensus::validate()	Injects active context into validation msg

Receive-Side Extraction

Message Type	Extraction Point	Helper Function
TMTransaction	PeerImp::onMessage(TMTransaction)	TxTracing::txReceiveSpan()
TMProposeSet	PeerImp::onMessage(TMProposeSet)	ConsensusReceiveTracing::proposalReceiveSpan()
TMValidation	PeerImp::onMessage(TMValidation)	ConsensusReceiveTracing::validationReceiveSpan()

Key Files

File	Role
src/xrpld/telemetry/PropagationHelpers.h	injectSpanContext() — SpanGuard to protobuf
include/xrpl/telemetry/TraceContextPropagator.h	OTel context <-> protobuf conversion primitives
src/xrpld/telemetry/ConsensusReceiveTracing.h	Proposal/validation receive span factories
src/xrpld/telemetry/TxTracing.h	Transaction receive span factory

Backwards Compatibility

Older peers that do not populate TraceContext fields in their messages will simply produce empty trace bytes on the receive side. The extraction helpers detect this and create standalone (root) spans instead of child spans. No errors are logged and no data is lost — the receive span is still created with all its normal attributes, it just lacks a cross-node parent link.

Example Tempo Queries

# Find cross-node transaction traces (tx.receive spans with no errors)
{name="tx.receive" && status != error}

# Find proposals received with cross-node parent context
{} >> {name="consensus.proposal.receive"}

# Trace a transaction across the network by its hash
{name =~ "tx.*" && span.tx_hash = "<hash>"}

# Find all spans in a cross-node consensus trace
{resource.service.name="xrpld" && span.consensus_round_id = "<round_id>"}

# Compare latency between sender and receiver for validations
{name = "consensus.validation.send" || name = "consensus.validation.receive"}

Prometheus Metrics (Spanmetrics)

The OTel Collector's spanmetrics connector automatically derives RED (Rate, Errors, Duration) metrics from every span. No custom metrics code is needed in xrpld.

Generated Metric Names

Prometheus Metric	Type	Description
span_calls_total	Counter	Total span invocations
span_duration_milliseconds_bucket	Histogram	Latency distribution buckets
span_duration_milliseconds_count	Histogram	Latency observation count
span_duration_milliseconds_sum	Histogram	Cumulative latency

Metric Labels

Every metric carries these standard labels:

Label	Source	Example
span_name	Span name	rpc.command.server_info
status_code	Span status	STATUS_CODE_UNSET, STATUS_CODE_ERROR
service_name	Resource attribute	xrpld
span_kind	Span kind	SPAN_KIND_INTERNAL

Additionally, span attributes configured as dimensions in the collector become metric labels. The span attribute keys are already underscore form (the naming convention forbids dots), so the label name matches the attribute name verbatim. Prometheus' dots → underscores sanitization only fires for dotted attribute names (e.g. resource attributes like service.name), which does not apply to these dimensions.

Span Attribute	Metric Label	Applies To
command	command	rpc.command.* spans
rpc_status	rpc_status	rpc.command.* spans
consensus_mode	consensus_mode	consensus.ledger_close spans
local	local	tx.process spans
proposal_trusted	proposal_trusted	peer.proposal.receive spans
validation_trusted	validation_trusted	peer.validation.receive spans

Histogram Buckets

Configured in otel-collector-config.yaml:

1ms, 5ms, 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 5s

System Metrics (OTel native -- beast::insight)

xrpld has a built-in metrics framework (beast::insight) that exports metrics natively via OTLP to the OTel Collector. These complement the span-derived RED metrics by providing system-level gauges, counters, and timers that don't map to individual trace spans.

Configuration

Add to xrpld.cfg:

[insight]
server=otel
endpoint=http://localhost:4318/v1/metrics
prefix=xrpld

The OTelCollector implementation exports metrics via OTLP/HTTP to the same OTel Collector that receives traces. No separate StatsD receiver is needed.

Fallback: Set server=statsd and address=127.0.0.1:8125 to use the legacy StatsD UDP path. This requires re-enabling the statsd receiver in otel-collector-config.yaml and uncommenting port 8125 in docker-compose.yml.

Metric Reference

Gauges

Prometheus Metric	Source	Description
ledgermaster_validated_ledger_age	LedgerMaster.h:373	Age of validated ledger (seconds)
ledgermaster_published_ledger_age	LedgerMaster.h:374	Age of published ledger (seconds)
state_accounting_{mode}_duration	NetworkOPs.cpp:774	Time in each operating mode (Disconnected/Connected/Syncing/Tracking/Full)
state_accounting_{mode}_transitions	NetworkOPs.cpp:780	Transition count per mode
peer_finder_active_inbound_peers	PeerfinderManager.cpp:214	Active inbound peer connections
peer_finder_active_outbound_peers	PeerfinderManager.cpp:215	Active outbound peer connections
overlay_peer_disconnects	OverlayImpl.h:557	Peer disconnect count
jobq_job_count	JobQueue.cpp:26	Current job queue depth (emitted as jobq_job_count: the JobQueue collector is wrapped in group("jobq"), so the registered job_count gauge gains the jobq_ prefix)
{category}_bytes_in/out	OverlayImpl.h:535	Overlay traffic bytes per category (57 categories)
{category}_messages_in/out	OverlayImpl.h:535	Overlay traffic messages per category

OTel MetricsRegistry Gauges

These gauges are exported via the OTel Metrics SDK PeriodicMetricReader (10s interval), NOT through beast::insight.

Prometheus Metric	Source	Description
server_info{metric="server_state"}	MetricsRegistry.cpp	Operating mode (0=DISCONNECTED .. 4=FULL)
server_info{metric="uptime"}	MetricsRegistry.cpp	Seconds since server start
server_info{metric="peers"}	MetricsRegistry.cpp	Total connected peers
server_info{metric="validated_ledger_seq"}	MetricsRegistry.cpp	Validated ledger sequence number
server_info{metric="ledger_current_index"}	MetricsRegistry.cpp	Current open ledger sequence
server_info{metric="peer_disconnects_resources"}	MetricsRegistry.cpp	Cumulative resource-related peer disconnects
server_info{metric="last_close_proposers"}	MetricsRegistry.cpp	Proposers in last closed round
server_info{metric="last_close_converge_time_ms"}	MetricsRegistry.cpp	Last close convergence time (ms)
build_info{version="<ver>"}	MetricsRegistry.cpp	Info-style metric (always 1)
complete_ledgers{bound="start|end",index="<N>"}	MetricsRegistry.cpp	Complete ledger range start/end pairs
db_metrics{metric="db_kb_total"}	MetricsRegistry.cpp	Total database size (KB)
db_metrics{metric="db_kb_ledger"}	MetricsRegistry.cpp	Ledger database size (KB)
db_metrics{metric="db_kb_transaction"}	MetricsRegistry.cpp	Transaction database size (KB)
db_metrics{metric="historical_perminute"}	MetricsRegistry.cpp	Historical ledger fetches per minute
cache_metrics{metric="AL_size"}	MetricsRegistry.cpp	AcceptedLedger cache size
nodestore_state{metric="node_reads_duration_us"}	MetricsRegistry.cpp	Cumulative read time (microseconds)
nodestore_state{metric="read_request_bundle"}	MetricsRegistry.cpp	Read request bundle count
nodestore_state{metric="read_threads_running"}	MetricsRegistry.cpp	Active read threads
nodestore_state{metric="read_threads_total"}	MetricsRegistry.cpp	Total read threads configured

Counters

Prometheus Metric	Source	Description
rpc_requests	ServerHandler.cpp:108	Total RPC request count
ledger_fetches	InboundLedgers.cpp:44	Ledger fetch request count
ledger_history_mismatch	LedgerHistory.cpp:16	Ledger hash mismatch count
warn	Logic.h:33	Resource manager warning count
drop	Logic.h:34	Resource manager drop count

Histograms

Prometheus Metric	Source	Description
rpc_time	ServerHandler.cpp:110	RPC response time (ms)
rpc_size	ServerHandler.cpp:109	RPC response size (bytes)
ios_latency	Application.cpp:438	I/O service loop latency (ms)
pathfind_fast	PathRequests.h:23	Fast pathfinding duration (ms)
pathfind_full	PathRequests.h:24	Full pathfinding duration (ms)

Deployment Tiers

Multiple xrpld instances can send telemetry to per-tier collectors that all forward to one Grafana stack. Four resource attributes segregate the data so one dashboard set serves every deployment:

Dimension	Attribute	Set by	Example values
Node	service.instance.id	xrpld cfg	alice-laptop, ci-runner-7
Service	service.name	xrpld cfg	xrpld, xrpld-validator
Network	xrpl.network.type	xrpld node	mainnet, testnet, devnet, perf
Environment	deployment.environment	collector	local, test, ci, prod
Work Item	xrpl.work.item	perf-iac	RIPD-7455 (empty outside perf runs)
Branch	xrpl.branch	perf-iac	baseline:<ref>:<commit>, test:<ref>:<commit>
Node Role	xrpl.node.role	perf-iac	validator, peer

Dashboards expose these as the template variables $node, $service_name, $xrpl_network_type, $deployment_environment, $xrpl_work_item, $xrpl_branch, and $xrpl_node_role (each variable name matches its Prometheus label). Select them top-down — work item → branch → node role → node for a perf comparison run, or environment → network → service → node for general use. Selecting All matches every value, including series lacking the label, so mixed old/new data never disappears.

The last three ($xrpl_work_item, $xrpl_branch, $xrpl_node_role) are populated only during perf-iac comparison runs, which stamp them as resource attributes from their own alloy pipeline. Outside those runs the labels are absent; leaving the filters on All keeps every dashboard rendering normally.

Who owns which attribute

• Node and service come from xrpld config (service_instance_id, service_name). Unique per process.
• Network is a property of the chain the node joined; the node derives it from [network_id] and stamps xrpl.network.type on all three signals.
• Environment is a property of where the collector runs; each collector serves one environment and stamps it.

The upsert vs insert rule

The collector's resource/tier processor uses two actions on purpose:

• deployment.environment → upsert (overwrite). The collector is the environment, so it is authoritative.
• xrpl.network.type → insert (fill only if absent). The node knows its real network, so the collector must not overwrite it — insert only supplies a value when the source did not (e.g. an older xrpld build). This is what lets a local node connected to mainnet report network=mainnet, not the collector's default.

Configuring a collector for a tier

Each tier runs its own collector. Set the two values in the resource/tier processor of the collector config (otel-collector-config.yaml for local backends, otel-collector-config.grafanacloud.yaml for Grafana Cloud):

processors:
  resource/tier:
    attributes:
      - key: deployment.environment
        value: <tier> # local | test | ci | prod
        action: upsert
      - key: xrpl.network.type
        value: <network> # mainnet | testnet | devnet (fallback only)
        action: insert

Suggested per-tier values:

Collector	deployment.environment	xrpl.network.type (fallback)
Developer laptop	local	devnet
Test machines	test	testnet
CI runs	ci	testnet
Production observer	prod	mainnet

The xrpl.network.type value is only a fallback: when the node stamps its own network (all current builds do), the node's value wins. Set it to the network the collector most commonly serves.

How the tier labels reach metrics

Resource attributes do not become Prometheus labels automatically. Two collector settings make it work, both already enabled:

• prometheus.resource_to_telemetry_conversion: enabled: true promotes resource attributes to metric labels on the local scrape surface.
• spanmetrics.resource_metrics_key_attributes lists the tier attributes so span-derived series stay grouped per node and tier.

Traces and logs carry resource attributes natively; Grafana Cloud ingests all three signals' attributes over OTLP directly.

Grafana Dashboards

Ten dashboards are pre-provisioned in docker/telemetry/grafana/dashboards/:

RPC Performance (rpc-performance)

Panel	Type	PromQL	Labels Used
RPC Request Rate by Command	timeseries	sum by (command) (rate(span_calls_total{span_name=~"rpc.command.*"}[5m]))	command
RPC Latency p95 by Command	timeseries	histogram_quantile(0.95, sum by (le, command) (rate(span_duration_milliseconds_bucket{span_name=~"rpc.command.*"}[5m])))	command
RPC Error Rate	bargauge	Error spans / total spans × 100, grouped by command	command, status_code
RPC Latency Heatmap	heatmap	sum(increase(span_duration_milliseconds_bucket{span_name=~"rpc.command.*"}[5m])) by (le)	le (bucket boundaries)
Overall RPC Throughput	timeseries	rpc.request + rpc.process rate	—
RPC Success vs Error	timeseries	by status_code (UNSET vs ERROR)	status_code
Top Commands by Volume	bargauge	topk(10, ...) by command	command
WebSocket Message Rate	stat	rpc.ws_message rate	—

Transaction Overview (transaction-overview)

Panel	Type	PromQL	Labels Used
Transaction Processing Rate	timeseries	rate(span_calls_total{span_name="tx.process"}[5m]) and tx.receive	span_name
Transaction Processing Latency	timeseries	histogram_quantile(0.95 / 0.50, ... {span_name="tx.process"})	—
Transaction Path Distribution	piechart	sum by (local) (rate(span_calls_total{span_name="tx.process"}[5m]))	local
Transaction Receive vs Suppressed	timeseries	rate(span_calls_total{span_name="tx.receive"}[5m])	—
TX Processing Duration Heatmap	heatmap	tx.process histogram buckets	le
TX Apply Duration per Ledger	timeseries	p95/p50 of tx.apply	—
Peer TX Receive Rate	timeseries	tx.receive rate	—
TX Apply Failed Rate	stat	rate(span_calls_total{span_name="tx.transactor",stage="apply",ter_result!~"tesSUCCESS|"})	stage, ter_result
TxQ Accept: Applied Ratio per Node	state-timeline	applied / (applied+failed) of span_calls_total{span_name="txq.accept_tx"} per node	txq_status, service_instance_id

Consensus Health (consensus-health)

Panel	Type	PromQL	Labels Used
Consensus Round Duration	timeseries	histogram_quantile(0.95 / 0.50, ... {span_name="consensus.accept"})	—
Consensus Proposals Sent Rate	timeseries	rate(span_calls_total{span_name="consensus.proposal.send"}[5m])	—
Ledger Close Duration	timeseries	histogram_quantile(0.95, ... {span_name="consensus.ledger_close"})	—
Validation Send Rate	stat	rate(span_calls_total{span_name="consensus.validation.send"}[5m])	—
Ledger Apply Duration	timeseries	histogram_quantile(0.95 / 0.50, ... {span_name="consensus.accept.apply"})	—
Close Time Agreement	timeseries	rate(span_calls_total{span_name="consensus.accept.apply"}[5m])	—
Consensus Mode Over Time	timeseries	consensus.ledger_close by consensus_mode	consensus_mode
Accept vs Close Rate	timeseries	consensus.accept vs consensus.ledger_close rate	—
Validation vs Close Rate	timeseries	consensus.validation.send vs consensus.ledger_close	—
Accept Duration Heatmap	heatmap	consensus.accept histogram buckets	le

Ledger Operations (ledger-operations)

Panel	Type	PromQL	Labels Used
Ledger Build Rate	stat	ledger.build call rate	—
Ledger Build Duration	timeseries	p95/p50 of ledger.build	—
Ledger Validation Rate	stat	ledger.validate call rate	—
Build Duration Heatmap	heatmap	ledger.build histogram buckets	le
TX Apply Duration	timeseries	p95/p50 of tx.apply	—
TX Apply Rate	timeseries	tx.apply call rate	—
Ledger Store Rate	stat	ledger.store call rate	—
Build vs Close Duration	timeseries	p95 ledger.build vs consensus.ledger_close	—

Peer Network (peer-network)

Requires trace_peer=1 in the [telemetry] config section.

Panel	Type	PromQL	Labels Used
Proposal Receive Rate	timeseries	peer.proposal.receive rate	—
Validation Receive Rate	timeseries	peer.validation.receive rate	—
Proposals Trusted vs Untrusted	piechart	by proposal_trusted	proposal_trusted
Validations Trusted vs Untrusted	piechart	by validation_trusted	validation_trusted

Node Health -- System Metrics (node-health)

Panel	Type	PromQL	Labels Used
Validated Ledger Age	stat	ledgermaster_validated_ledger_age	—
Published Ledger Age	stat	ledgermaster_published_ledger_age	—
Operating Mode (Time Share)	timeseries	rate(state_accounting_X_duration) / sum(rate(all modes))	—
Operating Mode Transitions	timeseries	state_accounting_*_transitions	—
I/O Latency	timeseries	histogram_quantile(0.95, ios_latency_bucket)	—
Job Queue Depth	timeseries	jobq_job_count	—
Ledger Fetch Rate	stat	rate(ledger_fetches[5m])	—
Ledger History Mismatches	stat	rate(ledger_history_mismatch[5m])	—
Key Jobs Execution Time	timeseries	acceptledger{quantile="$quantile"} (+ 10 more key jobs)	quantile
Key Jobs Dequeue Wait Time	timeseries	acceptledger_q{quantile="$quantile"} (+ 10 more)	quantile
FullBelowCache Size	timeseries	node_family_full_below_cache_size	—
FullBelowCache Hit Rate	gauge	node_family_full_below_cache_hit_rate	—
Ledger Publish Gap	stat	Published_Ledger_Age - Validated_Ledger_Age	—
State Duration Rate (Full vs Tracking)	timeseries	rate(state_accounting_full_duration[5m]) / 1000000	—
All Jobs Execution Time (Detail)	timeseries	{__name__=~"<all_jobs>", quantile="$quantile"}	quantile
All Jobs Dequeue Wait (Detail)	timeseries	{__name__=~"<all_jobs>_q", quantile="$quantile"}	quantile
Server State	stat	server_info{metric="server_state"}	metric
Uptime	stat	server_info{metric="uptime"}	metric
Peer Count	stat	server_info{metric="peers"}	metric
Validated Ledger Seq	stat	server_info{metric="validated_ledger_seq"}	metric
Build Version	stat	build_info	version
Complete Ledger Ranges	table	complete_ledgers	bound, index
Database Sizes	timeseries	db_metrics{metric=~"db_kb_.*"}	metric
Historical Fetch Rate	stat	db_metrics{metric="historical_perminute"}	metric

Network Traffic -- System Metrics (network-traffic)

Panel	Type	PromQL	Labels Used
Active Peers	timeseries	peer_finder_active_*_peers	—
Peer Disconnects	timeseries	increase(overlay_peer_disconnects[$__rate_interval])	—
Total Network Bytes	timeseries	rate(total_bytes_in/out[$__rate_interval])	—
Total Network Messages	timeseries	rate(total_messages_in/out[$__rate_interval])	—
Transaction Traffic	timeseries	rate(transactions_messages_in/out[$__rate_interval])	—
Proposal Traffic	timeseries	rate(proposals_messages_in/out[$__rate_interval])	—
Validation Traffic	timeseries	rate(validations_messages_in/out[$__rate_interval])	—
Traffic by Category	bargauge	topk(10, label_replace(sum by (service_instance_id)(rate(<metric>[$__rate_interval])),"__name__","<metric>","","") or …)	—
Duplicate Traffic (Wasted Bandwidth)	timeseries	rate(*_duplicate_bytes_in/out[$__rate_interval])	—
All Traffic Categories (Detail)	timeseries	topk(15, label_replace(sum by (service_instance_id)(rate(<metric>[$__rate_interval])),"__name__","<metric>","","") or …)	—

Why the per-category panels enumerate each metric. A bare rate({__name__=~".*_bytes_in"}[…]) fails on Mimir/Cloud with "vector cannot contain metrics with the same labelset": rate() drops the __name__ label, so the many matched counters collapse to identical labelsets. Wrapping in sum by (__name__, …) does not help (the inner vector is rejected before the outer sum). The working form enumerates each *_bytes_in metric and re-attaches its name with label_replace(..., "__name__", "<metric>", "", ""), so the existing {{__name__}} legend and the per-series display-name overrides keep working.

RPC & Pathfinding -- System Metrics (rpc-pathfinding)

Panel	Type	PromQL	Labels Used
RPC Request Rate	stat	rate(rpc_requests[5m])	—
RPC Response Time	timeseries	histogram_quantile(0.95, rpc_time_bucket)	—
RPC Response Size	timeseries	histogram_quantile(0.95, rpc_size_bucket)	—
RPC Response Time Heatmap	heatmap	rpc_time_bucket	—
Pathfinding Fast Duration	timeseries	histogram_quantile(0.95, pathfind_fast_bucket)	—
Pathfinding Full Duration	timeseries	histogram_quantile(0.95, pathfind_full_bucket)	—
Resource Warnings Rate	stat	rate(warn[5m])	—
Resource Drops Rate	stat	rate(drop[5m])	—

Span → Metric → Dashboard Summary

Span Name	Prometheus Metric Filter	Grafana Dashboard
rpc.http_request	{span_name="rpc.http_request"}	RPC Performance (Overall Throughput)
rpc.ws_upgrade	{span_name="rpc.ws_upgrade"}	-- (available but not paneled)
rpc.ws_message	{span_name="rpc.ws_message"}	RPC Performance (WebSocket Rate)
rpc.process	{span_name="rpc.process"}	RPC Performance (Overall Throughput)
rpc.command.*	{span_name=~"rpc.command.*"}	RPC Performance (Rate, Latency, Error, Top)
tx.process	{span_name="tx.process"}	Transaction Overview (Rate, Latency, Heatmap)
tx.receive	{span_name="tx.receive"}	Transaction Overview (Rate, Receive)
tx.apply	{span_name="tx.apply"}	Transaction Overview + Ledger Ops (Apply)
txq.enqueue	{span_name="txq.enqueue"}	-- (available but not paneled)
txq.apply_direct	{span_name="txq.apply_direct"}	-- (available but not paneled)
txq.batch_clear	{span_name="txq.batch_clear"}	-- (available but not paneled)
txq.accept	{span_name="txq.accept"}	-- (available but not paneled)
txq.accept_tx	{span_name="txq.accept_tx"}	-- (available but not paneled)
txq.cleanup	{span_name="txq.cleanup"}	-- (available but not paneled)
consensus.round	{span_name="consensus.round"}	-- (available but not paneled)
consensus.phase.open	{span_name="consensus.phase.open"}	-- (available but not paneled)
consensus.establish	{span_name="consensus.establish"}	-- (available but not paneled)
consensus.update_positions	{span_name="consensus.update_positions"}	-- (available but not paneled)
consensus.check	{span_name="consensus.check"}	-- (available but not paneled)
consensus.accept	{span_name="consensus.accept"}	Consensus Health (Duration, Rate, Heatmap)
consensus.proposal.send	{span_name="consensus.proposal.send"}	Consensus Health (Proposals Rate)
consensus.ledger_close	{span_name="consensus.ledger_close"}	Consensus Health (Close, Mode)
consensus.validation.send	{span_name="consensus.validation.send"}	Consensus Health (Validation Rate)
consensus.accept.apply	{span_name="consensus.accept.apply"}	Consensus Health (Apply Duration, Close Time)
consensus.mode_change	{span_name="consensus.mode_change"}	-- (available but not paneled)
consensus.proposal.receive	{span_name="consensus.proposal.receive"}	-- (available but not paneled)
consensus.validation.receive	{span_name="consensus.validation.receive"}	-- (available but not paneled)
ledger.build	{span_name="ledger.build"}	Ledger Ops (Build Rate, Duration, Heatmap)
ledger.validate	{span_name="ledger.validate"}	Ledger Ops (Validation Rate)
ledger.store	{span_name="ledger.store"}	Ledger Ops (Store Rate)
peer.proposal.receive	{span_name="peer.proposal.receive"}	Peer Network (Rate, Trusted/Untrusted)
peer.validation.receive	{span_name="peer.validation.receive"}	Peer Network (Rate, Trusted/Untrusted)

Alerting

xrpld provisions six Grafana alert rules on the health-critical metrics, so a stock stack alerts out of the box with no UI setup. Rules are provisioned from docker/telemetry/grafana/provisioning/alerting/ and load automatically when the Grafana container starts. They appear under Alerting → Alert rules, folder xrpld.

Alert catalogue

All rules evaluate every minute against the Prometheus datasource, over a 5-minute window, and group by service_instance_id so each node alerts on its own. Alerts fire only after the condition holds for the for dwell time.

Alert	Severity	Fires when	For
LedgerHistoryMismatch	critical	rate(ledger_history_mismatch_total) > 0	5m
LedgerCloseStalled	critical	rate(ledgers_closed_total) ≈ 0	3m
ValidationsMissed	warning	rate(validation_missed_total) > 0	5m
ValidationsNotChecked	warning	rate(validations_checked_total) ≈ 0	5m
JobQueueTxOverflow	warning	rate(jq_trans_overflow_total) > 0	5m
JobQueueLatencyHigh	warning	p99 job_queued_us > 1s	5m

Consensus / ledger health

LedgerHistoryMismatch — The node closed a ledger whose history diverges from the validated network chain. Likely causes: corrupted local state, a bug, or a node that fell out of sync and rebuilt incorrectly. Investigate the node's ledger acquisition logs; a healthy node never mismatches.

LedgerCloseStalled — No ledgers closed for 3 minutes. A healthy node closes one every ~3-5s. Likely causes: lost peer connectivity, consensus stall, or the process is hung. This rule also fires on NoData — if the series disappears the node is likely down. Check peer count and process health first.

Validator health

ValidationsMissed — This validator's validations are not agreeing with the validated ledger. Sustained misses risk removal from UNLs. Check clock sync, peer connectivity, and whether the node is keeping up with ledger close.

ValidationsNotChecked — The node has stopped checking incoming validations from peers. Likely causes: overlay/peer disconnection or a stalled validation pipeline. Fires on NoData as well.

Job queue / resource health

JobQueueTxOverflow — The transaction job queue is full and transactions are being dropped. The node is shedding load it cannot process. Check CPU, the JobQueueLatencyHigh alert, and offered load.

JobQueueLatencyHigh — p99 queue wait exceeds 1 second, i.e. jobs back up before running. The node is saturated. Correlate with CPU and the Job Queue dashboard.

Tuning thresholds

Thresholds live in docker/telemetry/grafana/provisioning/alerting/rules.yaml as the params array of each rule's C (threshold) node. Common tunables:

• JobQueueLatencyHigh — params: [1000000] is 1 000 000 µs (1s). Lower it for latency-sensitive deployments.
• LedgerCloseStalled / ValidationsNotChecked — use lt with a tiny epsilon (0.001) rather than 0, so floating-point rate noise near zero does not suppress the alert.

Edit the file and restart the Grafana container to reload:

docker compose -f docker/telemetry/docker-compose.yml restart grafana

Sending alerts somewhere real

Two contact points are provisioned in docker/telemetry/grafana/provisioning/alerting/contactpoints.yaml:

Contact point	Receivers	Gets
xrpld-default	Slack	warning-severity alerts
xrpld-critical	Slack + email	critical-severity alerts

The severity split lives in docker/telemetry/grafana/provisioning/alerting/policies.yaml: the root route sends everything to xrpld-default, and a child route matching severity = critical overrides to xrpld-critical. So a critical alert goes to Slack and email; a warning goes to Slack only. Both group by alertname + service_instance_id; critical alerts re-page hourly vs the 4h default.

Configure delivery (no secrets in git)

The Slack webhook and email address are not hard-coded — the YAML references ${SLACK_WEBHOOK_URL} and ${ALERT_EMAIL_TO}, which Grafana expands from the environment at startup. Supply them through a gitignored env file:

cp docker/telemetry/.env.alerting.example docker/telemetry/.env.alerting
# edit .env.alerting — this file is gitignored, never commit the webhook/address
docker compose -f docker/telemetry/docker-compose.yml up -d grafana

• Slack — set SLACK_WEBHOOK_URL to an incoming-webhook URL. Drives both tiers.
• Email — set ALERT_EMAIL_TO (comma-separated) and point the GF_SMTP_* vars at a real relay with GF_SMTP_ENABLED=true. Grafana can only send mail once SMTP is configured.

Any variable left blank disables that path; the stack still runs. To add a third destination (PagerDuty, Opsgenie, a custom webhook), add a receiver to the relevant contact point.

Verifying alert provisioning loaded

After the stack is up:

# All six rules present?
curl -s http://localhost:3000/api/v1/provisioning/alert-rules | jq '.[].title'

# Contact points present?
curl -s http://localhost:3000/api/v1/provisioning/contact-points | jq '.[].name'

Grafana logs a provisioning error and skips the file if the YAML is malformed:

docker compose -f docker/telemetry/docker-compose.yml logs grafana | grep -i alerting

Log-Trace Correlation

When xrpld is built with telemetry=ON, log lines emitted within an active OpenTelemetry span automatically include trace_id and span_id fields:

2024-Jan-15 10:30:45.123456 UTC LedgerMaster:NFO trace_id=abc123def456789012345678abcdef01 span_id=0123456789abcdef Validated ledger 42

This enables bidirectional navigation between logs and traces in Grafana:

• Tempo -> Loki: Click "Logs for this trace" on any trace in Grafana Tempo to see all log lines from that trace.
• Loki -> Tempo: Click the TraceID derived field link on any log line containing trace_id= to jump to the full trace in Tempo.

Log Ingestion Pipeline

Log files are ingested by the OTel Collector's filelog receiver, which tails debug.log files and parses them with a regex that extracts timestamp, partition, severity, trace_id, span_id, and message fields. Parsed entries are exported to Grafana Loki.

The receiver tails /var/log/xrpld/*/debug.log inside the collector container. docker-compose bind-mounts the host log root there; the source defaults to the repo-relative docker/telemetry/data/logs, which the telemetry configs write to (data/logs/<network>/debug.log) and which needs no root. To tail logs from elsewhere, set XRPLD_LOG_DIR before docker compose up (the integration test does this to point at its own workdir). The single trailing * matches one per-network or per-node subdirectory.

LogQL Query Examples

The OTel Collector emits logs to Loki with service_name="xrpld" (not job="xrpld").

# Find all logs for a specific trace
{service_name="xrpld"} |= "trace_id=abc123def456789012345678abcdef01"

# Error logs with trace context (log lines with ERR severity that have a trace_id)
{service_name="xrpld"} |= "ERR" |= "trace_id="

# All logs from a specific partition that were emitted during a span
{service_name="xrpld"} |= "LedgerMaster" | regexp `trace_id=(?P<trace_id>[a-f0-9]+)` | trace_id != ""

# Logs from a specific subsystem during a span (e.g. LedgerConsensus)
{service_name="xrpld"} |= "LedgerConsensus" |= "trace_id="

# Logs from the last hour containing trace context
{service_name="xrpld"} |= "trace_id=" | regexp `(?P<partition>\S+):(?P<sev>\S+)\s+trace_id=(?P<tid>[a-f0-9]+)`

# Count of traced vs untraced log lines
count_over_time({service_name="xrpld"} |= "trace_id=" [5m])

Verifying Log Correlation

1. Start the observability stack and xrpld with telemetry enabled.
2. Send an RPC request: curl http://localhost:5005 -d '{"method":"server_info"}'
3. Check the debug.log for trace_id= entries: grep trace_id= /path/to/debug.log
4. Open Grafana at http://localhost:3000 -> Explore -> Loki and search for {service_name="xrpld"} |= "trace_id=".
5. Click the TraceID link to navigate to the corresponding trace in Tempo.

Troubleshooting

No traces appearing in Tempo

1. Check xrpld logs for Telemetry starting message
2. Verify enabled=1 in the [telemetry] config section
3. Test collector connectivity: curl -v http://localhost:4318/v1/traces
4. Check collector logs: docker compose -f docker/telemetry/docker-compose.yml logs otel-collector
5. Verify Tempo is receiving data: open Grafana → Explore → select Tempo datasource → search by service.name = xrpld
6. Check Tempo logs: docker compose -f docker/telemetry/docker-compose.yml logs tempo

No system metrics in Prometheus

1. Check xrpld logs for OTelCollector starting message
2. Verify server=otel in the [insight] config section
3. Verify the endpoint in [insight] points to the OTLP/HTTP port (default: http://localhost:4318/v1/metrics)
4. Check that the otlp receiver is in the metrics pipeline receivers in otel-collector-config.yaml
5. Query Prometheus directly: curl 'http://localhost:9090/api/v1/query?query=jobq_job_count'

Server info gauge shows server_state=0

This is normal during startup. The server starts in DISCONNECTED mode (0) and progresses through CONNECTED (1), SYNCING (2), TRACKING (3), to FULL (4). Wait for the node to sync with the network.

Database metrics showing zero

The getKBUsed*() methods require SQLite databases to exist. If running with --standalone or before the first ledger is stored, these will be zero.

High memory usage

• Reduce trace volume with collector-side tail sampling (xrpld head sampling is fixed at 1.0 and is not configurable)
• Reduce max_queue_size and batch_size
• Disable high-volume trace categories: trace_peer=0

Collector connection failures

• Verify endpoint URL matches collector address
• Check firewall rules for ports 4317/4318
• If using TLS, verify certificate path with tls_ca_cert

No trace_id in log output

• Verify xrpld was built with telemetry=ON (the XRPL_ENABLE_TELEMETRY preprocessor flag)
• Verify enabled=1 in the [telemetry] config section
• Log lines only contain trace_id/span_id when emitted inside an active span — background logs outside of RPC/consensus/transaction processing will not have trace context
• Check that the specific trace category is enabled (e.g., trace_rpc=1)

No logs in Loki

• Verify the log file mount in docker-compose.yml points to the correct xrpld log directory (default source docker/telemetry/data/logs, or the XRPLD_LOG_DIR override) and that xrpld actually writes debug.log there
• Check OTel Collector logs for filelog receiver errors: docker compose logs otel-collector
• Verify Loki is running: curl http://localhost:3100/ready
• Check the filelog receiver glob /var/log/xrpld/*/debug.log matches your log layout — the log file must sit one subdirectory below the mount root

Performance Tuning

Scenario	Recommendation
Production mainnet	trace_peer=0; reduce volume via collector tail sampling
Testnet/devnet	Full tracing (head sampling fixed at 1.0)
Debugging specific issue	Full tracing (head sampling fixed at 1.0)
High-throughput node	Increase batch_size=1024, max_queue_size=4096

Disabling Telemetry

Set enabled=0 in config (runtime disable) or build without the flag:

cmake --preset default -Dtelemetry=OFF

When telemetry is compiled out, all trace macros expand to no-ops with zero overhead.

Validating Telemetry Stack

After deploying telemetry, use the Phase 10 workload tools to validate the full stack end-to-end.

Quick Validation

# Run the full validation suite (starts cluster, generates load, validates):
docker/telemetry/workload/run-full-validation.sh --xrpld .build/xrpld

# Check the report:
cat /tmp/xrpld-validation/reports/validation-report.json | jq '.summary'

What Gets Validated

Category	Checks	Description
Spans	16+ span types	All span names appear in Tempo with required attributes
Metrics	30+ metrics	SpanMetrics, StatsD gauges/counters, Phase 9 metrics
Logs	2 checks	trace_id/span_id present in Loki, cross-reference works
Dashboards	10 dashboards	All Grafana dashboards load without errors

Running Individual Tools

# RPC load only:
python3 docker/telemetry/workload/rpc_load_generator.py \
    --endpoints ws://localhost:6006 --rate 50 --duration 120

# Transaction mix only:
python3 docker/telemetry/workload/tx_submitter.py \
    --endpoint ws://localhost:6006 --tps 5 --duration 120

# Validation only (assumes load already ran):
python3 docker/telemetry/workload/validate_telemetry.py \
    --report /tmp/report.json

Interpreting Failures

• Span failures: Check that the relevant trace category is enabled in [telemetry] config (e.g., trace_rpc=1).
• Metric failures: Verify the OTel Collector is running and Prometheus is scraping port 8889. Check docker compose logs otel-collector.
• Dashboard failures: Ensure Grafana provisioning is mounted correctly. Check docker compose logs grafana.

Performance Benchmarking

Measure the overhead of the telemetry stack against a baseline:

docker/telemetry/workload/benchmark.sh --xrpld .build/xrpld --duration 300

Benchmark Thresholds

Metric	Target	Description
CPU overhead	< 3%	Average CPU increase across nodes
Memory overhead	< 5MB	Peak RSS increase per node
RPC p99 latency	< 2ms	Additional p99 latency for server_info
Throughput impact	< 5%	Reduction in ledger close rate
Consensus impact	< 1%	Increase in consensus round time

Tuning for Production

If benchmarks exceed thresholds:

1. Reduce sampling: sampling_ratio=0.01 (1% of traces)
2. Disable peer tracing: trace_peer=0 (highest volume category)
3. Increase batch delay: batch_delay_ms=10000 (less frequent exports)
4. Reduce queue size: max_queue_size=1024 (back-pressure earlier)

See docker/telemetry/workload/README.md for full documentation.