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
• Protocol Span Flow
  • Master overview
  • Client and peer ingress
  • Shared transaction apply pipeline
  • Consensus round
  • Accept, build, and finalize the ledger
  • Side flows: pathfinding and ledger acquire
  • Where telemetry parenting differs from protocol flow
• 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. For plain-language definitions of the XRP Ledger terms used in the dashboards, see the telemetry glossary.

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, current_ledger_seq	Transaction submission and processing
tx.receive	PeerImp.cpp	peer_id, tx_hash, tx_type, peer_version, suppressed, tx_status, current_ledger_seq	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, current_ledger_seq, current_ledger_hash	Ledger-aware checks stage
tx.transactor	Transactor.cpp	stage, tx_type, ter_result, applied, current_ledger_seq, current_ledger_hash	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.

current_ledger_seq is the current (open/in-flight) ledger index a span acted on — the ledger being worked on, not an established one — so a transaction's txID-keyed spans can be joined to the ledger trace it targeted (span.current_ledger_seq). The view-bearing stages (tx.preclaim, tx.transactor) also carry current_ledger_hash (the current ledger's parent hash); tx.preflight is stateless and omits both.

Transaction Queue Spans

Span Name	Source File	Attributes	Description
txq.enqueue	TxQ.cpp	tx_hash, tx_type, current_ledger_seq, current_ledger_hash	Enqueue decision; parents to tx.process on the submission path (explicit context), a root on the open-ledger rebuild path — current_ledger_seq correlates it to the ledger in both cases
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

Both peer receive spans are kConsumer inbound entry points started as fresh trace roots. They never inherit an ambient span left active on the peer thread, so they do not nest under an unrelated transaction's trace. The distributed child span that links back to the sending node is the separate consensus.*.receive / tx.receive span (see Cross-Node Trace Propagation).

---

Protocol Span Flow

This section maps every span type onto the real xrpld control flow and XRPL protocol order (verified against code and docs/consensus.md) — what the code actually executes next, in what order, with which loops and branches. Spans are drawn as labels on real operations, not as their OpenTelemetry parent links; the SDK's span parenting is listed separately in Where telemetry parenting differs from protocol flow.

These diagrams are the canonical key for linking the span hierarchy — every node and every branch is labelled with the span that represents that state or transition, so a span can be wired to its true protocol parent/child by reading the graph. They are therefore drawn exact, not simplified: every real loop, retry, recovery, and drop branch is shown even when it adds clutter.

Naming and edge conventions:

• Rectangle [ ] — a state/operation that emits a span; the first line is the exact span.name, the parenthetical below is the operation.
• Rounded ( ) with (no span) — a real protocol step that emits no span; shown so the flow stays continuous and is never mistaken for a missing span.
• Solid arrow — the code calls or sequences directly into the next operation. When the transition itself emits a span, the edge is labelled →[span.name]; otherwise it carries the branch condition.
• ↻ — the edge repeats (per tx, per peer, per dispute, per pass, per round).
• Dashed arrow — a conditional branch or an async job hand-off.
• Dotted ⇢ ctx — trace context crosses a node boundary over a protobuf peer message (sender.span ⇢ receiver.span); a different node continues the trace — not an in-process call.
• Red-bordered node — a terminal drop / abandon state.

Master overview

Five ingress origins feed two shared engines — the per-transaction apply pipeline and the consensus round — which converge on ledger build → store → validate. Pathfinding and ledger-acquire are side flows. A single ledger can take many consensus rounds to settle (see Consensus round).

flowchart TB
    classDef ingress fill:#1d4ed8,stroke:#1e3a8a,color:#fff;
    classDef engine  fill:#047857,stroke:#064e3b,color:#fff;
    classDef consensus fill:#b45309,stroke:#7c2d12,color:#fff;
    classDef ledger  fill:#6d28d9,stroke:#4c1d95,color:#fff;
    classDef side    fill:#0e7490,stroke:#155e75,color:#fff;
    classDef plain   fill:#334155,stroke:#0f172a,color:#fff;

    subgraph ING["Ingress (protocol entry points)"]
        direction TB
        RPC["rpc.http_request / rpc.ws_message<br/>rpc.ws_upgrade / grpc.MethodName<br/>(client transport in)"]:::ingress
        SUB(["submit command<br/>(no span)"]):::plain
        PRELAY["tx.receive<br/>(peer relay in)"]:::ingress
        PMSG["peer.proposal.receive<br/>peer.validation.receive<br/>(peer overlay in)"]:::ingress
    end

    TXP["tx.process<br/>(NetworkOPs::processTransaction)"]:::engine
    OPEN["txq.enqueue<br/>(open-ledger apply + TxQ decision)"]:::engine
    PIPE["tx.preflight → tx.preclaim → tx.transactor<br/>(SHARED apply pipeline)"]:::engine

    subgraph CONS["Consensus round"]
        direction TB
        ROUND["consensus.round<br/>(Open → Establish → Accepted)"]:::consensus
        ACC["consensus.accept → consensus.accept.apply"]:::consensus
    end

    subgraph LGR["Ledger finalize"]
        direction TB
        BUILD["ledger.build<br/>(tx.apply over agreed set)"]:::ledger
        STORE["ledger.store<br/>(built, NOT yet final)"]:::ledger
        VAL["ledger.validate<br/>(promoted at quorum)"]:::ledger
    end

    subgraph SIDE["Side flows"]
        direction TB
        PF["pathfind.update_all<br/>pathfind.request/compute/discover"]:::side
        ACQ["ledger.acquire<br/>(fetch missing / correct prior)"]:::side
    end

    RPC -.->|submit / submit_multisigned| SUB --> TXP
    RPC -.->|path_find / ripple_path_find| PF
    PRELAY --> TXP
    TXP --> OPEN
    OPEN -->|↻ up to 3 passes| PIPE
    OPEN -.->|txq.accept re-apply queued tx each close ↻| PIPE
    PMSG -->|peerProposal / recvValidation| ROUND
    ROUND --> ACC --> BUILD
    BUILD -->|↻ each tx × up to 3 passes| PIPE
    BUILD --> STORE
    PMSG -. "trusted validations arrive async → checkAccept quorum" .-> VAL
    ROUND -. "avalanche rounds ↻ (threshold 50→65→70→95%)" .-> ROUND
    ACC -->|endConsensus ↻ next round until a ledger validates| ROUND
    ROUND -.->|wrong-ledger: request correct prior| ACQ
    ACQ -.->|switch-ledger: resume round on correct prior| ROUND
    ACQ --> STORE
    VAL -.->|missing ledger| ACQ
    BUILD -.->|every close re-runs| PF

Client and peer ingress

RPC submit and peer relay converge at tx.process, the single NetworkOPs entry. gRPC serves ledger queries only — it has no submit path and never runs doCommand.

flowchart TB
    classDef span fill:#1d4ed8,stroke:#1e3a8a,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    HTTP["rpc.http_request<br/>(HTTP entry)"]:::span
    PROC["rpc.process<br/>(parse + batch)"]:::span
    CMD["rpc.command.NAME<br/>(one command)"]:::span
    WSU["rpc.ws_upgrade<br/>(WS handshake)"]:::span
    WSM["rpc.ws_message<br/>(one frame)"]:::span
    GRPC["grpc.MethodName<br/>(ledger query)"]:::span
    GH(["handler_ ctx<br/>(no span)"]):::plain
    SUBMIT(["doSubmit<br/>(no span)"]):::plain
    TXP["tx.process<br/>(NetworkOPs::processTransaction)"]:::span
    RELAYOUT(["Overlay::relay fan-out to N peers<br/>(no span; if applied / terQUEUED,<br/>shouldRelay, not tfInnerBatchTxn)"]):::plain
    PREDROP(["Diverged / needNetworkLedger<br/>(no span — dropped before tx.receive)"]):::drop
    RCV["tx.receive<br/>(peer TMTransaction in)"]:::span
    RCVDROP["tx.receive<br/>tx_status = rejected_inner_batch /<br/>suppressed / dropped_no_sync /<br/>dropped_queue_full"]:::drop
    CHK(["checkTransaction<br/>(JtTransaction worker, no span)"]):::plain
    PRELAY_IN(["TMTransaction in (no span)"]):::plain

    HTTP -->|processRequest| PROC
    PROC -->|↻ each batch request → doCommand| CMD
    WSU -. "each inbound frame → onWSMessage" .-> WSM
    WSM -->|doCommand| CMD
    GRPC --> GH
    CMD -.->|submit / submit_multisigned| SUBMIT
    SUBMIT -->|processTransaction| TXP
    TXP -.->|relay applied / queued tx| RELAYOUT

    PRELAY_IN -.->|tracking == Diverged / needNetworkLedger| PREDROP
    PRELAY_IN -->|else| RCV
    RCV -.->|inner-batch / dup / age>4min / JtTransaction full| RCVDROP
    RCV -->|addJob JtTransaction| CHK
    CHK -->|processTransaction, trusted=peer| TXP

    RELAYOUT -. "tx.process ⇢ tx.receive (span_id over TMTransaction)" .-> RCV

Ingress branches (all evidence in code):

• onHandoff: WS upgrade vs peer bundle vs status page vs legacy HTTP (ServerHandler.cpp:227).
• doSubmit: tx_blob present → submit signed blob; absent → server sign-and-submit (Submit.cpp:49).
• tx.process: local RPC → doTransactionSync; peer → doTransactionAsync (JtBatch) (NetworkOPs.cpp:1434).
• Pre-span peer drops (no tx.receive created): Diverged (PeerImp.cpp:1299) / needNetworkLedger (1302), before the span at ~1320.
• Post-span peer drops (span exists, tx_status set, no job enqueued): tfInnerBatchTxn (1348), HashRouter dup/BAD (1361), dropped_no_sync when validated-ledger age > 4 min (1416), dropped_queue_full when JtTransaction jobs > maxTransactions (1421).
• Relay fan-out: an accepted/queued tx.process relays to N peers via Overlay::relay, gated on applied || (non-FULL local) || terQUEUED, HashRouter shouldRelay, and not tfInnerBatchTxn; the span context is injected here (NetworkOPs.cpp:1797).

Inbound consensus messages take a two-stage handler — a fresh-root peer.*.receive span created first (kConsumer, always), then a consensus.*.receive span (only if not dropped) that carries the sender's context — before enqueuing a checkPropose / checkValidation worker job. The drop points are asymmetric: proposals drop entirely before consensus.proposal.receive, while validations can drop both before and after consensus.validation.receive.

flowchart TB
    classDef span fill:#0e7490,stroke:#155e75,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    PPR["peer.proposal.receive<br/>(freshRoot, always)"]:::span
    PPRDROP(["no consensus.proposal.receive<br/>(untrusted+relay-off / dup /<br/>untrusted+Diverged / untrusted+loaded)"]):::drop
    CPR["consensus.proposal.receive<br/>(carries sender ctx)"]:::span
    CP(["checkPropose worker<br/>(no span)"]):::plain
    SIGP(["sig-fail: charge, drop<br/>(no relay, no span)"]):::drop
    PTP(["processTrustedProposal → peerProposal<br/>(no span)"]):::plain

    PVR["peer.validation.receive<br/>(freshRoot, always)"]:::span
    pvrDrop1(["no consensus.validation.receive<br/>(!isCurrent / relay-off / dup)"]):::drop
    CVR["consensus.validation.receive<br/>(carries sender ctx)"]:::span
    cvrDrop2(["dropped after span<br/>(untrusted+Diverged /<br/>untrusted+loaded → no job/relay)"]):::drop
    CV(["checkValidation worker<br/>(no span)"]):::plain
    SIGV(["!isValid: charge, drop<br/>(no span)"]):::drop
    RV(["recvValidation → handleNewValidation<br/>(no span)"]):::plain
    RELAY(["Overlay::relay fan-out to N peers<br/>(no span)"]):::plain

    PPR -.->|4 drop conditions| PPRDROP
    PPR -->|else| CPR
    CPR -->|addJob JtProposalT/Ut| CP
    CP -.->|!checkSign| SIGP
    CP -->|isTrusted| PTP
    CP -.->|if relay| RELAY

    PVR -.->|3 drop conditions| pvrDrop1
    PVR -->|else| CVR
    CVR -.->|untrusted+Diverged / loaded| cvrDrop2
    CVR -->|addJob JtValidationT/Ut| CV
    CV -.->|!isValid| SIGV
    CV -->|recvValidation| RV
    CV -.->|if relay / cluster| RELAY

Consensus-message drop evidence:

• Both peer.proposal.receive and peer.validation.receive are freshRoot spans created at the top of onMessage (PeerImp.cpp:1766, 2389) — so they exist even for dropped messages.
• Proposal drops (all before consensus.proposal.receive at 1868): untrusted+relay-off (1807), duplicate (1832), untrusted+Diverged (1840), untrusted+loaded (1846).
• Validation drops (asymmetric around consensus.validation.receive at 2476): before — !isCurrent (2426), relay-off (2445), duplicate (2468); after — untrusted+Diverged (2489), untrusted+loaded (2506).
• Worker sig-fail drops (charged kFeeInvalidSignature, suppress processing and relay): checkPropose !checkSign (PeerImp.cpp:3105), checkValidation !isValid (3149).

Shared transaction apply pipeline

The apply pipeline is the single protocol tx-processing chain, expressed in code as one composed call (apply.cpp:118): doApply(preclaim(preflight(), …), …). C++ evaluates inner-to-outer, so preflight runs first, feeds preclaim, which feeds doApply. Each stage inspects the prior stage's TER and no-ops if it already failed.

Four invokers point into this one pipeline; the diagram draws it once.

flowchart TB
    classDef span fill:#047857,stroke:#064e3b,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef inv fill:#1d4ed8,stroke:#1e3a8a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    I1(["open-ledger applyOne (no span)<br/>↻ up to 3 passes"]):::inv
    I2["txq.apply_direct<br/>(fee ≥ required)"]:::span
    I3["txq.batch_clear / txq.accept_tx<br/>↻ per queued tx"]:::span
    I4["tx.apply<br/>(consensus set, ↻ each tx × 3 passes)"]:::span

    REPF(["txq path: rules/flags changed?<br/>re-run preflight (no span)"]):::plain
    FREE(["xrpl::apply() (no span)"]):::plain
    PF["tx.preflight<br/>(stateless checks)"]:::span
    PC["tx.preclaim<br/>(ledger-aware checks)"]:::span
    TR["tx.transactor<br/>(mutate stage)"]:::span
    CLS(["classify final TER (no span)"]):::plain
    OK(["Success / erase (no span)"]):::plain
    FAIL(["tef / tem / tel → hard fail, erase"]):::drop
    RETRY(["retriable ter → keep in set (no span)"]):::plain

    I1 --> FREE
    I2 --> FREE
    I3 --> REPF --> FREE
    I4 --> FREE
    FREE --> PF
    PF -->|preflight tesSUCCESS| PC
    PF -. "else → classify (no preclaim/transactor)" .-> CLS
    PC -->|likelyToClaimFee| TR
    PC -. "else → classify (no transactor)" .-> CLS
    TR --> CLS
    CLS --> OK
    CLS --> FAIL
    CLS --> RETRY
    RETRY -. "next pass while pass<3 and changes>0" .-> FREE
    RETRY -. "last pass → drop from set" .-> FAIL

Pipeline gates and retry (evidence):

• preclaim short-circuits if preflight !tesSUCCESS (applySteps.cpp:498); doApply short-circuits if !likelyToClaimFee (applySteps.cpp:532); the transactor mutates only when preclaim is tesSUCCESS (Transactor.cpp:1647).
• Final-TER classification: applied → Success; tef | tem | tel → hard Fail; else → Retry (apply.cpp:226).
• Multi-pass retry: both open-ledger applyOne and consensus tx.apply loop pass < LEDGER_TOTAL_PASSES (= 3); a Retry tx is kept for the next pass, and the final pass converts lingering retriable txs into drops (OpenLedger.h:237, BuildLedger.cpp:129; LEDGER_TOTAL_PASSES OpenLedger.h:29).
• TxQ re-preflight: the queue path re-runs preflight when the ledger's rules/flags changed since enqueue (TxQ.cpp:315).
• TxQ cross-ledger retry: a queued tx that fails with a retriable result keeps its slot with --retriesRemaining (kRetriesAllowed = 10) and is re-applied at a later ledger close; on retriesRemaining ≤ 0 or tef|tem it is dropped with an account retryPenalty (TxQ.cpp:1528).
• TxQ::apply outcome fork: preflight-reject / applied_direct / batch_clear / queued (terQUEUED) / reject (TxQ.cpp:762).

tx.apply is set-level, consensus-only. It wraps the retry-pass loop over the agreed set during buildLedger and exists on no other invoker. It is not a per-transaction span, and TxQ / open-ledger apply create no tx.apply.

Consensus round

beginConsensus → startRound starts the round (consensus.round, Open phase). The heartbeat timer drives Consensus::timerEntry each pass; the round stays in Establish across many heartbeats until the outcome is decided.

A single ledger can take many rounds to settle. Two nested multi-round mechanisms (see docs/consensus.md):

1. Avalanche rounds inside one Establish phase — each timerEntry runs phaseEstablish again (establishCounter_++) and raises the inclusion threshold 50% → 65% → 70% → 95% as the round ages (ConsensusParms.h:145). checkConsensus returning No keeps the node in Establish and loops; a round cannot even Expire before a minimum of avalancheCutoffs.size() × avMinRounds = 4 × 2 = 8 passes (Consensus.h:1938).
2. Retry across consensus rounds — a round can end MovedOn / Expired, meaning the network settled a different ledger. The node still builds a ledger, but the next round's checkLedger detects the wrong prior, switches to WrongLedger / SwitchedLedger mode, acquires the correct ledger, and re-deliberates. A ledger is only truly settled once trusted validations reach quorum (ledger.validate); the alternate is abandoned.

flowchart TB
    classDef span fill:#b45309,stroke:#7c2d12,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;

    BEGIN(["beginConsensus → startRound<br/>(Proposing OR Observing; no span)"]):::plain
    ROUND["consensus.round<br/>(one attempt at next ledger)"]:::span
    OPENS["consensus.phase.open<br/>(collect txs; buffer peer<br/>proposals / gotTxSet)"]:::span
    HB(["heartbeat → timerEntry<br/>(every LEDGER_MIN_CLOSE; no span)"]):::plain
    CKL(["checkLedger<br/>(correct prior ledger? no span)"]):::plain
    WRONG(["handleWrongLedger → leaveConsensus (no span):<br/>if Proposing send BOW-OUT,<br/>mode → Observing; acquire ledger"]):::plain
    MODE["consensus.mode_change<br/>(mode transition)"]:::span
    POPEN(["phaseOpen: shouldCloseLedger? (no span)"]):::plain
    CLOSE["consensus.ledger_close<br/>(close open ledger, seed disputes)"]:::span
    pSend["consensus.proposal.send<br/>(broadcast our position)"]:::span
    PEST["consensus.establish<br/>(phaseEstablish; avalanche round ↻<br/>threshold 50→65→70→95%)"]:::span
    UPOS["consensus.update_positions<br/>(add/drop disputed txs; child of establish)"]:::span
    acqTx(["acquireTxSet → gotTxSet<br/>(async peer tx set; no span)"]):::plain
    PAUSE(["shouldPause?<br/>(wait on laggards; no span)"]):::plain
    CHECK["consensus.check<br/>(checkConsensus; child of establish)"]:::span
    CTC(["haveCloseTimeConsensus?<br/>(else agree-to-disagree +1s; no span)"]):::plain
    ACCEPT["consensus.accept<br/>(round complete)"]:::span

    BEGIN --> ROUND --> OPENS
    HB -->|under mutex| CKL
    CKL -.->|wrong prior| WRONG
    WRONG --> MODE
    WRONG -. "recovered → re-enter Open (playbackProposals)" .-> OPENS
    WRONG -. "still missing → keep deliberating, defer to peers" .-> HB
    CKL -->|prior OK| POPEN
    HB -.->|phase==Open| POPEN
    HB -.->|phase==Establish| PEST
    POPEN -.->|shouldClose| CLOSE
    CLOSE -.->|mode==Proposing| pSend
    PEST --> UPOS
    UPOS -.->|position changed && Proposing| pSend
    UPOS -.->|disagreeing peer position| acqTx
    acqTx -. "gotTxSet ↻ → new disputes" .-> UPOS
    PEST --> PAUSE
    PAUSE -. "pausing → wait (loop)" .-> HB
    PAUSE -->|ready| CHECK
    CHECK -. "No / Expired < 8 passes → next avalanche round" .-> HB
    CHECK --> CTC
    CTC -. "no CT consensus → loop" .-> HB
    CTC -.->|Yes / MovedOn / Expired ≥ 8| ACCEPT
    ROUND -.->|mode set at start| MODE
    ACCEPT -. "endConsensus → next round ↻ (until a ledger validates)" .-> BEGIN

Consensus loops and branches (evidence):

• consensus.establish is the parent of update_positions and check: phaseEstablish creates the establish span (startEstablishTracing), and both child spans parent to its captured context (Consensus.h:2100, 1629, 1838).
• Avalanche-convergence loop (rounds within one ledger): repeated heartbeat → timerEntry → phaseEstablish bumps establishCounter_ and raises the inclusion threshold each pass; checkConsensus = No stays in Establish (NetworkOPs.cpp:1214; Consensus.h:1468; thresholds ConsensusParms.h:145).
• Retry-across-rounds loop (many rounds per settled ledger): MovedOn / Expired accepts a non-preferred ledger; the next round's checkLedger finds the wrong prior and recovers before re-deliberating (Consensus.h:1194); round-to-round via endConsensus → beginConsensus (NetworkOPs.cpp:2315).
• Two extra establish loop-backs before accept: shouldPause (laggard backpressure) and !haveCloseTimeConsensus_ (TX consensus but not close-time) each return and re-loop, distinct from checkConsensus == No (Consensus.h:1497, 1500); close time can "agree to disagree" at prior close + 1s (docs/consensus.md:163).
• acquireTxSet / gotTxSet loop: a disagreeing peer position triggers an async acquireTxSet; the later gotTxSet regenerates disputes and can extend the establish phase (Consensus.h:932).
• Bow-out / mode change: handleWrongLedger → leaveConsensus sends a bow-out proposal and demotes Proposing → Observing for the rest of the round (Consensus.h:1977); startRound begins in Proposing or Observing (docs/consensus.md:176).
• Buffered Open-phase inputs: peerProposal / gotTxSet arriving during Open are stored, then seeded as disputes at closeLedger (createDisputes); playbackProposals replays them at startRound / handleWrongLedger (docs/consensus.md:244; Consensus.h:817).
• Outcome fork after checkConsensus: No (loop) / Yes (onAccept) / MovedOn / Expired (Consensus.h:1516).
• Expired guard: a round cannot leave on Expired before avalancheCutoffs.size() × avMinRounds (= 8) passes — below that, Expired loops like No (Consensus.h:1938).
• The deterministic-vs-random trace-strategy branch at round start (RCLConsensus.cpp:1291) sets only the trace ID — it has zero protocol effect.

Accept, build, and finalize the ledger

onAccept enqueues a JtAccept job; doAccept runs on that worker (consensus.accept.apply). It builds the ledger (running the apply pipeline over the agreed set), cleans the queue, stores the ledger, optionally broadcasts a validation, and rebuilds the open ledger. A built ledger is not final — it is promoted to ledger.validate only when trusted validations reach quorum, an async, validation-driven path re-entered per incoming trusted validation; a built ledger that loses is abandoned.

flowchart TB
    classDef span fill:#6d28d9,stroke:#4c1d95,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    onAcc["consensus.accept<br/>(round complete)"]:::span
    APPLY["consensus.accept.apply<br/>(JtAccept worker)"]:::span
    BLCL(["buildLCL: replay data? (no span)"]):::plain
    BUILD["ledger.build<br/>(normal: apply agreed set)"]:::span
    RPLY["ledger.build<br/>(replay: TapNone, no tx.apply child)"]:::span
    TXAP["tx.apply<br/>(↻ each tx × up to 3 passes)"]:::span
    CLEAN["txq.cleanup<br/>(expire queue entries)"]:::span
    STORE["ledger.store<br/>(built, NOT yet final)"]:::span
    vSend["consensus.validation.send<br/>(broadcast our validation)"]:::span
    CACC(["consensusBuilt → checkAccept<br/>(quorum gate; no span)"]):::plain
    NEWVAL(["inbound trusted validation<br/>→ handleNewValidation → checkAccept<br/>(async, per validation; no span)"]):::plain
    VAL["ledger.validate<br/>(promote highest-seq ledger ≥ quorum)"]:::span
    LOSE(["built ledger loses:<br/>never promoted → abandoned"]):::drop
    OACC(["OpenLedger::accept<br/>(rebuild open ledger; no span)"]):::plain
    tqAcc["txq.accept<br/>(↻ drain queued txs)"]:::span
    swlStd(["switchLCL standalone:<br/>setFullLedger + tryAdvance (no span)"]):::plain
    swlNet(["switchLCL networked:<br/>checkAccept (no span)"]):::plain
    END(["endConsensus → next round ↻ (no span)"]):::plain

    onAcc -.->|addJob JtAccept| APPLY
    APPLY --> BLCL
    BLCL -->|normal path| BUILD --> TXAP
    BLCL -. "replay path" .-> RPLY
    APPLY --> CLEAN
    APPLY --> STORE
    APPLY -. "validating && isCompatible && !fail && canValidateSeq" .-> vSend
    APPLY --> CACC
    NEWVAL --> CACC
    CACC -.->|highest-seq trusted ledger ≥ quorum| VAL
    CACC -.->|tvc < quorum → no promotion| LOSE
    APPLY --> OACC
    OACC -->|TxQ::accept callback| tqAcc
    OACC --> swlStd
    OACC --> swlNet
    swlStd -. "marks full-validated (no ledger.validate span)" .-> END
    swlNet --> CACC
    onAcc --> END

• Order inside doAccept: buildLCL (build → tx.apply, then txq.cleanup, then ledger.store) → optional validate → consensusBuilt/checkAccept → OpenLedger::accept (rebuilds the open ledger; txq.accept runs in its callback) → switchLCL promotes the built ledger to the new LCL (RCLConsensus.cpp:812 then 833).
• buildLCL replay branch: if releaseReplay() has data, buildLedger replays the stored set with TapNone — it still emits ledger.build (via buildLedgerImpl) but applies txns directly with no tx.apply child and no 3-pass loop; else the normal consensus-set path runs tx.apply over 3 passes (RCLConsensus.cpp:929; BuildLedger.cpp:252).
• processClosedLedger (txq.cleanup) runs after build, before store (RCLConsensus.cpp:950 vs 953).
• ledger.validate is async + lossy: checkAccept is re-entered per incoming trusted validation (handleNewValidation → checkAccept, RCLValidations.cpp:193); it promotes the highest-seq trusted ledger whose valCount > neededValidations (LedgerMaster.cpp:1180), which may be a different ledger than the one this node built. Below quorum (tvc < minVal) it returns early with no promotion — a built ledger that loses is abandoned (LedgerMaster.cpp:980; docs/consensus.md:50). The ledger.validate span is emitted only inside checkAccept (LedgerMaster.cpp:987).
• validation-send guard: broadcast only if validating_ && isCompatible && !consensusFail && canValidateSeq(seq) — silently suppressed for incompatible ledgers or an already-validated seq (RCLConsensus.cpp:730).
• switchLCL: standalone → setFullLedger + tryAdvance — marks the ledger full-validated without emitting ledger.validate (that span lives only in checkAccept); networked → checkAccept (shared async quorum gate) (LedgerMaster.cpp:442).

Side flows: pathfinding and ledger acquire

Pathfinding — an RPC one-shot (path_find / ripple_path_find) plus an async recompute that fires on every ledger close for all active subscriptions, and also garbage-collects dead subscriptions:

flowchart TB
    classDef span fill:#0e7490,stroke:#155e75,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    REQ["pathfind.request<br/>(path_find / ripple_path_find)"]:::span
    CREATE(["subscribe: makePathRequest →<br/>persistent subscription (no span)"]):::plain
    COMP["pathfind.compute<br/>(doUpdate, one pass)"]:::span
    DISC["pathfind.discover<br/>(findPaths)"]:::span
    PFDR(["Pathfinder + RippleCalc<br/>↻ per source asset (no span)"]):::plain
    UP(["updatePaths (JtUpdatePf, every close; no span)"]):::plain
    UALL["pathfind.update_all<br/>(recompute all active)"]:::span
    DEAD(["dead subscriber → doAborting +<br/>remove_if erase"]):::drop

    REQ -.->|subcommand create| CREATE
    REQ -->|doUpdate| COMP -->|findPaths| DISC --> PFDR
    UP -->|once per close| UALL
    UALL -->|↻ each active request| COMP
    UALL -. "new request arrived → extra pass ↻" .-> UALL
    UALL -.->|dead / aborted| DEAD

Ledger acquire — a separate trace root (not part of the close flow) that fetches a missing or correct-prior ledger from peers, retries per peer/timer, and finishes with a reason-dependent store; checkAccept + tryAdvance run on any completed acquire:

flowchart TB
    classDef span fill:#0e7490,stroke:#155e75,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    HIST(["tryAdvance → doAdvance → fetchForHistory<br/>(Reason::HISTORY; no span)"]):::plain
    NEED(["checkAccept / handleNewValidation /<br/>consensus wrong-ledger (no span)"]):::plain
    INB(["InboundLedgers::acquire (no span)"]):::plain
    ACQ["ledger.acquire<br/>(InboundLedger::init)"]:::span
    TRIG(["trigger / addPeers / onTimer<br/>↻ per peer / chunk (no span)"]):::plain
    FAILED(["timeouts > 6 → failed_ →<br/>logFailure (NO store, NO checkAccept)"]):::drop
    DONE(["done() — complete && !failed (no span)"]):::plain
    ONF(["onLedgerFetched (no span)<br/>(HISTORY: no store)"]):::plain
    STORE["ledger.store<br/>(GENERIC / CONSENSUS)"]:::span
    CACC(["checkAccept + tryAdvance (no span)<br/>(↻ may publish/advance many ledgers)"]):::plain

    NEED -.->|GENERIC / CONSENSUS| INB --> ACQ
    HIST -.->|HISTORY| INB
    ACQ -.->|not complete| TRIG
    TRIG -. "retry ↻" .-> TRIG
    TRIG -.->|timeout cap| FAILED
    TRIG -.->|complete| DONE
    DONE -.->|reason == HISTORY| ONF
    DONE -.->|GENERIC / CONSENSUS| STORE
    DONE --> CACC
    CACC -. "advanceWork ↻ → further HISTORY acquire" .-> HIST

Side-flow evidence:

• Pathfind subscription lifecycle: path_find create inserts a persistent subscription (makePathRequest); update_all re-runs each active request every close, removes dead subscribers (doAborting + remove_if erase), and takes an extra pass when a new request arrived mid-run (PathRequestManager.cpp:103, 169, 181).
• Acquire outcome fork: timeouts_ > kLedgerTimeoutRetriesMax (= 6) sets failed_ → terminal logFailure, no store/checkAccept (InboundLedger.cpp:387).
• done() reason branch (store side only): HISTORY → onLedgerFetched, no storeLedger; else → storeLedger. But checkAccept + tryAdvance run for any complete_ && !failed_ acquire regardless of reason (InboundLedger.cpp:495 store switch; 507 reason-independent checkAccept/tryAdvance).
• tryAdvance multi-ledger loop: doAdvance runs do { … } while (advanceWork_), publishing a range of ledgers and recursively triggering further HISTORY acquire (LedgerMaster.cpp:1905).

Where telemetry parenting differs from protocol flow

The graph above is protocol control flow. The OpenTelemetry span parent links are built differently and, in several places, do not represent a real call edge. Read a trace with these in mind:

Telemetry does this	Real protocol flow
tx.process is a hashSpan root from txID — an independent trace root (TxTracing.h:63).	The real edge is the synchronous doSubmit → processTransaction call; it is not a child of rpc.command.submit.
tx.preflight / tx.preclaim / tx.transactor share one txID-derived trace ID.	That shared ID is a correlation trick, not a call edge. The real order is the composed apply() at apply.cpp:118. They are not children of tx.process or tx.apply.
consensus.round uses a deterministic trace ID from the previous ledger hash.	This makes all validators share one trace ID (a cross-node shared root), not a per-node parent. The real round-to-round edge is endConsensus → beginConsensus.
consensus.accept (main thread) and consensus.accept.apply (JtAccept worker) are wired via a captured context.	The real edge is the queued JtAccept job, a thread hand-off (RCLConsensus.cpp:483).
pathfind.update_all parents nothing from the original pathfind.request.	The causal link is the ledger-close job on JtUpdatePf, not span nesting.
ledger.acquire and its downstream ledger.store / ledger.validate.	Reached via the AcqDone job, not parent inheritance; ledger.acquire is its own root.
peer.*.receive (fresh kConsumer root) and consensus.*.receive on the same message.	Two sequential stages of one synchronous handler, not parent/child; on a duplicate/untrusted drop the consensus.*.receive is never created.
Receive spans adopt the sender's trace_id + span_id as a genuine cross-node parent.	Deliberate: the receive span becomes a child of a different node's span (a cross-node context marker, not an in-process edge). tx.receive is asymmetric — it borrows only the sender's span_id and re-derives its own trace_id from txID.

Known telemetry artifacts (from live audits, memory otel-span-hierarchy-audit): an RPC entry span's scope can leak across a reused coroutine worker, and the hashSpan roots (tx.*) — along with plain roots like ledger.acquire — can surface in Tempo as dangling "root span not yet received". These are exporter/parenting artifacts, not real control-flow parents.

---

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
"What tx work fed a given ledger?"	tx.* / txq.*	span.current_ledger_seq

Correlating a transaction to the ledger it was worked on

The tx.process, tx.receive, txq.enqueue, tx.preclaim, and tx.transactor spans carry current_ledger_seq — the open/in-flight ledger they acted on (not an established ledger). Because these spans are keyed on the transaction id (their own trace) while the ledger/consensus spans are keyed on the ledger, use the attribute to bridge the two id-spaces:

# All transaction-side work recorded against ledger N
{span.current_ledger_seq = N}

# Join to the ledger build/consensus trace for the same ledger
{name="ledger.build" && span.ledger_seq = N}

txq.enqueue and the view-bearing apply stages also carry current_ledger_hash (the current ledger's parent hash), which equals the consensus.round deterministic trace-id seed on the consensus-build path. tx.preflight is stateless and omits both attributes.

---

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 (spanmetrics connector, unit: ms):

1ms, 5ms, 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2s, 3s, 4s, 5s, 10s, 30s

Sub-second boundaries cover RPC/tx/ledger spans; 2s-4s resolve second-scale consensus spans (consensus.round, consensus.establish) that would otherwise pile into one 1s-5s bucket and make histogram_quantile a meaningless interpolation; 10s/30s give the ledger.acquire catch-up tail a measurable home. Boundaries must stay strictly ascending. The native beast::insight histograms (ms-scale RPC/IO timers) keep the original 1ms-5s buckets in Telemetry.cpp — they never exceed 5s, so they need no high-range buckets.

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)
server_info{metric="last_close_time"}	MetricsRegistry.cpp	Network close time of last closed ledger (NetClock secs since XRPL epoch). Age = time() - (value + 946684800); close interval = 1/rate(ledgers_closed_total), not a gauge delta
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
rpc_in_flight_requests	PerfLogImp.cpp	RPC requests currently executing (UpDownCounter)

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)

Adding a New Metric

Use the call-site macros in src/xrpld/telemetry/MetricMacros.h -- no MetricsRegistry.h/.cpp edit is needed for any of these:

Need	Macro
Monotonic tally (never decreases)	XRPL_METRIC_COUNTER_INC / _ADD [+ _LABELED]
Running total that can decrease	XRPL_METRIC_UPDOWN_ADD [+ _LABELED]
Distribution of values (latency, size)	XRPL_METRIC_HISTOGRAM_RECORD [+ _LABELED]
Last-value snapshot (not a distribution)	XRPL_METRIC_GAUGE_RECORD [+ _LABELED] -- requires an ABI v2 opentelemetry-cpp build; this repo currently builds ABI v1, so use the observable-gauge row below instead
Value your own code already tracks, sampled on a timer	XRPL_METRIC_OBSERVABLE_GAUGE_REGISTER / _COUNTER_REGISTER / _UPDOWN_REGISTER

#include <xrpld/telemetry/MetricMacros.h>

// Monotonic counter:
XRPL_METRIC_COUNTER_INC(app_, "my_new_thing_total", "Description of what this counts");

// Value that can go up and down, e.g. in-flight work (no _total suffix -- that
// is reserved for monotonic counters; an UpDownCounter is a current value):
XRPL_METRIC_UPDOWN_ADD(app_, "my_in_flight_requests", "Currently executing", 1);   // on start
XRPL_METRIC_UPDOWN_ADD(app_, "my_in_flight_requests", "Currently executing", -1);  // on finish

// Sampled from your own state, on the OTel export timer (register ONCE, in init code):
XRPL_METRIC_OBSERVABLE_GAUGE_REGISTER(app_, "my_thing_size", "Current size",
    [this] { return static_cast<int64_t>(myThing_.size()); });

Counters use a _total suffix by convention. A histogram whose values can exceed ~10,000 units (e.g. a microsecond duration beyond 10ms) still needs one line added to addMicrosecondHistogramView() in MetricsRegistry.cpp -- the only case that still touches a central file. There is no way to read a metric's current value back from application code -- OTel's API is write-only by design; keep your own state if your logic needs to both record and read a running value (see the Doxygen header in MetricMacros.h and "Use Case 4" in tasks/metric-macro-plan.md for the full explanation and the prometheus-cpp contrast rationale).

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.round"}) (full round, not consensus.ledger_close which is only the sub-ms onClose prologue)	consensus_mode
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.round (full round, not consensus.ledger_close which is only the sub-ms onClose prologue)	—
Ledger Close Interval & Age	timeseries	Interval: 1/rate(ledgers_closed_total); Age: time() - (server_info{metric="last_close_time"} + 946684800)	—

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

Diagnosing slow/stuck fresh sync

A fresh node that is slow to reach server_state=full, or never reaches it, is diagnosed from the Ledger Sync Health dashboard (uid ledger-sync-health).

Walk the dashboard rows top-down — the first row that looks wrong is the stage that is blocking:

1. Bootstrap (Domain 0) — can the node reach peers and form a quorum at all? Covers DNS resolution, outbound dial, protocol/network-ID negotiation, UNL fetch with trusted-key-vs-quorum, and clock skew. A node that never gets past this row can never validate, no matter how healthy the pipeline looks.
2. Sync pipeline — is ledger data actually arriving and being applied? Covers sync-state transitions, ledger/tx-set acquire progress, job-queue backlog, quorum and publish lag, back-fill, and persistence latency.

Signal definitions: telemetry-glossary.md "Fresh-node sync diagnostics". Signal index (instrument, emit site, panel): 09-data-collection-reference.md "Fresh-node sync diagnostics".

Note: the sync signals are native metrics, which are never sampled — unlike the span-derived (spanmetrics) series, they are always complete.

Bootstrap (Domain 0) — ordered diagnosis

Work the Bootstrap row in this order. Each step gates the next, so stop at the first one that is wrong and fix it before reading further panels.

1. DNS — did the node resolve its configured peers? Panel DNS Resolve Outcome Rate (dns_resolve_total). Any rate on outcome=empty means a name in [ips] or [ips_fixed] returned no address, so that peer is never dialled. Fix the hostname or point at an IP. If both outcomes are flat at zero the node resolved nothing at all — check that [ips]/[ips_fixed] is actually populated. Then check DNS Resolve Latency (p95) (dns_resolve_latency_ms): a seconds-scale p95 is a resolver timing out, which delays every dial behind it even when the resolution eventually succeeds.

2. Outbound dial — did the connections complete? Panel Outbound Dial Outcome Rate (overlay_connect_total). The outcome label names the stage that broke:

   • connected — success; this is the line that must be non-zero.
   • tcp_fail — no route, refused, or the peer port is closed or firewalled.
   • tls_fail — the TLS handshake failed.
   • upgrade_fail — TLS succeeded but the HTTP upgrade or protocol negotiation was rejected. This is the outcome that pairs with step 3.
   • timeout — the attempt never reached a terminal state. If every attempt lands on one failure outcome and connected stays at zero, the node has no outbound peers and cannot sync at all. Outbound Dial Latency (p95) (overlay_dial_latency_ms) covers successes and failures together, so a p95 pinned near the dial timeout means peers accept the TCP connection but never finish the handshake.

3. Handshake — why were peers rejected? Panel Handshake Negotiation Failures by Reason (handshake_negotiation_fail_total). This is where an upgrade_fail from step 2 gets its cause. The reason label is the check that rejected the peer; the ones worth acting on first:

   • wrong_network or invalid_network_id — most common fresh-node misconfiguration. The node is on a different network than its peers, so it will never reach a quorum however healthy everything else looks. Fix [network_id] and the peer list together.
   • clock_skew — the peer rejected this node's clock; go to step 5.
   • session_verify_failed, bad_public_key, no_session_signature — a misbehaving or mismatched peer rather than a local fault.
   • self_connection, local_ip_mismatch, remote_ip_mismatch — a low background rate is normal on a NAT'd host.

4. UNL — can the node ever form a quorum? Panel UNL Fetch Rate by Site & Outcome (unl_fetch_total), grouped by site and outcome. Read the outcome carefully — accepted is the only success value:

   • accepted — the list was applied.
   • same_sequence, known_sequence — normal no-op refreshes of a list the node already holds.
   • fetch_error, bad_status, parse_error — transport or content faults; the site is effectively unreachable.
   • stale, untrusted, invalid, unsupported_version — the list arrived but was rejected, so no keys are loaded from that site.
   • expired — the list was applied and its keys were loaded, but it is past its validity window, so the expiry sweep drops them again and the publisher does not count as available. Refresh the publisher blob, and check the local clock, rather than replacing validators.txt.
   • pending — the list is valid only from a future date and is held for rotation. Normal, not a fault. Then read UNL Trusted Keys vs Quorum and UNL Quorum Headroom (unl_quorum, metric=trusted_keys against metric=quorum). This is the "will never validate" check. If trusted_keys is zero, or sits at or below quorum (headroom zero or negative, shown red), the trusted UNL is too small to ever satisfy quorum: the node can track ledgers but will never declare one validated, and no amount of healthy acquire traffic changes that. A site stuck on fetch_error or expired in the panel above is the usual cause. A very large quorum with a deeply negative headroom is the distinct "quorum disabled" state: too many publishers are unavailable, so quorum has been switched off entirely rather than merely set high. Fix publisher reachability first — the key count is irrelevant until quorum is enabled again.

5. Clock — is local time disagreeing with the network? Panel Clock Close Offset (clock_close_offset_seconds, metric=offset). The signed series is negative when the local clock runs ahead of the network and positive when it lags; the magnitude series carries the threshold bands. A magnitude above 1 second that does not decay is suspicious and delays consensus participation. Note that server_info only reports close_time_offset once the magnitude reaches 60 seconds, so this panel sees skew long before the API does. A persistent offset is a local NTP fault, not a network one — and it is also the cause behind a clock_skew reason in step 3.

If all five steps are clean the bootstrap stage is healthy, and the problem is in the Sync pipeline row instead.

Sync pipeline — ordered diagnosis

Once bootstrap is clean, work the Sync pipeline row in this order. As above, each step gates the next: stop at the first one that is wrong.

1. Did the node ever sync at all? Panel Time to First FULL (sync_state, metric=initial_full_duration_us, shown in seconds). This is a one-shot measurement: it fills in the moment the node first reaches full and never changes again. So there are exactly two readings that matter — a value, meaning the node synced and this is how long it took, or a flat zero (red), meaning it has never reached full. A flat zero is the signal, not missing data; everything below explains why. Do not read a rising or falling trend into this panel — it cannot have one.

2. Is the node gated on the network ledger? Panel Network Ledger Gate (sync_state, metric=network_ledger_gate). A persistent 1 (red) means the node has never seen a complete ledger from the network, so it refuses submitted transactions and cannot reach full however healthy the acquire pipeline looks. This normally clears within the first minutes of startup; a 1 that never clears sends you back to the Bootstrap row (no peers, or no quorum) rather than deeper into the pipeline.

3. Is the server stalling rather than starving? Panels Server Stall (sync_state, metric=server_stall_seconds) and Server Stall Event Rate (server_stall_events_total). A non-zero stall means the main loop missed its heartbeat for at least 10 seconds, which is main-loop overload, not sync data starvation — so the fix is in job-queue depth and disk latency, not peer supply. Read the two panels together, as they separate two different faults that look identical in a log:

   • Large stall seconds with a flat event rate — one long unresolved stall. Note that past 600 seconds the server deliberately fails with a logic error, so a stall approaching that is about to end the process.
   • Small stall seconds with a rising event rate — repeated short stalls; the server keeps recovering and re-stalling, which points at periodic work (sweeps, large writes) rather than a single stuck operation.

4. How far behind is it, and is it converging? Panel Ledgers Behind Network (sync_state, metric=ledgers_behind). The target is the highest ledger any connected peer reports holding, so this is the gap the node must close. Trending down to 0 is healthy convergence; flat or rising means the node acquires slower than the network advances and will never converge on its own. One caveat when reading a zero: the value is floored at 0 and the target comes from peer reports, so a node with no peers — or whose peers have reported no range yet — also reads 0. Confirm against the peer count before treating a 0 here as "at the tip".

5. Which state edges is it actually taking? Panel Mode Transitions by Edge (state_changes_total, grouped by from and to). A clean fresh sync traverses each climb edge (disconnected→connected→syncing→tracking→full) roughly once. Repeated counts on a reverse edge such as full→connected, paired with connected→full, is flapping: the node keeps reaching full and losing it. Flapping with a healthy step 4 points back at step 3 (stalls) or at the Bootstrap row's clock and quorum panels, since those are what drop a node out of full once it has arrived. Use the Mode From and Mode To template variables to isolate one edge.

6. Is ledger acquisition progressing, or permanently stuck? Panel Missing SHAMap Nodes per Acquire (state/tx) (sync_acquire, metric=missing_state_nodes_max and missing_tx_nodes_max). This is the panel that separates "sync is slow" from "sync will never finish." Read the shape over several minutes, not the instantaneous value:

   • Falling toward zero — the acquire is progressing. Slow is not stuck.
   • Flat and non-zero — no peer is serving that tree. The node will sit here forever; no amount of waiting fixes it. Go to step 7.
   • Pinned at 256 — that is the per-sweep cap (kMissingNodesFind), so the real backlog is at least that large. Meaningful only in combination with the trend: pinned-and-falling is a large but progressing tree.
   • Zero on one tree, non-zero on the other — that tree is already complete; concentrate on the one still reporting nodes. One caveat: zero on both is only healthy if In-Flight Acquires (step 8) is non-zero. Zero everywhere with zero acquires in flight is an idle node, which says nothing about acquire health.

7. Is the node asking and getting nothing back? Panel Acquire Stall Rate (no progress) (sync_acquire_no_progress_total). This counts acquire timeouts in which not a single node arrived. A sustained rate here together with a flat missing-node count from step 6 is the definitive stuck-sync signature: the node is requesting and no peer is answering. Check the peer count and whether any connected peer actually holds the ledger range being requested — a peer set that cannot serve the range looks identical to no peers at all from inside the acquire.

8. Is the data arriving useful, or wasted? Panel Add-Node Outcomes (sync_addnode_total, stacked by outcome). Traffic-level metrics count all three outcomes as healthy throughput, which is why this split matters:

   • good — new, valid nodes. This is the only line that represents progress.
   • duplicate — nodes already held. A share that swamps good means peers keep re-sending known data, so bandwidth is busy while the acquire stands still.
   • invalid — nodes that failed validation. A rising share points at a specific misbehaving peer rather than a local fault. Then read Received-Data Stash Depth & In-Flight Acquires (sync_acquire, metric=received_data_depth and in_flight). A growing stash means node data is arriving faster than it can be applied, which is a job-queue or disk problem, not a peer-supply one — the opposite conclusion from step 7, and the two are distinguished only by this panel.

9. Is it disk-bound rather than peer-bound? Panels Acquire Source (local vs network) (sync_acquire_source_total) and SHAMap TreeNode Cache Hit Rate (shamap_cache_hit_rate). During a genuine fresh sync network dominates and the cache hit rate is low — both expected, because nothing is local yet. The diagnostic case is a node that should already be warm:

   • Sustained network on a range the node should already hold means the local store is not retaining data.
   • A persistently low tree-node hit rate means the working set does not fit the cache, or continuous re-acquisition is churning it, so every tree walk pays disk latency. Note this is the in-memory tree-node cache, one layer above the NuDB Cache Hit Ratio panel on the Ledger Data Sync dashboard: a miss here is what produces a node-store read there. A low rate on both is disk-bound sync; a low rate here with a healthy NuDB ratio is cache pressure alone. This is also the pairing that explains a large existing database syncing slower than a fresh one.

10. Is the work arriving but never getting a worker thread? Steps 6 to 9 all assume the node is at least trying to process what it receives. This step covers the case where it is not: the data arrived, the job was queued, and no thread ever ran it. Read the two levels in order, because the pool-wide answer supersedes the per-type one.

    • Pool first — Worker Pool Saturation (jobq_saturation, running_tasks / worker_threads) with Worker Pool Capacity & Total Backlog beside it. Below 80% the pool has spare capacity, so skip to the per-type read. At 100% the answer depends on total_waiting:
      • 100% with total_waiting near zero — the pool is merely busy at this instant. Not a fault.
      • 100% with total_waiting climbing — the pool is exhausted. Every job type is starved by it, so every other stage in this row will look slow at once. Stop here: fixing an individual subsystem cannot help while no thread is free to run its jobs. Look at what is holding the threads (long-running jobs, disk waits from step 9) or at the [workers] setting for the node size.
    • Then per type — Deferred Jobs by Type (starvation) (jobq_backlog, metric=deferred). This is the signal that exists nowhere else. A deferred job is one the queue accepted and then withheld because its type is already running at its concurrency limit, so it appears in neither waiting nor running, and neither the job counters nor the queue-wait histograms can show it. Any sustained non-zero value names the job type whose limit is the bottleneck. During a fresh sync watch job_type=ledgerData and job_type=ledgerRequest first: both run at a limit of 3, so they are the types that starve soonest, and starved ledgerData is exactly why the received-data stash in step 8 grows while the missing-node count in step 6 stays flat. Job Queue Occupancy by Type (waiting/running) gives the context — running pinned at the limit with waiting above zero confirms the limit, not the supply, is what is holding the type back. Note the difference from Job Queue Wait p95 By Type on the Ledger Data Sync dashboard: that panel measures how long jobs that already ran had waited, so it reports the past; these gauges report what is sitting in the queue right now, including the part being actively withheld.

11. Can the network even serve the ledgers this node needs? Steps 6 to 10 all assume some peer holds what the node is asking for. This step tests that assumption, and it is the one that separates "slow" from "impossible". Panel Peers Able to Serve Needed Sequence (peer_ledger_supply, metric=peers_reporting, peers_serving_validated and peers_serving_next). Read the two counts together — peers_reporting is the denominator that makes the rest meaningful:

    • peers_serving_next at zero with peers_reporting above zero — the decisive reading. Peers are connected and have advertised their ranges, and none of them holds the next ledger this node must acquire. No amount of waiting finishes the sync; the peer set itself has to change. Add peers that hold the range, or point the node at a full-history server. Everything in steps 6 to 10 will look starved as a consequence, so do not chase them.
    • peers_serving_next above zero but the sync is still slow — supply is fine and the fault is downstream. Go back to steps 6 to 10: the data is available, so the limit is acquire progress, local processing or worker threads.
    • peers_reporting at zero — nothing has advertised a range yet. This is not a supply gap; it means the node has no peers, or its peers have not sent a status change. Peers advertising an empty range are excluded from every field, so the two window fields read 0 meaning unknown, not genesis — do not read a zero window here as "peers serve from the start of history". Go back to the Bootstrap row, and to step 12 for why there are no peers. Then read Peer Ledger Supply Window (peer_ledger_supply, metric=supply_min_seq and supply_max_seq) against the sequence the node wants. This is what tells the two shapes of a supply gap apart: a needed sequence below supply_min_seq means the node is asking for history nobody kept, so it needs a full-history peer; a needed sequence above supply_max_seq means it is asking for a tip nobody has reached, which is a peer set lagging the real network rather than a history problem.

12. Is this node failing to get or keep peers, and why? Step 11 says whether the peer set can serve; this step says why the peer set is what it is. Panel PeerFinder Slot Census (peerfinder_slot_census) with PeerFinder Address Caches & Fixed Peers beside it. All nine fields come from one lock acquire, so occupancy and capacity can be compared directly — which is what separates three faults that otherwise look identical:

    • connecting non-zero while out_active stays below out_max — the node is dialling and the dials never complete. Without the attempt count this looks exactly like a node that is not dialling at all. Pair it with Outbound Dial Outcome Rate in the Bootstrap row for the stage that breaks.
    • bootcache and livecache both at 0 — there is nothing to dial. No seed addresses at all, so check [ips] and DNS in Bootstrap step 1.
    • fixed_active below fixed_configured — a peer named in the configuration is unreachable. fixed_configured is what was asked for and fixed_active is what was obtained, so any shortfall names a specific configured peer to check. Then split the traffic by direction. Inbound Peer Accept Outcomes (peer_accept_total, by outcome) covers connections offered to this node; the already-documented overlay_connect_total{outcome} in Bootstrap step 2 covers dials from it. Reading both is the only way to get the full in/out picture: a node refusing every inbound connection looks the same as one nobody dials until these are separated. On the inbound side resource_limit, no_slot and slot_refused are this node declining (load, capacity, or a duplicate), while protocol_mismatch, bad_cookie and handshake_error point at the peer or at a network-id mismatch. Then read Peer Disconnects by Reason (peer_disconnect_total, by reason and direction). One disconnect count cannot separate the two causes; the label can:
    • large_sendq, charge_resources — our fault. This node could not keep up with what it owed the peer, or charged it past the resource limit, so it shed the connection as backpressure. The fix is local capacity, not the peer list, and it sends you back to steps 9 and 10.
    • not_useful, ping_timeout, read_error — topology or network faults. The peer is on a different chain or unreachable, so the fix is the peer set.
    • graceful, shutdown, stopping — normal churn and clean teardown, not faults. A run dominated by these is healthy. Use direction to tell churn in the peers this node dials from churn in the peers that dial it. Finally, the mirror-image question: Ledger/Object Serve Refusals (serve_refused_total, by request and reason) is what this node refuses to serve OTHERS. It does not explain this node's own sync, but it explains its peers' — and a node that refuses everything is why some other operator is reading step 11 on their side. sendq_full and load_shed are self-inflicted: this node is too loaded or too far behind on its send queue to answer, so treat them as the serving-side symptom of the same overload steps 3, 9 and 10 cover. not_found is different — it is a genuine history gap, meaning the data was asked for and this node simply does not hold it, which is a configuration and retention question rather than a load one.

13. Is the node about to stop validating for good? Panel Amendment Block Countdown (amendment_block, metric=seconds_to_block) with Amendment Warned (metric=warned) beside it. This step outranks every other step in urgency, so check it whenever a sync looks wrong, not only after the ten above are clean:

    • seconds_to_block at -1 — healthy. The -1 is an explicit sentinel meaning nothing is pending, chosen so the healthy state is a distinct value rather than a missing series. Do not read it as a negative duration or as absent data.
    • seconds_to_block at any non-negative value — a countdown to a terminal state. When it expires the node becomes amendment-blocked: it stops validating and will never validate again without a software upgrade. The value is clamped at 0 rather than going negative, so a 0 means the activation is due or past due, not that it just started. Nothing else on this dashboard matters if this is counting down — plan the upgrade inside the window, because after it there is no operational fix. warned reaching 1 is the same condition seen as a flag: an unsupported amendment has reached majority. The existing validator_health{metric="amendment_blocked"} on the Validator Health dashboard is the after-the-fact companion — it reports the block once it has happened, when nothing can be done, whereas this countdown is the only actionable part. The blocking amendment's hash is not a metric label, deliberately: the network can vote on an arbitrary 256-bit amendment id, not drawn from this build's known features, so an id label would be unbounded cardinality and would mint a permanent new series per amendment. Get the hash from the log line in AmendmentTableImpl::doValidatedLedger ("Unsupported amendment ... reached majority at ...") via Loki, correlated to this series by node and time. Finally, read Byzantine Ledger Jumps (ledger_jump_total) in the same pass. Any non-zero rate means the node was fed a last-closed ledger it had not built on and discarded its own chain tip to follow. A single jump during a fresh sync can be benign as the node settles onto the network's chain. Repeated jumps are wrong-chain thrash: the node keeps switching between chains and never settles, so check the peer set from step 12 and the configured network id from Bootstrap step 3 — those are what put a node on the wrong chain in the first place. Nothing in the acquire pipeline can fix it.

14. Is the node store itself the bottleneck — and is it the write side? This is the step for the specific symptom "a node with a large existing database starts and syncs slower than a fresh one". Back-fill is write-bound, so no read-side panel can show it; check this step whenever a node with existing history is the slow one. Panel NodeStore Write vs Read Latency (us/op) (nodestore_latency, metric=write_mean_us and read_mean_us) with NodeStore Operation Rate (metric=write_count / read_count) beside it:

    • Write line rising during history back-fill — the backend cannot absorb writes fast enough. Sync will stay slow however many peers are available, so adding peers will not help. Check storage IOPS, the [node_db] backend and its tuning, and whether the online-delete / rotation cycle is competing with the back-fill writes. Correlate with nodestore_state{metric="write_load"} on the Ledger Data Sync dashboard.
    • Read line far above the write line — the read path, not the write path, is the cost. Read it together with SHAMap TreeNode Cache Hit Rate (step 7): a cold in-memory cache sends every tree walk to disk, and that shows up here as read latency rather than as a node-store fault.
    • Write rate at zero while the node is still behind the network — nothing is being persisted at all, so the stall is upstream of the node store. Go back to peer supply (step 12) and the acquire panels (steps 6-8); storage is not the problem.
    • Both panels use the rate of the mean divided by the rate of the count, which is why the count series exist. Read as an interval latency, not a since-boot average — on a long-running node the raw cumulative mean moves so slowly that a current stall is invisible in it.
    • Two limits to keep in mind. First, this is a mean, not a percentile: a tail that matters will move it, but there is no p99 here. That is a deliberate cost trade — a histogram would need one Record() per node object, and a single ledger write walks thousands of SHAMap nodes. Second, write_mean_us is currently emitted only for store paths that record their duration, which today is the [import_db] admin import. On an ordinary node you will see write_count climbing with no write_mean_us line: that is a known instrumentation gap, not a healthy zero, and the mean is deliberately omitted rather than drawn as 0 so it cannot be misread as "writes are instantaneous".

15. Is replay-based back-fill silently falling back to the slow path? Only relevant when [ledger_replay] is enabled. Panels Replay Fallback to Full Acquire (by stage) (ledger_replay_fallback_total) and Replay Outcomes (by terminal state) (ledger_replay_outcome_total):

    • Any sustained fallback rate — too few connected peers support the LedgerReplay protocol feature, so every historical ledger is fetched whole instead of as a delta. Back-fill still completes, just far slower, which is why this is easy to miss: nothing fails, the optimisation is simply gone. The stage label says which sub-task gave up — skiplist (fetching the list of historical ledger hashes) or delta (a single ledger's changes). Fix by peering with nodes that support the feature.
    • Failure outcomes climbing while success stays flat — replay runs but never completes. The outcome names the layer at fault: timeout means the deltas never arrived, so treat it as a peer-supply problem and read it with step 12; build_failed means a delta would not apply to its parent, and parameter_failed means a peer served a skip list inconsistent with the request — those two are data faults from the serving peers, not slowness, so the peer set is suspect rather than the network.
    • All series absent — expected when [ledger_replay] is not configured, or on a node with no history to back-fill. Absence here is not a regression; it means no replay task was ever created. For the same reason neither counter is asserted by the local validation harness.

16. The node has peers and a UNL, yet never declares a ledger validated — is it slow, stuck, or is only publishing behind? This is the last stage of the pipeline and the one every step above assumes away. Steps 1 to 15 all ask whether ledger data arrives and gets applied; this step asks whether the ledgers the node already holds ever pass the quorum gate, and whether the ones that pass ever reach clients. It is the step for the specific symptom "peers are connected, the trusted list is loaded, acquisition looks healthy, and server_state still never becomes full."

    • Slow or stuck? Panel Trusted Validations vs Quorum Target (ledger_quorum_publish, metric=trusted_validation_tally against quorum_target). Both are snapshots of the most recent gate evaluation, recorded whether it passed or failed, so a node that keeps failing still reports both numbers — which is exactly what separates the two cases:
      • Tally climbing toward the target — slow, not stuck. Validations are accumulating and the gate will pass. Keep waiting; nothing here needs fixing.
      • Tally flat below the target — stuck. Validations arrive and never reach quorum, so this node can hold every ledger it needs and still never declare one validated. Two causes: too few trusted validators are reachable, or the UNL / negative-UNL configuration excludes the ones that are. Go to Bootstrap step 4 (UNL Trusted Keys vs Quorum and UNL Quorum Headroom) — a trusted list that cannot satisfy quorum makes every panel in this row look starved as a consequence, so do not chase them.
      • Target reading about 9.2e18 (signed 64-bit maximum) — the explicit quorum-disabled sentinel. Too many list publishers are unavailable, so the trusted list switched quorum off entirely rather than merely setting it high, and the node can never validate however far the tally climbs. The value is reported as that maximum rather than being allowed to wrap to -1, precisely so it cannot be misread as a target the tally already exceeds. Fix publisher reachability first; the key count is irrelevant until quorum is enabled again.
      • Both series flat at 0 — the gate has never been evaluated at all, so nothing has yet been offered for validation. That is an upstream problem, not a quorum one: go back to the Bootstrap row. One reading caveat that matters more here than anywhere else in this row: judge the tally by its sustained floor over minutes, never by a single sample. Each series is a snapshot of the last evaluation, and the first evaluation of every round runs before peer validations for that ledger arrive, so a healthy node sawtooths.
    • Is the gate actually rejecting? Panel Pre-Accept Quorum Shortfall Rate (ledger_quorum_shortfall_total, stage=pre_accept). Read this one carefully, because a non-zero rate is not by itself a fault. Consensus issues this node's own validation and evaluates the gate immediately, before its peers' validations for that same ledger have arrived, so the first evaluation of each round tallies short and is retried as validations come in. A healthy cluster emits this counter every round. The fault signature is the combination: this rate climbing well above the ledger-close rate while the tally above stays flat below its target and Time to First Validated Ledger stays at zero. That is the retry loop never converging, rather than merely running early.
    • Did it ever validate at all? Panel Time to First Validated Ledger (ledger_quorum_publish, metric=time_to_first_validated_us, shown in seconds). A one-shot measurement, read exactly like Time to First FULL in step 1: a value means the node got there and this is how long it took, a flat zero means it never has. Reading the two together is what pins down the fault — a value on Time to First FULL beside a zero here means the node reached the full server state but has still never fully validated a ledger, which points at the quorum gate rather than at acquisition.
    • Validation fine, publishing behind? Panel Publish Lag (validated minus published) (ledger_quorum_publish, metric=publish_lag). This is the separate question, and the one no other panel can answer: the published sequence was never exported before, so this gap was not derivable from any other series. Publishing trails validation by design, so a small lag that drains each round is normal.
      • Lag flat at 0 or 1 — healthy.
      • Lag positive and growing — validation is healthy and the publish pipeline is not. The node itself is current while its clients and subscriptions see stale data. This is a local processing fault, not a peer or quorum one, so go back to steps 3, 9 and 10: a starved job queue or a stalling main loop is the usual cause.
      • Lag flat at 0 on a node that has never validated — not healthy, merely empty. There is nothing validated to publish, so read Trusted Validations vs Quorum Target first and ignore this panel until the gate passes.
    • Are the arriving validations even usable? Panel Trusted Validation Accept Rate by Status (consensus.validation.accept, by validation_status). The tally panels above say how many trusted validations were counted; this one says what happened to the validations that arrived, which is the missing half when the tally sits flat:
      • Nearly all current — normal. That is the only status that continues to the acceptance gate, so the tally is limited by how many validators are reachable, not by the validations themselves. Go back to Bootstrap step 4.
      • Rate concentrated in stale, bad_seq, multiple or conflicting — validations arrive and are counted for nothing. This is the difference between a node that is slow to validate and one that never will, and it is invisible in the tally, which simply stays low either way. stale in bulk usually means a clock or timing problem (check Clock Close Offset in Bootstrap); multiple or conflicting in bulk means a trusted validator is misbehaving or the node is seeing two chains.
      • accept_gated=true on a share of them — normal, and not a lost span: another thread was already accepting that ledger, so no acceptance followed this particular validation.
      • Flat at zero on a peered node — no trusted validations are arriving at all, which sends you back to Bootstrap step 4 rather than anywhere in this row. This panel is span-derived, so unlike the native panels above it inherits trace sampling — read it for the shape of the split, and the native tally for exact counts.

17. Which stage of the fetch is stuck, and which peer or object is it stuck on? Every step above reads a native metric, which is an aggregate: it says how much and how often, never which one. This step reads the span-derived panels at the bottom of the Sync pipeline row, which answer the "which" questions the aggregates structurally cannot — and each point on them is backed by a trace, so it can be clicked through to the individual fetch, request or dial. Two things to know before reading them. First, unlike every metric above, these series come from spans and are therefore subject to sampling and to the trace_ledger / trace_peer config flags — an absent series can mean "not enabled" rather than "not happening". Second, use them to localise a fault that the metric steps have already established, not to detect one.

    • Which acquire phase is stuck? Panels Ledger Acquire Phase Duration (p95 by phase) and Ledger Acquire Phase Outcomes (by phase & timeout) (ledger.acquire.header / .astree / .txtree). Step 6 says the missing-node count is flat; these say where. A ledger acquire is three sequential fetches, and the parent ledger.acquire span is flat, so its duration is the account-state tree's and hides the other two:
      • astree band hot, with timed_out=true and a non-zero missing_nodes — peers are not supplying account-state nodes. This is the common stuck-fresh-sync shape. Check Outbound Dial Outcomes below for refused / timeout spikes, and step 11 for whether any peer holds the range at all.
      • header band hot — the node is waiting to be told what to fetch. The header names both trees' root hashes, so until it arrives nothing else can even be requested. This is a peer-supply fault upstream of either tree, and it is invisible in step 6, whose missing-node counts are both still zero at this point.
      • txtree band hot while astree is quiet — unusual, and worth noting precisely because the transaction tree is normally small: the parent span's duration would not have shown it. Use the $timed_out template variable to isolate the timed-out phases. The two are separate dimensions on purpose: a phase can time out and still be retried by its parent acquire, so timed_out=true is not the same as the acquire having failed.
    • Are proposed transaction sets arriving? Panels Tx-Set Acquire Outcomes and Tx-Set Acquire Duration (p95) (txset.acquire). Nothing measured this before, so a consensus round stalled waiting on a transaction set looked identical to an idle one:
      • timeout or abandoned climbing — proposed sets never complete, so rounds wait on data rather than on agreement. Read it with the consensus panels rather than with the acquire steps above: this is the consensus path, not history back-fill.
      • complete series p95 approaching the round interval — sets do arrive, but so late that they delay the round they belong to. The outcome rate alone cannot show this, because those acquisitions succeed.
      • All series flat at zero — normal. It means this node already held every proposed set locally and never had to fetch one.
    • Which peer is the dial failing against? Panel Outbound Dial Outcomes (span-derived, per attempt) (peer.dial). The same five outcomes as overlay_connect_total in Bootstrap step 2, set from the same code path so the two cannot disagree — read the counter first for the rate, then come here for the identity. The span carries remote_endpoint, which the counter deliberately cannot: one metric series per peer address would be unbounded cardinality. Drill into a failing point to get the endpoint, which is what separates one unreachable peer from a broken local network — a distinction the aggregate rate cannot make. A dial torn down by shutdown ends its span with no outcome attribute; that is deliberate, and means "never concluded" rather than a lost span.
    • Are we serving others while starving ourselves? Panel Ledger Serve Rate by Object Type (ledger.serve). This is the supply side — what this node does for its peers — so it does not explain this node's own sync, but it does explain its peers'. Compare the as series here against the inbound astree phase above: healthy serving with starved receiving points at peer selection rather than at this node's capacity.
      • refused climbing — this node is declining requests; Ledger/Object Serve Refusals in step 12 gives the specific cause.
      • partial climbing — replies keep filling kSoftMaxReplyNodes, so peers need repeated round trips for one tree. Expected while serving large state trees; sustained on small requests it is worth a look.

18. Are consensus rounds themselves slowing down? Panels Consensus Round Duration Distribution (heatmap) and Consensus Round Duration (p50/p95) (consensus_round_duration_ms). A round that used to take 3-4 s and now takes 12 delays every ledger behind it, and until now this was only a span attribute — answering it fleet-wide meant raw trace queries. Being a native metric it is also never sampled, unlike every span-derived panel above.

    • Band drifting upward, or a second band high up — rounds are taking longer. Read it against the two panels that explain why: Tx-Set Acquire Duration (p95) (step 16 — rounds waiting on data rather than on agreement) and Trusted Validations vs Quorum Target (step 13 — validations arriving too late to close the round).
    • P95 climbing while P50 stays flat — a minority of rounds stall. This is the early form of what the heatmap later shows as a second band.
    • Both rising together — the whole network is slowing, not this node. Compare against another node using the $node variable before treating it as a local fault.
    • Buckets span 500 ms to 120 s deliberately. The SDK default stops at 10 s, which would put every slow round in one saturated bucket and read every quantile as exactly 10 s — and the consensus parameters allow a round up to 120 s before it is abandoned.

Following ONE slow ledger as a single trace

The steps above find which stage is slow across all ledgers. This finds why one specific ledger was slow, which is the question left when the aggregate panels look merely mediocre.

Every span that touches a ledger derives its trace id from that ledger's own hash, so they all share one trace even though each runs on a different thread and no context is passed between them. One TraceQL query returns the whole story:

{span.ledger_hash="<64-hex-ledger-hash>"}

What that trace contains, and what each part tells you:

Span	Thread	What a long one means
ledger.acquire (+ phases)	JtLedgerData worker	The node had to fetch this ledger and peers were slow to supply it
consensus.validation.accept	validation worker	Time this trusted validation spent driving the acceptance decision
ledger.validate	whichever thread hit the gate	The acceptance decision itself (LedgerMaster::checkAccept)
ledger.store	caller of storeLedger	Persisting the ledger — a slow one points at the node store

Read it this way:

1. Get a candidate hash. From a slow point on any per-ledger panel, or from a log line, or by searching for the shape directly — for example an acquire that never finished: {name="ledger.acquire" && span.outcome="abandoned"}.
2. Open the whole trace with the query above. The spans appear as siblings, not as a parent/child chain. That is deliberate and is the honest shape: none of them directly causes another, and their order changes with the sync path — checkAccept is reached from a peer thread (an arriving validation), from the acquire-completion job, and from the consensus thread (switchLCL). A chain would assert an order that does not hold.
3. Compare the spans' durations, which is the point of having them in one trace: whether this ledger was slow to arrive, slow to be accepted, or slow to be stored is now one glance instead of three separate searches that cannot be correlated.
4. If a validation span has no ledger.validate beside it, check its accept_gated attribute. true means another thread was already accepting that ledger, so no acceptance followed this validation — that is normal, not a lost span.
5. If the trace holds only one span, the join is broken rather than the ledger being fast: confirm with the span.trace_join.per_ledger check in validate_telemetry.py, which fails CI on exactly this regression.

flowchart TD
    KEY["Ledger hash (32 bytes)<br/>trace_id = hash[0:16]"]

    KEY --> ACQ["ledger.acquire<br/>JtLedgerData worker<br/>network fetch"]
    KEY --> VAL["consensus.validation.accept<br/>validation worker<br/>trusted validation arrives"]
    KEY --> CHK["ledger.validate<br/>checkAccept<br/>acceptance decision"]
    KEY --> STO["ledger.store<br/>storeLedger<br/>persist"]

    classDef seed fill:#1f3b57,stroke:#8ab4d8,stroke-width:2px,color:#ffffff
    classDef span fill:#f2f6fa,stroke:#4a6f8a,stroke-width:1px,color:#12232e
    class KEY seed
    class ACQ,VAL,CHK,STO span

Two limits worth knowing. The join needs the ledger hash: a stage that has only a sequence number cannot join, so a by-sequence lookup made before the hash is known will not appear. And the trace id is the hash's leading 16 bytes only — the ledger_hash attribute carries all 32, which is how you confirm two spans are genuinely the same ledger rather than a trace-id coincidence.

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.