rippled/OpenTelemetryPlan/09-data-collection-reference.md

Observability Data Collection Reference

Audience: Developers and operators. This is the single source of truth for all telemetry data collected by xrpld's observability stack.

Related docs: docs/telemetry-runbook.md (operator runbook with alerting and troubleshooting) | 03-implementation-strategy.md (code structure and performance optimization) | 04-code-samples.md (C++ instrumentation examples)

Data Flow Overview

graph LR
    subgraph xrpldNode["xrpld Node"]
        A["Trace Macros<br/>XRPL_TRACE_SPAN<br/>(OTLP/HTTP exporter)"]
        B["beast::insight<br/>OTel native metrics<br/>(OTLP/HTTP exporter)"]
    end

    subgraph collector["OTel Collector  :4317 / :4318"]
        direction TB
        R1["OTLP Receiver<br/>:4317 gRPC  |  :4318 HTTP<br/>(traces + metrics)"]
        BP["Batch Processor<br/>timeout 1s, batch 100"]
        SM["SpanMetrics Connector<br/>derives RED metrics<br/>from trace spans"]

        R1 --> BP
        BP --> SM
    end

    subgraph backends["Trace Backend"]
        D["Grafana Tempo  :3200<br/>TraceQL search &<br/>S3/GCS long-term storage"]
    end

    subgraph metrics["Metrics Stack"]
        E["Prometheus  :9090<br/>scrapes :8889<br/>span-derived + system metrics"]
    end

    subgraph viz["Visualization"]
        F["Grafana  :3000<br/>10 dashboards"]
    end

    A -->|"OTLP/HTTP :4318<br/>(traces + attributes)"| R1
    B -->|"OTLP/HTTP :4318<br/>(gauges, counters, histograms)"| R1

    BP -->|"OTLP/gRPC :4317"| D

    SM -->|"span_calls_total<br/>span_duration_ms<br/>(6 dimension labels)"| E
    R1 -->|"* gauges<br/>* counters<br/>* histograms"| E

    E -->|"Prometheus<br/>data source"| F
    D -->|"Tempo<br/>data source"| F

    style A fill:#4a90d9,color:#fff,stroke:#2a6db5
    style B fill:#4a90d9,color:#fff,stroke:#2a6db5
    style R1 fill:#5cb85c,color:#fff,stroke:#3d8b3d
    style BP fill:#449d44,color:#fff,stroke:#2d6e2d
    style SM fill:#449d44,color:#fff,stroke:#2d6e2d
    style D fill:#f0ad4e,color:#000,stroke:#c78c2e
    style E fill:#f0ad4e,color:#000,stroke:#c78c2e
    style F fill:#5bc0de,color:#000,stroke:#3aa8c1
    style xrpldNode fill:#1a2633,color:#ccc,stroke:#4a90d9
    style collector fill:#1a3320,color:#ccc,stroke:#5cb85c
    style backends fill:#332a1a,color:#ccc,stroke:#f0ad4e
    style metrics fill:#332a1a,color:#ccc,stroke:#f0ad4e
    style viz fill:#1a2d33,color:#ccc,stroke:#5bc0de

There are two independent telemetry pipelines entering a single OTel Collector via the same OTLP receiver:

1. OpenTelemetry Traces — Distributed spans with attributes, exported via OTLP/HTTP (:4318) to the collector's OTLP Receiver. The Batch Processor groups spans (1s timeout, batch size 100) before forwarding to trace backends. The SpanMetrics Connector derives RED metrics (rate, errors, duration) from every span and feeds them into the metrics pipeline.
2. beast::insight OTel Metrics — System-level gauges, counters, and histograms exported natively via OTLP/HTTP (:4318) to the same OTLP Receiver. These are batched and exported to Prometheus alongside span-derived metrics. The StatsD UDP transport has been replaced by native OTLP; server=statsd remains available as a fallback.

A third, narrower metrics path exists for instruments created at their call site through the XRPL_METRIC_* macros. These use the OTel Metrics SDK directly and reach the collector's OTLP receiver rather than the StatsD receiver, so their names carry no xrpld_ prefix. See §2a. Code in libxrpl cannot use these macros and always goes through beast::insight instead.

Trace backend — The collector exports traces via OTLP/gRPC to:

• Grafana Tempo — Preferred trace backend. Supports TraceQL queries at :3200, S3/GCS object storage for cost-effective long-term trace retention, and integrates natively with Grafana.

Further reading: 00-tracing-fundamentals.md for core OpenTelemetry concepts (traces, spans, context propagation, sampling). 07-observability-backends.md for production backend selection, collector placement, and sampling strategies.

---

1. OpenTelemetry Spans

1.1 Complete Span Inventory (~36 spans)

See also: 02-design-decisions.md §2.3 for naming conventions and the full span catalog with rationale. 04-code-samples.md §4.6 for span flow diagrams.

Span names vs. attribute keys: span names use dotted subsystem.operation form (e.g. rpc.http_request). Span attribute keys use the bare/underscore form from the 2026-05-13 naming redesign (e.g. tx_hash, not xrpl.tx.hash). The dotted xrpl.* form is reserved for OTel resource attributes set once at startup. See §1.2 for the full attribute inventory.

RPC Spans

Controlled by trace_rpc=1 in [telemetry] config.

Span Name	Parent	Source File	Description
rpc.http_request	—	ServerHandler.cpp	Top-level HTTP JSON-RPC request entry point
rpc.ws_message	—	ServerHandler.cpp	WebSocket message handling (one per inbound frame)
rpc.ws_upgrade	—	ServerHandler.cpp	WebSocket upgrade handshake (records handshake failures)
rpc.process	rpc.http_request	ServerHandler.cpp	RPC processing pipeline (single or batch request)
rpc.command.<name>	rpc.process	RPCHandler.cpp	Per-command span (e.g., rpc.command.server_info, rpc.command.ledger)

Where to find: Tempo → TraceQL: {resource.service.name="xrpld" && name=~"rpc.http_request|rpc.command.*"}

Grafana dashboard: RPC Performance (rpc-performance)

gRPC Spans

Controlled by trace_rpc=1 in [telemetry] config.

Span Name	Parent	Source File	Description
grpc.<MethodName>	—	GRPCServer.cpp	One flat span per gRPC method (e.g., grpc.GetLedger, grpc.GetLedgerData, grpc.GetLedgerDiff, grpc.GetLedgerEntry)

The method name is embedded in the span name (formed at the call site as grpc.<MethodName>), so dashboards break out per-method latency and error rates without TraceQL attribute filters.

Where to find: Tempo → TraceQL: {resource.service.name="xrpld" && name=~"grpc.*"}

Grafana dashboard: RPC Performance (rpc-performance)

Transaction Spans

Controlled by trace_transactions=1 in [telemetry] config.

Span Name	Parent	Source File	Description
tx.process	—	NetworkOPs.cpp	Transaction submission entry point (local or peer-relayed)
tx.receive	—	PeerImp.cpp	Raw transaction received from peer overlay (before deduplication)
tx.apply	ledger.build	BuildLedger.cpp	Transaction set applied to new ledger during consensus
tx.preflight	—	applySteps.cpp	Stateless checks stage (stage=preflight)
tx.preclaim	—	applySteps.cpp	Ledger-aware checks stage before fee claim (stage=preclaim)
tx.transactor	—	Transactor.cpp	Apply stage — the transactor runs (stage=apply)

The three apply-pipeline spans share a deterministic trace_id derived from txID[0:16], so preflight, preclaim, and transactor for one transaction group under a single trace even though they run sequentially and often on different threads. A transaction that hard-fails preflight or preclaim never reaches the later spans — the stage attribute identifies where it stopped.

Deterministic roots are true roots. Spans with a deterministic trace_id (the tx.* apply pipeline, tx.process, tx.receive, and consensus.round) are emitted as genuine trace roots with an empty parent_span_id. The chosen trace_id is injected through a custom DeterministicIdGenerator on the SDK's no-parent branch, so there is no synthetic placeholder parent — Tempo shows a clean root, not a "root span not yet received" warning. Cross-node correlation still works because every node derives the same trace_id from the shared hash.

Log-trace correlation is retained across coroutines. OTel context storage is coroutine-aware (backed by LocalValue), so the active span travels with a coroutine across yield() and resumes on whatever thread the scheduler picks. RPC, consensus, and transaction spans therefore keep per-line log-trace correlation, and their scopes are safe across coroutine yields. Job-handoff spans — transaction apply and receive, and consensus accept — are activated inside their worker bodies rather than at enqueue, so each worker's log lines carry the span's trace context.

Where to find: Tempo → TraceQL: {resource.service.name="xrpld" && name=~"tx.process|tx.receive"} or, for the apply pipeline: {resource.service.name="xrpld" && name=~"tx.preflight|tx.preclaim|tx.transactor"}

Grafana dashboard: Transaction Overview (transaction-overview)

Transaction Queue (TxQ) Spans

Controlled by trace_transactions=1 in [telemetry] config.

Span Name	Parent	Source File	Description
txq.enqueue	tx.process (submission path; root on open-ledger rebuild)	TxQ.cpp	Queue admission decision (apply/queue/reject). Parents to tx.process via explicit context on submit; correlates via current_ledger_seq on all paths
txq.apply_direct	txq.enqueue	TxQ.cpp	Direct apply attempt that bypasses the queue
txq.batch_clear	txq.enqueue	TxQ.cpp	Batch clear of an account's queued txs
txq.accept	—	TxQ.cpp	Ledger-close accept loop (drains the queue)
txq.accept_tx	txq.accept	TxQ.cpp	Per-queued-transaction apply inside the accept loop
txq.cleanup	—	TxQ.cpp	Post-close cleanup of expired queue entries

Where to find: Tempo → TraceQL: {resource.service.name="xrpld" && name=~"txq.*"}

Grafana dashboard: Transaction Overview (transaction-overview)

Consensus Spans

Controlled by trace_consensus=1 in [telemetry] config.

Span Name	Parent	Source File	Description
consensus.round	— (root)	RCLConsensus.cpp	Root span for one consensus round (deterministic trace per round)
consensus.phase.open	consensus.round	Consensus.h	Open phase — collecting transactions before close
consensus.proposal.send	consensus.round	RCLConsensus.cpp	Node broadcasts its transaction set proposal
consensus.ledger_close	consensus.round	RCLConsensus.cpp	Ledger close event triggered by consensus
consensus.establish	consensus.round	Consensus.h	Establish phase — converging on the transaction set
consensus.update_positions	consensus.round	Consensus.h	Position update with per-dispute vote details
consensus.check	consensus.round	Consensus.h	Consensus threshold check (agree/disagree tally)
consensus.accept	consensus.round	RCLConsensus.cpp	Consensus accepts a ledger (round complete)
consensus.accept.apply	consensus.accept	RCLConsensus.cpp	Ledger application with close-time details (jtACCEPT thread)
consensus.validation.send	consensus.round	RCLConsensus.cpp	Validation message sent after ledger accepted (follows-from link)
consensus.mode_change	consensus.round	RCLConsensus.cpp	Operating-mode transition during the round
consensus.proposal.receive	(context)	PeerImp.cpp	Proposal received from a peer (context-propagated into the round)
consensus.validation.receive	(context)	PeerImp.cpp	Validation received from a peer (context-propagated into the round)

The .receive spans are created per-message in the overlay and joined to the round trace via context propagation rather than direct parenting. The consensus.validation.send span uses a follows-from link off the round.

Where to find: Tempo → TraceQL: {resource.service.name="xrpld" && name=~"consensus.*"}

Grafana dashboard: Consensus Health (consensus-health)

Ledger Spans

Controlled by trace_ledger=1 in [telemetry] config.

Span Name	Parent	Source File	Description
ledger.build	—	BuildLedger.cpp	Build new ledger from accepted transaction set
ledger.validate	—	LedgerMaster.cpp	Ledger promoted to validated status
ledger.store	—	LedgerMaster.cpp	Ledger stored to database/history

Where to find: Tempo → TraceQL: {resource.service.name="xrpld" && name=~"ledger.*"}

Grafana dashboard: Ledger Operations (ledger-operations)

Peer Spans

Controlled by trace_peer in [telemetry] config. Enabled by default (high volume).

Span Name	Parent	Source File	Description
peer.proposal.receive	—	PeerImp.cpp	Consensus proposal received from peer
peer.validation.receive	—	PeerImp.cpp	Validation message received from peer

A — parent means the span is a fresh trace root (kConsumer): it is started via ScopedSpanGuard::freshRoot() at the inbound-message entry point and never inherits an ambient span left active on the peer thread, so it does not nest under an unrelated transaction's trace.

Where to find: Tempo → TraceQL: {resource.service.name="xrpld" && name=~"peer.*"}

Grafana dashboard: Peer Network (peer-network)

PathFind Spans

Controlled by trace_rpc=1 in [telemetry] config.

Span Name	Parent	Source File	Description
pathfind.request	rpc.command.<name>	PathFind.cpp	path_find RPC entry (doPathFind)
pathfind.compute	pathfind.request	PathRequest.cpp	Path computation for one request (PathRequest::doUpdate)
pathfind.discover	pathfind.compute	Pathfinder.cpp	Graph exploration (one per RPC call)
pathfind.update_all	—	PathRequest.cpp	Async recomputation of all active requests at ledger close

Note

: pathfind.request nests under the active rpc.command.<name> span. Because OTel context storage is coroutine-aware (backed by LocalValue), the rpc.command.* scope stays correct even though its generic dispatch (callMethod) also wraps handlers such as doRipplePathFind whose span is held across a coroutine yield() — the ambient context travels with the coroutine when it resumes, so there is no wrong-thread scope pop. The pathfind.request → compute → discover sub-tree therefore parents to rpc.command.<name>, giving an exact request-to-pathfind nesting.

Where to find: Tempo → TraceQL: {resource.service.name="xrpld" && name=~"pathfind.*"}

---

1.2 Complete Attribute Inventory (bare/underscore keys)

See also: 02-design-decisions.md §2.4.2 for attribute design rationale and privacy considerations.

Every span can carry key-value attributes that provide context for filtering and aggregation. Per the 2026-05-13 naming redesign, span-attribute keys use the bare field name (the span name already carries the domain), or the <domain>_<field> underscore form where a bare name would collide (e.g. rpc_status, grpc_status, tx_status, txq_status).

Dotted keys are resource attributes, never span attributes:

• xrpl.network.id and xrpl.network.type are resource attributes set once at startup on the OTel resource — not span attributes. They appear on every span's resource scope, queried as {resource.xrpl.network.id=...}.
• The ledger hash uses the bare ledger_hash key on every span that records it (both consensus.validation.send and peer.validation.receive) — there is no dotted span attribute.

RPC Attributes

Attribute	Type	Set On	Description
command	string	rpc.command.*, rpc.ws_message	RPC command name (e.g., server_info, ledger)
version	int64	rpc.command.*	API version number
rpc_role	string	rpc.command.*	Caller role: "admin" or "user"
rpc_status	string	rpc.command.*	Result: "success" or "error"
request_payload_size	int64	rpc.http_request	Bytes of inbound request payload
is_batch	boolean	rpc.process	true if the request is a JSON-RPC batch
batch_size	int64	rpc.process	Number of sub-requests in a batch
load_type	string	rpc.command.*	Resource cost category after execution

Tempo query: {span.command="server_info"} to find all server_info calls.

Prometheus label: command (used as a SpanMetrics dimension).

gRPC Attributes

Attribute	Type	Set On	Description
method	string	grpc.<MethodName>	gRPC method name (e.g., GetLedger)
grpc_role	string	grpc.<MethodName>	Caller role: "admin" or "user"
grpc_status	string	grpc.<MethodName>	Result: "success" or "error"

Tempo query: {span.method="GetLedger"} or {name="grpc.GetLedger"}.

Prometheus labels: method, grpc_role, grpc_status (SpanMetrics dimensions).

Transaction Attributes

Attribute	Type	Set On	Description
tx_hash	string	tx.process, tx.receive	Transaction hash (hex-encoded)
local	boolean	tx.process	true if locally submitted, false if peer-relayed
path	string	tx.process	Submission path: "sync" or "async"
tx_type	string	tx.process, tx.preflight, tx.preclaim, tx.transactor	Transaction type name (e.g., Payment)
fee	int64	tx.process	Transaction fee in drops
sequence	int64	tx.process	Transaction sequence number
suppressed	boolean	tx.receive	true if transaction was suppressed (duplicate)
tx_status	string	tx.receive	Transaction status (e.g., "known_bad")
peer_id	int64	tx.receive	Peer identifier (also set on peer spans)
peer_version	string	tx.receive	Peer protocol version string
stage	string	tx.preflight, tx.preclaim, tx.transactor	Apply-pipeline stage: preflight, preclaim, or apply
ter_result	string	tx.preflight, tx.preclaim, tx.transactor	Engine result token for that stage (e.g., tesSUCCESS, terPRE_SEQ)
applied	boolean	tx.transactor	true if the transaction was applied to the ledger
current_ledger_seq	int64	tx.process, tx.receive, tx.preclaim, tx.transactor	Seq of the ledger being worked on (open/in-flight, not established) — joins the txID-keyed spans to the ledger trace
current_ledger_hash	string	tx.preclaim, tx.transactor	Parent hash of that ledger (= consensus.round trace-id seed on the build path). View-bearing stages only; tx.preflight omits both

Tempo query: {span.tx_hash="<hash>"} to trace a specific transaction across nodes. Join a transaction's work to its ledger with {span.current_ledger_seq=<N>}.

Prometheus labels: local, suppressed, tx_type, ter_result, stage (SpanMetrics dimensions).

Transaction Queue (TxQ) Attributes

Attribute	Type	Set On	Description
tx_hash	string	txq.enqueue, txq.accept_tx	Transaction hash
tx_type	string	txq.enqueue	Transaction type name
current_ledger_seq	int64	txq.enqueue	Seq of the ledger being worked on — correlates the enqueue to the ledger trace
current_ledger_hash	string	txq.enqueue	Parent hash of that ledger (= consensus.round trace-id seed on the build path)
txq_status	string	txq.enqueue, txq.accept_tx	Queue outcome (e.g. queued, applied_direct, rejected)
fee_level_paid	int64	txq.enqueue	Fee level paid by the queued tx
required_fee_level	int64	txq.enqueue	Minimum fee level for inclusion
num_cleared	int64	txq.batch_clear	Entries cleared in a batch
queue_size	int64	txq.accept	Current TxQ depth
ledger_changed	boolean	txq.accept	Whether the ledger changed since last attempt
ter_code	int64	txq.accept_tx	Transaction engine result code
retries_remaining	int64	txq.accept_tx	Retries left before discard
ledger_seq	int64	txq.cleanup	Ledger sequence number
expired_count	int64	txq.cleanup	Number of expired entries cleared

Prometheus label: txq_status (SpanMetrics dimension).

Consensus Attributes

Attribute	Type	Set On	Description
consensus_ledger_id	string	consensus.round	Previous-ledger id anchoring the round
ledger_seq	int64	consensus.round, consensus.ledger_close, consensus.accept.apply, consensus.validation.send	Ledger sequence number
consensus_mode	string	consensus.round, consensus.ledger_close	Node mode: "Proposing", "Observing", "Wrong", etc.
consensus_round_id	int64	consensus.round	Round identifier
consensus_phase	string	consensus.round	Current phase name (updated on each transition)
trace_strategy	string	consensus.round	Trace-id strategy (deterministic / random)
previous_ledger_seq	int64	consensus.round	Sequence of the previous ledger
previous_proposers	int64	consensus.round	Proposer count in the previous round
previous_round_time_ms	int64	consensus.round	Duration of the previous round
consensus_round	int64	consensus.proposal.send	Proposal sequence number for the broadcast proposal
is_bow_out	boolean	consensus.proposal.send	Whether the proposal is a bow-out (resigning the round)
tx_count_open	int64	consensus.ledger_close	Transactions in the open ledger at close
close_time_resolution_ms	int64	consensus.ledger_close	Close-time rounding granularity
converge_percent	int64	consensus.establish, consensus.update_positions	Convergence percentage
establish_count	int64	consensus.establish	Establish-phase iteration count
proposers	int64	consensus.establish, consensus.update_positions, consensus.accept	Number of proposers
disputes_count	int64	consensus.establish, consensus.update_positions	Number of disputed transactions
tx_id	string	consensus.update_positions	Disputed transaction id (per-dispute event)
dispute_our_vote	boolean	consensus.update_positions	Our vote on the disputed tx
dispute_yays	int64	consensus.update_positions	Yes votes on the disputed tx
dispute_nays	int64	consensus.update_positions	No votes on the disputed tx
agree_count	int64	consensus.check	Agreeing proposer count
disagree_count	int64	consensus.check	Disagreeing proposer count
threshold_percent	int64	consensus.check	Agreement threshold percentage
consensus_result	string	consensus.check	Check outcome
quorum	int64	consensus.check, consensus.accept	Quorum required
round_time_ms	int64	consensus.accept, consensus.accept.apply	Total consensus round duration in milliseconds
consensus_state	string	consensus.accept.apply	Consensus outcome: "finished" or "moved_on"
close_time	int64	consensus.accept.apply	Agreed-upon ledger close time (epoch seconds)
close_time_correct	boolean	consensus.accept.apply	Whether validators agreed on close time
close_resolution_ms	int64	consensus.accept.apply	Close-time rounding granularity in milliseconds
proposing	boolean	consensus.accept.apply, consensus.validation.send	Whether this node was a proposer
parent_close_time	int64	consensus.accept.apply	Parent ledger close time
close_time_self	int64	consensus.accept.apply	This node's close-time vote
close_time_vote_bins	string	consensus.accept.apply	Distribution of close-time votes
resolution_direction	string	consensus.accept.apply	Whether close resolution increased/decreased/unchanged
tx_count	int64	consensus.accept.apply	Transactions in the accepted set
ledger_hash	string	consensus.validation.send	Full hash of the validated ledger (shared with peer)
full_validation	boolean	consensus.validation.send	Whether this is a full validation
validation_sign_time	int64	consensus.validation.send	Validation signing time
mode_old	string	consensus.mode_change	Operating mode before the transition
mode_new	string	consensus.mode_change	Operating mode after the transition

Tempo query: {span.consensus_mode="Proposing"} to find rounds where the node was proposing.

Prometheus labels: consensus_mode, consensus_state, consensus_phase, consensus_result, consensus_stalled, mode_new, close_time_correct (SpanMetrics dimensions).

Ledger Attributes

Attribute	Type	Set On	Description
ledger_seq	int64	ledger.build, ledger.validate, ledger.store	Ledger sequence number
close_time	int64	ledger.build	Ledger close time (epoch seconds)
close_time_correct	boolean	ledger.build	Whether close time was agreed upon by validators
close_resolution_ms	int64	ledger.build	Close time rounding granularity in milliseconds
tx_count	int64	tx.apply	Transactions applied to the ledger
tx_failed	int64	tx.apply	Failed transactions in the apply set
validations	int64	ledger.validate	Number of validations received for this ledger

The apply-step span tx.apply (child of ledger.build) carries tx_count/tx_failed; the parent ledger.build carries ledger_seq and the close-time attributes.

Tempo query: {span.ledger_seq=12345} to find all spans for a specific ledger.

Peer Attributes

Attribute	Type	Set On	Description
peer_id	int64	tx.receive, peer.proposal.receive, peer.validation.receive	Peer identifier
proposal_trusted	boolean	peer.proposal.receive	Whether the proposal came from a trusted validator
validation_trusted	boolean	peer.validation.receive	Whether the validation came from a trusted validator
full_validation	boolean	peer.validation.receive	Whether the validation is a full validation
ledger_hash	string	peer.validation.receive	Validated ledger hash (shared with consensus spans)

Prometheus labels: proposal_trusted, validation_trusted (SpanMetrics dimensions).

PathFind Attributes

Attribute	Type	Set On	Description
pathfind_source_account	string	pathfind.request	Originating account for the path search
pathfind_dest_account	string	pathfind.request	Destination account
pathfind_fast	boolean	pathfind.compute	Whether fast pathfinding mode is enabled
pathfind_search_level	int64	pathfind.discover	Depth of graph exploration
pathfind_num_paths	int64	pathfind.discover	Total paths produced
pathfind_ledger_index	int64	pathfind.update_all	Target ledger index
pathfind_num_requests	int64	pathfind.update_all	Active requests recomputed

---

1.3 SpanMetrics — Derived Prometheus Metrics

See also: 01-architecture-analysis.md §1.8.2 for how span-derived metrics map to operational insights.

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

Prometheus Metric	Type	Description
span_calls_total	Counter	Total span invocations
span_duration_milliseconds_bucket	Histogram	Latency distribution (buckets: 1, 5, 10, 25, 50, 100, 250, 500, 1000, 5000 ms)
span_duration_milliseconds_count	Histogram	Observation count
span_duration_milliseconds_sum	Histogram	Cumulative latency

Standard labels on every metric: span_name, status_code, service_name, span_kind

Additional dimension labels (configured in otel-collector-config.yaml). The Prometheus label is the bare span-attribute key verbatim — the SpanMetrics connector does not rewrite or prefix it:

Prometheus Label / Span Attribute	Type	Applies To
command	string	rpc.command.*
rpc_status	string	rpc.command.*
consensus_mode	string	consensus.round, consensus.ledger_close
close_time_correct	boolean	consensus.accept.apply
local	boolean	tx.process
suppressed	boolean	tx.receive
proposal_trusted	boolean	peer.proposal.receive
validation_trusted	boolean	peer.validation.receive
tx_type	string	tx.*, txq.enqueue
ter_result	string	tx.preflight, tx.preclaim, tx.transactor
stage	string	tx.preflight, tx.preclaim, tx.transactor
txq_status	string	txq.enqueue, txq.accept_tx
consensus_state	string	consensus.accept.apply
load_type	string	rpc.command.*
is_batch	boolean	rpc.process
mode_new	string	consensus.mode_change
consensus_stalled	boolean	consensus.check
consensus_phase	string	consensus.round
consensus_result	string	consensus.check
method	string	grpc.<MethodName>
grpc_role	string	grpc.<MethodName>
grpc_status	string	grpc.<MethodName>

The stage dimension (3 values: preflight, preclaim, apply) turns the apply-pipeline spans into per-stage RED metrics with no native instruments — the Transaction Overview dashboard charts rate, p95 latency, and failure rate by stage.

Sampling caveat: xrpld head sampling is fixed at 1.0 (every trace is recorded), so span-derived metrics are not undercounted at the node. If the collector is configured with tail sampling, span-derived metrics reflect only the retained traces, whereas native StatsD/meter metrics do not sample. Account for any collector-side tail sampling when reading absolute stage rates.

Where to query: Prometheus → span_calls_total{span_name="rpc.command.server_info"}

---

2. System Metrics (beast::insight — OTel native)

See also: 02-design-decisions.md for the beast::insight coexistence design. 06-implementation-phases.md for the Phase 6/7 metric inventory.

Migration complete: Phase 7 replaced the StatsD UDP transport with native OTel Metrics SDK export via OTLP/HTTP. The beast::insight::Collector interface and all metric names are preserved — only the wire protocol changed. [insight] server=statsd remains as a fallback.

These are system-level metrics emitted by xrpld's beast::insight framework via OTel OTLP/HTTP. They cover operational data that doesn't map to individual trace spans.

Configuration

# Recommended: native OTel metrics via OTLP/HTTP
[insight]
server=otel
endpoint=http://localhost:4318/v1/metrics
prefix=xrpld

Fallback (StatsD):

[insight]
server=statsd
address=127.0.0.1:8125
prefix=xrpld

2.1 Gauges

Prometheus Metric	Source File	Description	Typical Range
ledgermaster_validated_ledger_age	LedgerMaster.h	Seconds since last validated ledger	0–10 (healthy), >30 (stale)
ledgermaster_published_ledger_age	LedgerMaster.h	Seconds since last published ledger	0–10 (healthy)
state_accounting_disconnected_duration	NetworkOPs.cpp	Cumulative seconds in Disconnected state	Monotonic
state_accounting_connected_duration	NetworkOPs.cpp	Cumulative seconds in Connected state	Monotonic
state_accounting_syncing_duration	NetworkOPs.cpp	Cumulative seconds in Syncing state	Monotonic
state_accounting_tracking_duration	NetworkOPs.cpp	Cumulative seconds in Tracking state	Monotonic
state_accounting_full_duration	NetworkOPs.cpp	Cumulative seconds in Full state	Monotonic (should dominate)
state_accounting_disconnected_transitions	NetworkOPs.cpp	Count of transitions to Disconnected	Low
state_accounting_connected_transitions	NetworkOPs.cpp	Count of transitions to Connected	Low
state_accounting_syncing_transitions	NetworkOPs.cpp	Count of transitions to Syncing	Low
state_accounting_tracking_transitions	NetworkOPs.cpp	Count of transitions to Tracking	Low
state_accounting_full_transitions	NetworkOPs.cpp	Count of transitions to Full	Low (should be 1 after startup)
peer_finder_active_inbound_peers	PeerfinderManager.cpp	Active inbound peer connections	0–85
peer_finder_active_outbound_peers	PeerfinderManager.cpp	Active outbound peer connections	10–21
overlay_peer_disconnects	OverlayImpl.cpp	Cumulative peer disconnection count	Low growth
overlay_peer_disconnects_charges	OverlayImpl.cpp	Disconnects due to resource limit charges	Low growth (subset of above)
job_count	JobQueue.cpp	Current job queue depth	0–100 (healthy)

Grafana dashboard: Node Health (node-health)

2.2 Counters

Prometheus Metric	Source File	Description
rpc_requests	ServerHandler.cpp	Total RPC requests received
ledger_fetches	InboundLedgers.cpp	Inbound ledger fetch attempts
ledger_history_mismatch	LedgerHistory.cpp	Ledger hash mismatches detected
warn	Logic.h	Resource manager warnings issued
drop	Logic.h	Resource manager drops (connections rejected)

Note: With server=otel, warn and drop are properly exported as OTel Counter instruments. The previous StatsD |m type limitation no longer applies.

Grafana dashboard: RPC & Pathfinding (rpc-pathfinding)

2.3 Histograms (Event timers)

Prometheus Metric	Source File	Unit	Description
rpc_time	ServerHandler.cpp	ms	RPC response time distribution
rpc_size	ServerHandler.cpp	ms*	RPC response size (see note)
ios_latency	Application.cpp	ms	I/O service loop latency
pathfind_fast	PathRequests.h	ms	Fast pathfinding duration
pathfind_full	PathRequests.h	ms	Full pathfinding duration

Quantiles collected: 0th, 50th, 90th, 95th, 99th, 100th percentile.

* rpc_size instrument mismatch (known issue): response size in bytes is recorded through the millisecond-scaled event histogram (makeEvent), so it is exported as rpc_size_milliseconds_bucket with time-scaled boundaries that top out at 5000. Byte values above ~5 KB saturate in the last bucket, so the percentiles are not true byte sizes. The RPC & Pathfinding panel is flagged accordingly. A dedicated byte-unit histogram is needed to fix this; tracked separately.

Grafana dashboards: Node Health (ios_latency), RPC & Pathfinding (rpc_time, rpc_size, pathfind_*)

2.4 Overlay Traffic Metrics

For each of the 45+ overlay traffic categories (defined in TrafficCount.h), four gauges are emitted:

• {category}_bytes_in
• {category}_bytes_out
• {category}_messages_in
• {category}_messages_out

Key categories:

Category	Description
total	All traffic aggregated
overhead / overhead_overlay	Protocol overhead
transactions / transactions_duplicate	Transaction relay
proposals / proposals_untrusted / proposals_duplicate	Consensus proposals
validations / validations_untrusted / validations_duplicate	Consensus validations
ledger_data_get / ledger_data_share	Ledger data exchange
ledger_data_Transaction_Node_get/share	Transaction node data
ledger_data_Account_State_Node_get/share	Account state node data
ledger_data_Transaction_Set_candidate_get/share	Transaction set candidates
getObject / haveTxSet / ledgerData	Object requests
ping / status	Keepalive and status
set_get	Set requests

Grafana dashboards: Network Traffic (network-traffic), Overlay Traffic Detail (overlay-traffic-detail), Ledger Data & Sync (ledger-data-sync)

2.5 Per-Job-Type Saturation Gauges

JobTypeData has always tracked three per-type counters — waiting, running, and deferred (include/xrpl/core/JobTypeData.h) — but only the process-wide queue depth was exported. These three gauge families export them per job type, so queue pressure can be attributed to a type instead of only being visible as a single total.

For each of the 35 non-special job types, three gauges are created in the JobTypeData constructor and assigned in JobQueue::collect().

Metric	Source File	Description
jobq_{jobtype}_waiting	JobTypeData.h	Jobs enqueued for this type but not yet started
jobq_{jobtype}_running	JobTypeData.h	Jobs of this type currently executing
jobq_{jobtype}_deferred	JobTypeData.h	Jobs held back by this type's concurrency limit

Why deferred matters most. JobQueue::addJob() never rejects — when a type is at its concurrency limit it increments deferred and returns true (JobQueue.cpp, addRefCountedJob). Backpressure on a capped type therefore shows up only as latency, after the delay has already happened. A non-zero deferred reading precedes that, which makes it the leading indicator for a saturating type. It is structurally always zero for special types, which is why they are excluded — the same !info.special() guard that already gates the event timers.

Name derivation. These names are not literals anywhere in the source; the jobq segment is added at runtime. Application.cpp constructs the JobQueue with collectorManager_->group("jobq"), and GroupImp::makeName() (Groups.cpp) joins the group name and the metric name with a .. OTelCollector then lowercases the result and maps . to _, so JtLedgerReq (whose type name is ledgerRequest) exports as jobq_ledgerrequest_deferred. The same derivation applies to the event timers in §2.3 and to the queue-depth gauge in §2.1, which is why the latter appears as jobq_job_count rather than job_count.

Sampling granularity: these are gauges read by the collector hook once per second, not counters incremented at the event. A deferred spike shorter than the sampling interval can be missed. Saturation long enough to delay ledger sync persists across a sample, which is the case these gauges are for.

Pipeline: JobQueue.cpp is in libxrpl and reaches the exporter through beast::insight::Collector, like every other metric in this section. It cannot use the xrpld-only call-site metric macros used by §2a.

---

2a. Call-Site OTel Metrics (MetricsRegistry)

See also: §2 for the beast::insight metrics that use the StatsD transport instead.

Every metric in §2 goes through beast::insight. The metrics below are created directly at their call site through the XRPL_METRIC_* macros (src/xrpld/telemetry/MetricMacros.h) and exported by the OTel Metrics SDK rather than over StatsD. The macros create each instrument once and expand to an empty statement when telemetry is compiled out, so a call site needs no conditional compilation.

These names carry no xrpld_ prefix: the prefix belongs to the StatsD sender, and this path identifies the node through OTel resource attributes instead.

Note

: this family is emitted by MetricsRegistry, which arrives with the native-metrics work in a later phase than this document's own StatsD pipeline. It is catalogued here because this document is the single source of truth for the whole telemetry surface.

2a.1 Job Lifecycle Metrics

Recorded from the three PerfLog job hooks that JobQueue already calls.

Metric	Type	Unit	Labels	Description
job_queued_total	Counter	—	job_type, handler	Jobs enqueued
job_started_total	Counter	—	job_type, handler	Jobs started
job_finished_total	Counter	—	job_type, handler	Jobs completed
job_queued_us	Histogram	µs	job_type, handler	Queue wait time distribution
job_running_us	Histogram	µs	job_type, handler	Execution time distribution

job_queued_us and job_running_us register explicit microsecond buckets spanning 100 µs to 60 s. Without that view the SDK default buckets stop at 10 ms and every quantile reads as 10 ms.

The handler Label

job_type names the queue a job ran on, not who produced it. Several producers share one type — RcvGetLedger and RcvGetObjByHash both run as JtLedgerReq, and JtLedgerData has five (ProcessLData and GotStaleData in InboundLedgers.cpp, AcqDone and the InboundLedger TimeoutCounter job name in InboundLedger.cpp, and GotFetchPack in LedgerMaster.cpp) — so a queue-wait spike cannot be pinned to one of them by job_type alone. The handler label carries the name passed to addJob, which does distinguish them.

The value is not the raw name. It passes through MetricsRegistry::sanitiseHandler, which keeps the name only when it is non-empty and every character is an ASCII letter, and otherwise returns the literal other. The letter test is an explicit ASCII range check rather than a locale-dependent one, so it cannot admit non-ASCII bytes.

Why reduce the name. Two addJob names embed a ledger sequence number: LedgerPersistence.cpp builds "Pub" + std::to_string(ledger->seq()), and OrderBookDBImpl.cpp builds "OB" + std::to_string(ledger->seq() % 1000000000). Used raw, either would mint a new time series for every ledger. Both always contain digits, so both fold to other.

Every other production job name is a compile-time literal, which makes the label domain a function of the source rather than of traffic — it cannot grow at runtime. Names that are literals but still fail the rule also fold to other: GetConsL1 and GetConsL2 contain digits, and the coroutine names RPC-Client, WS-Client, and gRPC-Client contain a hyphen. A job name added later that fails the rule degrades to other rather than becoming unbounded, which is a stronger guarantee than a hand-maintained allowlist.

2a.2 GetObject Request Metrics

Emitted from the TMGetObjectByHash handler in src/xrpld/overlay/detail/PeerImp.cpp. Together with job_queued_us and job_running_us filtered to handler="RcvGetObjByHash", they split the handler's end-to-end time into queue wait, NodeStore lookup, and everything else — so slowness is attributable to a stage rather than merely observed.

Metric	Type	Unit	Labels	Description
getobject_lookup_us	Histogram	µs	—	Time in the NodeStore fetch loop
getobject_request_objects	Histogram	—	—	Objects requested per message
getobject_lookups_total	Counter	—	result	NodeStore lookup outcomes
getobject_rejected_total	Counter	—	reason	Requests refused before any NodeStore access
getobject_charge	Histogram	—	—	Dynamic resource charge applied to the request

Label	Values	Meaning
result	hit, miss	Whether the requested object was found in the NodeStore
reason	oversize, malformed_ledgerhash	Which admission check refused the request

Aggregation. getobject_lookup_us times the fetch loop once per request, and getobject_lookups_total is added once per request carrying the batch hit and miss totals. The loop is bounded by Tuning::kHardMaxReplyNodes (12288), so timing or counting each iteration would cost more than the lookups it measures. getobject_request_objects records the requested count, which is what the charge bands price on. getobject_charge records only the dynamic component returned by computeGetObjectByHashFee(); the admission-time base charge is a constant.

Bucket view required: getobject_lookup_us needs an explicit microsecond-bucket view like the two job histograms above. Without one the SDK default buckets stop at 10 ms, and a lookup loop running toward its 12288 cap exceeds that routinely — so the metric would saturate exactly when it is needed. The view is registered alongside the other three in MetricsRegistry.cpp. Note that MetricsRegistry does not exist on this branch — it is introduced on phase-9, which is where the registration lives.

Expected to read zero: getobject_rejected_total counts non-conforming requests, which a healthy peer does not send. A zero series does not prove the metric works — verify it with a deliberately oversized request.

---

3. Grafana Dashboard Reference

See also: 05-configuration-reference.md §5.8 for Grafana data source provisioning (Tempo, Prometheus) and TraceQL query examples.

3.1 Span-Derived Dashboards (5)

Dashboard	UID	Data Source	Key Panels
RPC Performance	rpc-performance	Prometheus (SpanMetrics)	Request rate by command, p95 latency by command, error rate, heatmap, top commands
Transaction Overview	transaction-overview	Prometheus (SpanMetrics)	Processing rate, latency p95/p50, local vs relay split, apply duration, heatmap
Consensus Health	consensus-health	Prometheus (SpanMetrics)	Round duration p95/p50, proposals rate, close duration, mode timeline, heatmap
Ledger Operations	ledger-operations	Prometheus (SpanMetrics)	Build rate, build duration, validation rate, store rate, build vs close comparison
Peer Network	peer-network	Prometheus (SpanMetrics)	Proposal receive rate, validation receive rate, trusted vs untrusted breakdown

3.2 System Metrics Dashboards (5)

Dashboard	UID	Data Source	Key Panels
Node Health	node-health	Prometheus (OTLP)	Ledger age, operating mode, I/O latency, job queue, fetch rate
Network Traffic	network-traffic	Prometheus (OTLP)	Active peers, disconnects, bytes in/out, messages in/out, traffic by category
RPC & Pathfinding	rpc-pathfinding	Prometheus (OTLP)	RPC rate, response time/size, pathfinding duration, resource warnings/drops
Overlay Traffic Detail	overlay-traffic-detail	Prometheus (OTLP)	Squelch, overhead, validator lists, set get/share, have/requested tx, proof paths
Ledger Data & Sync	ledger-data-sync	Prometheus (OTLP)	Ledger data exchange, legacy ledger share/get, getobject by type, traffic heatmap

3.3 Deployment-Tier Template Variables

Every dashboard carries four filtering template variables (each variable name matches its Prometheus label), letting one Grafana stack be sliced by tier:

Variable	Source label	Description
$node	exported_instance	Filter by xrpld node instance
$service_name	service_name	Filter by service (service.name, e.g. xrpld)
$deployment_environment	deployment_environment	Filter by deployment tier (local / test / ci / prod)
$xrpl_network_type	xrpl_network_type	Filter by network (mainnet / testnet / devnet)

See telemetry-runbook.md "Deployment Tiers" for how the tier attributes are set and reach metrics.

3.4 Accessing the Dashboards

1. Open Grafana at http://localhost:3000
2. Navigate to Dashboards → xrpld folder
3. All 10 dashboards are auto-provisioned from docker/telemetry/grafana/dashboards/

---

4. Tempo Trace Search Guide

See also: 08-appendix.md §8.2 for span hierarchy visualizations. 05-configuration-reference.md §5.8.5 for TraceQL query examples.

Finding Traces by Type

What to Find	Tempo TraceQL Query
All RPC calls	{resource.service.name="xrpld" && name="rpc.http_request"}
Specific RPC command	{resource.service.name="xrpld" && name="rpc.command.server_info"}
Slow RPC calls	{resource.service.name="xrpld" && name=~"rpc.command.*"} | duration > 100ms
Failed RPC calls	{span.rpc_status="error"}
gRPC method calls	{resource.service.name="xrpld" && name="grpc.GetLedger"}
Specific transaction	{span.tx_hash="<hex_hash>"}
Local transactions only	{span.local=true}
Consensus rounds	{resource.service.name="xrpld" && name="consensus.round"}
Rounds by mode	{span.consensus_mode="Proposing"}
Specific ledger	{span.ledger_seq=12345}
Peer proposals (trusted)	{span.proposal_trusted=true}

Trace Structure

A typical RPC trace shows the span hierarchy:

rpc.http_request (ServerHandler)
  └── rpc.process (ServerHandler)
       └── rpc.command.server_info (RPCHandler)

A consensus round groups its lifecycle spans under a single root (consensus.round); the build/ledger spans run as their own trees:

consensus.round                    (root — one per round)
  ├── consensus.phase.open         (open phase)
  ├── consensus.proposal.send      (broadcast proposal)
  ├── consensus.ledger_close       (close event)
  ├── consensus.establish          (establish phase)
  ├── consensus.update_positions   (position updates)
  ├── consensus.check              (threshold check)
  ├── consensus.accept             (accept result)
  │     └── consensus.accept.apply (apply, jtACCEPT thread)
  └── consensus.validation.send    (send validation, follows-from link)

ledger.build                       (build new ledger)
  └── tx.apply                     (apply transaction set)
ledger.validate                    (promote to validated)
ledger.store                       (persist to DB)

---

5. Prometheus Query Examples

See also: 05-configuration-reference.md §5.8.7 for correlating Prometheus system metrics with trace-derived metrics.

Span-Derived Metrics

# RPC request rate by command (last 5 minutes)
sum by (command) (rate(span_calls_total{span_name=~"rpc.command.*"}[5m]))

# RPC p95 latency by command
histogram_quantile(0.95, sum by (le, command) (rate(span_duration_milliseconds_bucket{span_name=~"rpc.command.*"}[5m])))

# Consensus round duration p95
histogram_quantile(0.95, sum by (le) (rate(span_duration_milliseconds_bucket{span_name="consensus.round"}[5m])))

# Transaction processing rate (local vs relay)
sum by (local) (rate(span_calls_total{span_name="tx.process"}[5m]))

# Trusted vs untrusted proposal rate
sum by (proposal_trusted) (rate(span_calls_total{span_name="peer.proposal.receive"}[5m]))

StatsD Metrics

# Validated ledger age (should be < 10s)
ledgermaster_validated_ledger_age

# Active peer count
peer_finder_active_inbound_peers + peer_finder_active_outbound_peers

# RPC response time p95
histogram_quantile(0.95, rpc_time_bucket)

# Total network bytes in (rate)
rate(total_bytes_in[5m])

# Operating mode (should be "Full" after startup)
state_accounting_full_duration

---

5a. Log-Trace Correlation (Phase 8)

Plan details: 06-implementation-phases.md §6.8.1 — motivation, architecture, Mermaid diagrams Task breakdown: Phase8_taskList.md — per-task implementation details

Phase 8 injects OTel trace context into xrpld's Logs::format() output, enabling log-trace correlation. When a log line is emitted within an active OTel span, the trace and span identifiers are automatically appended after the severity field:

Log Format

<timestamp> <partition>:<severity> trace_id=<32hex> span_id=<16hex> <message>

Example:

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

• trace_id=<hex32> — 32-character lowercase hex trace identifier. Links to the distributed trace in Tempo/Jaeger.
• span_id=<hex16> — 16-character lowercase hex span identifier. Identifies the specific span within the trace.
• Only present when the log is emitted within an active OTel span. Log lines outside of traced code paths have no trace context fields.

Implementation

The trace context injection is implemented in Logs::format() (src/libxrpl/basics/Log.cpp), guarded by #ifdef XRPL_ENABLE_TELEMETRY. It checks the thread-local runtime context value directly (via RuntimeContext::GetCurrent().GetValue(kSpanKey)) to avoid the heap allocation that GetSpan() performs on the no-span path. On threads without an active span, the cost is a thread-local read + variant type check (~15-20ns). On the active-span path, total cost is ~50ns per log call.

Log Ingestion Pipeline

xrpld debug.log -> OTel Collector filelog receiver -> regex_parser -> Loki exporter -> Grafana Loki

The OTel Collector's filelog receiver tails debug.log files and uses a regex_parser operator to extract structured fields:

Field	Type	Description
timestamp	datetime	Log timestamp
partition	string	Log partition (e.g., LedgerMaster, PeerImp)
severity	string	Severity code (TRC, DBG, NFO, WRN, ERR, FTL)
trace_id	string	32-hex trace identifier (optional)
span_id	string	16-hex span identifier (optional)
message	string	Log message body

Grafana Correlation

Bidirectional linking between logs and traces is configured via Grafana datasource provisioning:

• Tempo -> Loki (tracesToLogs): Clicking "Logs for this trace" on a Tempo trace view filters Loki logs by trace_id, showing all log lines from that trace.
• Loki -> Tempo (derivedFields): A regex-based derived field on the Loki datasource extracts trace_id from log lines and renders it as a clickable link to the corresponding trace in Tempo.

Loki Backend

Grafana Loki (v3.4.2) serves as the log storage backend. It receives log entries from the OTel Collector's otlphttp/loki exporter via the native OTLP endpoint at http://loki:3100/otlp.

LogQL Query Examples

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

# Error logs with trace context
{job="xrpld"} |= "ERR" |= "trace_id="

# Logs from a specific partition with trace context
{job="xrpld"} |= "LedgerMaster" | regexp `trace_id=(?P<trace_id>[a-f0-9]+)` | trace_id != ""

# Count traced log lines over time
count_over_time({job="xrpld"} |= "trace_id=" [5m])

---

6. Known Issues

Issue	Impact	Status
warn and drop metrics use non-standard StatsD |m meter type	Metrics silently dropped by OTel StatsD receiver	Phase 6 Task 6.1 — needs |m → |c change in StatsDCollector.cpp
job_count may not emit in standalone mode	Missing from Prometheus in some test configs	Requires active job queue activity
rpc_requests depends on [insight] config	Zero series if StatsD not configured	Requires [insight] server=statsd in xrpld.cfg
Peer tracing enabled by default	peer.* spans emit unless trace_peer=0	High volume — set trace_peer=0 to opt out on busy mainnet nodes

---

7. Privacy and Data Collection

The telemetry system is designed with privacy in mind:

• No private keys are ever included in spans or metrics
• No account balances or financial data is traced
• Transaction hashes are included (public on-ledger data) but not transaction contents
• Peer IDs are internal identifiers, not IP addresses
• All telemetry is opt-in — disabled by default at build time (-Dtelemetry=OFF)
• Sampling — head sampling is fixed at 1.0 (sample everything); reduce data volume with collector-side tail sampling
• Data stays local — the default stack sends data to localhost only

---

8. Configuration Quick Reference

Full reference: 05-configuration-reference.md §5.1 for all [telemetry] options with defaults, the config parser implementation, and collector YAML configurations (dev and production).

Minimal Setup (development)

[telemetry]
enabled=1

[insight]
server=statsd
address=127.0.0.1:8125
prefix=xrpld

Production Setup

[telemetry]
enabled=1
endpoint=http://otel-collector:4318/v1/traces
trace_peer=0
batch_size=1024
max_queue_size=4096

[insight]
server=statsd
address=otel-collector:8125
prefix=xrpld

Trace Category Toggle

Config Key	Default	Controls
trace_rpc	1	rpc.* spans
trace_transactions	1	tx.* spans
trace_consensus	1	consensus.* spans
trace_ledger	1	ledger.* spans
trace_peer	1	peer.* spans (high volume)