# xrpld Telemetry Operator Runbook ## 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](build/telemetry.md). ## Quick Start ### 1. Start the observability stack ```bash 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`: ```ini [telemetry] enabled=1 endpoint=http://localhost:4318/v1/traces ``` ### 3. Build with telemetry support ```bash conan install . --build=missing -o telemetry=True cmake --preset default -Dtelemetry=ON cmake --build --preset default ``` ## 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` | ## 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.` | RPCHandler.cpp | `command`, `version`, `rpc_role`, `rpc_status`, `load_type` | Per-command span (e.g., `rpc.command.server_info`) | ### Transaction Spans | Span Name | Source File | Attributes | Description | | --------------- | --------------- | --------------------------------------------------------------------------------- | ------------------------------------- | | `tx.process` | NetworkOPs.cpp | `tx_hash`, `local`, `path`, `tx_type`, `fee`, `sequence`, `ter_result`, `applied` | Transaction submission and processing | | `tx.receive` | PeerImp.cpp | `peer_id`, `tx_hash`, `tx_type`, `peer_version`, `suppressed`, `tx_status` | Transaction received from peer relay | | `tx.apply` | BuildLedger.cpp | `ledger_seq`, `tx_count`, `tx_failed` | Transaction set applied per ledger | | `tx.preflight` | applySteps.cpp | `stage`, `tx_type`, `ter_result` | Stateless checks stage | | `tx.preclaim` | applySteps.cpp | `stage`, `tx_type`, `ter_result` | Ledger-aware checks stage | | `tx.transactor` | Transactor.cpp | `stage`, `tx_type`, `ter_result`, `applied` | Apply stage (transactor runs) | The three apply-pipeline spans (`tx.preflight`, `tx.preclaim`, `tx.transactor`) share a deterministic `trace_id` from `txID[0:16]`, so they group under one trace per transaction. The `stage` attribute (`preflight` / `preclaim` / `apply`) drives the collector spanmetrics `stage` dimension, giving per-stage RED metrics on the _Transaction Overview_ dashboard. ### Transaction Queue Spans | Span Name | Source File | Attributes | Description | | ------------------ | ----------- | -------------------------------------------------------- | -------------------------------------------------- | | `txq.enqueue` | TxQ.cpp | `tx_hash`, `tx_type` | Transaction enqueue decision (child of tx.process) | | `txq.apply_direct` | TxQ.cpp | -- | Direct apply attempt (bypassing queue) | | `txq.batch_clear` | TxQ.cpp | -- | Batch clear of queued transactions for an account | | `txq.accept` | TxQ.cpp | `queue_size`, `ledger_changed` | Ledger-close accept loop over queued transactions | | `txq.accept_tx` | TxQ.cpp | `tx_hash`, `retries_remaining`, `ter_code`, `txq_status` | Per-transaction apply during accept | | `txq.cleanup` | TxQ.cpp | `ledger_seq` | Post-close cleanup of expired queue entries | ### PathFinding Spans | Span Name | Source File | Attributes | Description | | --------------------- | --------------------------------- | -------------------------------------------------- | ------------------------------------------------------- | | `pathfind.request` | PathFind.cpp / RipplePathFind.cpp | `pathfind_source_account`, `pathfind_dest_account` | Path-find RPC entry (accounts hashed; set when present) | | `pathfind.compute` | PathRequest.cpp | `pathfind_fast`, `pathfind_dest_currency` | Path computation for one request (`doUpdate`) | | `pathfind.discover` | PathRequest.cpp | `pathfind_search_level`, `pathfind_num_paths` | Graph exploration (one per RPC call in `findPaths`) | | `pathfind.update_all` | PathRequestManager.cpp | `pathfind_ledger_index`, `pathfind_num_requests` | Async recomputation of active requests on ledger close | ### Consensus Spans | Span Name | Source File | Attributes | Description | | ------------------------------ | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- | | `consensus.round` | RCLConsensus.cpp | `consensus_ledger_id`, `ledger_seq`, `consensus_mode`, `trace_strategy`, `consensus_round_id` | Root span for a consensus round (deterministic or random trace ID) | | `consensus.phase.open` | Consensus.h | -- | Open phase duration (child of round) | | `consensus.proposal.send` | RCLConsensus.cpp | `consensus_round`, `is_bow_out` | Consensus proposal broadcast | | `consensus.ledger_close` | RCLConsensus.cpp | `ledger_seq`, `consensus_mode` | Ledger close event | | `consensus.establish` | Consensus.h | `converge_percent`, `establish_count`, `proposers` | Establish phase duration (child of round) | | `consensus.update_positions` | Consensus.h | `converge_percent`, `proposers`, `disputes_count` | Position update and dispute resolution (see Events below) | | `consensus.check` | Consensus.h | `agree_count`, `disagree_count`, `converge_percent`, `have_close_time_consensus`, `threshold_percent`, `proposers_finished`, `consensus_stalled`, `establish_count`, `consensus_result` | Consensus threshold check | | `consensus.accept` | RCLConsensus.cpp | `proposers`, `round_time_ms`, `quorum`, `disputes_count`, `consensus_state` | Ledger accepted by consensus | | `consensus.accept.apply` | RCLConsensus.cpp | `ledger_seq`, `close_time`, `close_time_correct`, `close_resolution_ms`, `consensus_state`, `proposing`, `round_time_ms`, `parent_close_time`, `close_time_self`, `close_time_vote_bins`, `resolution_direction`, `tx_count` | Ledger application with close time details (see Events below) | | `consensus.validation.send` | RCLConsensus.cpp | `ledger_seq`, `proposing`, `ledger_hash`, `full_validation`, `validation_sign_time` | Validation sent after accept (follows-from link) | | `consensus.mode_change` | RCLConsensus.cpp | `mode_old`, `mode_new` | Consensus mode transition | | `consensus.proposal.receive` | PeerImp.cpp | `proposal_trusted`, `consensus_round` | Proposal received from peer (extracts parent context from TraceContext when present; falls back to standalone span for older peers) | | `consensus.validation.receive` | PeerImp.cpp | `validation_trusted`, `ledger_seq` | Validation received from peer (extracts parent context from TraceContext when present; falls back to standalone span for older peers) | #### Consensus Span Events | Parent Span | Event Name | Event Attributes | Description | | ---------------------------- | ----------------- | ----------------------------------------------------------- | ------------------------------------------------------- | | `consensus.update_positions` | `dispute.resolve` | `tx_id`, `dispute_our_vote`, `dispute_yays`, `dispute_nays` | Emitted per dispute when votes are tallied | | `consensus.accept.apply` | `tx.included` | `tx_id` | Emitted per transaction included in the accepted ledger | #### Close Time Queries (Tempo TraceQL) Span attributes are filtered with `span.` 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 = ""} # 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). --- ## 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. = && span. != } # Duration filter (no prefix needed) {name="" && duration > 500ms} # Regex match {name="" && span. =~ ".*"} # Multiple span names {name = "" || name = ""} # Name regex {name =~ ".*" && span. = } # Structural: find parent spans that have a matching child/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 = ""} # Find rounds where validators disagreed on close time {name="consensus.accept.apply" && span.close_time_correct = false} # Find both validation send and receive (compare sender vs receiver latency) {name = "consensus.validation.send" || name = "consensus.validation.receive"} ``` ### Cross-Subsystem Correlation ``` # Follow a transaction from receive through queue to ledger {name =~ "tx.*|txq.*" && span.tx_type = "Payment" && duration > 500ms} # Find all NFT-related activity across tx and txq spans {name =~ "tx.*|txq.*" && span.tx_type =~ "NFToken.*"} # Find all AMM activity across tx and txq spans {name =~ "tx.*|txq.*" && span.tx_type =~ "AMM.*"} # Find cross-node transaction receives (no errors) {name="tx.receive" && status != error} ``` ### Where to Look (Quick Reference) | Question | Span | Key Attributes | | ----------------------------------- | --------------------------- | ---------------------------------------- | | "Which tx type is slowest?" | `tx.process` | `span.tx_type` + duration | | "Why was my tx rejected?" | `tx.process` | `span.ter_result`, `span.applied` | | "What AMM operations happened?" | `tx.process` | `span.tx_type =~ "AMM.*"` | | "What DEX offers failed?" | `tx.process` | `span.tx_type`, `span.ter_result` | | "What NFT activity occurred?" | `tx.process`, `txq.enqueue` | `span.tx_type =~ "NFToken.*"` | | "Is the TxQ backing up?" | `txq.accept` | `span.queue_size`, `span.ledger_changed` | | "Why was my tx dropped from queue?" | `txq.accept_tx` | `span.txq_status`, `span.ter_code` | | "Are batch requests a problem?" | `rpc.process` | `span.is_batch`, `span.batch_size` | | "Which RPC is expensive?" | `rpc.command.*` | `span.load_type`, duration | | "Did consensus reach threshold?" | `consensus.check` | `span.consensus_result` | | "Was consensus outcome normal?" | `consensus.accept` | `span.consensus_state` | | "Did a validator bow out?" | `consensus.proposal.send` | `span.is_bow_out` | | "Which ledger was validated?" | `consensus.validation.send` | `span.ledger_hash` | | "Did close time agreement fail?" | `consensus.accept.apply` | `span.close_time_correct` | --- ## Cross-Node Trace Propagation xrpld propagates trace context across nodes via protobuf `TraceContext` fields embedded in peer-to-peer messages. When Node A sends a transaction, proposal, or validation, it injects its active span's trace/span IDs into the protobuf message. Node B extracts that context on receipt and creates a child span, linking the two nodes into a single distributed trace. ### How It Works ``` Node A (sender) Node B (receiver) +-----------------------------+ +-------------------------------+ | tx.process / consensus.* | | PeerImp::onMessage() | | | | | | | | v | | v | | SpanGuard::getTraceBytes() | | extract TraceContext from | | | | | protobuf message | | v | send | | | | injectSpanContext() --------|--------->| v | | sets TraceContext fields | proto | txReceiveSpan() | | (trace_id, span_id, flags) | msg | proposalReceiveSpan() | +-----------------------------+ | validationReceiveSpan() | | | | | v | | child span with parent link | +-------------------------------+ ``` ### Send-Side Injection | Message Type | Injection Point | Mechanism | | ------------- | -------------------------- | ------------------------------------------ | | TMTransaction | `NetworkOPs::apply()` | Injects `tx.process` span into relay msg | | TMProposeSet | `RCLConsensus::propose()` | Injects active context into proposal msg | | TMValidation | `RCLConsensus::validate()` | Injects active context into validation msg | ### Receive-Side Extraction | Message Type | Extraction Point | Helper Function | | ------------- | ----------------------------------- | -------------------------------------------------- | | TMTransaction | `PeerImp::onMessage(TMTransaction)` | `TxTracing::txReceiveSpan()` | | TMProposeSet | `PeerImp::onMessage(TMProposeSet)` | `ConsensusReceiveTracing::proposalReceiveSpan()` | | TMValidation | `PeerImp::onMessage(TMValidation)` | `ConsensusReceiveTracing::validationReceiveSpan()` | ### Key Files | File | Role | | ------------------------------------------------- | ----------------------------------------------- | | `src/xrpld/telemetry/PropagationHelpers.h` | `injectSpanContext()` — SpanGuard to protobuf | | `include/xrpl/telemetry/TraceContextPropagator.h` | OTel context <-> protobuf conversion primitives | | `src/xrpld/telemetry/ConsensusReceiveTracing.h` | Proposal/validation receive span factories | | `src/xrpld/telemetry/TxTracing.h` | Transaction receive span factory | ### Backwards Compatibility Older peers that do not populate `TraceContext` fields in their messages will simply produce empty trace bytes on the receive side. The extraction helpers detect this and create standalone (root) spans instead of child spans. No errors are logged and no data is lost — the receive span is still created with all its normal attributes, it just lacks a cross-node parent link. ### Example Tempo Queries ``` # Find cross-node transaction traces (tx.receive spans with no errors) {name="tx.receive" && status != error} # Find proposals received with cross-node parent context {} >> {name="consensus.proposal.receive"} # Trace a transaction across the network by its hash {name =~ "tx.*" && span.tx_hash = ""} # Find all spans in a cross-node consensus trace {resource.service.name="xrpld" && span.consensus_round_id = ""} # Compare latency between sender and receiver for validations {name = "consensus.validation.send" || name = "consensus.validation.receive"} ``` ## Prometheus Metrics (Spanmetrics) The OTel Collector's spanmetrics connector automatically derives RED (Rate, Errors, Duration) metrics from every span. No custom metrics code is needed in xrpld. ### Generated Metric Names | Prometheus Metric | Type | Description | | ----------------------------------- | --------- | ---------------------------- | | `span_calls_total` | Counter | Total span invocations | | `span_duration_milliseconds_bucket` | Histogram | Latency distribution buckets | | `span_duration_milliseconds_count` | Histogram | Latency observation count | | `span_duration_milliseconds_sum` | Histogram | Cumulative latency | ### Metric Labels Every metric carries these standard labels: | Label | Source | Example | | -------------- | ------------------ | ---------------------------------------- | | `span_name` | Span name | `rpc.command.server_info` | | `status_code` | Span status | `STATUS_CODE_UNSET`, `STATUS_CODE_ERROR` | | `service_name` | Resource attribute | `xrpld` | | `span_kind` | Span kind | `SPAN_KIND_INTERNAL` | Additionally, span attributes configured as dimensions in the collector become metric labels. The span attribute keys are already underscore form (the naming convention forbids dots), so the label name matches the attribute name verbatim. Prometheus' dots → underscores sanitization only fires for dotted attribute names (e.g. resource attributes like `service.name`), which does not apply to these dimensions. | Span Attribute | Metric Label | Applies To | | -------------------- | -------------------- | ------------------------------- | | `command` | `command` | `rpc.command.*` spans | | `rpc_status` | `rpc_status` | `rpc.command.*` spans | | `consensus_mode` | `consensus_mode` | `consensus.ledger_close` spans | | `local` | `local` | `tx.process` spans | | `proposal_trusted` | `proposal_trusted` | `peer.proposal.receive` spans | | `validation_trusted` | `validation_trusted` | `peer.validation.receive` spans | ### Histogram Buckets Configured in `otel-collector-config.yaml`: ``` 1ms, 5ms, 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 5s ``` ## System Metrics (OTel native -- beast::insight) xrpld has a built-in metrics framework (`beast::insight`) that exports metrics natively via OTLP to the OTel Collector. These complement the span-derived RED metrics by providing system-level gauges, counters, and timers that don't map to individual trace spans. ### Configuration Add to `xrpld.cfg`: ```ini [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 | | `job_count` | JobQueue.cpp:26 | Current job queue depth | | `{category}_bytes_in/out` | OverlayImpl.h:535 | Overlay traffic bytes per category (57 categories) | | `{category}_messages_in/out` | OverlayImpl.h:535 | Overlay traffic messages per category | #### OTel MetricsRegistry Gauges These gauges are exported via the OTel Metrics SDK `PeriodicMetricReader` (10s interval), NOT through beast::insight. | Prometheus Metric | Source | Description | | --------------------------------------------------- | ------------------- | ------------------------------------------------ | | `server_info{metric="server_state"}` | MetricsRegistry.cpp | Operating mode (0=DISCONNECTED .. 4=FULL) | | `server_info{metric="uptime"}` | MetricsRegistry.cpp | Seconds since server start | | `server_info{metric="peers"}` | MetricsRegistry.cpp | Total connected peers | | `server_info{metric="validated_ledger_seq"}` | MetricsRegistry.cpp | Validated ledger sequence number | | `server_info{metric="ledger_current_index"}` | MetricsRegistry.cpp | Current open ledger sequence | | `server_info{metric="peer_disconnects_resources"}` | MetricsRegistry.cpp | Cumulative resource-related peer disconnects | | `server_info{metric="last_close_proposers"}` | MetricsRegistry.cpp | Proposers in last closed round | | `server_info{metric="last_close_converge_time_ms"}` | MetricsRegistry.cpp | Last close convergence time (ms) | | `build_info{version=""}` | MetricsRegistry.cpp | Info-style metric (always 1) | | `complete_ledgers{bound="start\|end",index=""}` | 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` | ```cpp #include // 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(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` | | Environment | `deployment.environment` | collector | `local`, `test`, `ci`, `prod` | Dashboards expose these as the template variables `$node`, `$service_name`, `$xrpl_network_type`, and `$deployment_environment` (each variable name matches its Prometheus label). Select them top-down — environment → network → service → node. Selecting **All** matches every value, including series lacking the label, so mixed old/new data never disappears. ### 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): ```yaml processors: resource/tier: attributes: - key: deployment.environment value: # local | test | ci | prod action: upsert - key: xrpl.network.type value: # mainnet | testnet | devnet (fallback only) action: insert ``` Suggested per-tier values: | Collector | `deployment.environment` | `xrpl.network.type` (fallback) | | ------------------- | ------------------------ | ------------------------------ | | Developer laptop | `local` | `devnet` | | Test machines | `test` | `testnet` | | CI runs | `ci` | `testnet` | | Production observer | `prod` | `mainnet` | The `xrpl.network.type` value is only a fallback: when the node stamps its own network (all current builds do), the node's value wins. Set it to the network the collector most commonly serves. ### How the tier labels reach metrics Resource attributes do not become Prometheus labels automatically. Two collector settings make it work, both already enabled: - `prometheus.resource_to_telemetry_conversion: enabled: true` promotes resource attributes to metric labels on the local scrape surface. - `spanmetrics.resource_metrics_key_attributes` lists the tier attributes so span-derived series stay grouped per node and tier. Traces and logs carry resource attributes natively; Grafana Cloud ingests all three signals' attributes over OTLP directly. ## Grafana Dashboards Ten dashboards are pre-provisioned in `docker/telemetry/grafana/dashboards/`: ### RPC Performance (`rpc-performance`) | Panel | Type | PromQL | Labels Used | | --------------------------- | ---------- | -------------------------------------------------------------------------------------------------------------------------- | ------------------------ | | RPC Request Rate by Command | timeseries | `sum by (command) (rate(span_calls_total{span_name=~"rpc.command.*"}[5m]))` | `command` | | RPC Latency p95 by Command | timeseries | `histogram_quantile(0.95, sum by (le, command) (rate(span_duration_milliseconds_bucket{span_name=~"rpc.command.*"}[5m])))` | `command` | | RPC Error Rate | bargauge | Error spans / total spans × 100, grouped by `command` | `command`, `status_code` | | RPC Latency Heatmap | heatmap | `sum(increase(span_duration_milliseconds_bucket{span_name=~"rpc.command.*"}[5m])) by (le)` | `le` (bucket boundaries) | | Overall RPC Throughput | timeseries | `rpc.request` + `rpc.process` rate | — | | RPC Success vs Error | timeseries | by `status_code` (UNSET vs ERROR) | `status_code` | | Top Commands by Volume | bargauge | `topk(10, ...)` by `command` | `command` | | WebSocket Message Rate | stat | `rpc.ws_message` rate | — | ### Transaction Overview (`transaction-overview`) | Panel | Type | PromQL | Labels Used | | ---------------------------------- | -------------- | -------------------------------------------------------------------------------------------- | ----------------------------------- | | Transaction Processing Rate | timeseries | `rate(span_calls_total{span_name="tx.process"}[5m])` and `tx.receive` | `span_name` | | Transaction Processing Latency | timeseries | `histogram_quantile(0.95 / 0.50, ... {span_name="tx.process"})` | — | | Transaction Path Distribution | piechart | `sum by (local) (rate(span_calls_total{span_name="tx.process"}[5m]))` | `local` | | Transaction Receive vs Suppressed | timeseries | `rate(span_calls_total{span_name="tx.receive"}[5m])` | — | | TX Processing Duration Heatmap | heatmap | `tx.process` histogram buckets | `le` | | TX Apply Duration per Ledger | timeseries | p95/p50 of `tx.apply` | — | | Peer TX Receive Rate | timeseries | `tx.receive` rate | — | | TX Apply Failed Rate | stat | `rate(span_calls_total{span_name="tx.transactor",stage="apply",ter_result!~"tesSUCCESS\|"})` | `stage`, `ter_result` | | TxQ Accept: Applied Ratio per Node | state-timeline | applied / (applied+failed) of `span_calls_total{span_name="txq.accept_tx"}` per node | `txq_status`, `service_instance_id` | ### Consensus Health (`consensus-health`) | Panel | Type | PromQL | Labels Used | | ----------------------------- | ---------- | --------------------------------------------------------------------------- | ---------------- | | Consensus Round Duration | timeseries | `histogram_quantile(0.95 / 0.50, ... {span_name="consensus.accept"})` | — | | Consensus Proposals Sent Rate | timeseries | `rate(span_calls_total{span_name="consensus.proposal.send"}[5m])` | — | | Ledger Close Duration | timeseries | `histogram_quantile(0.95, ... {span_name="consensus.ledger_close"})` | — | | Validation Send Rate | stat | `rate(span_calls_total{span_name="consensus.validation.send"}[5m])` | — | | Ledger Apply Duration | timeseries | `histogram_quantile(0.95 / 0.50, ... {span_name="consensus.accept.apply"})` | — | | Close Time Agreement | timeseries | `rate(span_calls_total{span_name="consensus.accept.apply"}[5m])` | — | | Consensus Mode Over Time | timeseries | `consensus.ledger_close` by `consensus_mode` | `consensus_mode` | | Accept vs Close Rate | timeseries | `consensus.accept` vs `consensus.ledger_close` rate | — | | Validation vs Close Rate | timeseries | `consensus.validation.send` vs `consensus.ledger_close` | — | | Accept Duration Heatmap | heatmap | `consensus.accept` histogram buckets | `le` | ### Ledger Operations (`ledger-operations`) | Panel | Type | PromQL | Labels Used | | ----------------------- | ---------- | ---------------------------------------------- | ----------- | | Ledger Build Rate | stat | `ledger.build` call rate | — | | Ledger Build Duration | timeseries | p95/p50 of `ledger.build` | — | | Ledger Validation Rate | stat | `ledger.validate` call rate | — | | Build Duration Heatmap | heatmap | `ledger.build` histogram buckets | `le` | | TX Apply Duration | timeseries | p95/p50 of `tx.apply` | — | | TX Apply Rate | timeseries | `tx.apply` call rate | — | | Ledger Store Rate | stat | `ledger.store` call rate | — | | Build vs Close Duration | timeseries | p95 `ledger.build` vs `consensus.ledger_close` | — | ### Peer Network (`peer-network`) Requires `trace_peer=1` in the `[telemetry]` config section. | Panel | Type | PromQL | Labels Used | | -------------------------------- | ---------- | ------------------------------ | -------------------- | | Proposal Receive Rate | timeseries | `peer.proposal.receive` rate | — | | Validation Receive Rate | timeseries | `peer.validation.receive` rate | — | | Proposals Trusted vs Untrusted | piechart | by `proposal_trusted` | `proposal_trusted` | | Validations Trusted vs Untrusted | piechart | by `validation_trusted` | `validation_trusted` | ### Node Health -- System Metrics (`node-health`) | Panel | Type | PromQL | Labels Used | | -------------------------------------- | ---------- | ---------------------------------------------------------- | ---------------- | | Validated Ledger Age | stat | `ledgermaster_validated_ledger_age` | — | | Published Ledger Age | stat | `ledgermaster_published_ledger_age` | — | | Operating Mode (Time Share) | timeseries | `rate(state_accounting_X_duration) / sum(rate(all modes))` | — | | Operating Mode Transitions | timeseries | `state_accounting_*_transitions` | — | | I/O Latency | timeseries | `histogram_quantile(0.95, ios_latency_bucket)` | — | | Job Queue Depth | timeseries | `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__=~"", quantile="$quantile"}` | `quantile` | | All Jobs Dequeue Wait (Detail) | timeseries | `{__name__=~"_q", quantile="$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, rate(*_bytes_in[$__rate_interval]))` | — | | Duplicate Traffic (Wasted Bandwidth) | timeseries | `rate(*_duplicate_bytes_in/out[$__rate_interval])` | — | | All Traffic Categories (Detail) | timeseries | `topk(15, rate(*_bytes_in[$__rate_interval]))` | — | ### 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 `exported_instance` 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: ```bash 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` + `exported_instance`; 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: ```bash 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: ```bash # 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: ```bash 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//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"`). ```logql # 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[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\S+):(?P\S+)\s+trace_id=(?P[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=job_count'` ### Server info gauge shows server_state=0 This is normal during startup. The server starts in DISCONNECTED mode (0) and progresses through CONNECTED (1), SYNCING (2), TRACKING (3), to FULL (4). Wait for the node to sync with the network. ### Database metrics showing zero The `getKBUsed*()` methods require SQLite databases to exist. If running with `--standalone` or before the first ledger is stored, these will be zero. ### High memory usage - Reduce trace volume with collector-side tail sampling (xrpld head sampling is fixed at 1.0 and is not configurable) - Reduce `max_queue_size` and `batch_size` - Disable high-volume trace categories: `trace_peer=0` ### Collector connection failures - Verify endpoint URL matches collector address - Check firewall rules for ports 4317/4318 - If using TLS, verify certificate path with `tls_ca_cert` ### No trace_id in log output - Verify xrpld was built with `telemetry=ON` (the `XRPL_ENABLE_TELEMETRY` preprocessor flag) - Verify `enabled=1` in the `[telemetry]` config section - Log lines only contain `trace_id`/`span_id` when emitted inside an active span — background logs outside of RPC/consensus/transaction processing will not have trace context - Check that the specific trace category is enabled (e.g., `trace_rpc=1`) ### No logs in Loki - Verify the log file mount in docker-compose.yml points to the correct xrpld log directory (default source `docker/telemetry/data/logs`, or the `XRPLD_LOG_DIR` override) and that xrpld actually writes `debug.log` there - Check OTel Collector logs for filelog receiver errors: `docker compose logs otel-collector` - Verify Loki is running: `curl http://localhost:3100/ready` - Check the filelog receiver glob `/var/log/xrpld/*/debug.log` matches your log layout — the log file must sit one subdirectory below the mount root ## Performance Tuning | Scenario | Recommendation | | ------------------------ | --------------------------------------------------------- | | Production mainnet | `trace_peer=0`; reduce volume via collector tail sampling | | Testnet/devnet | Full tracing (head sampling fixed at 1.0) | | Debugging specific issue | Full tracing (head sampling fixed at 1.0) | | High-throughput node | Increase `batch_size=1024`, `max_queue_size=4096` | ## Disabling Telemetry Set `enabled=0` in config (runtime disable) or build without the flag: ```bash cmake --preset default -Dtelemetry=OFF ``` When telemetry is compiled out, all trace macros expand to no-ops with zero overhead.