ARCHIVE/rippled
1
0
Fork 0
mirror of https://github.com/XRPLF/rippled.git synced 2026-07-23 15:10:34 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
29df9c4ed4534a356343bdecdb34baec82e10fda
rippled/OpenTelemetryPlan/06-implementation-phases.md

89 KiB
Raw Blame History

Implementation Phases

Parent Document: OpenTelemetryPlan.md Related: Configuration Reference | Observability Backends

6.1 Phase Overview

TxQ = Transaction Queue

gantt
    title OpenTelemetry Implementation Timeline
    dateFormat  YYYY-MM-DD
    axisFormat  Week %W

    section Phase 1
    Core Infrastructure        :p1, 2024-01-01, 2w
    SDK Integration           :p1a, 2024-01-01, 4d
    Telemetry Interface       :p1b, after p1a, 3d
    Configuration & CMake     :p1c, after p1b, 3d
    Unit Tests                :p1d, after p1c, 2d
    Buffer & Integration      :p1e, after p1d, 2d

    section Phase 2
    RPC Tracing               :p2, after p1, 2w
    HTTP Context Extraction   :p2a, after p1, 2d
    RPC Handler Instrumentation :p2b, after p2a, 4d
    PathFinding Instrumentation :p2f, after p2b, 2d
    TxQ Instrumentation       :p2g, after p2f, 2d
    WebSocket Support         :p2c, after p2g, 2d
    Integration Tests         :p2d, after p2c, 2d
    Buffer & Review           :p2e, after p2d, 4d

    section Phase 3
    Transaction Tracing       :p3, after p2, 2w
    Protocol Buffer Extension :p3a, after p2, 2d
    PeerImp Instrumentation   :p3b, after p3a, 3d
    Fee Escalation Instrumentation :p3f, after p3b, 2d
    Relay Context Propagation :p3c, after p3f, 3d
    Multi-node Tests          :p3d, after p3c, 2d
    Buffer & Review           :p3e, after p3d, 4d

    section Phase 4
    Consensus Tracing         :p4, after p3, 2w
    Consensus Round Spans     :p4a, after p3, 3d
    Proposal Handling         :p4b, after p4a, 3d
    Establish Phase (4a)      :p4f, after p4b, 3d
    Validation Tests          :p4c, after p4f, 4d
    Buffer & Review           :p4e, after p4c, 4d

    section Phase 5
    Documentation & Deploy    :p5, after p4, 1w

    section Phase 6
    StatsD Metrics Bridge     :p6, after p5, 1w

    section Phase 7
    Native OTel Metrics       :p7, after p6, 2w

    section Phase 8
    Log-Trace Correlation     :p8, after p7, 1w

6.2 Phase 1: Core Infrastructure (Weeks 1-2)

Objective: Establish foundational telemetry infrastructure

Tasks

Task Description
1.1 Add OpenTelemetry C++ SDK to Conan/CMake
1.2 Implement Telemetry interface and factory
1.3 Implement SpanGuard RAII wrapper
1.4 Implement configuration parser
1.5 Integrate into ApplicationImp
1.6 Add conditional compilation (XRPL_ENABLE_TELEMETRY)
1.7 Create NullTelemetry no-op implementation
1.8 Unit tests for core infrastructure

Exit Criteria

  • OpenTelemetry SDK compiles and links
  • Telemetry can be enabled/disabled via config
  • Basic span creation works
  • No performance regression when disabled
  • Unit tests passing

6.3 Phase 2: RPC Tracing (Weeks 3-4)

TxQ = Transaction Queue

Objective: Complete tracing for all RPC operations

Tasks

Task Description
2.1 Implement W3C Trace Context HTTP header extraction
2.2 Instrument ServerHandler::onRequest()
2.3 Instrument RPCHandler::doCommand()
2.4 Add RPC-specific attributes
2.5 Instrument WebSocket handler
2.6 PathFinding instrumentation (pathfind.request, pathfind.compute spans)
2.7 TxQ instrumentation (txq.enqueue, txq.apply spans)
2.8 Integration tests for RPC tracing
2.9 Performance benchmarks
2.10 Documentation

Exit Criteria

  • All RPC commands traced
  • Trace context propagates from HTTP headers
  • WebSocket and HTTP both instrumented
  • <1ms overhead per RPC call
  • Integration tests passing

6.4 Phase 3: Transaction Tracing (Weeks 5-6)

Objective: Trace transaction lifecycle across network with deterministic cross-node correlation

Tasks

Task Description
3.1 Define TraceContext Protocol Buffer message
3.2 Implement protobuf context serialization
3.3 Instrument PeerImp::handleTransaction()
3.4 Instrument NetworkOPs::submitTransaction()
3.5 Instrument HashRouter integration
3.6 Fee escalation instrumentation (fee.escalate span)
3.7 Implement relay context propagation
3.8 Integration tests (multi-node)
3.9 Deterministic transaction trace ID (trace_id = txHash[0:16])
3.10 Performance benchmarks

Deterministic Trace ID (Task 3.9)

Transaction spans use deterministic trace IDs derived from the transaction hash: trace_id = txHash[0:16]. All nodes handling the same transaction independently produce spans under the same trace_id. Protobuf span_id propagation (Task 3.7) additionally provides parent-child relay ordering when available. See 02-design-decisions.md §2.5.0 for the design rationale and Phase3_taskList.md Task 3.9 for the full implementation spec.

Exit Criteria

  • Transaction traces span across nodes
  • Trace context in Protocol Buffer messages
  • HashRouter deduplication visible in traces
  • Multi-node integration tests passing
  • <5% overhead on transaction throughput
  • Deterministic trace_id: all nodes produce same trace_id for same transaction
  • Protobuf span_id propagation preserves parent-child ordering when available

6.5 Phase 4: Consensus Tracing (Weeks 7-8)

Objective: Full observability into consensus rounds

Tasks

Task Description Status
4.1 Instrument RCLConsensusAdaptor::startRound() ✅ Done (via 4a.2)
4.2 Instrument phase transitions ✅ Done
4.3 Instrument proposal handling ✅ Done
4.4 Instrument validation handling ✅ Done
4.5 Add consensus-specific attributes ✅ Done
4.6 Correlate with transaction traces ✅ Done
4.7 Build verification and testing ✅ Done
4.8 Validation span enrichment (ext. dashboard) ❌ Not done

Note: The original plan doc listed tasks 4.7-4.11 as "Validator list tracing", "Amendment voting tracing", "SHAMap sync tracing", "Multi-validator integration tests", and "Performance validation". These were descoped and replaced by the tasklist's 4.7 (build verification) and 4.8 (validation span enrichment). Validator, amendment, and SHAMap tracing are not implemented.

Spans Produced

Span Name Location Attributes
consensus.phase.open Consensus.h (none)
consensus.proposal.send RCLConsensus.cpp consensus_round
consensus.ledger_close RCLConsensus.cpp ledger_seq, consensus_mode
consensus.accept RCLConsensus.cpp proposers, round_time_ms, quorum
consensus.accept.apply RCLConsensus.cpp close_time, close_time_correct, close_resolution_ms, consensus_state, proposing, round_time_ms, ledger_seq, parent_close_time, close_time_self, close_time_vote_bins, resolution_direction
consensus.validation.send RCLConsensus.cpp ledger_seq, proposing

Exit Criteria

  • Complete consensus round traces
  • Phase transitions visible (open, establish, close, accept)
  • Proposals and validations traced — send and receive; relay deferred to Phase 4b
  • Close time agreement tracked (per avCT_CONSENSUS_PCT)
  • No impact on consensus timing
  • Multi-validator test network validated
  • Transaction-consensus correlation (Task 4.6) — tx.included events in doAccept
  • Validation span enrichment (Task 4.8) — not implemented

Implementation Status — Phase 4a Complete

Phase 4a (establish-phase gap fill & cross-node correlation) adds:

  • Deterministic trace ID derived from previousLedger.id() so all validators in the same round share the same trace_id (switchable via consensus_trace_strategy config: "deterministic" or "attribute"). See Configuration Reference for full configuration options.
  • Round lifecycle spans: consensus.round with round-to-round span links.
  • Establish phase: consensus.establish, consensus.update_positions (with dispute.resolve events), consensus.check (with threshold tracking).
  • Mode changes: consensus.mode_change spans.
  • Validation: consensus.validation.send with span link to round span (thread-safe cross-thread access via roundSpanContext_ snapshot).
  • Separation of concerns: telemetry extracted to private helpers (startRoundTracing, createValidationSpan, startEstablishTracing, updateEstablishTracing, endEstablishTracing).

See Phase4_taskList.md for the full spec and implementation notes.

6.5a Phase 4a: Establish-Phase Gap Fill & Cross-Node Correlation

Objective: Fill tracing gaps in the establish phase and establish cross-node correlation using deterministic trace IDs derived from previousLedger.id().

Approach: Direct instrumentation in Consensus.h and RCLConsensus.cpp. All spans use SpanGuard factory methods (span(), hashSpan(), linkedSpan()) with TraceCategory::Consensus gating. No macros used — all tracing via direct SpanGuard API calls.

Tasks

Task Description Effort Risk Status
4a.0 Prerequisites: extend SpanGuard & Telemetry APIs 1d Medium ✅ Done (no macros)
4a.1 Adaptor getTelemetry() method 0.5d Low ⏭️ Skipped (not needed)
4a.2 Switchable round span with deterministic traceID 2d High ✅ Done
4a.3 Span members in Consensus.h 0.5d Medium ✅ Done (with deviation)
4a.4 Instrument phaseEstablish() 1d Medium ✅ Done
4a.5 Instrument updateOurPositions() 1d Medium ✅ Done
4a.6 Instrument haveConsensus() (thresholds) 1d Medium ✅ Done
4a.7 Instrument mode changes 0.5d Low ✅ Done
4a.8 Reparent existing spans under round 0.5d Low ✅ Done
4a.9 Build verification and testing 1d Low ✅ Done

Total Effort: 9 days

Spans Produced

Span Name Location Key Attributes (actually set)
consensus.round RCLConsensus.cpp consensus_round_id, consensus_ledger_id, ledger_seq, consensus_mode, trace_strategy
consensus.establish Consensus.h converge_percent, establish_count, proposers
consensus.update_positions Consensus.h converge_percent, proposers, have_close_time_consensus, close_time_threshold, disputes_count, avalanche_threshold
consensus.check Consensus.h agree_count, disagree_count, converge_percent, have_close_time_consensus, threshold_percent, consensus_result
consensus.mode_change RCLConsensus.cpp mode_old, mode_new

Exit Criteria

  • Establish phase internals traced (establish, update_positions, check spans)
  • Establish phase fully traced — disputes_count, avalanche_threshold, dispute yays/nays all implemented
  • Cross-node correlation works via deterministic trace_id
  • Strategy switchable via config (deterministic / attribute)
  • Consecutive rounds linked via follows-from spans
  • Build passes with telemetry ON and OFF
  • No impact on consensus timing

See Phase4_taskList.md for full task details.

6.5b Phase 4b: Cross-Node Propagation (Future)

Objective: Wire TraceContextPropagator for P2P messages (proposals, validations) to enable true distributed tracing between nodes.

Status: Partially implemented. Send-side injection (proposals and validations) and receive-side extraction (consensus.{proposal,validation}. receive spans parented on the sender's context) are wired in Phase 4a. Remaining Phase 4b work: relay spans in share(RCLCxPeerPos) and multi-node validation of the propagation path.

Prerequisites: Phase 4a complete and validated.

See Phase4_taskList.md § Phase 4b for full design.

6.6 Phase 5: Documentation & Deployment (Week 9)

Objective: Production readiness

Tasks

Task Description Status
5.1 Operator runbook Complete
5.2 Grafana dashboards Complete
5.3 Alert definitions Deferred — post-MVP
5.4 Collector deployment examples Complete
5.5 Developer documentation Complete
5.6 Training materials Deferred — post-MVP
5.7 Final integration testing Complete

6.7 Phase 6: StatsD Metrics Integration (Week 10)

Objective: Bridge xrpld's existing beast::insight StatsD metrics into the OpenTelemetry collection pipeline, exposing 300+ pre-existing metrics alongside span-derived RED metrics in Prometheus/Grafana.

Background

xrpld has a mature metrics framework (beast::insight) that emits StatsD-format metrics over UDP. These metrics cover node health, peer networking, RPC performance, job queue, and overlay traffic — data that does not overlap with the span-based instrumentation from Phases 1-5. By adding a StatsD receiver to the OTel Collector, both metric sources converge in Prometheus.

Metric Inventory

Category Group Type Count Key Metrics
Node State State_Accounting Gauge 10 *_duration, *_transitions per operating mode
Ledger LedgerMaster Gauge 2 Validated_Ledger_Age, Published_Ledger_Age
Ledger Fetch — Counter 1 ledger_fetches
Ledger History ledger.history Counter 1 mismatch
RPC rpc Counter+Event 3 requests, time (histogram), size (histogram)
Job Queue — Gauge+Event 1 + 2×N job_count, per-job {name} and {name}_q
Peer Finder Peer_Finder Gauge 2 Active_Inbound_Peers, Active_Outbound_Peers
Overlay Overlay Gauge 1 Peer_Disconnects
Overlay Traffic per-category Gauge 4×57 = 228 Bytes_In/Out, Messages_In/Out per traffic category
Pathfinding — Event 2 pathfind_fast, pathfind_full (histograms)
I/O — Event 1 ios_latency (histogram)
Resource Mgr — Meter 2 warn, drop (rate counters)
Caches per-cache Gauge 2×N {cache}.size, {cache}.hit_rate

Total: ~255+ unique metrics (plus dynamic job-type and cache metrics)

Tasks

Task Description
6.1 DEFERRED Fix Meter wire format (|m → |c) in StatsDCollector.cpp — breaking change, tracked separately
6.2 Add statsd receiver to OTel Collector config
6.3 Expose UDP port 8125 in docker-compose.yml
6.4 Add [insight] config to integration test node configs
6.5 Create "Node Health" Grafana dashboard (16 panels)
6.6 Create "Network Traffic" Grafana dashboard (10 panels)
6.7 Create "RPC & Pathfinding (StatsD)" Grafana dashboard (8 panels)
6.8 Update integration test to verify StatsD metrics in Prometheus
6.9 Update TESTING.md and telemetry-runbook.md

Wire Format Fix (Task 6.1) — DEFERRED

The StatsDMeterImpl in StatsDCollector.cpp sends metrics with |m suffix, which is non-standard StatsD. The OTel StatsD receiver silently drops these. Fix: change |m to |c (counter), which is semantically correct since meters are increment-only counters. Only 2 metrics are affected (warn, drop in Resource Manager).

Status: Deferred as a separate change — this is a breaking change for any StatsD backend that previously consumed the custom |m type. The Resource Warnings and Resource Drops dashboard panels will show no data until this fix is applied.

New Grafana Dashboards

Node Health (statsd-node-health.json, uid: xrpld-statsd-node-health):

  • Validated/Published Ledger Age, Operating Mode Duration/Transitions, I/O Latency, Job Queue Depth, Ledger Fetch Rate, Ledger History Mismatches, Key Jobs Execution/Dequeue Time, FullBelowCache Size/Hit Rate, Ledger Publish Gap, State Duration Rate, All Jobs Detail

Network Traffic (statsd-network-traffic.json, uid: xrpld-statsd-network):

  • Active Inbound/Outbound Peers, Peer Disconnects, Total Bytes/Messages In/Out, Transaction/Proposal/Validation Traffic, Top Traffic Categories, Duplicate Traffic, All Traffic Categories Detail

RPC & Pathfinding (StatsD) (statsd-rpc-pathfinding.json, uid: xrpld-statsd-rpc):

  • RPC Request Rate, Response Time p95/p50, Response Size p95/p50, Pathfinding Fast/Full Duration, Resource Warnings/Drops, Response Time Heatmap

Exit Criteria

  • StatsD metrics visible in Prometheus (curl localhost:9090/api/v1/query?query=xrpld_LedgerMaster_Validated_Ledger_Age)
  • All 3 new Grafana dashboards load without errors
  • Integration test verifies at least core StatsD metrics (ledger age, peer counts, RPC requests)
  • Meter metrics (warn, drop) flow correctly after |m → |c fix — DEFERRED (breaking change, tracked separately)

6.8 Phase 7: Native OTel Metrics Migration (Weeks 11-12)

Objective: Replace StatsDCollector with a native OpenTelemetry Metrics SDK implementation behind the existing beast::insight::Collector interface, eliminating the StatsD UDP dependency and unifying traces and metrics into a single OTLP pipeline.

Motivation: Why Migrate from StatsD to Native OTel Metrics

The Phase 6 StatsD bridge was a pragmatic first step, but it retains inherent limitations that native OTel export resolves.

What We Gain

  1. Unified telemetry pipeline — Traces and metrics export via the same OTLP/HTTP endpoint to the same OTel Collector. One protocol, one endpoint, one config. Eliminates the split-brain architecture of "OTLP for traces, StatsD UDP for metrics."

  2. Eliminates StatsD UDP limitations — StatsD is fire-and-forget over UDP with no delivery guarantees, no backpressure, 1472-byte MTU packet fragmentation, and text-based encoding overhead. OTLP uses HTTP/gRPC with retries, binary protobuf encoding, and connection-level flow control.

  3. Fixes the |m wire format issue — The StatsDMeterImpl uses non-standard |m StatsD type that the OTel StatsD receiver silently drops. Native OTel counters eliminate this problem entirely (Phase 6 Task 6.1 — DEFERRED becomes resolved).

  4. Richer metric semantics — OTel Metrics SDK supports explicit histogram bucket boundaries, exemplars (linking metrics to traces), resource attributes, and metric views. StatsD has no concept of these.

  5. Removes infrastructure dependency — No more StatsD receiver needed in the OTel Collector. One less receiver to configure, monitor, and debug. Simplifies the collector YAML.

  6. Metric-to-trace correlation — OTel metrics and traces share the same resource attributes (service.name, service.instance.id). Grafana can link from a metric spike directly to the traces that caused it — impossible with StatsD-sourced metrics.

  7. Production-grade export — OTel's PeriodicMetricReader provides configurable export intervals, batch sizes, timeout handling, and graceful shutdown — all built into the SDK rather than hand-rolled in StatsDCollectorImp.

What We Lose

  1. StatsD ecosystem compatibility — Operators using external StatsD-compatible backends (Datadog Agent, Graphite, Telegraph) will need to switch to OTLP-compatible backends or keep server=statsd as a fallback.

  2. Simplicity of UDP — StatsD's UDP fire-and-forget model is dead simple and has zero connection management. OTLP/HTTP requires a TCP connection, TLS negotiation (in production), and retry logic. The OTel SDK handles this, but it's more moving parts.

  3. Slightly higher memory — OTel SDK maintains internal aggregation state for metrics before export. StatsD just formats and sends strings. Expected overhead: ~1-2 MB additional for metric state.

  4. Dependency on OTel C++ Metrics SDK stability — The Metrics SDK is GA since 1.0 and on version 1.18.0, but it's less battle-tested than the tracing SDK in the C++ ecosystem.

Decision

The gains (unified pipeline, delivery guarantees, metric-trace correlation, simpler collector config) significantly outweigh the losses. StatsDCollector is retained as a fallback via server=statsd for operators who need StatsD ecosystem compatibility during the transition period.

Architecture

Class Hierarchy (after Phase 7)

beast::insight::Collector (abstract interface — unchanged)
    |
    +-- StatsDCollector        (existing — retained as fallback, deprecated)
    |     +-- StatsDCounterImpl    -> StatsD |c over UDP
    |     +-- StatsDGaugeImpl      -> StatsD |g over UDP
    |     +-- StatsDMeterImpl      -> StatsD |m over UDP (non-standard)
    |     +-- StatsDEventImpl      -> StatsD |ms over UDP
    |     +-- StatsDHookImpl       -> 1s periodic callback
    |
    +-- NullCollector          (existing — unchanged, used when disabled)
    |     +-- NullCounterImpl      -> no-op
    |     +-- NullGaugeImpl        -> no-op
    |     +-- NullMeterImpl        -> no-op
    |     +-- NullEventImpl        -> no-op
    |     +-- NullHookImpl         -> no-op
    |
    +-- OTelCollector          (NEW — Phase 7)
          +-- OTelCounterImpl      -> otel::Counter<int64_t>
          +-- OTelGaugeImpl        -> otel::ObservableGauge<uint64_t>
          +-- OTelMeterImpl        -> otel::Counter<uint64_t>
          +-- OTelEventImpl        -> otel::Histogram<double>
          +-- OTelHookImpl         -> 1s periodic callback (same pattern)

Data Flow (after Phase 7)

graph LR
    subgraph xrpldNode["xrpld Node"]
        A["Trace Macros<br/>XRPL_TRACE_SPAN"]
        B["beast::insight<br/>OTelCollector"]
    end

    subgraph collector["OTel Collector  :4317 / :4318"]
        direction TB
        R1["OTLP Receiver<br/>:4317 gRPC  |  :4318 HTTP"]
        BP["Batch Processor"]
        SM["SpanMetrics Connector"]

        R1 --> BP
        BP --> SM
    end

    subgraph backends["Trace Backends"]
        D["Jaeger / Tempo"]
    end

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

    subgraph viz["Visualization"]
        F["Grafana  :3000"]
    end

    A -->|"OTLP/HTTP :4318<br/>(traces)"| R1
    B -->|"OTLP/HTTP :4318<br/>(metrics)"| R1

    BP -->|"OTLP/gRPC"| D
    SM -->|"RED metrics"| E
    R1 -->|"xrpld_* metrics<br/>(native OTLP)"| E

    E --> F
    D --> F

    style A fill:#4a90d9,color:#fff,stroke:#2a6db5
    style B fill:#d9534f,color:#fff,stroke:#b52d2d
    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

Key change: StatsD receiver removed from collector. Both traces and metrics enter via OTLP receiver on the same port.

Configuration

# [insight] section — new "otel" server option
[insight]
server=otel              # NEW: uses OTel OTLP metrics exporter
prefix=xrpld             # metric name prefix (preserved)

# Endpoint and auth inherited from [telemetry] section:
[telemetry]
enabled=1
endpoint=http://localhost:4318/v1/traces

The OTelCollector reads the OTLP endpoint from [telemetry] config (replacing /v1/traces with /v1/metrics for the metrics exporter). No additional config keys needed.

Backward compatibility: server=statsd continues to work exactly as before.

See Phase7_taskList.md for detailed per-task breakdown.

Instrument Type Mapping

beast::insight OTel Metrics SDK Rationale
Counter (int64, |c) Counter<int64_t> Direct 1:1 mapping
Gauge (uint64, |g) ObservableGauge<uint64_t> Async callback matches existing Hook polling pattern
Meter (uint64, |m) Counter<uint64_t> Fixes non-standard wire format; meters are semantically counters
Event (ms, |ms) Histogram<double> Duration distributions with explicit bucket boundaries
Hook (1s callback) PeriodicMetricReader alignment Same 1s collection interval

Tasks

Task Description
7.1 Add OTel Metrics SDK to build deps (conan/cmake)
7.2 Implement OTelCollector class (~400-500 lines)
7.3 Update CollectorManager — add server=otel
7.4 Update OTel Collector YAML (add metrics pipeline, remove StatsD receiver)
7.5 Preserve metric names in Prometheus (naming strategy)
7.6 Update Grafana dashboards (if names change)
7.7 Update integration tests
7.8 Update documentation (runbook, reference docs)

Exit Criteria

  • All 255+ metrics visible in Prometheus via OTLP pipeline (no StatsD receiver)
  • server=otel is the default in development docker-compose
  • server=statsd still works as a fallback
  • Existing Grafana dashboards display data correctly
  • Integration test passes with OTLP-only metrics pipeline
  • No performance regression vs StatsD baseline (< 1% CPU overhead)
  • Deferred Task 6.1 (|m wire format) no longer relevant

6.9 Phase 8: Log-Trace Correlation and Centralized Log Ingestion (Week 13)

Motivation

xrpld's beast::Journal logs and OpenTelemetry traces are currently two disjoint observability signals. When investigating an issue, operators must manually correlate timestamps between log files and Jaeger/Tempo traces. Phase 8 bridges this gap by injecting trace context (trace_id, span_id) into every log line emitted within an active span, and ingesting those logs into Grafana Loki via the OTel Collector's filelog receiver.

Gains

  1. One-click trace-to-log navigation — Click a trace in Tempo/Jaeger and immediately see the corresponding log lines in Loki, filtered by trace_id.
  2. Reverse lookup (log-to-trace) — Loki derived fields make trace_id values clickable links back to Tempo.
  3. Unified observability — All three pillars (traces, metrics, logs) flow through the same OTel Collector pipeline and are visible in a single Grafana instance.
  4. Zero new dependencies in xrpld — Uses existing OTel SDK headers (GetSpan, GetContext) already linked in Phase 1.
  5. Negligible overhead — The implementation checks the thread-local context value directly, avoiding heap allocation on the no-span path (~15-20ns). On the active-span path, total cost is ~50ns per log call. At typical logging rates, overhead is negligible.

Losses / Risks

  1. Log format change — Existing log parsers that rely on a fixed format will need updating to handle the optional trace_id=... span_id=... fields.
  2. Loki resource usage — Log ingestion adds storage and memory overhead to the observability stack (mitigated by retention policies).
  3. Filelog receiver complexity — The regex parser must be kept in sync with the log format; a format change in Logs::format() could break parsing.

Decision

The correlation value far outweighs the risks. The log format change is backward-compatible (fields are appended only when a span is active), and the filelog receiver regex is straightforward to maintain.

Architecture

Phase 8 has two independent sub-phases that can be developed in parallel:

  • Phase 8a (code change): Modify Logs::format() in src/libxrpl/basics/Log.cpp to append trace_id=<hex32> span_id=<hex16> when the current thread has an active OTel span. Guarded by #ifdef XRPL_ENABLE_TELEMETRY.
  • Phase 8b (infra only): Add Loki to the Docker Compose stack, configure the OTel Collector's filelog receiver to tail xrpld's log file, parse out structured fields (timestamp, partition, severity, trace_id, span_id, message), and export to Loki via OTLP. Configure Grafana Tempo↔Loki bidirectional linking.

Trace ID Injection Flow

flowchart LR
    subgraph xrpld["xrpld process"]
        JLOG["JLOG(j.info())"]
        Format["Logs::format()"]
        OTelCtx["OTel Context<br/>(thread-local)"]
        JLOG --> Format
        OTelCtx -.->|"GetSpan()→GetContext()"| Format
    end

    subgraph output["Log Output"]
        LogLine["2024-01-15T10:30:45.123Z<br/>LedgerMaster:NFO<br/>trace_id=abc123...<br/>span_id=def456...<br/>Validated ledger 42"]
    end

    Format --> LogLine

    style xrpld fill:#1a237e,stroke:#0d1642,color:#fff
    style output fill:#1b5e20,stroke:#0d3d14,color:#fff
    style JLOG fill:#283593,stroke:#1a237e,color:#fff
    style Format fill:#283593,stroke:#1a237e,color:#fff
    style OTelCtx fill:#283593,stroke:#1a237e,color:#fff
    style LogLine fill:#2e7d32,stroke:#1b5e20,color:#fff

Loki Ingestion Pipeline

flowchart LR
    subgraph collector["OTel Collector"]
        FR["filelog receiver<br/>tails debug.log"]
        RP["regex_parser<br/>extracts trace_id,<br/>span_id, severity"]
        BP["batch processor"]
        LE["otlp/loki exporter"]
        FR --> RP --> BP --> LE
    end

    LogFile["xrpld<br/>debug.log"] --> FR
    LE --> Loki["Grafana Loki<br/>:3100"]
    Loki <-->|"derivedFields ↔<br/>tracesToLogs"| Tempo["Grafana Tempo"]

    style collector fill:#e65100,stroke:#bf360c,color:#fff
    style FR fill:#f57c00,stroke:#e65100,color:#fff
    style RP fill:#f57c00,stroke:#e65100,color:#fff
    style BP fill:#f57c00,stroke:#e65100,color:#fff
    style LE fill:#f57c00,stroke:#e65100,color:#fff
    style LogFile fill:#1a237e,stroke:#0d1642,color:#fff
    style Loki fill:#4a148c,stroke:#2e0d57,color:#fff
    style Tempo fill:#4a148c,stroke:#2e0d57,color:#fff

Tasks

Task Description
8.1 Inject trace_id into Logs::format()
8.2 Add Loki to Docker Compose stack
8.3 Add filelog receiver to OTel Collector
8.4 Configure Grafana trace-to-log correlation
8.5 Update integration tests
8.6 Update documentation (runbook, reference docs)

Parallel work: Task 8.2 (Loki infra) can run in parallel with Task 8.1 (code change). Tasks 8.3–8.6 are sequential.

Exit Criteria

  • Log lines within active spans contain trace_id=<hex> span_id=<hex>
  • Log lines outside spans have no trace context (no empty fields)
  • Loki ingests xrpld logs via OTel Collector filelog receiver
  • Grafana Tempo → Loki one-click correlation works
  • Grafana Loki → Tempo reverse lookup works via derived field
  • Integration test verifies trace_id presence in logs
  • No performance regression from trace_id injection (< 0.1% overhead)

6.9 Risk Assessment

quadrantChart
    title Risk Assessment Matrix
    x-axis Low Impact --> High Impact
    y-axis Low Likelihood --> High Likelihood
    quadrant-1 Mitigate Immediately
    quadrant-2 Plan Mitigation
    quadrant-3 Accept Risk
    quadrant-4 Monitor Closely

    SDK Compat: [0.2, 0.18]
    Protocol Chg: [0.75, 0.72]
    Perf Overhead: [0.58, 0.42]
    Context Prop: [0.4, 0.55]
    Memory Leaks: [0.85, 0.25]

Risk Details

Risk Likelihood Impact Mitigation
Protocol changes break compatibility Medium High Use high field numbers, optional fields
Performance overhead unacceptable Medium Medium Sampling, conditional compilation
Context propagation complexity Medium Medium Phased rollout, extensive testing
SDK compatibility issues Low Medium Pin SDK version, fallback to no-op
Memory leaks in long-running nodes Low High Memory profiling, bounded queues

6.10 Success Metrics

Metric Target Measurement
Trace coverage >95% of transaction code paths (independent of sampling ratio) Sampling verification
CPU overhead <3% Benchmark tests
Memory overhead <10 MB Memory profiling
Latency impact (p99) <2% Performance tests
Trace completeness >99% spans with required attrs Validation script
Cross-node trace linkage >90% of multi-hop transactions Integration tests

6.11 Quick Wins and Crawl-Walk-Run Strategy

TxQ = Transaction Queue

This section outlines a prioritized approach to maximize ROI with minimal initial investment.

6.11.1 Crawl-Walk-Run Overview

flowchart TB
    subgraph crawl["🐢 CRAWL (Week 1-2)"]
        direction LR
        c1[Core SDK Setup] ~~~ c2[RPC Tracing Only] ~~~ c3[PathFinding + TxQ Tracing] ~~~ c4[Single Node]
    end

    subgraph walk["🚶 WALK (Week 3-5)"]
        direction LR
        w1[Transaction Tracing] ~~~ w2[Fee Escalation Tracing] ~~~ w3[Cross-Node Context] ~~~ w4[Basic Dashboards]
    end

    subgraph run["🏃 RUN (Week 6-9)"]
        direction LR
        r1[Consensus Tracing] ~~~ r2[Establish Phase<br/>& Cross-Node Correlation] ~~~ r3[StatsD Integration] ~~~ r4[Production Deploy]
    end

    crawl --> walk --> run

    style crawl fill:#1b5e20,stroke:#0d3d14,color:#fff
    style walk fill:#bf360c,stroke:#8c2809,color:#fff
    style run fill:#0d47a1,stroke:#082f6a,color:#fff
    style c1 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style c2 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style c3 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style c4 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style w1 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style w2 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style w3 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style w4 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style r1 fill:#0d47a1,stroke:#082f6a,color:#fff
    style r2 fill:#0d47a1,stroke:#082f6a,color:#fff
    style r3 fill:#0d47a1,stroke:#082f6a,color:#fff
    style r4 fill:#0d47a1,stroke:#082f6a,color:#fff

Reading the diagram:

  • CRAWL (Weeks 1-2): Minimal investment -- set up the SDK, instrument RPC and PathFinding/TxQ handlers, and verify on a single node. Delivers immediate latency visibility.
  • WALK (Weeks 3-5): Expand to transaction lifecycle tracing, fee escalation, cross-node context propagation, and basic Grafana dashboards. This is where distributed tracing starts working.
  • RUN (Weeks 6-9): Full consensus instrumentation, establish-phase gap fill, cross-node correlation, StatsD integration, and production deployment with sampling and alerting.
  • Arrows (crawl → walk → run): Each phase builds on the prior one; you cannot skip ahead because later phases depend on infrastructure established earlier.

6.11.2 Quick Wins (Immediate Value)

Quick Win Value When to Deploy
RPC Command Tracing High Week 2
RPC Latency Histograms High Week 2
Error Rate Dashboard Medium Week 2
Transaction Submit Tracing High Week 3
Consensus Round Duration Medium Week 6

6.11.3 CRAWL Phase (Weeks 1-2)

Goal: Get basic tracing working with minimal code changes.

What You Get:

  • RPC request/response traces for all commands
  • Latency breakdown per RPC command
  • PathFinding and TxQ tracing (directly impacts RPC latency)
  • Error visibility with stack traces
  • Basic Grafana dashboard

Code Changes: ~15 lines in ServerHandler.cpp, ~40 lines in new telemetry module

Why Start Here:

  • RPC is the lowest-risk, highest-visibility component
  • PathFinding and TxQ are RPC-adjacent and directly affect latency
  • Immediate value for debugging client issues
  • No cross-node complexity
  • Single file modification to existing code

6.11.4 WALK Phase (Weeks 3-5)

Goal: Add transaction lifecycle tracing across nodes.

What You Get:

  • End-to-end transaction traces from submit to relay
  • Fee escalation tracing within the transaction pipeline
  • Cross-node correlation (see transaction path)
  • HashRouter deduplication visibility
  • Relay latency metrics

Code Changes: ~120 lines across 4 files, plus protobuf extension

Why Do This Second:

  • Builds on RPC tracing (transactions submitted via RPC)
  • Fee escalation is integral to the transaction processing pipeline
  • Moderate complexity (requires context propagation)
  • High value for debugging transaction issues

6.11.5 RUN Phase (Weeks 6-9)

Goal: Full observability including consensus.

What You Get:

  • Complete consensus round visibility
  • Phase transition timing
  • Validator proposal tracking
  • Validator list and manifest tracing — descoped
  • Amendment voting tracing — descoped
  • SHAMap sync tracing — descoped
  • Full end-to-end traces (client → RPC → TX → consensus → ledger) — partial (tx-consensus correlation not yet done)

Code Changes: ~100 lines across 3 consensus files

Why Do This Last:

  • Highest complexity (consensus is critical path)
  • Validator, amendment, and SHAMap components were descoped (lower priority)
  • Requires thorough testing
  • Lower relative value (consensus issues are rarer)

6.11.6 ROI Prioritization Matrix

quadrantChart
    title Implementation ROI Matrix
    x-axis Low Effort --> High Effort
    y-axis Low Value --> High Value
    quadrant-1 Quick Wins - Do First
    quadrant-2 Major Projects - Plan Carefully
    quadrant-3 Nice to Have - Optional
    quadrant-4 Time Sinks - Avoid

    RPC Tracing: [0.15, 0.92]
    TX Submit Trace: [0.3, 0.78]
    TX Relay Trace: [0.5, 0.88]
    Consensus Trace: [0.72, 0.72]
    Peer Msg Trace: [0.85, 0.3]
    Ledger Acquire: [0.55, 0.52]

6.12 Definition of Done

TxQ = Transaction Queue | HA = High Availability

Clear, measurable criteria for each phase.

6.12.1 Phase 1: Core Infrastructure

Criterion Measurement Target
SDK Integration cmake --build succeeds with -DXRPL_ENABLE_TELEMETRY=ON ✅ Compiles
Runtime Toggle enabled=0 produces zero overhead <0.1% CPU difference
Span Creation Unit test creates and exports span Span appears in Tempo
Configuration All config options parsed correctly Config validation tests pass
Documentation Developer guide exists PR approved

Definition of Done: All criteria met, PR merged, no regressions in CI.

6.12.2 Phase 2: RPC Tracing

Criterion Measurement Target
Coverage All RPC commands instrumented 100% of commands
Context Extraction traceparent header propagates Integration test passes
Attributes Command, status, duration recorded Validation script confirms
Performance RPC latency overhead <1ms p99
Dashboard Grafana dashboard deployed Screenshot in docs

Definition of Done: RPC traces visible in Tempo for all commands, dashboard shows latency distribution.

6.12.3 Phase 3: Transaction Tracing

Criterion Measurement Target
Local Trace Submit → validate → TxQ traced Single-node test passes
Cross-Node Context propagates via protobuf Multi-node test passes
Deterministic TraceID Same trace_id on all nodes for same tx Multi-node test: query by txHash[0:16] returns all spans
Relay Ordering Protobuf span_id propagation creates parent-child Tempo trace tree shows relay chain
Graceful Degradation Old peer drops trace_context Spans still grouped by deterministic trace_id
Relay Visibility relay_count attribute correct Spot check 100 txs
HashRouter Deduplication visible in trace Duplicate txs show suppressed=true
Performance TX throughput overhead <5% degradation

Definition of Done: Transaction traces span 3+ nodes in test network with deterministic trace_id correlation, parent-child ordering via protobuf propagation, and performance within bounds.

6.12.4 Phase 4: Consensus Tracing

Criterion Measurement Target
Round Tracing startRound creates root span Unit test passes
Phase Visibility All phases have child spans Integration test confirms
Proposer Attribution Proposer ID in attributes Spot check 50 rounds
Timing Accuracy Phase durations match PerfLog <5% variance
No Consensus Impact Round timing unchanged Performance test passes

Definition of Done: Consensus rounds fully traceable, no impact on consensus timing.

6.12.5 Phase 5: Production Deployment

Criterion Measurement Target
Collector HA Multiple collectors deployed No single point of failure
Sampling Tail sampling configured 10% base + errors + slow
Retention Data retained per policy 7 days hot, 30 days warm
Alerting Alerts configured Error spike, high latency
Runbook Operator documentation Approved by ops team
Training Team trained Session completed

Definition of Done: Telemetry running in production, operators trained, alerts active.

6.12.6 Success Metrics Summary

Phase Primary Metric Secondary Metric Deadline
Phase 1 SDK compiles and runs Zero overhead when disabled End of Week 2
Phase 2 100% RPC coverage <1ms latency overhead End of Week 4
Phase 3 Cross-node traces work <5% throughput impact End of Week 6
Phase 4 Consensus fully traced No consensus timing impact End of Week 8
Phase 5 Production deployment Operators trained End of Week 9
Phase 6 StatsD metrics in Prometheus 3 dashboards operational End of Week 10
Phase 7 All metrics via OTLP No StatsD dependency End of Week 12
Phase 8 trace_id in logs + Loki Tempo↔Loki correlation End of Week 13

6.13 Recommended Implementation Order

Based on ROI analysis, implement in this exact order:

flowchart TB
    subgraph week1["Week 1"]
        t1[1. OpenTelemetry SDK<br/>Conan/CMake integration]
        t2[2. Telemetry interface<br/>SpanGuard, config]
    end

    subgraph week2["Week 2"]
        t3[3. RPC ServerHandler<br/>instrumentation]
        t4[4. Basic Tempo setup<br/>for testing]
    end

    subgraph week3["Week 3"]
        t5[5. Transaction submit<br/>tracing]
        t6[6. Grafana dashboard<br/>v1]
    end

    subgraph week4["Week 4"]
        t7[7. Protobuf context<br/>extension]
        t8[8. PeerImp tx.relay<br/>instrumentation]
    end

    subgraph week5["Week 5"]
        t9[9. Multi-node<br/>integration tests]
        t10[10. Performance<br/>benchmarks]
    end

    subgraph week6_8["Weeks 6-8"]
        t11[11. Consensus<br/>instrumentation]
        t12[12. Full integration<br/>testing]
    end

    subgraph week9["Week 9"]
        t13[13. Production<br/>deployment]
        t14[14. Documentation<br/>& training]
    end

    t1 --> t2 --> t3 --> t4
    t4 --> t5 --> t6
    t6 --> t7 --> t8
    t8 --> t9 --> t10
    t10 --> t11 --> t12
    t12 --> t13 --> t14

    style week1 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style week2 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style week3 fill:#bf360c,stroke:#8c2809,color:#fff
    style week4 fill:#bf360c,stroke:#8c2809,color:#fff
    style week5 fill:#bf360c,stroke:#8c2809,color:#fff
    style week6_8 fill:#0d47a1,stroke:#082f6a,color:#fff
    style week9 fill:#4a148c,stroke:#2e0d57,color:#fff
    style t1 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style t2 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style t3 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style t4 fill:#1b5e20,stroke:#0d3d14,color:#fff
    style t5 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style t6 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style t7 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style t8 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style t9 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style t10 fill:#ffe0b2,stroke:#ffcc80,color:#1e293b
    style t11 fill:#0d47a1,stroke:#082f6a,color:#fff
    style t12 fill:#0d47a1,stroke:#082f6a,color:#fff
    style t13 fill:#4a148c,stroke:#2e0d57,color:#fff
    style t14 fill:#4a148c,stroke:#2e0d57,color:#fff

Reading the diagram:

  • Week 1 (tasks 1-2): Foundation work -- integrate the OpenTelemetry SDK via Conan/CMake and build the Telemetry interface with SpanGuard and config parsing.
  • Week 2 (tasks 3-4): First observable output -- instrument ServerHandler for RPC tracing and stand up Tempo so developers can see traces immediately.
  • Weeks 3-5 (tasks 5-10): Transaction lifecycle -- add submit tracing, build the first Grafana dashboard, extend protobuf for cross-node context, instrument PeerImp relay, then validate with multi-node integration tests and performance benchmarks.
  • Weeks 6-8 (tasks 11-12): Consensus deep-dive -- instrument consensus rounds and phases, then run full integration testing across all instrumented paths.
  • Week 9 (tasks 13-14): Go-live -- deploy to production with sampling/alerting configured, and deliver documentation and operator training.
  • Arrow chain (t1 → ... → t14): Strict sequential dependency; each task's output is a prerequisite for the next.

Appendix: External Dashboard Parity

Cross-phase plan for reaching parity with the community xrpl-validator-dashboard. Previously a standalone design spec; merged here so the phase plan is self-contained.

Date: 2026-03-30 Status: Draft Source: realgrapedrop/xrpl-validator-dashboard Jira Epic: RIPD-5060

Summary

Integrate 29 missing metrics, 18 alert rules, and enriched span attributes from the community xrpl-validator-dashboard into xrpld's native OpenTelemetry instrumentation. Changes are distributed across phases 2, 3, 4, 6, 7, 9, 10, and 11 of the OTel PR chain.

Gap Analysis

Coverage Breakdown (86 external metrics)

Status Count Notes
Already covered 30 peer_count, load_factor, io_latency, uptime, overlay traffic
Partially covered 3 state_value encoding, NuDB granularity, validation_quorum
Missing 29 Validation agreement, ledger economy, peer quality, UNL health
N/A (external) 24 Monitor health, realtime duplicates, system metrics

Missing Metrics by Category

Category Metrics Count
Validation Agreement validations_sent_total, validations_checked_total, validation_agreements_total, validation_missed_total, validation_agreement_pct_1h/24h, validation_agreements_1h/24h, validation_missed_1h/24h, validation_event 11
Ledger Economy ledgers_closed_total, ledger_age_seconds, base_fee_xrp, reserve_base_xrp, reserve_inc_xrp, transaction_rate 6
State Tracking time_in_current_state_seconds, state_changes_total, validator_state_info 3
Peer Quality peers_insane, peer_latency_p90_ms 2
Validator Health amendment_blocked, unl_expiry_days 2
Upgrade Awareness peers_higher_version_pct, upgrade_recommended 2
Storage / Other ledger_nudb_bytes, jq_trans_overflow_total, initial_sync_duration_seconds 3

Alert Rules (18 total, from external dashboard)

Group Count Rules
Critical 8 Agreement <90%, not proposing, unhealthy state, amendment blocked, UNL expiring, IO latency, load factor, peer count <5
Network 3 Peer drop >10%/30%, P90 latency + disconnect correlation
Performance 7 CPU >80%, memory >90%, disk >85%, job queue overflow, upgrade recommended, tx rate drop, stale ledger

Branch-to-Change Mapping

Phase 2 — pratik/otel-phase2-rpc-tracing

Ref: Adds to existing Phase 2 task list. Consumed by Phase 7 (MetricsRegistry) and Phase 10 (validation checks).

Task 2.8: RPC Span Attribute Enrichment

Add node-level health context to every rpc.command.* span so operators can correlate RPC behavior with node state.

New span attributes on rpc.command.*:

Attribute Type Source Value Example
xrpl.node.amendment_blocked bool app_.getOPs().isAmendmentBlocked() true
xrpl.node.server_state string app_.getOPs().strOperatingMode() "full", "syncing"

File: src/xrpld/rpc/detail/RPCHandler.cpp (in the rpc.command.* span creation block, after existing setAttribute calls)

Rationale: RPC is the operator's primary interaction point. When a node is amendment-blocked or degraded, every RPC response is suspect. Tagging spans with this state enables Jaeger queries like {name=~"rpc.command.*"} | xrpl.node.amendment_blocked = true to find all RPCs served during a blocked period.

Exit Criteria:

  • rpc.command.server_info spans carry xrpl.node.amendment_blocked and xrpl.node.server_state attributes
  • No measurable latency impact (attribute values are cached atomics, not computed per-call)

Phase 3 — pratik/otel-phase3-tx-tracing

Ref: Adds to existing Phase 3 task list. Consumed by Phase 10 (validation checks).

Task 3.7: Transaction Span Peer Version Attribute

Add the relaying peer's xrpld version to transaction receive spans to enable version-mismatch correlation.

New span attribute on tx.receive:

Attribute Type Source Value Example
xrpl.peer.version string peer->getVersion() "xrpld-2.4.0"

File: src/xrpld/overlay/detail/PeerImp.cpp (in the tx.receive span block, after existing xrpl.peer.id setAttribute)

Rationale: Transaction relay is where version mismatches cause subtle serialization or validation bugs. Tracing "this tx came from a v2.3.0 peer" helps diagnose compatibility issues during network upgrades.

Exit Criteria:

  • tx.receive spans carry xrpl.peer.version attribute with a non-empty version string
  • Attribute is omitted (not empty-string) when getVersion() returns empty

Phase 4 — pratik/otel-phase4-consensus-tracing

Ref: Adds to existing Phase 4 task list. Provides the span-level foundation that Phase 7 (ValidationTracker) builds upon. Consumed by Phase 10 (validation checks).

Task 4.8: Consensus Validation Span Enrichment

Add ledger hash and validation type to validation spans on both send and receive paths. This enables trace-level agreement analysis — filter by ledger hash to see which validators agreed.

New span attributes on consensus.validation.send:

Attribute Type Source Value Example
xrpl.validation.ledger_hash string Ledger hash from validate() call args "A1B2C3..." (64-char hex)
xrpl.validation.full bool Whether this is a full validation true

New span attributes on peer.validation.receive:

Attribute Type Source Value Example
xrpl.peer.validation.ledger_hash string From deserialized STValidation object "A1B2C3..." (64-char hex)
xrpl.peer.validation.full bool From STValidation flags true

New span attributes on consensus.accept:

Attribute Type Source Value Example
xrpl.consensus.validation_quorum int64 app_.validators().quorum() 28
xrpl.consensus.proposers_validated int64 result.proposers from consensus result 35

Files:

  • src/xrpld/app/consensus/RCLConsensus.cpp (validation.send and accept spans)
  • src/xrpld/overlay/detail/PeerImp.cpp (peer.validation.receive span)

Rationale: The external dashboard's most valuable feature is validation agreement tracking. By recording the ledger hash on both outgoing and incoming validation spans, we create the raw data for agreement analysis at the trace level. Phase 7's ValidationTracker builds the metric-level aggregation on top of this.

Exit Criteria:

  • consensus.validation.send spans carry xrpl.validation.ledger_hash and xrpl.validation.full
  • peer.validation.receive spans carry xrpl.peer.validation.ledger_hash and xrpl.peer.validation.full
  • consensus.accept spans carry xrpl.consensus.validation_quorum and xrpl.consensus.proposers_validated
  • Ledger hash attributes match between send and receive for the same ledger

Phase 6 — pratik/otel-phase6-statsd

Ref: Adds to existing Phase 6 scope. No separate task list file exists for Phase 6 per project convention.

Addition: Bridge peerDisconnectsCharges_ metric

The overlay already tracks resource-limit disconnects via OverlayImpl::Stats::peerDisconnectsCharges_ (a beast::insight::Gauge). This metric is registered but not included in the StatsD bridge mapping.

What to do:

  • Ensure xrpld_Overlay_Peer_Disconnects_Charges appears in the StatsD-to-Prometheus metric name mapping
  • Verify the metric appears in Prometheus after StatsD bridge is active

File: src/xrpld/overlay/detail/OverlayImpl.cpp

Prometheus name: xrpld_Overlay_Peer_Disconnects_Charges

Phase 7 — pratik/otel-phase7-native-metrics

Ref: Adds to existing Phase 7 task list. This is the largest addition. Depends on Phase 4 span attributes for validation tracking context. Consumed by Phase 9 (dashboards), Phase 10 (validation), Phase 11 (alerts).

Task 7.8: ValidationTracker — Validation Agreement Computation

The most valuable missing component. A stateful class that tracks whether our validator's validations agree with network consensus, maintaining rolling 1h and 24h windows.

Architecture:


  consensus.validation.send ─────> ValidationTracker ──────> MetricsRegistry
  (records our validation          (reconciles after         (exports agreement
   for ledger X)                    8s grace period)          gauges every 10s)

  ledger.validate ───────────────> ValidationTracker
  (records which ledger            (marks ledger X as
   network validated)               agreed or missed)

Design:

/// Tracks validation agreement between this node and network consensus.
///
///  ValidationTracker
///  ├── recordOurValidation(ledgerHash, ledgerSeq)  // called when we send
///  ├── recordNetworkValidation(ledgerHash, seq)     // called on ledger validate
///  ├── reconcile()                                  // called periodically (timer)
///  ├── agreementPct1h() -> double                   // 0.0-100.0
///  ├── agreementPct24h() -> double
///  ├── agreements1h() -> uint64_t
///  ├── missed1h() -> uint64_t
///  ├── agreements24h() -> uint64_t
///  ├── missed24h() -> uint64_t
///  ├── totalAgreements() -> uint64_t
///  ├── totalMissed() -> uint64_t
///  ├── totalValidationsSent() -> uint64_t
///  └── totalValidationsChecked() -> uint64_t        // all network validations seen
class ValidationTracker
{
    // Ring buffer of pending ledger events (max 1000)
    struct LedgerEvent {
        uint256 ledgerHash;
        LedgerIndex seq;
        TimePoint closeTime;
        bool weValidated = false;     // did we send a validation for this ledger?
        bool networkValidated = false; // did network validate this ledger?
        bool reconciled = false;       // has 8s grace period elapsed?
        bool agreed = false;           // after reconciliation: did we agree?
    };

    // Sliding window deques for pre-computed window stats
    struct WindowEvent {
        TimePoint time;
        bool agreed;
    };
    std::deque<WindowEvent> window1h_;  // events in last 1 hour
    std::deque<WindowEvent> window24h_; // events in last 24 hours

    // Reconciliation: 8s grace period after ledger close.
    // If our validation hasn't arrived by then, mark as missed.
    // 5-minute late repair: if a late validation arrives, correct the miss.
    static constexpr auto kGracePeriod = std::chrono::seconds(8);
    static constexpr auto kLateRepairWindow = std::chrono::minutes(5);
};

Recording sites (modifications to consensus code from Phase 7 branch):

Hook Point File What to Record
validate() in doAccept() RCLConsensus.cpp tracker.recordOurValidation(ledgerHash, seq)
onValidation() callback RCLValidations path tracker.recordNetworkValidation(...) — increment validationsChecked
LedgerMaster fully-validated LedgerMaster.cpp tracker.recordNetworkValidation(validatedHash, seq)

Key new files:

  • src/xrpld/telemetry/ValidationTracker.h
  • src/xrpld/telemetry/detail/ValidationTracker.cpp

Key modified files:

  • src/xrpld/telemetry/MetricsRegistry.h (add ValidationTracker member)
  • src/xrpld/telemetry/MetricsRegistry.cpp (add gauge callback reading from tracker)
  • src/xrpld/app/consensus/RCLConsensus.cpp (add recording hooks)
  • src/xrpld/app/ledger/detail/LedgerMaster.cpp (add recording hook)

Exit Criteria:

  • ValidationTracker correctly tracks agreement with 8s grace period
  • 5-minute late repair corrects false-positive misses
  • Thread-safe (atomics + mutex for window deques)
  • Rolling windows correctly evict stale entries
  • Unit tests for: normal agreement, missed validation, late repair, window eviction

Task 7.9: Validator Health Observable Gauges

New MetricsRegistry observable gauge for amendment, UNL, and quorum health.

Gauge Name Label metric= Type Source
xrpld_validator_health amendment_blocked int64 app_.getOPs().isAmendmentBlocked() → 0/1
unl_blocked int64 app_.getOPs().isUNLBlocked() → 0/1
unl_expiry_days double app_.validators().expires() → days until expiry
validation_quorum int64 app_.validators().quorum()

File: src/xrpld/telemetry/MetricsRegistry.cpp (new gauge callback in registerAsyncGauges())

Exit Criteria:

  • All 4 label values emitted every 10s
  • unl_expiry_days is negative when expired, positive when active
  • Values visible in Prometheus

Task 7.10: Peer Quality Observable Gauges

New MetricsRegistry observable gauge for peer health aggregates.

Gauge Name Label metric= Type Source
xrpld_peer_quality peer_latency_p90_ms double Iterate peers, compute P90 from latency_
peers_insane_count int64 Count peers with tracking_ == diverged
peers_higher_version_pct double Compare getVersion() to own version
upgrade_recommended int64 1 if peers_higher_version_pct > 60%

Implementation note: The callback iterates app_.overlay().foreach(...) to collect per-peer latency and version data. This runs every 10s on the metrics reader thread — acceptable overhead for ~50-200 peers.

File: src/xrpld/telemetry/MetricsRegistry.cpp

Exit Criteria:

  • P90 latency computed correctly (sort peer latencies, pick 90th percentile)
  • Insane count matches peers RPC output
  • Version comparison handles format variations (e.g., "xrpld-2.4.0-rc1")
  • Values visible in Prometheus

Task 7.11: Ledger Economy Observable Gauges

New MetricsRegistry observable gauge for fee and ledger metrics.

Gauge Name Label metric= Type Source
xrpld_ledger_economy base_fee_xrp double app_.getFeeTrack().getBaseFee() → drops
reserve_base_xrp double From validated ledger fee settings
reserve_inc_xrp double From validated ledger fee settings
ledger_age_seconds double now - lastValidatedCloseTime
transaction_rate double Derived: tx count delta / time delta

File: src/xrpld/telemetry/MetricsRegistry.cpp

Exit Criteria:

  • Fee values match server_info RPC output
  • ledger_age_seconds increases monotonically between ledger closes, resets on close
  • transaction_rate is smoothed (rolling average, not instantaneous)

Task 7.12: State Tracking Observable Gauges

New MetricsRegistry observable gauge for node state duration.

Gauge Name Label metric= Type Source
xrpld_state_tracking state_value int64 0-7 numeric encoding matching external dashboard
time_in_current_state_seconds double now - lastModeChangeTime

State value encoding:

xrpld's OperatingMode enum maps 0-4 (DISCONNECTED through FULL). The external dashboard extends this to 0-6 by combining operating mode with consensus participation:

Value State Source
0 disconnected OperatingMode::DISCONNECTED
1 connected OperatingMode::CONNECTED
2 syncing OperatingMode::SYNCING
3 tracking OperatingMode::TRACKING
4 full OperatingMode::FULL and not validating
5 validating OperatingMode::FULL and mConsensus.validating() is true
6 proposing OperatingMode::FULL and consensus mode is proposing

Note: Values 5-6 require checking both OperatingMode and ConsensusMode. The callback should derive these from app_.getOPs().getOperatingMode() combined with mConsensus.mode(). If operating mode is FULL and consensus is proposing → 6; if FULL and validating → 5; otherwise use the raw OperatingMode enum value.

File: src/xrpld/telemetry/MetricsRegistry.cpp

Exit Criteria:

  • state_value matches external dashboard encoding
  • time_in_current_state_seconds resets on mode change

Task 7.13: Storage Detail Observable Gauge

Gauge Name Label metric= Type Source
xrpld_storage_detail nudb_bytes int64 NuDB backend file size (filesystem stat)

File: src/xrpld/telemetry/MetricsRegistry.cpp

Exit Criteria:

  • NuDB file size reported in bytes
  • Gracefully returns 0 if NuDB not configured

Task 7.14: New Synchronous Counters

New counters incremented at event sites. Declared in MetricsRegistry, recording sites added in consensus/overlay/network code.

Counter Name Increment Site Source File
xrpld_ledgers_closed_total onAccept() in consensus RCLConsensus.cpp
xrpld_validations_sent_total validate() in consensus RCLConsensus.cpp
xrpld_validations_checked_total Network validation received LedgerMaster.cpp
xrpld_validation_agreements_total ValidationTracker reconciliation ValidationTracker.cpp
xrpld_validation_missed_total ValidationTracker reconciliation ValidationTracker.cpp
xrpld_state_changes_total setMode() in NetworkOPs NetworkOPs.cpp
xrpld_jq_trans_overflow_total Job queue overflow path JobQueue.cpp

Key modified files:

  • src/xrpld/telemetry/MetricsRegistry.h/.cpp (counter declarations)
  • src/xrpld/app/consensus/RCLConsensus.cpp (recording: ledgers_closed, validations_sent)
  • src/xrpld/app/ledger/detail/LedgerMaster.cpp (recording: validations_checked)
  • src/xrpld/app/misc/NetworkOPs.cpp (recording: state_changes)

Exit Criteria:

  • All 7 counters monotonically increase during normal operation
  • Counter values match expected rates (e.g., ledgers_closed ≈ 1 per 3-5s)
  • Values visible in Prometheus

Task 7.15: Validation Agreement Observable Gauge

Reads from the ValidationTracker (Task 7.8) to export rolling window stats.

Gauge Name Label metric= Type Source
xrpld_validation_agreement agreement_pct_1h double tracker.agreementPct1h()
agreements_1h int64 tracker.agreements1h()
missed_1h int64 tracker.missed1h()
agreement_pct_24h double tracker.agreementPct24h()
agreements_24h int64 tracker.agreements24h()
missed_24h int64 tracker.missed24h()

File: src/xrpld/telemetry/MetricsRegistry.cpp

Exit Criteria:

  • Agreement percentages in range [0.0, 100.0]
  • Window stats match manual count from validation counters
  • Percentages stabilize after 1h/24h of operation

Phase 9 — pratik/otel-phase9-metric-gap-fill

Ref: Adds to existing Phase 9 task list. Depends on Phase 7 gauges/counters. Consumed by Phase 10 (dashboard load checks).

Task 9.11: Validator Health Dashboard

New Grafana dashboard: validator-health.json

Panel Type PromQL
Agreement % (1h) stat xrpld_validation_agreement{metric="agreement_pct_1h"}
Agreement % (24h) stat xrpld_validation_agreement{metric="agreement_pct_24h"}
Agreements vs Missed (1h) bargauge agreements_1h and missed_1h side by side
Agreements vs Missed (24h) bargauge agreements_24h and missed_24h side by side
Validation Rate stat rate(xrpld_validations_sent_total[5m]) * 60
Validations Checked Rate stat rate(xrpld_validations_checked_total[5m]) * 60
Amendment Blocked stat xrpld_validator_health{metric="amendment_blocked"}
UNL Expiry (days) stat xrpld_validator_health{metric="unl_expiry_days"}
Validation Quorum stat xrpld_validator_health{metric="validation_quorum"}
State Value Timeline timeseries xrpld_state_tracking{metric="state_value"}
Time in Current State stat xrpld_state_tracking{metric="time_in_current_state_seconds"}
State Changes Rate stat rate(xrpld_state_changes_total[1h])
Ledgers Closed Rate stat rate(xrpld_ledgers_closed_total[5m]) * 60

Dashboard conventions: $node template variable for service_instance_id filtering, dark theme, matching existing panel sizes and color schemes.

Task 9.12: Peer Quality Dashboard

New Grafana dashboard: peer-quality.json

Panel Type PromQL
P90 Peer Latency timeseries xrpld_peer_quality{metric="peer_latency_p90_ms"}
Insane/Diverged Peers stat xrpld_peer_quality{metric="peers_insane_count"}
Higher Version Peers % stat xrpld_peer_quality{metric="peers_higher_version_pct"}
Upgrade Recommended stat xrpld_peer_quality{metric="upgrade_recommended"}
Resource Disconnects timeseries xrpld_Overlay_Peer_Disconnects_Charges
Inbound vs Outbound bargauge xrpld_Peer_Finder_Active_Inbound_Peers, ..._Outbound_Peers

Task 9.13: Ledger Economy Dashboard Panels

Add a "Ledger Economy" row to the existing node-health.json dashboard:

Panel Type PromQL
Base Fee (drops) stat xrpld_ledger_economy{metric="base_fee_xrp"}
Reserve Base (drops) stat xrpld_ledger_economy{metric="reserve_base_xrp"}
Reserve Inc (drops) stat xrpld_ledger_economy{metric="reserve_inc_xrp"}
Ledger Age stat xrpld_ledger_economy{metric="ledger_age_seconds"}
Transaction Rate timeseries xrpld_ledger_economy{metric="transaction_rate"}

Phase 10 — pratik/otel-phase10-workload-validation

Ref: Adds to existing Phase 10 task list. Validates all additions from Phases 2-9.

Task 10.6: External Dashboard Parity Validation Checks

Add checks to validate_telemetry.py for all new span attributes and metrics.

New span attribute checks (~8):

Span Name New Attribute
rpc.command.server_info xrpl.node.amendment_blocked
rpc.command.server_info xrpl.node.server_state
tx.receive xrpl.peer.version
consensus.validation.send xrpl.validation.ledger_hash
consensus.validation.send xrpl.validation.full
peer.validation.receive xrpl.peer.validation.ledger_hash
consensus.accept xrpl.consensus.validation_quorum
consensus.accept xrpl.consensus.proposers_validated

New metric existence checks (~13):

Metric Name
xrpld_validation_agreement{metric="agreement_pct_1h"}
xrpld_validation_agreement{metric="agreement_pct_24h"}
xrpld_validator_health{metric="amendment_blocked"}
xrpld_validator_health{metric="unl_expiry_days"}
xrpld_peer_quality{metric="peer_latency_p90_ms"}
xrpld_peer_quality{metric="peers_insane_count"}
xrpld_ledger_economy{metric="base_fee_xrp"}
xrpld_ledger_economy{metric="transaction_rate"}
xrpld_state_tracking{metric="state_value"}
xrpld_ledgers_closed_total
xrpld_validations_sent_total
xrpld_state_changes_total
xrpld_storage_detail{metric="nudb_bytes"}

New dashboard load checks (~3):

Dashboard
validator-health
peer-quality
node-health (updated)

New metric value sanity checks (~4):

Check Condition
validation_agreement_pct_1h in [0, 100]
unl_expiry_days > 0 (not expired)
peer_latency_p90_ms > 0 (peers exist)
state_value in [0, 7]

Total new checks: ~28 (bringing total from 73 to ~101)

Phase 11 — (future branch)

Ref: Adds to existing Phase 11 task list. Depends on Phase 7 metrics and Phase 9 dashboards.

Task 11.9: Alert Rules from External Dashboard

Port 18 alert rules from the external xrpl-validator-dashboard to Grafana alerting provisioning.

Critical Group (8 rules, eval interval 10s):

Rule Condition For
Agreement Below 90% xrpld_validation_agreement{metric="agreement_pct_24h"} < 90 30s
Not Proposing xrpld_state_tracking{metric="state_value"} < 6 10s
Unhealthy State xrpld_state_tracking{metric="state_value"} < 4 10s
Amendment Blocked xrpld_validator_health{metric="amendment_blocked"} == 1 1m
UNL Expiring xrpld_validator_health{metric="unl_expiry_days"} < 14 1h
High IO Latency histogram_quantile(0.95, xrpld_ios_latency_bucket) > 50 1m
High Load Factor xrpld_load_factor_metrics{metric="load_factor"} > 1000 1m
Peer Count Critical xrpld_server_info{metric="peers"} < 5 1m

Network Group (3 rules, eval interval 10s):

Rule Condition For
Peer Drop >10% delta(xrpld_server_info{metric="peers"}[30s]) / ... * 100 < -10 30s
Peer Drop >30% Same formula, threshold -30 30s
P90 Latency + Disconnects peer_latency_p90_ms > 500 AND rate(disconnects) > 0 2m

Performance Group (7 rules, eval interval 10s):

Rule Condition For
CPU High Per-core CPU > 80% 2m
Memory Critical Memory usage > 90% 1m
Disk Warning Disk usage > 85% 2m
Job Queue Overflow rate(xrpld_jq_trans_overflow_total[5m]) > 0 1m
Upgrade Recommended xrpld_peer_quality{metric="peers_higher_version_pct"} > 60 1m
TX Rate Drop Transaction rate dropped > 50% in 5m window 5m
Stale Ledger xrpld_ledger_economy{metric="ledger_age_seconds"} > 30 1m

Notification channels: Template configs for Email/SMTP, Discord, Slack, PagerDuty.

Files:

  • docker/telemetry/grafana/alerting/alert-rules.yaml (new or extend existing)
  • docker/telemetry/grafana/alerting/contact-points.yaml
  • docker/telemetry/grafana/alerting/notification-policies.yaml

Task 11.10: Dual-Datasource Architecture Documentation

Document the external dashboard's "fast path" pattern as a future optimization for real-time panels:

  • Pattern: A lightweight Prometheus scrape endpoint (separate from OTLP pipeline) that polls critical metrics every 2-5s, bypassing the 10s OTLP metric reader interval and Prometheus scrape interval.
  • Use case: Real-time state panels (server state, ledger age, peer count) where 10-15s latency is too slow.
  • Decision: Document as a future option, not implement now. Current 10s interval is acceptable for v1.

File: OpenTelemetryPlan/Phase11_taskList.md (documentation task, no code)

Documentation Updates

docs/telemetry-runbook.md (on Phase 9 branch)

Add new sections after "Phase 9: OTel Metrics Alerting Rules":

  1. Validator Health Monitoring — explains agreement tracking, amendment blocked, UNL expiry, with example PromQL queries
  2. Peer Quality Monitoring — explains P90 latency, insane peers, version awareness
  3. Ledger Economy Monitoring — explains fee/reserve gauges, transaction rate, ledger age
  4. Validation Agreement Explained — operator-facing explanation of the reconciliation algorithm (8s grace, 5m late repair), what "missed" means, and when to worry

OpenTelemetryPlan/09-data-collection-reference.md (on Phase 9 branch)

Add new metric tables in a "Phase 7+: External Dashboard Parity" section covering all 29 new metrics with their gauge names, label values, types, and sources.

Cross-Phase Dependency Chain

Phase 2 (span attrs: amendment_blocked, server_state)
Phase 3 (span attrs: peer.version)
Phase 4 (span attrs: validation.ledger_hash, validation.full, quorum)
Phase 6 (StatsD bridge: peerDisconnectsCharges)
    │
    ├── all above rebase into ──>
    │
Phase 7 (ValidationTracker + 7 gauges + 7 counters + agreement gauge)
    │
Phase 9 (3 dashboards + ledger economy panels + runbook + data-collection-ref)
    │
Phase 10 (28 new validation checks in validate_telemetry.py)
    │
Phase 11 (18 alert rules + dual-datasource docs)

Rebase Strategy

After committing changes to each branch (starting from Phase 2):

  1. Commit on pratik/otel-phase2-rpc-tracing
  2. Rebase phase3 onto phase2, resolve conflicts (task list files only — low risk)
  3. Commit on phase3, rebase phase4 onto phase3
  4. Continue through chain: 4 → 5 → 5b → 6 → 7 → 8 → 9 → 10
  5. Force-push-with-lease all affected branches

Since these are documentation-only changes (task list .md files), merge conflicts should be minimal — each file is unique to its branch.

Previous: Configuration Reference | Next: Observability Backends | Back to: Overview