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rippled/OpenTelemetryPlan/06-implementation-phases.md
Pratik Mankawde 6738f8b9ab docs: update Phase 9 docs and dashboard for push_metrics.py parity gauges 
- Add Task 9.7a to Phase9_taskList.md documenting new gauges
- Add metric tables to 09-data-collection-reference.md (server_info,
  build_info, complete_ledgers, db_metrics, extended cache/nodestore)
- Update metric counts from ~50 to ~68 in 06-implementation-phases.md
- Add OTel MetricsRegistry gauge reference to telemetry-runbook.md
- Add 11 new panels to system-node-health.json Grafana dashboard
  (server state, uptime, peers, validated seq, last close info,
  build version, complete ledgers, db sizes, historical fetch rate,
  peer disconnects)
- Fix leftover merge conflict marker in 08-appendix.md
- Add ripplex/mseconds to cspell dictionary

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-31 16:39:40 +01:00

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# Implementation Phases
> **Parent Document**: [OpenTelemetryPlan.md](./OpenTelemetryPlan.md)
> **Related**: [Configuration Reference](./05-configuration-reference.md) | [Observability Backends](./07-observability-backends.md)
---
## 6.1 Phase Overview
> **TxQ** = Transaction Queue
```mermaid
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
Validator List & Manifest Tracing :p4f, after p4b, 2d
Amendment Voting Tracing :p4g, after p4f, 2d
SHAMap Sync Tracing :p4h, after p4g, 2d
Validation Tests :p4c, after p4h, 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
section Phase 9 (Future)
Internal Metric Gap Fill :p9, after p8, 2.5w
section Phase 10 (Future)
Workload Validation :p10, after p9, 2w
section Phase 11 (Future)
Third-Party Collection :p11, after p10, 3w
```
---
## 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
### 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 | Performance benchmarks |
### 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
---
## 6.5 Phase 4: Consensus Tracing (Weeks 7-8)
**Objective**: Full observability into consensus rounds
### Tasks
| Task | Description |
| ---- | ---------------------------------------------- |
| 4.1 | Instrument `RCLConsensusAdaptor::startRound()` |
| 4.2 | Instrument phase transitions |
| 4.3 | Instrument proposal handling |
| 4.4 | Instrument validation handling |
| 4.5 | Add consensus-specific attributes |
| 4.6 | Correlate with transaction traces |
| 4.7 | Validator list and manifest tracing |
| 4.8 | Amendment voting tracing |
| 4.9 | SHAMap sync tracing |
| 4.10 | Multi-validator integration tests |
| 4.11 | Performance validation |
### Spans Produced
| Span Name | Location | Attributes |
| --------------------------- | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `consensus.proposal.send` | `RCLConsensus.cpp:177` | `xrpl.consensus.round` |
| `consensus.ledger_close` | `RCLConsensus.cpp:282` | `xrpl.consensus.ledger.seq`, `xrpl.consensus.mode` |
| `consensus.accept` | `RCLConsensus.cpp:395` | `xrpl.consensus.proposers`, `xrpl.consensus.round_time_ms` |
| `consensus.accept.apply` | `RCLConsensus.cpp:521` | `xrpl.consensus.close_time`, `close_time_correct`, `close_resolution_ms`, `state`, `proposing`, `round_time_ms`, `ledger.seq`, `parent_close_time`, `close_time_self`, `close_time_vote_bins`, `resolution_direction` |
| `consensus.validation.send` | `RCLConsensus.cpp:753` | `xrpl.consensus.proposing` |
### Exit Criteria
- [x] Complete consensus round traces
- [x] Phase transitions visible
- [x] Proposals and validations traced
- [x] Close time agreement tracked (per `avCT_CONSENSUS_PCT`)
- [x] No impact on consensus timing
- [ ] Multi-validator test network validated
### 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](./05-configuration-reference.md) for full
configuration options. The `consensus_trace_strategy` option will be
documented in the configuration reference as part of Phase 4a implementation.
- **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](./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`. Long-lived spans use
direct SpanGuard members; short-lived scoped spans use `XRPL_TRACE_*` macros.
### Tasks
| Task | Description | Effort | Risk |
| ---- | ------------------------------------------------ | ------ | ------ |
| 4a.0 | Prerequisites: extend SpanGuard & Telemetry APIs | 1d | Medium |
| 4a.1 | Adaptor `getTelemetry()` method | 0.5d | Low |
| 4a.2 | Switchable round span with deterministic traceID | 2d | High |
| 4a.3 | Span members in `Consensus.h` | 0.5d | Medium |
| 4a.4 | Instrument `phaseEstablish()` | 1d | Medium |
| 4a.5 | Instrument `updateOurPositions()` | 1d | Medium |
| 4a.6 | Instrument `haveConsensus()` (thresholds) | 1d | Medium |
| 4a.7 | Instrument mode changes | 0.5d | Low |
| 4a.8 | Reparent existing spans under round | 0.5d | Low |
| 4a.9 | Build verification and testing | 1d | Low |
**Total Effort**: 9 days
### Spans Produced
| Span Name | Location | Key Attributes |
| ---------------------------- | ------------------ | ---------------------------------------------------------------- |
| `consensus.round` | `RCLConsensus.cpp` | `round_id`, `ledger_id`, `ledger.seq`, `mode`; link → prev round |
| `consensus.establish` | `Consensus.h` | `converge_percent`, `establish_count`, `proposers` |
| `consensus.update_positions` | `Consensus.h` | `disputes_count`, `converge_percent`, `proposers_agreed/total` |
| `consensus.check` | `Consensus.h` | `agree/disagree_count`, `threshold_percent`, `result` |
| `consensus.mode_change` | `RCLConsensus.cpp` | `mode.old`, `mode.new` |
### Exit Criteria
- [ ] Establish phase internals fully traced (disputes, convergence, thresholds)
- [ ] 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](./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**: Design documented, NOT implemented. Protobuf fields (field 1001)
and `TraceContextPropagator` class exist. Wiring deferred until Phase 4a is
validated in a multi-node environment.
**Prerequisites**: Phase 4a complete and validated.
See [Phase4_taskList.md § Phase 4b](./Phase4_taskList.md) for full design.
---
## 6.6 Phase 5: Documentation & Deployment (Week 9)
**Objective**: Production readiness
### Tasks
| Task | Description |
| ---- | ----------------------------- |
| 5.1 | Operator runbook |
| 5.2 | Grafana dashboards |
| 5.3 | Alert definitions |
| 5.4 | Collector deployment examples |
| 5.5 | Developer documentation |
| 5.6 | Training materials |
| 5.7 | Final integration testing |
---
## 6.7 Phase 6: StatsD Metrics Integration (Week 10)
**Objective**: Bridge rippled'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
rippled 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 (8 panels) |
| 6.6 | Create "Network Traffic" Grafana dashboard (8 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:706` 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: `rippled-statsd-node-health`):
- Validated/Published Ledger Age, Operating Mode Duration/Transitions, I/O Latency, Job Queue Depth, Ledger Fetch Rate, Ledger History Mismatches
**Network Traffic** (`statsd-network-traffic.json`, uid: `rippled-statsd-network`):
- Active Inbound/Outbound Peers, Peer Disconnects, Total Bytes/Messages In/Out, Transaction/Proposal/Validation Traffic, Top Traffic Categories
**RPC & Pathfinding (StatsD)** (`statsd-rpc-pathfinding.json`, uid: `rippled-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=rippled_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; resolved by Phase 7's OTel Counter mapping)
---
## 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)
```mermaid
graph LR
subgraph rippledNode["rippled 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 -->|"rippled_* 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 rippledNode 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
```ini
# [insight] section — new "otel" server option
[insight]
server=otel # NEW: uses OTel OTLP metrics exporter
prefix=rippled # 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](./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
---
## 6.8.1 Phase 8: Log-Trace Correlation and Centralized Log Ingestion (Week 13)
### Motivation
rippled'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 rippled** — Uses existing OTel SDK headers (`GetSpan`, `GetContext`) already linked in Phase 1.
5. **Negligible overhead** — `GetSpan()` + `GetContext()` are thread-local reads (<10ns/call). At ~1000 JLOG calls/min, this adds <10us/min.
#### 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 rippled'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
```mermaid
flowchart LR
subgraph rippled["rippled 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 rippled 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
```mermaid
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["rippled<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 rippled 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)
---
## 6.8.2 Phase 9: Internal Metric Instrumentation Gap Fill (Weeks 14-15) — Future Enhancement
> **Status**: Planned, not yet implemented.
### Motivation
Phases 1-8 establish trace spans, StatsD metrics bridge, native OTel metrics, and log-trace correlation. However, ~68 metrics that exist inside rippled's `get_counts`, `server_info`, TxQ, PerfLog, and `CountedObject` systems have **no time-series export path**. These are the metrics that exchanges, payment processors, analytics providers, validators, and researchers need most — NodeStore I/O performance, cache hit rates, per-RPC-method counters, transaction queue depth, fee escalation levels, and live object instance counts.
### Architecture
Hybrid approach — two instrumentation strategies based on proximity to existing code:
```mermaid
flowchart TB
subgraph rippled["rippled process"]
subgraph existing["Existing beast::insight registrations"]
NS["NodeStore I/O<br/>(Database.cpp)"]
end
subgraph newreg["New OTel MetricsRegistry"]
CR["Cache Hit Rates<br/>(async gauge callbacks)"]
TQ["TxQ Metrics<br/>(async gauge callbacks)"]
PL["PerfLog RPC/Job<br/>(counters + histograms)"]
CO["CountedObjects<br/>(async gauge callbacks)"]
LF["Load Factors<br/>(async gauge callbacks)"]
end
end
subgraph export["Export Pipelines"]
BI["beast::insight<br/>OTelCollector (Phase 7)"]
OS["OTel Metrics SDK<br/>PeriodicMetricReader"]
end
NS --> BI
CR --> OS
TQ --> OS
PL --> OS
CO --> OS
LF --> OS
BI --> OTLP["OTLP/HTTP :4318<br/>/v1/metrics"]
OS --> OTLP
style rippled fill:#1a2633,color:#ccc,stroke:#4a90d9
style existing fill:#2a4a6b,color:#fff,stroke:#4a90d9
style newreg fill:#2a4a6b,color:#fff,stroke:#4a90d9
style export fill:#1a3320,color:#ccc,stroke:#5cb85c
style NS fill:#4a90d9,color:#fff,stroke:#2a6db5
style CR fill:#5cb85c,color:#fff,stroke:#3d8b3d
style TQ fill:#5cb85c,color:#fff,stroke:#3d8b3d
style PL fill:#5cb85c,color:#fff,stroke:#3d8b3d
style CO fill:#5cb85c,color:#fff,stroke:#3d8b3d
style LF fill:#5cb85c,color:#fff,stroke:#3d8b3d
style BI fill:#449d44,color:#fff,stroke:#2d6e2d
style OS fill:#449d44,color:#fff,stroke:#2d6e2d
style OTLP fill:#f0ad4e,color:#000,stroke:#c78c2e
```
- **beast::insight extensions** (blue): NodeStore I/O metrics added near existing `Database.cpp` registrations — exported via Phase 7's `OTelCollector`.
- **OTel MetricsRegistry** (green): New centralized class using `ObservableGauge` async callbacks for cache, TxQ, PerfLog, CountedObjects, and load factors — polled at 10s intervals by `PeriodicMetricReader`.
### Third-Party Consumer Context
| Consumer Category | Key Metrics They Need From Phase 9 |
| ---------------------- | --------------------------------------------------------------- |
| Exchanges | Fee escalation levels, TxQ depth, settlement latency |
| Payment Processors | Load factors, io_latency, transaction throughput |
| Analytics Providers | NodeStore I/O, cache hit rates, counted objects |
| Validators / Operators | Per-job execution times, PerfLog RPC counters, consensus timing |
| Academic Researchers | Consensus performance time-series, fee market dynamics |
| Institutional Custody | Server health scores, reserve calculations, node availability |
### Tasks
| Task | Description |
| ---- | ----------------------------------------- |
| 9.1 | NodeStore I/O metrics |
| 9.2 | Cache hit rate metrics + MetricsRegistry |
| 9.3 | TxQ metrics |
| 9.4 | PerfLog per-RPC metrics |
| 9.5 | PerfLog per-job metrics |
| 9.6 | Counted object instance metrics |
| 9.7 | Fee escalation & load factor metrics |
| 9.7a | push_metrics.py parity gauges |
| 9.8 | New Grafana dashboards (2 new, 2 updated) |
| 9.9 | Update documentation |
| 9.10 | Integration tests |
See [Phase9_taskList.md](./Phase9_taskList.md) for detailed per-task breakdown.
### Exit Criteria
- [ ] All ~68 new metrics visible in Prometheus via OTLP pipeline
- [ ] `MetricsRegistry` class registers/deregisters cleanly with OTel SDK
- [ ] 2 new Grafana dashboards operational (Fee Market, Job Queue)
- [ ] No performance regression (< 0.5% CPU overhead from new callbacks)
- [ ] Documentation updated with full new metric inventory
---
## 6.8.3 Phase 10: Synthetic Workload Generation & Telemetry Validation (Weeks 16-17) — Future Enhancement
> **Status**: Planned, not yet implemented.
### Motivation
Before the telemetry stack (Phases 1-9) can be considered production-ready, we need automated proof that all 16 spans, 22 attributes, 300+ metrics, 10 Grafana dashboards, and log-trace correlation work correctly under realistic load. This phase establishes a reusable CI-integrated validation suite and performance benchmark baseline.
### Architecture
```mermaid
flowchart LR
subgraph harness["Docker Compose Workload Harness"]
direction TB
V1["Validator 1"] ~~~ V2["Validator 2"] ~~~ V3["Validator 3"]
V4["Validator 4"] ~~~ V5["Validator 5"]
end
subgraph generators["Workload Generators"]
RPC["RPC Load Generator<br/>(configurable RPS,<br/>command distribution)"]
TX["Transaction Submitter<br/>(Payment, Offer, NFT,<br/>Escrow, AMM mix)"]
end
subgraph validation["Validation Suite"]
SV["Span Validator<br/>(Jaeger/Tempo API)"]
MV["Metric Validator<br/>(Prometheus API)"]
LV["Log-Trace Validator<br/>(Loki API)"]
DV["Dashboard Validator<br/>(Grafana API)"]
BM["Benchmark Suite<br/>(CPU, memory, latency<br/>ON vs OFF comparison)"]
end
generators --> harness
harness --> validation
style harness fill:#1a2633,color:#ccc,stroke:#4a90d9
style generators fill:#1a3320,color:#ccc,stroke:#5cb85c
style validation fill:#332a1a,color:#ccc,stroke:#f0ad4e
style V1 fill:#4a90d9,color:#fff,stroke:#2a6db5
style V2 fill:#4a90d9,color:#fff,stroke:#2a6db5
style V3 fill:#4a90d9,color:#fff,stroke:#2a6db5
style V4 fill:#4a90d9,color:#fff,stroke:#2a6db5
style V5 fill:#4a90d9,color:#fff,stroke:#2a6db5
style RPC fill:#5cb85c,color:#fff,stroke:#3d8b3d
style TX fill:#5cb85c,color:#fff,stroke:#3d8b3d
style SV fill:#f0ad4e,color:#000,stroke:#c78c2e
style MV fill:#f0ad4e,color:#000,stroke:#c78c2e
style LV fill:#f0ad4e,color:#000,stroke:#c78c2e
style DV fill:#f0ad4e,color:#000,stroke:#c78c2e
style BM fill:#f0ad4e,color:#000,stroke:#c78c2e
```
### Tasks
| Task | Description |
| ---- | -------------------------------------- |
| 10.1 | Multi-node test harness (5 validators) |
| 10.2 | RPC load generator |
| 10.3 | Transaction submitter (6+ tx types) |
| 10.4 | Telemetry validation suite |
| 10.5 | Performance benchmark suite |
| 10.6 | CI integration |
| 10.7 | Documentation |
See [Phase10_taskList.md](./Phase10_taskList.md) for detailed per-task breakdown.
### Exit Criteria
- [ ] 5-node validator cluster starts and reaches consensus in docker-compose
- [ ] Validation suite confirms all 16 spans, 22 attributes, 300+ metrics
- [ ] All 10 Grafana dashboards render data (no empty panels)
- [ ] Benchmark shows < 3% CPU overhead, < 5MB memory overhead
- [ ] CI workflow runs validation on telemetry branch changes
---
## 6.8.4 Phase 11: Third-Party Data Collection Pipelines (Weeks 18-20) — Future Enhancement
> **Status**: Planned, not yet implemented.
### Motivation
rippled has no native Prometheus/OTLP metrics export for data accessible only via JSON-RPC (`server_info`, `get_counts`, `fee`, `peers`, `validators`, `feature`). Every external consumer — exchanges, payment processors, analytics providers, validators, compliance firms, DeFi protocols, researchers, custodians, and CBDC platforms — must build custom JSON-RPC polling and conversion pipelines. This phase centralizes that work into a reusable custom OTel Collector receiver.
### Architecture
```mermaid
flowchart LR
subgraph receiver["Custom OTel Collector Receiver (Go)"]
direction TB
SI["server_info<br/>collector"]
GC["get_counts<br/>collector"]
FE["fee<br/>collector"]
PE["peers<br/>collector"]
VA["validators<br/>collector"]
DX["DEX/AMM<br/>collector<br/>(optional)"]
end
rippled["rippled<br/>Admin RPC<br/>:5005"] -->|"JSON-RPC<br/>poll every 30s"| receiver
receiver -->|"xrpl_* metrics"| PROM["Prometheus<br/>:9090"]
receiver -->|"OTLP export"| OTLP["Any OTLP-<br/>compatible<br/>backend"]
PROM --> GF["Grafana<br/>4 new dashboards"]
PROM --> AL["Prometheus<br/>Alerting Rules"]
style receiver fill:#1a3320,color:#ccc,stroke:#5cb85c
style SI fill:#5cb85c,color:#fff,stroke:#3d8b3d
style GC fill:#5cb85c,color:#fff,stroke:#3d8b3d
style FE fill:#5cb85c,color:#fff,stroke:#3d8b3d
style PE fill:#5cb85c,color:#fff,stroke:#3d8b3d
style VA fill:#5cb85c,color:#fff,stroke:#3d8b3d
style DX fill:#449d44,color:#fff,stroke:#2d6e2d
style rippled fill:#4a90d9,color:#fff,stroke:#2a6db5
style PROM fill:#f0ad4e,color:#000,stroke:#c78c2e
style OTLP fill:#f0ad4e,color:#000,stroke:#c78c2e
style GF fill:#5bc0de,color:#000,stroke:#3aa8c1
style AL fill:#d9534f,color:#fff,stroke:#b52d2d
```
### Third-Party Consumer Gap Analysis
| Consumer Category | Data Unlocked by Phase 11 |
| ---------------------- | ------------------------------------------------------------ |
| Exchanges | Real-time fee estimates, TxQ capacity, server health scores |
| Payment Processors | Settlement latency percentiles, corridor health |
| Analytics Providers | Validator metrics, network topology, amendment voting status |
| DeFi / AMM | AMM pool TVL, DEX order book depth, trade volumes |
| Validators / Operators | Per-peer latency, version distribution, UNL health, alerting |
| Compliance | Transaction volume trends, network growth metrics |
| Academic Researchers | Consensus performance time-series, decentralization metrics |
| CBDC / Tokenization | Token supply tracking, trust line adoption, freeze status |
| Institutional Custody | Multi-sig status, escrow tracking, reserve calculations |
| Wallet Providers | Server health for node selection, fee prediction data |
### Tasks
| Task | Description |
| ----- | ------------------------------------- |
| 11.1 | OTel Collector receiver scaffold (Go) |
| 11.2 | server_info / server_state collector |
| 11.3 | get_counts collector |
| 11.4 | Peer topology collector |
| 11.5 | Validator & amendment collector |
| 11.6 | Fee & TxQ collector |
| 11.7 | DEX & AMM collector (optional) |
| 11.8 | Prometheus alerting rules |
| 11.9 | New Grafana dashboards (4) |
| 11.10 | Integration with Phase 10 validation |
| 11.11 | Documentation |
See [Phase11_taskList.md](./Phase11_taskList.md) for detailed per-task breakdown.
### Exit Criteria
- [ ] Custom OTel Collector receiver exports all `xrpl_*` metrics to Prometheus
- [ ] 4 new Grafana dashboards operational (Validator Health, Network Topology, Fee Market, DEX/AMM)
- [ ] Prometheus alerting rules fire correctly for simulated failures
- [ ] Receiver handles rippled restart/unavailability gracefully
- [ ] Go receiver has unit tests with >80% coverage
---
## 6.9 Risk Assessment
```mermaid
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 |
---
## 6.9 Quick Wins and Crawl-Walk-Run Strategy
> **TxQ** = Transaction Queue
This section outlines a prioritized approach to maximize ROI with minimal initial investment.
### 6.9.1 Crawl-Walk-Run Overview
<div align="center">
```mermaid
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[Validator, Amendment,<br/>SHAMap Tracing] ~~~ r3[Full Correlation] ~~~ 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
```
</div>
**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, validator/amendment/SHAMap tracing, end-to-end correlation, 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.
### 6.9.2 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 |
### 6.9.3 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
### 6.9.4 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
### 6.9.5 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
- Amendment voting tracing
- SHAMap sync tracing
- Full end-to-end traces (client → RPC → TX → consensus → ledger)
**Code Changes**: ~100 lines across 3 consensus files, plus validator/amendment/SHAMap modules
**Why Do This Last**:
- Highest complexity (consensus is critical path)
- Validator, amendment, and SHAMap components are lower priority
- Requires thorough testing
- Lower relative value (consensus issues are rarer)
### 6.9.6 ROI Prioritization Matrix
```mermaid
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 |
| 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, 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 | Status |
| -------- | -------------------------------- | --------------------------- | -------------- | ------------------ |
| Phase 1 | SDK compiles and runs | Zero overhead when disabled | End of Week 2 | Active |
| Phase 2 | 100% RPC coverage | <1ms latency overhead | End of Week 4 | Active |
| Phase 3 | Cross-node traces work | <5% throughput impact | End of Week 6 | Active |
| Phase 4 | Consensus fully traced | No consensus timing impact | End of Week 8 | Active |
| Phase 5 | Production deployment | Operators trained | End of Week 9 | Active |
| Phase 6 | StatsD metrics in Prometheus | 3 dashboards operational | End of Week 10 | Active |
| Phase 7 | All metrics via OTLP | No StatsD dependency | End of Week 12 | Active |
| Phase 8 | trace_id in logs + Loki | Tempo↔Loki correlation | End of Week 13 | Active |
| Phase 9 | 68+ new internal metrics in Prom | 2 new dashboards | End of Week 15 | Future Enhancement |
| Phase 10 | Full telemetry stack validated | < 3% CPU overhead proven | End of Week 17 | Future Enhancement |
| Phase 11 | Third-party metrics via receiver | 4 new dashboards + alerting | End of Week 20 | Future Enhancement |
---
## 6.13 Recommended Implementation Order
Based on ROI analysis, implement in this exact order:
```mermaid
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.
---
_Previous: [Configuration Reference](./05-configuration-reference.md)_ | _Next: [Observability Backends](./07-observability-backends.md)_ | _Back to: [Overview](./OpenTelemetryPlan.md)_
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