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Separate plan from tasks: move Phase 7 plan into 06-implementation-phases.md, remove Phase 8 content

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- Move Phase 7 motivation (gains/losses/decision) and architecture (class
  hierarchy, data flow diagram, config) from Phase7_taskList.md into
  06-implementation-phases.md §6.8
- Strip Phase7_taskList.md to tasks only (7.1-7.8 + summary table)
- Remove Phase8_taskList.md — belongs on Phase 8 branch
- Remove §6.8.1 (Phase 8) from 06-implementation-phases.md
- Remove §5a (Phase 8 log correlation) from 09-data-collection-reference.md
- Remove Phase 8 row from OpenTelemetryPlan.md phase table

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Pratik Mankawde
2026-03-06 15:55:47 +00:00
parent 85a2220312
commit 702cf63c62
5 changed files with 144 additions and 524 deletions
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179
OpenTelemetryPlan/06-implementation-phases.md
View File

@@ -352,15 +352,146 @@ The `StatsDMeterImpl` in `StatsDCollector.cpp:706` sends metrics with `|m` suffi
**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.
### Why Migrate
### Motivation: Why Migrate from StatsD to Native OTel Metrics
The Phase 6 StatsD bridge was a pragmatic first step, but it retains inherent limitations: UDP fire-and-forget with no delivery guarantees, non-standard `|m` wire format, 1472-byte MTU fragmentation, and a split-brain architecture where traces use OTLP but metrics use StatsD. Phase 7 resolves all of these by implementing a new `OTelCollectorImpl` behind the unchanged `beast::insight::Collector` interface — zero changes at call sites.
The Phase 6 StatsD bridge was a pragmatic first step, but it retains inherent limitations that native OTel export resolves.
**What we gain**: Unified OTLP pipeline, delivery guarantees, metric-trace correlation via shared resource attributes, explicit histogram buckets, simpler collector config (no StatsD receiver), and the `|m` meter issue is resolved by mapping to OTel `Counter<uint64_t>`.
#### What We Gain
**What we lose**: StatsD ecosystem compatibility (mitigated: `server=statsd` retained as fallback), slightly higher memory (~1-2 MB for OTel aggregation state), and dependency on OTel C++ Metrics SDK stability (mitigated: SDK 1.18.0 is GA).
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."
See [Phase7_taskList.md](./Phase7_taskList.md) for full rationale, architecture diagrams, and detailed task breakdown.
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
@@ -399,43 +530,6 @@ See [Phase7_taskList.md](./Phase7_taskList.md) for full rationale, architecture
---
## 6.8.1 Phase 8: Log-Trace Correlation and Loki Ingestion (Week 13)
**Objective**: Inject trace context (trace_id, span_id) into rippled's Journal log output and add Grafana Loki as a centralized log backend with bidirectional trace-log correlation in Grafana.
### Sub-Phases
**Phase 8a** (code change): Modify `Logs::format()` to read the thread-local OTel span context and prepend `trace_id=<hex> span_id=<hex>` to every log line emitted within an active span. Zero changes to the ~2,242 JLOG call sites — injection is transparent.
**Phase 8b** (infra only): Add Grafana Loki to the Docker observability stack and configure the OTel Collector's filelog receiver to parse rippled's log format, extract trace_id, and export to Loki. Configure Grafana's Tempo-to-Loki and Loki-to-Tempo derived field links for one-click correlation.
See [Phase8_taskList.md](./Phase8_taskList.md) for full motivation, architecture diagrams, and detailed task breakdown.
### Tasks
| Task | Description | Sub-Phase | Effort | Risk |
| ---- | ------------------------------------------ | --------- | ------ | ------ |
| 8.1 | Inject trace_id into `Logs::format()` | 8a | 1d | Low |
| 8.2 | Add Loki to Docker Compose stack | 8b | 0.5d | Low |
| 8.3 | Add filelog receiver to OTel Collector | 8b | 1d | Medium |
| 8.4 | Configure Grafana trace-to-log correlation | 8b | 0.5d | Low |
| 8.5 | Update integration tests | 8a + 8b | 0.5d | Low |
| 8.6 | Update documentation | 8a + 8b | 1d | Low |
**Total Effort**: 4.5 days
### Exit Criteria
- [ ] Log lines within active spans contain `trace_id=<hex> span_id=<hex>`
- [ ] Log lines outside spans have no trace context (clean — 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)
---
## 6.9 Risk Assessment
```mermaid
@@ -720,7 +814,6 @@ Clear, measurable criteria for each phase.
| 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 all logs | Loki ingestion working | End of Week 13 |
---

6
OpenTelemetryPlan/09-data-collection-reference.md
View File

@@ -486,12 +486,6 @@ rippled_State_Accounting_Full_duration
---
## 5a. Future: Log-Trace Correlation (Phase 8)
> **Planned**: [Phase8_taskList.md](./Phase8_taskList.md) adds `trace_id` and `span_id` to every JLOG log line emitted within an active OTel span. Combined with Grafana Loki ingestion, this enables one-click navigation between traces (Tempo) and logs (Loki). No changes to JLOG call sites — injection is transparent in `Logs::format()`.
---
## 6. Known Issues
| Issue | Impact | Status |

5
OpenTelemetryPlan/OpenTelemetryPlan.md
View File

@@ -157,7 +157,7 @@ OpenTelemetry Collector configurations are provided for development (with Jaeger
## 6. Implementation Phases
The implementation spans 13 weeks across 8 phases:
The implementation spans 12 weeks across 7 phases:
| Phase | Duration | Focus | Key Deliverables |
| ----- | ----------- | --------------------- | ----------------------------------------------------------- |
@@ -168,9 +168,8 @@ The implementation spans 13 weeks across 8 phases:
| 5 | Week 9 | Documentation | Runbook, Dashboards, Training |
| 6 | Week 10 | StatsD Metrics Bridge | OTel Collector StatsD receiver, 3 Grafana dashboards |
| 7 | Weeks 11-12 | Native OTel Metrics | OTelCollector impl, OTLP metrics export, StatsD deprecation |
| 8 | Week 13 | Log-Trace Correlation | trace_id in JLOG output, Loki ingestion, Tempo-Loki linking |
**Total Effort**: 65.1 developer-days with 2 developers
**Total Effort**: 60.6 developer-days with 2 developers
➡️ **[View full Implementation Phases](./06-implementation-phases.md)**

163
OpenTelemetryPlan/Phase7_taskList.md
View File

@@ -8,163 +8,12 @@
### Related Plan Documents
| Document | Relevance |
| -------------------------------------------------------------------- | -------------------------------------------------------------------------- |
| [02-design-decisions.md](./02-design-decisions.md) | Collector interface design, beast::insight coexistence strategy (replaced) |
| [05-configuration-reference.md](./05-configuration-reference.md) | `[insight]` and `[telemetry]` config sections |
| [06-implementation-phases.md](./06-implementation-phases.md) | Phase 7 summary, exit criteria, success metrics (§6.8) |
| [09-data-collection-reference.md](./09-data-collection-reference.md) | Complete metric inventory that must be preserved |
---
## Motivation: Why Migrate from StatsD to Native OTel Metrics
### 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)
```
### Instrument Type Mapping
| beast::insight Type | OTel Metrics SDK Instrument | Rationale |
| --------------------------------------- | ----------------------------------------------- | ------------------------------------------------------------------------------------------------------------ |
| `Counter` (int64, delta, `\|c`) | `Counter<int64_t>` | Direct 1:1 — both are monotonic delta counters |
| `Gauge` (uint64, current value, `\|g`) | `ObservableGauge<uint64_t>` with async callback | OTel gauges use async observation via callbacks; the existing Hook pattern already provides periodic polling |
| `Meter` (uint64, increment-only, `\|m`) | `Counter<uint64_t>` | Meters are semantically counters — this fixes the non-standard `\|m` wire format issue from Phase 6 Task 6.1 |
| `Event` (ms duration, `\|ms`) | `Histogram<double>` with explicit buckets | Duration distributions — use same buckets as SpanMetrics: [1, 5, 10, 25, 50, 100, 250, 500, 1000, 5000] ms |
| `Hook` (1s periodic callback) | `PeriodicMetricReader` callback alignment | Collection interval matches existing 1s period; hooks fire gauge observations before export |
### 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.
| Document | Relevance |
| -------------------------------------------------------------------- | --------------------------------------------------------------- |
| [06-implementation-phases.md](./06-implementation-phases.md) | Phase 7 plan: motivation, architecture, exit criteria (§6.8) |
| [02-design-decisions.md](./02-design-decisions.md) | Collector interface design, beast::insight coexistence strategy |
| [05-configuration-reference.md](./05-configuration-reference.md) | `[insight]` and `[telemetry]` config sections |
| [09-data-collection-reference.md](./09-data-collection-reference.md) | Complete metric inventory that must be preserved |
---

315
OpenTelemetryPlan/Phase8_taskList.md
View File

@@ -1,315 +0,0 @@
# Phase 8: Log-Trace Correlation and Centralized Log Ingestion — Task List
> **Goal**: Inject trace context (trace_id, span_id) into rippled's Journal log output for log-trace correlation, and add OTel Collector filelog receiver to ingest logs into Grafana Loki for unified observability.
>
> **Scope**: Two independent sub-phases — 8a (code change: trace_id in logs) and 8b (infra only: filelog receiver to Loki). No changes to the `beast::Journal` public API.
>
> **Branch**: `pratik/otel-phase8-log-correlation` (from `pratik/otel-phase7-native-metrics`)
### Related Plan Documents
| Document | Relevance |
| ---------------------------------------------------------------- | ------------------------------------------------------------- |
| [06-implementation-phases.md](./06-implementation-phases.md) | Phase 8 summary and exit criteria (§6.8.1) |
| [07-observability-backends.md](./07-observability-backends.md) | Loki backend recommendation, Grafana data source provisioning |
| [Phase7_taskList.md](./Phase7_taskList.md) | Prerequisite — native OTel metrics pipeline must be working |
| [05-configuration-reference.md](./05-configuration-reference.md) | `[telemetry]` config (trace_id injection toggle) |
---
## Motivation
### The Problem
rippled emits ~2,242 JLOG calls across 194 files. When investigating an issue (e.g., a consensus failure or a slow RPC), operators must manually correlate timestamps between Jaeger traces and log files — a tedious, error-prone process that loses context.
### What We Gain
1. **One-click trace-to-log correlation** — Grafana's Tempo → Loki link lets operators click a trace span and see all log lines emitted during that span's lifetime. No manual timestamp matching.
2. **Log-to-trace reverse lookup** — An error in the logs like `"Transaction failed"` can be traced back to the exact distributed trace via the `trace_id` field. Grafana Loki's LogQL: `{job="rippled"} |= "trace_id=abc123"`.
3. **Structured log context** — trace_id and span_id make logs queryable by trace, enabling aggregate analysis (e.g., "show all logs for consensus rounds that took >5s").
4. **Zero call-site changes** — The injection happens in `Logs::format()` or `Logs::write()`, transparently to the ~2,242 JLOG call sites.
5. **Centralized log storage** — Loki provides indexed, compressed log retention with LogQL queries, replacing ad-hoc `grep` on individual node log files.
### What We Lose
1. **Slightly larger log lines** — Each log line grows by ~70 characters (`trace_id=<32hex> span_id=<16hex>`). At ~1000 lines/min, this adds ~70 KB/min of log volume.
2. **Thread-local dependency** — trace_id is only available when a span is active on the current thread. JLOG calls outside any span context will have empty trace_id (this is fine — it indicates non-traced code paths).
3. **Loki infrastructure** — Adds another backend (Loki) to the observability stack. Mitigated: Loki is lightweight and can run as a single binary for development.
---
## Architecture
### Phase 8a: Trace ID Injection into Logs
```
Thread with active OTel span:
JLOG(journal_.info()) << "Processing transaction " << txHash;
Flow:
1. ScopedStream destructor calls sink.write(severity, text)
2. Logs::write() calls Logs::format()
3. format() checks thread-local OTel span context
4. If span is active: prepends "trace_id=<hex> span_id=<hex>" to message
5. Output: "2026-03-06T12:34:56.789Z Consensus:INF trace_id=abc123 span_id=def456 Processing transaction ..."
```
**Key design decision**: Inject in `Logs::format()` (the static formatting function) by accessing the thread-local OTel span context via `opentelemetry::trace::GetSpan(opentelemetry::context::RuntimeContext::GetCurrent())`. This is a read-only, lock-free operation.
**Conditional**: Only inject when `[telemetry] enabled=1` and a span is active. When telemetry is disabled at build time (`-Dtelemetry=OFF`), the injection compiles to nothing.
### Phase 8b: Filelog Receiver to Loki
```mermaid
graph LR
subgraph rippledNode["rippled Node"]
A["JLOG output<br/>with trace_id"]
end
A -->|"stdout / debug.log"| FL
subgraph collector["OTel Collector"]
FL["Filelog Receiver<br/>parses rippled log format"]
BP["Batch Processor"]
FL --> BP
end
BP -->|"OTLP/gRPC"| L["Grafana Loki<br/>:3100"]
L --> G["Grafana :3000<br/>Explore: Loki + Tempo"]
style A fill:#4a90d9,color:#fff,stroke:#2a6db5
style FL fill:#5cb85c,color:#fff,stroke:#3d8b3d
style BP fill:#449d44,color:#fff,stroke:#2d6e2d
style L fill:#9c27b0,color:#fff,stroke:#6a1b9a
style G fill:#5bc0de,color:#000,stroke:#3aa8c1
style rippledNode fill:#1a2633,color:#ccc,stroke:#4a90d9
style collector fill:#1a3320,color:#ccc,stroke:#5cb85c
```
**OTel Collector filelog receiver** parses rippled's log format, extracts `trace_id` and `span_id` as log attributes, and exports to Loki via OTLP. Grafana correlates Tempo traces with Loki logs via the shared `trace_id`.
---
## Task 8.1: Inject trace_id into Logs::format()
**Objective**: Add OTel trace context to every log line that is emitted within an active span.
**What to do**:
- Edit `src/libxrpl/basics/Log.cpp`:
- In `Logs::format()` (around line 346), after severity is appended, check for active OTel span:
```cpp
#ifdef XRPL_ENABLE_TELEMETRY
auto span = opentelemetry::trace::GetSpan(
opentelemetry::context::RuntimeContext::GetCurrent());
auto ctx = span->GetContext();
if (ctx.IsValid())
{
// Append trace context as structured fields
char traceId[33], spanId[17];
ctx.trace_id().ToLowerBase16(traceId);
ctx.span_id().ToLowerBase16(spanId);
output += "trace_id=";
output.append(traceId, 32);
output += " span_id=";
output.append(spanId, 16);
output += ' ';
}
#endif
```
- Add `#include` for OTel context headers, guarded by `#ifdef XRPL_ENABLE_TELEMETRY`
- Edit `include/xrpl/basics/Log.h`:
- No changes needed — format() signature unchanged
**Key modified files**:
- `src/libxrpl/basics/Log.cpp`
**Performance note**: `GetSpan()` and `GetContext()` are thread-local reads with no locking — measured at <10ns per call. With ~1000 JLOG calls/min, this adds <10us/min of overhead.
---
## Task 8.2: Add Loki to Docker Compose Stack
**Objective**: Add Grafana Loki as a log storage backend in the development observability stack.
**What to do**:
- Edit `docker/telemetry/docker-compose.yml`:
- Add Loki service:
```yaml
loki:
image: grafana/loki:2.9.0
ports:
- "3100:3100"
command: -config.file=/etc/loki/local-config.yaml
```
- Add Loki as a Grafana data source in provisioning
- Create `docker/telemetry/grafana/provisioning/datasources/loki.yaml`:
- Configure Loki data source with derived fields linking `trace_id` to Tempo
**Key new files**:
- `docker/telemetry/grafana/provisioning/datasources/loki.yaml`
**Key modified files**:
- `docker/telemetry/docker-compose.yml`
---
## Task 8.3: Add Filelog Receiver to OTel Collector
**Objective**: Configure the OTel Collector to tail rippled's log file and export to Loki.
**What to do**:
- Edit `docker/telemetry/otel-collector-config.yaml`:
- Add `filelog` receiver:
```yaml
receivers:
filelog:
include: [/var/log/rippled/debug.log]
operators:
- type: regex_parser
regex: '^(?P<timestamp>\S+)\s+(?P<partition>\S+):(?P<severity>\S+)\s+(?:trace_id=(?P<trace_id>[a-f0-9]+)\s+span_id=(?P<span_id>[a-f0-9]+)\s+)?(?P<message>.*)$'
timestamp:
parse_from: attributes.timestamp
layout: "%Y-%m-%dT%H:%M:%S.%fZ"
```
- Add logs pipeline:
```yaml
service:
pipelines:
logs:
receivers: [filelog]
processors: [batch]
exporters: [otlp/loki]
```
- Add Loki exporter:
```yaml
exporters:
otlp/loki:
endpoint: loki:3100
tls:
insecure: true
```
- Mount rippled's log directory into the collector container via docker-compose volume
**Key modified files**:
- `docker/telemetry/otel-collector-config.yaml`
- `docker/telemetry/docker-compose.yml`
---
## Task 8.4: Configure Grafana Trace-to-Log Correlation
**Objective**: Enable one-click navigation from Tempo traces to Loki logs in Grafana.
**What to do**:
- Edit Grafana Tempo data source provisioning to add `tracesToLogs` configuration:
```yaml
tracesToLogs:
datasourceUid: loki
filterByTraceID: true
filterBySpanID: false
tags: ["partition", "severity"]
```
- Edit Grafana Loki data source provisioning to add `derivedFields` linking trace_id back to Tempo:
```yaml
derivedFields:
- datasourceUid: tempo
matcherRegex: "trace_id=(\\w+)"
name: TraceID
url: "$${__value.raw}"
```
**Key modified files**:
- `docker/telemetry/grafana/provisioning/datasources/loki.yaml`
- `docker/telemetry/grafana/provisioning/datasources/` (Tempo data source file)
---
## Task 8.5: Update Integration Tests
**Objective**: Verify trace_id appears in logs and Loki correlation works.
**What to do**:
- Edit `docker/telemetry/integration-test.sh`:
- After sending RPC requests (which create spans), grep rippled's log output for `trace_id=`
- Verify trace_id matches a trace visible in Jaeger
- Optionally: query Loki via API to confirm log ingestion
**Key modified files**:
- `docker/telemetry/integration-test.sh`
---
## Task 8.6: Update Documentation
**Objective**: Document the log correlation feature in runbook and reference docs.
**What to do**:
- Edit `docs/telemetry-runbook.md`:
- Add "Log-Trace Correlation" section explaining how to use Grafana Tempo → Loki linking
- Add LogQL query examples for filtering by trace_id
- Edit `OpenTelemetryPlan/09-data-collection-reference.md`:
- Add new section "3. Log Correlation" between SpanMetrics and StatsD sections
- Document the log format with trace_id injection
- Document Loki as a new backend
- Edit `docker/telemetry/TESTING.md`:
- Add log correlation verification steps
**Key modified files**:
- `docs/telemetry-runbook.md`
- `OpenTelemetryPlan/09-data-collection-reference.md`
- `docker/telemetry/TESTING.md`
---
## Summary Table
| Task | Description | Sub-Phase | New Files | Modified Files | Effort | Risk | Depends On |
| ---- | ------------------------------------------ | --------- | --------- | -------------- | ------ | ------ | ---------- |
| 8.1 | Inject trace_id into Logs::format() | 8a | 0 | 1 | 1d | Low | Phase 7 |
| 8.2 | Add Loki to Docker Compose stack | 8b | 1 | 1 | 0.5d | Low | — |
| 8.3 | Add filelog receiver to OTel Collector | 8b | 0 | 2 | 1d | Medium | 8.1, 8.2 |
| 8.4 | Configure Grafana trace-to-log correlation | 8b | 0 | 2 | 0.5d | Low | 8.3 |
| 8.5 | Update integration tests | 8a + 8b | 0 | 1 | 0.5d | Low | 8.4 |
| 8.6 | Update documentation | 8a + 8b | 0 | 3 | 1d | Low | 8.5 |
**Total Effort**: 4.5 days
**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** (from [06-implementation-phases.md §6.8.1](./06-implementation-phases.md)):
- [ ] 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)