# xrpld Telemetry Operator Runbook

## Table of Contents

- [Overview](#overview)
- [Quick Start](#quick-start)
- [Configuration Reference](#configuration-reference)
- [Exporting to Grafana Cloud](#exporting-to-grafana-cloud)
- [Span Reference](#span-reference)
  - [RPC Spans](#rpc-spans)
  - [Transaction Spans](#transaction-spans)
  - [Transaction Queue Spans](#transaction-queue-spans)
  - [PathFinding Spans](#pathfinding-spans)
  - [Consensus Spans](#consensus-spans)
  - [Ledger Spans](#ledger-spans)
  - [Peer Spans](#peer-spans)
- [Protocol Span Flow](#protocol-span-flow)
  - [Master overview](#master-overview)
  - [Client and peer ingress](#client-and-peer-ingress)
  - [Shared transaction apply pipeline](#shared-transaction-apply-pipeline)
  - [Consensus round](#consensus-round)
  - [Accept, build, and finalize the ledger](#accept-build-and-finalize-the-ledger)
  - [Side flows: pathfinding and ledger acquire](#side-flows-pathfinding-and-ledger-acquire)
  - [Where telemetry parenting differs from protocol flow](#where-telemetry-parenting-differs-from-protocol-flow)
- [Insights and Sample Queries](#insights-and-sample-queries)
  - [Transaction Workflow Analysis](#transaction-workflow-analysis)
  - [DEX (OfferCreate / OfferCancel)](#dex-offercreate--offercancel)
  - [Apply Pipeline by Stage](#apply-pipeline-by-stage)
  - [Transaction Queue Health](#transaction-queue-health)
  - [RPC Debugging](#rpc-debugging)
  - [PathFinding Performance](#pathfinding-performance)
  - [Consensus Health](#consensus-health)
  - [Cross-Subsystem Correlation](#cross-subsystem-correlation)
- [Cross-Node Trace Propagation](#cross-node-trace-propagation)
- [Prometheus Metrics (Spanmetrics)](#prometheus-metrics-spanmetrics)
- [System Metrics (OTel native -- beast::insight)](#system-metrics-otel-native----beastinsight)
- [Grafana Dashboards](#grafana-dashboards)
- [Alerting](#alerting)
- [Log-Trace Correlation](#log-trace-correlation)
- [Troubleshooting](#troubleshooting)
- [Performance Tuning](#performance-tuning)
- [Disabling Telemetry](#disabling-telemetry)
- [Validating Telemetry Stack](#validating-telemetry-stack)
- [Performance Benchmarking](#performance-benchmarking)

## Overview

xrpld supports OpenTelemetry distributed tracing to provide visibility into RPC requests, transaction processing, and consensus rounds.

This runbook covers operating a running node and querying its traces. For
building xrpld with telemetry support and the internal architecture, see
[build/telemetry.md](build/telemetry.md). For plain-language definitions of the
XRP Ledger terms used in the dashboards, see the
[telemetry glossary](telemetry-glossary.md).

## Quick Start

### 1. Start the observability stack

```bash
docker compose -f docker/telemetry/docker-compose.yml up -d
```

This starts:

- **OTel Collector** on ports 4317 (gRPC) and 4318 (HTTP), and 13133 (health)
- **Tempo** on http://localhost:3200 (trace backend)
- **Prometheus** on http://localhost:9090
- **Loki** on http://localhost:3100 (log aggregation)
- **Grafana** on http://localhost:3000 (Tempo pre-configured as datasource)

### 2. Enable telemetry in xrpld

Add to your `xrpld.cfg`:

```ini
[telemetry]
enabled=1
endpoint=http://localhost:4318/v1/traces
```

### 3. Build with telemetry support

```bash
conan install . --build=missing -o telemetry=True
cmake --preset default -Dtelemetry=ON
cmake --build --preset default
```

### 4. Run against a live network

Two ready-made configs connect a tracking node (no validator credentials) to a
public network with all tracing and native metrics enabled:

| Config                                         | Network |
| ---------------------------------------------- | ------- |
| `docker/telemetry/xrpld-telemetry.cfg`         | Devnet  |
| `docker/telemetry/xrpld-telemetry-mainnet.cfg` | Mainnet |

```bash
.build/xrpld --conf docker/telemetry/xrpld-telemetry-mainnet.cfg
```

Both set `[insight] server=otel` (native metrics → collector → Prometheus, which
drives the dashboards) and `service_instance_id`, exposed by Prometheus as the
`service_instance_id` label that the `$node` dashboard variable filters on. The
mainnet config logs to `/var/log/xrpld/mainnet/debug.log` — the path
the collector's filelog receiver tails for log-trace correlation.

Metrics begin flowing as soon as the node connects to peers (`server_state`
≥ `connected`); full ledger and consensus panels populate after sync
(`server_state` = `full`). Check progress with:

```bash
curl -s http://localhost:5005 -d '{"method":"server_info"}' |
    jq '.result.info | {server_state, peers, complete_ledgers}'
```

> Mainnet sync is bandwidth- and disk-heavy. For a quick check use the devnet
> config or the standalone test in `docker/telemetry/TESTING.md`, which
> generates spans without waiting for a live sync.

## Configuration Reference

| Option                     | Default                           | Description                                               |
| -------------------------- | --------------------------------- | --------------------------------------------------------- |
| `enabled`                  | `0`                               | Master switch for telemetry                               |
| `endpoint`                 | `http://localhost:4318/v1/traces` | OTLP/HTTP endpoint                                        |
| `service_name`             | `xrpld`                           | OpenTelemetry service name resource attribute             |
| `service_instance_id`      | node public key                   | OpenTelemetry service instance ID resource attribute      |
| `trace_rpc`                | `1`                               | Enable RPC request tracing                                |
| `trace_transactions`       | `1`                               | Enable transaction tracing                                |
| `trace_consensus`          | `1`                               | Enable consensus tracing                                  |
| `trace_peer`               | `1`                               | Enable peer message tracing (high volume)                 |
| `trace_ledger`             | `1`                               | Enable ledger tracing                                     |
| `consensus_trace_strategy` | `deterministic`                   | Consensus trace ID strategy (`deterministic` or `random`) |
| `batch_size`               | `512`                             | Max spans per batch export                                |
| `batch_delay_ms`           | `5000`                            | Delay between batch exports                               |
| `max_queue_size`           | `2048`                            | Max spans queued before dropping                          |
| `use_tls`                  | `0`                               | Use TLS for exporter connection                           |
| `tls_ca_cert`              | (empty)                           | Path to CA certificate bundle                             |
| `tls_client_cert`          | (empty)                           | Client cert (PEM) for mutual TLS; empty = one-way TLS     |
| `tls_client_key`           | (empty)                           | Private key (PEM) for `tls_client_cert`                   |

## Exporting to Grafana Cloud

The collector can ship traces, metrics, and logs to a hosted **Grafana
Cloud** stack instead of (or alongside) the local Tempo/Prometheus/Loki
backends. This is a runtime choice — no xrpld rebuild and no change to the
base stack. xrpld still exports to the local collector exactly as before;
the collector adds one OTLP/HTTP exporter that forwards all three signals to
the Grafana Cloud OTLP gateway, which fans them out to hosted Tempo, Mimir,
and Loki.

### Credentials

Find these under **Grafana Cloud → Connections → OpenTelemetry (OTLP)**:

| Value                         | Used as           | Notes                                              |
| ----------------------------- | ----------------- | -------------------------------------------------- |
| `GRAFANA_CLOUD_OTLP_ENDPOINT` | exporter endpoint | Full gateway URL incl. `/otlp` path                |
| `GRAFANA_CLOUD_INSTANCE_ID`   | Basic-auth user   | Numeric stack/instance id                          |
| `GRAFANA_CLOUD_API_TOKEN`     | Basic-auth pass   | Access-policy token with `*:write` for all signals |

### Enable

1. Copy the template and fill in the three values:

   ```bash
   cp docker/telemetry/.env.grafanacloud.example docker/telemetry/.env.grafanacloud
   # edit .env.grafanacloud — this file is gitignored, never commit tokens
   ```

2. Bring the stack up with the base file **and** the Grafana Cloud override:

   ```bash
   docker compose -f docker/telemetry/docker-compose.yml \
       -f docker/telemetry/docker-compose.grafanacloud.yaml up -d
   ```

To return to local-only export, bring the stack up with just the base
`docker-compose.yml`.

### Files

| File                                      | Role                                                                                           |
| ----------------------------------------- | ---------------------------------------------------------------------------------------------- |
| `otel-collector-config.grafanacloud.yaml` | Collector config: local backends **plus** a Grafana Cloud OTLP exporter on all three pipelines |
| `docker-compose.grafanacloud.yaml`        | Override that mounts that config and injects the credentials                                   |
| `.env.grafanacloud.example`               | Credential template (copy to `.env.grafanacloud`)                                              |

### Local + cloud vs cloud-only

The prepared config **dual-exports**: data goes to both the local stack and
Grafana Cloud, so the on-box backends remain a fallback. For cloud-only,
remove the local exporters (`debug`, `otlp/tempo`, `prometheus`,
`otlphttp/loki`) from the respective pipelines in
`otel-collector-config.grafanacloud.yaml`, leaving only
`otlphttp/grafanacloud`.

> **Note**: shipping logs to Grafana Cloud requires keeping xrpld file
> logging on (at least `warning` level) so the collector's filelog receiver
> has a `debug.log` to tail. Traces and metrics are unaffected by log level.

### Importing dashboards to Grafana Cloud

Shipping data (above) is independent of installing the dashboards. The local
stack auto-provisions dashboards from a mounted folder
(`grafana/provisioning/dashboards/dashboards.yaml`, `type: file`); Grafana
Cloud cannot read your filesystem, so its dashboards must be imported over the
HTTP API or the UI.

The dashboard JSON in `docker/telemetry/grafana/dashboards/` references its
backends through datasource **template variables** (`${DS_PROMETHEUS}`,
`${DS_TEMPO}`) rather than fixed UIDs. On import, Grafana binds each variable
to a datasource of the matching type — auto-selecting it when only one exists
(the usual case: one Mimir, one Tempo). This is what makes the same files work
unchanged on both the local stack and Cloud.

> Dashboards are parameterized by `grafana/parameterize-datasources.py`. If you
> add a dashboard exported with hardcoded UIDs, re-run that script (idempotent)
> before committing so it stays portable.

To import:

1. In Grafana Cloud, go to **Dashboards → New → Import**.
2. Upload a file from `docker/telemetry/grafana/dashboards/` (or paste its
   JSON), then click **Load**.
3. At the datasource prompt, confirm the auto-selected **Prometheus/Mimir**
   datasource — and **Tempo** for dashboards that query traces — then
   **Import**.
4. Repeat per dashboard. Only `consensus-health` uses Tempo; the rest need
   only the Prometheus/Mimir datasource.

## Span Reference

All spans instrumented in xrpld, grouped by subsystem:

### RPC Spans

| Span Name            | Source File       | Attributes                                                  | Description                                           |
| -------------------- | ----------------- | ----------------------------------------------------------- | ----------------------------------------------------- |
| `rpc.http_request`   | ServerHandler.cpp | `request_payload_size`                                      | Top-level HTTP RPC request                            |
| `rpc.ws_upgrade`     | ServerHandler.cpp | —                                                           | WebSocket upgrade handshake                           |
| `rpc.ws_message`     | ServerHandler.cpp | `command`                                                   | WebSocket RPC message                                 |
| `rpc.process`        | ServerHandler.cpp | `is_batch`, `batch_size`                                    | RPC processing (child of rpc.http_request/ws_message) |
| `rpc.command.<name>` | RPCHandler.cpp    | `command`, `version`, `rpc_role`, `rpc_status`, `load_type` | Per-command span (e.g., `rpc.command.server_info`)    |

### Transaction Spans

| Span Name       | Source File     | Attributes                                                                                              | Description                           |
| --------------- | --------------- | ------------------------------------------------------------------------------------------------------- | ------------------------------------- |
| `tx.process`    | NetworkOPs.cpp  | `tx_hash`, `local`, `path`, `tx_type`, `fee`, `sequence`, `ter_result`, `applied`, `current_ledger_seq` | Transaction submission and processing |
| `tx.receive`    | PeerImp.cpp     | `peer_id`, `tx_hash`, `tx_type`, `peer_version`, `suppressed`, `tx_status`, `current_ledger_seq`        | Transaction received from peer relay  |
| `tx.apply`      | BuildLedger.cpp | `ledger_seq`, `tx_count`, `tx_failed`                                                                   | Transaction set applied per ledger    |
| `tx.preflight`  | applySteps.cpp  | `stage`, `tx_type`, `ter_result`                                                                        | Stateless checks stage                |
| `tx.preclaim`   | applySteps.cpp  | `stage`, `tx_type`, `ter_result`, `current_ledger_seq`, `current_ledger_hash`                           | Ledger-aware checks stage             |
| `tx.transactor` | Transactor.cpp  | `stage`, `tx_type`, `ter_result`, `applied`, `current_ledger_seq`, `current_ledger_hash`                | Apply stage (transactor runs)         |

The three apply-pipeline spans (`tx.preflight`, `tx.preclaim`, `tx.transactor`)
share a deterministic `trace_id` from `txID[0:16]`, so they group under one
trace per transaction. The `stage` attribute (`preflight` / `preclaim` /
`apply`) drives the collector spanmetrics `stage` dimension, giving per-stage
RED metrics on the _Transaction Overview_ dashboard.

`current_ledger_seq` is the current (open/in-flight) ledger index a span acted on
— the ledger being worked on, not an established one — so a transaction's
txID-keyed spans can be joined to the ledger trace it targeted
(`span.current_ledger_seq`). The view-bearing stages (`tx.preclaim`,
`tx.transactor`) also carry `current_ledger_hash` (the current ledger's parent
hash); `tx.preflight` is stateless and omits both.

### Transaction Queue Spans

| Span Name          | Source File | Attributes                                                        | Description                                                                                                                                                                                  |
| ------------------ | ----------- | ----------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `txq.enqueue`      | TxQ.cpp     | `tx_hash`, `tx_type`, `current_ledger_seq`, `current_ledger_hash` | Enqueue decision; parents to `tx.process` on the submission path (explicit context), a root on the open-ledger rebuild path — `current_ledger_seq` correlates it to the ledger in both cases |
| `txq.apply_direct` | TxQ.cpp     | --                                                                | Direct apply attempt (bypassing queue)                                                                                                                                                       |
| `txq.batch_clear`  | TxQ.cpp     | --                                                                | Batch clear of queued transactions for an account                                                                                                                                            |
| `txq.accept`       | TxQ.cpp     | `queue_size`, `ledger_changed`                                    | Ledger-close accept loop over queued transactions                                                                                                                                            |
| `txq.accept_tx`    | TxQ.cpp     | `tx_hash`, `retries_remaining`, `ter_code`, `txq_status`          | Per-transaction apply during accept                                                                                                                                                          |
| `txq.cleanup`      | TxQ.cpp     | `ledger_seq`                                                      | Post-close cleanup of expired queue entries                                                                                                                                                  |

### PathFinding Spans

| Span Name             | Source File                       | Attributes                                         | Description                                             |
| --------------------- | --------------------------------- | -------------------------------------------------- | ------------------------------------------------------- |
| `pathfind.request`    | PathFind.cpp / RipplePathFind.cpp | `pathfind_source_account`, `pathfind_dest_account` | Path-find RPC entry (accounts hashed; set when present) |
| `pathfind.compute`    | PathRequest.cpp                   | `pathfind_fast`, `pathfind_dest_currency`          | Path computation for one request (`doUpdate`)           |
| `pathfind.discover`   | PathRequest.cpp                   | `pathfind_search_level`, `pathfind_num_paths`      | Graph exploration (one per RPC call in `findPaths`)     |
| `pathfind.update_all` | PathRequestManager.cpp            | `pathfind_ledger_index`, `pathfind_num_requests`   | Async recomputation of active requests on ledger close  |

### Consensus Spans

| Span Name                      | Source File      | Attributes                                                                                                                                                                                                                   | Description                                                                                                                           |
| ------------------------------ | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `consensus.round`              | RCLConsensus.cpp | `consensus_ledger_id`, `ledger_seq`, `consensus_mode`, `trace_strategy`, `consensus_round_id`                                                                                                                                | Root span for a consensus round (deterministic or random trace ID)                                                                    |
| `consensus.phase.open`         | Consensus.h      | --                                                                                                                                                                                                                           | Open phase duration (child of round)                                                                                                  |
| `consensus.proposal.send`      | RCLConsensus.cpp | `consensus_round`, `is_bow_out`                                                                                                                                                                                              | Consensus proposal broadcast                                                                                                          |
| `consensus.ledger_close`       | RCLConsensus.cpp | `ledger_seq`, `consensus_mode`                                                                                                                                                                                               | Ledger close event                                                                                                                    |
| `consensus.establish`          | Consensus.h      | `converge_percent`, `establish_count`, `proposers`                                                                                                                                                                           | Establish phase duration (child of round)                                                                                             |
| `consensus.update_positions`   | Consensus.h      | `converge_percent`, `proposers`, `disputes_count`                                                                                                                                                                            | Position update and dispute resolution (see Events below)                                                                             |
| `consensus.check`              | Consensus.h      | `agree_count`, `disagree_count`, `converge_percent`, `have_close_time_consensus`, `threshold_percent`, `proposers_finished`, `consensus_stalled`, `establish_count`, `consensus_result`                                      | Consensus threshold check                                                                                                             |
| `consensus.accept`             | RCLConsensus.cpp | `proposers`, `round_time_ms`, `quorum`, `disputes_count`, `consensus_state`                                                                                                                                                  | Ledger accepted by consensus                                                                                                          |
| `consensus.accept.apply`       | RCLConsensus.cpp | `ledger_seq`, `close_time`, `close_time_correct`, `close_resolution_ms`, `consensus_state`, `proposing`, `round_time_ms`, `parent_close_time`, `close_time_self`, `close_time_vote_bins`, `resolution_direction`, `tx_count` | Ledger application with close time details (see Events below)                                                                         |
| `consensus.validation.send`    | RCLConsensus.cpp | `ledger_seq`, `proposing`, `ledger_hash`, `full_validation`, `validation_sign_time`                                                                                                                                          | Validation sent after accept (follows-from link)                                                                                      |
| `consensus.mode_change`        | RCLConsensus.cpp | `mode_old`, `mode_new`                                                                                                                                                                                                       | Consensus mode transition                                                                                                             |
| `consensus.proposal.receive`   | PeerImp.cpp      | `proposal_trusted`, `consensus_round`                                                                                                                                                                                        | Proposal received from peer (extracts parent context from TraceContext when present; falls back to standalone span for older peers)   |
| `consensus.validation.receive` | PeerImp.cpp      | `validation_trusted`, `ledger_seq`                                                                                                                                                                                           | Validation received from peer (extracts parent context from TraceContext when present; falls back to standalone span for older peers) |

#### Consensus Span Events

| Parent Span                  | Event Name        | Event Attributes                                            | Description                                             |
| ---------------------------- | ----------------- | ----------------------------------------------------------- | ------------------------------------------------------- |
| `consensus.update_positions` | `dispute.resolve` | `tx_id`, `dispute_our_vote`, `dispute_yays`, `dispute_nays` | Emitted per dispute when votes are tallied              |
| `consensus.accept.apply`     | `tx.included`     | `tx_id`                                                     | Emitted per transaction included in the accepted ledger |

#### Close Time Queries (Tempo TraceQL)

Span attributes are filtered with `span.<attr>` inside `{}`. Combine conditions with `&&`.

```
# Find rounds where validators disagreed on close time
{name="consensus.accept.apply" && span.close_time_correct = false}

# Find consensus failures (moved_on)
{name="consensus.accept.apply" && span.consensus_state = "moved_on"}

# Find slow ledger applications (>5s)
{name="consensus.accept.apply" && duration > 5000ms}

# Find specific ledger's consensus details
{name="consensus.accept.apply" && span.ledger_seq = 92345678}

# Find all spans in a consensus round (deterministic trace strategy)
{name="consensus.round" && span.consensus_round_id = "<round_id>"}

# Find dispute resolutions
{name="consensus.update_positions"} >> {event:name="dispute.resolve"}
```

### Ledger Spans

| Span Name         | Source File          | Attributes                            | Description                   |
| ----------------- | -------------------- | ------------------------------------- | ----------------------------- |
| `ledger.build`    | BuildLedger.cpp:31   | `ledger_seq`, `tx_count`, `tx_failed` | Ledger build during consensus |
| `ledger.validate` | LedgerMaster.cpp:915 | `ledger_seq`, `validations`           | Ledger promoted to validated  |
| `ledger.store`    | LedgerMaster.cpp:409 | `ledger_seq`                          | Ledger stored in history      |

### Peer Spans

| Span Name                 | Source File      | Attributes                      | Description                   |
| ------------------------- | ---------------- | ------------------------------- | ----------------------------- |
| `peer.proposal.receive`   | PeerImp.cpp:1667 | `peer_id`, `proposal_trusted`   | Proposal received from peer   |
| `peer.validation.receive` | PeerImp.cpp:2264 | `peer_id`, `validation_trusted` | Validation received from peer |

Both peer receive spans are `kConsumer` inbound entry points started as fresh
trace roots. They never inherit an ambient span left active on the peer thread,
so they do not nest under an unrelated transaction's trace. The distributed
child span that links back to the sending node is the separate
`consensus.*.receive` / `tx.receive` span (see Cross-Node Trace Propagation).

---

## Protocol Span Flow

This section maps every span type onto the **real xrpld control flow and XRPL
protocol order** (verified against code and [docs/consensus.md](consensus.md)) —
what the code actually executes next, in what order, with which loops and
branches. Spans are drawn as **labels on real operations**, not as their
OpenTelemetry parent links; the SDK's span parenting is listed separately in
[Where telemetry parenting differs from protocol flow](#where-telemetry-parenting-differs-from-protocol-flow).

These diagrams are the **canonical key for linking the span hierarchy** — every
node and every branch is labelled with the span that represents that state or
transition, so a span can be wired to its true protocol parent/child by reading
the graph. They are therefore drawn **exact, not simplified**: every real loop,
retry, recovery, and drop branch is shown even when it adds clutter.

Naming and edge conventions:

- **Rectangle `[ ]`** — a state/operation that **emits a span**; the first line is
  the exact `span.name`, the parenthetical below is the operation.
- **Rounded `( )` with `(no span)`** — a real protocol step that emits **no span**;
  shown so the flow stays continuous and is never mistaken for a missing span.
- **Solid arrow** — the code calls or sequences directly into the next operation.
  When the transition itself emits a span, the edge is labelled `→[span.name]`;
  otherwise it carries the branch condition.
- **`↻`** — the edge repeats (per tx, per peer, per dispute, per pass, per round).
- **Dashed arrow** — a conditional branch or an async job hand-off.
- **Dotted `⇢ ctx`** — trace context crosses a node boundary over a protobuf peer
  message (`sender.span ⇢ receiver.span`); a different node continues the trace —
  **not** an in-process call.
- **Red-bordered node** — a terminal **drop / abandon** state.

### Master overview

Five ingress origins feed two shared engines — the per-transaction **apply
pipeline** and the **consensus round** — which converge on ledger build → store →
validate. Pathfinding and ledger-acquire are side flows. A single ledger can take
**many consensus rounds** to settle (see [Consensus round](#consensus-round)).

```mermaid
flowchart TB
    classDef ingress fill:#1d4ed8,stroke:#1e3a8a,color:#fff;
    classDef engine  fill:#047857,stroke:#064e3b,color:#fff;
    classDef consensus fill:#b45309,stroke:#7c2d12,color:#fff;
    classDef ledger  fill:#6d28d9,stroke:#4c1d95,color:#fff;
    classDef side    fill:#0e7490,stroke:#155e75,color:#fff;
    classDef plain   fill:#334155,stroke:#0f172a,color:#fff;

    subgraph ING["Ingress (protocol entry points)"]
        direction TB
        RPC["rpc.http_request / rpc.ws_message<br/>rpc.ws_upgrade / grpc.MethodName<br/>(client transport in)"]:::ingress
        SUB(["submit command<br/>(no span)"]):::plain
        PRELAY["tx.receive<br/>(peer relay in)"]:::ingress
        PMSG["peer.proposal.receive<br/>peer.validation.receive<br/>(peer overlay in)"]:::ingress
    end

    TXP["tx.process<br/>(NetworkOPs::processTransaction)"]:::engine
    OPEN["txq.enqueue<br/>(open-ledger apply + TxQ decision)"]:::engine
    PIPE["tx.preflight → tx.preclaim → tx.transactor<br/>(SHARED apply pipeline)"]:::engine

    subgraph CONS["Consensus round"]
        direction TB
        ROUND["consensus.round<br/>(Open → Establish → Accepted)"]:::consensus
        ACC["consensus.accept → consensus.accept.apply"]:::consensus
    end

    subgraph LGR["Ledger finalize"]
        direction TB
        BUILD["ledger.build<br/>(tx.apply over agreed set)"]:::ledger
        STORE["ledger.store<br/>(built, NOT yet final)"]:::ledger
        VAL["ledger.validate<br/>(promoted at quorum)"]:::ledger
    end

    subgraph SIDE["Side flows"]
        direction TB
        PF["pathfind.update_all<br/>pathfind.request/compute/discover"]:::side
        ACQ["ledger.acquire<br/>(fetch missing / correct prior)"]:::side
    end

    RPC -.->|submit / submit_multisigned| SUB --> TXP
    RPC -.->|path_find / ripple_path_find| PF
    PRELAY --> TXP
    TXP --> OPEN
    OPEN -->|↻ up to 3 passes| PIPE
    OPEN -.->|txq.accept re-apply queued tx each close ↻| PIPE
    PMSG -->|peerProposal / recvValidation| ROUND
    ROUND --> ACC --> BUILD
    BUILD -->|↻ each tx × up to 3 passes| PIPE
    BUILD --> STORE
    PMSG -. "trusted validations arrive async → checkAccept quorum" .-> VAL
    ROUND -. "avalanche rounds ↻ (threshold 50→65→70→95%)" .-> ROUND
    ACC -->|endConsensus ↻ next round until a ledger validates| ROUND
    ROUND -.->|wrong-ledger: request correct prior| ACQ
    ACQ -.->|switch-ledger: resume round on correct prior| ROUND
    ACQ --> STORE
    VAL -.->|missing ledger| ACQ
    BUILD -.->|every close re-runs| PF
```

### Client and peer ingress

RPC submit and peer relay **converge** at `tx.process`, the single NetworkOPs
entry. gRPC serves ledger queries only — it has no submit path and never runs
`doCommand`.

```mermaid
flowchart TB
    classDef span fill:#1d4ed8,stroke:#1e3a8a,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    HTTP["rpc.http_request<br/>(HTTP entry)"]:::span
    PROC["rpc.process<br/>(parse + batch)"]:::span
    CMD["rpc.command.NAME<br/>(one command)"]:::span
    WSU["rpc.ws_upgrade<br/>(WS handshake)"]:::span
    WSM["rpc.ws_message<br/>(one frame)"]:::span
    GRPC["grpc.MethodName<br/>(ledger query)"]:::span
    GH(["handler_ ctx<br/>(no span)"]):::plain
    SUBMIT(["doSubmit<br/>(no span)"]):::plain
    TXP["tx.process<br/>(NetworkOPs::processTransaction)"]:::span
    RELAYOUT(["Overlay::relay fan-out to N peers<br/>(no span; if applied / terQUEUED,<br/>shouldRelay, not tfInnerBatchTxn)"]):::plain
    PREDROP(["Diverged / needNetworkLedger<br/>(no span — dropped before tx.receive)"]):::drop
    RCV["tx.receive<br/>(peer TMTransaction in)"]:::span
    RCVDROP["tx.receive<br/>tx_status = rejected_inner_batch /<br/>suppressed / dropped_no_sync /<br/>dropped_queue_full"]:::drop
    CHK(["checkTransaction<br/>(JtTransaction worker, no span)"]):::plain
    PRELAY_IN(["TMTransaction in (no span)"]):::plain

    HTTP -->|processRequest| PROC
    PROC -->|↻ each batch request → doCommand| CMD
    WSU -. "each inbound frame → onWSMessage" .-> WSM
    WSM -->|doCommand| CMD
    GRPC --> GH
    CMD -.->|submit / submit_multisigned| SUBMIT
    SUBMIT -->|processTransaction| TXP
    TXP -.->|relay applied / queued tx| RELAYOUT

    PRELAY_IN -.->|tracking == Diverged / needNetworkLedger| PREDROP
    PRELAY_IN -->|else| RCV
    RCV -.->|inner-batch / dup / age>4min / JtTransaction full| RCVDROP
    RCV -->|addJob JtTransaction| CHK
    CHK -->|processTransaction, trusted=peer| TXP

    RELAYOUT -. "tx.process ⇢ tx.receive (span_id over TMTransaction)" .-> RCV
```

Ingress branches (all evidence in code):

- `onHandoff`: WS upgrade vs peer bundle vs status page vs legacy HTTP
  ([ServerHandler.cpp:227](../src/xrpld/rpc/detail/ServerHandler.cpp#L227)).
- `doSubmit`: `tx_blob` present → submit signed blob; absent → server
  sign-and-submit ([Submit.cpp:49](../src/xrpld/rpc/handlers/transaction/Submit.cpp#L49)).
- `tx.process`: local RPC → `doTransactionSync`; peer → `doTransactionAsync`
  (JtBatch) ([NetworkOPs.cpp:1434](../src/xrpld/app/misc/NetworkOPs.cpp#L1434)).
- **Pre-span peer drops** (no `tx.receive` created): `Diverged`
  ([PeerImp.cpp:1299](../src/xrpld/overlay/detail/PeerImp.cpp#L1299)) /
  `needNetworkLedger` ([1302](../src/xrpld/overlay/detail/PeerImp.cpp#L1302)),
  before the span at ~1320.
- **Post-span peer drops** (span exists, `tx_status` set, no job enqueued):
  `tfInnerBatchTxn` ([1348](../src/xrpld/overlay/detail/PeerImp.cpp#L1348)),
  HashRouter dup/`BAD` ([1361](../src/xrpld/overlay/detail/PeerImp.cpp#L1361)),
  `dropped_no_sync` when validated-ledger age > 4 min
  ([1416](../src/xrpld/overlay/detail/PeerImp.cpp#L1416)), `dropped_queue_full`
  when `JtTransaction` jobs > `maxTransactions`
  ([1421](../src/xrpld/overlay/detail/PeerImp.cpp#L1421)).
- **Relay fan-out**: an accepted/queued `tx.process` relays to N peers via
  `Overlay::relay`, gated on `applied || (non-FULL local) || terQUEUED`,
  HashRouter `shouldRelay`, and not `tfInnerBatchTxn`; the span context is
  injected here ([NetworkOPs.cpp:1797](../src/xrpld/app/misc/NetworkOPs.cpp#L1797)).

Inbound consensus messages take a two-stage handler — a fresh-root `peer.*.receive`
span created first (kConsumer, always), then a `consensus.*.receive` span (only if
not dropped) that carries the sender's context — before enqueuing a `checkPropose`
/ `checkValidation` worker job. The drop points are **asymmetric**: proposals drop
entirely before `consensus.proposal.receive`, while validations can drop both
before and after `consensus.validation.receive`.

```mermaid
flowchart TB
    classDef span fill:#0e7490,stroke:#155e75,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    PPR["peer.proposal.receive<br/>(freshRoot, always)"]:::span
    PPRDROP(["no consensus.proposal.receive<br/>(untrusted+relay-off / dup /<br/>untrusted+Diverged / untrusted+loaded)"]):::drop
    CPR["consensus.proposal.receive<br/>(carries sender ctx)"]:::span
    CP(["checkPropose worker<br/>(no span)"]):::plain
    SIGP(["sig-fail: charge, drop<br/>(no relay, no span)"]):::drop
    PTP(["processTrustedProposal → peerProposal<br/>(no span)"]):::plain

    PVR["peer.validation.receive<br/>(freshRoot, always)"]:::span
    pvrDrop1(["no consensus.validation.receive<br/>(!isCurrent / relay-off / dup)"]):::drop
    CVR["consensus.validation.receive<br/>(carries sender ctx)"]:::span
    cvrDrop2(["dropped after span<br/>(untrusted+Diverged /<br/>untrusted+loaded → no job/relay)"]):::drop
    CV(["checkValidation worker<br/>(no span)"]):::plain
    SIGV(["!isValid: charge, drop<br/>(no span)"]):::drop
    RV(["recvValidation → handleNewValidation<br/>(no span)"]):::plain
    RELAY(["Overlay::relay fan-out to N peers<br/>(no span)"]):::plain

    PPR -.->|4 drop conditions| PPRDROP
    PPR -->|else| CPR
    CPR -->|addJob JtProposalT/Ut| CP
    CP -.->|!checkSign| SIGP
    CP -->|isTrusted| PTP
    CP -.->|if relay| RELAY

    PVR -.->|3 drop conditions| pvrDrop1
    PVR -->|else| CVR
    CVR -.->|untrusted+Diverged / loaded| cvrDrop2
    CVR -->|addJob JtValidationT/Ut| CV
    CV -.->|!isValid| SIGV
    CV -->|recvValidation| RV
    CV -.->|if relay / cluster| RELAY
```

Consensus-message drop evidence:

- Both `peer.proposal.receive` and `peer.validation.receive` are `freshRoot`
  spans created at the top of `onMessage`
  ([PeerImp.cpp:1766](../src/xrpld/overlay/detail/PeerImp.cpp#L1766),
  [2389](../src/xrpld/overlay/detail/PeerImp.cpp#L2389)) — so they exist even for
  dropped messages.
- **Proposal drops (all before `consensus.proposal.receive` at
  [1868](../src/xrpld/overlay/detail/PeerImp.cpp#L1868))**: untrusted+relay-off
  ([1807](../src/xrpld/overlay/detail/PeerImp.cpp#L1807)), duplicate
  ([1832](../src/xrpld/overlay/detail/PeerImp.cpp#L1832)), untrusted+Diverged
  ([1840](../src/xrpld/overlay/detail/PeerImp.cpp#L1840)), untrusted+loaded
  ([1846](../src/xrpld/overlay/detail/PeerImp.cpp#L1846)).
- **Validation drops (asymmetric around `consensus.validation.receive` at
  [2476](../src/xrpld/overlay/detail/PeerImp.cpp#L2476))**: before — `!isCurrent`
  ([2426](../src/xrpld/overlay/detail/PeerImp.cpp#L2426)), relay-off
  ([2445](../src/xrpld/overlay/detail/PeerImp.cpp#L2445)), duplicate
  ([2468](../src/xrpld/overlay/detail/PeerImp.cpp#L2468)); after — untrusted+Diverged
  ([2489](../src/xrpld/overlay/detail/PeerImp.cpp#L2489)), untrusted+loaded
  ([2506](../src/xrpld/overlay/detail/PeerImp.cpp#L2506)).
- **Worker sig-fail drops** (charged `kFeeInvalidSignature`, suppress processing
  and relay): `checkPropose !checkSign`
  ([PeerImp.cpp:3105](../src/xrpld/overlay/detail/PeerImp.cpp#L3105)),
  `checkValidation !isValid`
  ([3149](../src/xrpld/overlay/detail/PeerImp.cpp#L3149)).

### Shared transaction apply pipeline

The apply pipeline is the **single protocol tx-processing chain**, expressed in
code as one composed call
([apply.cpp:118](../src/libxrpl/tx/apply.cpp#L118)):
`doApply(preclaim(preflight(), …), …)`. C++ evaluates inner-to-outer, so
`preflight` runs first, feeds `preclaim`, which feeds `doApply`. Each stage
inspects the prior stage's `TER` and no-ops if it already failed.

**Four invokers** point into this one pipeline; the diagram draws it once.

```mermaid
flowchart TB
    classDef span fill:#047857,stroke:#064e3b,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef inv fill:#1d4ed8,stroke:#1e3a8a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    I1(["open-ledger applyOne (no span)<br/>↻ up to 3 passes"]):::inv
    I2["txq.apply_direct<br/>(fee ≥ required)"]:::span
    I3["txq.batch_clear / txq.accept_tx<br/>↻ per queued tx"]:::span
    I4["tx.apply<br/>(consensus set, ↻ each tx × 3 passes)"]:::span

    REPF(["txq path: rules/flags changed?<br/>re-run preflight (no span)"]):::plain
    FREE(["xrpl::apply() (no span)"]):::plain
    PF["tx.preflight<br/>(stateless checks)"]:::span
    PC["tx.preclaim<br/>(ledger-aware checks)"]:::span
    TR["tx.transactor<br/>(mutate stage)"]:::span
    CLS(["classify final TER (no span)"]):::plain
    OK(["Success / erase (no span)"]):::plain
    FAIL(["tef / tem / tel → hard fail, erase"]):::drop
    RETRY(["retriable ter → keep in set (no span)"]):::plain

    I1 --> FREE
    I2 --> FREE
    I3 --> REPF --> FREE
    I4 --> FREE
    FREE --> PF
    PF -->|preflight tesSUCCESS| PC
    PF -. "else → classify (no preclaim/transactor)" .-> CLS
    PC -->|likelyToClaimFee| TR
    PC -. "else → classify (no transactor)" .-> CLS
    TR --> CLS
    CLS --> OK
    CLS --> FAIL
    CLS --> RETRY
    RETRY -. "next pass while pass<3 and changes>0" .-> FREE
    RETRY -. "last pass → drop from set" .-> FAIL
```

Pipeline gates and retry (evidence):

- `preclaim` short-circuits if preflight `!tesSUCCESS`
  ([applySteps.cpp:498](../src/libxrpl/tx/applySteps.cpp#L498)); `doApply`
  short-circuits if `!likelyToClaimFee`
  ([applySteps.cpp:532](../src/libxrpl/tx/applySteps.cpp#L532)); the transactor
  mutates only when preclaim is `tesSUCCESS`
  ([Transactor.cpp:1647](../src/libxrpl/tx/Transactor.cpp#L1647)).
- **Final-TER classification**: `applied` → Success; `tef | tem | tel` → hard Fail;
  else → Retry ([apply.cpp:226](../src/libxrpl/tx/apply.cpp#L226)).
- **Multi-pass retry**: both open-ledger `applyOne` and consensus `tx.apply` loop
  `pass < LEDGER_TOTAL_PASSES` (= 3); a `Retry` tx is kept for the next pass, and
  the final pass converts lingering retriable txs into drops
  ([OpenLedger.h:237](../src/xrpld/app/ledger/OpenLedger.h#L237),
  [BuildLedger.cpp:129](../src/xrpld/app/ledger/detail/BuildLedger.cpp#L129);
  `LEDGER_TOTAL_PASSES` [OpenLedger.h:29](../src/xrpld/app/ledger/OpenLedger.h#L29)).
- **TxQ re-preflight**: the queue path re-runs `preflight` when the ledger's
  rules/flags changed since enqueue
  ([TxQ.cpp:315](../src/xrpld/app/misc/detail/TxQ.cpp#L315)).
- **TxQ cross-ledger retry**: a queued tx that fails with a retriable result keeps its slot with
  `--retriesRemaining` (`kRetriesAllowed` = 10) and is re-applied at a **later**
  ledger close; on `retriesRemaining ≤ 0` or `tef|tem` it is dropped with an
  account `retryPenalty` ([TxQ.cpp:1528](../src/xrpld/app/misc/detail/TxQ.cpp#L1528)).
- `TxQ::apply` outcome fork: preflight-reject / `applied_direct` / `batch_clear` /
  `queued` (`terQUEUED`) / reject
  ([TxQ.cpp:762](../src/xrpld/app/misc/detail/TxQ.cpp#L762)).

> **`tx.apply` is set-level, consensus-only.** It wraps the retry-pass loop over
> the agreed set during `buildLedger` and exists on **no other** invoker. It is
> not a per-transaction span, and TxQ / open-ledger apply create no `tx.apply`.

### Consensus round

`beginConsensus → startRound` starts the round (`consensus.round`, `Open` phase).
The **heartbeat timer** drives `Consensus::timerEntry` each pass; the round stays
in `Establish` across many heartbeats until the outcome is decided.

> **A single ledger can take many rounds to settle.** Two nested multi-round
> mechanisms (see [docs/consensus.md](consensus.md)):
>
> 1. **Avalanche rounds inside one Establish phase** — each `timerEntry` runs
>    `phaseEstablish` again (`establishCounter_++`) and raises the inclusion
>    threshold **50% → 65% → 70% → 95%** as the round ages
>    ([ConsensusParms.h:145](../src/xrpld/consensus/ConsensusParms.h#L145)).
>    `checkConsensus` returning `No` keeps the node in `Establish` and loops; a
>    round cannot even `Expire` before a minimum of
>    `avalancheCutoffs.size() × avMinRounds = 4 × 2 = 8` passes
>    ([Consensus.h:1938](../src/xrpld/consensus/Consensus.h#L1938)).
> 2. **Retry across consensus rounds** — a round can end `MovedOn` / `Expired`,
>    meaning the network settled a _different_ ledger. The node still builds a
>    ledger, but the **next** round's `checkLedger` detects the wrong prior,
>    switches to `WrongLedger` / `SwitchedLedger` mode, acquires the correct
>    ledger, and re-deliberates. A ledger is only truly settled once trusted
>    **validations reach quorum** (`ledger.validate`); the alternate is abandoned.

```mermaid
flowchart TB
    classDef span fill:#b45309,stroke:#7c2d12,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;

    BEGIN(["beginConsensus → startRound<br/>(Proposing OR Observing; no span)"]):::plain
    ROUND["consensus.round<br/>(one attempt at next ledger)"]:::span
    OPENS["consensus.phase.open<br/>(collect txs; buffer peer<br/>proposals / gotTxSet)"]:::span
    HB(["heartbeat → timerEntry<br/>(every LEDGER_MIN_CLOSE; no span)"]):::plain
    CKL(["checkLedger<br/>(correct prior ledger? no span)"]):::plain
    WRONG(["handleWrongLedger → leaveConsensus (no span):<br/>if Proposing send BOW-OUT,<br/>mode → Observing; acquire ledger"]):::plain
    MODE["consensus.mode_change<br/>(mode transition)"]:::span
    POPEN(["phaseOpen: shouldCloseLedger? (no span)"]):::plain
    CLOSE["consensus.ledger_close<br/>(close open ledger, seed disputes)"]:::span
    pSend["consensus.proposal.send<br/>(broadcast our position)"]:::span
    PEST["consensus.establish<br/>(phaseEstablish; avalanche round ↻<br/>threshold 50→65→70→95%)"]:::span
    UPOS["consensus.update_positions<br/>(add/drop disputed txs; child of establish)"]:::span
    acqTx(["acquireTxSet → gotTxSet<br/>(async peer tx set; no span)"]):::plain
    PAUSE(["shouldPause?<br/>(wait on laggards; no span)"]):::plain
    CHECK["consensus.check<br/>(checkConsensus; child of establish)"]:::span
    CTC(["haveCloseTimeConsensus?<br/>(else agree-to-disagree +1s; no span)"]):::plain
    ACCEPT["consensus.accept<br/>(round complete)"]:::span

    BEGIN --> ROUND --> OPENS
    HB -->|under mutex| CKL
    CKL -.->|wrong prior| WRONG
    WRONG --> MODE
    WRONG -. "recovered → re-enter Open (playbackProposals)" .-> OPENS
    WRONG -. "still missing → keep deliberating, defer to peers" .-> HB
    CKL -->|prior OK| POPEN
    HB -.->|phase==Open| POPEN
    HB -.->|phase==Establish| PEST
    POPEN -.->|shouldClose| CLOSE
    CLOSE -.->|mode==Proposing| pSend
    PEST --> UPOS
    UPOS -.->|position changed && Proposing| pSend
    UPOS -.->|disagreeing peer position| acqTx
    acqTx -. "gotTxSet ↻ → new disputes" .-> UPOS
    PEST --> PAUSE
    PAUSE -. "pausing → wait (loop)" .-> HB
    PAUSE -->|ready| CHECK
    CHECK -. "No / Expired < 8 passes → next avalanche round" .-> HB
    CHECK --> CTC
    CTC -. "no CT consensus → loop" .-> HB
    CTC -.->|Yes / MovedOn / Expired ≥ 8| ACCEPT
    ROUND -.->|mode set at start| MODE
    ACCEPT -. "endConsensus → next round ↻ (until a ledger validates)" .-> BEGIN
```

Consensus loops and branches (evidence):

- **`consensus.establish` is the parent of `update_positions` and `check`**:
  `phaseEstablish` creates the establish span (`startEstablishTracing`), and both
  child spans parent to its captured context
  ([Consensus.h:2100](../src/xrpld/consensus/Consensus.h#L2100),
  [1629](../src/xrpld/consensus/Consensus.h#L1629),
  [1838](../src/xrpld/consensus/Consensus.h#L1838)).
- **Avalanche-convergence loop (rounds within one ledger)**: repeated
  `heartbeat → timerEntry → phaseEstablish` bumps `establishCounter_` and raises
  the inclusion threshold each pass; `checkConsensus` = `No` stays in `Establish`
  ([NetworkOPs.cpp:1214](../src/xrpld/app/misc/NetworkOPs.cpp#L1214);
  [Consensus.h:1468](../src/xrpld/consensus/Consensus.h#L1468);
  thresholds [ConsensusParms.h:145](../src/xrpld/consensus/ConsensusParms.h#L145)).
- **Retry-across-rounds loop (many rounds per settled ledger)**: `MovedOn` /
  `Expired` accepts a non-preferred ledger; the next round's `checkLedger` finds
  the wrong prior and recovers before re-deliberating
  ([Consensus.h:1194](../src/xrpld/consensus/Consensus.h#L1194)); round-to-round
  via `endConsensus → beginConsensus`
  ([NetworkOPs.cpp:2315](../src/xrpld/app/misc/NetworkOPs.cpp#L2315)).
- **Two extra establish loop-backs before accept**: `shouldPause` (laggard
  backpressure) and `!haveCloseTimeConsensus_` (TX consensus but not close-time)
  each `return` and re-loop, distinct from `checkConsensus == No`
  ([Consensus.h:1497](../src/xrpld/consensus/Consensus.h#L1497),
  [1500](../src/xrpld/consensus/Consensus.h#L1500)); close time can
  "agree to disagree" at prior close + 1s ([docs/consensus.md:163](consensus.md)).
- **acquireTxSet / gotTxSet loop**: a disagreeing peer position triggers an async
  `acquireTxSet`; the later `gotTxSet` regenerates disputes and can extend the
  establish phase ([Consensus.h:932](../src/xrpld/consensus/Consensus.h#L932)).
- **Bow-out / mode change**: `handleWrongLedger → leaveConsensus` sends a bow-out
  proposal and demotes Proposing → Observing for the rest of the round
  ([Consensus.h:1977](../src/xrpld/consensus/Consensus.h#L1977)); `startRound`
  begins in Proposing **or** Observing ([docs/consensus.md:176](consensus.md)).
- **Buffered Open-phase inputs**: `peerProposal` / `gotTxSet` arriving during Open
  are stored, then seeded as disputes at `closeLedger` (`createDisputes`);
  `playbackProposals` replays them at `startRound` / `handleWrongLedger`
  ([docs/consensus.md:244](consensus.md);
  [Consensus.h:817](../src/xrpld/consensus/Consensus.h#L817)).
- **Outcome fork** after `checkConsensus`: `No` (loop) / `Yes` (onAccept) /
  `MovedOn` / `Expired` ([Consensus.h:1516](../src/xrpld/consensus/Consensus.h#L1516)).
- **Expired guard**: a round cannot leave on `Expired` before
  `avalancheCutoffs.size() × avMinRounds` (= 8) passes — below that, `Expired`
  loops like `No` ([Consensus.h:1938](../src/xrpld/consensus/Consensus.h#L1938)).
- The **deterministic-vs-random trace-strategy** branch at round start
  ([RCLConsensus.cpp:1291](../src/xrpld/app/consensus/RCLConsensus.cpp#L1291)) sets
  only the trace ID — it has **zero protocol effect**.

### Accept, build, and finalize the ledger

`onAccept` enqueues a `JtAccept` job; `doAccept` runs on that worker
(`consensus.accept.apply`). It builds the ledger (running the apply pipeline over
the agreed set), cleans the queue, stores the ledger, optionally broadcasts a
validation, and rebuilds the open ledger. A built ledger is **not final** — it is
promoted to `ledger.validate` only when trusted validations reach quorum, an
**async, validation-driven** path re-entered per incoming trusted validation; a
built ledger that loses is **abandoned**.

```mermaid
flowchart TB
    classDef span fill:#6d28d9,stroke:#4c1d95,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    onAcc["consensus.accept<br/>(round complete)"]:::span
    APPLY["consensus.accept.apply<br/>(JtAccept worker)"]:::span
    BLCL(["buildLCL: replay data? (no span)"]):::plain
    BUILD["ledger.build<br/>(normal: apply agreed set)"]:::span
    RPLY["ledger.build<br/>(replay: TapNone, no tx.apply child)"]:::span
    TXAP["tx.apply<br/>(↻ each tx × up to 3 passes)"]:::span
    CLEAN["txq.cleanup<br/>(expire queue entries)"]:::span
    STORE["ledger.store<br/>(built, NOT yet final)"]:::span
    vSend["consensus.validation.send<br/>(broadcast our validation)"]:::span
    CACC(["consensusBuilt → checkAccept<br/>(quorum gate; no span)"]):::plain
    NEWVAL(["inbound trusted validation<br/>→ handleNewValidation → checkAccept<br/>(async, per validation; no span)"]):::plain
    VAL["ledger.validate<br/>(promote highest-seq ledger ≥ quorum)"]:::span
    LOSE(["built ledger loses:<br/>never promoted → abandoned"]):::drop
    OACC(["OpenLedger::accept<br/>(rebuild open ledger; no span)"]):::plain
    tqAcc["txq.accept<br/>(↻ drain queued txs)"]:::span
    swlStd(["switchLCL standalone:<br/>setFullLedger + tryAdvance (no span)"]):::plain
    swlNet(["switchLCL networked:<br/>checkAccept (no span)"]):::plain
    END(["endConsensus → next round ↻ (no span)"]):::plain

    onAcc -.->|addJob JtAccept| APPLY
    APPLY --> BLCL
    BLCL -->|normal path| BUILD --> TXAP
    BLCL -. "replay path" .-> RPLY
    APPLY --> CLEAN
    APPLY --> STORE
    APPLY -. "validating && isCompatible && !fail && canValidateSeq" .-> vSend
    APPLY --> CACC
    NEWVAL --> CACC
    CACC -.->|highest-seq trusted ledger ≥ quorum| VAL
    CACC -.->|tvc < quorum → no promotion| LOSE
    APPLY --> OACC
    OACC -->|TxQ::accept callback| tqAcc
    OACC --> swlStd
    OACC --> swlNet
    swlStd -. "marks full-validated (no ledger.validate span)" .-> END
    swlNet --> CACC
    onAcc --> END
```

- Order inside `doAccept`: `buildLCL` (build → `tx.apply`, then `txq.cleanup`,
  then `ledger.store`) → optional `validate` → `consensusBuilt`/`checkAccept` →
  `OpenLedger::accept` (rebuilds the open ledger; `txq.accept` runs in its
  callback) → `switchLCL` promotes the built ledger to the new LCL
  ([RCLConsensus.cpp:812](../src/xrpld/app/consensus/RCLConsensus.cpp#L812) then
  [833](../src/xrpld/app/consensus/RCLConsensus.cpp#L833)).
- **buildLCL replay branch**: if `releaseReplay()` has data, `buildLedger` replays
  the stored set with `TapNone` — it **still emits `ledger.build`** (via
  `buildLedgerImpl`) but applies txns directly with **no `tx.apply` child** and no
  3-pass loop; else the normal consensus-set path runs `tx.apply` over 3 passes
  ([RCLConsensus.cpp:929](../src/xrpld/app/consensus/RCLConsensus.cpp#L929);
  [BuildLedger.cpp:252](../src/xrpld/app/ledger/detail/BuildLedger.cpp#L252)).
- `processClosedLedger` (`txq.cleanup`) runs **after** build, **before** store
  ([RCLConsensus.cpp:950](../src/xrpld/app/consensus/RCLConsensus.cpp#L950) vs
  [953](../src/xrpld/app/consensus/RCLConsensus.cpp#L953)).
- **`ledger.validate` is async + lossy**: `checkAccept` is re-entered per incoming
  trusted validation (`handleNewValidation → checkAccept`,
  [RCLValidations.cpp:193](../src/xrpld/app/consensus/RCLValidations.cpp#L193));
  it promotes the **highest-seq** trusted ledger whose `valCount > neededValidations`
  ([LedgerMaster.cpp:1180](../src/xrpld/app/ledger/detail/LedgerMaster.cpp#L1180)),
  which may be a **different** ledger than the one this node built. Below quorum
  (`tvc < minVal`) it returns early with no promotion — a built ledger that loses
  is abandoned ([LedgerMaster.cpp:980](../src/xrpld/app/ledger/detail/LedgerMaster.cpp#L980);
  [docs/consensus.md:50](consensus.md)). The `ledger.validate` span is emitted only
  inside `checkAccept` ([LedgerMaster.cpp:987](../src/xrpld/app/ledger/detail/LedgerMaster.cpp#L987)).
- **validation-send guard**: broadcast only if
  `validating_ && isCompatible && !consensusFail && canValidateSeq(seq)` — silently
  suppressed for incompatible ledgers or an already-validated seq
  ([RCLConsensus.cpp:730](../src/xrpld/app/consensus/RCLConsensus.cpp#L730)).
- **switchLCL**: standalone → `setFullLedger` + `tryAdvance` — marks the ledger
  full-validated **without** emitting `ledger.validate` (that span lives only in
  `checkAccept`); networked → `checkAccept` (shared async quorum gate)
  ([LedgerMaster.cpp:442](../src/xrpld/app/ledger/detail/LedgerMaster.cpp#L442)).

### Side flows: pathfinding and ledger acquire

**Pathfinding** — an RPC one-shot (`path_find` / `ripple_path_find`) plus an async
recompute that fires on **every ledger close** for all active subscriptions, and
also garbage-collects dead subscriptions:

```mermaid
flowchart TB
    classDef span fill:#0e7490,stroke:#155e75,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    REQ["pathfind.request<br/>(path_find / ripple_path_find)"]:::span
    CREATE(["subscribe: makePathRequest →<br/>persistent subscription (no span)"]):::plain
    COMP["pathfind.compute<br/>(doUpdate, one pass)"]:::span
    DISC["pathfind.discover<br/>(findPaths)"]:::span
    PFDR(["Pathfinder + RippleCalc<br/>↻ per source asset (no span)"]):::plain
    UP(["updatePaths (JtUpdatePf, every close; no span)"]):::plain
    UALL["pathfind.update_all<br/>(recompute all active)"]:::span
    DEAD(["dead subscriber → doAborting +<br/>remove_if erase"]):::drop

    REQ -.->|subcommand create| CREATE
    REQ -->|doUpdate| COMP -->|findPaths| DISC --> PFDR
    UP -->|once per close| UALL
    UALL -->|↻ each active request| COMP
    UALL -. "new request arrived → extra pass ↻" .-> UALL
    UALL -.->|dead / aborted| DEAD
```

**Ledger acquire** — a **separate trace root** (not part of the close flow) that
fetches a missing or correct-prior ledger from peers, retries per peer/timer, and
finishes with a reason-dependent store; `checkAccept` + `tryAdvance` run on **any**
completed acquire:

```mermaid
flowchart TB
    classDef span fill:#0e7490,stroke:#155e75,color:#fff;
    classDef plain fill:#334155,stroke:#0f172a,color:#fff;
    classDef drop fill:#7f1d1d,stroke:#ef4444,color:#fff;

    HIST(["tryAdvance → doAdvance → fetchForHistory<br/>(Reason::HISTORY; no span)"]):::plain
    NEED(["checkAccept / handleNewValidation /<br/>consensus wrong-ledger (no span)"]):::plain
    INB(["InboundLedgers::acquire (no span)"]):::plain
    ACQ["ledger.acquire<br/>(InboundLedger::init)"]:::span
    TRIG(["trigger / addPeers / onTimer<br/>↻ per peer / chunk (no span)"]):::plain
    FAILED(["timeouts > 6 → failed_ →<br/>logFailure (NO store, NO checkAccept)"]):::drop
    DONE(["done() — complete && !failed (no span)"]):::plain
    ONF(["onLedgerFetched (no span)<br/>(HISTORY: no store)"]):::plain
    STORE["ledger.store<br/>(GENERIC / CONSENSUS)"]:::span
    CACC(["checkAccept + tryAdvance (no span)<br/>(↻ may publish/advance many ledgers)"]):::plain

    NEED -.->|GENERIC / CONSENSUS| INB --> ACQ
    HIST -.->|HISTORY| INB
    ACQ -.->|not complete| TRIG
    TRIG -. "retry ↻" .-> TRIG
    TRIG -.->|timeout cap| FAILED
    TRIG -.->|complete| DONE
    DONE -.->|reason == HISTORY| ONF
    DONE -.->|GENERIC / CONSENSUS| STORE
    DONE --> CACC
    CACC -. "advanceWork ↻ → further HISTORY acquire" .-> HIST
```

Side-flow evidence:

- **Pathfind subscription lifecycle**: `path_find` create inserts a persistent
  subscription (`makePathRequest`); `update_all` re-runs each active request every
  close, removes dead subscribers (`doAborting` + `remove_if` erase), and takes an
  extra pass when a new request arrived mid-run
  ([PathRequestManager.cpp:103](../src/xrpld/rpc/detail/PathRequestManager.cpp#L103),
  [169](../src/xrpld/rpc/detail/PathRequestManager.cpp#L169),
  [181](../src/xrpld/rpc/detail/PathRequestManager.cpp#L181)).
- **Acquire outcome fork**: `timeouts_ > kLedgerTimeoutRetriesMax` (= 6) sets
  `failed_` → terminal `logFailure`, no store/checkAccept
  ([InboundLedger.cpp:387](../src/xrpld/app/ledger/detail/InboundLedger.cpp#L387)).
- **done() reason branch (store side only)**: `HISTORY` → `onLedgerFetched`, **no**
  `storeLedger`; else → `storeLedger`. But `checkAccept` + `tryAdvance` run for
  **any** `complete_ && !failed_` acquire regardless of reason
  ([InboundLedger.cpp:495](../src/xrpld/app/ledger/detail/InboundLedger.cpp#L495)
  store switch; [507](../src/xrpld/app/ledger/detail/InboundLedger.cpp#L507)
  reason-independent checkAccept/tryAdvance).
- **tryAdvance multi-ledger loop**: `doAdvance` runs `do { … } while (advanceWork_)`,
  publishing a range of ledgers and recursively triggering further HISTORY acquire
  ([LedgerMaster.cpp:1905](../src/xrpld/app/ledger/detail/LedgerMaster.cpp#L1905)).

### Where telemetry parenting differs from protocol flow

The graph above is protocol control flow. The OpenTelemetry span **parent links**
are built differently and, in several places, do **not** represent a real
call edge. Read a trace with these in mind:

| Telemetry does this                                                                                                                   | Real protocol flow                                                                                                                                                                                                                                    |
| ------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tx.process` is a `hashSpan` root from `txID` — an independent trace root ([TxTracing.h:63](../src/xrpld/telemetry/TxTracing.h#L63)). | The real edge is the synchronous `doSubmit → processTransaction` call; it is **not** a child of `rpc.command.submit`.                                                                                                                                 |
| `tx.preflight` / `tx.preclaim` / `tx.transactor` share one `txID`-derived trace ID.                                                   | That shared ID is a correlation trick, not a call edge. The real order is the composed `apply()` at [apply.cpp:118](../src/libxrpl/tx/apply.cpp#L118). They are **not** children of `tx.process` or `tx.apply`.                                       |
| `consensus.round` uses a deterministic trace ID from the previous ledger hash.                                                        | This makes **all validators share one trace ID** (a cross-node shared root), not a per-node parent. The real round-to-round edge is `endConsensus → beginConsensus`.                                                                                  |
| `consensus.accept` (main thread) and `consensus.accept.apply` (JtAccept worker) are wired via a captured context.                     | The real edge is the queued `JtAccept` job, a thread hand-off ([RCLConsensus.cpp:483](../src/xrpld/app/consensus/RCLConsensus.cpp#L483)).                                                                                                             |
| `pathfind.update_all` parents nothing from the original `pathfind.request`.                                                           | The causal link is the ledger-close job on `JtUpdatePf`, not span nesting.                                                                                                                                                                            |
| `ledger.acquire` and its downstream `ledger.store` / `ledger.validate`.                                                               | Reached via the `AcqDone` job, not parent inheritance; `ledger.acquire` is its own root.                                                                                                                                                              |
| `peer.*.receive` (fresh `kConsumer` root) and `consensus.*.receive` on the same message.                                              | Two **sequential stages of one synchronous handler**, not parent/child; on a duplicate/untrusted drop the `consensus.*.receive` is never created.                                                                                                     |
| Receive spans adopt the sender's `trace_id` + `span_id` as a genuine cross-node parent.                                               | Deliberate: the receive span becomes a child of a **different node's** span (a cross-node context marker, not an in-process edge). `tx.receive` is asymmetric — it borrows only the sender's `span_id` and re-derives its own `trace_id` from `txID`. |

> **Known telemetry artifacts** (from live audits, memory `otel-span-hierarchy-audit`):
> an RPC entry span's scope can leak across a reused coroutine worker, and the
> `hashSpan` roots (`tx.*`) — along with plain roots like `ledger.acquire` — can
> surface in Tempo as dangling "root span not yet received". These are
> exporter/parenting artifacts, not real control-flow parents.

---

## Insights and Sample Queries

This section shows what questions you can answer using the span attributes, with example Tempo TraceQL queries.

**TraceQL syntax note:** span attributes must be referenced with the `span.` prefix inside `{}`.
Conditions are combined with `&&`. The `|` pipeline operator is not supported on this Tempo version.

```
# General pattern
{name="<span-name>" && span.<attr> = <value> && span.<attr2> != <value2>}

# Duration filter (no prefix needed)
{name="<span-name>" && duration > 500ms}

# Regex match
{name="<span-name>" && span.<attr> =~ "<pattern>.*"}

# Multiple span names
{name = "<span-a>" || name = "<span-b>"}

# Name regex
{name =~ "<pattern>.*" && span.<attr> = <value>}

# Structural: find parent spans that have a matching child/event
{name="<parent>"} >> {event:name="<event-name>"}
```

### Transaction Workflow Analysis

```
# Find all AMM transactions (AMMDeposit, AMMWithdraw, AMMVote)
{name="tx.process" && span.tx_type =~ "AMM.*"}

# Find a specific AMM operation
{name="tx.process" && span.tx_type = "AMMDeposit"}
{name="tx.process" && span.tx_type = "AMMWithdraw"}
{name="tx.process" && span.tx_type = "AMMVote"}

# Find Payment transactions that failed
{name="tx.process" && span.tx_type = "Payment" && span.ter_result != "tesSUCCESS"}

# Find Payment failures due to path issues
{name="tx.process" && span.tx_type = "Payment" && span.ter_result =~ "tecPATH.*"}

# Compare latency of different transaction types
{name="tx.process" && span.tx_type = "OfferCreate"}
{name="tx.process" && span.tx_type = "Payment"}

# Find high-fee transactions (fee > 1 XRP = 1000000 drops)
{name="tx.process" && span.fee > 1000000}

# Find transactions that were not applied
{name="tx.process" && span.applied = false}

# Find NFTokenMint across tx and txq spans
{name =~ "tx.*|txq.*" && span.tx_type = "NFTokenMint"}

# Find all NFT-related activity
{name =~ "tx.*|txq.*" && span.tx_type =~ "NFToken.*"}

# Find TrustSet transactions (IOU trust lines)
{name="tx.process" && span.tx_type = "TrustSet"}

# Find oracle price updates
{name="tx.process" && span.tx_type = "OracleSet"}
```

### DEX (OfferCreate / OfferCancel)

```
# All DEX offer creates
{name="tx.process" && span.tx_type = "OfferCreate"}

# Offers killed (ImmediateOrCancel/FillOrKill with no fill)
{name="tx.process" && span.tx_type = "OfferCreate" && span.ter_result = "tecKILLED"}

# Offers that failed due to insufficient funds
{name="tx.process" && span.tx_type = "OfferCreate" && span.ter_result = "tecUNFUNDED_OFFER"}

# Offers failed due to insufficient reserve to place the offer
{name="tx.process" && span.tx_type = "OfferCreate" && span.ter_result = "tecINSUF_RESERVE_OFFER"}

# Offer cancellations
{name="tx.process" && span.tx_type = "OfferCancel"}

# OfferCreate transactions received from peers (cross-node relay)
{name="tx.receive" && span.tx_type = "OfferCreate"}
```

### Apply Pipeline by Stage

```
# All three stages of one transaction (preflight -> preclaim -> apply)
{name=~"tx.preflight|tx.preclaim|tx.transactor"}

# Transactions that failed at the preclaim stage
{name="tx.preclaim"} | ter_result != "tesSUCCESS"

# Transactions that hard-failed preflight (never reached preclaim/apply)
{name="tx.preflight"} | ter_result != "tesSUCCESS"
```

PromQL on the span-derived metrics (dashboard: _Transaction Overview_):

```
# Per-stage throughput — the funnel preflight >= preclaim >= apply
sum by (stage) (rate(span_calls_total{span_name=~"tx.preflight|tx.preclaim|tx.transactor"}[5m]))

# Per-stage p95 latency
histogram_quantile(0.95, sum by (le, stage) (rate(span_duration_milliseconds_bucket{span_name=~"tx.preflight|tx.preclaim|tx.transactor"}[5m])))

# Per-stage failure rate (ter_result != tesSUCCESS; a failing ter completes the
# span normally, so filter on the attribute, not status_code which only flags exceptions)
sum by (stage) (rate(span_calls_total{span_name=~"tx.preflight|tx.preclaim|tx.transactor", ter_result!~"tesSUCCESS|"}[5m]))
```

> **Alerting**: a rising `tx.preflight` / `tx.preclaim` failure rate points to
> malformed or stale-sequence submissions (often spam or a misbehaving client);
> a rising `tx.transactor` failure rate points to apply-time problems. Alert per
> stage rather than on a single aggregate so the failing stage is obvious.

> **Sampling caveat**: these stage metrics are span-derived and inherit the
> **tracer head-sampling** ratio (`sampling_ratio`). At `sampling_ratio < 1.0`
> they undercount proportionally — treat them as relative trends, not absolute
> transaction counts. Native StatsD metrics are unsampled.

### Transaction Queue Health

```
# Find transactions rejected from the queue
{name="txq.accept_tx" && span.txq_status = "failed"}

# Find transactions being retried
{name="txq.accept_tx" && span.txq_status = "retried"}

# Find transactions that exhausted retries
{name="txq.accept_tx" && span.txq_status = "retried" && span.retries_remaining = 0}

# Which transaction types get queued most often?
{name="txq.enqueue" && span.tx_type = "Payment"}
{name="txq.enqueue" && span.tx_type = "OfferCreate"}
{name="txq.enqueue" && span.tx_type =~ "NFToken.*"}

# Find ledger closes that applied queued transactions
{name="txq.accept" && span.ledger_changed = true}
```

### RPC Debugging

```
# Find batch RPC requests
{name="rpc.process" && span.is_batch = true}

# Find large RPC payloads (>100KB)
{name="rpc.http_request" && span.request_payload_size > 100000}

# Find resource-heavy RPC commands (by load_type)
{name =~ "rpc.command.*" && span.load_type = "exceptioned RPC"}

# Find a specific WebSocket command
{name="rpc.ws_message" && span.command = "subscribe"}

# Find server_info calls
{name="rpc.command.server_info"}

# Find slow pathfinding with many source assets
{name="pathfind.discover" && span.pathfind_num_source_assets > 10}
```

### PathFinding Performance

```
# Find pathfinding for specific currencies
{name="pathfind.compute" && span.pathfind_dest_currency = "USD"}

# Find expensive pathfinding (many source assets to explore)
{name="pathfind.discover" && span.pathfind_num_source_assets > 20}

# Find slow pathfinding requests
{name="pathfind.compute" && duration > 1000ms}
```

### Consensus Health

```
# Find rounds where consensus timed out (expired)
{name="consensus.accept" && span.consensus_state = "expired"}

# Find rounds where we moved on without full agreement
{name="consensus.accept" && span.consensus_state = "moved_on"}

# Find rounds with many disputes
{name="consensus.accept" && span.disputes_count > 5}

# Find slow consensus rounds (>5s)
{name="consensus.accept" && span.round_time_ms > 5000}

# Find bow-out proposals (node resigned from round)
{name="consensus.proposal.send" && span.is_bow_out = true}

# Correlate validation with its ledger
{name="consensus.validation.send" && span.ledger_hash = "<hash>"}

# Find rounds where validators disagreed on close time
{name="consensus.accept.apply" && span.close_time_correct = false}

# Find both validation send and receive (compare sender vs receiver latency)
{name = "consensus.validation.send" || name = "consensus.validation.receive"}
```

### Cross-Subsystem Correlation

```
# Follow a transaction from receive through queue to ledger
{name =~ "tx.*|txq.*" && span.tx_type = "Payment" && duration > 500ms}

# Find all NFT-related activity across tx and txq spans
{name =~ "tx.*|txq.*" && span.tx_type =~ "NFToken.*"}

# Find all AMM activity across tx and txq spans
{name =~ "tx.*|txq.*" && span.tx_type =~ "AMM.*"}

# Find cross-node transaction receives (no errors)
{name="tx.receive" && status != error}
```

### Where to Look (Quick Reference)

| Question                            | Span                        | Key Attributes                           |
| ----------------------------------- | --------------------------- | ---------------------------------------- |
| "Which tx type is slowest?"         | `tx.process`                | `span.tx_type` + duration                |
| "Why was my tx rejected?"           | `tx.process`                | `span.ter_result`, `span.applied`        |
| "What AMM operations happened?"     | `tx.process`                | `span.tx_type =~ "AMM.*"`                |
| "What DEX offers failed?"           | `tx.process`                | `span.tx_type`, `span.ter_result`        |
| "What NFT activity occurred?"       | `tx.process`, `txq.enqueue` | `span.tx_type =~ "NFToken.*"`            |
| "Is the TxQ backing up?"            | `txq.accept`                | `span.queue_size`, `span.ledger_changed` |
| "Why was my tx dropped from queue?" | `txq.accept_tx`             | `span.txq_status`, `span.ter_code`       |
| "Are batch requests a problem?"     | `rpc.process`               | `span.is_batch`, `span.batch_size`       |
| "Which RPC is expensive?"           | `rpc.command.*`             | `span.load_type`, duration               |
| "Did consensus reach threshold?"    | `consensus.check`           | `span.consensus_result`                  |
| "Was consensus outcome normal?"     | `consensus.accept`          | `span.consensus_state`                   |
| "Did a validator bow out?"          | `consensus.proposal.send`   | `span.is_bow_out`                        |
| "Which ledger was validated?"       | `consensus.validation.send` | `span.ledger_hash`                       |
| "Did close time agreement fail?"    | `consensus.accept.apply`    | `span.close_time_correct`                |
| "What tx work fed a given ledger?"  | `tx.*` / `txq.*`            | `span.current_ledger_seq`                |

### Correlating a transaction to the ledger it was worked on

The `tx.process`, `tx.receive`, `txq.enqueue`, `tx.preclaim`, and `tx.transactor`
spans carry `current_ledger_seq` — the open/in-flight ledger they acted on (not
an established ledger). Because these spans are keyed on the transaction id (their
own trace) while the ledger/consensus spans are keyed on the ledger, use the
attribute to bridge the two id-spaces:

```
# All transaction-side work recorded against ledger N
{span.current_ledger_seq = N}

# Join to the ledger build/consensus trace for the same ledger
{name="ledger.build" && span.ledger_seq = N}
```

`txq.enqueue` and the view-bearing apply stages also carry `current_ledger_hash`
(the current ledger's parent hash), which equals the `consensus.round`
deterministic trace-id seed on the consensus-build path. `tx.preflight` is
stateless and omits both attributes.

---

## Cross-Node Trace Propagation

xrpld propagates trace context across nodes via protobuf `TraceContext` fields
embedded in peer-to-peer messages. When Node A sends a transaction, proposal,
or validation, it injects its active span's trace/span IDs into the protobuf
message. Node B extracts that context on receipt and creates a child span,
linking the two nodes into a single distributed trace.

### How It Works

```
Node A (sender)                          Node B (receiver)
+-----------------------------+          +-------------------------------+
| tx.process / consensus.*    |          | PeerImp::onMessage()          |
|   |                         |          |   |                           |
|   v                         |          |   v                           |
| SpanGuard::getTraceBytes()  |          | extract TraceContext from      |
|   |                         |          | protobuf message               |
|   v                         |   send   |   |                           |
| injectSpanContext() --------|--------->|   v                           |
| sets TraceContext fields    |  proto   | txReceiveSpan()               |
| (trace_id, span_id, flags) |  msg     | proposalReceiveSpan()         |
+-----------------------------+          | validationReceiveSpan()       |
                                         |   |                           |
                                         |   v                           |
                                         | child span with parent link   |
                                         +-------------------------------+
```

### Send-Side Injection

| Message Type  | Injection Point            | Mechanism                                  |
| ------------- | -------------------------- | ------------------------------------------ |
| TMTransaction | `NetworkOPs::apply()`      | Injects `tx.process` span into relay msg   |
| TMProposeSet  | `RCLConsensus::propose()`  | Injects active context into proposal msg   |
| TMValidation  | `RCLConsensus::validate()` | Injects active context into validation msg |

### Receive-Side Extraction

| Message Type  | Extraction Point                    | Helper Function                                    |
| ------------- | ----------------------------------- | -------------------------------------------------- |
| TMTransaction | `PeerImp::onMessage(TMTransaction)` | `TxTracing::txReceiveSpan()`                       |
| TMProposeSet  | `PeerImp::onMessage(TMProposeSet)`  | `ConsensusReceiveTracing::proposalReceiveSpan()`   |
| TMValidation  | `PeerImp::onMessage(TMValidation)`  | `ConsensusReceiveTracing::validationReceiveSpan()` |

### Key Files

| File                                              | Role                                            |
| ------------------------------------------------- | ----------------------------------------------- |
| `src/xrpld/telemetry/PropagationHelpers.h`        | `injectSpanContext()` — SpanGuard to protobuf   |
| `include/xrpl/telemetry/TraceContextPropagator.h` | OTel context <-> protobuf conversion primitives |
| `src/xrpld/telemetry/ConsensusReceiveTracing.h`   | Proposal/validation receive span factories      |
| `src/xrpld/telemetry/TxTracing.h`                 | Transaction receive span factory                |

### Backwards Compatibility

Older peers that do not populate `TraceContext` fields in their messages will
simply produce empty trace bytes on the receive side. The extraction helpers
detect this and create standalone (root) spans instead of child spans. No
errors are logged and no data is lost — the receive span is still created with
all its normal attributes, it just lacks a cross-node parent link.

### Example Tempo Queries

```
# Find cross-node transaction traces (tx.receive spans with no errors)
{name="tx.receive" && status != error}

# Find proposals received with cross-node parent context
{} >> {name="consensus.proposal.receive"}

# Trace a transaction across the network by its hash
{name =~ "tx.*" && span.tx_hash = "<hash>"}

# Find all spans in a cross-node consensus trace
{resource.service.name="xrpld" && span.consensus_round_id = "<round_id>"}

# Compare latency between sender and receiver for validations
{name = "consensus.validation.send" || name = "consensus.validation.receive"}
```

## Prometheus Metrics (Spanmetrics)

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

### Generated Metric Names

| Prometheus Metric                   | Type      | Description                  |
| ----------------------------------- | --------- | ---------------------------- |
| `span_calls_total`                  | Counter   | Total span invocations       |
| `span_duration_milliseconds_bucket` | Histogram | Latency distribution buckets |
| `span_duration_milliseconds_count`  | Histogram | Latency observation count    |
| `span_duration_milliseconds_sum`    | Histogram | Cumulative latency           |

### Metric Labels

Every metric carries these standard labels:

| Label          | Source             | Example                                  |
| -------------- | ------------------ | ---------------------------------------- |
| `span_name`    | Span name          | `rpc.command.server_info`                |
| `status_code`  | Span status        | `STATUS_CODE_UNSET`, `STATUS_CODE_ERROR` |
| `service_name` | Resource attribute | `xrpld`                                  |
| `span_kind`    | Span kind          | `SPAN_KIND_INTERNAL`                     |

Additionally, span attributes configured as dimensions in the collector
become metric labels. The span attribute keys are already underscore form
(the naming convention forbids dots), so the label name matches the attribute
name verbatim. Prometheus' dots → underscores sanitization only fires for
dotted attribute names (e.g. resource attributes like `service.name`), which
does not apply to these dimensions.

| Span Attribute       | Metric Label         | Applies To                      |
| -------------------- | -------------------- | ------------------------------- |
| `command`            | `command`            | `rpc.command.*` spans           |
| `rpc_status`         | `rpc_status`         | `rpc.command.*` spans           |
| `consensus_mode`     | `consensus_mode`     | `consensus.ledger_close` spans  |
| `local`              | `local`              | `tx.process` spans              |
| `proposal_trusted`   | `proposal_trusted`   | `peer.proposal.receive` spans   |
| `validation_trusted` | `validation_trusted` | `peer.validation.receive` spans |

### Histogram Buckets

Configured in `otel-collector-config.yaml` (spanmetrics connector, `unit: ms`):

```
1ms, 5ms, 10ms, 25ms, 50ms, 100ms, 250ms, 500ms, 1s, 2s, 3s, 4s, 5s, 10s, 30s
```

Sub-second boundaries cover RPC/tx/ledger spans; 2s-4s resolve second-scale
consensus spans (`consensus.round`, `consensus.establish`) that would otherwise
pile into one 1s-5s bucket and make `histogram_quantile` a meaningless
interpolation; 10s/30s give the `ledger.acquire` catch-up tail a measurable home.
Boundaries must stay strictly ascending. The native beast::insight histograms
(ms-scale RPC/IO timers) keep the original 1ms-5s buckets in
`Telemetry.cpp` — they never exceed 5s, so they need no high-range buckets.

## System Metrics (OTel native -- beast::insight)

xrpld has a built-in metrics framework (`beast::insight`) that exports metrics natively via OTLP to the OTel Collector. These complement the span-derived RED metrics by providing system-level gauges, counters, and timers that don't map to individual trace spans.

### Configuration

Add to `xrpld.cfg`:

```ini
[insight]
server=otel
endpoint=http://localhost:4318/v1/metrics
prefix=xrpld
```

The `OTelCollector` implementation exports metrics via OTLP/HTTP to the same OTel Collector that receives traces. No separate StatsD receiver is needed.

> **Fallback**: Set `server=statsd` and `address=127.0.0.1:8125` to use the legacy StatsD UDP path. This requires re-enabling the `statsd` receiver in `otel-collector-config.yaml` and uncommenting port 8125 in `docker-compose.yml`.

### Metric Reference

#### Gauges

| Prometheus Metric                     | Source                    | Description                                                                |
| ------------------------------------- | ------------------------- | -------------------------------------------------------------------------- |
| `ledgermaster_validated_ledger_age`   | LedgerMaster.h:373        | Age of validated ledger (seconds)                                          |
| `ledgermaster_published_ledger_age`   | LedgerMaster.h:374        | Age of published ledger (seconds)                                          |
| `state_accounting_{mode}_duration`    | NetworkOPs.cpp:774        | Time in each operating mode (Disconnected/Connected/Syncing/Tracking/Full) |
| `state_accounting_{mode}_transitions` | NetworkOPs.cpp:780        | Transition count per mode                                                  |
| `peer_finder_active_inbound_peers`    | PeerfinderManager.cpp:214 | Active inbound peer connections                                            |
| `peer_finder_active_outbound_peers`   | PeerfinderManager.cpp:215 | Active outbound peer connections                                           |
| `overlay_peer_disconnects`            | OverlayImpl.h:557         | Peer disconnect count                                                      |
| `jobq_job_count`                      | JobQueue.cpp:26           | Current job queue depth (all types)                                        |
| `jobq_{jobtype}_waiting`              | JobTypeData.h             | Jobs of this type enqueued but not yet running                             |
| `jobq_{jobtype}_running`              | JobTypeData.h             | Jobs of this type currently executing                                      |
| `jobq_{jobtype}_deferred`             | JobTypeData.h             | Jobs of this type held back because the type's concurrency limit was hit   |
| `{category}_bytes_in/out`             | OverlayImpl.h:535         | Overlay traffic bytes per category (57 categories)                         |
| `{category}_messages_in/out`          | OverlayImpl.h:535         | Overlay traffic messages per category                                      |

Note that `job_count` is exported as `jobq_job_count`: the JobQueue is
constructed with `collectorManager_->group("jobq")` (Application.cpp:386),
`GroupImp::makeName()` joins prefix and name with a `.` (Groups.cpp:42), and
`OTelCollectorImp::formatName()` then turns the `.` into `_` and lowercases the
whole string (OTelCollector.cpp:860). The same mechanism produces the
`jobq_{jobtype}_*` names above and the pre-existing
`jobq_{jobtype}_milliseconds` timing family.

#### Per-Job-Type Queue Saturation

The three `jobq_{jobtype}_{waiting,running,deferred}` families expose the
per-type counters that `JobTypeData` already maintained but never exported.
`{jobtype}` is the lowercased `JobTypes` name, so `JtLedgerReq` ("ledgerRequest")
becomes `jobq_ledgerrequest_waiting` / `_running` / `_deferred`.

They are emitted for every **non-special** job type — 35 of the 46 declared
types. A "special" type is one whose concurrency limit is 0
(`JobTypeInfo::special()`, JobTypeInfo.h:71-74); the limit logic never applies
to it, so its `deferred` is always zero. The gauge members are declared at
JobTypeData.h:78-80 and created at :100-102, next to the existing
`dequeue`/`execute` events and under the same `!info.special()` guard (:95). They
are published by `JobQueue::collect()` under the `mutex_` that already guards the
counters (JobQueue.cpp:66-93) — no new locking.

**`deferred` is the leading indicator.** `JobQueue::addJob()` never rejects
work — when a type is at its limit, `addRefCountedJob()` increments `deferred`
and returns `true` anyway (JobQueue.cpp:131-142), and `finishJob()` drains one
deferred job per completion (JobQueue.cpp:353-362). Backpressure on a capped type
therefore shows up **only as latency**, after the harm is done. `deferred > 0`
says the cap is being hit _now_, before the duration histograms move.

Limits that matter for ledger sync (JobTypes.h:54-77):

| Job type       | Metric prefix         | Limit | Producers                                                                       |
| -------------- | --------------------- | ----- | ------------------------------------------------------------------------------- |
| `JtPack`       | `jobq_makefetchpack_` | 1     | `MakeFetchPack`                                                                 |
| `JtLedgerReq`  | `jobq_ledgerrequest_` | 3     | `RcvGetLedger`, `RcvGetObjByHash`                                               |
| `JtLedgerData` | `jobq_ledgerdata_`    | 3     | `ProcessLData`, `GotStaleData`, `GotFetchPack`, `AcqDone`, `InboundLedger`      |
| `JtUpdatePf`   | `jobq_updatepaths_`   | 1     | `PthFindNewReq`, `PthFindOBDB`, `PthFindNewLed`, `OB<seq>` — see the note below |
| `JtTxnData`    | `jobq_fetchtxndata_`  | 5     | `TxAcq`, `ComplAcquire`, `RcvPeerData`                                          |

> **`JtUpdatePf` has four producers, three of them individually visible.** All
> four run the same `updatePaths()` work but arrive under different names. Three
> come through `LedgerMaster::newPFWork()` (LedgerMaster.cpp:1545), which passes
> its caller's name straight to `addJob`: `PthFindNewReq` (:1512),
> `PthFindOBDB` (:1533), and `PthFindNewLed` (:1984). All three are all-letters,
> so each is its own `handler` series. The fourth is
> `"OB" + std::to_string(seq)` (OrderBookDBImpl.cpp:84), which contains digits
> and therefore folds to `handler="other"` — order-book rebuild traffic is the
> only one of the four that is not directly attributable. Do not read the whole
> type as invisible: three of its four producers are named.

> **Sampling caveat**: these are gauges read by the `JobQueue::collect()` hook,
> which the beast::insight `PeriodicMetricReader` drives every 1 s
> (Telemetry.cpp:441). A `deferred` spike shorter than the sample interval can be
> missed entirely. Treat a non-zero reading as real saturation, but do not treat
> a zero reading as proof that no saturation occurred — cross-check
> `job_queued_us` for the same type.

#### OTel MetricsRegistry Gauges

These gauges are exported via the OTel Metrics SDK `PeriodicMetricReader` (10s interval), NOT through beast::insight.

| Prometheus Metric                                   | Source              | Description                                                                                                                                                                         |
| --------------------------------------------------- | ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `server_info{metric="server_state"}`                | MetricsRegistry.cpp | Operating mode (0=DISCONNECTED .. 4=FULL)                                                                                                                                           |
| `server_info{metric="uptime"}`                      | MetricsRegistry.cpp | Seconds since server start                                                                                                                                                          |
| `server_info{metric="peers"}`                       | MetricsRegistry.cpp | Total connected peers                                                                                                                                                               |
| `server_info{metric="validated_ledger_seq"}`        | MetricsRegistry.cpp | Validated ledger sequence number                                                                                                                                                    |
| `server_info{metric="ledger_current_index"}`        | MetricsRegistry.cpp | Current open ledger sequence                                                                                                                                                        |
| `server_info{metric="peer_disconnects_resources"}`  | MetricsRegistry.cpp | Cumulative resource-related peer disconnects                                                                                                                                        |
| `server_info{metric="last_close_proposers"}`        | MetricsRegistry.cpp | Proposers in last closed round                                                                                                                                                      |
| `server_info{metric="last_close_converge_time_ms"}` | MetricsRegistry.cpp | Last close convergence time (ms)                                                                                                                                                    |
| `server_info{metric="last_close_time"}`             | MetricsRegistry.cpp | Network close time of last closed ledger (NetClock secs since XRPL epoch). Age = `time() - (value + 946684800)`; close interval = `1/rate(ledgers_closed_total)`, not a gauge delta |
| `build_info{version="<ver>"}`                       | MetricsRegistry.cpp | Info-style metric (always 1)                                                                                                                                                        |
| `complete_ledgers{bound="start\|end",index="<N>"}`  | MetricsRegistry.cpp | Complete ledger range start/end pairs                                                                                                                                               |
| `db_metrics{metric="db_kb_total"}`                  | MetricsRegistry.cpp | Total database size (KB)                                                                                                                                                            |
| `db_metrics{metric="db_kb_ledger"}`                 | MetricsRegistry.cpp | Ledger database size (KB)                                                                                                                                                           |
| `db_metrics{metric="db_kb_transaction"}`            | MetricsRegistry.cpp | Transaction database size (KB)                                                                                                                                                      |
| `db_metrics{metric="historical_perminute"}`         | MetricsRegistry.cpp | Historical ledger fetches per minute                                                                                                                                                |
| `cache_metrics{metric="AL_size"}`                   | MetricsRegistry.cpp | AcceptedLedger cache size                                                                                                                                                           |
| `nodestore_state{metric="node_reads_duration_us"}`  | MetricsRegistry.cpp | Cumulative read time (microseconds)                                                                                                                                                 |
| `nodestore_state{metric="node_writes_duration_us"}` | MetricsRegistry.cpp | Cumulative write time (microseconds)                                                                                                                                                |
| `nodestore_state{metric="read_request_bundle"}`     | MetricsRegistry.cpp | Read request bundle count                                                                                                                                                           |
| `nodestore_state{metric="read_threads_running"}`    | MetricsRegistry.cpp | Active read threads                                                                                                                                                                 |
| `nodestore_state{metric="read_threads_total"}`      | MetricsRegistry.cpp | Total read threads configured                                                                                                                                                       |
| `rpc_in_flight_requests`                            | PerfLogImp.cpp      | RPC requests currently executing (UpDownCounter)                                                                                                                                    |

#### Sync Diagnosis Signals

More label values on the same `nodestore_state` gauge. They exist to separate the
two different reasons a node is slow to reach `full` — see
[Slow to reach `full`](#slow-to-reach-full). The `nudb_*` group is published only
when the writable backend is NuDB; a memory or RocksDB backend omits those four
label values rather than reporting them as zero.

| Prometheus Metric                                    | Source              | Description                                                 |
| ---------------------------------------------------- | ------------------- | ----------------------------------------------------------- |
| `nodestore_state{metric="read_mean_us"}`             | MetricsRegistry.cpp | Mean time per backend read (microseconds)                   |
| `nodestore_state{metric="write_mean_us"}`            | MetricsRegistry.cpp | Mean time per backend write (microseconds)                  |
| `nodestore_state{metric="nudb_writers_in_flight"}`   | MetricsRegistry.cpp | Threads inside a NuDB insert right now                      |
| `nodestore_state{metric="nudb_writer_depth_x100"}`   | MetricsRegistry.cpp | Mean queue depth at the NuDB insert mutex, ×100             |
| `nodestore_state{metric="nudb_insert_mean_us"}`      | MetricsRegistry.cpp | Mean NuDB insert time, queueing included (microseconds)     |
| `nodestore_state{metric="nudb_insert_max_us"}`       | MetricsRegistry.cpp | Slowest single NuDB insert seen (microseconds)              |
| `nodestore_state{metric="acquire_deferrals"}`        | MetricsRegistry.cpp | Timer jobs skipped because the lane was full, **all lanes** |
| `nodestore_state{metric="acquire_timeouts"}`         | MetricsRegistry.cpp | Timer bodies that ran and advanced retry, **all lanes**     |
| `nodestore_state{metric="acquire_ledger_deferrals"}` | MetricsRegistry.cpp | Deferrals from ledger acquisition alone                     |
| `nodestore_state{metric="acquire_ledger_timeouts"}`  | MetricsRegistry.cpp | Timeouts from ledger acquisition alone                      |
| `nodestore_state{metric="acquire_give_ups"}`         | MetricsRegistry.cpp | Acquisitions that exhausted their retry budget              |
| `nodestore_state{metric="acquire_aborts"}`           | MetricsRegistry.cpp | Acquisitions destroyed before finishing                     |
| `nodestore_state{metric="acquire_aborts_partial"}`   | MetricsRegistry.cpp | Subset of aborts that discarded partly built maps           |
| `nodestore_state{metric="acquire_completions"}`      | MetricsRegistry.cpp | Acquisitions that finished successfully                     |
| `nodestore_state{metric="acquire_sweep_evictions"}`  | MetricsRegistry.cpp | Acquisitions evicted by the 1-minute sweep                  |

`nudb_writer_depth_x100` is fixed-point: divide by 100 to read it. The depth sits
just above 1.0 even under load, so an integer gauge would truncate the whole
signal away. It is `depthSum / depthSamples`, both accumulated when an insert
**enters** the critical section, so an insert still in flight is part of the mean.

`acquire_deferrals` and `acquire_timeouts` sum every `TimeoutCounter` subclass —
inbound ledgers, transaction sets and the three ledger-replay tasks — because both
are recorded in that shared base. They answer "is any lane deferring", not "is
ledger acquisition deferring". Use `acquire_ledger_deferrals` and
`acquire_ledger_timeouts` for the ledger-acquisition diagnosis; see
[The deferral/timeout pair](#the-deferraltimeout-pair).

#### Counters

| Prometheus Metric         | Source                | Description                    |
| ------------------------- | --------------------- | ------------------------------ |
| `rpc_requests`            | ServerHandler.cpp:108 | Total RPC request count        |
| `ledger_fetches`          | InboundLedgers.cpp:44 | Ledger fetch request count     |
| `ledger_history_mismatch` | LedgerHistory.cpp:16  | Ledger hash mismatch count     |
| `warn`                    | Logic.h:33            | Resource manager warning count |
| `drop`                    | Logic.h:34            | Resource manager drop count    |

#### Histograms

| Prometheus Metric | Source                | Description                    |
| ----------------- | --------------------- | ------------------------------ |
| `rpc_time`        | ServerHandler.cpp:110 | RPC response time (ms)         |
| `rpc_size`        | ServerHandler.cpp:109 | RPC response size (bytes)      |
| `ios_latency`     | Application.cpp:438   | I/O service loop latency (ms)  |
| `pathfind_fast`   | PathRequests.h:23     | Fast pathfinding duration (ms) |
| `pathfind_full`   | PathRequests.h:24     | Full pathfinding duration (ms) |

#### Job Instruments

These five come from the `PerfLog` job hooks, not from beast::insight, so they
are exported by the `MetricsRegistry` meter. `job_queued_us` and `job_running_us`
have explicit microsecond bucket views registered
(`addMicrosecondHistogramView()`, MetricsRegistry.cpp:253-254) spanning 100 µs to
60 s; without those the SDK default buckets stop at 10 ms and every quantile
saturates.

| Prometheus Metric    | Kind      | Labels                | Description                          |
| -------------------- | --------- | --------------------- | ------------------------------------ |
| `job_queued_total`   | Counter   | `job_type`, `handler` | Jobs enqueued                        |
| `job_started_total`  | Counter   | `job_type`, `handler` | Jobs dequeued and started            |
| `job_finished_total` | Counter   | `job_type`, `handler` | Jobs run to completion               |
| `job_queued_us`      | Histogram | `job_type`, `handler` | Time spent waiting in the queue (µs) |
| `job_running_us`     | Histogram | `job_type`, `handler` | Time spent executing (µs)            |

#### The `handler` Label

`job_type` names the queue a job ran on, not the code that submitted it. Several
job types have more than one producer, so `job_type` alone cannot attribute a
latency spike. The clearest case: `RcvGetLedger` (PeerImp.cpp:1566) and
`RcvGetObjByHash` (PeerImp.cpp:2603) both submit to `JtLedgerReq`, so both report
as `job_type="ledgerRequest"`. `JtLedgerData` has five producers, `JtUpdatePf` has
four, and `JtAdvance` has four.

The `handler` label carries the name string passed to `addJob()` — the specific
call site. It resolves all of those at once, not just the GetObject path.

**The value is sanitized, not raw.** A raw job name would be unbounded, because
two production job names embed a ledger sequence number:

- `"Pub" + std::to_string(ledger->seq())` (LedgerPersistence.cpp:84)
- `"OB" + std::to_string(ledger->seq() % 1000000000)` (OrderBookDBImpl.cpp:84)

A raw label would mint a new Prometheus series for every ledger — unbounded
growth at ~1 series every 3-5 s, forever.
`MetricsRegistry::sanitiseHandler()` (declared inline in
`src/xrpld/telemetry/MetricsRegistry.h`) therefore applies one rule:

- Keep the name when it is **non-empty and every character is an ASCII letter**.
- Otherwise return the constant `"other"`. An empty name, a digit, a hyphen, or
  any punctuation all fall here.

Both dynamic names always contain digits, so both collapse to `"other"`. Because
the rule is a pure function of compile-time string literals, the label domain is
fixed at build time and cannot grow at runtime. That is a stronger guarantee than
an allowlist, which would silently mislabel any job added later; this rule
degrades to `"other"` instead.

**Five static job names also fall into `"other"`.** Sweeping every `addJob` /
`addRefCountedJob` / `postCoro` / `newPFWork` / `TimeoutCounter::jobName` call
site outside `src/test` finds 48 distinct static name literals. 43 are
all-letters and pass through; five are not, and two more are built at runtime
from a ledger sequence. The domain is therefore **44 values** (43 names plus
`"other"`):

| Name          | Why it is not all-letters | Call site              |
| ------------- | ------------------------- | ---------------------- |
| `GetConsL1`   | digit                     | RCLConsensus.cpp:169   |
| `GetConsL2`   | digit                     | RCLValidations.cpp:135 |
| `gRPC-Client` | hyphen                    | GRPCServer.cpp:156     |
| `RPC-Client`  | hyphen                    | ServerHandler.cpp:332  |
| `WS-Client`   | hyphen                    | ServerHandler.cpp:376  |

> The consequence worth remembering: `handler="other"` is a **mixed bucket**, not
> a residual. It holds the two per-ledger dynamic names _and_ those five static
> ones, so `GetConsL1` and `GetConsL2` — two different `JtAdvance` producers —
> are not separable, and neither are the three RPC client-session names. Do not
> read a handler breakdown as exhaustive. The GetObject path is unaffected:
> `RcvGetObjByHash` and `RcvGetLedger` are all-letters and pass through as
> distinct series.
>
> The count is "at the time of writing" — it is a property of the source, not of
> the rule. Re-derive it from the call sites after adding a job rather than
> trusting this number.

#### GetObject Request Metrics

Five instruments on the `TMGetObjectByHash` query path, recorded via the
`XRPL_METRIC_*` macros at their call sites in `PeerImp.cpp`. Label cardinality is
fixed and tiny: two `result` values, two `reason` values.

| Prometheus Metric           | Kind      | Labels                                          | Description                                           |
| --------------------------- | --------- | ----------------------------------------------- | ----------------------------------------------------- |
| `getobject_lookup_us`       | Histogram | none                                            | Time inside the NodeStore fetch loop (µs)             |
| `getobject_request_objects` | Histogram | none                                            | Objects requested per request                         |
| `getobject_lookups_total`   | Counter   | `result` = `hit` \| `miss`                      | NodeStore hit/miss volume                             |
| `getobject_rejected_total`  | Counter   | `reason` = `oversize` \| `malformed_ledgerhash` | Requests refused before any NodeStore access          |
| `getobject_charge`          | Histogram | none                                            | Dynamic component of the differential resource charge |

Aggregation choices worth knowing when reading these:

- `getobject_lookup_us` times the **whole fetch loop once**
  (`processGetObjectByHash()`, PeerImp.cpp:2713-2742 — the iteration cap is set
  at :2713 and the loop ends at :2742), not each iteration. The loop can run up
  to `kHardMaxReplyNodes` = 12288 times (Tuning.h:30); timing each
  `fetchNodeObject()` would cost more than the lookups. It needs an
  `addMicrosecondHistogramView()` entry for the same reason the job histograms
  do — a 12288-lookup loop routinely exceeds 10 ms, so without the view the
  metric saturates exactly when it matters.
- `getobject_lookups_total` is incremented **once per request with the batch
  totals**, not once per object. A 12288-iteration loop incrementing per object
  would be a measurable hot-path cost for no extra information.
- `getobject_request_objects` records `packet.objects_size()` — the _requested_
  count, which is what the charge bands price on, not the count actually found.
- `getobject_charge` records only the **dynamic** part returned by
  `computeGetObjectByHashFee()` (PeerImp.cpp:3658-3681), applied at
  PeerImp.cpp:2757-2758 just after the loop. The admission-time base charge is a
  constant (`kFeeModerateBurdenPeer`, PeerImp.cpp:2634) and is already implied.
- `getobject_rejected_total` counts the two early returns in
  `onMessage(TMGetObjectByHash)`: the malformed-ledgerhash check (PeerImp.cpp:2569,
  counter at :2573) and the oversize gate (PeerImp.cpp:2585, counter at :2591).
  Both fire before the job is enqueued, so a rejected request contributes to no
  other GetObject metric.

> On a healthy local network `getobject_rejected_total` reads zero — no honest
> peer sends an oversized request. Verify its panel with a synthetic oversized
> request; do not assume it works because the query parses.

#### Adding a New Metric

<!-- cspell:ignore ISTOGRAM -->
<!-- The all-caps macro name XRPL_METRIC_HISTOGRAM_RECORD trips cspell's
     compound-word splitter, which emits the subword "ISTOGRAM"; ignore it here. -->

Use the call-site macros in `src/xrpld/telemetry/MetricMacros.h` -- no
`MetricsRegistry.h`/`.cpp` edit is needed for any of these:

| Need                                                   | Macro                                                                                                                                                                     |
| ------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Monotonic tally (never decreases)                      | `XRPL_METRIC_COUNTER_INC` / `_ADD` [+ `_LABELED`]                                                                                                                         |
| Running total that can decrease                        | `XRPL_METRIC_UPDOWN_ADD` [+ `_LABELED`]                                                                                                                                   |
| Distribution of values (latency, size)                 | `XRPL_METRIC_HISTOGRAM_RECORD` [+ `_LABELED`]                                                                                                                             |
| Last-value snapshot (not a distribution)               | `XRPL_METRIC_GAUGE_RECORD` [+ `_LABELED`] -- requires an ABI v2 opentelemetry-cpp build; this repo currently builds ABI v1, so use the observable-gauge row below instead |
| Value your own code already tracks, sampled on a timer | `XRPL_METRIC_OBSERVABLE_GAUGE_REGISTER` / `_COUNTER_REGISTER` / `_UPDOWN_REGISTER`                                                                                        |

```cpp
#include <xrpld/telemetry/MetricMacros.h>

// Monotonic counter:
XRPL_METRIC_COUNTER_INC(app_, "my_new_thing_total", "Description of what this counts");

// Value that can go up and down, e.g. in-flight work (no _total suffix -- that
// is reserved for monotonic counters; an UpDownCounter is a current value):
XRPL_METRIC_UPDOWN_ADD(app_, "my_in_flight_requests", "Currently executing", 1);   // on start
XRPL_METRIC_UPDOWN_ADD(app_, "my_in_flight_requests", "Currently executing", -1);  // on finish

// Sampled from your own state, on the OTel export timer (register ONCE, in init code):
XRPL_METRIC_OBSERVABLE_GAUGE_REGISTER(app_, "my_thing_size", "Current size",
    [this] { return static_cast<int64_t>(myThing_.size()); });
```

Counters use a `_total` suffix by convention. A histogram whose values can
exceed ~10,000 units (e.g. a microsecond duration beyond 10ms) still needs one
line added to `addMicrosecondHistogramView()` in `MetricsRegistry.cpp` -- the
only case that still touches a central file. There is no way to read a metric's
current value back from application code -- OTel's API is write-only by design;
keep your own state if your logic needs to both record and read a running value
(see the Doxygen header in `MetricMacros.h` and "Use Case 4" in
`tasks/metric-macro-plan.md` for the full explanation and the `prometheus-cpp`
contrast rationale).

## Deployment Tiers

Multiple xrpld instances can send telemetry to per-tier collectors that all
forward to one Grafana stack. Four resource attributes segregate the data so
one dashboard set serves every deployment:

| Dimension   | Attribute                | Set by     | Example values                                   |
| ----------- | ------------------------ | ---------- | ------------------------------------------------ |
| Node        | `service.instance.id`    | xrpld cfg  | `alice-laptop`, `ci-runner-7`                    |
| Service     | `service.name`           | xrpld cfg  | `xrpld`, `xrpld-validator`                       |
| Network     | `xrpl.network.type`      | xrpld node | `mainnet`, `testnet`, `devnet`, `perf`           |
| Environment | `deployment.environment` | collector  | `local`, `test`, `ci`, `prod`                    |
| Work Item   | `xrpl.work.item`         | perf-iac   | `RIPD-7455` (empty outside perf runs)            |
| Branch      | `xrpl.branch`            | perf-iac   | `baseline:<ref>:<commit>`, `test:<ref>:<commit>` |
| Node Role   | `xrpl.node.role`         | perf-iac   | `validator`, `peer`                              |

Dashboards expose these as the template variables `$node`, `$service_name`,
`$xrpl_network_type`, `$deployment_environment`, `$xrpl_work_item`,
`$xrpl_branch`, and `$xrpl_node_role` (each variable name matches its
Prometheus label). Select them top-down — work item → branch → node role →
node for a perf comparison run, or environment → network → service → node for
general use. Selecting **All** matches every value, including series lacking
the label, so mixed old/new data never disappears.

The last three (`$xrpl_work_item`, `$xrpl_branch`, `$xrpl_node_role`) are
populated only during perf-iac comparison runs, which stamp them as resource
attributes from their own alloy pipeline. Outside those runs the labels are
absent; leaving the filters on **All** keeps every dashboard rendering
normally.

### Who owns which attribute

- **Node and service** come from xrpld config (`service_instance_id`,
  `service_name`). Unique per process.
- **Network** is a property of the chain the node joined; the node derives it
  from `[network_id]` and stamps `xrpl.network.type` on all three signals.
- **Environment** is a property of where the collector runs; each collector
  serves one environment and stamps it.

### The upsert vs insert rule

The collector's `resource/tier` processor uses two actions on purpose:

- `deployment.environment` → **`upsert`** (overwrite). The collector _is_ the
  environment, so it is authoritative.
- `xrpl.network.type` → **`insert`** (fill only if absent). The node knows
  its real network, so the collector must not overwrite it — `insert` only
  supplies a value when the source did not (e.g. an older xrpld build). This
  is what lets a local node connected to mainnet report `network=mainnet`,
  not the collector's default.

### Configuring a collector for a tier

Each tier runs its own collector. Set the two values in the `resource/tier`
processor of the collector config (`otel-collector-config.yaml` for local
backends, `otel-collector-config.grafanacloud.yaml` for Grafana Cloud):

```yaml
processors:
  resource/tier:
    attributes:
      - key: deployment.environment
        value: <tier> # local | test | ci | prod
        action: upsert
      - key: xrpl.network.type
        value: <network> # mainnet | testnet | devnet (fallback only)
        action: insert
```

Suggested per-tier values:

| Collector           | `deployment.environment` | `xrpl.network.type` (fallback) |
| ------------------- | ------------------------ | ------------------------------ |
| Developer laptop    | `local`                  | `devnet`                       |
| Test machines       | `test`                   | `testnet`                      |
| CI runs             | `ci`                     | `testnet`                      |
| Production observer | `prod`                   | `mainnet`                      |

The `xrpl.network.type` value is only a fallback: when the node stamps its
own network (all current builds do), the node's value wins. Set it to the
network the collector most commonly serves.

### How the tier labels reach metrics

Resource attributes do not become Prometheus labels automatically. Two
collector settings make it work, both already enabled:

- `prometheus.resource_to_telemetry_conversion: enabled: true` promotes
  resource attributes to metric labels on the local scrape surface.
- `spanmetrics.resource_metrics_key_attributes` lists the tier attributes so
  span-derived series stay grouped per node and tier.

Traces and logs carry resource attributes natively; Grafana Cloud ingests all
three signals' attributes over OTLP directly.

## Grafana Dashboards

Fifteen dashboards are pre-provisioned in `docker/telemetry/grafana/dashboards/`.
Fourteen are Prometheus-backed; `log-derived-insights` is the only Loki/LogQL
board and is documented last, together with the LogQL-specific traps it exposed.

> Nine dashboards have a reference section below. `fee-market`, `job-queue`,
> `ledger-data-sync`, `overlay-traffic-detail`, `peer-quality`, and
> `validator-health` are provisioned but not yet documented here — their panel
> descriptions carry the same six-heading reference format, so open the panel
> info icon in Grafana until a section is written.

### RPC Performance (`rpc-performance`)

| Panel                       | Type       | PromQL                                                                                                                     | Labels Used              |
| --------------------------- | ---------- | -------------------------------------------------------------------------------------------------------------------------- | ------------------------ |
| RPC Request Rate by Command | timeseries | `sum by (command) (rate(span_calls_total{span_name=~"rpc.command.*"}[5m]))`                                                | `command`                |
| RPC Latency p95 by Command  | timeseries | `histogram_quantile(0.95, sum by (le, command) (rate(span_duration_milliseconds_bucket{span_name=~"rpc.command.*"}[5m])))` | `command`                |
| RPC Error Rate              | bargauge   | Error spans / total spans × 100, grouped by `command`                                                                      | `command`, `status_code` |
| RPC Latency Heatmap         | heatmap    | `sum(increase(span_duration_milliseconds_bucket{span_name=~"rpc.command.*"}[5m])) by (le)`                                 | `le` (bucket boundaries) |
| Overall RPC Throughput      | timeseries | `rpc.request` + `rpc.process` rate                                                                                         | —                        |
| RPC Success vs Error        | timeseries | by `status_code` (UNSET vs ERROR)                                                                                          | `status_code`            |
| Top Commands by Volume      | bargauge   | `topk(10, ...)` by `command`                                                                                               | `command`                |
| WebSocket Message Rate      | stat       | `rpc.ws_message` rate                                                                                                      | —                        |

### Transaction Overview (`transaction-overview`)

| Panel                              | Type           | PromQL                                                                                       | Labels Used                         |
| ---------------------------------- | -------------- | -------------------------------------------------------------------------------------------- | ----------------------------------- |
| Transaction Processing Rate        | timeseries     | `rate(span_calls_total{span_name="tx.process"}[5m])` and `tx.receive`                        | `span_name`                         |
| Transaction Processing Latency     | timeseries     | `histogram_quantile(0.95 / 0.50, ... {span_name="tx.process"})`                              | —                                   |
| Transaction Path Distribution      | piechart       | `sum by (local) (increase(span_calls_total{span_name="tx.process"}[5m]))`                    | `local`                             |
| Transaction Receive vs Suppressed  | timeseries     | `rate(span_calls_total{span_name="tx.receive"}[5m])`                                         | —                                   |
| TX Processing Duration Heatmap     | heatmap        | `tx.process` histogram buckets                                                               | `le`                                |
| TX Apply Duration per Ledger       | timeseries     | p95/p50 of `tx.apply`                                                                        | —                                   |
| Peer TX Receive Rate               | timeseries     | `tx.receive` rate                                                                            | —                                   |
| TX Apply Failed Rate               | stat           | `rate(span_calls_total{span_name="tx.transactor",stage="apply",ter_result!~"tesSUCCESS\|"})` | `stage`, `ter_result`               |
| TxQ Accept: Applied Ratio per Node | state-timeline | applied / (applied+failed) of `span_calls_total{span_name="txq.accept_tx"}` per node         | `txq_status`, `service_instance_id` |

### Consensus Health (`consensus-health`)

| Panel                         | Type       | PromQL                                                                                                                                             | Labels Used      |
| ----------------------------- | ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| Consensus Round Duration      | timeseries | `histogram_quantile(0.95 / 0.50, ... {span_name="consensus.accept"})`                                                                              | —                |
| Consensus Proposals Sent Rate | timeseries | `rate(span_calls_total{span_name="consensus.proposal.send"}[5m])`                                                                                  | —                |
| Ledger Close Duration         | timeseries | `histogram_quantile(0.95, ... {span_name="consensus.round"})` (full round, not `consensus.ledger_close` which is only the sub-ms onClose prologue) | `consensus_mode` |
| Validation Send Rate          | stat       | `rate(span_calls_total{span_name="consensus.validation.send"}[5m])`                                                                                | —                |
| Ledger Apply Duration         | timeseries | `histogram_quantile(0.95 / 0.50, ... {span_name="consensus.accept.apply"})`                                                                        | —                |
| Close Time Agreement          | timeseries | `rate(span_calls_total{span_name="consensus.accept.apply"}[5m])`                                                                                   | —                |
| Consensus Mode Over Time      | timeseries | `consensus.ledger_close` by `consensus_mode`                                                                                                       | `consensus_mode` |
| Accept vs Close Rate          | timeseries | `consensus.accept` vs `consensus.ledger_close` rate                                                                                                | —                |
| Validation vs Close Rate      | timeseries | `consensus.validation.send` vs `consensus.ledger_close`                                                                                            | —                |
| Accept Duration Heatmap       | heatmap    | `consensus.accept` histogram buckets                                                                                                               | `le`             |

### Ledger Operations (`ledger-operations`)

| Panel                       | Type       | PromQL                                                                                                                       | Labels Used |
| --------------------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------- | ----------- |
| Ledger Build Rate           | stat       | `ledger.build` call rate                                                                                                     | —           |
| Ledger Build Duration       | timeseries | p95/p50 of `ledger.build`                                                                                                    | —           |
| Ledger Validation Rate      | stat       | `ledger.validate` call rate                                                                                                  | —           |
| Build Duration Heatmap      | heatmap    | `ledger.build` histogram buckets                                                                                             | `le`        |
| TX Apply Duration           | timeseries | p95/p50 of `tx.apply`                                                                                                        | —           |
| TX Apply Rate               | timeseries | `tx.apply` call rate                                                                                                         | —           |
| Ledger Store Rate           | stat       | `ledger.store` call rate                                                                                                     | —           |
| Build vs Close Duration     | timeseries | p95 `ledger.build` vs `consensus.round` (full round, not `consensus.ledger_close` which is only the sub-ms onClose prologue) | —           |
| Ledger Close Interval & Age | timeseries | Interval: `1/rate(ledgers_closed_total)`; Age: `time() - (server_info{metric="last_close_time"} + 946684800)`                | —           |

### Peer Network (`peer-network`)

Requires `trace_peer=1` in the `[telemetry]` config section.

| Panel                            | Type       | PromQL                                                                    | Labels Used          |
| -------------------------------- | ---------- | ------------------------------------------------------------------------- | -------------------- |
| Proposal Receive Rate            | timeseries | `peer.proposal.receive` rate                                              | —                    |
| Validation Receive Rate          | timeseries | `peer.validation.receive` rate                                            | —                    |
| Proposals Trusted vs Untrusted   | piechart   | `increase()` counts in the selected window, split by `proposal_trusted`   | `proposal_trusted`   |
| Validations Trusted vs Untrusted | piechart   | `increase()` counts in the selected window, split by `validation_trusted` | `validation_trusted` |

### Node Health -- System Metrics (`node-health`)

| Panel                                                        | Type       | PromQL                                                                                                                                                     | Labels Used      |
| ------------------------------------------------------------ | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- |
| Validated Ledger Age                                         | stat       | `ledgermaster_validated_ledger_age`                                                                                                                        | —                |
| Published Ledger Age                                         | stat       | `ledgermaster_published_ledger_age`                                                                                                                        | —                |
| Operating Mode (Time Share)                                  | timeseries | `rate(state_accounting_X_duration) / sum(rate(all modes))`                                                                                                 | —                |
| Operating Mode Transitions                                   | timeseries | `state_accounting_*_transitions`                                                                                                                           | —                |
| I/O Latency                                                  | timeseries | `histogram_quantile(0.95, ios_latency_bucket)`                                                                                                             | —                |
| Job Queue Depth                                              | timeseries | `jobq_job_count`                                                                                                                                           | —                |
| Ledger Fetch Rate                                            | stat       | `rate(ledger_fetches[5m])`                                                                                                                                 | —                |
| Ledger History Mismatches                                    | stat       | `rate(ledger_history_mismatch[5m])`                                                                                                                        | —                |
| Key Jobs Execution Time                                      | timeseries | `acceptledger{quantile="$quantile"}` (+ 10 more key jobs)                                                                                                  | `quantile`       |
| Key Jobs Dequeue Wait Time                                   | timeseries | `acceptledger_q{quantile="$quantile"}` (+ 10 more)                                                                                                         | `quantile`       |
| FullBelowCache Size                                          | timeseries | `node_family_full_below_cache_size`                                                                                                                        | —                |
| FullBelowCache Hit Rate                                      | gauge      | `node_family_full_below_cache_hit_rate`                                                                                                                    | —                |
| Ledger Publish Gap                                           | stat       | `Published_Ledger_Age - Validated_Ledger_Age`                                                                                                              | —                |
| State Duration Rate (Full vs Tracking)                       | timeseries | `rate(state_accounting_full_duration[5m]) / 1000000`                                                                                                       | —                |
| All Jobs Execution Time (Detail)                             | timeseries | `{__name__=~"<all_jobs>", quantile="$quantile"}`                                                                                                           | `quantile`       |
| All Jobs Dequeue Wait (Detail)                               | timeseries | `{__name__=~"<all_jobs>_q", quantile="$quantile"}`                                                                                                         | `quantile`       |
| Server State                                                 | stat       | `server_info{metric="server_state"}`                                                                                                                       | `metric`         |
| Uptime                                                       | stat       | `server_info{metric="uptime"}`                                                                                                                             | `metric`         |
| Peer Count                                                   | stat       | `server_info{metric="peers"}`                                                                                                                              | `metric`         |
| Validated Ledger Seq -- Lag Behind Network Tip               | stat       | `max by (xrpl_network_type) (server_info{metric="validated_ledger_seq"}) - on(xrpl_network_type) group_right() server_info{metric="validated_ledger_seq"}` | `metric`         |
| Validated Ledger Seq -- Convergence (Max - Min, per network) | stat       | `max by (xrpl_network_type) (server_info{metric="validated_ledger_seq"}) - min by (xrpl_network_type) (server_info{metric="validated_ledger_seq"})`        | `metric`         |
| Build Version                                                | stat       | `build_info`                                                                                                                                               | `version`        |
| Complete Ledger Ranges                                       | table      | `complete_ledgers`                                                                                                                                         | `bound`, `index` |
| Database Sizes                                               | timeseries | `db_metrics{metric=~"db_kb_.*"}`                                                                                                                           | `metric`         |
| Historical Fetch Rate                                        | stat       | `db_metrics{metric="historical_perminute"}`                                                                                                                | `metric`         |

### Network Traffic -- System Metrics (`network-traffic`)

| Panel                                | Type       | PromQL                                                                                                                     | Labels Used |
| ------------------------------------ | ---------- | -------------------------------------------------------------------------------------------------------------------------- | ----------- |
| Active Peers                         | timeseries | `peer_finder_active_*_peers`                                                                                               | —           |
| Peer Disconnects                     | timeseries | `increase(overlay_peer_disconnects[$__rate_interval])`                                                                     | —           |
| Total Network Bytes                  | timeseries | `rate(total_bytes_in/out[$__rate_interval])`                                                                               | —           |
| Total Network Messages               | timeseries | `rate(total_messages_in/out[$__rate_interval])`                                                                            | —           |
| Transaction Traffic                  | timeseries | `rate(transactions_messages_in/out[$__rate_interval])`                                                                     | —           |
| Proposal Traffic                     | timeseries | `rate(proposals_messages_in/out[$__rate_interval])`                                                                        | —           |
| Validation Traffic                   | timeseries | `rate(validations_messages_in/out[$__rate_interval])`                                                                      | —           |
| Traffic by Category                  | bargauge   | `topk(10, label_replace(sum by (service_instance_id)(rate(<metric>[$__rate_interval])),"__name__","<metric>","","") or …)` | —           |
| Duplicate Traffic (Wasted Bandwidth) | timeseries | `rate(*_duplicate_bytes_in/out[$__rate_interval])`                                                                         | —           |
| All Traffic Categories (Detail)      | timeseries | `topk(15, label_replace(sum by (service_instance_id)(rate(<metric>[$__rate_interval])),"__name__","<metric>","","") or …)` | —           |

> **Why the per-category panels enumerate each metric.** A bare
> `rate({__name__=~".*_bytes_in"}[…])` fails on Mimir/Cloud with _"vector
> cannot contain metrics with the same labelset"_: `rate()` drops the
> `__name__` label, so the many matched counters collapse to identical
> labelsets. Wrapping in `sum by (__name__, …)` does **not** help (the inner
> vector is rejected before the outer `sum`). The working form enumerates each
> `*_bytes_in` metric and re-attaches its name with `label_replace(...,
"__name__", "<metric>", "", "")`, so the existing `{{__name__}}` legend and
> the per-series display-name overrides keep working.

### RPC & Pathfinding -- System Metrics (`rpc-pathfinding`)

| Panel                     | Type       | PromQL                                           | Labels Used |
| ------------------------- | ---------- | ------------------------------------------------ | ----------- |
| RPC Request Rate          | stat       | `rate(rpc_requests[5m])`                         | —           |
| RPC Response Time         | timeseries | `histogram_quantile(0.95, rpc_time_bucket)`      | —           |
| RPC Response Size         | timeseries | `histogram_quantile(0.95, rpc_size_bucket)`      | —           |
| RPC Response Time Heatmap | heatmap    | `rpc_time_bucket`                                | —           |
| Pathfinding Fast Duration | timeseries | `histogram_quantile(0.95, pathfind_fast_bucket)` | —           |
| Pathfinding Full Duration | timeseries | `histogram_quantile(0.95, pathfind_full_bucket)` | —           |
| Resource Warnings Rate    | stat       | `rate(warn_total[$__rate_interval])`             | —           |
| Resource Drops Rate       | stat       | `rate(drop_total[$__rate_interval])`             | —           |

### Span → Metric → Dashboard Summary

| Span Name                      | Prometheus Metric Filter                     | Grafana Dashboard                             |
| ------------------------------ | -------------------------------------------- | --------------------------------------------- |
| `rpc.http_request`             | `{span_name="rpc.http_request"}`             | RPC Performance (Overall Throughput)          |
| `rpc.ws_upgrade`               | `{span_name="rpc.ws_upgrade"}`               | -- (available but not paneled)                |
| `rpc.ws_message`               | `{span_name="rpc.ws_message"}`               | RPC Performance (WebSocket Rate)              |
| `rpc.process`                  | `{span_name="rpc.process"}`                  | RPC Performance (Overall Throughput)          |
| `rpc.command.*`                | `{span_name=~"rpc.command.*"}`               | RPC Performance (Rate, Latency, Error, Top)   |
| `tx.process`                   | `{span_name="tx.process"}`                   | Transaction Overview (Rate, Latency, Heatmap) |
| `tx.receive`                   | `{span_name="tx.receive"}`                   | Transaction Overview (Rate, Receive)          |
| `tx.apply`                     | `{span_name="tx.apply"}`                     | Transaction Overview + Ledger Ops (Apply)     |
| `txq.enqueue`                  | `{span_name="txq.enqueue"}`                  | -- (available but not paneled)                |
| `txq.apply_direct`             | `{span_name="txq.apply_direct"}`             | -- (available but not paneled)                |
| `txq.batch_clear`              | `{span_name="txq.batch_clear"}`              | -- (available but not paneled)                |
| `txq.accept`                   | `{span_name="txq.accept"}`                   | -- (available but not paneled)                |
| `txq.accept_tx`                | `{span_name="txq.accept_tx"}`                | -- (available but not paneled)                |
| `txq.cleanup`                  | `{span_name="txq.cleanup"}`                  | -- (available but not paneled)                |
| `consensus.round`              | `{span_name="consensus.round"}`              | -- (available but not paneled)                |
| `consensus.phase.open`         | `{span_name="consensus.phase.open"}`         | -- (available but not paneled)                |
| `consensus.establish`          | `{span_name="consensus.establish"}`          | -- (available but not paneled)                |
| `consensus.update_positions`   | `{span_name="consensus.update_positions"}`   | -- (available but not paneled)                |
| `consensus.check`              | `{span_name="consensus.check"}`              | -- (available but not paneled)                |
| `consensus.accept`             | `{span_name="consensus.accept"}`             | Consensus Health (Duration, Rate, Heatmap)    |
| `consensus.proposal.send`      | `{span_name="consensus.proposal.send"}`      | Consensus Health (Proposals Rate)             |
| `consensus.ledger_close`       | `{span_name="consensus.ledger_close"}`       | Consensus Health (Close, Mode)                |
| `consensus.validation.send`    | `{span_name="consensus.validation.send"}`    | Consensus Health (Validation Rate)            |
| `consensus.accept.apply`       | `{span_name="consensus.accept.apply"}`       | Consensus Health (Apply Duration, Close Time) |
| `consensus.mode_change`        | `{span_name="consensus.mode_change"}`        | -- (available but not paneled)                |
| `consensus.proposal.receive`   | `{span_name="consensus.proposal.receive"}`   | -- (available but not paneled)                |
| `consensus.validation.receive` | `{span_name="consensus.validation.receive"}` | -- (available but not paneled)                |
| `ledger.build`                 | `{span_name="ledger.build"}`                 | Ledger Ops (Build Rate, Duration, Heatmap)    |
| `ledger.validate`              | `{span_name="ledger.validate"}`              | Ledger Ops (Validation Rate)                  |
| `ledger.store`                 | `{span_name="ledger.store"}`                 | Ledger Ops (Store Rate)                       |
| `peer.proposal.receive`        | `{span_name="peer.proposal.receive"}`        | Peer Network (Rate, Trusted/Untrusted)        |
| `peer.validation.receive`      | `{span_name="peer.validation.receive"}`      | Peer Network (Rate, Trusted/Untrusted)        |

## Alerting

xrpld provisions thirteen Grafana alert rules on the health-critical metrics, so
a stock stack alerts out of the box with no UI setup. Rules are provisioned from
`docker/telemetry/grafana/provisioning/alerting/` and load automatically when
the Grafana container starts. They appear under **Alerting → Alert rules**,
folder **xrpld**.

> **All rules ship `isPaused: true`.** Thresholds are tuned against a small
> dev/devnet population, so every rule is deactivated on arrival — compare it
> against your own baseline, then unpause. The key is camelCase: `is_paused` is
> **silently ignored** by the provisioning loader (no error, no warning) and
> leaves the rule live. Note the sibling field `notification_settings` _is_
> snake_case.

### Alert catalogue

All rules evaluate every minute against the Prometheus datasource and aggregate
`by (service_instance_id)` so each node alerts on its own. Every expr selects
`{service_name="xrpld"}` — the same Prometheus may also host a legacy statsd
fleet exporting some of these names (`state_accounting_*` in particular) with no
xrpld resource attributes, and without the selector those series get summed in.
Alerts fire only after the condition holds for the `for` dwell time.

| Alert                     | Severity | Fires when                                         | For |
| ------------------------- | -------- | -------------------------------------------------- | --- |
| `LedgerHistoryMismatch`   | critical | `increase(ledger_history_mismatch_total[15m])` > 0 | 2m  |
| `LedgerCloseStalled`      | critical | `rate(ledgers_closed_total)` ≈ 0                   | 3m  |
| `ValidatedLedgerStale`    | critical | `ledgermaster_validated_ledger_age` > 60s          | 5m  |
| `ValidationsMissed`       | warning  | validator miss _ratio_ > 0.1                       | 15m |
| `ValidationsNotChecked`   | warning  | `rate(validations_checked_total)` ≈ 0              | 5m  |
| `JobQueueTxOverflow`      | warning  | `increase(jq_trans_overflow_total[15m])` > 0       | 2m  |
| `JobQueueLatencyHigh`     | warning  | p99 `job_queued_us` > 1s                           | 5m  |
| `NodeStoreIOLatencyHigh`  | warning  | p95 `ios_latency_milliseconds` > 1s                | 10m |
| `NodeStateFlapping`       | warning  | > 3 re-entries into FULL per hour                  | 15m |
| `NodeNotFull`             | warning  | `server_state` < 4 (FULL)                          | 15m |
| `ManifestJobQueueConvoy`  | warning  | `jobq_manifest_waiting` > 3                        | 10m |
| `ManifestFloodInbound`    | warning  | `rate(overhead_manifest_bytes_in)` > 512 kB/s      | 10m |
| `PeerResourceDisconnects` | warning  | > 5 resource-driven peer disconnects per 30m       | 5m  |

Two expression idioms recur and are load-bearing — do not "simplify" them away:

- **Sparse counters use `increase(...[15m])` with a short `for`,** not
  `rate(...[5m])` with `for: 5m`. A single increment keeps `rate[5m]` nonzero for
  only ~4 minutes of dwell, so a 5-minute `for` can never be satisfied and the
  rule silently never fires for the one-off events it exists to catch.
- **"Node stopped doing X" rules synthesise an explicit zero** via
  `or (0 * max_over_time(...[1h]))`, because `sum by()` returns rows only for
  still-reporting nodes: one dead node's row simply disappears from the result,
  so `noDataState` never triggers unless _every_ node vanishes at once.

#### Consensus / ledger health

**LedgerHistoryMismatch** — The node closed a ledger whose history diverges
from the validated network chain. Likely causes: corrupted local state, a bug,
or a node that fell out of sync and rebuilt incorrectly. Investigate the node's
ledger acquisition logs; a healthy node never mismatches.

**LedgerCloseStalled** — No ledgers closed for 3 minutes. A healthy node closes
one every ~3-5s. Likely causes: lost peer connectivity, consensus stall, or the
process is hung. This rule also fires on _NoData_ — if the series disappears the
node is likely down. Check peer count and process health first.

**ValidatedLedgerStale** — The validated ledger has fallen more than 60s behind.
This is the clearest single "is this node healthy" signal on XRPL: it is the
symptom nearly every consensus or sync failure eventually produces, so it is
often the first thing to check and the last thing to clear. Measured over 7 days:
p50 2s, p95 4s, p99 5s on every node.

> **The `< 1209600` clause in this rule's expression is required — do not remove
> it.** When a node holds no validated ledger at all,
> `LedgerMaster::getValidatedLedgerAge()` returns `weeks{2}` (1 209 600 s) as a
> **sentinel**, not a measurement. Without the clause the rule reads that as "14
> days stale" and fires on every node during startup — measured, it produced
> sustained firing on all nine nodes over a six-day window, healthy ones included.
> A node genuinely stuck without a validated ledger is caught by
> `LedgerCloseStalled` and `NodeNotFull` instead.

#### Validator health

**ValidationsMissed** — This validator's validations are not agreeing with the
validated ledger. Sustained misses risk removal from UNLs. Check clock sync,
peer connectivity, and whether the node is keeping up with ledger close.

> **Why this is a ratio gated on `validations_sent_total`, not
> `rate(validation_missed_total) > 0`:** `ValidationTracker` classifies a ledger
> as a miss whenever `weValidated && networkValidated` is not _both_ true. A node
> that does not validate never sets `weValidated`, so **every** reconciled ledger
> counts as a miss and the raw rate is permanently nonzero — the measured miss
> ratio is exactly `1.0` on non-validating nodes. No threshold can separate "not
> a validator" from "validator disagreeing", so the rule gates on
> `validations_sent_total > 0` to exclude non-validators entirely, and then
> measures the ratio among nodes that genuinely do validate.

**ValidationsNotChecked** — The node has stopped checking incoming validations
from peers. Likely causes: overlay/peer disconnection or a stalled validation
pipeline. Fires on NoData as well.

#### Job queue / resource health

**JobQueueTxOverflow** — The transaction job queue is full and transactions are
being dropped. The node is shedding load it cannot process. Check CPU, the
`JobQueueLatencyHigh` alert, and offered load.

**JobQueueLatencyHigh** — p99 queue wait exceeds 1 second, i.e. jobs back up
before running. The node is saturated. Correlate with CPU and the Job Queue
dashboard.

**NodeStoreIOLatencyHigh** — p95 node-store IO latency exceeds 1s. Sustained
store latency is the usual _upstream cause_ of state flapping and sync stalls, so
this often fires alongside `NodeStateFlapping` and explains it. Check disk
utilisation and whether the node store sits on a slow volume — moving it to a
local NVMe has previously cut time-to-`full` by more than 3x. Measured p99-of-p95
is 37-49ms on healthy nodes and 488-566ms on nodes that are actively flapping.

#### Node operating state

**NodeStateFlapping** — The node is oscillating `full → syncing/connected → full`
instead of holding sync. Measured: a flapping node re-enters `full` 4-6 times per
hour sustained, while a healthy node manages 0-1, so the `> 3` threshold sits
between the two populations with roughly a 3x margin.

The rule counts `state_accounting_full_transitions`, which counts transitions
_into_ `full` and is exported as a cumulative gauge — `increase()` is therefore
correct, and its counter-reset correction turns a process restart into a small
positive delta rather than a false spike. `state_changes_total` cannot be used
here: it carries no from/to labels, so it cannot tell a flap from a normal
startup walk.

**The `uptime > 3600` gate is load-bearing.** Every node walks
`disconnected → connected → syncing → tracking → full` once at boot; without the
gate, every restart pages. The trade-off is deliberate: flapping confined to the
first hour after boot is not alerted.

Investigate in this order: `NodeStoreIOLatencyHigh` (most common cause), peer
connectivity, then clock sync.

**NodeNotFull** — The node has been below `FULL` for 15m
(`0`=disconnected, `1`=connected, `2`=syncing, `3`=tracking, `4`=full). This is
deliberately a _separate_ rule from `NodeStateFlapping`: a node that drops to
syncing and stays there produces no further full-transitions, so the flapping
counter by definition cannot catch it.

#### Overlay / manifests

**ManifestJobQueueConvoy** — Manifest jobs are backing up in the job queue. Peers
send `TMManifests` dumps up to ~57MB (just under `kMaximumMessageSize`, see
`overlay/Message.h`), and `JtManifest` is registered with `maxLimit`
(`core/JobTypes.h`), so every peer's dump runs concurrently and they convoy on
`ManifestCache::mutex_`; `OverlayImpl::onManifests` also re-verifies the blob a
second time on Accept. Measured effect: each `RcvManifests` job took 16-18s and
the entire 8-worker pool was occupied.

This is the most reliable manifest-flood signal because `jobq_manifest_waiting`
is `0` at the 99.9th percentile on every node over 24h — any sustained backlog is
a genuine outlier rather than normal variance.

**ManifestFloodInbound** — Inbound manifest byte-rate exceeds 512 kB/s. Catches the
wire-level cause (a peer shipping oversized dumps) even when the job pool absorbs
it without a visible backlog. Measured over 7 days: healthy p95 0.2-0.5 kB/s and
p99 1.0-1.8 kB/s, against peaks up to 2.7 MB/s during real storms — so the
threshold sits ~280x above healthy p99 and ~5x below the peaks.

> An earlier revision used 50 kB/s, justified from a 24-hour window. Over a full
> week that produced ~41 sustained 5-minute firings across six **healthy** nodes,
> i.e. routine paging. Prefer a 7-day sample when tuning any threshold here; 24
> hours is too short to expose weekly variation.

> **Both manifest rules deliberately suppress startup.** The manifest storm at
> boot is _measured normal behaviour_, so `ManifestFloodInbound` carries an
> `uptime > 1800` gate and `ManifestJobQueueConvoy` relies on a 10m dwell that the
> startup burst does not outlast. A flood confined to the first 30 minutes after
> boot will therefore not alert.

**PeerResourceDisconnects** — The node dropped more than 5 peers in 30m for
exceeding resource budgets. Sustained disconnects starve the node of peers and
precede sync loss.

### Tuning thresholds

Thresholds live in
`docker/telemetry/grafana/provisioning/alerting/rules.yaml` as the `params`
array of each rule's `C` (threshold) node. Common tunables:

- **`JobQueueLatencyHigh`** — `params: [1000000]` is 1 000 000 µs (1s). Lower
  it for latency-sensitive deployments.
- **`LedgerCloseStalled` / `ValidationsNotChecked`** — use `lt` with a tiny
  epsilon (`0.001`) rather than `0`, so floating-point rate noise near zero
  does not suppress the alert.

Edit the file and restart the Grafana container to reload:

```bash
docker compose -f docker/telemetry/docker-compose.yml restart grafana
```

### Sending alerts somewhere real

Two contact points are provisioned in
`docker/telemetry/grafana/provisioning/alerting/contactpoints.yaml`:

| Contact point    | Receivers     | Gets                     |
| ---------------- | ------------- | ------------------------ |
| `xrpld-default`  | Slack         | warning-severity alerts  |
| `xrpld-critical` | Slack + email | critical-severity alerts |

The severity split lives in
`docker/telemetry/grafana/provisioning/alerting/policies.yaml`: the root route
sends everything to `xrpld-default`, and a child route matching
`severity = critical` overrides to `xrpld-critical`. So a critical alert goes
to Slack **and** email; a warning goes to Slack only. Both group by
`alertname` + `service_instance_id`; critical alerts re-page hourly vs the 4h default.

#### Configure delivery (no secrets in git)

The Slack webhook and email address are **not** hard-coded. `contactpoints.yaml`
ships deliberately unroutable placeholders — an `https://hooks.slack.invalid/…`
host and an `…@xrpld.invalid` address — which keep provisioning valid so the
stack boots with zero configuration while alerts route nowhere.

To enable delivery, edit those two values **in place** with a real webhook and
address, and do not commit the result.

```bash
cp docker/telemetry/.env.alerting.example docker/telemetry/.env.alerting
# edit .env.alerting — gitignored; holds the SMTP relay settings
$EDITOR docker/telemetry/grafana/provisioning/alerting/contactpoints.yaml
docker compose -f docker/telemetry/docker-compose.yml up -d grafana
```

- **Slack** — replace the placeholder `url:` with an incoming-webhook URL. Drives
  both tiers.
- **Email** — replace the placeholder `addresses:` (comma- or semicolon-separated)
  **and** point the `GF_SMTP_*` vars in `.env.alerting` at a real relay with
  `GF_SMTP_ENABLED=true`. Grafana can only send mail once SMTP is configured.

Three traps worth knowing before you edit this file:

- **Do not substitute `${SLACK_WEBHOOK_URL}` / `${ALERT_EMAIL_TO}` here.** Grafana
  expands `${VAR}` but does **not** support `${VAR:-default}`, so an unset variable
  expands to empty, fails validation, and Grafana **exits 1** — taking the whole
  telemetry stack down, not just alerting. A blank variable does not "disable that
  path"; it breaks startup.
- **Never empty a `receivers:` list to disable a tier.** A contact point with no
  receivers ceases to exist, the policy tree then references a missing receiver,
  and Grafana refuses to boot. Point the route at a contact point that still
  exists instead.
- **File provisioning is upsert-only.** Deleting a receiver from the YAML does not
  remove it from an instance that already booted with it — the old receiver keeps
  delivering. Removal needs an explicit `deleteContactPoints:` block listing the
  uid (a commented example sits at the bottom of `contactpoints.yaml`).

To add a third destination (PagerDuty, Opsgenie, a custom webhook), add a receiver
to the relevant contact point.

#### Panel screenshots on alerts need a matching render token

Alert notifications that carry a panel image are rendered by the `renderer`
sidecar, not by Grafana itself. Grafana 13 enables the `renderAuthJWT` feature
toggle by default, so the renderer rejects any request whose token is missing or
still the `-` default — notifications then arrive with no image.

`docker-compose.yml` feeds both sides from one variable, so they cannot drift:
`GF_RENDERING_RENDERER_TOKEN` on the `grafana` service and `AUTH_TOKEN` on the
`renderer` service both read `${GF_RENDERING_RENDERER_TOKEN}`, defaulting to a
local development value. Override it in the environment to use your own:

```bash
GF_RENDERING_RENDERER_TOKEN=$(openssl rand -hex 16) \
    docker compose -f docker/telemetry/docker-compose.yml up -d grafana renderer
```

If images stop appearing, check that the two containers agree — a token set on
only one side fails exactly this way:

```bash
docker compose -f docker/telemetry/docker-compose.yml exec grafana \
    printenv GF_RENDERING_RENDERER_TOKEN
docker compose -f docker/telemetry/docker-compose.yml exec renderer \
    printenv AUTH_TOKEN
```

#### Deploying alerts to Grafana Cloud

Grafana Cloud has **no provisioning filesystem**, so these `apiVersion: 1` files
cannot be loaded there. Cloud deployment goes through the REST API via
`docker/telemetry/upload_alerts_to_grafana.py`, which reads the same tracked
`rules.yaml` as the single source of truth (so local and Cloud cannot drift) and
applies the Cloud-specific transforms: the local `prometheus` datasource uid is
swapped for the Cloud one, the `folder:` _name_ becomes an existing `folderUID`,
and `interval` becomes integer seconds.

```bash
cd docker/telemetry
python3 upload_alerts_to_grafana.py --dry-run # always dry-run first
python3 upload_alerts_to_grafana.py           # create rules, paused
python3 upload_alerts_to_grafana.py --verify  # read back what is deployed
```

Credentials come from `.env.grafanaserviceapi` (gitignored, a service-account
token with `alert.rules:write`); the recipient address comes from `ALERT_EMAIL_TO`
in `.env.alerting`. Neither is ever written to a tracked file. Use
`--no-delivery` to land the rules before a recipient is chosen, and `--activate`
only once the thresholds have been checked against the target fleet's baseline.

> **The Cloud notification policy tree must not be pushed.** There is exactly one
> policy tree per org and the PUT endpoint **replaces it wholesale**. On a shared
> stack the root receiver and its sibling routes belong to other teams, so pushing
> an xrpld-shaped tree would silently re-route their alerts. The uploader
> therefore never touches the tree; instead each rule carries
> `notification_settings.receiver`, which routes that rule directly to the xrpld
> contact point and bypasses the tree entirely. Verify with a before/after hash of
> `GET /api/v1/provisioning/policies`.

### Verifying alert provisioning loaded

After the stack is up:

```bash
# All thirteen rules present, and is each one paused?
curl -s http://localhost:3000/api/v1/provisioning/alert-rules |
    jq -r '.[] | "\(.title)\tpaused=\(.isPaused)"'

# Contact points present?
curl -s http://localhost:3000/api/v1/provisioning/contact-points | jq '.[].name'
```

Check `paused=true` explicitly rather than assuming it: a mis-spelled
`is_paused` is dropped without any error and the rule provisions **live**.

Grafana logs a provisioning error and skips the file if the YAML is malformed:

```bash
docker compose -f docker/telemetry/docker-compose.yml logs grafana | grep -i alerting
```

A malformed _expression_ fails differently and more quietly — the rule loads but
every evaluation errors. After a threshold or expr change, confirm each rule's
query still returns data:

```bash
# Should print a numeric value per node, and no empty results
curl -sG http://localhost:9090/api/v1/query \
    --data-urlencode 'query=sum by (service_instance_id) (rate(ledgers_closed_total{service_name="xrpld"}[5m]))' |
    jq '.data.result | length'
```

## Log-Trace Correlation

When xrpld is built with `telemetry=ON`, log lines emitted within an active OpenTelemetry span automatically include `trace_id` and `span_id` fields:

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

This enables bidirectional navigation between logs and traces in Grafana:

- **Tempo -> Loki**: Click "Logs for this trace" on any trace in Grafana Tempo to see all log lines from that trace.
- **Loki -> Tempo**: Click the `TraceID` derived field link on any log line containing `trace_id=` to jump to the full trace in Tempo.

### Log Ingestion Pipeline

Log files are ingested by the OTel Collector's `filelog` receiver, which tails `debug.log` files and parses them with a regex that extracts `timestamp`, `partition`, `severity`, `trace_id`, `span_id`, and `message` fields. Parsed entries are exported to Grafana Loki.

The receiver tails `/var/log/xrpld/*/debug.log` inside the collector container. docker-compose bind-mounts the host log root there; the source defaults to the repo-relative `docker/telemetry/data/logs`, which the telemetry configs write to (`data/logs/<network>/debug.log`) and which needs no root. To tail logs from elsewhere, set `XRPLD_LOG_DIR` before `docker compose up` (the integration test does this to point at its own workdir). The single trailing `*` matches one per-network or per-node subdirectory.

### LogQL Query Examples

The OTel Collector emits logs to Loki with `service_name="xrpld"` (not `job="xrpld"`).

For log-derived panels built on these queries, see the
[Log-Derived Insights](#log-derived-insights-log-derived-insights) dashboard and its
LogQL trap list — `partition`, `severity`, and `xrpl_network_type` are
**structured metadata**, not stream labels, so they must be filtered with `|`
after the selector and cannot be discovered by `label_values()`.

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

# Error logs with trace context (log lines with ERR severity that have a trace_id).
# Use the severity field, not `|= "ERR"`: a line filter also matches the literal
# "ERR" anywhere in the message body (measured: 4 DBG lines per 6h on devnet).
{service_name="xrpld"} | severity = `ERR` | trace_id != ""

# All logs from a specific partition that were emitted during a span.
# Prefer the structured-metadata filter over a line match: `|= "LedgerMaster"`
# also matches the substring anywhere in the message body.
{service_name="xrpld"} | partition = `LedgerMaster` | trace_id != ""

# Logs from a specific subsystem during a span (e.g. LedgerConsensus)
{service_name="xrpld"} | partition = `LedgerConsensus` | trace_id != ""

# Logs from the last hour containing trace context. `partition`, `severity`, and
# `trace_id` are already parsed into structured metadata by the collector's
# filelog receiver, so re-extracting them with regexp is unnecessary work.
{service_name="xrpld"} | trace_id != ""

# Count of traced vs untraced log lines
sum(count_over_time({service_name="xrpld"} | trace_id != "" [5m]))
sum(count_over_time({service_name="xrpld"} | trace_id = "" [5m]))
```

### Verifying Log Correlation

1. Start the observability stack and xrpld with telemetry enabled.
2. Send an RPC request: `curl http://localhost:5005 -d '{"method":"server_info"}'`
3. Check the debug.log for `trace_id=` entries: `grep trace_id= /path/to/debug.log`
4. Open Grafana at http://localhost:3000 -> Explore -> Loki and search for `{service_name="xrpld"} | trace_id != ""`.
5. Click the TraceID link to navigate to the corresponding trace in Tempo.

### Log-Derived Insights (`log-derived-insights`)

The only **Loki/LogQL** dashboard. It surfaces detail that no metric or span
records, by parsing `debug.log` text. 41 panels in 10 rows: 8 stat, 18
timeseries, 2 table, 1 state-timeline, 1 logs, 1 text, across 35 queries.

> **REQUIRES DEBUG LOGS for most rows.** xrpld's default threshold is `Info`
> (`Severity thresh = Severity::Info`, `app/main/Main.cpp`). Rows tagged `[DBG]`
> read `DBG`-severity lines that a default node never writes, so those panels are
> **empty** on an unmodified node — and an empty panel means _not collecting_, not
> _no problem_. Rows tagged `[DEFAULT OK]` work as shipped.
>
> Enable per partition rather than globally (`Resource` alone emits ~329k
> lines/6h):
>
> ```
> log_level ManifestCache debug
> log_level Resource debug
> log_level InboundLedger debug
> log_level Peer debug
> log_level PeerFinder debug
> ```
>
> Those five cover every `[DBG]` row. The `[MIXED]` stat row additionally
> reads `LedgerConsensus` and `LoadMonitor`, both of which already emit at
> the default level, so its error/consensus/breach/sync panels populate
> without any change — only its manifest, fee, and fetch-waste panels need
> debug enabled.

| Row                                     | Gate           | Key panels                                                                                                                                                                                 |
| --------------------------------------- | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Worst Offenders — Node Ranking          | `[MIXED]`      | 8 stat panels ranking nodes by error volume, attack-like input, total fee charged, manifest rejections, consensus problems, job latency breaches, sync instability, and ledger fetch waste |
| Node Operating State Transitions        | `[DEFAULT OK]` | Transition rate and state timeline from `STATE->` (`NetworkOPsImp::setMode`, info)                                                                                                         |
| Log Volume & Severity Mix               | `[DEFAULT OK]` | Line rate by severity; top-N partitions by rate                                                                                                                                            |
| Manifests — Disposition & Producers     | `[DBG]`        | Disposition rate; accept-vs-reject; top-N master keys                                                                                                                                      |
| Resource Fee Charges — Load Attribution | `[DBG]`        | Charge rate by reason; fee-weighted load; top-N peers by IP and public key                                                                                                                 |
| Ledger Acquisition Efficiency           | `[DBG]`        | Duplicate ratio; good vs duplicate vs timeout                                                                                                                                              |
| Peer Lifecycle & Disconnects            | `[DBG]`        | Disconnect reason breakdown; handshake and accept rate                                                                                                                                     |
| Consensus Phase & Mode                  | `[DEFAULT OK]` | Phase transitions; operating-mode proxy; quorum and trusted-set size                                                                                                                       |
| Slow Job Latency Breaches               | `[DEFAULT OK]` | Run p99, wait p99, breach rate by job (`LoadMonitor`, >500ms only)                                                                                                                         |
| Error & Warning Stream                  | `[DEFAULT OK]` | WRN/ERR/FTL rate by partition; live log tail                                                                                                                                               |

Filters: `$service_name`, `$deployment_environment`, `$node`,
`$xrpl_network_type`, `$severity`, plus log-derived `$consensus_phase`,
`$consensus_mode`, `$manifest_action`, `$charge_reason`, and `$topn`.

#### LogQL traps this dashboard exposed

Ten mistakes that fail **silently** — each cost a debugging cycle, so check them
before adding any LogQL panel.

1. **`partition` is structured metadata, not a stream label.**
   `{service_name="xrpld", partition="ManifestCache"}` returns **zero rows with
   no error**. Correct form: `{service_name="xrpld"} | partition = \`ManifestCache\``.
Stream labels are only `service_name`, `service_instance_id`,
`deployment_environment`. Everything else — `partition`, `severity`,
`xrpl_network_type`, `message`, `trace_id` — is structured metadata.

2. **`label_values()` cannot see structured metadata.** A `query`-type template
   variable over `xrpl_network_type`, `severity`, or `partition` returns an empty
   dropdown; only true stream labels populate. Use a `custom` variable with
   enumerated values instead. This is why filters appeared blank.

3. **A target with no datasource `uid` resolves to the DEFAULT datasource.**
   The Prometheus dashboards use `{"type": "prometheus"}` with no uid and work
   only because Prometheus _is_ the default. A Loki target written the same way
   sends LogQL to Prometheus and returns nothing. Always pin
   `{"type": "loki", "uid": "${DS_LOKI}"}`.

4. **`$__rate_interval` is Prometheus-only — Loki panels must use `[$__auto]`.**
   Grafana does not substitute `$__rate_interval` for a Loki target, so Loki
   receives the literal string and fails with
   `parse error: not a valid duration string: "$__rate_interval"`, which surfaces
   as "No data". The other 14 dashboards all use `$__rate_interval` because they
   are Prometheus-backed; do **not** align LogQL panels to that convention.

5. **Loki caps a query at 2000 series.** Any per-key or per-IP aggregation must be
   wrapped in `topk(N, ...)` or it fails with HTTP 400. A true distinct-key count
   over a large key space is therefore not possible in a panel.

6. **Loki tables need `labelsToFields` plus `reduce`.** Loki attaches labels to
   the Value field instead of returning columns, so a table panel renders bare
   Time/Value without `labelsToFields`. Grafana also runs a Loki table target as a
   **range** query even when `instant: true` is set, producing one row per series
   _per timestamp_ — visible as the same key repeated many times. Use
   `reduce(lastNotNull, labelsToFields)` then `organize`, and note the value
   column is then named `Last *`, which any field override must match.

7. **Title Case legends need `label_format`, not value mappings.** A label-driven
   legend renders the raw log value (`full`, `moderate peer request`). Grafana
   value mappings do not help — they map the metric _value_, not label text in
   `displayName`. Rewrite the label in the query:
   `| label_format state=\`{{if eq .state "full"}}Full{{else}}{{.state}}{{end}}\``.

8. **`unwrap` must be the last pipeline stage.** Any label filter or
   `label_format` placed after `| unwrap <field>` makes the query invalid and it
   returns zero frames.

9. **Non-matching lines yield an empty label.** A line in the selected partition
   that does not match the panel's `regexp` still passes through with an empty
   extracted label, which renders as a blank legend entry. Guard with
   `| <label> != \`\`` before aggregating.

10. **Stat panels need `[$__range]`, not `[$__auto]`.** Grafana runs a Loki stat
    target as a range query even with `instant: true`, so `lastNotNull` reads only
    the final bucket — a window total shows as a single-bucket count. Aggregate
    over `$__range` and reduce with `max`.

Also worth knowing: the **Grafana Cloud image renderer cannot query Loki** in this
stack. A minimal probe dashboard with a hardcoded datasource uid, a literal
expression and no template variables still rendered "No data", while the identical
expression returned 241 points through `/api/ds/query`. Verify LogQL panels with
`/api/ds/query` per target, not with panel-image rendering.

## Troubleshooting

### No traces appearing in Tempo

1. Check xrpld logs for `Telemetry starting` message
2. Verify `enabled=1` in the `[telemetry]` config section
3. Test collector connectivity: `curl -v http://localhost:4318/v1/traces`
4. Check collector logs: `docker compose -f docker/telemetry/docker-compose.yml logs otel-collector`
5. Verify Tempo is receiving data: open Grafana → Explore → select Tempo datasource → search by `service.name = xrpld`
6. Check Tempo logs: `docker compose -f docker/telemetry/docker-compose.yml logs tempo`

### No system metrics in Prometheus

1. Check xrpld logs for `OTelCollector starting` message
2. Verify `server=otel` in the `[insight]` config section
3. Verify the endpoint in `[insight]` points to the OTLP/HTTP port (default: `http://localhost:4318/v1/metrics`)
4. Check that the `otlp` receiver is in the metrics pipeline receivers in `otel-collector-config.yaml`
5. Query Prometheus directly: `curl 'http://localhost:9090/api/v1/query?query=jobq_job_count'`

### Server info gauge shows server_state=0

This is normal during startup. The server starts in DISCONNECTED mode (0) and
progresses through CONNECTED (1), SYNCING (2), TRACKING (3), to FULL (4).
Wait for the node to sync with the network.

### Database metrics showing zero

The `getKBUsed*()` methods require SQLite databases to exist. If running with
`--standalone` or before the first ledger is stored, these will be zero.

### Slow TMGetObjectByHash service

Use this when a peer reports slow object fetches, or when `job_queued_us` /
`job_running_us` for `job_type="ledgerRequest"` rises. The goal is to name the
cause, not to confirm the slowness.

End-to-end handler time splits into three additive parts, and each has its own
signal:

```mermaid
flowchart LR
    A["`**Request arrives**
    onMessage
    TMGetObjectByHash`"] --> B["`**1. Queue wait**
    job_queued_us
    handler=RcvGetObjByHash`"]
    B --> C["`**2. NodeStore lookup**
    getobject_lookup_us`"]
    C --> D["`**3. Everything else**
    protobuf, serialization,
    reply construction`"]
    D --> E["`**Reply sent**`"]

    C -.-> F["`job_running_us
    handler=RcvGetObjByHash
    covers steps 2 + 3`"]
    D -.-> F

    style A fill:#1f4e79,color:#ffffff
    style B fill:#7b3f00,color:#ffffff
    style C fill:#2d5016,color:#ffffff
    style D fill:#4a148c,color:#ffffff
    style E fill:#1f4e79,color:#ffffff
    style F fill:#37474f,color:#ffffff
```

**Reading the diagram**

- Step 1 is time the job sat in `JtLedgerReq` before a worker picked it up. Only
  `job_queued_us` measures it.
- Steps 2 and 3 both happen inside the worker, so `job_running_us` covers them
  together. `getobject_lookup_us` isolates step 2 alone.
- Step 3 is therefore not measured directly. Derive it:
  `job_running_us − getobject_lookup_us`. That subtraction is what makes the set
  able to name a cause instead of just reporting a duration.

**Procedure** — work through these in order. The first row that matches is the
answer.

> **Scope every query to one node.** All snippets below carry
> `service_instance_id=~"$node"`. On a shared Grafana stack an unscoped selector
> aggregates across every node and branch reporting to it, so another node's
> saturation would be attributed to this one. Substitute the node's public key
> for `$node` when querying Prometheus directly rather than from a dashboard.

1. Split queue wait from run time. Compare the two p99s for the handler:

   ```promql
   histogram_quantile(0.99, sum by (le) (rate(job_queued_us_bucket{handler="RcvGetObjByHash", service_instance_id=~"$node"}[5m])))
   ```

   ```promql
   histogram_quantile(0.99, sum by (le) (rate(job_running_us_bucket{handler="RcvGetObjByHash", service_instance_id=~"$node"}[5m])))
   ```

2. If run time is the larger term, split it against the fetch loop:

   ```promql
   histogram_quantile(0.99, sum by (le) (rate(getobject_lookup_us_bucket{service_instance_id=~"$node"}[5m])))
   ```

3. Match the outcome below.

| Observation                                                                | Root cause and next step                                                                                                                                                                                                                                                            |
| -------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `job_queued_us{handler="RcvGetObjByHash"}` high, `job_running_us` normal   | **Queue contention** — the work is cheap, the wait is not. Confirm with `jobq_ledgerrequest_deferred{service_instance_id=~"$node"} > 0`, then compare `job_queued_us{handler="RcvGetLedger"}`: if it is also high, both producers are starved by the limit of 3, not by each other. |
| `job_running_us` high and within ~10% of `getobject_lookup_us`             | **NodeStore is the bottleneck** — nearly all run time is in the fetch loop. Check `rate(getobject_lookups_total{result="miss"}[5m])` and the existing NuDB / `nodestore_state` panels. A miss-heavy mix means real disk seeks.                                                      |
| `job_running_us` high but `getobject_lookup_us` low                        | **Cost is outside the fetch loop** — protobuf, serialization, or reply construction. Storage is fine. Look at reply size: a large `getobject_request_objects` with a high hit rate means big replies to build and send.                                                             |
| `getobject_request_objects` p99 large                                      | **Peers are sending big batches** — the work is real, not a regression. Nothing is broken; the node is being asked to do more. Decide whether to accept the load or price it higher.                                                                                                |
| `rate(getobject_rejected_total{reason="oversize"}[5m])` rising             | **Non-conforming traffic** — requests above `kHardMaxReplyNodes` are being refused before any NodeStore access. Check `getobject_charge` to confirm the pricing escalates for the requests that _are_ accepted.                                                                     |
| `rate(getobject_rejected_total{reason="malformed_ledgerhash"}[5m])` rising | **Malformed requests** — a peer is sending a ledgerhash that is not 32 bytes. Refused at the gate; no queue or storage cost incurred.                                                                                                                                               |
| All GetObject metrics normal, `jobq_*_deferred` high on another type       | **This path is exonerated** — the slowness is elsewhere. Find the saturated type with `topk(5, {__name__=~"jobq_.*_deferred", service_instance_id=~"$node"} > 0)` and investigate that producer instead.                                                                            |

Row 2 says "within ~10%", not "equal", deliberately: `job_running_us` also
covers the charge computation (PeerImp.cpp:2757) and the reply `send()` that
follow the loop, so it is always the larger of the two. Treat a small residual as
normal and only a large one as a signal — that is what row 3 is for.

The last row matters as much as the others: the set can rule this path _out_,
which a slowness-only metric cannot.

**Caveats**

- `handler` collapses to `"other"` for any job name that is not all ASCII
  letters, so a handler breakdown is not exhaustive — see
  [The `handler` Label](#the-handler-label). This does not affect
  `RcvGetObjByHash` or `RcvGetLedger`; both pass through as distinct series.
- `jobq_*_deferred` is sampled at 1 s. A zero reading does not prove no
  saturation occurred — see the sampling caveat under
  [Per-Job-Type Queue Saturation](#per-job-type-queue-saturation).
- The two families compared in this procedure are exported on **different
  cadences by separate meter providers**: the `jobq_*` gauges every 1 s
  (`Telemetry.cpp` global provider) and `job_queued_us` / `job_running_us` /
  `getobject_*` every 10 s (the private `MetricsRegistry` provider). A short
  spike can therefore appear in one family a step before the other. When
  correlating them, widen the window rather than reading a single scrape, and
  do not conclude the two disagree from one interval's difference.
- A request rejected at either gate contributes to no other GetObject metric, so
  a rejection spike will _not_ show up as latency. Check the rejection counters
  before concluding that traffic is normal.

### Slow to reach `full`

Use this when a node takes far longer than expected to sync. Two completely
different bottlenecks look identical from outside: in both, the `ledgerData` job
lane sits pinned at its concurrency cap of 3 with jobs waiting behind it.

**Lane occupancy on its own distinguishes nothing.** It is true in both cases, so
it is never a diagnosis. Two tuning experiments were spent before that was known —
do not repeat them. Read the storage-side signals below instead.

The two modes and the signal that separates them:

```mermaid
flowchart TB
    L["`**ledgerData lane at cap 3**
    jobs waiting behind it
    TRUE IN BOTH MODES
    diagnoses nothing`"]

    L --> W["`**Mode W — write-bound**
    fresh or empty store`"]
    L --> R["`**Mode R — cold-read-bound**
    populated store, cold pages`"]

    W --> W1["`reads cheap and always miss
    data comes from peers`"]
    W1 --> W2["`cost is on the WRITE side
    one global mutex per insert
    so inserts queue`"]
    W2 --> W3["`**Look at:** writer depth
    above ~1.2, insert mean
    well above service time`"]

    R --> R1["`no write contention
    writer depth 1.00`"]
    R1 --> R2["`reads pay disk latency
    on every fetch —
    found but cold`"]
    R2 --> R3["`**Look at:** read mean
    several times a warm read,
    then the found rate to split
    cold-but-held from real misses`"]

    style L fill:#7b3f00,color:#ffffff
    style W fill:#1f4e79,color:#ffffff
    style R fill:#4a148c,color:#ffffff
    style W1 fill:#37474f,color:#ffffff
    style W2 fill:#37474f,color:#ffffff
    style W3 fill:#2d5016,color:#ffffff
    style R1 fill:#37474f,color:#ffffff
    style R2 fill:#37474f,color:#ffffff
    style R3 fill:#2d5016,color:#ffffff
```

#### `node_reads_hit` is a found count, not a cache-hit rate

This is the most misleading signal on the board, so read it first.
`fetchHitCount_` is incremented whenever the fetch **returned an object**
(`src/libxrpl/nodestore/Database.cpp:246-255`) — not when a cache served it. So
`node_reads_hit / node_reads_total` is the fraction of fetches that **found**
something, and it can read ~100% while every one of those fetches went to disk.

A ~100% "hit rate" at over 100 µs per read is therefore not a contradiction. It is
the cold-read signature: the data is on disk, found every time, and paid for every
time.

An incident report supplied to this project describes a devnet client-handler node
that had not reached `full` after roughly 25 minutes while reading at 112.7 µs per
fetch, against an otherwise-identical peer that reached `full` in 4.4 minutes at
4.95 µs per fetch. Both reported a found rate of ~99.98%. Those figures come from
that report, not from a run on our own hosts. Note that the found rate is identical
on the healthy peer and the stalled one, which is exactly why the found rate is
never a trigger on its own — see the decision rule below. Read this incident
alongside [Honest limits of this diagnosis](#honest-limits-of-this-diagnosis)
before concluding that cold reads caused the 25 minutes; on our own hardware they
did not produce anything like it.

A node configured with `online_delete` runs `DatabaseRotatingImp`, which has **no
NodeObject cache** at all (0 `cache_` references in
`src/libxrpl/nodestore/DatabaseRotatingImp.cpp` versus 11 in
`DatabaseNodeImp.cpp`), so every fetch reaches the backend. That is why the
cold-read mode shows up on exactly those nodes.

#### The decision rule

**Procedure** — all of it assumes the `ledgerData` lane is already at its cap; if
it is not, this procedure does not apply.

Answer two questions, in this order. Neither one alone is a diagnosis.

**Question 1 — are reads expensive?** Read `read_mean_us`.

- Under ~10 µs: reads are **cheap**. Both a clean store and a healthy populated
  store land here.
- Over ~20 µs: reads are **expensive**.
- Between the two: break the tie on the tail. Take `max_over_time` of
  `read_mean_us` across the window. Over ~100 µs counts as expensive;
  otherwise treat it as cheap. There is no read-max gauge —
  `nudb_insert_max_us` is a **write** signal and does not answer this.

**Question 2 — is the write path queueing?** Read
`nudb_writer_depth_x100 / 100`. Above ~1.2 is queueing at the insert mutex.
Depth of 1.00 is not. On a non-NuDB backend the series is absent, so this
question has no answer and only question 1 applies.

Then read the answer off the pair:

| Reads     | Write path      | Root cause and next step                                                                                                                                                                                                                                      |
| --------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Cheap     | Depth ~1.00     | **Not a storage bottleneck.** Nothing is queueing at either side. Look outside storage: work is either not arriving from peers or being discarded before it lands. Check `acquire_ledger_deferrals` against `acquire_ledger_timeouts`, and the sweep counter. |
| Cheap     | Depth over ~1.2 | **Serialized write path.** The queue is at the backend's insert mutex, not at the device. Tuning the disk will not help; see the note below. This is the clean-store mode.                                                                                    |
| Expensive | Depth ~1.00     | Split on the **found rate**. At 50% or above, **cold reads on data the node already has** — the walk pays disk latency on objects it holds. Below 50%, **genuinely disk-bound on real misses**, and storage hardware is the right thing to change.            |
| Expensive | Depth over ~1.2 | **Both paths queueing.** Rarer, and neither fix on its own will be enough. Treat the larger of the two costs as the lead.                                                                                                                                     |

**Why the rule is shaped this way.** Three points about the thresholds, each
learned from a dataset that an earlier version of this table got wrong:

- **Read cost is a relative judgement, so the band has a floor and a ceiling, not
  one cut.** A cold read on our box measured 31.8 µs mean; a cold read on the
  devnet node in the reported incident measured 112.7 µs. A single "over 100 µs"
  cut would call our own cold-read run healthy. Cheap and expensive are set at
  ~10 µs and ~20 µs with the peak breaking ties in between, because what matters
  is whether reads cost several times a warm read, not whether they cross one
  absolute number.
- **The found rate is a splitter, never a trigger.** A high found rate on its own
  is the normal, healthy state of a populated store — the healthy peer in the
  reported incident read 99.98% found at 4.95 µs per read and was fine. Only ask
  the found rate once reads are already known to be expensive; then it separates
  "slow on data we have" from "slow because we are missing".
- **Writer depth answers before the found rate.** Depth above 1 means the queue is
  at the insert mutex, which no amount of read-side tuning addresses. It is also
  the only signal that is unambiguous on a clean store, where the found rate is
  near zero and read cost is uninformative.

Completions and sweeps do not pick the row. They say how **severe** the read case
is once the row is picked: a populated store with cold pages can still finish (our
reference run reached `full` in 260 s with 8 completions) or can fail to finish at
all, which is what the reported incident describes. Same cause, different severity.

Queries, scoped to one node as everywhere else in this runbook:

```promql
# Are acquisitions finishing? (per minute)
increase(nodestore_state{metric="acquire_completions", service_instance_id=~"$node"}[1m])

# Question 1 -- read cost, in microseconds per read
nodestore_state{metric="read_mean_us", service_instance_id=~"$node"}

# The tail, for the 10-20 us tie-break. No read-max gauge exists, so take the
# highest value the mean reached over the window.
max_over_time(nodestore_state{metric="read_mean_us", service_instance_id=~"$node"}[15m])

# Question 2 -- write-side queueing. Depth is fixed-point, divide by 100.
nodestore_state{metric="nudb_writer_depth_x100", service_instance_id=~"$node"} / 100

# The insert time that goes with that depth
nodestore_state{metric="nudb_insert_mean_us", service_instance_id=~"$node"}

# The found rate, which splits the expensive-read row only. Do not read it on
# its own: a high found rate is normal and healthy on a populated store.
  rate(nodestore_state{metric="node_reads_hit", service_instance_id=~"$node"}[5m])
/ rate(nodestore_state{metric="node_reads_total", service_instance_id=~"$node"}[5m])

# The deferral/timeout pair, scoped to ledger acquisition. Use these two, not
# the all-lane acquire_deferrals / acquire_timeouts -- see the pair section below.
increase(nodestore_state{metric="acquire_ledger_deferrals", service_instance_id=~"$node"}[5m])
increase(nodestore_state{metric="acquire_ledger_timeouts", service_instance_id=~"$node"}[5m])
```

#### Measured reference points

**Provenance.** The two columns below are our own measurements: node2 on the AWS
dev box, build `e3c2f8279a`, 2026-07-27/28, same host and same binary for both
runs, differing only in the state of the store. Use them as the shape to compare
against, not as thresholds. The read figures below come from the `read_mean_us`
gauge, the only read-latency signal exported; the "highest sample" row is the
largest value that gauge reached over the run, not a read-latency percentile. The third dataset in this section — the 25-minute devnet stall and its
healthy peer — is **not** ours; it comes from an incident report supplied to this
project and is kept separate for that reason.

**Three rows below were measured on a build that got them wrong.** Both runs
predate the measurement fixes, so read those rows as bounds rather than values:

- **Completions** were only counted in `InboundLedger::done()`, so an acquisition
  satisfied entirely from the local store — `init()` sets `complete_` and returns
  without ever calling `done()` — was never counted. Mode W's `0` is therefore not
  evidence that the node completed nothing; it reached `full`, which it could not
  have done without completing acquisitions. The count is now taken at both exits
  behind an idempotent latch, so on a current build a zero means zero.
- **Writer depth** was summed at insert entry but divided by a sample count that
  only advanced at insert exit, so in-flight inserts — the deep, slow ones —
  contributed depth to the numerator and nothing to the denominator. The mean was
  biased **down**, worst exactly when queueing was worst. Mode W's 1.60 is a lower
  bound on the true depth.
- **Queueing per insert** is derived from that depth, so its 37 % is a lower bound
  too. See the derivation below.

| Signal                         | Mode W: clean store      | Mode R: populated store, cold pages |
| ------------------------------ | ------------------------ | ----------------------------------- |
| Time to `full`                 | 510 s                    | 260 s                               |
| `read_mean_us`                 | 8.8 µs                   | 31.8 µs                             |
| `read_mean_us`, highest sample | 9 µs                     | 223 µs                              |
| Found rate                     | 0.00 %                   | 88.3 %                              |
| Insert time, mean              | 20.0 µs                  | 15.9 µs                             |
| Writer depth, mean             | ≥ 1.60 (biased low)      | 1.00                                |
| Queueing per insert            | ≥ 37 % (derived from ↑)  | 0 %                                 |
| Deferrals over run, all lanes  | +5441                    | +1845                               |
| Timeouts over run, all lanes   | +687                     | +399                                |
| Completions over run           | 0 (under-counted, see ↑) | 8 (under-counted, see ↑)            |
| Sweep evictions                | +127                     | +38                                 |

Applying the decision rule: Mode W reads cheap (8.8 µs) with depth 1.60, so it is
the serialized write path. Mode R reads expensive (31.8 µs mean, 223 µs peak) with
depth 1.00 and a found rate well above 50%, so it is cold reads on data the node
holds. The rule reaches both answers without the found rate deciding either mode on
its own — and both answers survive the corrected measurements, because a
depth-1.60 lower bound is still above the 1.2 threshold and Mode R's 1.00 is a
floor that cannot be biased below itself.

The deferral and timeout rows are the **all-lane** counters, the only ones that
existed when these runs were taken. They cannot be attributed to ledger
acquisition; the eight-to-one ratio in Mode W is a whole-node figure. Re-measure
with `acquire_ledger_deferrals` / `acquire_ledger_timeouts` before quoting a ratio
as a ledger-acquisition fingerprint.

**The reported incident, for contrast — not our measurement.** Figures from an
incident report supplied to this project. No writer-depth data was captured, so the
rule reaches its answer from question 1 alone.

| Signal         | Stalled client handler | Healthy peer |
| -------------- | ---------------------- | ------------ |
| Time to `full` | not reached in ~25 min | 4.4 min      |
| `read_mean_us` | 112.7 µs               | 4.95 µs      |
| Found rate     | ~99.98 %               | ~99.98 %     |
| Writer depth   | not captured           | not captured |

The healthy peer is the reason the found rate is a splitter and not a trigger: it
reported the same ~99.98% as the stalled node and was fine. What separates them is
read cost — 4.95 µs is a warm read, 112.7 µs is not.

What healthy looks like: read mean in the single-digit microseconds, writer depth
at 1.00, queueing near 0%, and `acquire_completions` advancing. Any one of a read
mean several times a warm read, a writer depth above ~1.2, or completions flat at
zero is worth chasing.

**Completions flat at zero only means something on a current build.** Until the
counter was moved to cover both exits, an acquisition served from the local store
was never counted, so a build predating that fix could read zero while completing
steadily. Check the build before treating a zero on archived data as a symptom.

**How the 37% is derived, and why it is a lower bound.** It is not measured
directly — it comes from the two gauges by Little's Law. With mean queue depth L
and mean insert time W, the service time is `S = W / L` and the queueing component
is `W − S`. Mode W's 20.0 µs at depth 1.60 gives S = 12.5 µs, so 7.5 µs of every
insert — 37% — was spent waiting for the mutex rather than writing.

That 37% is **not exact**: the L it was computed from came from the biased
estimator described above, which understated depth. A larger L gives a smaller S
and a larger `W − S`, so the true queueing share of Mode W was **at least** 37%.
Quote it as a floor. Mode R's depth of exactly 1.00 is unaffected — 1.00 is the
minimum a depth can be, so no bias can have pushed it there — which is why its 0%
stands as measured.

**Why the write path serializes.** NuDB takes one global mutex per insert
(`nudb/impl/basic_store.ipp:288`). It is a Conan dependency and is not patched
here, so this is a property to observe and design around, not a bug to fix
locally. `nudb_writer_depth_x100` is the queue length at that mutex.

#### The deferral/timeout pair

Read these two together or not at all. The livelock fingerprint is **deferrals
rising while timeouts stay flat**.

**Read the ledger-scoped pair, not the all-lane pair.** Both events are recorded in
`TimeoutCounter`, a base class shared by five subclasses — `InboundLedger`,
`TransactionAcquire`, `LedgerReplayTask`, `LedgerDeltaAcquire` and
`SkipListAcquire` — each with its own job limit. `acquire_deferrals` and
`acquire_timeouts` therefore pool every lane, so a replay lane sitting at its own
limit produces the fingerprint shape while ledger acquisition is perfectly healthy.
That is a false positive on a headline diagnosis. `acquire_ledger_deferrals` and
`acquire_ledger_timeouts` count the same two events for the `InboundLedger` lane
only (`src/xrpld/app/ledger/detail/TimeoutCounter.h`, `isLedgerAcquisition()`), and
they are the pair this procedure means. The all-lane totals remain useful for one
question only: whether _any_ lane is deferring.

A deferral happens when the acquisition timer job finds its lane's job count at or
above the acquisition's own limit — 5 for `InboundLedger`
(`src/xrpld/app/ledger/detail/InboundLedger.cpp:86`), compared against
`getJobCountTotal()` in `TimeoutCounter::queueJob()`
(`src/xrpld/app/ledger/detail/TimeoutCounter.cpp:62-64`). That is not the same as
the `ledgerData` lane's concurrency cap of 3 (`include/xrpl/core/JobTypes.h:63`):
the gate counts running plus queued, so it fires at 3 running plus 2 queued. The
timer is re-armed but its **body does not run**, so the retry counter never
advances and the 6-timeout give-up becomes unreachable — the give-up path is
disarmed and the acquisition can never end on its own. Neither counter alone shows
this: deferrals rising looks like ordinary backpressure, and timeouts flat looks
like health. Only the divergence is diagnostic. See the counter documentation in
`src/xrpld/app/ledger/AcquireStats.h`.

Two more pairs from the same family:

- `acquire_sweep_evictions` rising while `acquire_completions` stays at zero →
  partial work is being discarded and redone. The sweep drops any acquisition
  idle for more than one minute
  (`src/xrpld/app/ledger/detail/InboundLedgers.cpp:400`), taking whatever it had
  built with it.
- `acquire_aborts_partial` rising → the expensive form of an abort, where partly
  built maps were thrown away. `acquire_aborts` alone does not separate the cheap
  case from this one.

#### Honest limits of this diagnosis

- **Our populated-store run was twice as fast, not slower** — 260 s against 510 s,
  despite reads being roughly 4× more expensive. Reusing local data beats fetching
  from peers even when every read is cold. Slow cold reads therefore do **not** on
  their own explain the ~25-minute stall in the reported devnet incident. The
  decision rule identifies the _mode_ correctly in both cases; it does not claim
  that the mode alone accounts for that duration.
- Something compounds it there, and we have not confirmed what. The most likely
  candidate is a much larger store, where the walk takes long enough that the
  1-minute sweep destroys partial work faster than it can complete — which is why
  the sweep and completion counters are in the table. **This is an unconfirmed
  hypothesis.** Treat it as the next thing to test, not as the answer. It was also
  partly suggested by Mode W's zero completions, which we now know was a counting
  defect rather than a stalled node, so the hypothesis has lost one of its
  supports and needs re-testing on a current build before it is pursued.
- **Three of the numbers above were measured with instruments that were since
  corrected**: completions (missed local-store hits), writer depth (mean biased
  low), and the deferral/timeout pair (pooled across five job lanes). The modes
  and the decision rule are unaffected — each survives the correction, as noted
  where it appears — but no figure in the reference table should be quoted as an
  exact measurement without re-running on a build that has all three fixes.
- The `nudb_*` label values are absent entirely on a non-NuDB writable backend.
  Absent is not zero — a missing series means "not applicable", so a panel showing
  a gap there is correct behaviour.
- **`write_load` and `nudb_writers_in_flight` are the same number on NuDB.** Both
  read the same atomic: `NuDBBackend::getWriteLoad()` returns `concurrentWriters`
  (`src/libxrpl/nodestore/backend/NuDBFactory.cpp:355-361`), which is also what
  `WriteStats::concurrentWriters` reports. Their agreement confirms nothing — it is
  one signal plotted twice. On RocksDB `write_load` is a genuinely different
  quantity, the larger of the recorded load and the pending batch size
  (`src/libxrpl/nodestore/BatchWriter.cpp:47-53`), so it is a batch-queue length
  rather than a thread count.
- **`stored_object_bytes` is not the size of the store on disk.** It reports the
  cumulative object-payload bytes this process has written — the same value as
  `node_written_bytes`, from the same accessor — so it excludes keys, padding and
  the log, and it resets with the process. A ratio of the two is a constant 1.0 and
  measures nothing. This label value was called `nudb_bytes` before Phase 9; it
  comes from `node_store::Database` rather than the NuDB backend, so it is not part
  of the `nudb_*` family above and reads the same on RocksDB.
- These gauges are sampled on the `MetricsRegistry` reader's 10 s cadence, while
  the `jobq_*` lane gauges are sampled at 1 s by a different provider. Widen the
  window when correlating them rather than reading a single scrape; see the caveat
  under [Slow TMGetObjectByHash service](#slow-tmgetobjectbyhash-service).
- `read_mean_us` and `write_mean_us` are omitted rather than reported as zero when
  nothing has been read or written yet, so an idle node legitimately shows no
  series.

Existing panels that already carry part of this picture, on the _Ledger Data &
Sync_ dashboard: **NuDB Read Latency**, **NuDB Read Found Ratio**, **NuDB Read
Pressure**, and **Job Queue Backlog and Deferred by Type** for the lane occupancy
that this procedure tells you to distrust on its own.

The pair this procedure asks for is on **Ledger Acquire Deferrals vs Timeouts
(Ledger Lane Only)**, in the _Sync Bottleneck Discrimination_ row. The adjacent
**Acquire Deferrals vs Timeouts (All Lanes)** plots the pooled totals; read it only
to see whether some other acquisition lane is also under pressure.

**NuDB Read Found Ratio** plots `node_reads_hit / node_reads_total`. That is the
found rate, not a cache hit ratio: the underlying counter increments whenever a
fetch returned an object, and a node with `online_delete` has no object cache at
all. A rate near 1.0 alongside an expensive read time is the cold-read signature,
not a sign the cache is working.

### High memory usage

- Reduce trace volume with collector-side tail sampling (xrpld head sampling is
  fixed at 1.0 and is not configurable)
- Reduce `max_queue_size` and `batch_size`
- Disable high-volume trace categories: `trace_peer=0`

### Collector connection failures

- Verify endpoint URL matches collector address
- Check firewall rules for ports 4317/4318
- If using TLS, verify certificate path with `tls_ca_cert`

### No trace_id in log output

- Verify xrpld was built with `telemetry=ON` (the `XRPL_ENABLE_TELEMETRY` preprocessor flag)
- Verify `enabled=1` in the `[telemetry]` config section
- Log lines only contain `trace_id`/`span_id` when emitted inside an active span — background logs outside of RPC/consensus/transaction processing will not have trace context
- Check that the specific trace category is enabled (e.g., `trace_rpc=1`)

### No logs in Loki

- Verify the log file mount in docker-compose.yml points to the correct xrpld log directory (default source `docker/telemetry/data/logs`, or the `XRPLD_LOG_DIR` override) and that xrpld actually writes `debug.log` there
- Check OTel Collector logs for filelog receiver errors: `docker compose logs otel-collector`
- Verify Loki is running: `curl http://localhost:3100/ready`
- Check the filelog receiver glob `/var/log/xrpld/*/debug.log` matches your log layout — the log file must sit one subdirectory below the mount root

## Performance Tuning

| Scenario                 | Recommendation                                            |
| ------------------------ | --------------------------------------------------------- |
| Production mainnet       | `trace_peer=0`; reduce volume via collector tail sampling |
| Testnet/devnet           | Full tracing (head sampling fixed at 1.0)                 |
| Debugging specific issue | Full tracing (head sampling fixed at 1.0)                 |
| High-throughput node     | Increase `batch_size=1024`, `max_queue_size=4096`         |

## Disabling Telemetry

Set `enabled=0` in config (runtime disable) or build without the flag:

```bash
cmake --preset default -Dtelemetry=OFF
```

When telemetry is compiled out, all trace macros expand to no-ops with zero overhead.

## Validating Telemetry Stack

After deploying telemetry, use the Phase 10 workload tools to validate the full stack end-to-end.

### Quick Validation

```bash
# Run the full validation suite (starts cluster, generates load, validates):
docker/telemetry/workload/run-full-validation.sh --xrpld .build/xrpld

# Check the report:
cat /tmp/xrpld-validation/reports/validation-report.json | jq '.summary'
```

### What Gets Validated

| Category   | Checks         | Description                                             |
| ---------- | -------------- | ------------------------------------------------------- |
| Spans      | 16+ span types | All span names appear in Tempo with required attributes |
| Metrics    | 30+ metrics    | SpanMetrics, StatsD gauges/counters, Phase 9 metrics    |
| Logs       | 2 checks       | trace_id/span_id present in Loki, cross-reference works |
| Dashboards | 15 dashboards  | All Grafana dashboards load without errors              |

### Running Individual Tools

```bash
# RPC load only:
python3 docker/telemetry/workload/rpc_load_generator.py \
    --endpoints ws://localhost:6006 --rate 50 --duration 120

# Transaction mix only:
python3 docker/telemetry/workload/tx_submitter.py \
    --endpoint ws://localhost:6006 --tps 5 --duration 120

# Validation only (assumes load already ran):
python3 docker/telemetry/workload/validate_telemetry.py \
    --report /tmp/report.json
```

### Interpreting Failures

- **Span failures**: Check that the relevant trace category is enabled in `[telemetry]` config (e.g., `trace_rpc=1`).
- **Metric failures**: Verify the OTel Collector is running and Prometheus is scraping port 8889. Check `docker compose logs otel-collector`.
- **Dashboard failures**: Ensure Grafana provisioning is mounted correctly. Check `docker compose logs grafana`.

## Performance Benchmarking

Measure the overhead of the telemetry stack against a baseline:

```bash
docker/telemetry/workload/benchmark.sh --xrpld .build/xrpld --duration 300
```

### Benchmark Thresholds

| Metric            | Target | Description                            |
| ----------------- | ------ | -------------------------------------- |
| CPU overhead      | < 3%   | Average CPU increase across nodes      |
| Memory overhead   | < 5MB  | Peak RSS increase per node             |
| RPC p99 latency   | < 2ms  | Additional p99 latency for server_info |
| Throughput impact | < 5%   | Reduction in ledger close rate         |
| Consensus impact  | < 1%   | Increase in consensus round time       |

### Tuning for Production

If benchmarks exceed thresholds:

1. **Reduce sampling**: `sampling_ratio=0.01` (1% of traces)
2. **Disable peer tracing**: `trace_peer=0` (highest volume category)
3. **Increase batch delay**: `batch_delay_ms=10000` (less frequent exports)
4. **Reduce queue size**: `max_queue_size=1024` (back-pressure earlier)

See `docker/telemetry/workload/README.md` for full documentation.