- Add Tempo 2.7.2 service to docker-compose with local storage - Add otlp/tempo exporter to OTel Collector traces pipeline - Add Tempo Grafana datasource provisioning with node graph - Update 05-configuration-reference.md examples with Tempo - OTel Collector fans traces to both Jaeger and Tempo simultaneously Jaeger provides a standalone UI at :16686 for quick lookups. Tempo is queryable via Grafana Explore using TraceQL and is the recommended backend for production (supports S3/GCS storage). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
26 KiB
Configuration Reference
Parent Document: OpenTelemetryPlan.md Related: Code Samples | Implementation Phases
5.1 rippled Configuration
5.1.1 Configuration File Section
Add to cfg/xrpld-example.cfg:
# ═══════════════════════════════════════════════════════════════════════════════
# TELEMETRY (OpenTelemetry Distributed Tracing)
# ═══════════════════════════════════════════════════════════════════════════════
#
# Enables distributed tracing for transaction flow, consensus, and RPC calls.
# Traces are exported to an OpenTelemetry Collector using OTLP protocol.
#
# [telemetry]
#
# # Enable/disable telemetry (default: 0 = disabled)
# enabled=1
#
# # Exporter type: "otlp_grpc" (default), "otlp_http", or "none"
# exporter=otlp_grpc
#
# # OTLP endpoint (default: localhost:4317 for gRPC, localhost:4318 for HTTP)
# endpoint=localhost:4317
#
# # Use TLS for exporter connection (default: 0)
# use_tls=0
#
# # Path to CA certificate for TLS (optional)
# # tls_ca_cert=/path/to/ca.crt
#
# # Sampling ratio: 0.0-1.0 (default: 1.0 = 100% sampling)
# # Use lower values in production to reduce overhead
# sampling_ratio=0.1
#
# # Batch processor settings
# batch_size=512 # Spans per batch (default: 512)
# batch_delay_ms=5000 # Max delay before sending batch (default: 5000)
# max_queue_size=2048 # Max queued spans (default: 2048)
#
# # Component-specific tracing (default: all enabled except peer)
# trace_transactions=1 # Transaction relay and processing
# trace_consensus=1 # Consensus rounds and proposals
# trace_rpc=1 # RPC request handling
# trace_peer=0 # Peer messages (high volume, disabled by default)
# trace_ledger=1 # Ledger acquisition and building
#
# # Service identification (automatically detected if not specified)
# # service_name=rippled
# # service_instance_id=<node_public_key>
[telemetry]
enabled=0
5.1.2 Configuration Options Summary
| Option | Type | Default | Description |
|---|---|---|---|
enabled |
bool | false |
Enable/disable telemetry |
exporter |
string | "otlp_grpc" |
Exporter type: otlp_grpc, otlp_http, none |
endpoint |
string | localhost:4317 |
OTLP collector endpoint |
use_tls |
bool | false |
Enable TLS for exporter connection |
tls_ca_cert |
string | "" |
Path to CA certificate file |
sampling_ratio |
float | 1.0 |
Sampling ratio (0.0-1.0) |
batch_size |
uint | 512 |
Spans per export batch |
batch_delay_ms |
uint | 5000 |
Max delay before sending batch (ms) |
max_queue_size |
uint | 2048 |
Maximum queued spans |
trace_transactions |
bool | true |
Enable transaction tracing |
trace_consensus |
bool | true |
Enable consensus tracing |
trace_rpc |
bool | true |
Enable RPC tracing |
trace_peer |
bool | false |
Enable peer message tracing (high volume) |
trace_ledger |
bool | true |
Enable ledger tracing |
service_name |
string | "rippled" |
Service name for traces |
service_instance_id |
string | <node_pubkey> |
Instance identifier |
5.2 Configuration Parser
// src/libxrpl/telemetry/TelemetryConfig.cpp
#include <xrpl/telemetry/Telemetry.h>
#include <xrpl/basics/Log.h>
namespace xrpl {
namespace telemetry {
Telemetry::Setup
setup_Telemetry(
Section const& section,
std::string const& nodePublicKey,
std::string const& version)
{
Telemetry::Setup setup;
// Basic settings
setup.enabled = section.value_or("enabled", false);
setup.serviceName = section.value_or("service_name", "rippled");
setup.serviceVersion = version;
setup.serviceInstanceId = section.value_or(
"service_instance_id", nodePublicKey);
// Exporter settings
setup.exporterType = section.value_or("exporter", "otlp_grpc");
if (setup.exporterType == "otlp_grpc")
setup.exporterEndpoint = section.value_or("endpoint", "localhost:4317");
else if (setup.exporterType == "otlp_http")
setup.exporterEndpoint = section.value_or("endpoint", "localhost:4318");
setup.useTls = section.value_or("use_tls", false);
setup.tlsCertPath = section.value_or("tls_ca_cert", "");
// Sampling
setup.samplingRatio = section.value_or("sampling_ratio", 1.0);
if (setup.samplingRatio < 0.0 || setup.samplingRatio > 1.0)
{
Throw<std::runtime_error>(
"telemetry.sampling_ratio must be between 0.0 and 1.0");
}
// Batch processor
setup.batchSize = section.value_or("batch_size", 512u);
setup.batchDelay = std::chrono::milliseconds{
section.value_or("batch_delay_ms", 5000u)};
setup.maxQueueSize = section.value_or("max_queue_size", 2048u);
// Component filtering
setup.traceTransactions = section.value_or("trace_transactions", true);
setup.traceConsensus = section.value_or("trace_consensus", true);
setup.traceRpc = section.value_or("trace_rpc", true);
setup.tracePeer = section.value_or("trace_peer", false);
setup.traceLedger = section.value_or("trace_ledger", true);
return setup;
}
} // namespace telemetry
} // namespace xrpl
5.3 Application Integration
5.3.1 ApplicationImp Changes
// src/xrpld/app/main/Application.cpp (modified)
#include <xrpl/telemetry/Telemetry.h>
class ApplicationImp : public Application
{
// ... existing members ...
// Telemetry (must be constructed early, destroyed late)
std::unique_ptr<telemetry::Telemetry> telemetry_;
public:
ApplicationImp(...)
{
// Initialize telemetry early (before other components)
auto telemetrySection = config_->section("telemetry");
auto telemetrySetup = telemetry::setup_Telemetry(
telemetrySection,
toBase58(TokenType::NodePublic, nodeIdentity_.publicKey()),
BuildInfo::getVersionString());
// Set network attributes
telemetrySetup.networkId = config_->NETWORK_ID;
telemetrySetup.networkType = [&]() {
if (config_->NETWORK_ID == 0) return "mainnet";
if (config_->NETWORK_ID == 1) return "testnet";
if (config_->NETWORK_ID == 2) return "devnet";
return "custom";
}();
telemetry_ = telemetry::make_Telemetry(
telemetrySetup,
logs_->journal("Telemetry"));
// ... rest of initialization ...
}
void start() override
{
// Start telemetry first
if (telemetry_)
telemetry_->start();
// ... existing start code ...
}
void stop() override
{
// ... existing stop code ...
// Stop telemetry last (to capture shutdown spans)
if (telemetry_)
telemetry_->stop();
}
telemetry::Telemetry& getTelemetry() override
{
assert(telemetry_);
return *telemetry_;
}
};
5.3.2 Application Interface Addition
// include/xrpl/app/main/Application.h (modified)
namespace telemetry { class Telemetry; }
class Application
{
public:
// ... existing virtual methods ...
/** Get the telemetry system for distributed tracing */
virtual telemetry::Telemetry& getTelemetry() = 0;
};
5.4 CMake Integration
5.4.1 Find OpenTelemetry Module
# cmake/FindOpenTelemetry.cmake
# Find OpenTelemetry C++ SDK
#
# This module defines:
# OpenTelemetry_FOUND - System has OpenTelemetry
# OpenTelemetry::api - API library target
# OpenTelemetry::sdk - SDK library target
# OpenTelemetry::otlp_grpc_exporter - OTLP gRPC exporter target
# OpenTelemetry::otlp_http_exporter - OTLP HTTP exporter target
find_package(opentelemetry-cpp CONFIG QUIET)
if(opentelemetry-cpp_FOUND)
set(OpenTelemetry_FOUND TRUE)
# Create imported targets if not already created by config
if(NOT TARGET OpenTelemetry::api)
add_library(OpenTelemetry::api ALIAS opentelemetry-cpp::api)
endif()
if(NOT TARGET OpenTelemetry::sdk)
add_library(OpenTelemetry::sdk ALIAS opentelemetry-cpp::sdk)
endif()
if(NOT TARGET OpenTelemetry::otlp_grpc_exporter)
add_library(OpenTelemetry::otlp_grpc_exporter ALIAS
opentelemetry-cpp::otlp_grpc_exporter)
endif()
else()
# Try pkg-config fallback
find_package(PkgConfig QUIET)
if(PKG_CONFIG_FOUND)
pkg_check_modules(OTEL opentelemetry-cpp QUIET)
if(OTEL_FOUND)
set(OpenTelemetry_FOUND TRUE)
# Create imported targets from pkg-config
add_library(OpenTelemetry::api INTERFACE IMPORTED)
target_include_directories(OpenTelemetry::api INTERFACE
${OTEL_INCLUDE_DIRS})
endif()
endif()
endif()
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(OpenTelemetry
REQUIRED_VARS OpenTelemetry_FOUND)
5.4.2 CMakeLists.txt Changes
# CMakeLists.txt (additions)
# ═══════════════════════════════════════════════════════════════════════════════
# TELEMETRY OPTIONS
# ═══════════════════════════════════════════════════════════════════════════════
option(XRPL_ENABLE_TELEMETRY
"Enable OpenTelemetry distributed tracing support" OFF)
if(XRPL_ENABLE_TELEMETRY)
find_package(OpenTelemetry REQUIRED)
# Define compile-time flag
add_compile_definitions(XRPL_ENABLE_TELEMETRY)
message(STATUS "OpenTelemetry tracing: ENABLED")
else()
message(STATUS "OpenTelemetry tracing: DISABLED")
endif()
# ═══════════════════════════════════════════════════════════════════════════════
# TELEMETRY LIBRARY
# ═══════════════════════════════════════════════════════════════════════════════
if(XRPL_ENABLE_TELEMETRY)
add_library(xrpl_telemetry
src/libxrpl/telemetry/Telemetry.cpp
src/libxrpl/telemetry/TelemetryConfig.cpp
src/libxrpl/telemetry/TraceContext.cpp
)
target_include_directories(xrpl_telemetry
PUBLIC
${CMAKE_CURRENT_SOURCE_DIR}/include
)
target_link_libraries(xrpl_telemetry
PUBLIC
OpenTelemetry::api
OpenTelemetry::sdk
OpenTelemetry::otlp_grpc_exporter
PRIVATE
xrpl_basics
)
# Add to main library dependencies
target_link_libraries(xrpld PRIVATE xrpl_telemetry)
else()
# Create null implementation library
add_library(xrpl_telemetry
src/libxrpl/telemetry/NullTelemetry.cpp
)
target_include_directories(xrpl_telemetry
PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/include
)
endif()
5.5 OpenTelemetry Collector Configuration
5.5.1 Development Configuration
# otel-collector-dev.yaml
# Minimal configuration for local development
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
http:
endpoint: 0.0.0.0:4318
processors:
batch:
timeout: 1s
send_batch_size: 100
exporters:
# Console output for debugging
logging:
verbosity: detailed
sampling_initial: 5
sampling_thereafter: 200
# Jaeger for trace visualization
jaeger:
endpoint: jaeger:14250
tls:
insecure: true
# Grafana Tempo for trace storage
otlp/tempo:
endpoint: tempo:4317
tls:
insecure: true
service:
pipelines:
traces:
receivers: [otlp]
processors: [batch]
exporters: [logging, jaeger, otlp/tempo]
5.5.2 Production Configuration
# otel-collector-prod.yaml
# Production configuration with filtering, sampling, and multiple backends
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
tls:
cert_file: /etc/otel/server.crt
key_file: /etc/otel/server.key
ca_file: /etc/otel/ca.crt
processors:
# Memory limiter to prevent OOM
memory_limiter:
check_interval: 1s
limit_mib: 1000
spike_limit_mib: 200
# Batch processing for efficiency
batch:
timeout: 5s
send_batch_size: 512
send_batch_max_size: 1024
# Tail-based sampling (keep errors and slow traces)
tail_sampling:
decision_wait: 10s
num_traces: 100000
expected_new_traces_per_sec: 1000
policies:
# Always keep error traces
- name: errors
type: status_code
status_code:
status_codes: [ERROR]
# Keep slow consensus rounds (>5s)
- name: slow-consensus
type: latency
latency:
threshold_ms: 5000
# Keep slow RPC requests (>1s)
- name: slow-rpc
type: and
and:
and_sub_policy:
- name: rpc-spans
type: string_attribute
string_attribute:
key: xrpl.rpc.command
values: [".*"]
enabled_regex_matching: true
- name: latency
type: latency
latency:
threshold_ms: 1000
# Probabilistic sampling for the rest
- name: probabilistic
type: probabilistic
probabilistic:
sampling_percentage: 10
# Attribute processing
attributes:
actions:
# Hash sensitive data
- key: xrpl.tx.account
action: hash
# Add deployment info
- key: deployment.environment
value: production
action: upsert
exporters:
# Grafana Tempo for long-term storage
otlp/tempo:
endpoint: tempo.monitoring:4317
tls:
insecure: false
ca_file: /etc/otel/tempo-ca.crt
# Elastic APM for correlation with logs
otlp/elastic:
endpoint: apm.elastic:8200
headers:
Authorization: "Bearer ${ELASTIC_APM_TOKEN}"
extensions:
health_check:
endpoint: 0.0.0.0:13133
zpages:
endpoint: 0.0.0.0:55679
service:
extensions: [health_check, zpages]
pipelines:
traces:
receivers: [otlp]
processors: [memory_limiter, tail_sampling, attributes, batch]
exporters: [otlp/tempo, otlp/elastic]
5.6 Docker Compose Development Environment
# docker-compose-telemetry.yaml
version: "3.8"
services:
# OpenTelemetry Collector
otel-collector:
image: otel/opentelemetry-collector-contrib:0.92.0
container_name: otel-collector
command: ["--config=/etc/otel-collector-config.yaml"]
volumes:
- ./otel-collector-dev.yaml:/etc/otel-collector-config.yaml:ro
ports:
- "4317:4317" # OTLP gRPC
- "4318:4318" # OTLP HTTP
- "13133:13133" # Health check
depends_on:
- jaeger
# Jaeger for trace visualization
jaeger:
image: jaegertracing/all-in-one:1.53
container_name: jaeger
environment:
- COLLECTOR_OTLP_ENABLED=true
ports:
- "16686:16686" # UI
- "14250:14250" # gRPC
# Grafana Tempo for trace storage (recommended for production)
tempo:
image: grafana/tempo:2.7.2
container_name: tempo
command: ["-config.file=/etc/tempo.yaml"]
volumes:
- ./tempo.yaml:/etc/tempo.yaml:ro
- tempo-data:/var/tempo
ports:
- "3200:3200" # HTTP API
# Grafana for dashboards
grafana:
image: grafana/grafana:10.2.3
container_name: grafana
environment:
- GF_AUTH_ANONYMOUS_ENABLED=true
- GF_AUTH_ANONYMOUS_ORG_ROLE=Admin
volumes:
- ./grafana/provisioning:/etc/grafana/provisioning:ro
- ./grafana/dashboards:/var/lib/grafana/dashboards:ro
ports:
- "3000:3000"
depends_on:
- jaeger
- tempo
# Prometheus for metrics (optional, for correlation)
prometheus:
image: prom/prometheus:v2.48.1
container_name: prometheus
volumes:
- ./prometheus.yaml:/etc/prometheus/prometheus.yml:ro
ports:
- "9090:9090"
networks:
default:
name: rippled-telemetry
5.7 Configuration Architecture
flowchart TB
subgraph config["Configuration Sources"]
cfgFile["xrpld.cfg<br/>[telemetry] section"]
cmake["CMake<br/>XRPL_ENABLE_TELEMETRY"]
end
subgraph init["Initialization"]
parse["setup_Telemetry()"]
factory["make_Telemetry()"]
end
subgraph runtime["Runtime Components"]
tracer["TracerProvider"]
exporter["OTLP Exporter"]
processor["BatchProcessor"]
end
subgraph collector["Collector Pipeline"]
recv["Receivers"]
proc["Processors"]
exp["Exporters"]
end
cfgFile --> parse
cmake -->|"compile flag"| parse
parse --> factory
factory --> tracer
tracer --> processor
processor --> exporter
exporter -->|"OTLP"| recv
recv --> proc
proc --> exp
style config fill:#e3f2fd,stroke:#1976d2
style runtime fill:#e8f5e9,stroke:#388e3c
style collector fill:#fff3e0,stroke:#ff9800
5.8 Grafana Integration
Step-by-step instructions for integrating rippled traces with Grafana.
5.8.1 Data Source Configuration
Tempo (Recommended)
# grafana/provisioning/datasources/tempo.yaml
apiVersion: 1
datasources:
- name: Tempo
type: tempo
access: proxy
url: http://tempo:3200
jsonData:
httpMethod: GET
tracesToLogs:
datasourceUid: loki
tags: ["service.name", "xrpl.tx.hash"]
mappedTags: [{ key: "trace_id", value: "traceID" }]
mapTagNamesEnabled: true
filterByTraceID: true
serviceMap:
datasourceUid: prometheus
nodeGraph:
enabled: true
search:
hide: false
lokiSearch:
datasourceUid: loki
Jaeger
# grafana/provisioning/datasources/jaeger.yaml
apiVersion: 1
datasources:
- name: Jaeger
type: jaeger
access: proxy
url: http://jaeger:16686
jsonData:
tracesToLogs:
datasourceUid: loki
tags: ["service.name"]
Elastic APM
# grafana/provisioning/datasources/elastic-apm.yaml
apiVersion: 1
datasources:
- name: Elasticsearch-APM
type: elasticsearch
access: proxy
url: http://elasticsearch:9200
database: "apm-*"
jsonData:
esVersion: "8.0.0"
timeField: "@timestamp"
logMessageField: message
logLevelField: log.level
5.8.2 Dashboard Provisioning
# grafana/provisioning/dashboards/dashboards.yaml
apiVersion: 1
providers:
- name: "rippled-dashboards"
orgId: 1
folder: "rippled"
folderUid: "rippled"
type: file
disableDeletion: false
updateIntervalSeconds: 30
options:
path: /var/lib/grafana/dashboards/rippled
5.8.3 Example Dashboard: RPC Performance
{
"title": "rippled RPC Performance",
"uid": "rippled-rpc-performance",
"panels": [
{
"title": "RPC Latency by Command",
"type": "heatmap",
"datasource": "Tempo",
"targets": [
{
"queryType": "traceql",
"query": "{resource.service.name=\"rippled\" && span.xrpl.rpc.command != \"\"} | histogram_over_time(duration) by (span.xrpl.rpc.command)"
}
],
"gridPos": { "h": 8, "w": 12, "x": 0, "y": 0 }
},
{
"title": "RPC Error Rate",
"type": "timeseries",
"datasource": "Tempo",
"targets": [
{
"queryType": "traceql",
"query": "{resource.service.name=\"rippled\" && status.code=error} | rate() by (span.xrpl.rpc.command)"
}
],
"gridPos": { "h": 8, "w": 12, "x": 12, "y": 0 }
},
{
"title": "Top 10 Slowest RPC Commands",
"type": "table",
"datasource": "Tempo",
"targets": [
{
"queryType": "traceql",
"query": "{resource.service.name=\"rippled\" && span.xrpl.rpc.command != \"\"} | avg(duration) by (span.xrpl.rpc.command) | topk(10)"
}
],
"gridPos": { "h": 8, "w": 24, "x": 0, "y": 8 }
},
{
"title": "Recent Traces",
"type": "table",
"datasource": "Tempo",
"targets": [
{
"queryType": "traceql",
"query": "{resource.service.name=\"rippled\"}"
}
],
"gridPos": { "h": 8, "w": 24, "x": 0, "y": 16 }
}
]
}
5.8.4 Example Dashboard: Transaction Tracing
{
"title": "rippled Transaction Tracing",
"uid": "rippled-tx-tracing",
"panels": [
{
"title": "Transaction Throughput",
"type": "stat",
"datasource": "Tempo",
"targets": [
{
"queryType": "traceql",
"query": "{resource.service.name=\"rippled\" && name=\"tx.receive\"} | rate()"
}
],
"gridPos": { "h": 4, "w": 6, "x": 0, "y": 0 }
},
{
"title": "Cross-Node Relay Count",
"type": "timeseries",
"datasource": "Tempo",
"targets": [
{
"queryType": "traceql",
"query": "{resource.service.name=\"rippled\" && name=\"tx.relay\"} | avg(span.xrpl.tx.relay_count)"
}
],
"gridPos": { "h": 8, "w": 12, "x": 0, "y": 4 }
},
{
"title": "Transaction Validation Errors",
"type": "table",
"datasource": "Tempo",
"targets": [
{
"queryType": "traceql",
"query": "{resource.service.name=\"rippled\" && name=\"tx.validate\" && status.code=error}"
}
],
"gridPos": { "h": 8, "w": 12, "x": 12, "y": 4 }
}
]
}
5.8.5 TraceQL Query Examples
Common queries for rippled traces:
# Find all traces for a specific transaction hash
{resource.service.name="rippled" && span.xrpl.tx.hash="ABC123..."}
# Find slow RPC commands (>100ms)
{resource.service.name="rippled" && name=~"rpc.command.*"} | duration > 100ms
# Find consensus rounds taking >5 seconds
{resource.service.name="rippled" && name="consensus.round"} | duration > 5s
# Find failed transactions with error details
{resource.service.name="rippled" && name="tx.validate" && status.code=error}
# Find transactions relayed to many peers
{resource.service.name="rippled" && name="tx.relay"} | span.xrpl.tx.relay_count > 10
# Compare latency across nodes
{resource.service.name="rippled" && name="rpc.command.account_info"} | avg(duration) by (resource.service.instance.id)
5.8.6 Correlation with PerfLog
To correlate OpenTelemetry traces with existing PerfLog data:
Step 1: Configure Loki to ingest PerfLog
# promtail-config.yaml
scrape_configs:
- job_name: rippled-perflog
static_configs:
- targets:
- localhost
labels:
job: rippled
__path__: /var/log/rippled/perf*.log
pipeline_stages:
- json:
expressions:
trace_id: trace_id
ledger_seq: ledger_seq
tx_hash: tx_hash
- labels:
trace_id:
ledger_seq:
tx_hash:
Step 2: Add trace_id to PerfLog entries
Modify PerfLog to include trace_id when available:
// In PerfLog output, add trace_id from current span context
void logPerf(Json::Value& entry) {
auto span = opentelemetry::trace::GetSpan(
opentelemetry::context::RuntimeContext::GetCurrent());
if (span && span->GetContext().IsValid()) {
char traceIdHex[33];
span->GetContext().trace_id().ToLowerBase16(traceIdHex);
entry["trace_id"] = std::string(traceIdHex, 32);
}
// ... existing logging
}
Step 3: Configure Grafana trace-to-logs link
In Tempo data source configuration, set up the derived field:
jsonData:
tracesToLogs:
datasourceUid: loki
tags: ["trace_id", "xrpl.tx.hash"]
filterByTraceID: true
filterBySpanID: false
5.8.7 Correlation with Insight/StatsD Metrics
To correlate traces with existing Beast Insight metrics:
Step 1: Export Insight metrics to Prometheus
# prometheus.yaml
scrape_configs:
- job_name: "rippled-statsd"
static_configs:
- targets: ["statsd-exporter:9102"]
Step 2: Add exemplars to metrics
OpenTelemetry SDK automatically adds exemplars (trace IDs) to metrics when using the Prometheus exporter. This links metrics spikes to specific traces.
Step 3: Configure Grafana metric-to-trace link
# In Prometheus data source
jsonData:
exemplarTraceIdDestinations:
- name: trace_id
datasourceUid: tempo
Step 4: Dashboard panel with exemplars
{
"title": "RPC Latency with Trace Links",
"type": "timeseries",
"datasource": "Prometheus",
"targets": [
{
"expr": "histogram_quantile(0.99, rate(rippled_rpc_duration_seconds_bucket[5m]))",
"exemplar": true
}
]
}
This allows clicking on metric data points to jump directly to the related trace.
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