distributed-tracing
Cloud, DevOps & SystèmesImplement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
Documentation
Distributed Tracing
Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.
Do not use this skill when
Instructions
resources/implementation-playbook.md.Purpose
Track requests across distributed systems to understand latency, dependencies, and failure points.
Use this skill when
Distributed Tracing Concepts
Trace Structure
Trace (Request ID: abc123)
↓
Span (frontend) [100ms]
↓
Span (api-gateway) [80ms]
├→ Span (auth-service) [10ms]
└→ Span (user-service) [60ms]
└→ Span (database) [40ms]Key Components
Jaeger Setup
Kubernetes Deployment
# Deploy Jaeger Operator
kubectl create namespace observability
kubectl create -f https://github.com/jaegertracing/jaeger-operator/releases/download/v1.51.0/jaeger-operator.yaml -n observability
# Deploy Jaeger instance
kubectl apply -f - <<EOF
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
name: jaeger
namespace: observability
spec:
strategy: production
storage:
type: elasticsearch
options:
es:
server-urls: http://elasticsearch:9200
ingress:
enabled: true
EOFDocker Compose
version: '3.8'
services:
jaeger:
image: jaegertracing/all-in-one:latest
ports:
- "5775:5775/udp"
- "6831:6831/udp"
- "6832:6832/udp"
- "5778:5778"
- "16686:16686" # UI
- "14268:14268" # Collector
- "14250:14250" # gRPC
- "9411:9411" # Zipkin
environment:
- COLLECTOR_ZIPKIN_HOST_PORT=:9411Reference: See references/jaeger-setup.md
Application Instrumentation
OpenTelemetry (Recommended)
#### Python (Flask)
from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from flask import Flask
# Initialize tracer
resource = Resource(attributes={SERVICE_NAME: "my-service"})
provider = TracerProvider(resource=resource)
processor = BatchSpanProcessor(JaegerExporter(
agent_host_name="jaeger",
agent_port=6831,
))
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
# Instrument Flask
app = Flask(__name__)
FlaskInstrumentor().instrument_app(app)
@app.route('/api/users')
def get_users():
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("get_users") as span:
span.set_attribute("user.count", 100)
# Business logic
users = fetch_users_from_db()
return {"users": users}
def fetch_users_from_db():
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("database_query") as span:
span.set_attribute("db.system", "postgresql")
span.set_attribute("db.statement", "SELECT * FROM users")
# Database query
return query_database()#### Node.js (Express)
const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node');
const { JaegerExporter } = require('@opentelemetry/exporter-jaeger');
const { BatchSpanProcessor } = require('@opentelemetry/sdk-trace-base');
const { registerInstrumentations } = require('@opentelemetry/instrumentation');
const { HttpInstrumentation } = require('@opentelemetry/instrumentation-http');
const { ExpressInstrumentation } = require('@opentelemetry/instrumentation-express');
// Initialize tracer
const provider = new NodeTracerProvider({
resource: { attributes: { 'service.name': 'my-service' } }
});
const exporter = new JaegerExporter({
endpoint: 'http://jaeger:14268/api/traces'
});
provider.addSpanProcessor(new BatchSpanProcessor(exporter));
provider.register();
// Instrument libraries
registerInstrumentations({
instrumentations: [
new HttpInstrumentation(),
new ExpressInstrumentation(),
],
});
const express = require('express');
const app = express();
app.get('/api/users', async (req, res) => {
const tracer = trace.getTracer('my-service');
const span = tracer.startSpan('get_users');
try {
const users = await fetchUsers();
span.setAttributes({ 'user.count': users.length });
res.json({ users });
} finally {
span.end();
}
});#### Go
package main
import (
"context"
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/exporters/jaeger"
"go.opentelemetry.io/otel/sdk/resource"
sdktrace "go.opentelemetry.io/otel/sdk/trace"
semconv "go.opentelemetry.io/otel/semconv/v1.4.0"
)
func initTracer() (*sdktrace.TracerProvider, error) {
exporter, err := jaeger.New(jaeger.WithCollectorEndpoint(
jaeger.WithEndpoint("http://jaeger:14268/api/traces"),
))
if err != nil {
return nil, err
}
tp := sdktrace.NewTracerProvider(
sdktrace.WithBatcher(exporter),
sdktrace.WithResource(resource.NewWithAttributes(
semconv.SchemaURL,
semconv.ServiceNameKey.String("my-service"),
)),
)
otel.SetTracerProvider(tp)
return tp, nil
}
func getUsers(ctx context.Context) ([]User, error) {
tracer := otel.Tracer("my-service")
ctx, span := tracer.Start(ctx, "get_users")
defer span.End()
span.SetAttributes(attribute.String("user.filter", "active"))
users, err := fetchUsersFromDB(ctx)
if err != nil {
span.RecordError(err)
return nil, err
}
span.SetAttributes(attribute.Int("user.count", len(users)))
return users, nil
}Reference: See references/instrumentation.md
Context Propagation
HTTP Headers
traceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01
tracestate: congo=t61rcWkgMzEPropagation in HTTP Requests
#### Python
from opentelemetry.propagate import inject
headers = {}
inject(headers) # Injects trace context
response = requests.get('http://downstream-service/api', headers=headers)#### Node.js
const { propagation } = require('@opentelemetry/api');
const headers = {};
propagation.inject(context.active(), headers);
axios.get('http://downstream-service/api', { headers });Tempo Setup (Grafana)
Kubernetes Deployment
apiVersion: v1
kind: ConfigMap
metadata:
name: tempo-config
data:
tempo.yaml: |
server:
http_listen_port: 3200
distributor:
receivers:
jaeger:
protocols:
thrift_http:
grpc:
otlp:
protocols:
http:
grpc:
storage:
trace:
backend: s3
s3:
bucket: tempo-traces
endpoint: s3.amazonaws.com
querier:
frontend_worker:
frontend_address: tempo-query-frontend:9095
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: tempo
spec:
replicas: 1
template:
spec:
containers:
- name: tempo
image: grafana/tempo:latest
args:
- -config.file=/etc/tempo/tempo.yaml
volumeMounts:
- name: config
mountPath: /etc/tempo
volumes:
- name: config
configMap:
name: tempo-configReference: See assets/jaeger-config.yaml.template
Sampling Strategies
Probabilistic Sampling
# Sample 1% of traces
sampler:
type: probabilistic
param: 0.01Rate Limiting Sampling
# Sample max 100 traces per second
sampler:
type: ratelimiting
param: 100Adaptive Sampling
from opentelemetry.sdk.trace.sampling import ParentBased, TraceIdRatioBased
# Sample based on trace ID (deterministic)
sampler = ParentBased(root=TraceIdRatioBased(0.01))Trace Analysis
Finding Slow Requests
Jaeger Query:
service=my-service
duration > 1sFinding Errors
Jaeger Query:
service=my-service
error=true
tags.http.status_code >= 500Service Dependency Graph
Jaeger automatically generates service dependency graphs showing:
Best Practices
Integration with Logging
Correlated Logs
import logging
from opentelemetry import trace
logger = logging.getLogger(__name__)
def process_request():
span = trace.get_current_span()
trace_id = span.get_span_context().trace_id
logger.info(
"Processing request",
extra={"trace_id": format(trace_id, '032x')}
)Troubleshooting
No traces appearing:
High latency overhead:
Reference Files
references/jaeger-setup.md - Jaeger installationreferences/instrumentation.md - Instrumentation patternsassets/jaeger-config.yaml.template - Jaeger configurationRelated Skills
prometheus-configuration - For metricsgrafana-dashboards - For visualizationslo-implementation - For latency SLOsCompétences similaires
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