Instrument Distributed Tracing

Implement OpenTelemetry distributed tracing to track requests across microservices and identify performance bottlenecks.

When to Use

• Debugging latency issues in distributed systems with multiple services
• Understanding request flow and dependencies between microservices
• Identifying slow database queries or external API calls within a transaction
• Correlating traces with logs and metrics for root cause analysis
• Measuring end-to-end latency from user request to response
• Migrating from legacy tracing systems (Zipkin, Jaeger) to OpenTelemetry
• Establishing SLO compliance through detailed latency percentile tracking

Inputs

• Required: List of services to instrument (languages and frameworks)
• Required: Tracing backend choice (Jaeger, Tempo, Zipkin, or vendor SaaS)
• Optional: Existing instrumentation libraries (OpenTracing, Zipkin)
• Optional: Sampling strategy requirements (percentage, rate limiting)
• Optional: Custom span attributes for business-specific metadata

Procedure

See Extended Examples for complete configuration files and templates.

Step 1: Set Up Tracing Backend

Deploy Jaeger or Grafana Tempo to receive and store traces.

Option A: Jaeger all-in-one (development/testing):

# docker-compose.yml
version: '3.8'
services:
  jaeger:
    image: jaegertracing/all-in-one:1.51
    ports:
      - "5775:5775/udp"   # Zipkin compact thrift
      - "6831:6831/udp"   # Jaeger compact thrift
      - "6832:6832/udp"   # Jaeger binary thrift
      - "5778:5778"       # Serve configs
      - "16686:16686"     # Jaeger UI
      - "14268:14268"     # Jaeger HTTP thrift
      - "14250:14250"     # Jaeger GRPC
      - "9411:9411"       # Zipkin compatible endpoint
    environment:
      - COLLECTOR_ZIPKIN_HOST_PORT=:9411
      - COLLECTOR_OTLP_ENABLED=true
    restart: unless-stopped

Option B: Grafana Tempo (production, scalable):

# docker-compose.yml
version: '3.8'
services:
  tempo:
    image: grafana/tempo:2.3.0
    command: ["-config.file=/etc/tempo.yaml"]
    volumes:
      - ./tempo.yaml:/etc/tempo.yaml
      - tempo-data:/tmp/tempo
    ports:
      - "3200:3200"   # Tempo HTTP
      - "4317:4317"   # OTLP gRPC
      - "4318:4318"   # OTLP HTTP
      - "9411:9411"   # Zipkin
    restart: unless-stopped

volumes:
  tempo-data:

Tempo configuration (

tempo.yaml

):

server:
  http_listen_port: 3200

distributor:
  receivers:
    jaeger:
# ... (see EXAMPLES.md for complete configuration)

For production with S3 storage:

storage:
  trace:
    backend: s3
    s3:
      bucket: tempo-traces
      endpoint: s3.amazonaws.com
      region: us-east-1
    wal:
      path: /tmp/tempo/wal
    pool:
      max_workers: 100
      queue_depth: 10000

Expected: Tracing backend accessible, ready to receive traces via OTLP, Jaeger UI or Grafana shows "no traces" initially.

On failure:

• Verify ports not already in use:

  netstat -tulpn | grep -E '(4317|16686|3200)'

• Check container logs:

  docker logs jaeger

  or

  docker logs tempo

• Test OTLP endpoint:

  curl http://localhost:4318/v1/traces -v

• For Tempo: validate config syntax with

  tempo -config.file=/etc/tempo.yaml -verify-config

Step 2: Instrument Applications (Auto-Instrumentation)

Use OpenTelemetry auto-instrumentation for common frameworks to minimize code changes.

Python with Flask:

pip install opentelemetry-distro opentelemetry-exporter-otlp
opentelemetry-bootstrap -a install

# app.py
from flask import Flask
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
# ... (see EXAMPLES.md for complete configuration)

Go with Gin framework:

go get go.opentelemetry.io/otel
go get go.opentelemetry.io/otel/exporters/otlp/otlptrace/otlptracegrpc
go get go.opentelemetry.io/otel/sdk/trace
go get go.opentelemetry.io/contrib/instrumentation/github.com/gin-gonic/gin/otelgin

package main

import (
    "context"
    "github.com/gin-gonic/gin"
    "go.opentelemetry.io/otel"
# ... (see EXAMPLES.md for complete configuration)

Node.js with Express:

npm install @opentelemetry/api \
            @opentelemetry/sdk-node \
            @opentelemetry/auto-instrumentations-node \
            @opentelemetry/exporter-trace-otlp-grpc

// tracing.js
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-grpc');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { Resource } = require('@opentelemetry/resources');
const { SemanticResourceAttributes } = require('@opentelemetry/semantic-conventions');
# ... (see EXAMPLES.md for complete configuration)

Expected: Traces from instrumented services appear in Jaeger UI or Grafana, HTTP requests automatically create spans.

On failure:

• Check exporter endpoint is reachable from application
• Verify environment variables:

  OTEL_EXPORTER_OTLP_ENDPOINT=http://tempo:4317

• Enable debug logging:

  OTEL_LOG_LEVEL=debug

  (Python),

  OTEL_LOG_LEVEL=DEBUG

  (Node.js)
• Test with simple span: manually create a span to verify export pipeline
• Check for version conflicts between OpenTelemetry packages

Step 3: Add Manual Instrumentation

Create custom spans for business logic, database queries, and external calls.

Python manual spans:

from opentelemetry import trace

tracer = trace.get_tracer(__name__)

def process_order(order_id):
    # Create a span for the entire operation
# ... (see EXAMPLES.md for complete configuration)

Go manual spans:

import (
    "context"
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/attribute"
    "go.opentelemetry.io/otel/codes"
    "go.opentelemetry.io/otel/trace"
# ... (see EXAMPLES.md for complete configuration)

Span attributes best practices:

• Use semantic conventions:

  http.method

  ,

  http.status_code

  ,

  db.system

  ,

  db.statement

• Add business context:

  user.id

  ,

  order.id

  ,

  product.category

• Include resource identifiers:

  instance.id

  ,

  region

  ,

  availability_zone

• Record errors:

  span.RecordError(err)

  and

  span.SetStatus(codes.Error, message)

• Add events for significant milestones:

  span.AddEvent("cache_miss")

Expected: Custom spans appear in trace view, parent-child relationships correct, attributes visible in span details, errors highlighted.

On failure:

• Verify context propagation: parent span context passed to child
• Check span names are descriptive and follow naming conventions
• Ensure spans are ended (use

  defer span.End()

  in Go,

  with

  blocks in Python)
• Review attribute types: strings, ints, bools, floats only
• Validate semantic conventions: use standard attribute names where applicable

Step 4: Implement Context Propagation

Ensure trace context flows across service boundaries and async operations.

HTTP headers propagation (W3C Trace Context):

# Client side (Python with requests)
import requests
from opentelemetry import trace
from opentelemetry.propagate import inject

tracer = trace.get_tracer(__name__)
# ... (see EXAMPLES.md for complete configuration)

// Server side (Go with Gin)
import (
    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/propagation"
)

# ... (see EXAMPLES.md for complete configuration)

Message queue propagation (Kafka):

# Producer
from opentelemetry.propagate import inject
from kafka import KafkaProducer

producer = KafkaProducer(bootstrap_servers=['kafka:9092'])

# ... (see EXAMPLES.md for complete configuration)

# Consumer
from opentelemetry.propagate import extract

def process_message(msg):
    # Extract trace context from Kafka headers
    headers = {k: v.decode('utf-8') for k, v in msg.headers}
    ctx = extract(headers)

    # Continue the trace
    with tracer.start_as_current_span("process_order_event", context=ctx):
        order_id = json.loads(msg.value)['order_id']
        handle_order(order_id)

Async operations (Python asyncio):

import asyncio
from opentelemetry import trace, context

async def async_operation():
    # Capture current context
    token = context.attach(context.get_current())
    try:
        with tracer.start_as_current_span("async_database_query"):
            await asyncio.sleep(0.1)  # Simulated async work
            return "result"
    finally:
        context.detach(token)

Expected: Traces span multiple services, trace IDs consistent across service boundaries, parent-child relationships preserved.

On failure:

• Verify W3C Trace Context propagator configured:

  otel.propagation.set_global_textmap(TraceContextTextMapPropagator())

• Check headers are passed in HTTP requests
• For Kafka: ensure headers supported by broker version (v0.11+)
• Debug with header inspection: log

  traceparent

  header value
• Use trace visualization to identify broken trace links

Step 5: Configure Sampling Strategies

Implement sampling to reduce trace volume and cost while maintaining visibility.

Sampling strategies:

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import (
    ParentBased,
    TraceIdRatioBased,
    StaticSampler,
    Decision
# ... (see EXAMPLES.md for complete configuration)

Tail-based sampling with Tempo:

Configure in

tempo.yaml

:

overrides:
  defaults:
    metrics_generator:
      processors: [service-graphs, span-metrics]
      storage:
        path: /tmp/tempo/generator/wal
        remote_write:
          - url: http://prometheus:9090/api/v1/write
            send_exemplars: true

    # Tail sampling (requires tempo-query)
    ingestion_rate_limit_bytes: 5000000
    ingestion_burst_size_bytes: 10000000

Use Grafana Tempo's TraceQL for dynamic sampling:

# Sample traces with errors
{ status = error }

# Sample slow traces (>1s)
{ duration > 1s }

# Sample specific services
{ resource.service.name = "checkout-service" }

Expected: Trace volume reduced to target percentage, error traces always sampled, sampling decision visible in trace metadata.

On failure:

• Verify sampler applied before tracer provider initialization
• Check sampling decision attribute in exported spans
• For tail sampling: ensure sufficient buffering (

  ingestion_burst_size_bytes

  )
• Monitor dropped traces:

  otel_traces_dropped_total

  metric
• Test with synthetic high-volume traffic to validate sampling rate

Step 6: Correlate Traces with Metrics and Logs

Link traces to metrics and logs for unified observability.

Add trace IDs to logs (Python):

import logging
from opentelemetry import trace

# Custom log formatter with trace context
class TraceFormatter(logging.Formatter):
    def format(self, record):
# ... (see EXAMPLES.md for complete configuration)

Generate metrics from traces (Tempo):

# tempo.yaml
metrics_generator:
  registry:
    external_labels:
      cluster: production
  storage:
# ... (see EXAMPLES.md for complete configuration)

This generates Prometheus metrics:

• traces_service_graph_request_total

  - request count between services

• traces_span_metrics_duration_seconds

  - span duration histogram

• traces_spanmetrics_calls_total

  - span call counts

Query traces from metrics (Grafana):

Add exemplar support to Prometheus datasource in Grafana:

datasources:
  - name: Prometheus
    type: prometheus
    url: http://prometheus:9090
    jsonData:
      exemplarTraceIdDestinations:
        - name: trace_id
          datasourceName: Tempo

In Grafana dashboard, enable exemplars:

{
  "fieldConfig": {
    "defaults": {
      "custom": {
        "showExemplars": true
      }
    }
  }
}

Expected: Clicking metric exemplars opens trace, logs show trace IDs, traces link to logs, unified debugging across signals.

On failure:

• Verify exemplar support enabled in Prometheus (requires v2.26+)
• Check trace ID format matches (32-char hex)
• Ensure metrics generator enabled in Tempo config
• Validate remote write endpoint accessible from Tempo
• Test exemplar queries:

  histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) and on() exemplar

Validation

• Tracing backend receives spans from all instrumented services
• Traces show correct parent-child relationships across services
• Span attributes include semantic conventions and business context
• Context propagates correctly across HTTP calls and message queues
• Sampling strategy reduces trace volume to target percentage
• Error traces always sampled (if using error-aware sampling)
• Trace IDs appear in application logs with correct format
• Grafana shows traces linked from metrics via exemplars
• Log panels have data links to trace viewer
• Trace retention matches configured storage policy

Common Pitfalls

• Context not propagated: Forgetting to pass

  context

  to downstream calls breaks traces. Always pass context explicitly.
• Spans never ended: Missing

  defer span.End()

  (Go) or

  with

  blocks (Python) causes spans to remain open and memory leaks.
• Over-instrumentation: Creating spans for every function causes trace bloat. Focus on service boundaries, database calls, and external APIs.
• Missing error recording: Not calling

  span.RecordError()

  loses valuable debugging information. Always record errors in spans.
• High cardinality attributes: Using unbounded values (user IDs, request bodies) as span attributes causes storage issues. Use sampling or aggregate labels.
• Incorrect span kind: Using wrong span kind (CLIENT vs SERVER vs INTERNAL) affects service graph generation. Follow semantic conventions.
• Sampling before context: Sampling decisions must respect parent trace context. Use

  ParentBased

  sampler to honor upstream sampling.

Related Skills

• correlate-observability-signals

  - Unified debugging with metrics, logs, and traces linked by trace IDs

• setup-prometheus-monitoring

  - Generate metrics from traces using Tempo metrics generator

• configure-log-aggregation

  - Add trace IDs to logs for correlation with distributed traces

• build-grafana-dashboards

  - Visualize trace-derived metrics and exemplar links in dashboards