Instrument Distributed Tracing

OTel tracing → track req across microservices → find perf bottleneck.

Use When

• Latency debug → multi-service distributed sys
• Req flow + deps between microservices
• Slow DB query / external API in txn
• Correlate traces w/ logs+metrics → root cause
• E2E latency (user req → res)
• Legacy tracing (Zipkin, Jaeger) → OTel migration
• SLO compliance → latency percentile

In

• Req: Svc list (lang + framework)
• Req: Backend (Jaeger, Tempo, Zipkin, SaaS)
• Opt: Existing lib (OpenTracing, Zipkin)
• Opt: Sampling strat (%, rate limit)
• Opt: Custom span attrs → biz metadata

Do

See Extended Examples for complete config files.

Step 1: Backend

Deploy Jaeger / Grafana Tempo → receive+store traces.

Opt A: Jaeger all-in-one (dev/test):

# 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

Opt B: Grafana Tempo (prod, 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 config (tempo.yaml):

server:
  http_listen_port: 3200

distributor:
  receivers:
    jaeger:
# ... (see EXAMPLES.md)

Prod w/ 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

→ Backend up, OTLP ready, Jaeger UI / Grafana → "no traces" initially.

If err:

• Ports busy? netstat -tulpn | grep -E '(4317|16686|3200)'
• Container logs: docker logs jaeger / docker logs tempo
• Test OTLP: curl http://localhost:4318/v1/traces -v
• Tempo: tempo -config.file=/etc/tempo.yaml -verify-config

Step 2: Instrument Apps (Auto)

OTel auto-instrument → common frameworks → min code change.

Python w/ 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)

Go w/ Gin:

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)

Node.js w/ 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)

→ Traces from svcs appear in Jaeger UI / Grafana. HTTP req → spans auto.

If err:

• Exporter endpoint reachable from app?
• Env vars: OTEL_EXPORTER_OTLP_ENDPOINT=http://tempo:4317
• Debug log: OTEL_LOG_LEVEL=debug (Py), OTEL_LOG_LEVEL=DEBUG (Node)
• Test w/ simple span → verify export pipeline
• Ver conflicts between OTel pkgs?

Step 3: Manual Instrument

Custom spans → biz logic, DB query, external call.

Py 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)

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)

Span attrs best practice:

• Semantic conv: http.method, http.status_code, db.system, db.statement
• Biz ctx: user.id, order.id, product.category
• Resource id: instance.id, region, availability_zone
• Errs: span.RecordError(err) + span.SetStatus(codes.Error, message)
• Events: span.AddEvent("cache_miss")

→ Custom spans in trace view. Parent-child correct. Attrs visible. Errs highlighted.

If err:

• Ctx propagation → parent span ctx → child?
• Span names descriptive + naming conv?
• Spans ended (defer span.End() Go, with Py)?
• Attr types: str, int, bool, float only
• Semantic conv: standard attr names

Step 4: Ctx Propagation

Trace ctx flows across svc boundary + async ops.

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)

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

# ... (see EXAMPLES.md)

Msg queue propagation (Kafka):

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

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

# ... (see EXAMPLES.md)

# 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 ops (Py 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)

→ Traces span multi svcs. Trace IDs consistent. Parent-child preserved.

If err:

• W3C propagator set: otel.propagation.set_global_textmap(TraceContextTextMapPropagator())
• Headers passed in HTTP req?
• Kafka: header support v0.11+
• Debug → log traceparent value
• Viz → find broken trace links

Step 5: Sampling

Sampling → reduce trace vol + cost, keep visibility.

Sampling strats:

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

Tail-based sampling w/ Tempo:

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

Grafana Tempo TraceQL → dynamic sampling:

# Sample traces with errors
{ status = error }

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

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

→ Trace vol → target %. Err traces always sampled. Sampling in span metadata.

If err:

• Sampler applied before tracer provider init
• Sampling decision attr in exported spans?
• Tail sampling: ingestion_burst_size_bytes sufficient buffering
• Dropped traces: otel_traces_dropped_total
• Test synth high-vol traffic → validate rate

Step 6: Correlate Traces w/ Metrics+Logs

Link traces → metrics+logs → unified obs.

Add trace IDs → logs (Py):

import logging
from opentelemetry import trace

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

Gen metrics from traces (Tempo):

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

Prometheus metrics produced:

• traces_service_graph_request_total — req count between svcs
• traces_span_metrics_duration_seconds — span duration histogram
• traces_spanmetrics_calls_total — span call counts

Query traces from metrics (Grafana):

Exemplar support → Prometheus datasource:

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

Enable exemplars in dashboard:

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

→ Click metric exemplar → trace opens. Logs → trace IDs. Traces → logs. Unified debug.

If err:

• Exemplar support enabled Prometheus (v2.26+)
• Trace ID format match (32-char hex)
• Metrics gen enabled Tempo config
• Remote write reachable from Tempo
• Test: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) and on() exemplar

Check

• Backend receives spans from all svcs
• Traces → parent-child correct across svcs
• Span attrs → semantic conv + biz ctx
• Ctx propagates across HTTP+MQ
• Sampling → target %
• Err traces always sampled
• Trace IDs in logs (correct format)
• Grafana → traces via exemplars
• Log panels → trace viewer links
• Retention matches storage policy

Traps

• Ctx not propagated: Forget pass context → broken trace. Pass explicit.
• Spans never ended: Miss defer span.End() / with → mem leak
• Over-instrument: Span per fn → trace bloat. Focus svc boundary, DB, external API.
• Miss err record: No span.RecordError() → lose debug info. Always record errs.
• High-card attrs: Unbounded vals (user ID, bodies) → storage issues. Sample / aggregate.
• Wrong span kind: CLIENT vs SERVER vs INTERNAL → svc graph wrong. Follow semantic conv.
• Sample before ctx: Sampling respects parent. Use ParentBased → honor upstream.

→

• correlate-observability-signals — unified debug w/ metrics+logs+traces
• setup-prometheus-monitoring — gen metrics from traces (Tempo gen)
• configure-log-aggregation — trace IDs in logs
• build-grafana-dashboards — viz trace-derived metrics + exemplar links