Microservices: Distributed Tracing

Microservices: Distributed Tracing

Propagate trace context and emit spans across services using OpenTelemetry.

When to Use

• A request spans multiple services and you need to understand where latency comes from
• You have intermittent failures or slowdowns that are hard to reproduce
• You need to answer "which service caused this request to fail?" without grepping N log files
• You're optimizing performance and need a waterfall view of all calls within a single request

Instructions

OpenTelemetry setup (Node.js — must be first import):

// instrumentation.ts — must be imported BEFORE everything else
import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { Resource } from '@opentelemetry/resources';
import { SemanticResourceAttributes } from '@opentelemetry/semantic-conventions';
import { HttpInstrumentation } from '@opentelemetry/instrumentation-http';
import { ExpressInstrumentation } from '@opentelemetry/instrumentation-express';
import { PgInstrumentation } from '@opentelemetry/instrumentation-pg';
import { RedisInstrumentation } from '@opentelemetry/instrumentation-redis-4';

const sdk = new NodeSDK({
  resource: new Resource({
    [SemanticResourceAttributes.SERVICE_NAME]: 'order-service',
    [SemanticResourceAttributes.SERVICE_VERSION]: process.env.APP_VERSION ?? '0.0.0',
    [SemanticResourceAttributes.DEPLOYMENT_ENVIRONMENT]: process.env.NODE_ENV ?? 'development',
  }),
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT ?? 'http://otel-collector:4318/v1/traces',
  }),
  instrumentations: [
    new HttpInstrumentation(), // auto-instruments fetch/http
    new ExpressInstrumentation(), // auto-instruments Express routes
    new PgInstrumentation(), // auto-instruments pg queries
    new RedisInstrumentation(), // auto-instruments Redis calls
  ],
});

sdk.start();

process.on('SIGTERM', async () => {
  await sdk.shutdown();
});

Manual spans for custom operations:

import { trace, context, SpanStatusCode, SpanKind } from '@opentelemetry/api';

const tracer = trace.getTracer('order-service');

async function processOrder(orderId: string): Promise<OrderResult> {
  // Create a span for a custom operation not auto-instrumented
  return tracer.startActiveSpan('processOrder', async (span) => {
    span.setAttributes({
      'order.id': orderId,
      'order.service': 'order-service',
    });

    try {
      // Child spans are automatically created for DB/HTTP calls inside here
      const order = await db.order.findUnique({ where: { id: orderId } });
      if (!order) throw new Error(`Order ${orderId} not found`);

      span.setAttribute('order.status', order.status);
      span.setAttribute('order.total', order.total);

      const result = await fulfillOrder(order);

      span.setStatus({ code: SpanStatusCode.OK });
      return result;
    } catch (err) {
      span.recordException(err as Error);
      span.setStatus({ code: SpanStatusCode.ERROR, message: (err as Error).message });
      throw err;
    } finally {
      span.end();
    }
  });
}

// Outgoing HTTP call with trace context propagation
async function callPaymentService(orderId: string, amount: number): Promise<PaymentResult> {
  return tracer.startActiveSpan(
    'payment.charge',
    { kind: SpanKind.CLIENT, attributes: { 'order.id': orderId } },
    async (span) => {
      try {
        // fetch() is auto-instrumented — W3C trace context headers injected automatically
        const response = await fetch(`${PAYMENT_SERVICE_URL}/charges`, {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({ orderId, amount }),
        });

        span.setAttribute('http.status_code', response.status);

        if (!response.ok) {
          span.setStatus({ code: SpanStatusCode.ERROR });
          throw new Error(`Payment failed: HTTP ${response.status}`);
        }

        const result = await response.json();
        span.setAttribute('payment.charge_id', result.chargeId);
        return result;
      } finally {
        span.end();
      }
    }
  );
}

Correlation ID for log correlation:

import { trace, context } from '@opentelemetry/api';

// Middleware: expose trace ID in response headers for client-side correlation
app.use((req, res, next) => {
  const span = trace.getActiveSpan();
  if (span) {
    const { traceId, spanId } = span.spanContext();
    res.setHeader('X-Trace-Id', traceId);
    res.setHeader('X-Span-Id', spanId);

    // Add to every log entry automatically
    logger.setContext({ traceId, spanId });
  }
  next();
});

// In structured logs — include traceId so logs and traces can be linked in Jaeger/Grafana
logger.info('Order created', {
  orderId,
  traceId: trace.getActiveSpan()?.spanContext().traceId,
});

OpenTelemetry Collector (YAML config):

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      http:
        endpoint: 0.0.0.0:4318
      grpc:
        endpoint: 0.0.0.0:4317

processors:
  batch:
    timeout: 1s
    send_batch_size: 1024
  memory_limiter:
    limit_mib: 512

exporters:
  jaeger:
    endpoint: jaeger:14250
  otlp/tempo:
    endpoint: tempo:4317

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [jaeger, otlp/tempo]

Details

Trace context propagation: OpenTelemetry uses the W3C TraceContext standard (traceparent and tracestate headers). Auto-instrumentation injects these headers on outgoing HTTP calls and extracts them from incoming requests. Cross-service traces are linked by the same traceId.

Sampling: Tracing every request is expensive. Use sampling:

import { TraceIdRatioBased } from '@opentelemetry/sdk-trace-base';
// Sample 10% of requests in production
const sampler = new TraceIdRatioBased(0.1);
// Always sample errors — head-based vs. tail-based sampling

Anti-patterns:

• Adding trace context manually when auto-instrumentation handles it — creates duplicate spans
• Not setting span status on error — spans appear successful in dashboards
• Sampling too aggressively — rare errors may not be captured

Backends:

• Jaeger (open source, self-hosted)
• Tempo + Grafana (open source, integrates with Prometheus)
• Datadog, Honeycomb, Dynatrace (commercial, powerful)

Source

microservices.io/patterns/observability/distributed-tracing.html

Process

1. Read the instructions and examples in this document.
2. Apply the patterns to your implementation, adapting to your specific context.
3. Verify your implementation against the details and edge cases listed above.

Harness Integration

• Type: knowledge — this skill is a reference document, not a procedural workflow.
• No tools or state — consumed as context by other skills and agents.

Success Criteria

• The patterns described in this document are applied correctly in the implementation.
• Edge cases and anti-patterns listed in this document are avoided.