Node.js OpenTelemetry in Production: Distributed Tracing, Custom Spans, and OTLP

Distributed systems fail in distributed ways. A request enters your API gateway, fans out across a dozen microservices, hits three databases, publishes to a message queue, and somewhere in that chain — something goes wrong. Without distributed tracing, you're debugging with a flashlight in a blackout. OpenTelemetry is the flashlight upgrade.

This guide covers everything you need to instrument Node.js services for production: auto-instrumentation, manual spans, OTLP exporters, Jaeger and Grafana Tempo integration, W3C trace context propagation, and sampling strategies that keep your backend from drowning in telemetry data.

---

What Is OpenTelemetry?

OpenTelemetry (OTel) is a CNCF (Cloud Native Computing Foundation) incubating project that provides a vendor-neutral, open-source observability framework for generating, collecting, and exporting telemetry data — traces, metrics, and logs.

The key design principle is API/SDK separation:

• API: Stable interfaces your application code calls. Libraries instrument against the API. If no SDK is present, all calls are no-ops — zero overhead.
• SDK: The implementation. Swappable, configurable, and loaded at startup by your application (not your library dependencies).
• OTLP (OpenTelemetry Protocol): The wire protocol for exporting telemetry to any backend — Jaeger, Tempo, Datadog, Honeycomb, New Relic, or your own collector pipeline.

This means you instrument once and export anywhere. No more vendor lock-in at the instrumentation layer.

---

Installation

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

For metrics and log correlation (covered later):

npm install \
  @opentelemetry/sdk-metrics \
  @opentelemetry/exporter-metrics-otlp-grpc \
  pino

---

Complete tracing.js Setup

Create src/tracing.js — this file must be required before your application code:

// src/tracing.js
'use strict';

const { NodeSDK } = require('@opentelemetry/sdk-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-grpc');
const { BatchSpanProcessor, ParentBasedSampler, TraceIdRatioBasedSampler } = require('@opentelemetry/sdk-trace-base');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { Resource } = require('@opentelemetry/resources');
const { SemanticResourceAttributes } = require('@opentelemetry/semantic-conventions');
const { credentials, CompressionAlgorithms } = require('@grpc/grpc-js');

const isProd = process.env.NODE_ENV === 'production';

// Resource describes this service instance
const resource = new Resource({
  [SemanticResourceAttributes.SERVICE_NAME]: process.env.OTEL_SERVICE_NAME || 'my-service',
  [SemanticResourceAttributes.SERVICE_VERSION]: process.env.npm_package_version || '0.0.0',
  [SemanticResourceAttributes.DEPLOYMENT_ENVIRONMENT]: process.env.NODE_ENV || 'development',
  [SemanticResourceAttributes.SERVICE_INSTANCE_ID]: process.env.HOSTNAME || require('os').hostname(),
});

// OTLP gRPC exporter — configure via env or programmatically
const traceExporter = new OTLPTraceExporter({
  url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT || 'grpc://localhost:4317',
  credentials: isProd
    ? credentials.createSsl()
    : credentials.createInsecure(),
  compression: CompressionAlgorithms.gzip,
  timeoutMillis: 10_000,
});

// BatchSpanProcessor buffers spans and exports in batches
const spanProcessor = new BatchSpanProcessor(traceExporter, {
  maxQueueSize: isProd ? 2048 : 512,
  maxExportBatchSize: isProd ? 512 : 128,
  scheduledDelayMillis: isProd ? 5000 : 1000,
  exportTimeoutMillis: 30_000,
});

// Sampling: 100% in dev, 10% in prod (parent-based to keep trace continuity)
const sampler = new ParentBasedSampler({
  root: isProd
    ? new TraceIdRatioBasedSampler(
        parseFloat(process.env.OTEL_TRACES_SAMPLER_ARG || '0.1')
      )
    : new TraceIdRatioBasedSampler(1.0),
});

const sdk = new NodeSDK({
  resource,
  traceExporter,
  spanProcessor,
  sampler,
  instrumentations: [
    getNodeAutoInstrumentations({
      '@opentelemetry/instrumentation-http': {
        ignoreIncomingRequestHook: (req) => {
          // Don't trace health checks or readiness probes
          const ignored = ['/health', '/ready', '/metrics', '/favicon.ico'];
          return ignored.some((path) => req.url?.startsWith(path));
        },
        ignoreOutgoingRequestHook: (req) => {
          // Don't trace calls to internal metadata services
          return req.hostname === '169.254.169.254';
        },
        headersToSpanAttributes: {
          server: {
            requestHeaders: ['x-request-id', 'x-tenant-id'],
          },
        },
      },
      '@opentelemetry/instrumentation-redis': {
        dbStatementSerializer: (cmdName, cmdArgs) => {
          // Mask sensitive Redis commands — log command name only
          const sensitive = ['auth', 'set', 'setex', 'mset'];
          if (sensitive.includes(cmdName.toLowerCase())) {
            return `${cmdName} [REDACTED]`;
          }
          return `${cmdName} ${cmdArgs.join(' ')}`;
        },
      },
      '@opentelemetry/instrumentation-pg': {
        enhancedDatabaseReporting: false, // Don't capture SQL params in prod
        addSqlCommenterCommentToQueries: true,
      },
      '@opentelemetry/instrumentation-express': {
        ignoreLayers: [/^cors$/, /^compression$/],
      },
    }),
  ],
});

// Start the SDK before importing any instrumented libraries
sdk.start();
console.log('[otel] Tracing initialized');

// Graceful shutdown — flush pending spans before process exit
const shutdown = async () => {
  try {
    await sdk.shutdown();
    console.log('[otel] Tracing shut down cleanly');
  } catch (err) {
    console.error('[otel] Error shutting down tracing', err);
  } finally {
    process.exit(0);
  }
};

process.on('SIGTERM', shutdown);
process.on('SIGINT', shutdown);

module.exports = { sdk };

Require it at startup:

# package.json start script
node -r ./src/tracing.js src/server.js

Or with --require flag:

node --require ./src/tracing.js src/server.js

---

Manual Spans: Full Order Processing Example

Auto-instrumentation covers HTTP, databases, queues, and DNS. But business logic — order validation, pricing calculation, fraud checks — needs manual spans to be meaningful.

// src/services/orderService.js
'use strict';

const { trace, SpanKind, SpanStatusCode, context, propagation } = require('@opentelemetry/api');

const tracer = trace.getTracer('order-service', '1.0.0');

async function processOrder(orderId, customerId, items) {
  return tracer.startActiveSpan(
    'order.process',
    {
      kind: SpanKind.INTERNAL,
      attributes: {
        'order.id': orderId,
        'customer.id': customerId,
        'order.item_count': items.length,
      },
    },
    async (span) => {
      try {
        // Validate inventory
        const inventory = await tracer.startActiveSpan(
          'order.validate_inventory',
          { kind: SpanKind.INTERNAL },
          async (childSpan) => {
            try {
              const result = await checkInventory(items);
              childSpan.addEvent('inventory.checked', {
                'inventory.available': result.allAvailable,
                'inventory.items_checked': items.length,
              });
              if (!result.allAvailable) {
                childSpan.setStatus({
                  code: SpanStatusCode.ERROR,
                  message: 'Insufficient inventory',
                });
                throw new Error(`Out of stock: ${result.unavailableSkus.join(', ')}`);
              }
              return result;
            } finally {
              childSpan.end();
            }
          }
        );

        // Calculate pricing
        const pricing = await tracer.startActiveSpan(
          'order.calculate_pricing',
          { kind: SpanKind.INTERNAL },
          async (childSpan) => {
            try {
              const result = await calculatePricing(items, customerId);
              childSpan.setAttributes({
                'order.subtotal_cents': result.subtotalCents,
                'order.discount_cents': result.discountCents,
                'order.tax_cents': result.taxCents,
                'order.total_cents': result.totalCents,
              });
              return result;
            } catch (err) {
              childSpan.recordException(err);
              childSpan.setStatus({ code: SpanStatusCode.ERROR, message: err.message });
              throw err;
            } finally {
              childSpan.end();
            }
          }
        );

        // Charge payment
        const payment = await tracer.startActiveSpan(
          'order.charge_payment',
          { kind: SpanKind.CLIENT }, // External call = SpanKind.CLIENT
          async (childSpan) => {
            try {
              childSpan.setAttributes({
                'payment.provider': 'stripe',
                'payment.amount_cents': pricing.totalCents,
                'payment.currency': 'USD',
              });
              const result = await chargePayment(customerId, pricing.totalCents);
              childSpan.addEvent('payment.authorized', {
                'payment.charge_id': result.chargeId,
              });
              return result;
            } catch (err) {
              childSpan.recordException(err);
              childSpan.setStatus({ code: SpanStatusCode.ERROR, message: err.message });
              throw err;
            } finally {
              childSpan.end();
            }
          }
        );

        span.addEvent('order.completed', {
          'order.charge_id': payment.chargeId,
          'order.total_cents': pricing.totalCents,
        });
        span.setStatus({ code: SpanStatusCode.OK });

        return { orderId, chargeId: payment.chargeId, total: pricing.totalCents };
      } catch (err) {
        span.recordException(err);
        span.setStatus({ code: SpanStatusCode.ERROR, message: err.message });
        throw err;
      } finally {
        span.end();
      }
    }
  );
}

module.exports = { processOrder };

Key patterns:

• startActiveSpan automatically parents child spans through async context
• SpanKind.CLIENT for outbound calls, SpanKind.SERVER for inbound, SpanKind.PRODUCER/CONSUMER for queues
• Always call span.end() in finally — a span that never ends is a memory leak
• recordException captures the full stack trace on the span
• addEvent captures point-in-time structured events within a span's lifetime

---

W3C Trace Context Propagation for Message Queues

HTTP instrumentation handles propagation automatically. Message queues do not. You must manually inject context into message headers when producing and extract it when consuming.

// src/messaging/producer.js
const { context, propagation, trace, SpanKind, SpanStatusCode } = require('@opentelemetry/api');

async function publishOrderCreated(orderId, channel) {
  const tracer = trace.getTracer('order-producer');

  return tracer.startActiveSpan(
    'rabbitmq.publish order.created',
    { kind: SpanKind.PRODUCER },
    async (span) => {
      try {
        span.setAttributes({
          'messaging.system': 'rabbitmq',
          'messaging.destination': 'order.created',
          'messaging.destination_kind': 'topic',
          'messaging.message_id': orderId,
        });

        // Inject current trace context into message headers
        const headers = {};
        propagation.inject(context.active(), headers);

        await channel.publish('orders', 'order.created', Buffer.from(JSON.stringify({ orderId })), {
          headers,
          contentType: 'application/json',
          messageId: orderId,
          persistent: true,
        });

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

// src/messaging/consumer.js
const { context, propagation, trace, SpanKind, SpanStatusCode, ROOT_CONTEXT } = require('@opentelemetry/api');

async function handleOrderCreated(msg, channel) {
  const tracer = trace.getTracer('order-consumer');

  // Extract trace context from message headers — links this span to the producer's trace
  const extractedContext = propagation.extract(ROOT_CONTEXT, msg.properties.headers || {});

  return context.with(extractedContext, async () => {
    return tracer.startActiveSpan(
      'rabbitmq.process order.created',
      { kind: SpanKind.CONSUMER },
      async (span) => {
        try {
          const payload = JSON.parse(msg.content.toString());
          span.setAttributes({
            'messaging.system': 'rabbitmq',
            'messaging.destination': 'order.created',
            'messaging.operation': 'process',
            'messaging.message_id': payload.orderId,
          });

          await fulfillOrder(payload.orderId);

          channel.ack(msg);
          span.setStatus({ code: SpanStatusCode.OK });
        } catch (err) {
          span.recordException(err);
          span.setStatus({ code: SpanStatusCode.ERROR, message: err.message });
          channel.nack(msg, false, true);
          throw err;
        } finally {
          span.end();
        }
      }
    );
  });
}

---

Baggage API for Tenant IDs and Feature Flags

Baggage propagates key-value pairs alongside trace context — useful for routing decisions, tenant isolation, and feature flags that need to flow through your entire call chain.

const { propagation, context, baggageEntryMetadataFromString } = require('@opentelemetry/api');

// Middleware: attach tenant ID to baggage at the edge
function tenantBaggageMiddleware(req, res, next) {
  const tenantId = req.headers['x-tenant-id'];
  const featureFlags = req.headers['x-feature-flags'];

  if (tenantId) {
    let bag = propagation.getBaggage(context.active()) || propagation.createBaggage();
    bag = bag.setEntry('tenant.id', {
      value: tenantId,
      metadata: baggageEntryMetadataFromString(''),
    });

    if (featureFlags) {
      bag = bag.setEntry('feature.flags', {
        value: featureFlags,
        metadata: baggageEntryMetadataFromString(''),
      });
    }

    const ctx = propagation.setBaggage(context.active(), bag);
    return context.with(ctx, next);
  }

  next();
}

// Downstream service: read baggage from context
function getTenantFromBaggage() {
  const bag = propagation.getBaggage(context.active());
  return bag?.getEntry('tenant.id')?.value ?? 'unknown';
}

---

OTLP Exporter Configuration

Environment variable approach (recommended for production — no code changes needed per environment):

# Endpoint
OTEL_EXPORTER_OTLP_ENDPOINT=https://otel-collector.internal:4317
OTEL_EXPORTER_OTLP_PROTOCOL=grpc  # or http/protobuf, http/json

# Auth headers (Grafana Cloud, Honeycomb, etc.)
OTEL_EXPORTER_OTLP_HEADERS="Authorization=Basic <base64-encoded-credentials>"

# Compression
OTEL_EXPORTER_OTLP_COMPRESSION=gzip

# Timeout
OTEL_EXPORTER_OTLP_TIMEOUT=10000

# Service identity
OTEL_SERVICE_NAME=order-service
OTEL_RESOURCE_ATTRIBUTES=service.version=1.2.3,deployment.environment=production

# Sampling
OTEL_TRACES_SAMPLER=parentbased_traceidratio
OTEL_TRACES_SAMPLER_ARG=0.05

Programmatic gRPC with mTLS (for internal mesh communication):

const { credentials } = require('@grpc/grpc-js');
const fs = require('fs');

const traceExporter = new OTLPTraceExporter({
  url: 'grpc://otel-collector.internal:4317',
  credentials: credentials.createSsl(
    fs.readFileSync('/etc/ssl/ca.crt'),
    fs.readFileSync('/etc/ssl/client.key'),
    fs.readFileSync('/etc/ssl/client.crt')
  ),
  metadata: {
    'x-service-token': process.env.OTEL_SERVICE_TOKEN,
  },
  compression: CompressionAlgorithms.gzip,
  timeoutMillis: 10_000,
});

---

BatchSpanProcessor Tuning

Parameter	Development	Production
maxQueueSize	512	2048–8192
maxExportBatchSize	128	512
scheduledDelayMillis	1000 ms	5000 ms
exportTimeoutMillis	30000 ms	30000 ms
Sampler rate	100%	5–10%
Exporter protocol	gRPC insecure	gRPC TLS or HTTP/protobuf

In development, low scheduledDelayMillis means spans appear in Jaeger within seconds. In production, larger batches reduce network calls and lower per-span export overhead. If your queue fills (maxQueueSize reached), spans are dropped — monitor the otel_bsp_dropped_spans metric.

---

Jaeger for Local Development

# docker-compose.yml
version: '3.8'
services:
  jaeger:
    image: jaegertracing/all-in-one:1.57
    environment:
      COLLECTOR_OTLP_ENABLED: 'true'
      SPAN_STORAGE_TYPE: badger
      BADGER_EPHEMERAL: 'false'
      BADGER_DIRECTORY_VALUE: /badger/data
      BADGER_DIRECTORY_KEY: /badger/key
    ports:
      - '16686:16686'   # Jaeger UI
      - '4317:4317'     # OTLP gRPC
      - '4318:4318'     # OTLP HTTP
    volumes:
      - jaeger-data:/badger

volumes:
  jaeger-data:

docker-compose up -d jaeger

# Verify OTLP receiver is active
curl -s http://localhost:16686/api/services | jq .

# Point your app at it
export OTEL_EXPORTER_OTLP_ENDPOINT=grpc://localhost:4317
node -r ./src/tracing.js src/server.js

Open http://localhost:16686 to browse traces. The all-in-one image includes the collector, agent, query service, and UI — perfect for local work, not for production.

---

Grafana Tempo Production Architecture

In production, you run a dedicated OpenTelemetry Collector between your services and the backend. This decouples your services from backend changes, enables tail-based sampling, and adds metadata enrichment.

# otel-collector-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
        auth:
          authenticator: basicauth/server
      http:
        endpoint: 0.0.0.0:4318

processors:
  memory_limiter:
    check_interval: 1s
    limit_mib: 512
    spike_limit_mib: 128

  batch:
    timeout: 5s
    send_batch_size: 1024
    send_batch_max_size: 2048

  resource:
    attributes:
      - key: cluster.name
        value: prod-us-east-1
        action: upsert

  # Tail-based sampling — sample 100% of error traces, 5% of success
  tail_sampling:
    decision_wait: 10s
    num_traces: 100000
    expected_new_traces_per_sec: 1000
    policies:
      - name: errors-policy
        type: status_code
        status_code: { status_codes: [ERROR] }
      - name: slow-traces-policy
        type: latency
        latency: { threshold_ms: 2000 }
      - name: sample-remaining
        type: probabilistic
        probabilistic: { sampling_percentage: 5 }

exporters:
  otlp/tempo:
    endpoint: tempo.monitoring.svc.cluster.local:4317
    tls:
      insecure: false
      ca_file: /etc/ssl/certs/ca.crt

  logging:
    verbosity: basic

extensions:
  basicauth/server:
    htpasswd:
      file: /etc/otelcol/htpasswd
  health_check:
    endpoint: 0.0.0.0:13133
  pprof:
    endpoint: 0.0.0.0:1777

service:
  extensions: [basicauth/server, health_check, pprof]
  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter, resource, tail_sampling, batch]
      exporters: [otlp/tempo]
  telemetry:
    metrics:
      address: 0.0.0.0:8888

The tail_sampling processor waits for complete traces before making sampling decisions — unlike head-based sampling, it can sample 100% of error traces retroactively.

---

Sampling Strategies

Head-based sampling (decision at trace root, propagated downstream):

const {
  ParentBasedSampler,
  TraceIdRatioBasedSampler,
  AlwaysOnSampler,
  AlwaysOffSampler,
} = require('@opentelemetry/sdk-trace-base');

// 5% of new traces, inherit parent decision for continuations
const sampler = new ParentBasedSampler({
  root: new TraceIdRatioBasedSampler(0.05),
  remoteParentSampled: new AlwaysOnSampler(),    // Trust upstream if they sampled
  remoteParentNotSampled: new AlwaysOffSampler(), // Trust upstream if they dropped
  localParentSampled: new AlwaysOnSampler(),
  localParentNotSampled: new AlwaysOffSampler(),
});

Custom sampler — always sample traces with specific attributes:

const { SamplingDecision } = require('@opentelemetry/sdk-trace-base');

class PriorityCustomerSampler {
  shouldSample(context, traceId, spanName, spanKind, attributes) {
    // Always sample priority customers
    if (attributes['customer.tier'] === 'enterprise') {
      return {
        decision: SamplingDecision.RECORD_AND_SAMPLED,
        attributes: { 'sampling.reason': 'enterprise-customer' },
      };
    }
    // Otherwise 5% sample rate
    const hash = parseInt(traceId.substring(0, 8), 16);
    if ((hash & 0xffff) < 0xffff * 0.05) {
      return { decision: SamplingDecision.RECORD_AND_SAMPLED };
    }
    return { decision: SamplingDecision.NOT_RECORD };
  }

  toString() { return 'PriorityCustomerSampler'; }
}

---

Trace-to-Log Correlation with Pino

Correlating logs to traces is the most impactful observability improvement after tracing itself. With a trace ID in every log line, you can jump from a Grafana error alert directly to the relevant trace.

// src/logger.js
'use strict';

const pino = require('pino');
const { trace, context } = require('@opentelemetry/api');

// Pino mixin — called on every log entry to inject current span context
function otelMixin() {
  const span = trace.getActiveSpan();
  if (!span || !span.isRecording()) {
    return {};
  }

  const spanContext = span.spanContext();
  return {
    traceId: spanContext.traceId,
    spanId: spanContext.spanId,
    traceFlags: `0${spanContext.traceFlags.toString(16)}`,
    // Grafana/Loki trace correlation field
    'trace_id': spanContext.traceId,
  };
}

const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  mixin: otelMixin,
  transport: process.env.NODE_ENV === 'development'
    ? { target: 'pino-pretty' }
    : undefined,
  formatters: {
    level(label) { return { level: label }; },
  },
  timestamp: pino.stdTimeFunctions.isoTime,
});

module.exports = logger;

Log output in production (JSON, correlatable):

{
  "level": "info",
  "time": "2026-03-29T14:32:01.234Z",
  "traceId": "4bf92f3577b34da6a3ce929d0e0e4736",
  "spanId": "00f067aa0ba902b7",
  "traceFlags": "01",
  "msg": "Order processed successfully",
  "orderId": "ord_abc123",
  "totalCents": 4999
}

In Grafana, configure the Loki datasource with a Derived Field that extracts traceId and links to your Tempo datasource. Every log line becomes a clickable link to the full trace.

---

Production Checklist

• [ ] tracing.js is loaded with --require before any application code
• [ ] SIGTERM and SIGINT handlers call sdk.shutdown() to flush pending spans
• [ ] /health, /ready, and /metrics endpoints are excluded from tracing via ignoreIncomingRequestHook
• [ ] Redis commands containing credentials use dbStatementSerializer to redact values
• [ ] SQL instrumentation has enhancedDatabaseReporting: false in production (no param capture)
• [ ] ParentBasedSampler wraps your root sampler to ensure trace continuity across services
• [ ] Sampling rate is set via OTEL_TRACES_SAMPLER_ARG env var, not hardcoded
• [ ] BatchSpanProcessor maxQueueSize is sized for expected burst throughput
• [ ] OTLP exporter uses gzip compression
• [ ] OTLP exporter uses TLS in production
• [ ] OTel Collector runs as a sidecar or DaemonSet — services don't export directly to backends
• [ ] Tail-based sampling at the Collector captures 100% of error and slow traces
• [ ] Pino (or Winston) mixin injects traceId and spanId into every log line
• [ ] Grafana Loki is configured with Derived Fields linking traceId to Tempo
• [ ] otel_bsp_dropped_spans metric is monitored and alerted on
• [ ] Span names follow <service>.<operation> convention, not dynamic strings (avoid cardinality explosion)
• [ ] All manual spans use try/catch/finally with span.end() in finally
• [ ] recordException is called on caught errors before re-throwing
• [ ] Resource attributes include service.name, service.version, and deployment.environment
• [ ] Baggage propagation is used for cross-cutting concerns (tenant ID, request ID) — not span attributes alone

---

Conclusion

OpenTelemetry transforms debugging distributed systems from intuition-driven guesswork into structured investigation. With auto-instrumentation covering the standard library (HTTP, databases, queues), manual spans adding business context, W3C trace context flowing through message brokers, and tail-based sampling capturing every error trace at the Collector — you have full observability without vendor lock-in.

The investment is front-loaded: set up tracing.js correctly once, and every service in your fleet benefits. Add the Pino mixin, configure Grafana's Loki-to-Tempo links, and you have a single pane of glass from alert to log to trace to root cause.

Start with Jaeger locally, graduate to Grafana Tempo in production, and let the OTel Collector handle the routing, enrichment, and sampling between them. Your future self — debugging a 3am production incident — will thank you.

---

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