Distributed Tracing
Distributed tracing — the mechanism for tracking a request's journey through multiple services. Each step is a span (name, start_time, duration, attributes), linked by a shared trace-ID. Standard: W3C Trace Context (traceparent header). Tooling: OpenTelemetry SDK instruments code, Collector ships to a backend (Jaeger, Tempo, Datadog APM). Finds bottlenecks in seconds — instead of grepping logs.
Below: details, example, related terms, FAQ.
Details
- Trace = tree of spans linked by parent_span_id
- W3C traceparent: "00-{traceId}-{spanId}-{flags}" in HTTP headers
- Sampling: 1-10 % in production (otherwise cost explodes)
- OpenTelemetry auto-instrumentation: Java, Python, Node, Go, .NET
- Backends: Jaeger (self-host), Tempo (Grafana), Honeycomb, Datadog, New Relic
Example
// Node.js: auto-instrument everything
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
new NodeSDK({
traceExporter: new OTLPTraceExporter({ url: 'http://collector:4318/v1/traces' }),
instrumentations: [getNodeAutoInstrumentations()]
}).start();Related
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Frequently Asked Questions
Worth it for a monolith?
Monolith + single DB — no, plain logs are enough. 3+ microservices — must-have for debugging.
Sampling overhead?
At 1 % sampling — < 1 % CPU on the hot path. At 100 % — 5-10 % in high-throughput services.
Difference vs logging?
Logs — text with context, sorted by time. Trace — structured tree with millisecond durations.