When your application spans dozens of microservices debugging becomes a nightmare. A single user request might touch ten different services and you're stuck grepping through logs trying to piece together what happened. This is where distributed tracing saves you.
Distributed tracing tracks a request as it flows through your system. Each service adds metadata about its work creating a complete picture of the request's journey. You get visibility into:
Traces represent a single request flowing through your system. A trace is made up of spans.
Spans represent a unit of work. Each service creates spans for the operations it performs. Spans have:
Trace Context is how you connect spans across services. When Service A calls Service B it passes along trace metadata (trace ID, span ID) in HTTP headers or message metadata. This is called context propagation.
Traditional logging falls apart in distributed systems. Logs tell you what happened in one service but connecting the dots across services is manual and painful.
Distributed tracing gives you:
Root Cause Analysis - See exactly which service is slow or failing. If checkout is slow you can see whether it's the payment service, inventory service, or database causing the bottleneck.
Performance Optimization - Identify slow operations even if they don't fail. Maybe your services are healthy but you're making 20 sequential database calls when you could batch them.
Understanding System Behavior - See how requests actually flow through your system. Your architecture diagrams lie but traces don't.
OpenTelemetry (OTel) is the industry standard for instrumentation. It provides:
The beauty of OTel is vendor neutrality. Instrument your code once and swap backends without changing your application code.
Start with HTTP boundaries. Instrument your HTTP servers and clients first. This gives you service-to-service visibility immediately.
Add database tracing next. Database queries are often the bottleneck. Most ORMs have OTel integrations.
Instrument critical paths. Don't trace everything. Focus on user-facing workflows and known problem areas. Too much data becomes noise.
Propagate context everywhere. This is the hardest part. Every time you cross a service boundary (HTTP, message queue, background job) you need to propagate trace context. Miss one and your traces break.
Over-instrumentation. Creating spans for every function call generates massive overhead. Span creation is cheap but not free. Be selective.
Sampling confusion. At scale you can't keep every trace. Sample intelligently - keep all errors and slow requests, sample normal requests. Head-based sampling (decide at the start) is simple but can miss issues. Tail-based sampling (decide after the trace completes) catches more problems but is harder to implement.
Missing context propagation. If you forget to propagate trace context in even one place your traces become disconnected fragments. This is especially tricky with async code, background jobs, and message queues.
High cardinality tags. Don't use user IDs or unique identifiers as tag values. Backends index tags and high cardinality kills performance. Use user IDs in span names or logs instead.
Tracing is merging with metrics and logs. The "three pillars of observability" are converging. OTel now supports metrics and logs alongside traces. Soon you'll query your system asking "show me slow checkout traces" and drill into metrics and logs without leaving your tracing UI.