A slow checkout can involve an API gateway, an inventory service, and a queue worker. Distributed tracing connects operations in those systems into a trace. Each operation, or span, records a portion of the work and carries context that lets engineers see how it relates to the rest of the request.
Fix missing context before comparing backends
Instrument one path that crosses HTTP and a message queue. Verify that producers pass trace context and consumers extract it. For asynchronous work, verify that your instrumentation expresses the intended relationship, such as a parent-child link or a span link, for the messaging pattern. A broken propagation path can make two parts of the same transaction look like unrelated events.
Compare the backends with the same trace data
| Tool | Where it fits | Evaluation question |
|---|---|---|
| Jaeger | A dedicated distributed tracing backend and search UI | Can your deployment ingest and query the expected span volume? |
| Zipkin | Environments with existing Zipkin instrumentation or operational knowledge | Can you preserve propagation and reporting during migration? |
| Grafana Tempo | Teams investigating traces alongside metrics and logs in Grafana | Do storage, retention, and search meet incident-response needs? |
Head sampling makes a decision early and limits data volume quickly. Tail sampling can use characteristics observed later, such as errors or long duration, but requires buffering and enough visibility into each trace. Decide what evidence you need before setting a sample rate.
Use a rare error in the pilot and check whether the trace survives collection and sampling. Confirm that the responder can find it from a service name, time window, or trace ID. A complete, findable trace is the outcome that matters.