Distributed tracing helps you follow one request across microservices, see where it spent time, and locate the operation associated with an error. It works only when services create useful spans, propagate trace context across every relevant boundary, and successfully export data to a backend; a trace that looks incomplete can reflect a problem at any of those stages.
What distributed tracing shows you
A trace connects the work done for a request across components. Its spans represent logical operations, such as handling an HTTP request or calling another service, and carry details such as an operation name, start time, duration, and diagnostic data. Jaeger describes a trace as a directed acyclic graph of spans, which can be inspected as a tree or timeline in a tracing interface. Jaeger architecture documentation
That view is useful when a request fails or takes longer than expected: instead of seeing only an aggregate latency or an isolated error log, you can follow the request’s recorded operations and identify which span is slow or marked as failed. A trace is request-level causal detail, not a replacement for metrics that reveal patterns across many requests or logs that record event-specific context.
Trace IDs are not the trace itself
A trace ID identifies related work, but does not contain all the span data. Jaeger documents that trace identifiers and baggage can travel with requests while span details are exported asynchronously to a backend. Consequently, finding a trace ID in one place does not prove that every service created, exported, or retained its spans. Jaeger architecture documentation
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How to follow a request across services
OpenTelemetry is a vendor-neutral, open-source framework for instrumenting, generating, collecting, and exporting traces, metrics, and logs. Its tracing API defines span context with trace and span identifiers, trace flags, and trace state; that context is used to connect operations across process boundaries. OpenTelemetry documentation OpenTelemetry Tracing API
1. Start with a specific request
Choose a failing request or a latency outlier, then find its trace in your backend using the available trace ID or filters. Begin at the root span and follow the recorded child operations. Note the slowest or failing span, its service and operation identity, status, timestamps, and relevant attributes. Where instrumentation exposes the distinction, check whether the time reflects work inside a service or waiting on a downstream call.
2. Check context at every boundary
For HTTP, the W3C Trace Context standard defines the traceparent field and optional tracestate field for carrying trace context. OpenTelemetry propagators inject context into outgoing requests and extract it from incoming requests so independently instrumented services can associate their work with the same trace. W3C Trace Context OpenTelemetry overview
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Inspect both sides of a boundary: confirm that the caller injects context, the receiver extracts it, and the instrumentation and propagator are compatible. A service may produce spans but start a separate trace if it does not receive the caller’s context. The Python OpenTelemetry guidance notes that common framework instrumentation libraries can propagate context automatically and recommends using suitable instrumentation libraries rather than implementing propagation manually. OpenTelemetry Python propagation
3. Instrument work that automatic libraries do not cover
Automatic instrumentation can cover common frameworks, but verify each transport and execution boundary separately. Messaging systems, gateways, custom transports, and background jobs can break the causal chain if context is not carried with the work and extracted when it resumes. For a queued task, for example, the producer must place the relevant context in the message or associated metadata, and the consumer must restore it before creating the task’s spans.
Add manual spans for business-specific operations or unsupported boundaries when those operations matter to diagnosis. Use clear operation names and attributes that help distinguish the work. Avoid high-cardinality or sensitive attributes unless your data policy permits them; such data can complicate storage and querying or expose information you should not collect.
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4. Correlate traces with logs and metrics
Where your systems support it, include trace identifiers and resource attributes in logs and use matching service identity in telemetry. This lets you move from an individual request to related events or broader service behavior. Traces answer where work in a request was recorded; metrics show aggregate behavior, while logs provide event details.
What a tracing deployment needs
A functioning tracing path has more than instrumentation: instrumented SDKs create spans, an exporter sends them onward, a collector or ingestion endpoint may receive and process them, storage retains them, and a query interface lets you find and inspect them. Jaeger documents both an all-in-one deployment option and scalable system architectures, including direct-to-storage and Kafka-assisted paths. Its architecture documentation also describes collectors and a query service that exposes retrieval APIs and a UI. Jaeger architecture documentation
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The cited Jaeger architecture page is for version 1.76 and is archived; it should not be treated as current installation guidance. Jaeger’s sampling documentation says classic Jaeger SDKs are deprecated and recommends OpenTelemetry SDKs. Check the documentation for the Jaeger version you intend to deploy before following version-specific configuration or installation steps. Jaeger sampling documentation
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose sampling with trace completeness in mind
Sampling limits which traces are retained or exported, trading diagnostic coverage against telemetry volume and operational cost. The choice affects what you can conclude from the traces you do see: an unsampled request may have no stored trace, and the presence of one sampled span does not guarantee that all spans reached storage.
| Approach | How the decision works | Operational consequence |
|---|---|---|
| Head-based sampling | The decision is made near the trace origin and propagated with the trace context. | Services can follow a consistent decision, helping retain a complete sampled trace; a decision made early cannot use the final outcome of the request. Jaeger sampling documentation |
| Tail-based sampling | A collector makes a decision after receiving spans, allowing rules based on outcomes such as errors or high latency. | All spans for a trace need to reach the same Collector instance for effective decisions, so the collection and routing design must keep a trace together. Datadog OpenTelemetry sampling documentation |
These are different control points, not interchangeable guarantees. Select rules based on the requests you need to investigate, then verify what the SDKs, collectors, and backend actually retain. Datadog documents tail-sampling capabilities and the same-Collector constraint for its OpenTelemetry delivery guidance; that is a vendor’s documented capability, not an independent comparison of tracing platforms. Datadog OpenTelemetry sampling documentation
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Why spans are missing or a trace ends abruptly
Similar symptoms can arise at different points in the pipeline. Check the stages separately rather than assuming that every gap means a propagation failure.
- Instrumentation: Confirm that the service and operation are instrumented and that spans are being created. Automatic instrumentation does not guarantee coverage of custom code, every transport, or background execution.
- Propagation: Check the context injected by the caller and extracted by the receiver, including
traceparenton HTTP boundaries and any configured propagator compatibility at service or vendor boundaries. Verify that messaging and background work explicitly carry context. - Sampling: Determine whether a sampling decision excluded the trace or some telemetry. Check the configured sampling policy and how the decision is propagated; for tail sampling, check whether the spans for the trace reach the same Collector instance.
- Export and collection: Check SDK exporter configuration and the health of the exporter endpoint, collector, or ingestion service. A span created in an application is not necessarily a span successfully delivered to storage.
- Storage and query: Check backend retention, filters, search constraints, and whether the query interface can retrieve the data. A missing result can reflect storage or query behavior rather than absent instrumentation.
These stages align with Jaeger’s description of context propagation and asynchronous span export: a propagated identifier and exported span data follow related but distinct paths. Jaeger architecture documentation
Decide between self-hosted and managed tracing
Jaeger provides an open-source self-hosted option, while managed platforms can provide an ingestion pipeline and service-level controls. The appropriate choice depends on operational capacity and data requirements; compare the actual configuration and terms for the deployment under consideration rather than assuming a universal cost or capability advantage.
- Instrumentation coverage: Check automatic framework support and plan manual spans for business-specific operations or unsupported boundaries.
- Propagation interoperability: Confirm W3C Trace Context support, handling for legacy or vendor-specific formats at boundaries, and explicit context transfer for queues and background work.
- Sampling and completeness: Decide whether early head-based decisions or outcome-informed tail-based rules suit the diagnostic need, and whether the collector topology can keep spans for each trace together.
- Operations and governance: Evaluate storage and scaling responsibilities, retention, processing, access controls, ingestion volume, and how sensitive attributes are handled.
- Total cost: Compare current provider terms and the costs of the complete data path; pricing and service terms are not established by the documentation cited here.
Further reading
Distributed Tracing in Practice by Austin Parker, Daniel Spoonhower, Jonathan Mace, Ben Sigelman, and Rebecca Isaacs covers instrumentation, deployment, overhead, sampling, and OpenTelemetry context. O’Reilly lists it as a 327-page book published in April 2020; use current standards and software documentation for version-specific implementation details. O’Reilly publisher catalog
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