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To diagnose failures that cross service and infrastructure boundaries, collect and correlate metrics, logs, and distributed traces. Give telemetry consistent service and resource identity, instrument both workloads and the platform they run on, and monitor the collection pipeline itself. Start with broad signals, then add technology-specific detail when those signals narrow the problem.
What each signal tells you
Metrics, traces, and logs answer different questions. Used together, they help move from detecting an incident to locating the failing operation and understanding its context.
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| Signal | What it shows | Useful for |
|---|---|---|
| Metrics | Aggregated measurements over time, such as request rate, errors, latency, or resource use. | Spotting changes in system or service behavior and narrowing when or where an incident began. |
| Distributed traces | The sequence of operations a single request takes through services and dependencies. | Finding a slow or failed operation, dependency, or unexpected path across service boundaries. |
| Logs | Timestamped records of events, errors, and other significant activity. | Inspecting event-level details associated with a service or request. |
OpenTelemetry describes a trace as the path taken by a single request as it propagates through multiple services. See its observability primer for the signal concepts.
Make telemetry identifiable and correlatable
Use consistent identity attributes so an operator can distinguish a logical service from its namespace or system and from individual service instances. OpenTelemetry’s service semantic conventions describe service identity in distributed architectures. Its general semantic conventions establish shared attribute names and meanings for telemetry, supporting consistent interpretation across instrumentation and backends.
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Use conventions supported by your SDKs and backend, and check their stability before treating a field as a durable interface. Consistent names help tools and people interpret signals across services; inconsistent names can make otherwise useful telemetry harder to correlate.
Trace the request across service boundaries
Enable tracing at ingress, in application services, and for relevant dependencies. Preserve parent-child span relationships and propagate trace context on service calls. If context is not propagated, each service may show an isolated segment rather than a connected request path.
Use a trace waterfall or equivalent view to inspect operation timing, failed dependencies, and unexpected call paths. A trace can show where an individual request spent time; aggregate metrics help establish whether that behavior is part of a broader incident.
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Pair traces with structured logs
Emit structured, timestamped logs for errors and significant events. Where the instrumentation supports it, include trace context in log records so an operator can move from an error event to the corresponding request trace.
In Kubernetes, container output is commonly written to standard output and standard error, while system components also produce logs that can help with troubleshooting. The official Kubernetes logging documentation explains these sources and the role of cluster logging. Log availability and retention depend on the cluster’s configuration.
Start with broad service and infrastructure indicators
Establish a baseline that helps determine whether users or systems are affected, then use more detailed telemetry to investigate the cause.
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Service-level signals
- Rate: request volume or another relevant measure of activity.
- Errors: failed requests or operations.
- Duration: latency or operation time.
- Saturation: whether a constrained resource is approaching or at capacity.
OpenTelemetry’s signals guidance discusses general-purpose signals, including saturation, rate, errors, duration, high-level spans, and error logs. Treat an aggregate metric as a way to narrow an incident, not as proof of its root cause.
Runtime and host signals
Add CPU, memory, network, process, and container measurements that match the runtime and failure modes you need to diagnose. The OpenTelemetry system semantic conventions define conventions for system-related telemetry; available measurements depend on the instrumentation and deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Cover the Kubernetes platform as well as workloads
In Kubernetes, observability should include relevant control-plane components, kubelet and container or runtime logs, workload telemetry, and object status—not only application metrics. Kubernetes components expose metrics, containerized workloads produce logs, and traces can help show request movement through applications and cluster components.
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The Kubernetes observability documentation describes metrics endpoints for components including kube-apiserver, kubelet, kube-scheduler, kube-controller-manager, and kube-proxy, along with logging and tracing. Which components and exporters are available depends on the Kubernetes release and cluster configuration. Check the official documentation for the version you operate; trace-export capabilities are version-specific.
Monitor the telemetry pipeline itself
Telemetry can be delayed, dropped, or fail to export, so a missing signal does not necessarily mean the application is healthy. Inspect internal telemetry from SDKs and collectors to monitor collection, processing, and export. OpenTelemetry’s SDK metrics specification describes internal signals for processors, exporters, and metric readers.
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Compare configurations against the incident you need to diagnose
- Cross-signal context: Are metrics, logs, and traces all collected, and can service context connect them?
- Layer coverage: Are the application and the relevant platform layers represented, including cluster, container, process, and system signals?
- Baseline and depth: Can teams interpret the baseline consistently across services, and can they add deeper signals for the databases, runtimes, or infrastructure involved?
- Pipeline visibility: Can operators tell whether telemetry is being collected and exported, and are the relevant features mature in the deployed versions?
These checks help assess coverage and diagnostic usefulness; the cited guidance does not establish a vendor ranking or product recommendation.
Implementation details to verify
OpenTelemetry conventions and maturity can change, while Kubernetes capabilities vary by release and cluster setup. Confirm attribute stability, signal support, and configuration against the exact SDK, Collector, Kubernetes release, and backend you use. The guidance here is operational rather than a tested configuration for a particular environment.
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