Introduction to OpenTelemetry

When logs, metrics, and traces originate separately, the path of a request remains invisible. Using a prepared application, you place the API, SDK, instrumentation libraries, OTLP, Collector, and backends in one data flow. An architecture map then shows where telemetry is produced, processed, stored, and displayed in Grafana, and whether development or platform teams own each responsibility.

Automatic instrumentation quickly produces technical spans, but its scope and quality vary by language and framework. In your chosen track, you enable the suitable mechanism, send the same HTTP request, and inspect the service name, duration, status, and parent-child relationships. Comparing this result with the unobserved baseline provides reliable evidence for the deliberate code changes that follow.

An automatically generated HTTP span does not explain why an order step fails. You add a business span, an event, and selected attributes in the application code, then examine how the API, SDK, and instrumentation scope interact. The resulting trace answers a concrete diagnostic question without placing customer data or unbounded identifiers in the telemetry.

A trace breaks apart when one service neither accepts incoming context nor propagates it on an outgoing call. You follow a request across several services, inspect the W3C Trace Context, and repair a broken parent-child relationship. You then distinguish baggage from span attributes. The completed trace confirms that cause and duration remain connected across every service boundary.

Traces explain individual requests, metrics reveal patterns, and logs provide event details. For the same failure, you add a metric with controlled cardinality and connect structured logs to the request through trace and span IDs. Through a prepared reference path, exemplars, and provisioned Grafana correlations, you narrow the issue to relevant traces and logs. All three pieces of evidence must indicate the same cause.

Inconsistent service names and unbounded attributes make telemetry expensive and difficult to compare. You apply resources, instrumentation scope, and suitable semantic conventions, replace a high-cardinality attribute with a dimension whose value range is bounded, and avoid a sensitive field at the source. You then configure head sampling and document its blind spots; tail sampling remains a later architectural decision.

Kubernetes does not collect application telemetry automatically. You deploy the instrumented service as a Deployment, set the OTLP endpoint, protocol, and resource attributes through workload configuration, and send a request through a Service to the prepared reference pipeline. Collector output and the trace confirm that application and network destination align. This exposes what runs in Kubernetes and what is stored only in a backend.

A Collector can run as a sidecar, an agent on each node, or a central gateway service. You compare these patterns by source proximity, network path, Kubernetes enrichment, and operational ownership, then map the sample application's path. For the lab, you choose a version-pinned gateway route. A decision matrix records when development can configure it and when the platform must take over.

A Collector configuration processes only components connected into pipelines in the service block. From a scaffold, you build separate paths for traces, metrics, and logs with an OTLP receiver, selected processors, and the debug exporter. otelcol validate, test data, and version-pinned internal metrics prove that every pipeline starts, accepts data, and does not silently discard a signal type.

Without workload context, identical service names from several namespaces remain difficult to distinguish. With prepared permissions and a pod UID set through the Downward API, the Collector enriches signals with namespace, pod, and deployment data. You then filter an unwanted record, remove one remaining sensitive attribute, and position the memory limiter and batching deliberately. The comparison shows searchable context without confidential values.

OpenTelemetry does not store telemetry permanently. You replace the reference export with your Collector pipelines, connect them to Tempo, Prometheus, and Loki through version-pinned endpoints, and send a reference request. In Tempo, you inspect the trace; in Prometheus, the metric; and in Loki, the log. Prepared attribute mappings keep selected service and correlation details discoverable, while a checklist confirms complete export.

Raw data does not yet reveal whether a service is becoming slower, less reliable, or more heavily used. From version-pinned HTTP metrics and prepared queries, you build a Grafana dashboard for request rate, error ratio, and latency. You then include prepared exemplar and data-source correlations to narrow the issue to relevant traces and logs. The result requires no deep dive into PromQL or LogQL.

A gateway or sidecar does not replace correct context propagation in the application. Using a prepared Istio route configured through Gateway API, with an OpenTelemetry provider and fixed sampling, you compare direct and mediated requests, inspect traceparent, and repair missing propagation. A trace across edge, proxies, and services proves the fix. The Service Mesh Training covers routing, mTLS, and mesh operations.

A slow or unavailable backend must not hide data loss. During a prepared failure, you observe the queue, retries, backpressure, and internal Collector metrics, distinguish temporary from permanent errors, and correct the endpoint or pipeline. Debug output and returning signals confirm recovery. The exercise also clarifies why even persistent queues cannot provide an absolute delivery guarantee.

The final scenario shows increased latency alongside a gap in the telemetry path. Using the dashboard, trace, correlated log, Collector debug output, and internal metrics, you separate the application fault from the observability fault, repair both causes, and repeat the reference request. Normal service behaviour and three connected signals prove success; a runbook and transfer checklist preserve the method for your team.