Introduction to OpenTelemetry

OpenTelemetry produces, processes, and exports telemetry but does not permanently store or visualize it. You map APIs, SDKs, instrumentation libraries, OTLP, the Collector, and backends to one data path. You clearly distinguish application, platform, and observability-backend responsibilities for traces, metrics, and logs across the full path.

Automatic instrumentation produces technical telemetry for supported frameworks and libraries without deliberate business-code changes. In your language track, you enable the appropriate mechanism and inspect service names, duration, status, and parent-child relationships. You determine which additional business context the application itself must subsequently provide.

Technical HTTP spans do not explain which business operation matters for diagnosis. You add custom spans, events, and selected attributes through the API and SDK. You then inspect instrumentation scope and semantic conventions and avoid sensitive or unbounded values directly at the application code source.

Distributed traces require context that is injected into outgoing calls and extracted by receivers. You follow one request across several services, inspect W3C Trace Context, and correct missing propagation. You also distinguish baggage from span attributes and sensitive application data across internal and external service boundaries.

Traces describe individual flows, metrics reveal patterns, and logs provide event details. For the same service, you add a bounded-cardinality metric and connect structured logs through trace and span IDs. In Grafana, you investigate how prepared correlations guide movement between all three signal types.

Inconsistent service names and unbounded attributes make telemetry comparison and operation harder. You use resources, instrumentation scope, and semantic conventions and replace a high-cardinality value with a bounded dimension. You then compare head sampling with later tail-sampling decisions and record their respective blind spots.

Kubernetes does not collect application telemetry automatically. In the DevNinjas Dojo, our cloud learning environment, you deploy the instrumented service, set the OTLP endpoint, protocol, and resource attributes through workload configuration, and inspect the data path to a prepared Collector pipeline. You distinguish what is processed in the cluster from what is stored permanently in the connected observability backend.

Collectors can run as sidecars, agents on each node, or centralized gateway services. You compare these patterns by source proximity, network path, Kubernetes enrichment, and operational ownership. For the exercises, you choose a gateway path and distinguish when development configures it and when a platform team should take over.

Collector pipelines connect receivers, optional processors, and exporters for one signal type. From a scaffold, you build separate paths for traces, metrics, and logs using OTLP, batching, and debug output. You use configuration validation and internal telemetry to isolate component faults and missing data paths.

Without workload context, identically named services from several namespaces are hard to distinguish. With prepared permissions, the Collector enriches signals with namespace, pod, and deployment data. You then filter unwanted records, remove a sensitive attribute, and examine the roles of the memory limiter and batching in pipeline operation.

OpenTelemetry requires backends for permanent storage, querying, and visualization. You connect your Collector pipelines to prepared Tempo, Prometheus, and Loki instances and inspect export for each signal type. You assess attribute mappings that keep service and correlation details reliably and consistently discoverable in each connected backend.

Raw data does not yet reveal whether a service is becoming slower, producing more errors, or experiencing heavier load. From prepared queries, you build a Grafana dashboard for request rate, error ratio, and latency. You then use data-source correlations to move from an anomaly to related traces and logs.

A gateway or sidecar does not replace correct context propagation in the application. Through a prepared Istio route, you compare direct and mediated requests, inspect trace context, and correct missing propagation. Routing, mTLS, and production operation of a complete service mesh remain part of the advanced follow-up training.

A slow or unavailable backend can cause backlog and data loss in the telemetry path. During a prepared failure, you observe queues, retries, backpressure, and internal Collector metrics and distinguish temporary from permanent faults. You then assess correction paths and the limits of persistent queues.

A prepared scenario combines increased latency with a gap in the telemetry path. You use a dashboard, trace, correlated log, and Collector telemetry to separate application and observability faults. Finally, you transfer the diagnostic criteria to instrumentation, pipeline, and backend interfaces in your own technical application and platform environment.