observability-engineering

Correlate logs, metrics, and traces by using trace IDs as a common identifier across all three data types. Instrument your applications with OpenTelemetry to generate trace IDs, then propagate those IDs through your logging and metrics pipeline so you can query related events together in dashboards and backends like Jaeger, Tempo, or ELK.

Set up distributed tracing by instrumenting your services with OpenTelemetry, deploying a tracing backend like Jaeger or Tempo to collect spans, and configuring trace sampling to control costs. This approach works across microservices and containerized environments to show request flows and latency bottlenecks.

Implement structured logging by emitting logs as JSON with consistent fields, then aggregate them using tools like ELK or Loki. Include trace IDs in log entries to link logs to traces, and configure your aggregation backend to parse and index structured fields for searchable, correlated visibility.

Yes, OpenTelemetry is vendor-neutral and supports any compatible tracing backend (Jaeger, Tempo, others) and logging system. This flexibility lets you avoid vendor lock-in and switch tools without re-instrumenting your code.

Reduce costs by configuring sampling to collect a representative subset of traces rather than all traces, using cost-optimized storage for logs and metrics, and setting SLO-based alerts to focus on meaningful events. This balances deep visibility with budget constraints across cloud-native deployments.

In service mesh environments, leverage built-in observability features to collect metrics and traces automatically, use dashboards to visualize service dependencies and latencies, and correlate mesh telemetry with application-level traces to diagnose issues at both network and application layers.