SRE Observability Setup
Monitoring tells you when something is wrong. Observability tells you why. Anubiz Engineering implements comprehensive observability — metrics for trends, logs for context, traces for request flow — instrumented with SRE principles so every signal serves an operational purpose.
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Metrics Instrumentation
We instrument RED metrics (Rate, Errors, Duration) for every service and USE metrics (Utilization, Saturation, Errors) for every infrastructure component. Custom business metrics — signup rate, checkout completion, API usage by tenant — are instrumented alongside operational metrics. Prometheus client libraries emit metrics from your application code, and service mesh telemetry captures inter-service communication without code changes.
Structured Logging
Logs are structured JSON with consistent fields: timestamp, service name, trace ID, request ID, user ID, log level, and message. We configure log aggregation through Loki, Elasticsearch, or CloudWatch Logs with retention policies that balance cost and debuggability. Log-based alerts catch error patterns that metrics miss: specific exception types, authentication failures, and business logic violations.
Distributed Tracing
When a request touches five services, you need to trace its journey. We instrument distributed tracing with OpenTelemetry, propagating trace context across HTTP, gRPC, and message queues. Trace data flows to Jaeger, Tempo, or Datadog APM. Engineers debug latency issues by viewing the full request waterfall — seeing exactly which service and which database query added the 800ms spike.
Correlation and Context
The real power of observability is correlation. We link metrics, logs, and traces through shared identifiers: clicking a latency spike in Grafana jumps to exemplar traces, clicking a trace shows correlated logs for each span, and log entries link back to the trace that generated them. This reduces debugging time from hours of grep to minutes of clicking through connected data.
Why Anubiz Engineering
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