Observability
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Notes
- Sentry 5 Error tracking. It captures an exception with its stack trace, request context and release, then groups repeated occurrences into one issue you can…
- Grafana 4 Grafana is a query, visualisation and alerting layer over data it does not own.
- Prometheus 5 A time-series database that scrapes metrics from HTTP endpoints on an interval.
- Datadog 7 Commercial SaaS observability platform. One vendor for metrics, logs, APM (traces), real-user monitoring, synthetic tests, security monitoring, and more.
- ELK Stack (Elasticsearch, Logstash, Kibana) The classic self-hosted log aggregation stack: centralize logs from every service, make them full-text searchable, dashboard them.
- Jaeger 7 Open-source distributed tracing system. Originally built at Uber (2015), donated to CNCF (graduated 2019).
- OpenTelemetry 8 The CNCF-graduated standard for emitting observability data: traces, metrics and logs.
- The Three Pillars of Observability 4 Metrics, logs and traces, plus profiles as a fourth signal.
- Choosing an Observability Stack 6 Sentry, Datadog, Grafana, Prometheus, Jaeger, ELK, OpenTelemetry — interviewers will ask "which would you pick?" The strong answer is "depends on these…
- Trace context in async Python 7 Tracing is instrumented once and then breaks silently.
- Sampling Strategies for Tracing 6 You can't store every span from every request at scale — cost and storage.
- SLO, SLI, SLA, and Error Budgets 6 The Google SRE framework for turning "is the service healthy?" into a number that decides engineering work.
- Structured logging in Python Structured logging means emitting machine-parseable events (one JSON object per line) instead of prose.
- Cost as a signal 6 Latency has an owner, a dashboard and an alert.
- Incident response, runbooks and postmortems 4 Observability tells you something is wrong.