Understanding Distributed Systems with OpenTelemetry
summary
Claims that OpenTelemetry gives a single set of APIs and automatic instrumentation to observe distributed systems, then demonstrates it: instrumenting a Go fibonacci recursive app, injecting context propagation, exporting to stdout and Jaeger and OTLP backends, and wiring through an OTLP collector to LightStep and Honeycomb. The live demo generates deep traces — one recorded example yields 97 spans for a single request — and the exported traces and metrics visualize request latency and where time is spent, showing the instrumentation works end to end.
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chapters (7)
- 0:00Start
- 1:10Level-setting Are you responsible for writing software? Are you responsible for operating software?
- 5:19Why observability? • Microservices create complex interactions. • Failures don't exactly repeat • Debugging multi-tenancy is painful. • Monitoring no longer can help us.
- 7:20What is observability? • We need to answer questions about our systems. What characteristics did the queries that timed out at 500ms share in common? Service versions? Browser plugins? • Instrumentation produces data. • Querying data answers our questions.
- 8:24Telemetry aids observability Telemetry data isn't observability itself. • Instrumentation code is how we get telemetry. • Telemetry data can include traces, logs, and/or metrics. All different views into the same underlying truth.
- 28:28Context Propagation • Distributed context is an abstract data type that
- 31:55SDKs, Exporters, and Collector Services, Oh My! • OpenTelemetry's SDK implements trace & span creation. • An exporter can be instantiated to send the data collected by Open Telemetry to the backend of your choice.
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