Video Lesson
OpenTelemetry Fundamentals
OpenTelemetry (OTel) is the industry standard for instrumentation. It provides APIs and SDKs for generating traces, metrics, and logs in a vendor-neutral way. Key concepts: traces, spans, context propagation, and the OTel Collector.
We will instrument a multi-service application with OpenTelemetry, adding both automatic instrumentation (HTTP, database, gRPC) and custom spans for business-critical operations.
Trace Analysis and Visualization
Trace visualization tools (Jaeger, Zipkin, Tempo) display the waterfall view of a request flowing through services. Each span shows duration, status, and attributes.
We will set up Grafana Tempo for trace storage and learn to read trace waterfalls effectively — identifying which spans contribute most to latency, spotting sequential calls that could be parallelized, and finding error cascades.
AI-Assisted Performance Analysis
AI can analyze trace data at scale to identify: recurring bottleneck patterns, unusual latency distributions, service dependency anomalies, and performance regression after deployments.
We will build a workflow where AI tools analyze trace summaries and generate performance reports with actionable optimization recommendations.
Hands-On Exercises
- Add OpenTelemetry instrumentation to a sample Node.js or Python application — capture at least 3 custom spans
- Use trace data to identify the top 3 latency contributors in a multi-service request flow
- Write a prompt for an AI tool to analyze trace data and suggest performance optimizations — evaluate the response quality