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03 – OpenTelemetry Ecosystem & Instrumentation

Learning Objectives​

  • Understand OTel SDKs, auto vs manual instrumentation.
  • Build a Collector pipeline: receivers → processors → exporters.
  • Position Tempo within the pipeline.

Lecture Summary​

  • SDKs generate spans inside your app; agents/auto-instrumentation hook into common frameworks.
  • The Collector decouples app code from backends and centralizes sampling, batching, transforms.
  • Exporters send processed data to Tempo (OTLP), vendors, or queues.

Collector Pipeline Diagram​

Example: Minimal Collector Config​

Create otel-collector-config.yaml:

receivers:
  otlp:
    protocols:
      grpc:
      http:

processors:
  batch:
  memory_limiter:
    check_interval: 2s
    limit_mib: 400

exporters:
  otlp:
    endpoint: tempo:4317
    tls:
      insecure: true

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [otlp]

Hands-on Lab​

  1. Start the Collector with the config above (Docker in Section 5 provides the container).
  2. Verify it listens on 4317/4318.
  3. Point a sample app’s OTLP exporter to http://collector:4318 (HTTP) or collector:4317 (gRPC).

Deliverables​

  • A running Collector accepting OTLP.

Quiz (Self-check)​

  • Name three common Collector processors and their purpose.
  • Which signal(s) can OTel send besides traces?

Resources​

  • OpenTelemetry Collector Contrib
  • OTel Auto-Instrumentation for Node.js, Python, Go, and Java