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jaeger deep dive [b5aa9179]

Tags: kubernetes (k8s)

1. Sampling

  • sampling is to reduce costs of sampling
  • trace data is usually very repetitive
  • sampling -> consistent (all or nothing), no missing parts of the trace

  • head based sampling

    • easy to implement
    • spread out configuration since there may be multiple

  • remote sampling

    • reads configuration from central tier in the backend

  • tail based sampling

    • can be much more advanced
    • can catch anomalies
    • requires temp storage of all tarces
    • applications constantly incur performance

2. opentracing

  • opentelemetry implementation of the opentracing api
  • allows you to change tracing systems

  • jaeger components in opentelemetry

    • opentelemetry sdk's have jaeger grpc exporter and jaeger propagation
    • this means anything exporting with open telemetry can use jaeger

  • opentracing shim

    • all existing opentracing libs with opentelemtry sdk
  • jaeger client libs support w3c trace context

  • deploying jaeger onto kubernetes (k8s)

    • helm charts

    • jaeger operator -> most advanced, allInOne and production deployment

      • auto provision with Kafka
    • plain kube manifest