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Introduction:

This document describes how to trace/map the request across multiple sub-systems involved in process of serving/showing the details.

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Here we use the headers map as the carrier. The Tracer object knows which headers it needs to read in order to reconstruct the tracer state and any Baggage.

Pros:

1. Can able to find the time taken on each sub-modules easily based on SpanId

2. Time spent of each sub-system will be available directly in the event itself

3. In sub-systems, we can add multiple spans

Cons:

1. More efforts required compared to Solution1.

2. Multiple telemetry events will be logged for the same workflow(like impression & trace)

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Integrate with external distributed tracing tools like Zipkin, Jaeger etc..

JAEGER:

Architecture diagram:

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• Integration with spring application: Video- https://www.youtube.com/watch?v=hpnLUFRY4_Y

• Integration with NodeJs application: https://blog.risingstack.com/distributed-tracing-opentracing-node-js/

Pros:

1. The simple configuration code changes will handle the request traceability.

2. Jaegar UI can be used to visualize the trace events.

Cons:

1. Workflows can’t be traceable. Each API will be considered as one trace.

2. Connecting multiple applications/servers to the single jaeger instance to trace the workflow of the API has to explore.

Reference links:

• Opentracing specifications:

  https://github.com/opentracing/specification/blob/master/specification.md

• Span: Opentracing definition

  https://opentracing.io/docs/overview/spans/

• Open tracing Javascript implementation sample
  https://github.com/opentracing/opentracing-javascript

• Best practices:

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Jaeger Client node: https://github.com/jaegertracing/jaeger-client-node

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Integration with spring application: Video- https://www.youtube.com/watch?v=hpnLUFRY4_Y

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Trace ability of request across subsystems

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