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Recommendation Service

This service is responsible to get a list of recommended products for the user based on existing product ids the user is browsing.

Recommendation service source

Auto-instrumentation

This Python based service, makes use of the OpenTelemetry auto-instrumentor for Python, accomplished by leveraging the opentelemetry-instrument Python wrapper to run the scripts. This can be done in the ENTRYPOINT command for the service's Dockerfile.

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ENTRYPOINT [ "opentelemetry-instrument", "python", "recommendation_server.py" ]

Traces

Initializing Tracing

The OpenTelemetry SDK is initialized in the __main__ code block. This code will create a tracer provider, and establish a Span Processor to use. Export endpoints, resource attributes, and service name are automatically set by the OpenTelemetry auto instrumentor based on environment variables.

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    tracer = trace.get_tracer_provider().get_tracer("recommendationservice")

Add attributes to auto-instrumented spans

Within the execution of auto-instrumented code you can get current span from context.

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    span = trace.get_current_span()

Adding attributes to a span is accomplished using set_attribute on the span object. In the ListRecommendations function an attribute is added to the span.

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    span.set_attribute("app.products_recommended.count", len(prod_list))

Create new spans

New spans can be created and placed into active context using start_as_current_span from an OpenTelemetry Tracer object. When used in conjunction with a with block, the span will automatically be ended when the block ends execution. This is done in the get_product_list function.

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    with tracer.start_as_current_span("get_product_list") as span:

Metrics

Initializing Metrics

The OpenTelemetry SDK is initialized in the __main__ code block. This code will create a meter provider. Export endpoints, resource attributes, and service name are automatically set by the OpenTelemetry auto instrumentor based on environment variables.

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    meter = metrics.get_meter_provider().get_meter("recommendationservice")

Custom metrics

The following custom metrics are currently available:

  • app_recommendations_counter: Cumulative count of # recommended products per service call

Auto-instrumented metrics

The following metrics are available through auto-instrumentation, courtesy of the opentelemetry-instrumentation-system-metrics, which is installed as part of opentelemetry-bootstrap on building the recommendationservice Docker image:

  • runtime.cpython.cpu_time
  • runtime.cpython.memory
  • runtime.cpython.gc_count

Logs

TBD