[recommendation] - add manual tracing instrumentation

src/recommendationservice/recommendation_server.py

@@ -37,21 +37,14 @@
 tracer_provider = TracerProvider()
 trace.set_tracer_provider(tracer_provider)
 tracer_provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
+tracer = trace.get_tracer("recommendationservice")
 
 
 class RecommendationService(demo_pb2_grpc.RecommendationServiceServicer):
  def ListRecommendations(self, request, context):
- max_responses = 5
- # fetch list of products from product catalog stub
- cat_response = product_catalog_stub.ListProducts(demo_pb2.Empty())
- product_ids = [x.id for x in cat_response.products]
- filtered_products = list(set(product_ids)-set(request.product_ids))
- num_products = len(filtered_products)
- num_return = min(max_responses, num_products)
- # sample list of indicies to return
- indices = random.sample(range(num_products), num_return)
- # fetch product ids from indices
- prod_list = [filtered_products[i] for i in indices]
+ prod_list = get_product_list(request.product_ids)
+ span = trace.get_current_span()
+ span.set_attribute("app.products_recommended.count", len(prod_list))
  logger.info("[Recv ListRecommendations] product_ids={}".format(prod_list))
  # build and return response
  response = demo_pb2.ListRecommendationsResponse()
@@ -66,12 +59,34 @@ def Watch(self, request, context):
  return health_pb2.HealthCheckResponse(
  status=health_pb2.HealthCheckResponse.UNIMPLEMENTED)
 
+
+def get_product_list(request_product_ids):
+ with tracer.start_as_current_span("get_product_list") as span:
+ max_responses = 5
+ # fetch list of products from product catalog stub
+ cat_response = product_catalog_stub.ListProducts(demo_pb2.Empty())
+ product_ids = [x.id for x in cat_response.products]
+ span.set_attribute("app.products.count", len(product_ids))
+
+ filtered_products = list(set(product_ids) - set(request_product_ids))
+ num_products = len(filtered_products)
+ span.set_attribute("app.filtered_products.count", num_products)
+
+ num_return = min(max_responses, num_products)
+ # sample list of indicies to return
+ indices = random.sample(range(num_products), num_return)
+ # fetch product ids from indices
+ prod_list = [filtered_products[i] for i in indices]
+ return prod_list
+
+
 def must_map_env(key: str):
  value = os.environ.get(key)
  if value is None:
  raise Exception(f'{key} environment variable must be set')
  return value
 
+
 if __name__ == "__main__":
  logger.info("initializing recommendationservice")