def ListRecommendations(self, request, context):
tag = ''
for key, value in context.invocation_metadata():
if key == "x-dynatrace":
tag = value
incall = oneagent.get_sdk().trace_incoming_remote_call(
'ListRecommendations',
'RecommendationService',
'grpc://hipstershop.RecommendationService',
protocol_name='gRPC',
str_tag=tag)
with incall:
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]
logger.info(
"[Recv ListRecommendations] product_ids={}".format(prod_list))
# build and return response
response = demo_pb2.ListRecommendationsResponse()
response.product_ids.extend(prod_list)
return response
def ListRecommendations(self, request, context):
wasm_header = None
for key, value in context.invocation_metadata():
if key == "x-wasm-path":
logger.warn("x-wasm-path type: %s, value: %s", type(value),
repr(value))
wasm_header = value
break
metadata = (("x-wasm-path",
wasm_header), ) if wasm_header is not None else None
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]
logger.info(
"[Recv ListRecommendations] product_ids={}".format(prod_list))
# build and return response
response = demo_pb2.ListRecommendationsResponse()
response.product_ids.extend(prod_list)
return response
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]
logger.info("[Recv ListRecommendations] product_ids={}".format(prod_list))
# build and return response
response = demo_pb2.ListRecommendationsResponse()
response.product_ids.extend(prod_list)
a = 0
b = 1
for i in range(100000):
res = a+b
a = b
b = res
return response
def ListRecommendations(self, request, context):
# manually populate service map
# this can be removed once 7.8 is out and the python agent adds this by itself
product_catalog_destination_info = {
"address": os.environ.get('PRODUCT_CATALOG_SERVICE_ADDR', ''),
"port": int(os.environ.get('PORT', 8080)),
"service": {
"name": "grpc",
"resource": os.environ.get('PRODUCT_CATALOG_SERVICE_ADDR', ''),
"type": "external"
},
}
trace_parent = self.extract_trace_parent(context)
transaction = client.begin_transaction('request',
trace_parent=trace_parent)
request_dict = MessageToDict(request)
elasticapm.label(**{'request': request_dict})
max_responses = 5
# fetch list of products from product catalog stub
list_product_req = demo_pb2.Empty()
with elasticapm.capture_span(
'/hipstershop.ProductCatalogService/ListProducts',
labels=MessageToDict(list_product_req),
extra={"destination":
product_catalog_destination_info}) as span:
trace_parent = transaction.trace_parent.copy_from(
span_id=span.id, trace_options=TracingOptions(recorded=True))
cat_response, call = product_catalog_stub.ListProducts.with_call(
list_product_req,
metadata=[(constants.TRACEPARENT_HEADER_NAME,
trace_parent.to_string())])
with elasticapm.capture_span('CalculateRecommendations',
span_subtype='grpc',
span_action='calculate') as span:
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]
logger.info(
'[Recv ListRecommendations] product_ids={}'.format(prod_list),
extra=get_extra_logging_payload())
# build and return response
response = demo_pb2.ListRecommendationsResponse()
response.product_ids.extend(prod_list)
elasticapm.label(**{'response': MessageToDict(response)})
elasticapm.set_custom_context({
'request': request_dict,
'response': MessageToDict(response)
})
client.end_transaction(
'/hipstershop.RecommendationService/ListRecommendations',
'success')
return response
def ListRecommendations(self, request, context):
newrelic.agent.set_transaction_name(
'hipstershop.RecommendationService/ListRecommendations')
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]
logger.info(
"[Recv ListRecommendations] product_ids={}".format(prod_list))
# build and return response
response = demo_pb2.ListRecommendationsResponse()
response.product_ids.extend(prod_list)
return response
def ListRecommendations(self, request, context):
max_responses = 5
# fetch list of products from product catalog stub
# otel context propagation in grpc instrumentation is broken so we manually
# inject the context
metadata = {}
propagate.inject(metadata)
cat_response = product_catalog_stub.ListProducts(demo_pb2.Empty(),
metadata=metadata)
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]
logger.info(
"[Recv ListRecommendations] product_ids={}".format(prod_list))
# build and return response
response = demo_pb2.ListRecommendationsResponse()
response.product_ids.extend(prod_list)
return response
def ListRecommendations(self, request, context):
response = demo_pb2.ListRecommendationsResponse()
response.product_ids[:] = recommend.findMatchingProducts(
request.product_category)
#logging.info('Returning recommendations...')
return response
