diff --git a/src/service/clmcservice/graphapi/tests.py b/src/service/clmcservice/graphapi/tests.py
index d55d002983ee9d8b0855d4f68d7281a9c3f9fc74..021877ef215899e6a13a1debb66808e6a853fc67 100644
--- a/src/service/clmcservice/graphapi/tests.py
+++ b/src/service/clmcservice/graphapi/tests.py
@@ -119,7 +119,7 @@ class TestGraphAPI(object):
body = dumps(dict(service_function_chain="sfc", service_function_chain_instance="sfc_1", service_functions=service_functions))
request = testing.DummyRequest()
request.body = body.encode(request.charset)
- with pytest.raises(HTTPBadRequest, message="A bad request error must have been raised in case of missing timestamp parameters."):
+ with pytest.raises(HTTPBadRequest):
GraphAPI(request).build_temporal_graph()
# Create a valid build request and send it to the API endpoint
@@ -135,8 +135,13 @@ class TestGraphAPI(object):
request.body = body.encode(request.charset)
response = GraphAPI(request).build_temporal_graph()
graph_subresponse = response.pop("graph")
+ # remove the "from" and "to" keys, these will be returned in the graph_subresponse
+ build_json_body.pop("from")
+ build_json_body.pop("to")
assert response == build_json_body, "Response must contain the request body"
assert graph_subresponse.get("uuid") is not None, "Request UUID must be attached to the response."
+ assert graph_subresponse["time_range"]["from"] == from_timestamp * 10**9 # timestamp returned in nanoseconds
+ assert graph_subresponse["time_range"]["to"] == to_timestamp * 10**9 # timestamp returned in nanoseconds
request_id = graph_subresponse["uuid"]
graph_1_id = request_id
@@ -194,8 +199,13 @@ class TestGraphAPI(object):
request.body = body.encode(request.charset)
response = GraphAPI(request).build_temporal_graph()
graph_subresponse = response.pop("graph")
+ # remove the "from" and "to" keys, these will be returned in the graph_subresponse
+ build_json_body.pop("from")
+ build_json_body.pop("to")
assert response == build_json_body, "Response must contain the request body"
assert graph_subresponse.get("uuid") is not None, "Request UUID must be attached to the response."
+ assert graph_subresponse["time_range"]["from"] == from_timestamp * 10**9 # timestamp returned in nanoseconds
+ assert graph_subresponse["time_range"]["to"] == to_timestamp * 10**9 # timestamp returned in nanoseconds
request_id = graph_subresponse["uuid"]
graph_2_id = request_id
@@ -331,8 +341,13 @@ class TestGraphAPI(object):
request.body = body.encode(request.charset)
response = GraphAPI(request).build_temporal_graph()
graph_subresponse = response.pop("graph")
+ # remove the "from" and "to" keys, these will be returned in the graph_subresponse
+ build_json_body.pop("from")
+ build_json_body.pop("to")
assert response == build_json_body, "Response must contain the request body"
assert graph_subresponse.get("uuid") is not None, "Request UUID must be attached to the response."
+ assert graph_subresponse["time_range"]["from"] == from_timestamp * 10**9 # timestamp returned in nanoseconds
+ assert graph_subresponse["time_range"]["to"] == to_timestamp * 10**9 # timestamp returned in nanoseconds
request_id = graph_subresponse["uuid"]
# test some more error case handling of the RTT API endpoint
@@ -402,8 +417,13 @@ class TestGraphAPI(object):
request.body = body.encode(request.charset)
response = GraphAPI(request).build_temporal_graph()
graph_subresponse = response.pop("graph")
+ # remove the "from" and "to" keys, these will be returned in the graph_subresponse
+ build_json_body.pop("from")
+ build_json_body.pop("to")
assert response == build_json_body, "Response must contain the request body"
assert graph_subresponse.get("uuid") is not None, "Request UUID must be attached to the response."
+ assert graph_subresponse["time_range"]["from"] == from_timestamp * 10**9 # timestamp returned in nanoseconds
+ assert graph_subresponse["time_range"]["to"] == to_timestamp * 10**9 # timestamp returned in nanoseconds
request_id = graph_subresponse["uuid"]
# go through the set of input/output (expected) parameters and assert actual results match with expected ones
diff --git a/src/service/clmcservice/graphapi/utilities.py b/src/service/clmcservice/graphapi/utilities.py
index d10b97d12b0115bfa1ba52f9028d23b0c34620fd..090f7c31a80aa0be65ec1bcf680595e0567cc605 100644
--- a/src/service/clmcservice/graphapi/utilities.py
+++ b/src/service/clmcservice/graphapi/utilities.py
@@ -32,7 +32,7 @@ GRAPH_ROUND_TRIP_TIME_URL_PARAMS = ("startpoint", "endpoint")
GRAPH_BUILD_QUERY_PARAMS = {"from", "to", "service_function_chain", "service_function_chain_instance", "service_functions"}
GRAPH_BUILD_SF_QUERY_PARAMS = {"response_time_field", "request_size_field", "response_size_field", "measurement_name"}
-INFLUX_QUERY_TEMPLATE = 'SELECT {0} AS mean_response_time, {1} AS mean_request_size, {2} AS mean_response_size FROM "{3}"."{4}".{5} WHERE "flame_sfc"=\'{6}\' and "flame_sfci"=\'{7}\' and time>={8} and time<{9} GROUP BY "flame_sfe", "flame_location", "flame_sf"'
+INFLUX_QUERY_TEMPLATE = 'SELECT {0} AS mean_response_time, {1} AS mean_request_size, {2} AS mean_response_size FROM "{3}"."{4}".{5} WHERE "flame_sfc"=\'{6}\' and "flame_sfci"=\'{7}\' and "flame_sfp"=\'{8}\' and time>={9} and time<{10} GROUP BY "flame_sfe", "flame_location", "flame_sf"'
# in cypher the syntax is {name: 'value'}, here we use {{name: 'value'}} to escape the curly braces when applying the python format function
@@ -211,8 +211,8 @@ def build_temporal_subgraph(request_id, from_timestamp, to_timestamp, json_queri
compute_nodes = set() # a set is used to keep track of all compute nodes that are found while building the graph, which is then used to retrieve the network latencies
# traverse the list of service functions
- for service_function in json_queries["service_functions"]:
- query_data = json_queries["service_functions"][service_function]
+ for service_function_package in json_queries["service_functions"]:
+ query_data = json_queries["service_functions"][service_function_package]
response_time_field = query_data["response_time_field"]
request_size_field = query_data["request_size_field"]
@@ -220,47 +220,49 @@ def build_temporal_subgraph(request_id, from_timestamp, to_timestamp, json_queri
measurement = query_data["measurement_name"]
# build up the query by setting the placeholders in the query template
- query_to_execute = INFLUX_QUERY_TEMPLATE.format(response_time_field, request_size_field, response_size_field, db, rp, measurement, sfc, sfci, from_timestamp, to_timestamp)
-
- # create a node for the service function if it doesn't exist
- service_function_package_node = find_or_create_node(graph, "ServiceFunctionPackage", name=service_function)
- # crate a utilizedBy edge between the service function and the service function chain
- find_or_create_edge(graph, "utilizedBy", service_function_package_node, service_function_chain_node)
+ query_to_execute = INFLUX_QUERY_TEMPLATE.format(response_time_field, request_size_field, response_size_field, db, rp, measurement, sfc, sfci, service_function_package, from_timestamp, to_timestamp)
log.info("Executing query: {0}".format(query_to_execute))
result = influx_client.query(query_to_execute) # execute the query
-
- # iterate through each result item
- for item in result.items():
- metadata, result_points = item # each result item is a tuple of two elements
-
- # metadata consists of the result tags and the measurement name
- # measurement = metadata[0]
- tags = metadata[1]
-
- result_point = next(result_points) # get the result point dictionary
- response_time = result_point["mean_response_time"] # extract the response time of the SF from the result
- request_size = result_point["mean_request_size"] # extract the avg request size of the SF from the result
- response_size = result_point["mean_response_size"] # extract the avg response size of the SF from the result
-
- # create a ServiceFunction node from the tag value (if it is not already created)
- service_function_node = find_or_create_node(graph, "ServiceFunction", name=tags["flame_sf"])
- # create an edge between the the service function and the package (if it is not already created)
- find_or_create_edge(graph, "instanceOf", service_function_node, service_function_package_node)
- # crate a utilizedBy edge between the service function and the service function chain instance
- find_or_create_edge(graph, "utilizedBy", service_function_node, service_function_chain_instance_node)
-
- # create an Endpoint node from the tag value (if it is not already created)
- ipendpoint_node = find_or_create_node(graph, "Endpoint", name=tags["flame_sfe"], response_time=response_time, request_size=request_size, response_size=response_size, uuid=request_id)
- # create an edge between the service function and the endpoint (if it is not already created)
- find_or_create_edge(graph, "realisedBy", service_function_node, ipendpoint_node)
-
- # create a Cluster node from the tag value (if it is not already created)
- compute_node = find_or_create_node(graph, "Cluster", name=tags["flame_location"])
- # create an edge between the endpoint and the compute node (if it is not already created)
- find_or_create_edge(graph, "hostedBy", ipendpoint_node, compute_node)
-
- compute_nodes.add(compute_node) # add the compute node to the set of compute nodes
+ result_items = result.items()
+
+ if len(result_items) > 0:
+ # create a node for the service function if it doesn't exist
+ service_function_package_node = find_or_create_node(graph, "ServiceFunctionPackage", name=service_function_package)
+ # crate a utilizedBy edge between the service function and the service function chain
+ find_or_create_edge(graph, "utilizedBy", service_function_package_node, service_function_chain_node)
+
+ # iterate through each result item
+ for item in result_items:
+ metadata, result_points = item # each result item is a tuple of two elements
+
+ # metadata consists of the result tags and the measurement name
+ # measurement = metadata[0]
+ tags = metadata[1]
+
+ result_point = next(result_points) # get the result point dictionary
+ response_time = result_point["mean_response_time"] # extract the response time of the SF from the result
+ request_size = result_point["mean_request_size"] # extract the avg request size of the SF from the result
+ response_size = result_point["mean_response_size"] # extract the avg response size of the SF from the result
+
+ # create a ServiceFunction node from the tag value (if it is not already created)
+ service_function_node = find_or_create_node(graph, "ServiceFunction", name=tags["flame_sf"])
+ # create an edge between the the service function and the package (if it is not already created)
+ find_or_create_edge(graph, "instanceOf", service_function_node, service_function_package_node)
+ # crate a utilizedBy edge between the service function and the service function chain instance
+ find_or_create_edge(graph, "utilizedBy", service_function_node, service_function_chain_instance_node)
+
+ # create an Endpoint node from the tag value (if it is not already created)
+ ipendpoint_node = find_or_create_node(graph, "Endpoint", name=tags["flame_sfe"], response_time=response_time, request_size=request_size, response_size=response_size, uuid=request_id)
+ # create an edge between the service function and the endpoint (if it is not already created)
+ find_or_create_edge(graph, "realisedBy", service_function_node, ipendpoint_node)
+
+ # create a Cluster node from the tag value (if it is not already created)
+ compute_node = find_or_create_node(graph, "Cluster", name=tags["flame_location"])
+ # create an edge between the endpoint and the compute node (if it is not already created)
+ find_or_create_edge(graph, "hostedBy", ipendpoint_node, compute_node)
+
+ compute_nodes.add(compute_node) # add the compute node to the set of compute nodes
log.info("Finished building graph for service function chain {0} from database {1} with retention policy {2}".format(sfci, db, rp))
diff --git a/src/service/clmcservice/graphapi/views.py b/src/service/clmcservice/graphapi/views.py
index 9bac0e146306b5e86d6dcd935c81f58d219befc5..21f218c00e914bd4136b5b7f601bcca6b2ce417a 100644
--- a/src/service/clmcservice/graphapi/views.py
+++ b/src/service/clmcservice/graphapi/views.py
@@ -82,6 +82,9 @@ class GraphAPI(object):
build_temporal_subgraph(request_id, from_timestamp, to_timestamp, json_queries, graph, influx_client)
json_queries['graph'] = {"uuid": request_id, "time_range": {"from": from_timestamp, "to": to_timestamp}}
+ json_queries.pop("from")
+ json_queries.pop("to")
+
return json_queries
@view_config(route_name='graph_manage', request_method='DELETE')