550 lines
25 KiB
Python
550 lines
25 KiB
Python
# (c) Copyright 2017 SUSE LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import json
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import unittest
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import mock
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from oslo_config import cfg
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from pyspark.streaming.kafka import OffsetRange
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from tests.functional.spark_context_test import SparkContextTest
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from tests.functional.test_resources.fetch_quantity_data.data_provider \
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import DataProvider
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from tests.functional.test_resources.mock_component_manager \
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import MockComponentManager
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from tests.functional.test_resources.mock_data_driven_specs_repo \
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import MockDataDrivenSpecsRepo
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from monasca_transform.config.config_initializer import ConfigInitializer
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from monasca_transform.driver.mon_metrics_kafka \
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import MonMetricsKafkaProcessor
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from monasca_transform.transform import RddTransformContext
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from monasca_transform.transform import TransformContextUtils
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from tests.functional.messaging.adapter import DummyAdapter
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class TestPodNetUsageAgg(SparkContextTest):
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def setUp(self):
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super(TestPodNetUsageAgg, self).setUp()
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# configure the system with a dummy messaging adapter
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ConfigInitializer.basic_config(
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default_config_files=[
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'tests/functional/test_resources/config/'
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'test_config_with_dummy_messaging_adapter.conf'])
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# reset metric_id list dummy adapter
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if not DummyAdapter.adapter_impl:
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DummyAdapter.init()
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DummyAdapter.adapter_impl.metric_list = []
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def get_pre_transform_specs_json_all(self):
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"""get pre_transform_specs driver table info."""
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pre_transform_spec_json = """
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{"event_processing_params":{"set_default_zone_to":"1",
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"set_default_geolocation_to":"1",
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"set_default_region_to":"W"},
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"event_type":"pod.net.in_bytes_sec",
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"metric_id_list":["pod_net_in_b_per_sec_total_all"],
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"required_raw_fields_list":["creation_time",
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"meta#tenantId",
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"dimensions#namespace",
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"dimensions#pod_name",
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"dimensions#app"],
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"service_id":"host_metrics"}"""
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return [json.loads(pre_transform_spec_json)]
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def get_transform_specs_json_all(self):
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"""get transform_specs driver table info."""
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transform_spec_json_all = """
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{"aggregation_params_map":{
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"aggregation_pipeline":{"source":"streaming",
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"usage":"fetch_quantity",
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"setters":["rollup_quantity",
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"set_aggregated_metric_name",
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"set_aggregated_period"],
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"insert":["prepare_data",
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"insert_data_pre_hourly"]},
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"aggregated_metric_name":"pod.net.in_bytes_sec_agg",
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"aggregation_period":"hourly",
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"aggregation_group_by_list": ["tenant_id",
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"dimensions#app",
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"dimensions#namespace",
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"dimensions#pod_name",
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"dimensions#interface",
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"dimensions#deployment"],
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"usage_fetch_operation": "avg",
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"filter_by_list": [],
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"setter_rollup_group_by_list":[],
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"setter_rollup_operation": "sum",
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"dimension_list":["aggregation_period",
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"dimensions#app",
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"dimensions#namespace",
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"dimensions#pod_name"],
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"pre_hourly_operation":"avg",
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"pre_hourly_group_by_list":["default"]},
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"metric_group":"pod_net_in_b_per_sec_total_all",
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"metric_id":"pod_net_in_b_per_sec_total_all"}"""
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return [json.loads(transform_spec_json_all)]
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def get_pre_transform_specs_json_namespace(self):
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"""get pre_transform_specs driver table info."""
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pre_transform_spec_json = """
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{"event_processing_params":{"set_default_zone_to":"1",
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"set_default_geolocation_to":"1",
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"set_default_region_to":"W"},
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"event_type":"pod.net.in_bytes_sec",
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"metric_id_list":["pod_net_in_b_per_sec_per_namespace"],
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"required_raw_fields_list":["creation_time",
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"meta#tenantId",
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"dimensions#namespace",
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"dimensions#pod_name",
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"dimensions#app"],
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"service_id":"host_metrics"}"""
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return [json.loads(pre_transform_spec_json)]
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def get_transform_specs_json_namespace(self):
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"""get transform_specs driver table info."""
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transform_spec_json_namespace = """
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{"aggregation_params_map":{
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"aggregation_pipeline":{"source":"streaming",
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"usage":"fetch_quantity",
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"setters":["rollup_quantity",
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"set_aggregated_metric_name",
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"set_aggregated_period"],
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"insert":["prepare_data",
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"insert_data_pre_hourly"]},
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"aggregated_metric_name":"pod.net.in_bytes_sec_agg",
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"aggregation_period":"hourly",
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"aggregation_group_by_list": ["tenant_id",
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"dimensions#app",
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"dimensions#namespace",
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"dimensions#pod_name"],
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"usage_fetch_operation": "avg",
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"filter_by_list": [],
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"setter_rollup_group_by_list":["dimensions#namespace"],
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"setter_rollup_operation": "sum",
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"dimension_list":["aggregation_period",
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"dimensions#app",
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"dimensions#namespace",
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"dimensions#pod_name"],
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"pre_hourly_operation":"avg",
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"pre_hourly_group_by_list":["aggregation_period",
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"dimensions#namespace]'"]},
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"metric_group":"pod_net_in_b_per_sec_per_namespace",
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"metric_id":"pod_net_in_b_per_sec_per_namespace"}"""
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return [json.loads(transform_spec_json_namespace)]
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def get_pre_transform_specs_json_app(self):
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"""get pre_transform_specs driver table info."""
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pre_transform_spec_json = """
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{"event_processing_params":{"set_default_zone_to":"1",
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"set_default_geolocation_to":"1",
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"set_default_region_to":"W"},
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"event_type":"pod.net.in_bytes_sec",
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"metric_id_list":["pod_net_in_b_per_sec_per_app"],
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"required_raw_fields_list":["creation_time",
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"meta#tenantId",
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"dimensions#namespace",
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"dimensions#pod_name",
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"dimensions#app"],
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"service_id":"host_metrics"}"""
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return [json.loads(pre_transform_spec_json)]
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def get_transform_specs_json_app(self):
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"""get transform_specs driver table info."""
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transform_spec_json_app = """
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{"aggregation_params_map":{
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"aggregation_pipeline":{"source":"streaming",
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"usage":"fetch_quantity",
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"setters":["rollup_quantity",
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"set_aggregated_metric_name",
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"set_aggregated_period"],
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"insert":["prepare_data",
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"insert_data_pre_hourly"]},
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"aggregated_metric_name":"pod.net.in_bytes_sec_agg",
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"aggregation_period":"hourly",
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"aggregation_group_by_list": ["tenant_id",
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"dimensions#app",
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"dimensions#namespace",
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"dimensions#pod_name"],
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"usage_fetch_operation": "avg",
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"filter_by_list": [],
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"setter_rollup_group_by_list":["dimensions#app"],
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"setter_rollup_operation": "sum",
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"dimension_list":["aggregation_period",
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"dimensions#app",
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"dimensions#namespace",
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"dimensions#pod_name"],
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"pre_hourly_operation":"avg",
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"pre_hourly_group_by_list":["geolocation",
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"region",
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"zone",
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"aggregated_metric_name",
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"aggregation_period",
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"dimensions#app"]},
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"metric_group":"pod_net_in_b_per_sec_per_app",
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"metric_id":"pod_net_in_b_per_sec_per_app"}"""
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return [json.loads(transform_spec_json_app)]
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@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
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'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_insert_component_manager')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_setter_component_manager')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_usage_component_manager')
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def test_pod_net_in_usage_all(self,
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usage_manager,
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setter_manager,
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insert_manager,
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data_driven_specs_repo):
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# load components
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usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
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setter_manager.return_value = \
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MockComponentManager.get_setter_cmpt_mgr()
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insert_manager.return_value = \
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MockComponentManager.get_insert_cmpt_mgr()
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# init mock driver tables
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data_driven_specs_repo.return_value = \
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MockDataDrivenSpecsRepo(self.spark_context,
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self.get_pre_transform_specs_json_all(),
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self.get_transform_specs_json_all())
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# Create an emulated set of Kafka messages (these were gathered
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# by extracting Monasca messages from the Metrics queue on mini-mon).
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# Create an RDD out of the mocked Monasca metrics
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with open(DataProvider.fetch_quantity_data_path) as f:
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raw_lines = f.read().splitlines()
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raw_tuple_list = [eval(raw_line) for raw_line in raw_lines]
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rdd_monasca = self.spark_context.parallelize(raw_tuple_list)
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# decorate mocked RDD with dummy kafka offsets
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myOffsetRanges = [
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OffsetRange("metrics", 1, 10, 20)] # mimic rdd.offsetRanges()
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transform_context = TransformContextUtils.get_context(
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offset_info=myOffsetRanges,
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batch_time_info=self.get_dummy_batch_time())
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rdd_monasca_with_offsets = rdd_monasca.map(
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lambda x: RddTransformContext(x, transform_context))
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# Call the primary method in mon_metrics_kafka
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MonMetricsKafkaProcessor.rdd_to_recordstore(
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rdd_monasca_with_offsets)
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# get the metrics that have been submitted to the dummy message adapter
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metrics = DummyAdapter.adapter_impl.metric_list
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pod_net_usage_agg_metric = [
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value for value in metrics
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if value.get('metric').get('name') ==
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'pod.net.in_bytes_sec_agg' and
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value.get('metric').get('dimensions').get('app') ==
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'all' and
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value.get('metric').get('dimensions').get('namespace') ==
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'all' and
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value.get('metric').get('dimensions').get('pod_name') ==
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'all'][0]
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self.assertTrue(pod_net_usage_agg_metric is not None)
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self.assertEqual('pod.net.in_bytes_sec_agg',
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pod_net_usage_agg_metric
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.get('metric').get('name'))
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self.assertEqual('all',
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pod_net_usage_agg_metric
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.get("metric").get('dimensions').get('app'))
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self.assertEqual('all',
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pod_net_usage_agg_metric
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.get("metric").get('dimensions').get('namespace'))
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self.assertEqual('all',
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pod_net_usage_agg_metric
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.get("metric").get('dimensions').get('pod_name'))
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self.assertEqual(145.88,
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pod_net_usage_agg_metric
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.get('metric').get('value'))
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self.assertEqual('useast',
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pod_net_usage_agg_metric
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.get('meta').get('region'))
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self.assertEqual(cfg.CONF.messaging.publish_kafka_project_id,
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pod_net_usage_agg_metric
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.get('meta').get('tenantId'))
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self.assertEqual('hourly',
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pod_net_usage_agg_metric
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.get('metric').get('dimensions')
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.get('aggregation_period'))
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self.assertEqual(6.0,
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pod_net_usage_agg_metric
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.get('metric').get('value_meta').get('record_count'))
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self.assertEqual('2017-01-24 20:14:47',
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pod_net_usage_agg_metric
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.get('metric').get('value_meta')
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.get('firstrecord_timestamp_string'))
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self.assertEqual('2017-01-24 20:15:47',
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pod_net_usage_agg_metric
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.get('metric').get('value_meta')
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.get('lastrecord_timestamp_string'))
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@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
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'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_insert_component_manager')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_setter_component_manager')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_usage_component_manager')
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def test_pod_net_in_usage_namespace(self,
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usage_manager,
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setter_manager,
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insert_manager,
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data_driven_specs_repo):
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# load components
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usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
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setter_manager.return_value = \
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MockComponentManager.get_setter_cmpt_mgr()
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insert_manager.return_value = \
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MockComponentManager.get_insert_cmpt_mgr()
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# init mock driver tables
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data_driven_specs_repo.return_value = \
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MockDataDrivenSpecsRepo(self.spark_context,
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self.get_pre_transform_specs_json_namespace(),
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self.get_transform_specs_json_namespace())
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# Create an emulated set of Kafka messages (these were gathered
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# by extracting Monasca messages from the Metrics queue on mini-mon).
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# Create an RDD out of the mocked Monasca metrics
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with open(DataProvider.fetch_quantity_data_path) as f:
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raw_lines = f.read().splitlines()
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raw_tuple_list = [eval(raw_line) for raw_line in raw_lines]
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rdd_monasca = self.spark_context.parallelize(raw_tuple_list)
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# decorate mocked RDD with dummy kafka offsets
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myOffsetRanges = [
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OffsetRange("metrics", 1, 10, 20)] # mimic rdd.offsetRanges()
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transform_context = TransformContextUtils.get_context(
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offset_info=myOffsetRanges,
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batch_time_info=self.get_dummy_batch_time())
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rdd_monasca_with_offsets = rdd_monasca.map(
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lambda x: RddTransformContext(x, transform_context))
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# Call the primary method in mon_metrics_kafka
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MonMetricsKafkaProcessor.rdd_to_recordstore(
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rdd_monasca_with_offsets)
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# get the metrics that have been submitted to the dummy message adapter
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metrics = DummyAdapter.adapter_impl.metric_list
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pod_net_usage_agg_metric = [
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value for value in metrics
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if value.get('metric').get('name') ==
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'pod.net.in_bytes_sec_agg' and
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value.get('metric').get('dimensions').get('app') ==
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'all' and
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value.get('metric').get('dimensions').get('namespace') ==
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'website' and
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value.get('metric').get('dimensions').get('pod_name') ==
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'all'][0]
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self.assertTrue(pod_net_usage_agg_metric is not None)
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self.assertEqual('pod.net.in_bytes_sec_agg',
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pod_net_usage_agg_metric
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.get('metric').get('name'))
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self.assertEqual('all',
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pod_net_usage_agg_metric
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.get("metric").get('dimensions').get('app'))
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self.assertEqual('website',
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pod_net_usage_agg_metric
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.get("metric").get('dimensions').get('namespace'))
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self.assertEqual('all',
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pod_net_usage_agg_metric
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.get("metric").get('dimensions').get('pod_name'))
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self.assertEqual(22.94,
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pod_net_usage_agg_metric
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.get('metric').get('value'))
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self.assertEqual('useast',
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pod_net_usage_agg_metric
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.get('meta').get('region'))
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self.assertEqual(cfg.CONF.messaging.publish_kafka_project_id,
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pod_net_usage_agg_metric
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.get('meta').get('tenantId'))
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self.assertEqual('hourly',
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pod_net_usage_agg_metric
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.get('metric').get('dimensions')
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.get('aggregation_period'))
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self.assertEqual(3.0,
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pod_net_usage_agg_metric
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.get('metric').get('value_meta').get('record_count'))
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self.assertEqual('2017-01-24 20:14:47',
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pod_net_usage_agg_metric
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.get('metric').get('value_meta')
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.get('firstrecord_timestamp_string'))
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self.assertEqual('2017-01-24 20:15:47',
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pod_net_usage_agg_metric
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.get('metric').get('value_meta')
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.get('lastrecord_timestamp_string'))
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@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
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'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_insert_component_manager')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_setter_component_manager')
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@mock.patch('monasca_transform.transform.builder.'
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'generic_transform_builder.GenericTransformBuilder.'
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'_get_usage_component_manager')
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def test_pod_net_in_usage_app(self,
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usage_manager,
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setter_manager,
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insert_manager,
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data_driven_specs_repo):
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# load components
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usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
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setter_manager.return_value = \
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MockComponentManager.get_setter_cmpt_mgr()
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insert_manager.return_value = \
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MockComponentManager.get_insert_cmpt_mgr()
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# init mock driver tables
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data_driven_specs_repo.return_value = \
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MockDataDrivenSpecsRepo(self.spark_context,
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self.get_pre_transform_specs_json_app(),
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self.get_transform_specs_json_app())
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# Create an emulated set of Kafka messages (these were gathered
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|
# by extracting Monasca messages from the Metrics queue on mini-mon).
|
|
|
|
# Create an RDD out of the mocked Monasca metrics
|
|
with open(DataProvider.fetch_quantity_data_path) as f:
|
|
raw_lines = f.read().splitlines()
|
|
raw_tuple_list = [eval(raw_line) for raw_line in raw_lines]
|
|
|
|
rdd_monasca = self.spark_context.parallelize(raw_tuple_list)
|
|
|
|
# decorate mocked RDD with dummy kafka offsets
|
|
myOffsetRanges = [
|
|
OffsetRange("metrics", 1, 10, 20)] # mimic rdd.offsetRanges()
|
|
|
|
transform_context = TransformContextUtils.get_context(
|
|
offset_info=myOffsetRanges,
|
|
batch_time_info=self.get_dummy_batch_time())
|
|
|
|
rdd_monasca_with_offsets = rdd_monasca.map(
|
|
lambda x: RddTransformContext(x, transform_context))
|
|
|
|
# Call the primary method in mon_metrics_kafka
|
|
MonMetricsKafkaProcessor.rdd_to_recordstore(
|
|
rdd_monasca_with_offsets)
|
|
|
|
# get the metrics that have been submitted to the dummy message adapter
|
|
metrics = DummyAdapter.adapter_impl.metric_list
|
|
|
|
pod_net_usage_agg_metric = [
|
|
value for value in metrics
|
|
if value.get('metric').get('name') ==
|
|
'pod.net.in_bytes_sec_agg' and
|
|
value.get('metric').get('dimensions').get('app') ==
|
|
'junk' and
|
|
value.get('metric').get('dimensions').get('namespace') ==
|
|
'all' and
|
|
value.get('metric').get('dimensions').get('pod_name') ==
|
|
'all'][0]
|
|
|
|
self.assertTrue(pod_net_usage_agg_metric is not None)
|
|
|
|
self.assertEqual('pod.net.in_bytes_sec_agg',
|
|
pod_net_usage_agg_metric
|
|
.get('metric').get('name'))
|
|
|
|
self.assertEqual('junk',
|
|
pod_net_usage_agg_metric
|
|
.get("metric").get('dimensions').get('app'))
|
|
|
|
self.assertEqual('all',
|
|
pod_net_usage_agg_metric
|
|
.get("metric").get('dimensions').get('namespace'))
|
|
|
|
self.assertEqual('all',
|
|
pod_net_usage_agg_metric
|
|
.get("metric").get('dimensions').get('pod_name'))
|
|
|
|
self.assertEqual(122.94,
|
|
pod_net_usage_agg_metric
|
|
.get('metric').get('value'))
|
|
self.assertEqual('useast',
|
|
pod_net_usage_agg_metric
|
|
.get('meta').get('region'))
|
|
|
|
self.assertEqual(cfg.CONF.messaging.publish_kafka_project_id,
|
|
pod_net_usage_agg_metric
|
|
.get('meta').get('tenantId'))
|
|
|
|
self.assertEqual('hourly',
|
|
pod_net_usage_agg_metric
|
|
.get('metric').get('dimensions')
|
|
.get('aggregation_period'))
|
|
|
|
self.assertEqual(3.0,
|
|
pod_net_usage_agg_metric
|
|
.get('metric').get('value_meta').get('record_count'))
|
|
self.assertEqual('2017-01-24 20:14:47',
|
|
pod_net_usage_agg_metric
|
|
.get('metric').get('value_meta')
|
|
.get('firstrecord_timestamp_string'))
|
|
self.assertEqual('2017-01-24 20:15:47',
|
|
pod_net_usage_agg_metric
|
|
.get('metric').get('value_meta')
|
|
.get('lastrecord_timestamp_string'))
|
|
|
|
if __name__ == "__main__":
|
|
print("PATH *************************************************************")
|
|
import sys
|
|
print(sys.path)
|
|
print("PATH==============================================================")
|
|
unittest.main()
|