add test cases for fetch_quantity_prehourly_instance_usage
Removed dependency of yaml module. Change-Id: I87ce80d420bc75ddbef2c8454f088be25f9ff908
This commit is contained in:
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# Copyright 2016 Hewlett Packard Enterprise Development Company LP
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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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from monasca_transform.component.insert import InsertComponent
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from oslo_config import cfg
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from tests.unit.messaging.adapter import DummyAdapter
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class DummyInsertPreHourly(InsertComponent):
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"""Insert component that writes metric data to
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to kafka queue
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"""
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@staticmethod
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def insert(transform_context, instance_usage_df):
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"""write instance usage data to kafka"""
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transform_spec_df = transform_context.transform_spec_df_info
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agg_params = transform_spec_df.select("metric_id"
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).collect()[0].asDict()
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metric_id = agg_params['metric_id']
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cfg.CONF.set_override('adapter',
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'tests.unit.messaging.adapter:DummyAdapter',
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group='messaging')
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# Approach 1
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# using foreachPartition to iterate through elements in an
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# RDD is the recommended approach so as to not overwhelm kafka with the
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# zillion connections (but in our case the MessageAdapter does
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# store the adapter_impl so we should not create many producers)
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# using foreachpartitions was causing some serialization (cpickle)
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# problems where few libs like kafka.SimpleProducer and oslo_config.cfg
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# were not available
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#
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# removing _write_metrics_from_partition for now in favor of
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# Approach 2
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#
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# instance_usage_df_agg_params = instance_usage_df.rdd.map(
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# lambda x: InstanceUsageDataAggParams(x,
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# agg_params))
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# instance_usage_df_agg_params.foreachPartition(
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# DummyInsert._write_metrics_from_partition)
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#
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# Approach # 2
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#
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# using collect() to fetch all elements of an RDD
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# and write to kafka
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#
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for instance_usage_row in instance_usage_df.collect():
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instance_usage_dict = InsertComponent\
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._get_instance_usage_pre_hourly(instance_usage_row, metric_id)
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DummyAdapter.send_metric(instance_usage_dict)
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return instance_usage_df
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@ -16,10 +16,10 @@ import unittest
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from pyspark.streaming.kafka import OffsetRange
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from monasca_transform.component.insert.dummy_insert import DummyInsert
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from monasca_transform.config.config_initializer import ConfigInitializer
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from monasca_transform.processor.pre_hourly_processor import PreHourlyProcessor
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from tests.unit.component.insert.dummy_insert import DummyInsert
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from tests.unit.messaging.adapter import DummyAdapter
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from tests.unit.spark_context_test import SparkContextTest
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from tests.unit.test_resources.metrics_pre_hourly_data.data_provider \
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# Copyright 2016 Hewlett Packard Enterprise Development Company LP
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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 os
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class DataProvider(object):
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_resource_path = 'tests/unit/test_resources/'\
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'fetch_quantity_data_second_stage/'
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fetch_quantity_data_path = os.path.join(
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_resource_path, "fetch_quantity_data_second_stage.txt")
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{'usage_hour': '16', 'geolocation': 'all', 'record_count': 4.0, 'resource_uuid': 'all', 'host': 'mini-mon', 'aggregation_period': 'prehourly', 'usage_minute': 'all', 'service_group': 'all', 'lastrecord_timestamp_string': '2016-01-20 16:50:00', 'aggregated_metric_name': 'mem.total_mb_agg', 'user_id': 'all', 'zone': 'all', 'tenant_id': 'all', 'region': 'all', 'usage_date': '2016-01-20', 'firstrecord_timestamp_string': '2016-01-20 16:40:00', 'processing_meta': {'metric_id': 'mem_total_all'}, 'firstrecord_timestamp_unix': 1453308000.0, 'service_id': 'all', 'project_id': 'all', 'lastrecord_timestamp_unix': 1453308600.0, 'quantity': 1234.0}
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{'usage_hour': '16', 'geolocation': 'all', 'record_count': 7.0, 'resource_uuid': 'all', 'host': 'mini-mon', 'aggregation_period': 'prehourly', 'usage_minute': 'all', 'service_group': 'all', 'lastrecord_timestamp_string': '2016-01-20 17:00:00', 'aggregated_metric_name': 'mem.total_mb_agg', 'user_id': 'all', 'zone': 'all', 'tenant_id': 'all', 'region': 'all', 'usage_date': '2016-01-20', 'firstrecord_timestamp_string': '2016-01-20 16:50:00', 'processing_meta': {'metric_id': 'mem_total_all'}, 'firstrecord_timestamp_unix': 1453308600.0, 'service_id': 'all', 'project_id': 'all', 'lastrecord_timestamp_unix': 1453309200.0, 'quantity': 1213.0}
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{'usage_hour': '16', 'geolocation': 'all', 'record_count': 8.0, 'resource_uuid': 'all', 'host': 'mini-mon', 'aggregation_period': 'prehourly', 'usage_minute': 'all', 'service_group': 'all', 'lastrecord_timestamp_string': '2016-01-20 17:10:00', 'aggregated_metric_name': 'mem.total_mb_agg', 'user_id': 'all', 'zone': 'all', 'tenant_id': 'all', 'region': 'all', 'usage_date': '2016-01-20', 'firstrecord_timestamp_string': '2016-01-20 17:00:00', 'processing_meta': {'metric_id': 'mem_total_all'}, 'firstrecord_timestamp_unix': 1453309200.0, 'service_id': 'all', 'project_id': 'all', 'lastrecord_timestamp_unix': 1453309800.0, 'quantity': 1314.0}
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{'usage_hour': '16', 'geolocation': 'all', 'record_count': 5.0, 'resource_uuid': 'all', 'host': 'mini-mon', 'aggregation_period': 'prehourly', 'usage_minute': 'all', 'service_group': 'all', 'lastrecord_timestamp_string': '2016-01-20 17:20:00', 'aggregated_metric_name': 'mem.total_mb_agg', 'user_id': 'all', 'zone': 'all', 'tenant_id': 'all', 'region': 'all', 'usage_date': '2016-01-20', 'firstrecord_timestamp_string': '2016-01-20 17:10:00', 'processing_meta': {'metric_id': 'mem_total_all'}, 'firstrecord_timestamp_unix': 1453309800.0, 'service_id': 'all', 'project_id': 'all', 'lastrecord_timestamp_unix': 1453310400.0, 'quantity': 2318.0}
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{'usage_hour': '16', 'geolocation': 'all', 'record_count': 9.0, 'resource_uuid': 'all', 'host': 'mini-mon', 'aggregation_period': 'prehourly', 'usage_minute': 'all', 'service_group': 'all', 'lastrecord_timestamp_string': '2016-01-20 17:30:00', 'aggregated_metric_name': 'mem.total_mb_agg', 'user_id': 'all', 'zone': 'all', 'tenant_id': 'all', 'region': 'all', 'usage_date': '2016-01-20', 'firstrecord_timestamp_string': '2016-01-20 17:20:00', 'processing_meta': {'metric_id': 'mem_total_all'}, 'firstrecord_timestamp_unix': 1453310400.0, 'service_id': 'all', 'project_id': 'all', 'lastrecord_timestamp_unix': 1453311000.0, 'quantity': 1218.0}
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{'usage_hour': '16', 'geolocation': 'all', 'record_count': 6.0, 'resource_uuid': 'all', 'host': 'mini-mon', 'aggregation_period': 'prehourly', 'usage_minute': 'all', 'service_group': 'all', 'lastrecord_timestamp_string': '2016-01-20 17:40:00', 'aggregated_metric_name': 'mem.total_mb_agg', 'user_id': 'all', 'zone': 'all', 'tenant_id': 'all', 'region': 'all', 'usage_date': '2016-01-20', 'firstrecord_timestamp_string': '2016-01-20 17:30:00', 'processing_meta': {'metric_id': 'mem_total_all'}, 'firstrecord_timestamp_unix': 1453311000.0, 'service_id': 'all', 'project_id': 'all', 'lastrecord_timestamp_unix': 1453311600.0, 'quantity': 1382.0}
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@ -16,9 +16,7 @@ from stevedore.extension import Extension
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from stevedore.extension import ExtensionManager
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from monasca_transform.component.insert.dummy_insert import DummyInsert
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from monasca_transform.component.insert.prepare_data import PrepareData
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from monasca_transform.component.setter.rollup_quantity \
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import RollupQuantity
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from monasca_transform.component.setter.set_aggregated_metric_name \
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@ -31,6 +29,9 @@ from monasca_transform.component.usage.fetch_quantity \
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import FetchQuantity
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from monasca_transform.component.usage.fetch_quantity_util \
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import FetchQuantityUtil
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from tests.unit.component.insert.dummy_insert import DummyInsert
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from tests.unit.component.insert.dummy_insert_pre_hourly \
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import DummyInsertPreHourly
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class MockComponentManager(object):
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PrepareData(),
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None),
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Extension('insert_data',
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'monasca_transform.component.insert.dummy_insert:'
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'tests.unit.component.insert.dummy_insert:'
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'DummyInsert',
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DummyInsert(),
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None),
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Extension('insert_data_pre_hourly',
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'monasca_transform.component.insert.dummy_insert:'
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'tests.unit.component.insert.dummy_insert:'
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'DummyInsert',
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DummyInsert(),
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None),
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])
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@staticmethod
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def get_insert_pre_hourly_cmpt_mgr():
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return ExtensionManager.make_test_instance([Extension(
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'prepare_data',
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'monasca_transform.component.insert.prepare_data:PrepareData',
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PrepareData(),
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None),
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Extension('insert_data',
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'tests.unit.component.insert.dummy_insert:'
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'DummyInsert',
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DummyInsert(),
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None),
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Extension('insert_data_pre_hourly',
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'tests.unit.component.insert.'
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'dummy_insert_pre_hourly:'
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'DummyInsertPreHourly',
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DummyInsertPreHourly(),
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None),
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])
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import json
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def dump_as_ascii_string(dict_obj):
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return json.dumps(dict_obj, ensure_ascii=True)
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@ -0,0 +1,985 @@
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# Copyright 2016 Hewlett Packard Enterprise Development Company LP
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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 mock
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import unittest
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from oslo_config import cfg
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from pyspark.sql import SQLContext
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from pyspark.streaming.kafka import OffsetRange
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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.processor.pre_hourly_processor import PreHourlyProcessor
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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.unit.component.insert.dummy_insert import DummyInsert
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from tests.unit.messaging.adapter import DummyAdapter
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from tests.unit.spark_context_test import SparkContextTest
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from tests.unit.test_resources.fetch_quantity_data.data_provider \
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import DataProvider
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from tests.unit.test_resources.fetch_quantity_data_second_stage.data_provider \
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import DataProvider as SecondStageDataProvider
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from tests.unit.test_resources.mock_component_manager \
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import MockComponentManager
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from tests.unit.test_resources.mock_data_driven_specs_repo \
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import MockDataDrivenSpecsRepo
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from tests.unit.usage import dump_as_ascii_string
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class TestFetchQuantityInstanceUsageAgg(SparkContextTest):
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def setUp(self):
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super(TestFetchQuantityInstanceUsageAgg, 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/unit/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(self):
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"""get pre_transform_specs driver table info."""
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pre_transform_specs_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":"mem.total_mb",
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"metric_id_list":["mem_total_all"],
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"required_raw_fields_list":["creation_time"],
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"service_id":"host_metrics"}"""
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return [json.loads(pre_transform_specs_json)]
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def get_transform_specs_json_by_operation(self,
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usage_fetch_operation):
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"""get transform_specs driver table info."""
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transform_specs_json = """
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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": "mem.total_mb_agg",
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"aggregation_period": "hourly",
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"aggregation_group_by_list": ["host", "metric_id"],
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"usage_fetch_operation": "%s",
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"setter_rollup_group_by_list": ["host"],
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"setter_rollup_operation": "sum",
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"pre_hourly_operation":"%s",
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"pre_hourly_group_by_list":["default"],
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"dimension_list":["aggregation_period",
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"host",
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"project_id"]
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},
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"metric_group":"mem_total_all",
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"metric_id":"mem_total_all"}"""
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transform_specs_json_operation = \
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transform_specs_json % (usage_fetch_operation,
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usage_fetch_operation)
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return [json.loads(transform_specs_json_operation)]
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@mock.patch('monasca_transform.processor.pre_hourly_processor.KafkaInsert',
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DummyInsert)
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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.'
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'builder.generic_transform_builder.GenericTransformBuilder.'
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'_get_usage_component_manager')
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def test_fetch_quantity_max(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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# test operation
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test_operation = "max"
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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_pre_hourly_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(),
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self.get_transform_specs_json_by_operation(
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test_operation))
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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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instance_usage_list = DummyAdapter.adapter_impl.metric_list
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instance_usage_list = map(dump_as_ascii_string,
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instance_usage_list)
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DummyAdapter.adapter_impl.metric_list = []
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instance_usage_rdd = self.spark_context.parallelize(
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instance_usage_list)
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sql_context = SQLContext(self.spark_context)
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instance_usage_df = sql_context.read.json(instance_usage_rdd)
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PreHourlyProcessor.do_transform(instance_usage_df)
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metrics = DummyAdapter.adapter_impl.metric_list
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mem_total_mb_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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'mem.total_mb_agg' and
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value.get('metric').get('dimensions').get('host') ==
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'mini-mon'][0]
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self.assertTrue(mem_total_mb_agg_metric is not None)
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self.assertEqual('mem.total_mb_agg',
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mem_total_mb_agg_metric
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.get('metric').get('name'))
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self.assertEqual(8192.0,
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mem_total_mb_agg_metric
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.get('metric').get('value'))
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self.assertEqual('useast',
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mem_total_mb_agg_metric
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.get('meta').get('region'))
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self.assertEqual(cfg.CONF.messaging.publish_kafka_tenant_id,
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mem_total_mb_agg_metric
|
||||
.get('meta').get('tenantId'))
|
||||
self.assertEqual('mini-mon',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('host'))
|
||||
self.assertEqual('all',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('project_id'))
|
||||
self.assertEqual('hourly',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions')
|
||||
.get('aggregation_period'))
|
||||
self.assertEqual(4.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta').get('record_count'))
|
||||
self.assertEqual('2016-01-20 16:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('firstrecord_timestamp_string'))
|
||||
self.assertEqual('2016-01-20 16:40:46',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('lastrecord_timestamp_string'))
|
||||
|
||||
@mock.patch('monasca_transform.processor.pre_hourly_processor.KafkaInsert',
|
||||
DummyInsert)
|
||||
@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
|
||||
'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_insert_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_setter_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_usage_component_manager')
|
||||
def test_fetch_quantity_min(self,
|
||||
usage_manager,
|
||||
setter_manager,
|
||||
insert_manager,
|
||||
data_driven_specs_repo):
|
||||
|
||||
# test operation
|
||||
test_operation = "min"
|
||||
|
||||
# load components
|
||||
usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
|
||||
setter_manager.return_value = \
|
||||
MockComponentManager.get_setter_cmpt_mgr()
|
||||
insert_manager.return_value = \
|
||||
MockComponentManager.get_insert_pre_hourly_cmpt_mgr()
|
||||
|
||||
# init mock driver tables
|
||||
data_driven_specs_repo.return_value = \
|
||||
MockDataDrivenSpecsRepo(self.spark_context,
|
||||
self.get_pre_transform_specs_json(),
|
||||
self.get_transform_specs_json_by_operation(
|
||||
test_operation))
|
||||
|
||||
# Create an emulated set of Kafka messages (these were gathered
|
||||
# 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
|
||||
instance_usage_list = DummyAdapter.adapter_impl.metric_list
|
||||
instance_usage_list = map(dump_as_ascii_string,
|
||||
instance_usage_list)
|
||||
DummyAdapter.adapter_impl.metric_list = []
|
||||
instance_usage_rdd = self.spark_context.parallelize(
|
||||
instance_usage_list)
|
||||
sql_context = SQLContext(self.spark_context)
|
||||
instance_usage_df = sql_context.read.json(instance_usage_rdd)
|
||||
PreHourlyProcessor.do_transform(instance_usage_df)
|
||||
|
||||
metrics = DummyAdapter.adapter_impl.metric_list
|
||||
mem_total_mb_agg_metric = [
|
||||
value for value in metrics
|
||||
if value.get('metric').get('name') ==
|
||||
'mem.total_mb_agg' and
|
||||
value.get('metric').get('dimensions').get('host') ==
|
||||
'mini-mon'][0]
|
||||
|
||||
self.assertTrue(mem_total_mb_agg_metric is not None)
|
||||
|
||||
self.assertEqual('mem.total_mb_agg',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('name'))
|
||||
|
||||
self.assertEqual(1024.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value'))
|
||||
self.assertEqual('useast',
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('region'))
|
||||
|
||||
self.assertEqual(cfg.CONF.messaging.publish_kafka_tenant_id,
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('tenantId'))
|
||||
self.assertEqual('mini-mon',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('host'))
|
||||
self.assertEqual('all',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('project_id'))
|
||||
self.assertEqual('hourly',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions')
|
||||
.get('aggregation_period'))
|
||||
self.assertEqual(4.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta').get('record_count'))
|
||||
self.assertEqual('2016-01-20 16:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('firstrecord_timestamp_string'))
|
||||
self.assertEqual('2016-01-20 16:40:46',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('lastrecord_timestamp_string'))
|
||||
|
||||
@mock.patch('monasca_transform.processor.pre_hourly_processor.KafkaInsert',
|
||||
DummyInsert)
|
||||
@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
|
||||
'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_insert_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_setter_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_usage_component_manager')
|
||||
def test_fetch_quantity_avg(self,
|
||||
usage_manager,
|
||||
setter_manager,
|
||||
insert_manager,
|
||||
data_driven_specs_repo):
|
||||
|
||||
# test operation
|
||||
test_operation = "avg"
|
||||
|
||||
# load components
|
||||
usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
|
||||
setter_manager.return_value = \
|
||||
MockComponentManager.get_setter_cmpt_mgr()
|
||||
insert_manager.return_value = \
|
||||
MockComponentManager.get_insert_pre_hourly_cmpt_mgr()
|
||||
|
||||
# init mock driver tables
|
||||
data_driven_specs_repo.return_value = \
|
||||
MockDataDrivenSpecsRepo(self.spark_context,
|
||||
self.get_pre_transform_specs_json(),
|
||||
self.get_transform_specs_json_by_operation(
|
||||
test_operation))
|
||||
|
||||
# Create an emulated set of Kafka messages (these were gathered
|
||||
# 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
|
||||
instance_usage_list = DummyAdapter.adapter_impl.metric_list
|
||||
instance_usage_list = map(dump_as_ascii_string,
|
||||
instance_usage_list)
|
||||
DummyAdapter.adapter_impl.metric_list = []
|
||||
instance_usage_rdd = self.spark_context.parallelize(
|
||||
instance_usage_list)
|
||||
sql_context = SQLContext(self.spark_context)
|
||||
instance_usage_df = sql_context.read.json(instance_usage_rdd)
|
||||
PreHourlyProcessor.do_transform(instance_usage_df)
|
||||
|
||||
metrics = DummyAdapter.adapter_impl.metric_list
|
||||
mem_total_mb_agg_metric = [
|
||||
value for value in metrics
|
||||
if value.get('metric').get('name') ==
|
||||
'mem.total_mb_agg' and
|
||||
value.get('metric').get('dimensions').get('host') ==
|
||||
'mini-mon'][0]
|
||||
|
||||
self.assertTrue(mem_total_mb_agg_metric is not None)
|
||||
|
||||
self.assertEqual('mem.total_mb_agg',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('name'))
|
||||
|
||||
self.assertEqual(3840.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value'))
|
||||
self.assertEqual('useast',
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('region'))
|
||||
|
||||
self.assertEqual(cfg.CONF.messaging.publish_kafka_tenant_id,
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('tenantId'))
|
||||
self.assertEqual('mini-mon',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('host'))
|
||||
self.assertEqual('all',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('project_id'))
|
||||
self.assertEqual('hourly',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions')
|
||||
.get('aggregation_period'))
|
||||
self.assertEqual(4.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta').get('record_count'))
|
||||
self.assertEqual('2016-01-20 16:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('firstrecord_timestamp_string'))
|
||||
self.assertEqual('2016-01-20 16:40:46',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('lastrecord_timestamp_string'))
|
||||
|
||||
@mock.patch('monasca_transform.processor.pre_hourly_processor.KafkaInsert',
|
||||
DummyInsert)
|
||||
@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
|
||||
'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_insert_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_setter_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_usage_component_manager')
|
||||
def test_fetch_quantity_sum(self,
|
||||
usage_manager,
|
||||
setter_manager,
|
||||
insert_manager,
|
||||
data_driven_specs_repo):
|
||||
|
||||
# test operation
|
||||
test_operation = "sum"
|
||||
|
||||
# load components
|
||||
usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
|
||||
setter_manager.return_value = \
|
||||
MockComponentManager.get_setter_cmpt_mgr()
|
||||
insert_manager.return_value = \
|
||||
MockComponentManager.get_insert_pre_hourly_cmpt_mgr()
|
||||
|
||||
# init mock driver tables
|
||||
data_driven_specs_repo.return_value = \
|
||||
MockDataDrivenSpecsRepo(self.spark_context,
|
||||
self.get_pre_transform_specs_json(),
|
||||
self.get_transform_specs_json_by_operation(
|
||||
test_operation))
|
||||
|
||||
# Create an emulated set of Kafka messages (these were gathered
|
||||
# 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
|
||||
instance_usage_list = DummyAdapter.adapter_impl.metric_list
|
||||
instance_usage_list = map(dump_as_ascii_string,
|
||||
instance_usage_list)
|
||||
DummyAdapter.adapter_impl.metric_list = []
|
||||
instance_usage_rdd = self.spark_context.parallelize(
|
||||
instance_usage_list)
|
||||
sql_context = SQLContext(self.spark_context)
|
||||
instance_usage_df = sql_context.read.json(instance_usage_rdd)
|
||||
PreHourlyProcessor.do_transform(instance_usage_df)
|
||||
|
||||
metrics = DummyAdapter.adapter_impl.metric_list
|
||||
mem_total_mb_agg_metric = [
|
||||
value for value in metrics
|
||||
if value.get('metric').get('name') ==
|
||||
'mem.total_mb_agg' and
|
||||
value.get('metric').get('dimensions').get('host') ==
|
||||
'mini-mon'][0]
|
||||
|
||||
self.assertTrue(mem_total_mb_agg_metric is not None)
|
||||
|
||||
self.assertEqual('mem.total_mb_agg',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('name'))
|
||||
|
||||
self.assertEqual(15360.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value'))
|
||||
self.assertEqual('useast',
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('region'))
|
||||
|
||||
self.assertEqual(cfg.CONF.messaging.publish_kafka_tenant_id,
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('tenantId'))
|
||||
self.assertEqual('mini-mon',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('host'))
|
||||
self.assertEqual('all',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('project_id'))
|
||||
self.assertEqual('hourly',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions')
|
||||
.get('aggregation_period'))
|
||||
self.assertEqual(4.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta').get('record_count'))
|
||||
self.assertEqual('2016-01-20 16:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('firstrecord_timestamp_string'))
|
||||
self.assertEqual('2016-01-20 16:40:46',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('lastrecord_timestamp_string'))
|
||||
|
||||
@mock.patch('monasca_transform.processor.pre_hourly_processor.KafkaInsert',
|
||||
DummyInsert)
|
||||
@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
|
||||
'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_insert_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_setter_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_usage_component_manager')
|
||||
def test_fetch_quantity_max_second_stage(self,
|
||||
usage_manager,
|
||||
setter_manager,
|
||||
insert_manager,
|
||||
data_driven_specs_repo):
|
||||
|
||||
# test operation
|
||||
test_operation = "max"
|
||||
|
||||
# load components
|
||||
usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
|
||||
setter_manager.return_value = \
|
||||
MockComponentManager.get_setter_cmpt_mgr()
|
||||
insert_manager.return_value = \
|
||||
MockComponentManager.get_insert_pre_hourly_cmpt_mgr()
|
||||
|
||||
# init mock driver tables
|
||||
data_driven_specs_repo.return_value = \
|
||||
MockDataDrivenSpecsRepo(self.spark_context,
|
||||
self.get_pre_transform_specs_json(),
|
||||
self.get_transform_specs_json_by_operation(
|
||||
test_operation))
|
||||
|
||||
# Create an emulated set of Kafka messages (these were gathered
|
||||
# by extracting Monasca messages from the Metrics queue on mini-mon).
|
||||
|
||||
# Create an RDD out of the mocked Monasca metrics
|
||||
with open(SecondStageDataProvider.fetch_quantity_data_path) as f:
|
||||
raw_lines = f.read().splitlines()
|
||||
raw_tuple_list = [eval(raw_line) for raw_line in raw_lines]
|
||||
instance_usage_list = map(dump_as_ascii_string,
|
||||
raw_tuple_list)
|
||||
|
||||
# get the instance usage that have been
|
||||
# submitted to the dummy message adapter
|
||||
# create a json RDD from instance_usage_list
|
||||
instance_usage_rdd = self.spark_context.parallelize(
|
||||
instance_usage_list)
|
||||
|
||||
sql_context = SQLContext(self.spark_context)
|
||||
instance_usage_df = sql_context.read.json(
|
||||
instance_usage_rdd)
|
||||
|
||||
# call pre hourly processor
|
||||
PreHourlyProcessor.do_transform(instance_usage_df)
|
||||
metrics = DummyAdapter.adapter_impl.metric_list
|
||||
mem_total_mb_agg_metric = [
|
||||
value for value in metrics
|
||||
if value.get('metric').get('name') ==
|
||||
'mem.total_mb_agg' and
|
||||
value.get('metric').get('dimensions').get('host') ==
|
||||
'mini-mon'][0]
|
||||
self.assertTrue(mem_total_mb_agg_metric is not None)
|
||||
self.assertEqual('mem.total_mb_agg',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('name'))
|
||||
|
||||
self.assertEqual(2318.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value'))
|
||||
self.assertEqual('useast',
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('region'))
|
||||
|
||||
self.assertEqual(cfg.CONF.messaging.publish_kafka_tenant_id,
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('tenantId'))
|
||||
self.assertEqual('mini-mon',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('host'))
|
||||
self.assertEqual('all',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('project_id'))
|
||||
self.assertEqual('prehourly',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions')
|
||||
.get('aggregation_period'))
|
||||
|
||||
self.assertEqual(39.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta').get('record_count'))
|
||||
self.assertEqual('2016-01-20 16:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('firstrecord_timestamp_string'))
|
||||
self.assertEqual('2016-01-20 17:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('lastrecord_timestamp_string'))
|
||||
|
||||
@mock.patch('monasca_transform.processor.pre_hourly_processor.KafkaInsert',
|
||||
DummyInsert)
|
||||
@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
|
||||
'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_insert_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_setter_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_usage_component_manager')
|
||||
def test_fetch_quantity_min_second_stage(self,
|
||||
usage_manager,
|
||||
setter_manager,
|
||||
insert_manager,
|
||||
data_driven_specs_repo):
|
||||
|
||||
# test operation
|
||||
test_operation = "min"
|
||||
# load components
|
||||
usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
|
||||
setter_manager.return_value = \
|
||||
MockComponentManager.get_setter_cmpt_mgr()
|
||||
insert_manager.return_value = \
|
||||
MockComponentManager.get_insert_pre_hourly_cmpt_mgr()
|
||||
|
||||
# init mock driver tables
|
||||
data_driven_specs_repo.return_value = \
|
||||
MockDataDrivenSpecsRepo(self.spark_context,
|
||||
self.get_pre_transform_specs_json(),
|
||||
self.get_transform_specs_json_by_operation(
|
||||
test_operation))
|
||||
|
||||
# Create an emulated set of Kafka messages (these were gathered
|
||||
# by extracting Monasca messages from the Metrics queue on mini-mon).
|
||||
|
||||
# Create an RDD out of the mocked Monasca metrics
|
||||
with open(SecondStageDataProvider.fetch_quantity_data_path) as f:
|
||||
raw_lines = f.read().splitlines()
|
||||
raw_tuple_list = [eval(raw_line) for raw_line in raw_lines]
|
||||
instance_usage_list = map(dump_as_ascii_string,
|
||||
raw_tuple_list)
|
||||
|
||||
# create a json RDD from instance_usage_list
|
||||
instance_usage_rdd = self.spark_context.parallelize(
|
||||
instance_usage_list)
|
||||
|
||||
sql_context = SQLContext(self.spark_context)
|
||||
instance_usage_df = sql_context.read.json(
|
||||
instance_usage_rdd)
|
||||
|
||||
PreHourlyProcessor.do_transform(instance_usage_df)
|
||||
|
||||
metrics = DummyAdapter.adapter_impl.metric_list
|
||||
mem_total_mb_agg_metric = [
|
||||
value for value in metrics
|
||||
if value.get('metric').get('name') ==
|
||||
'mem.total_mb_agg' and
|
||||
value.get('metric').get('dimensions').get('host') ==
|
||||
'mini-mon'][0]
|
||||
self.assertTrue(mem_total_mb_agg_metric is not None)
|
||||
|
||||
self.assertEqual('mem.total_mb_agg',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('name'))
|
||||
|
||||
self.assertEqual(1213.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value'))
|
||||
self.assertEqual('useast',
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('region'))
|
||||
|
||||
self.assertEqual(cfg.CONF.messaging.publish_kafka_tenant_id,
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('tenantId'))
|
||||
self.assertEqual('mini-mon',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('host'))
|
||||
self.assertEqual('all',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('project_id'))
|
||||
self.assertEqual('prehourly',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions')
|
||||
.get('aggregation_period'))
|
||||
|
||||
self.assertEqual(39.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta').get('record_count'))
|
||||
self.assertEqual('2016-01-20 16:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('firstrecord_timestamp_string'))
|
||||
self.assertEqual('2016-01-20 17:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('lastrecord_timestamp_string'))
|
||||
|
||||
@mock.patch('monasca_transform.processor.pre_hourly_processor.KafkaInsert',
|
||||
DummyInsert)
|
||||
@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
|
||||
'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_insert_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_setter_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_usage_component_manager')
|
||||
def test_fetch_quantity_avg_second_stage(self,
|
||||
usage_manager,
|
||||
setter_manager,
|
||||
insert_manager,
|
||||
data_driven_specs_repo):
|
||||
|
||||
# test operation
|
||||
test_operation = "avg"
|
||||
# load components
|
||||
usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
|
||||
setter_manager.return_value = \
|
||||
MockComponentManager.get_setter_cmpt_mgr()
|
||||
insert_manager.return_value = \
|
||||
MockComponentManager.get_insert_pre_hourly_cmpt_mgr()
|
||||
|
||||
# init mock driver tables
|
||||
data_driven_specs_repo.return_value = \
|
||||
MockDataDrivenSpecsRepo(self.spark_context,
|
||||
self.get_pre_transform_specs_json(),
|
||||
self.get_transform_specs_json_by_operation(
|
||||
test_operation))
|
||||
|
||||
# Create an emulated set of Kafka messages (these were gathered
|
||||
# by extracting Monasca messages from the Metrics queue on mini-mon).
|
||||
|
||||
# Create an RDD out of the mocked Monasca metrics
|
||||
with open(SecondStageDataProvider.fetch_quantity_data_path) as f:
|
||||
raw_lines = f.read().splitlines()
|
||||
raw_tuple_list = [eval(raw_line) for raw_line in raw_lines]
|
||||
instance_usage_list = map(dump_as_ascii_string,
|
||||
raw_tuple_list)
|
||||
|
||||
# create a json RDD from instance_usage_list
|
||||
instance_usage_rdd = self.spark_context.parallelize(
|
||||
instance_usage_list)
|
||||
|
||||
sql_context = SQLContext(self.spark_context)
|
||||
instance_usage_df = sql_context.read.json(
|
||||
instance_usage_rdd)
|
||||
|
||||
# call pre hourly processor
|
||||
PreHourlyProcessor.do_transform(instance_usage_df)
|
||||
|
||||
metrics = DummyAdapter.adapter_impl.metric_list
|
||||
mem_total_mb_agg_metric = [
|
||||
value for value in metrics
|
||||
if value.get('metric').get('name') ==
|
||||
'mem.total_mb_agg' and
|
||||
value.get('metric').get('dimensions').get('host') ==
|
||||
'mini-mon'][0]
|
||||
self.assertTrue(mem_total_mb_agg_metric is not None)
|
||||
|
||||
self.assertEqual('mem.total_mb_agg',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('name'))
|
||||
|
||||
self.assertEqual(1446.5,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value'))
|
||||
self.assertEqual('useast',
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('region'))
|
||||
|
||||
self.assertEqual(cfg.CONF.messaging.publish_kafka_tenant_id,
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('tenantId'))
|
||||
self.assertEqual('mini-mon',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('host'))
|
||||
self.assertEqual('all',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('project_id'))
|
||||
self.assertEqual('prehourly',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions')
|
||||
.get('aggregation_period'))
|
||||
|
||||
self.assertEqual(39.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta').get('record_count'))
|
||||
self.assertEqual('2016-01-20 16:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('firstrecord_timestamp_string'))
|
||||
self.assertEqual('2016-01-20 17:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('lastrecord_timestamp_string'))
|
||||
|
||||
@mock.patch('monasca_transform.processor.pre_hourly_processor.KafkaInsert',
|
||||
DummyInsert)
|
||||
@mock.patch('monasca_transform.data_driven_specs.data_driven_specs_repo.'
|
||||
'DataDrivenSpecsRepoFactory.get_data_driven_specs_repo')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_insert_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_setter_component_manager')
|
||||
@mock.patch('monasca_transform.transform.builder.'
|
||||
'generic_transform_builder.GenericTransformBuilder.'
|
||||
'_get_usage_component_manager')
|
||||
def test_fetch_quantity_sum_second_stage(self,
|
||||
usage_manager,
|
||||
setter_manager,
|
||||
insert_manager,
|
||||
data_driven_specs_repo):
|
||||
|
||||
# test operation
|
||||
test_operation = "sum"
|
||||
# load components
|
||||
usage_manager.return_value = MockComponentManager.get_usage_cmpt_mgr()
|
||||
setter_manager.return_value = \
|
||||
MockComponentManager.get_setter_cmpt_mgr()
|
||||
insert_manager.return_value = \
|
||||
MockComponentManager.get_insert_pre_hourly_cmpt_mgr()
|
||||
|
||||
# init mock driver tables
|
||||
data_driven_specs_repo.return_value = \
|
||||
MockDataDrivenSpecsRepo(self.spark_context,
|
||||
self.get_pre_transform_specs_json(),
|
||||
self.get_transform_specs_json_by_operation(
|
||||
test_operation))
|
||||
|
||||
# Create an emulated set of Kafka messages (these were gathered
|
||||
# by extracting Monasca messages from the Metrics queue on mini-mon).
|
||||
|
||||
# Create an RDD out of the mocked Monasca metrics
|
||||
with open(SecondStageDataProvider.fetch_quantity_data_path) as f:
|
||||
raw_lines = f.read().splitlines()
|
||||
raw_tuple_list = [eval(raw_line) for raw_line in raw_lines]
|
||||
instance_usage_list = map(dump_as_ascii_string,
|
||||
raw_tuple_list)
|
||||
|
||||
# create a json RDD from instance_usage_list
|
||||
instance_usage_rdd = self.spark_context.parallelize(
|
||||
instance_usage_list)
|
||||
|
||||
sql_context = SQLContext(self.spark_context)
|
||||
instance_usage_df = sql_context.read.json(
|
||||
instance_usage_rdd)
|
||||
|
||||
# call pre hourly processor
|
||||
PreHourlyProcessor.do_transform(instance_usage_df)
|
||||
|
||||
metrics = DummyAdapter.adapter_impl.metric_list
|
||||
mem_total_mb_agg_metric = [
|
||||
value for value in metrics
|
||||
if value.get('metric').get('name') ==
|
||||
'mem.total_mb_agg' and
|
||||
value.get('metric').get('dimensions').get('host') ==
|
||||
'mini-mon'][0]
|
||||
self.assertTrue(mem_total_mb_agg_metric is not None)
|
||||
|
||||
self.assertEqual('mem.total_mb_agg',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('name'))
|
||||
|
||||
self.assertEqual(8679.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value'))
|
||||
self.assertEqual('useast',
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('region'))
|
||||
|
||||
self.assertEqual(cfg.CONF.messaging.publish_kafka_tenant_id,
|
||||
mem_total_mb_agg_metric
|
||||
.get('meta').get('tenantId'))
|
||||
self.assertEqual('mini-mon',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('host'))
|
||||
self.assertEqual('all',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions').get('project_id'))
|
||||
self.assertEqual('prehourly',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('dimensions')
|
||||
.get('aggregation_period'))
|
||||
|
||||
self.assertEqual(39.0,
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta').get('record_count'))
|
||||
self.assertEqual('2016-01-20 16:40:00',
|
||||
mem_total_mb_agg_metric
|
||||
.get('metric').get('value_meta')
|
||||
.get('firstrecord_timestamp_string'))
|
||||
self.assertEqual('2016-01-20 17:40:00',
|
||||
mem_total_mb_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()
|
Loading…
Reference in New Issue