openstack
/
monasca-transform
Code Issues Proposed changes

add test cases for fetch_quantity_prehourly_instance_usage 

Removed dependency of yaml module.

Change-Id: I87ce80d420bc75ddbef2c8454f088be25f9ff908
This commit is contained in:
Michael Dong 2016-07-19 14:11:11 -07:00
parent c498564929
commit acba1782ad
11 changed files with 1115 additions and 5 deletions
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0
tests/unit/component/__init__.py Normal file
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0
tests/unit/component/insert/__init__.py Normal file
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0
monasca_transform/component/insert/dummy_insert.py → tests/unit/component/insert/dummy_insert.py
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69
tests/unit/component/insert/dummy_insert_pre_hourly.py Normal file
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@ -0,0 +1,69 @@
# Copyright 2016 Hewlett Packard Enterprise Development Company LP
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from monasca_transform.component.insert import InsertComponent
from oslo_config import cfg
from tests.unit.messaging.adapter import DummyAdapter
class DummyInsertPreHourly(InsertComponent):
"""Insert component that writes metric data to
to kafka queue
"""
@staticmethod
def insert(transform_context, instance_usage_df):
"""write instance usage data to kafka"""
transform_spec_df = transform_context.transform_spec_df_info
agg_params = transform_spec_df.select("metric_id"
).collect()[0].asDict()
metric_id = agg_params['metric_id']
cfg.CONF.set_override('adapter',
'tests.unit.messaging.adapter:DummyAdapter',
group='messaging')
# Approach 1
# using foreachPartition to iterate through elements in an
# RDD is the recommended approach so as to not overwhelm kafka with the
# zillion connections (but in our case the MessageAdapter does
# store the adapter_impl so we should not create many producers)
# using foreachpartitions was causing some serialization (cpickle)
# problems where few libs like kafka.SimpleProducer and oslo_config.cfg
# were not available
#
# removing _write_metrics_from_partition for now in favor of
# Approach 2
#
# instance_usage_df_agg_params = instance_usage_df.rdd.map(
# lambda x: InstanceUsageDataAggParams(x,
# agg_params))
# instance_usage_df_agg_params.foreachPartition(
# DummyInsert._write_metrics_from_partition)
#
# Approach # 2
#
# using collect() to fetch all elements of an RDD
# and write to kafka
#
for instance_usage_row in instance_usage_df.collect():
instance_usage_dict = InsertComponent\
._get_instance_usage_pre_hourly(instance_usage_row, metric_id)
DummyAdapter.send_metric(instance_usage_dict)
return instance_usage_df

2
tests/unit/processor/test_pre_hourly_processor_agg.py
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@ -16,10 +16,10 @@ import unittest
from pyspark.streaming.kafka import OffsetRange
from monasca_transform.component.insert.dummy_insert import DummyInsert
from monasca_transform.config.config_initializer import ConfigInitializer
from monasca_transform.processor.pre_hourly_processor import PreHourlyProcessor
from tests.unit.component.insert.dummy_insert import DummyInsert
from tests.unit.messaging.adapter import DummyAdapter
from tests.unit.spark_context_test import SparkContextTest
from tests.unit.test_resources.metrics_pre_hourly_data.data_provider \

0
tests/unit/test_resources/fetch_quantity_data_second_stage/__init__.py Normal file
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24
tests/unit/test_resources/fetch_quantity_data_second_stage/data_provider.py Normal file
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@ -0,0 +1,24 @@
# Copyright 2016 Hewlett Packard Enterprise Development Company LP
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import os
class DataProvider(object):
_resource_path = 'tests/unit/test_resources/'\
'fetch_quantity_data_second_stage/'
fetch_quantity_data_path = os.path.join(
_resource_path, "fetch_quantity_data_second_stage.txt")

6
tests/unit/test_resources/fetch_quantity_data_second_stage/fetch_quantity_data_second_stage.txt Normal file
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@ -0,0 +1,6 @@
{'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}
{'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}
{'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}
{'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}
{'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}
{'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}

29
tests/unit/test_resources/mock_component_manager.py
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@ -16,9 +16,7 @@ from stevedore.extension import Extension
from stevedore.extension import ExtensionManager
from monasca_transform.component.insert.dummy_insert import DummyInsert
from monasca_transform.component.insert.prepare_data import PrepareData
from monasca_transform.component.setter.rollup_quantity \
import RollupQuantity
from monasca_transform.component.setter.set_aggregated_metric_name \
@ -31,6 +29,9 @@ from monasca_transform.component.usage.fetch_quantity \
import FetchQuantity
from monasca_transform.component.usage.fetch_quantity_util \
import FetchQuantityUtil
from tests.unit.component.insert.dummy_insert import DummyInsert
from tests.unit.component.insert.dummy_insert_pre_hourly \
import DummyInsertPreHourly
class MockComponentManager(object):
@ -88,13 +89,33 @@ class MockComponentManager(object):
PrepareData(),
None),
Extension('insert_data',
'monasca_transform.component.insert.dummy_insert:'
'tests.unit.component.insert.dummy_insert:'
'DummyInsert',
DummyInsert(),
None),
Extension('insert_data_pre_hourly',
'monasca_transform.component.insert.dummy_insert:'
'tests.unit.component.insert.dummy_insert:'
'DummyInsert',
DummyInsert(),
None),
])
@staticmethod
def get_insert_pre_hourly_cmpt_mgr():
return ExtensionManager.make_test_instance([Extension(
'prepare_data',
'monasca_transform.component.insert.prepare_data:PrepareData',
PrepareData(),
None),
Extension('insert_data',
'tests.unit.component.insert.dummy_insert:'
'DummyInsert',
DummyInsert(),
None),
Extension('insert_data_pre_hourly',
'tests.unit.component.insert.'
'dummy_insert_pre_hourly:'
'DummyInsertPreHourly',
DummyInsertPreHourly(),
None),
])

5
tests/unit/usage/__init__.py
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@ -0,0 +1,5 @@
import json
def dump_as_ascii_string(dict_obj):
return json.dumps(dict_obj, ensure_ascii=True)

985
tests/unit/usage/test_fetch_quantity_instance_usage_agg.py Normal file
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@ -0,0 +1,985 @@
# Copyright 2016 Hewlett Packard Enterprise Development Company LP
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import json
import mock
import unittest
from oslo_config import cfg
from pyspark.sql import SQLContext
from pyspark.streaming.kafka import OffsetRange
from monasca_transform.config.config_initializer import ConfigInitializer
from monasca_transform.driver.mon_metrics_kafka \
import MonMetricsKafkaProcessor
from monasca_transform.processor.pre_hourly_processor import PreHourlyProcessor
from monasca_transform.transform import RddTransformContext
from monasca_transform.transform import TransformContextUtils
from tests.unit.component.insert.dummy_insert import DummyInsert
from tests.unit.messaging.adapter import DummyAdapter
from tests.unit.spark_context_test import SparkContextTest
from tests.unit.test_resources.fetch_quantity_data.data_provider \
import DataProvider
from tests.unit.test_resources.fetch_quantity_data_second_stage.data_provider \
import DataProvider as SecondStageDataProvider
from tests.unit.test_resources.mock_component_manager \
import MockComponentManager
from tests.unit.test_resources.mock_data_driven_specs_repo \
import MockDataDrivenSpecsRepo
from tests.unit.usage import dump_as_ascii_string
class TestFetchQuantityInstanceUsageAgg(SparkContextTest):
def setUp(self):
super(TestFetchQuantityInstanceUsageAgg, self).setUp()
# configure the system with a dummy messaging adapter
ConfigInitializer.basic_config(
default_config_files=[
'tests/unit/test_resources/config/'
'test_config_with_dummy_messaging_adapter.conf'])
# reset metric_id list dummy adapter
if not DummyAdapter.adapter_impl:
DummyAdapter.init()
DummyAdapter.adapter_impl.metric_list = []
def get_pre_transform_specs_json(self):
"""get pre_transform_specs driver table info."""
pre_transform_specs_json = """
{"event_processing_params":{"set_default_zone_to":"1",
"set_default_geolocation_to":"1",
"set_default_region_to":"W"},
"event_type":"mem.total_mb",
"metric_id_list":["mem_total_all"],
"required_raw_fields_list":["creation_time"],
"service_id":"host_metrics"}"""
return [json.loads(pre_transform_specs_json)]
def get_transform_specs_json_by_operation(self,
usage_fetch_operation):
"""get transform_specs driver table info."""
transform_specs_json = """
{"aggregation_params_map":{
"aggregation_pipeline":{"source":"streaming",
"usage":"fetch_quantity",
"setters":["rollup_quantity",
"set_aggregated_metric_name",
"set_aggregated_period"],
"insert":["prepare_data",
"insert_data_pre_hourly"]},
"aggregated_metric_name": "mem.total_mb_agg",
"aggregation_period": "hourly",
"aggregation_group_by_list": ["host", "metric_id"],
"usage_fetch_operation": "%s",
"setter_rollup_group_by_list": ["host"],
"setter_rollup_operation": "sum",
"pre_hourly_operation":"%s",
"pre_hourly_group_by_list":["default"],
"dimension_list":["aggregation_period",
"host",
"project_id"]
},
"metric_group":"mem_total_all",
"metric_id":"mem_total_all"}"""
transform_specs_json_operation = \
transform_specs_json % (usage_fetch_operation,
usage_fetch_operation)
return [json.loads(transform_specs_json_operation)]
@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(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(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(8192.0,
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_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()