def storage_account():
test_case = AzureMgmtTestCase("__init__")
rg_preparer = ResourceGroupPreparer()
storage_preparer = StorageAccountPreparer(name_prefix='pyacrstorage')
# Set what the decorator is supposed to set for us
for prep in [rg_preparer, storage_preparer]:
prep.live_test = False
prep.test_class_instance = test_case
# Create
rg_name = create_random_name("pystorage", 24)
storage_name = create_random_name("pyacrstorage", 24)
try:
rg = rg_preparer.create_resource(rg_name)
StorageTestCase._RESOURCE_GROUP = rg['resource_group']
try:
storage_dict = storage_preparer.create_resource(
storage_name, resource_group=rg['resource_group'])
# Now the magic begins
StorageTestCase._STORAGE_ACCOUNT = storage_dict['storage_account']
StorageTestCase._STORAGE_KEY = storage_dict['storage_account_key']
yield
finally:
storage_preparer.remove_resource(
storage_name, resource_group=rg['resource_group'])
StorageTestCase._STORAGE_ACCOUNT = None
StorageTestCase._STORAGE_KEY = None
finally:
rg_preparer.remove_resource(rg_name)
StorageTestCase._RESOURCE_GROUP = None
def _create_data_feed(self, name):
name = create_random_name(name)
return self.admin_client.create_data_feed(
DataFeed(
name=name,
source=SQLServerDataFeed(
connection_string=self.sql_server_connection_string,
query='select * from adsample2 where Timestamp = @StartTime'
),
granularity=DataFeedGranularity(
granularity_type="Daily",
),
schema=DataFeedSchema(
metrics=[
DataFeedMetric(name="cost"),
DataFeedMetric(name="revenue")
],
dimensions=[
DataFeedDimension(name="category"),
DataFeedDimension(name="city")
],
timestamp_column="Timestamp"
),
ingestion_settings=DataFeedIngestionSettings(
ingestion_begin_time=datetime.datetime(2019, 10, 1),
)
)
)
async def _create_alert_config_for_update(self, name):
try:
detection_config, data_feed = await self._create_data_feed_and_detection_config(name)
alert_config_name = create_random_name(name)
alert_config = await self.admin_client.create_alert_configuration(
name=alert_config_name,
cross_metrics_operator="AND",
metric_alert_configurations=[
MetricAlertConfiguration(
detection_configuration_id=detection_config.id,
alert_scope=MetricAnomalyAlertScope(
scope_type="TopN",
top_n_group_in_scope=TopNGroupScope(
top=5,
period=10,
min_top_count=9
)
),
alert_conditions=MetricAnomalyAlertConditions(
metric_boundary_condition=MetricBoundaryCondition(
direction="Both",
companion_metric_id=data_feed.metric_ids['cost'],
lower=1.0,
upper=5.0
)
)
),
MetricAlertConfiguration(
detection_configuration_id=detection_config.id,
alert_scope=MetricAnomalyAlertScope(
scope_type="SeriesGroup",
series_group_in_scope={'city': 'Shenzhen'}
),
alert_conditions=MetricAnomalyAlertConditions(
severity_condition=SeverityCondition(
min_alert_severity="Low",
max_alert_severity="High"
)
)
),
MetricAlertConfiguration(
detection_configuration_id=detection_config.id,
alert_scope=MetricAnomalyAlertScope(
scope_type="WholeSeries"
),
alert_conditions=MetricAnomalyAlertConditions(
severity_condition=SeverityCondition(
min_alert_severity="Low",
max_alert_severity="High"
)
)
)
],
hook_ids=[]
)
return alert_config, data_feed, detection_config
except Exception as e:
self.admin_client.delete_data_feed(data_feed.id)
raise e
def create_random_name(self, prefix, length):
self.test_resources_count += 1
moniker = '{}{:06}'.format(prefix, self.test_resources_count)
if self.in_recording:
name = create_random_name(prefix, length)
self.name_replacer.register_name_pair(name, moniker)
return name
return moniker
def create_random_name(self, prefix, length):
self.test_resources_count += 1
moniker = '{}{:06}'.format(prefix, self.test_resources_count)
if self.in_recording:
name = create_random_name(prefix, length)
self.name_replacer.register_name_pair(name, moniker)
return name
return moniker
def _create_detection_config_for_update(self, name):
try:
data_feed = self._create_data_feed(name)
detection_config_name = create_random_name("testupdated")
detection_config = self.admin_client.create_detection_configuration(
name=detection_config_name,
metric_id=data_feed.metric_ids['cost'],
description="My test metric anomaly detection configuration",
whole_series_detection_condition=MetricDetectionCondition(
condition_operator="AND",
smart_detection_condition=SmartDetectionCondition(
sensitivity=50,
anomaly_detector_direction="Both",
suppress_condition=SuppressCondition(min_number=5,
min_ratio=5)),
hard_threshold_condition=HardThresholdCondition(
anomaly_detector_direction="Both",
suppress_condition=SuppressCondition(min_number=5,
min_ratio=5),
lower_bound=0,
upper_bound=100),
change_threshold_condition=ChangeThresholdCondition(
change_percentage=50,
shift_point=30,
within_range=True,
anomaly_detector_direction="Both",
suppress_condition=SuppressCondition(min_number=2,
min_ratio=2))),
series_detection_conditions=[
MetricSingleSeriesDetectionCondition(
series_key={
"city": "Shenzhen",
"category": "Jewelry"
},
smart_detection_condition=SmartDetectionCondition(
anomaly_detector_direction="Both",
sensitivity=63,
suppress_condition=SuppressCondition(
min_number=1, min_ratio=100)))
],
series_group_detection_conditions=[
MetricSeriesGroupDetectionCondition(
series_group_key={"city": "Sao Paulo"},
smart_detection_condition=SmartDetectionCondition(
anomaly_detector_direction="Both",
sensitivity=63,
suppress_condition=SuppressCondition(
min_number=1, min_ratio=100)))
])
return detection_config, data_feed
except Exception as e:
self.admin_client.delete_data_feed(data_feed.id)
raise e
async def _create_data_feed_and_detection_config(self, name):
data_feed = await self._create_data_feed(name)
detection_config_name = create_random_name(name)
detection_config = await self.admin_client.create_detection_configuration(
name=detection_config_name,
metric_id=data_feed.metric_ids['cost'],
description="testing",
whole_series_detection_condition=MetricDetectionCondition(
smart_detection_condition=SmartDetectionCondition(
sensitivity=50,
anomaly_detector_direction="Both",
suppress_condition=SuppressCondition(min_number=5,
min_ratio=5))))
return detection_config, data_feed
def _create_data_feed_for_update(self, name):
data_feed_name = create_random_name(name)
return self.admin_client.create_data_feed(
DataFeed(
name=data_feed_name,
source=SQLServerDataFeed(
connection_string=self.sql_server_connection_string,
query='select * from adsample2 where Timestamp = @StartTime'
),
granularity=DataFeedGranularity(
granularity_type="Daily",
),
schema=DataFeedSchema(
metrics=[
DataFeedMetric(name="cost", display_name="display cost", description="the cost"),
DataFeedMetric(name="revenue", display_name="display revenue", description="the revenue")
],
dimensions=[
DataFeedDimension(name="category", display_name="display category"),
DataFeedDimension(name="city", display_name="display city")
],
timestamp_column="Timestamp"
),
ingestion_settings=DataFeedIngestionSettings(
ingestion_begin_time=datetime.datetime(2019, 10, 1),
data_source_request_concurrency=0,
ingestion_retry_delay=-1,
ingestion_start_offset=-1,
stop_retry_after=-1,
),
options=DataFeedOptions(
admin_emails=["*****@*****.**"],
data_feed_description="my first data feed",
missing_data_point_fill_settings=DataFeedMissingDataPointFillSettings(
fill_type="SmartFilling"
),
rollup_settings=DataFeedRollupSettings(
rollup_type="NoRollup",
rollup_method="None",
),
viewer_emails=["viewers"],
access_mode="Private",
action_link_template="action link template"
)
)
)
def _create_data_feed(self, name):
name = create_random_name(name)
return self.admin_client.create_data_feed(
name=name,
source=SqlServerDataFeedSource(
connection_string=self.sql_server_connection_string,
query="select * from adsample2 where Timestamp = @StartTime"),
granularity="Daily",
schema=DataFeedSchema(
metrics=[
DataFeedMetric(name="cost"),
DataFeedMetric(name="revenue")
],
dimensions=[
DataFeedDimension(name="category"),
DataFeedDimension(name="city")
],
),
ingestion_settings="2019-10-01T00:00:00Z",
)
