def test_create_metric_with_colon_in_dimension_value(self):
name = data_utils.rand_name('name')
key = 'url'
value = 'http://localhost:8070/v2.0'
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
end_timestamp = int(round((time.time() + 3600 * 24) * 1000))
end_time_iso = helpers.timestamp_to_iso(end_timestamp)
metric = helpers.create_metric(name=name, dimensions={key: value})
resp, response_body = self.monasca_client.create_metrics(metric)
self.assertEqual(204, resp.status)
query_param = '?name=' + name + '&start_time=' + time_iso + \
'&end_time=' + end_time_iso + \
'&dimensions=' + key + ':' + value
for i in range(constants.MAX_RETRIES):
resp, response_body = self.monasca_client. \
list_measurements(query_param)
self.assertEqual(200, resp.status)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name:
self._verify_list_measurements_element(element, key, value)
measurement = element['measurements'][0]
self._verify_list_measurements_measurement(
measurement, metric, None, None)
return
time.sleep(constants.RETRY_WAIT_SECS)
if i == constants.MAX_RETRIES - 1:
error_msg = "Failed test_create_metric: " \
"timeout on waiting for metrics: at least " \
"one metric is needed. Current number of " \
"metrics = 0"
self.fail(error_msg)
def test_create_metrics(self):
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
end_timestamp = int(round(timestamp + 3600 * 24 * 1000))
end_time_iso = helpers.timestamp_to_iso(end_timestamp)
value_meta_key1 = data_utils.rand_name('meta_key')
value_meta_value1 = data_utils.rand_name('meta_value')
value_meta_key2 = data_utils.rand_name('value_meta_key')
value_meta_value2 = data_utils.rand_name('value_meta_value')
metrics = [
helpers.create_metric(name=name,
dimensions={key: value},
timestamp=timestamp,
value=1.23,
value_meta={
value_meta_key1: value_meta_value1
}),
helpers.create_metric(name=name,
dimensions={key: value},
timestamp=timestamp + 6000,
value=4.56,
value_meta={
value_meta_key2: value_meta_value2
})
]
resp, response_body = self.monasca_client.create_metrics(metrics)
self.assertEqual(204, resp.status)
query_param = '?name=' + name + '&start_time=' + str(time_iso) + \
'&end_time=' + str(end_time_iso)
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.\
list_measurements(query_param)
self.assertEqual(200, resp.status)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name \
and len(element['measurements']) == 2:
self._verify_list_measurements_element(element, key, value)
first_measurement = element['measurements'][0]
second_measurement = element['measurements'][1]
self._verify_list_measurements_measurement(
first_measurement, metrics[0], value_meta_key1,
value_meta_value1)
self._verify_list_measurements_measurement(
second_measurement, metrics[1], value_meta_key2,
value_meta_value2)
return
time.sleep(constants.RETRY_WAIT_SECS)
if i == constants.MAX_RETRIES - 1:
error_msg = "Failed test_create_metrics: " \
"timeout on waiting for metrics: at least " \
"one metric is needed. Current number of " \
"metrics = 0"
self.fail(error_msg)
def test_create_metrics(self):
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
end_timestamp = int(round(timestamp + 3600 * 24 * 1000))
end_time_iso = helpers.timestamp_to_iso(end_timestamp)
value_meta_key1 = data_utils.rand_name('meta_key')
value_meta_value1 = data_utils.rand_name('meta_value')
value_meta_key2 = data_utils.rand_name('value_meta_key')
value_meta_value2 = data_utils.rand_name('value_meta_value')
metrics = [
helpers.create_metric(name=name,
dimensions={key: value},
timestamp=timestamp,
value=1.23,
value_meta={
value_meta_key1: value_meta_value1
}),
helpers.create_metric(name=name,
dimensions={key: value},
timestamp=timestamp + 6000,
value=4.56,
value_meta={
value_meta_key2: value_meta_value2
})
]
resp, response_body = self.monasca_client.create_metrics(metrics)
self.assertEqual(204, resp.status)
query_param = '?name=' + name + '&start_time=' + str(time_iso) + \
'&end_time=' + str(end_time_iso)
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.\
list_measurements(query_param)
self.assertEqual(200, resp.status)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name \
and len(element['measurements']) == 2:
self._verify_list_measurements_element(element, key, value)
first_measurement = element['measurements'][0]
second_measurement = element['measurements'][1]
self._verify_list_measurements_measurement(
first_measurement, metrics[0], value_meta_key1,
value_meta_value1)
self._verify_list_measurements_measurement(
second_measurement, metrics[1], value_meta_key2,
value_meta_value2)
return
time.sleep(constants.RETRY_WAIT_SECS)
if i == constants.MAX_RETRIES - 1:
error_msg = "Failed test_create_metrics: " \
"timeout on waiting for metrics: at least " \
"one metric is needed. Current number of " \
"metrics = 0"
self.fail(error_msg)
def resource_setup(cls):
super(TestStatistics, cls).resource_setup()
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value1 = data_utils.rand_name('value1')
value2 = data_utils.rand_name('value2')
cls._test_name = name
cls._test_key = key
cls._test_value1 = value1
cls._start_timestamp = int(time.time() * 1000)
metrics = [
helpers.create_metric(name=name,
dimensions={key: value1},
timestamp=cls._start_timestamp,
value=metric_value1),
helpers.create_metric(name=name,
dimensions={key: value2},
timestamp=cls._start_timestamp + 1000,
value=metric_value2)
]
cls.monasca_client.create_metrics(metrics)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
query_param = '?name=' + str(name) + '&start_time=' + \
start_time_iso + '&merge_metrics=true' + '&end_time=' + \
helpers.timestamp_to_iso(cls._start_timestamp + 1000 * 2)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
cls._start_time_iso = start_time_iso
num_measurements = 0
for i in xrange(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.\
list_measurements(query_param)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name:
if len(element['measurements']
) >= MIN_REQUIRED_MEASUREMENTS:
cls._end_timestamp = cls._start_timestamp + 1000 * 3
cls._end_time_iso = helpers.timestamp_to_iso(
cls._end_timestamp)
return
else:
num_measurements = len(element['measurements'])
break
time.sleep(constants.RETRY_WAIT_SECS)
assert False, "Required {} measurements, found {}".format(
MIN_REQUIRED_MEASUREMENTS, num_measurements)
def test_list_statistics_with_no_merge_metrics(self):
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
metric3 = helpers.create_metric(
name=self._test_name,
dimensions={key: value},
timestamp=self._start_timestamp + 2000)
self.monasca_client.create_metrics(metric3)
query_param = '?name=' + str(self._test_name) + '&start_time=' + \
self._start_time_iso + '&end_time=' + helpers.\
timestamp_to_iso(self._start_timestamp + 1000 * 4) + \
'&merge_metrics=True'
for i in range(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.\
list_measurements(query_param)
elements = response_body['elements']
for element in elements:
if str(element['name']) == self._test_name and len(
element['measurements']) == 3:
end_time_iso = helpers.timestamp_to_iso(
self._start_timestamp + 1000 * 4)
query_parms = '?name=' + str(self._test_name) + \
'&statistics=avg' + '&start_time=' + \
str(self._start_time_iso) + '&end_time=' +\
str(end_time_iso) + '&period=100000'
self.assertRaises(exceptions.Conflict,
self.monasca_client.list_statistics,
query_parms)
return
time.sleep(constants.RETRY_WAIT_SECS)
self._check_timeout(i, constants.MAX_RETRIES, elements, 3)
def test_list_statistics_with_no_merge_metrics(self):
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
metric3 = helpers.create_metric(
name=self._test_name,
dimensions={key: value},
timestamp=self._start_timestamp + 2000)
self.monasca_client.create_metrics(metric3)
query_param = '?name=' + str(self._test_name) + '&start_time=' + \
self._start_time_iso + '&end_time=' + helpers.\
timestamp_to_iso(self._start_timestamp + 1000 * 4) + \
'&merge_metrics=True'
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.\
list_measurements(query_param)
elements = response_body['elements']
for element in elements:
if str(element['name']) == self._test_name and len(
element['measurements']) == 3:
end_time_iso = helpers.timestamp_to_iso(
self._start_timestamp + 1000 * 4)
query_parms = '?name=' + str(self._test_name) + \
'&statistics=avg' + '&start_time=' + \
str(self._start_time_iso) + '&end_time=' +\
str(end_time_iso) + '&period=100000'
self.assertRaises(exceptions.Conflict,
self.monasca_client.list_statistics,
query_parms)
return
time.sleep(constants.RETRY_WAIT_SECS)
self._check_timeout(i, constants.MAX_RETRIES, elements, 3)
def resource_setup(cls):
super(TestStatistics, cls).resource_setup()
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value1 = data_utils.rand_name('value1')
value2 = data_utils.rand_name('value2')
cls._test_name = name
cls._test_key = key
cls._test_value1 = value1
cls._start_timestamp = int(time.time() * 1000)
metrics = [
helpers.create_metric(name=name,
dimensions={key: value1},
timestamp=cls._start_timestamp,
value=metric_value1),
helpers.create_metric(name=name,
dimensions={key: value2},
timestamp=cls._start_timestamp + 1000,
value=metric_value2)
]
cls.monasca_client.create_metrics(metrics)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
query_param = '?name=' + str(name) + '&start_time=' + \
start_time_iso + '&merge_metrics=true' + '&end_time=' + \
helpers.timestamp_to_iso(cls._start_timestamp + 1000 * 2)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
cls._start_time_iso = start_time_iso
num_measurements = 0
for i in xrange(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.\
list_measurements(query_param)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name:
if len(element['measurements']) >= MIN_REQUIRED_MEASUREMENTS:
cls._end_timestamp = cls._start_timestamp + 1000 * 3
cls._end_time_iso = helpers.timestamp_to_iso(
cls._end_timestamp)
return
else:
num_measurements = len(element['measurements'])
break
time.sleep(constants.RETRY_WAIT_SECS)
assert False, "Required {} measurements, found {}".format(MIN_REQUIRED_MEASUREMENTS, num_measurements)
def resource_setup(cls):
super(TestDimensions, cls).resource_setup()
metric_name1 = data_utils.rand_name()
name1 = "name_1"
name2 = "name_2"
value1 = "value_1"
value2 = "value_2"
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
metric1 = helpers.create_metric(name=metric_name1,
dimensions={name1: value1,
name2: value2
})
cls.monasca_client.create_metrics(metric1)
metric1 = helpers.create_metric(name=metric_name1,
dimensions={name1: value2})
cls.monasca_client.create_metrics(metric1)
metric_name2 = data_utils.rand_name()
name3 = "name_3"
value3 = "value_3"
metric2 = helpers.create_metric(name=metric_name2,
dimensions={name3: value3})
cls.monasca_client.create_metrics(metric2)
metric_name3 = data_utils.rand_name()
metric3 = helpers.create_metric(name=metric_name3,
dimensions={name1: value3})
cls.monasca_client.create_metrics(metric3)
cls._test_metric1 = metric1
cls._test_metric2 = metric2
cls._test_metric_names = {metric_name1, metric_name2, metric_name3}
cls._dim_names_metric1 = [name1, name2]
cls._dim_names_metric2 = [name3]
cls._dim_names = cls._dim_names_metric1 + cls._dim_names_metric2
cls._dim_values_for_metric1 = [value1, value2]
cls._dim_values = [value1, value2, value3]
param = '?start_time=' + time_iso
returned_name_set = set()
for i in range(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.list_metrics(
param)
elements = response_body['elements']
for element in elements:
returned_name_set.add(str(element['name']))
if cls._test_metric_names.issubset(returned_name_set):
return
time.sleep(constants.RETRY_WAIT_SECS)
assert False, 'Unable to initialize metrics'
def resource_setup(cls):
super(TestDimensions, cls).resource_setup()
metric_name1 = data_utils.rand_name()
name1 = "name_1"
name2 = "name_2"
value1 = "value_1"
value2 = "value_2"
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
metric1 = helpers.create_metric(name=metric_name1,
dimensions={name1: value1,
name2: value2
})
cls.monasca_client.create_metrics(metric1)
metric1 = helpers.create_metric(name=metric_name1,
dimensions={name1: value2})
cls.monasca_client.create_metrics(metric1)
metric_name2 = data_utils.rand_name()
name3 = "name_3"
value3 = "value_3"
metric2 = helpers.create_metric(name=metric_name2,
dimensions={name3: value3})
cls.monasca_client.create_metrics(metric2)
metric_name3 = data_utils.rand_name()
metric3 = helpers.create_metric(name=metric_name3,
dimensions={name1: value3})
cls.monasca_client.create_metrics(metric3)
cls._test_metric1 = metric1
cls._test_metric2 = metric2
cls._test_metric_names = {metric_name1, metric_name2, metric_name3}
cls._dim_names_metric1 = [name1, name2]
cls._dim_names_metric2 = [name3]
cls._dim_names = cls._dim_names_metric1 + cls._dim_names_metric2
cls._dim_values_for_metric1 = [value1, value2]
cls._dim_values = [value1, value2, value3]
param = '?start_time=' + time_iso
returned_name_set = set()
for i in xrange(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.list_metrics(
param)
elements = response_body['elements']
for element in elements:
returned_name_set.add(str(element['name']))
if cls._test_metric_names.issubset(returned_name_set):
return
time.sleep(constants.RETRY_WAIT_SECS)
assert False, 'Unable to initialize metrics'
def test_create_metric(self):
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
end_timestamp = int(round((time.time() + 3600 * 24) * 1000))
end_time_iso = helpers.timestamp_to_iso(end_timestamp)
value_meta_key = data_utils.rand_name('value_meta_key')
value_meta_value = data_utils.rand_name('value_meta_value')
metric = helpers.create_metric(
name=name,
dimensions={key: value},
timestamp=timestamp,
value=1.23,
value_meta={value_meta_key: value_meta_value})
resp, response_body = self.monasca_client.create_metrics(metric)
self.assertEqual(204, resp.status)
query_param = '?name=' + name + '&start_time=' + time_iso + \
'&end_time=' + end_time_iso
for i in range(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.\
list_measurements(query_param)
self.assertEqual(200, resp.status)
elements = response_body['elements']
for element in elements:
# check if metric is there and dimension info already available
if (str(element['name']) == name
and len(element['dimensions']) > 0):
self._verify_list_measurements_element(element, key, value)
measurement = element['measurements'][0]
self._verify_list_measurements_measurement(
measurement, metric, value_meta_key, value_meta_value)
return
time.sleep(constants.RETRY_WAIT_SECS)
if i == constants.MAX_RETRIES - 1:
error_msg = "Failed test_create_metric: " \
"timeout on waiting for metrics: at least " \
"one metric is needed. Current number of " \
"metrics = 0"
self.fail(error_msg)
def test_create_metric_with_no_dimensions(self):
name = data_utils.rand_name('name')
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
end_timestamp = int(round(timestamp + 3600 * 24 * 1000))
end_time_iso = helpers.timestamp_to_iso(end_timestamp)
value_meta_key = data_utils.rand_name('value_meta_key')
value_meta_value = data_utils.rand_name('value_meta_value')
metric = helpers.create_metric(
name=name,
dimensions=None,
timestamp=timestamp,
value=1.23,
value_meta={value_meta_key: value_meta_value})
resp, response_body = self.monasca_client.create_metrics(metric)
self.assertEqual(204, resp.status)
query_param = '?name=' + str(name) + '&start_time=' + str(time_iso) \
+ '&end_time=' + str(end_time_iso)
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.\
list_measurements(query_param)
self.assertEqual(200, resp.status)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name:
self._verify_list_measurements_element(element,
test_key=None,
test_value=None)
if len(element['measurements']) > 0:
measurement = element['measurements'][0]
self._verify_list_measurements_measurement(
measurement, metric, value_meta_key,
value_meta_value)
return
time.sleep(constants.RETRY_WAIT_SECS)
if i == constants.MAX_RETRIES - 1:
error_msg = "Failed test_create_metric_with_no_dimensions: " \
"timeout on waiting for metrics: at least " \
"one metric is needed. Current number of " \
"metrics = 0"
self.fail(error_msg)
def resource_setup(cls):
super(TestStatistics, cls).resource_setup()
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
cls._test_name = name
cls._test_key = key
cls._test_value = value
cls._start_timestamp = int(time.time() * 1000)
metrics = [
helpers.create_metric(name=cls._test_name,
dimensions={cls._test_key: cls._test_value},
timestamp=cls._start_timestamp,
value=metric_value1),
helpers.create_metric(name=cls._test_name,
dimensions={cls._test_key: cls._test_value},
timestamp=cls._start_timestamp + 1000,
value=metric_value2)
]
cls.monasca_client.create_metrics(metrics)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
query_param = '?name=' + str(name) + '&start_time=' + \
start_time_iso + '&end_time=' + \
helpers.timestamp_to_iso(cls._start_timestamp + 1000 * 2)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
cls._start_time_iso = start_time_iso
for i in xrange(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.\
list_measurements(query_param)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name and len(
element['measurements']) == 2:
cls._end_timestamp = cls._start_timestamp + 1000 * 3
cls._end_time_iso = helpers.timestamp_to_iso(
cls._end_timestamp)
return
time.sleep(constants.RETRY_WAIT_SECS)
assert False, "Failed to find enough measurements to test"
def resource_setup(cls):
super(TestStatistics, cls).resource_setup()
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
cls._test_name = name
cls._test_key = key
cls._test_value = value
cls._start_timestamp = int(time.time() * 1000)
metrics = [
helpers.create_metric(name=cls._test_name,
dimensions={cls._test_key: cls._test_value},
timestamp=cls._start_timestamp,
value=metric_value1),
helpers.create_metric(name=cls._test_name,
dimensions={cls._test_key: cls._test_value},
timestamp=cls._start_timestamp + 1000,
value=metric_value2)
]
cls.monasca_client.create_metrics(metrics)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
query_param = '?name=' + str(name) + '&start_time=' + \
start_time_iso + '&end_time=' + \
helpers.timestamp_to_iso(cls._start_timestamp + 1000 * 2)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
cls._start_time_iso = start_time_iso
for i in xrange(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.\
list_measurements(query_param)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name and len(
element['measurements']) == 2:
cls._end_timestamp = cls._start_timestamp + 1000 * 3
cls._end_time_iso = helpers.timestamp_to_iso(
cls._end_timestamp)
return
time.sleep(constants.RETRY_WAIT_SECS)
assert False, "Failed to find enough measurements to test"
def test_list_measurements_success(self):
start_timestamp = int(time.time() * 1000)
start_time = str(helpers.timestamp_to_iso(start_timestamp))
parms = '?name=foo&start_time=' + start_time
resp, response_body = self.monasca_client.list_measurements(parms)
#
# Validate the call succeeds with empty result (we didn't
# create any metrics to get measurements for)
#
self.assertEqual(200, resp.status)
self.assertEqual(0, len(response_body['elements']))
self.assertTrue('/v2.0/metrics/measurements' in response_body['links'][0]['href'])
def test_create_metric_with_no_dimensions(self):
name = data_utils.rand_name('name')
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
end_timestamp = int(round(timestamp + 3600 * 24 * 1000))
end_time_iso = helpers.timestamp_to_iso(end_timestamp)
value_meta_key = data_utils.rand_name('value_meta_key')
value_meta_value = data_utils.rand_name('value_meta_value')
metric = helpers.create_metric(name=name,
dimensions=None,
timestamp=timestamp,
value=1.23,
value_meta={
value_meta_key: value_meta_value})
resp, response_body = self.monasca_client.create_metrics(metric)
self.assertEqual(204, resp.status)
query_param = '?name=' + str(name) + '&start_time=' + str(time_iso) \
+ '&end_time=' + str(end_time_iso)
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.\
list_measurements(query_param)
self.assertEqual(200, resp.status)
elements = response_body['elements']
for element in elements:
if str(element['name']) == name:
self._verify_list_measurements_element(
element, test_key=None, test_value=None)
if len(element['measurements']) > 0:
measurement = element['measurements'][0]
self._verify_list_measurements_measurement(
measurement, metric, value_meta_key,
value_meta_value)
return
time.sleep(constants.RETRY_WAIT_SECS)
if i == constants.MAX_RETRIES - 1:
error_msg = "Failed test_create_metric_with_no_dimensions: " \
"timeout on waiting for metrics: at least " \
"one metric is needed. Current number of " \
"metrics = 0"
self.fail(error_msg)
def test_create_metric_with_multibyte_character(self):
name = data_utils.rand_name('ｎａｍｅ').decode('utf8')
key = data_utils.rand_name('ｋｅｙ').decode('utf8')
value = data_utils.rand_name('ｖａｌｕｅ').decode('utf8')
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
end_timestamp = int(round((time.time() + 3600 * 24) * 1000))
end_time_iso = helpers.timestamp_to_iso(end_timestamp)
value_meta_key = data_utils.rand_name('value_meta_ｋｅｙ').decode('utf8')
value_meta_value = data_utils.rand_name('value_meta_ｖａｌｕｅ').decode(
'utf8')
metric = helpers.create_metric(
name=name,
dimensions={key: value},
timestamp=timestamp,
value=1.23,
value_meta={value_meta_key: value_meta_value})
resp, response_body = self.monasca_client.create_metrics(metric)
self.assertEqual(204, resp.status)
query_param = '?name=' + urlparse.quote(name.encode('utf8')) + \
'&start_time=' + time_iso + '&end_time=' + end_time_iso
for i in range(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.\
list_measurements(query_param)
self.assertEqual(200, resp.status)
elements = response_body['elements']
for element in elements:
if element['name'] == name:
self._verify_list_measurements_element(element, key, value)
measurement = element['measurements'][0]
self._verify_list_measurements_measurement(
measurement, metric, value_meta_key, value_meta_value)
return
time.sleep(constants.RETRY_WAIT_SECS)
if i == constants.MAX_RETRIES - 1:
error_msg = "Failed test_create_metric: " \
"timeout on waiting for metrics: at least " \
"one metric is needed. Current number of " \
"metrics = 0"
self.fail(error_msg)
def test_list_statistics_success(self):
start_timestamp = int(time.time() * 1000)
start_time = str(helpers.timestamp_to_iso(start_timestamp))
query_parms = '?name=foo&statistics=avg&start_time=' + start_time
resp, response_body = self.monasca_client.list_statistics(query_parms)
#
# Validate the call succeeds with empty result (we didn't
# create any metrics to get statistics for)
#
self.assertEqual(200, resp.status)
self.assertEqual(0, len(response_body['elements']))
self.assertTrue(
'/v2.0/metrics/statistics' in response_body['links'][0]['href'])
def test_list_metrics_with_time_args(self):
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value_org = data_utils.rand_name('value')
now = int(round(time.time() * 1000))
#
# Built start and end time args before and after the measurement.
#
start_iso = helpers.timestamp_to_iso(now - 1000)
end_timestamp = int(round(now + 1000))
end_iso = helpers.timestamp_to_iso(end_timestamp)
metric = helpers.create_metric(name=name,
dimensions={key: value_org},
timestamp=now)
self.monasca_client.create_metrics(metric)
for timer in xrange(constants.MAX_RETRIES):
query_parms = '?name=' + name + '&start_time=' + start_iso + '&end_time=' + end_iso
resp, response_body = self.monasca_client.list_metrics(query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements']
if elements:
dimensions = elements[0]
dimension = dimensions['dimensions']
value = dimension[unicode(key)]
self.assertEqual(value_org, str(value))
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
if timer == constants.MAX_RETRIES - 1:
skip_msg = "Skipped test_list_metrics_with_time_args: " \
"timeout on waiting for metrics: at least one " \
"metric is needed. Current number of metrics " \
"= 0"
raise self.skipException(skip_msg)
def test_list_metrics_with_time_args(self):
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value_org = data_utils.rand_name('value')
now = int(round(time.time() * 1000))
#
# Built start and end time args before and after the measurement.
#
start_iso = helpers.timestamp_to_iso(now - 1000)
end_timestamp = int(round(now + 1000))
end_iso = helpers.timestamp_to_iso(end_timestamp)
metric = helpers.create_metric(name=name,
dimensions={key: value_org},
timestamp=now)
self.monasca_client.create_metrics(metric)
for timer in xrange(constants.MAX_RETRIES):
query_parms = '?name=' + name + '&start_time=' + start_iso + '&end_time=' + end_iso
resp, response_body = self.monasca_client.list_metrics(query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements']
if elements:
dimensions = elements[0]
dimension = dimensions['dimensions']
value = dimension[unicode(key)]
self.assertEqual(value_org, str(value))
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
if timer == constants.MAX_RETRIES - 1:
skip_msg = "Skipped test_list_metrics_with_time_args: " \
"timeout on waiting for metrics: at least one " \
"metric is needed. Current number of metrics " \
"= 0"
raise self.skipException(skip_msg)
def test_list_measurements_with_endtime(self):
time_iso = helpers.timestamp_to_iso(
self._start_timestamp + ONE_SECOND * 2)
query_parms = '?name=' + str(self._names_list[0]) + \
'&merge_metrics=true' \
'&start_time=' + str(self._start_time) + \
'&end_time=' + str(time_iso)
resp, response_body = self.monasca_client.list_measurements(
query_parms)
self._verify_list_measurements(resp, response_body)
elements = response_body['elements']
self._verify_list_measurements_elements(elements=elements,
test_key=None, test_value=None)
measurements = elements[0]['measurements']
self._verify_list_measurements_meas_len(measurements=measurements,
test_len=NUM_MEASUREMENTS)
def test_list_measurements_with_endtime(self):
time_iso = helpers.timestamp_to_iso(
self._start_timestamp + ONE_SECOND * 2)
query_parms = '?name=' + str(self._names_list[0]) + \
'&merge_metrics=true' \
'&start_time=' + str(self._start_time) + \
'&end_time=' + str(time_iso)
resp, response_body = self.monasca_client.list_measurements(
query_parms)
self._verify_list_measurements(resp, response_body)
elements = response_body['elements']
self._verify_list_measurements_elements(elements=elements,
test_key=None, test_value=None)
measurements = elements[0]['measurements']
self._verify_list_measurements_meas_len(measurements=measurements,
test_len=NUM_MEASUREMENTS)
def resource_setup(cls):
super(TestMetricsNames, cls).resource_setup()
name1 = data_utils.rand_name('name1')
name2 = data_utils.rand_name('name2')
name3 = data_utils.rand_name('name3')
key = data_utils.rand_name()
key1 = data_utils.rand_name()
value = data_utils.rand_name()
value1 = data_utils.rand_name()
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
metric1 = helpers.create_metric(name=name1,
dimensions={key: value})
metric2 = helpers.create_metric(name=name2,
dimensions={key1: value1})
metric3 = helpers.create_metric(name=name3,
dimensions={key: value})
cls._test_metric_names = {name1, name2, name3}
cls._expected_names_list = list(cls._test_metric_names)
cls._expected_names_list.sort()
cls._test_metric_names_with_same_dim = [name1, name3]
cls._test_metrics = [metric1, metric2, metric3]
cls._dimensions_param = key + ':' + value
cls.monasca_client.create_metrics(cls._test_metrics)
query_param = '?start_time=' + time_iso
returned_name_set = set()
for i in xrange(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.list_metrics(query_param)
elements = response_body['elements']
for element in elements:
returned_name_set.add(str(element['name']))
if cls._test_metric_names.issubset(returned_name_set):
return
time.sleep(constants.RETRY_WAIT_SECS)
assert False, 'Unable to initialize metrics'
def resource_setup(cls):
super(TestMetricsNames, cls).resource_setup()
name1 = data_utils.rand_name('name1')
name2 = data_utils.rand_name('name2')
name3 = data_utils.rand_name('name3')
key = data_utils.rand_name()
key1 = data_utils.rand_name()
value = data_utils.rand_name()
value1 = data_utils.rand_name()
timestamp = int(round(time.time() * 1000))
time_iso = helpers.timestamp_to_iso(timestamp)
metric1 = helpers.create_metric(name=name1,
dimensions={key: value})
metric2 = helpers.create_metric(name=name2,
dimensions={key1: value1})
metric3 = helpers.create_metric(name=name3,
dimensions={key: value})
cls._test_metric_names = {name1, name2, name3}
cls._expected_names_list = list(cls._test_metric_names)
cls._expected_names_list.sort()
cls._test_metric_names_with_same_dim = [name1, name3]
cls._test_metrics = [metric1, metric2, metric3]
cls._dimensions_param = key + ':' + value
cls.monasca_client.create_metrics(cls._test_metrics)
query_param = '?start_time=' + time_iso
returned_name_set = set()
for i in range(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.list_metrics(query_param)
elements = response_body['elements']
for element in elements:
returned_name_set.add(str(element['name']))
if cls._test_metric_names.issubset(returned_name_set):
return
time.sleep(constants.RETRY_WAIT_SECS)
assert False, 'Unable to initialize metrics'
def test_list_statistics_with_offset_limit(self):
start_timestamp = int(time.time() * 1000)
name = data_utils.rand_name()
metric = [
helpers.create_metric(name=name, timestamp=start_timestamp + 0,
dimensions={'key1': 'value-1',
'key2': 'value-1'},
value=1),
helpers.create_metric(name=name, timestamp=start_timestamp + 500,
dimensions={'key1': 'value-2',
'key2': 'value-2'},
value=2),
helpers.create_metric(name=name, timestamp=start_timestamp + 1000,
dimensions={'key1': 'value-3',
'key2': 'value-3'},
value=3),
helpers.create_metric(name=name, timestamp=start_timestamp + 1500,
dimensions={'key1': 'value-4',
'key2': 'value-4'},
value=4)
]
self.monasca_client.create_metrics(metric)
query_parms = '?name=' + name
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.list_metrics(query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements']
if elements:
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
self._check_timeout(i, constants.MAX_RETRIES, elements, 4)
start_time = helpers.timestamp_to_iso(start_timestamp)
end_timestamp = start_timestamp + 4000
end_time = helpers.timestamp_to_iso(end_timestamp)
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg' \
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1'
resp, body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
elements = body['elements'][0]['statistics']
first_element = elements[0]
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg'\
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1' + '&limit=4'
resp, response_body = self.monasca_client.list_statistics(
query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements'][0]['statistics']
self.assertEqual(4, len(elements))
self.assertEqual(first_element, elements[0])
for index in xrange(1, 5):
max_limit = 5 - index
for limit in xrange(1, max_limit):
offset_timestamp = start_timestamp + (1000 * index)
offset = timeutils.iso8601_from_timestamp(offset_timestamp / 1000)
last_index = index + limit
expected_elements = elements[index:last_index]
query_parms = '?name=' + name + '&merge_metrics=true' + \
'&statistics=avg' + '&start_time=' + \
str(start_time) + '&end_time=' + \
str(end_time) + '&period=1' + '&limit=' + \
str(limit) + '&offset=' + str(offset)
resp, response_body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
if not response_body['elements']:
self.fail("No metrics returned")
if not response_body['elements'][0]['statistics']:
self.fail("No statistics returned")
new_elements = response_body['elements'][0]['statistics']
self.assertEqual(limit, len(new_elements))
# bug in the python API causes limit 1 to not have matching timestamps
if limit > 1:
self.assertEqual(expected_elements, new_elements)
def test_list_statistics_with_offset_limit(self):
start_timestamp = int(time.time() * 1000)
name = data_utils.rand_name()
metric = [
helpers.create_metric(name=name, timestamp=start_timestamp + 0,
dimensions={'key1': 'value-1',
'key2': 'value-1'},
value=1),
helpers.create_metric(name=name, timestamp=start_timestamp + 1000,
dimensions={'key1': 'value-2',
'key2': 'value-2'},
value=2),
helpers.create_metric(name=name, timestamp=start_timestamp + 2000,
dimensions={'key1': 'value-3',
'key2': 'value-3'},
value=3),
helpers.create_metric(name=name, timestamp=start_timestamp + 3000,
dimensions={'key1': 'value-4',
'key2': 'value-4'},
value=4)
]
self.monasca_client.create_metrics(metric)
query_parms = '?name=' + name
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.list_metrics(query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements']
if elements:
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
self._check_timeout(i, constants.MAX_RETRIES, elements, 4)
start_time = helpers.timestamp_to_iso(start_timestamp)
end_timestamp = start_timestamp + 4000
end_time = helpers.timestamp_to_iso(end_timestamp)
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg' \
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1'
resp, body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
elements = body['elements'][0]['statistics']
first_element = elements[0]
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg'\
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1' + '&limit=4'
resp, response_body = self.monasca_client.list_statistics(
query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements'][0]['statistics']
self.assertEqual(4, len(elements))
self.assertEqual(first_element, elements[0])
for index in xrange(1, 4):
max_limit = 4 - index
# Get first offset from api
query_parms = '?name=' + str(name) + \
'&merge_metrics=true&start_time=' + elements[index - 1][0] + \
'&end_time=' + end_time + \
'&limit=1'
resp, response_body = self.monasca_client.list_measurements(query_parms)
for link in response_body['links']:
if link['rel'] == 'next':
next_link = link['href']
if not next_link:
self.fail("No next link returned with query parameters: {}".formet(query_parms))
offset = helpers.get_query_param(next_link, "offset")
# python api returns exact timestamp, but the test needs a rounded number
offset_period_index = offset.find('.')
offset = offset[:offset_period_index] + 'Z'
for limit in xrange(1, max_limit):
expected_elements = [elem for elem in elements if elem[0] > offset]
expected_elements = expected_elements[:limit]
query_parms = '?name=' + name + '&merge_metrics=true' + \
'&statistics=avg' + '&start_time=' + \
str(start_time) + '&end_time=' + \
str(end_time) + '&period=1' + '&limit=' + \
str(limit) + '&offset=' + str(offset)
resp, response_body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
if not response_body['elements']:
self.fail("No metrics returned")
if not response_body['elements'][0]['statistics']:
self.fail("No statistics returned")
new_elements = response_body['elements'][0]['statistics']
self.assertEqual(limit, len(new_elements))
# bug in the python API causes limit 1 to not have matching timestamps
if limit > 1:
self.assertEqual(expected_elements, new_elements)
def resource_setup(cls):
super(TestStatistics, cls).resource_setup()
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value1 = data_utils.rand_name('value1')
value2 = data_utils.rand_name('value2')
cls._test_name = name
cls._test_key = key
cls._test_value1 = value1
cls._start_timestamp = int(time.time() * 1000)
metrics = [
helpers.create_metric(name=name,
dimensions={key: value1},
timestamp=cls._start_timestamp,
value=1.23),
helpers.create_metric(name=name,
dimensions={key: value2},
timestamp=cls._start_timestamp + 1000,
value=4.56)
]
cls.metric_values = [m['value'] for m in metrics]
cls.monasca_client.create_metrics(metrics)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
query_param = '?name=' + str(name) + '&start_time=' + \
start_time_iso + '&merge_metrics=true' + '&end_time=' + \
helpers.timestamp_to_iso(cls._start_timestamp + 1000 * 2)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
cls._start_time_iso = start_time_iso
num_measurements = 0
for i in xrange(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.\
list_measurements(query_param)
elements = response_body['elements']
if len(elements) > 0:
num_measurements = len(elements[0]['measurements'])
if num_measurements >= MIN_REQUIRED_MEASUREMENTS:
break
time.sleep(constants.RETRY_WAIT_SECS)
if num_measurements < MIN_REQUIRED_MEASUREMENTS:
assert False, "Required {} measurements, found {}".format(MIN_REQUIRED_MEASUREMENTS, num_measurements)
cls._end_timestamp = cls._start_timestamp + 3000
cls._end_time_iso = helpers.timestamp_to_iso(cls._end_timestamp)
name2 = data_utils.rand_name("group-by")
cls._group_by_metric_name = name2
cls._group_by_end_time_iso = helpers.timestamp_to_iso(cls._start_timestamp + 4000)
group_by_metrics = [
helpers.create_metric(name=name2, dimensions={'key1': 'value1', 'key2': 'value5', 'key3': 'value7'},
timestamp=cls._start_timestamp + 1, value=2),
helpers.create_metric(name=name2, dimensions={'key1': 'value2', 'key2': 'value5', 'key3': 'value7'},
timestamp=cls._start_timestamp + 1001, value=3),
helpers.create_metric(name=name2, dimensions={'key1': 'value3', 'key2': 'value6', 'key3': 'value7'},
timestamp=cls._start_timestamp + 2001, value=5),
helpers.create_metric(name=name2, dimensions={'key1': 'value4', 'key2': 'value6', 'key3': 'value8'},
timestamp=cls._start_timestamp + 3001, value=7),
]
cls.monasca_client.create_metrics(group_by_metrics)
query_param = '?name=' + str(name2) + \
'&start_time=' + start_time_iso + \
'&merge_metrics=true' + \
'&end_time=' + cls._group_by_end_time_iso
num_measurements = 0
for i in xrange(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client. \
list_measurements(query_param)
elements = response_body['elements']
if len(elements) > 0:
num_measurements = len(elements[0]['measurements'])
if num_measurements >= len(group_by_metrics):
break
time.sleep(constants.RETRY_WAIT_SECS)
if num_measurements < len(group_by_metrics):
assert False, "Required {} measurements, found {}".format(len(group_by_metrics),
response_body)
def test_list_statistics_with_offset_limit(self):
start_timestamp = int(time.time() * 1000)
name = data_utils.rand_name()
metric = [
helpers.create_metric(name=name, timestamp=start_timestamp + 1,
dimensions={'key1': 'value-1',
'key2': 'value-1'},
value=1),
helpers.create_metric(name=name, timestamp=start_timestamp + 1001,
dimensions={'key1': 'value-2',
'key2': 'value-2'},
value=2),
helpers.create_metric(name=name, timestamp=start_timestamp + 2001,
dimensions={'key1': 'value-3',
'key2': 'value-3'},
value=3),
helpers.create_metric(name=name, timestamp=start_timestamp + 3001,
dimensions={'key1': 'value-4',
'key2': 'value-4'},
value=4)
]
num_metrics = len(metric)
self.monasca_client.create_metrics(metric)
query_parms = '?name=' + name
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.list_metrics(query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements']
if elements:
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
self._check_timeout(i, constants.MAX_RETRIES, elements, num_metrics)
start_time = helpers.timestamp_to_iso(start_timestamp)
end_timestamp = start_timestamp + 4001
end_time = helpers.timestamp_to_iso(end_timestamp)
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg' \
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1'
resp, body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
elements = body['elements'][0]['statistics']
first_element = elements[0]
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg'\
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1' + '&limit=' + str(num_metrics)
resp, response_body = self.monasca_client.list_statistics(
query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements'][0]['statistics']
self.assertEqual(num_metrics, len(elements))
self.assertEqual(first_element, elements[0])
for limit in xrange(1, num_metrics):
start_index = 0
params = [('name', name),
('merge_metrics', 'true'),
('statistics', 'avg'),
('start_time', str(start_time)),
('end_time', str(end_time)),
('period', 1),
('limit', limit)]
offset = None
while True:
num_expected_elements = limit
if (num_expected_elements + start_index) > num_metrics:
num_expected_elements = num_metrics - start_index
these_params = list(params)
# If not the first call, use the offset returned by the last call
if offset:
these_params.extend([('offset', str(offset))])
query_parms = '?' + urlencode(these_params)
resp, response_body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
if not response_body['elements']:
self.fail("No metrics returned")
if not response_body['elements'][0]['statistics']:
self.fail("No statistics returned")
new_elements = response_body['elements'][0]['statistics']
self.assertEqual(num_expected_elements, len(new_elements))
expected_elements = elements[start_index:start_index + limit]
self.assertEqual(expected_elements, new_elements)
start_index += num_expected_elements
if start_index >= num_metrics:
break
# Get the next set
offset = self._get_offset(response_body)
def resource_setup(cls):
super(TestDimensions, cls).resource_setup()
start_timestamp = int(round(time.time() * 1000))
start_time_iso = helpers.timestamp_to_iso(start_timestamp)
# NOTE (brtknr): use interval of a day because the tag based queries
# appear to only support smallest granularity of a day, and disregard
# time of day, which is fine for most use cases.
day = 60 * 60 * 24 * 1000
end_timestamp = start_timestamp + 10 * day
end_time_iso = helpers.timestamp_to_iso(end_timestamp)
metric_name1 = data_utils.rand_name()
name1 = "name_1"
name2 = "name_2"
value1 = "value_1"
value2 = "value_2"
timestamp1 = start_timestamp - day
timestamp2 = start_timestamp + day
timestamp3 = start_timestamp + day + day
timestamp4 = end_timestamp + day
metric1 = helpers.create_metric(name=metric_name1,
timestamp=timestamp1,
dimensions={
name1: value1,
name2: value2,
})
cls.monasca_client.create_metrics(metric1)
metric1 = helpers.create_metric(name=metric_name1,
timestamp=timestamp2,
dimensions={name1: value2})
cls.monasca_client.create_metrics(metric1)
metric_name2 = data_utils.rand_name()
name3 = "name_3"
value3 = "value_3"
value4 = "value_4"
metric2 = helpers.create_metric(name=metric_name2,
timestamp=timestamp3,
dimensions={
name1: value3,
name3: value4,
})
cls.monasca_client.create_metrics(metric2)
metric_name3 = data_utils.rand_name()
metric3 = helpers.create_metric(name=metric_name3,
timestamp=timestamp4,
dimensions={name2: value3})
cls.monasca_client.create_metrics(metric3)
cls._test_metric1 = metric1
cls._test_metric2 = metric2
cls._test_metric_names = {metric_name1, metric_name2, metric_name3}
cls._dim_names_metric1 = [name1, name2]
cls._dim_names_metric1_in_timerange = [name1]
cls._dim_names_metric2 = [name1, name3]
cls._dim_names_metric2_in_timerange = [name1, name3]
cls._dim_names = sorted(
set(cls._dim_names_metric1 + cls._dim_names_metric2))
cls._dim_names_in_timerange = sorted(
set(cls._dim_names_metric1_in_timerange +
cls._dim_names_metric2_in_timerange))
cls._dim_name1 = name1
cls._dim_name1_values_for_metric1 = [value1, value2]
cls._dim_name1_values_for_metric1_in_timerange = [value2]
cls._dim_name1_values = [value1, value2, value3]
cls._dim_name1_values_in_timerange = [value2, value3]
cls._start_time = start_time_iso
cls._end_time = end_time_iso
param = '?start_time=' + start_time_iso
returned_name_set = set()
for i in range(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.list_metrics(param)
elements = response_body['elements']
metric_name1_count = 0
for element in elements:
returned_name_set.add(str(element['name']))
if (str(element['name']) == metric_name1):
metric_name1_count += 1
# Java version of influxdb never returns both metric1 in the list but Python does.
if cls._test_metric_names.issubset(returned_name_set) \
and (metric_name1_count == 2 or i == constants.MAX_RETRIES - 1):
return
time.sleep(constants.RETRY_WAIT_SECS)
assert False, 'Unable to initialize metrics'
def resource_setup(cls):
super(TestStatistics, cls).resource_setup()
name = data_utils.rand_name('name')
key = data_utils.rand_name('key')
value1 = data_utils.rand_name('value1')
value2 = data_utils.rand_name('value2')
cls._test_name = name
cls._test_key = key
cls._test_value1 = value1
cls._start_timestamp = int(time.time() * 1000)
metrics = [
helpers.create_metric(name=name,
dimensions={key: value1},
timestamp=cls._start_timestamp,
value=1.23),
helpers.create_metric(name=name,
dimensions={key: value2},
timestamp=cls._start_timestamp + 1000,
value=4.56)
]
cls.metric_values = [m['value'] for m in metrics]
cls.monasca_client.create_metrics(metrics)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
query_param = '?name=' + str(name) + '&start_time=' + \
start_time_iso + '&merge_metrics=true' + '&end_time=' + \
helpers.timestamp_to_iso(cls._start_timestamp + 1000 * 2)
start_time_iso = helpers.timestamp_to_iso(cls._start_timestamp)
cls._start_time_iso = start_time_iso
num_measurements = 0
for i in range(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client.\
list_measurements(query_param)
elements = response_body['elements']
if len(elements) > 0:
num_measurements = len(elements[0]['measurements'])
if num_measurements >= MIN_REQUIRED_MEASUREMENTS:
break
time.sleep(constants.RETRY_WAIT_SECS)
if num_measurements < MIN_REQUIRED_MEASUREMENTS:
assert False, "Required {} measurements, found {}".format(MIN_REQUIRED_MEASUREMENTS,
num_measurements)
cls._end_timestamp = cls._start_timestamp + 3000
cls._end_time_iso = helpers.timestamp_to_iso(cls._end_timestamp)
name2 = data_utils.rand_name("group-by")
cls._group_by_metric_name = name2
cls._group_by_end_time_iso = helpers.timestamp_to_iso(cls._start_timestamp + 4000)
group_by_metrics = [
helpers.create_metric(name=name2,
dimensions={'key1': 'value1', 'key2': 'value5', 'key3': 'value7'},
timestamp=cls._start_timestamp + 1, value=2),
helpers.create_metric(name=name2,
dimensions={'key1': 'value2', 'key2': 'value5', 'key3': 'value7'},
timestamp=cls._start_timestamp + 1001, value=3),
helpers.create_metric(name=name2,
dimensions={'key1': 'value3', 'key2': 'value6', 'key3': 'value7'},
timestamp=cls._start_timestamp + 2001, value=5),
helpers.create_metric(name=name2,
dimensions={'key1': 'value4', 'key2': 'value6', 'key3': 'value8'},
timestamp=cls._start_timestamp + 3001, value=7),
]
cls.monasca_client.create_metrics(group_by_metrics)
query_param = '?name=' + str(name2) + \
'&start_time=' + start_time_iso + \
'&merge_metrics=true' + \
'&end_time=' + cls._group_by_end_time_iso
num_measurements = 0
for i in range(constants.MAX_RETRIES):
resp, response_body = cls.monasca_client. \
list_measurements(query_param)
elements = response_body['elements']
if len(elements) > 0:
num_measurements = len(elements[0]['measurements'])
if num_measurements >= len(group_by_metrics):
break
time.sleep(constants.RETRY_WAIT_SECS)
if num_measurements < len(group_by_metrics):
assert False, "Required {} measurements, found {}".format(len(group_by_metrics),
response_body)
def test_list_statistics_with_offset_limit(self):
start_timestamp = int(time.time() * 1000)
name = data_utils.rand_name()
metric = [
helpers.create_metric(name=name, timestamp=start_timestamp + 1,
dimensions={'key1': 'value-1',
'key2': 'value-1'},
value=1),
helpers.create_metric(name=name, timestamp=start_timestamp + 1001,
dimensions={'key1': 'value-2',
'key2': 'value-2'},
value=2),
helpers.create_metric(name=name, timestamp=start_timestamp + 2001,
dimensions={'key1': 'value-3',
'key2': 'value-3'},
value=3),
helpers.create_metric(name=name, timestamp=start_timestamp + 3001,
dimensions={'key1': 'value-4',
'key2': 'value-4'},
value=4)
]
num_metrics = len(metric)
self.monasca_client.create_metrics(metric)
query_parms = '?name=' + name
for i in range(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.list_metrics(query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements']
if elements and len(elements) == num_metrics:
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
self._check_timeout(i, constants.MAX_RETRIES, elements, num_metrics)
start_time = helpers.timestamp_to_iso(start_timestamp)
end_timestamp = start_timestamp + 4001
end_time = helpers.timestamp_to_iso(end_timestamp)
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg' \
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1'
resp, body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
elements = body['elements'][0]['statistics']
first_element = elements[0]
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg'\
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1' + '&limit=' + str(num_metrics)
resp, response_body = self.monasca_client.list_statistics(
query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements'][0]['statistics']
self.assertEqual(num_metrics, len(elements))
self.assertEqual(first_element, elements[0])
for limit in range(1, num_metrics):
start_index = 0
params = [('name', name),
('merge_metrics', 'true'),
('statistics', 'avg'),
('start_time', str(start_time)),
('end_time', str(end_time)),
('period', 1),
('limit', limit)]
offset = None
while True:
num_expected_elements = limit
if (num_expected_elements + start_index) > num_metrics:
num_expected_elements = num_metrics - start_index
these_params = list(params)
# If not the first call, use the offset returned by the last call
if offset:
these_params.extend([('offset', str(offset))])
query_parms = '?' + urlencode(these_params)
resp, response_body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
if not response_body['elements']:
self.fail("No metrics returned")
if not response_body['elements'][0]['statistics']:
self.fail("No statistics returned")
new_elements = response_body['elements'][0]['statistics']
self.assertEqual(num_expected_elements, len(new_elements))
expected_elements = elements[start_index:start_index + limit]
self.assertEqual(expected_elements, new_elements)
start_index += num_expected_elements
if start_index >= num_metrics:
break
# Get the next set
offset = self._get_offset(response_body)
def test_list_statistics_with_offset_limit(self):
start_timestamp = int(time.time() * 1000)
name = data_utils.rand_name()
metric = [
helpers.create_metric(name=name,
timestamp=start_timestamp + 0,
dimensions={
'key1': 'value-1',
'key2': 'value-1'
},
value=1),
helpers.create_metric(name=name,
timestamp=start_timestamp + 500,
dimensions={
'key1': 'value-2',
'key2': 'value-2'
},
value=2),
helpers.create_metric(name=name,
timestamp=start_timestamp + 1000,
dimensions={
'key1': 'value-3',
'key2': 'value-3'
},
value=3),
helpers.create_metric(name=name,
timestamp=start_timestamp + 1500,
dimensions={
'key1': 'value-4',
'key2': 'value-4'
},
value=4)
]
self.monasca_client.create_metrics(metric)
query_parms = '?name=' + name
for i in xrange(constants.MAX_RETRIES):
resp, response_body = self.monasca_client.list_metrics(query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements']
if elements:
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
self._check_timeout(i, constants.MAX_RETRIES, elements, 4)
start_time = helpers.timestamp_to_iso(start_timestamp)
end_timestamp = start_timestamp + 4000
end_time = helpers.timestamp_to_iso(end_timestamp)
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg' \
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1'
resp, body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
elements = body['elements'][0]['statistics']
first_element = elements[0]
query_parms = '?name=' + name + '&merge_metrics=true&statistics=avg'\
+ '&start_time=' + str(start_time) + '&end_time=' + \
str(end_time) + '&period=1' + '&limit=4'
resp, response_body = self.monasca_client.list_statistics(query_parms)
self.assertEqual(200, resp.status)
elements = response_body['elements'][0]['statistics']
self.assertEqual(4, len(elements))
self.assertEqual(first_element, elements[0])
for index in xrange(1, 5):
max_limit = 5 - index
for limit in xrange(1, max_limit):
offset_timestamp = start_timestamp + (1000 * index)
offset = timeutils.iso8601_from_timestamp(offset_timestamp /
1000)
last_index = index + limit
expected_elements = elements[index:last_index]
query_parms = '?name=' + name + '&merge_metrics=true' + \
'&statistics=avg' + '&start_time=' + \
str(start_time) + '&end_time=' + \
str(end_time) + '&period=1' + '&limit=' + \
str(limit) + '&offset=' + str(offset)
resp, response_body = self.monasca_client.list_statistics(
query_parms)
self.assertEqual(200, resp.status)
if not response_body['elements']:
self.fail("No metrics returned")
if not response_body['elements'][0]['statistics']:
self.fail("No statistics returned")
new_elements = response_body['elements'][0]['statistics']
self.assertEqual(limit, len(new_elements))
# bug in the python API causes limit 1 to not have matching timestamps
if limit > 1:
self.assertEqual(expected_elements, new_elements)
def resource_setup(cls):
super(TestMeasurements, cls).resource_setup()
start_timestamp = int(time.time() * 1000)
start_time = str(helpers.timestamp_to_iso(start_timestamp))
metrics = []
name1 = data_utils.rand_name()
name2 = data_utils.rand_name()
cls._names_list = [name1, name2]
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
cls._key = key
cls._value = value
cls._start_timestamp = start_timestamp
for i in xrange(NUM_MEASUREMENTS):
metric = helpers.create_metric(
name=name1,
timestamp=start_timestamp + (i * 10),
value=i)
metrics.append(metric)
cls.monasca_client.create_metrics(metrics)
# Create metric2 for test_list_measurements_with_dimensions
metric2 = helpers.create_metric(
name=name1, timestamp=start_timestamp + ONE_SECOND * 2,
dimensions={key: value}, value=NUM_MEASUREMENTS)
cls.monasca_client.create_metrics(metric2)
# Create metric3 for test_list_measurements_with_offset_limit
metric3 = [
helpers.create_metric(
name=name2, timestamp=start_timestamp + ONE_SECOND * 3,
dimensions={'key1': 'value1', 'key2': 'value5', 'key3': 'value7'}),
helpers.create_metric(
name=name2, timestamp=start_timestamp + ONE_SECOND * 3 + 10,
dimensions={'key1': 'value2', 'key2': 'value5', 'key3': 'value7'}),
helpers.create_metric(
name=name2, timestamp=start_timestamp + ONE_SECOND * 3 + 20,
dimensions={'key1': 'value3', 'key2': 'value6', 'key3': 'value7'}),
helpers.create_metric(
name=name2, timestamp=start_timestamp + ONE_SECOND * 3 + 30,
dimensions={'key1': 'value4', 'key2': 'value6', 'key3': 'value8'})
]
cls.monasca_client.create_metrics(metric3)
# Create metric3 for test_list_measurements_with_no_merge_metrics
metric4 = helpers.create_metric(
name=name1, timestamp=start_timestamp + ONE_SECOND * 4,
dimensions={'key-1': 'value-1'},
value=NUM_MEASUREMENTS + 1)
cls.monasca_client.create_metrics(metric4)
end_time = str(helpers.timestamp_to_iso(
start_timestamp + NUM_MEASUREMENTS + ONE_SECOND * 5))
queries = []
queries.append('?name={}&start_time={}&end_time={}&merge_metrics=true'.
format(name1, start_time, end_time))
queries.append('?name={}&start_time={}&end_time={}&merge_metrics=true'.
format(name2, start_time, end_time))
for timer in xrange(constants.MAX_RETRIES):
responses = map(cls.monasca_client.list_measurements, queries)
resp_first = responses[0][0]
response_body_first = responses[0][1]
resp_second = responses[1][0]
response_body_second = responses[1][1]
if resp_first.status == 200 and resp_second.status == 200 \
and len(response_body_first['elements']) == 1 \
and len(response_body_second['elements']) == 1:
len_meas_first = len(
response_body_first['elements'][0]['measurements'])
len_meas_second = len(
response_body_second['elements'][0]['measurements'])
if len_meas_first == NUM_MEASUREMENTS + 2 \
and len_meas_second == 4:
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
else:
time.sleep(constants.RETRY_WAIT_SECS)
cls._start_time = start_time
cls._end_time = end_time
def resource_setup(cls):
super(TestMeasurements, cls).resource_setup()
start_timestamp = int(time.time() * 1000)
start_time = str(helpers.timestamp_to_iso(start_timestamp))
metrics = []
name1 = data_utils.rand_name()
name2 = data_utils.rand_name()
cls._names_list = [name1, name2]
key = data_utils.rand_name('key')
value = data_utils.rand_name('value')
cls._key = key
cls._value = value
cls._start_timestamp = start_timestamp
for i in xrange(NUM_MEASUREMENTS):
metric = helpers.create_metric(
name=name1,
timestamp=start_timestamp + (i * 10),
value=i)
metrics.append(metric)
cls.monasca_client.create_metrics(metrics)
# Create metric2 for test_list_measurements_with_dimensions
metric2 = helpers.create_metric(
name=name1, timestamp=start_timestamp + ONE_SECOND * 2,
dimensions={key: value}, value=NUM_MEASUREMENTS)
cls.monasca_client.create_metrics(metric2)
# Create metric3 for test_list_measurements_with_offset_limit
metric3 = [
helpers.create_metric(
name=name2, timestamp=start_timestamp + ONE_SECOND * 3,
dimensions={'key1': 'value1', 'key2': 'value5', 'key3': 'value7'}),
helpers.create_metric(
name=name2, timestamp=start_timestamp + ONE_SECOND * 3 + 10,
dimensions={'key1': 'value2', 'key2': 'value5', 'key3': 'value7'}),
helpers.create_metric(
name=name2, timestamp=start_timestamp + ONE_SECOND * 3 + 20,
dimensions={'key1': 'value3', 'key2': 'value6', 'key3': 'value7'}),
helpers.create_metric(
name=name2, timestamp=start_timestamp + ONE_SECOND * 3 + 30,
dimensions={'key1': 'value4', 'key2': 'value6', 'key3': 'value8'})
]
cls.monasca_client.create_metrics(metric3)
# Create metric3 for test_list_measurements_with_no_merge_metrics
metric4 = helpers.create_metric(
name=name1, timestamp=start_timestamp + ONE_SECOND * 4,
dimensions={'key-1': 'value-1'},
value=NUM_MEASUREMENTS + 1)
cls.monasca_client.create_metrics(metric4)
end_time = str(helpers.timestamp_to_iso(
start_timestamp + NUM_MEASUREMENTS + ONE_SECOND * 5))
queries = []
queries.append('?name={}&start_time={}&end_time={}&merge_metrics=true'.
format(name1, start_time, end_time))
queries.append('?name={}&start_time={}&end_time={}&merge_metrics=true'.
format(name2, start_time, end_time))
for timer in xrange(constants.MAX_RETRIES):
responses = map(cls.monasca_client.list_measurements, queries)
resp_first = responses[0][0]
response_body_first = responses[0][1]
resp_second = responses[1][0]
response_body_second = responses[1][1]
if resp_first.status == 200 and resp_second.status == 200 \
and len(response_body_first['elements']) == 1 \
and len(response_body_second['elements']) == 1:
len_meas_first = len(
response_body_first['elements'][0]['measurements'])
len_meas_second = len(
response_body_second['elements'][0]['measurements'])
if len_meas_first == NUM_MEASUREMENTS + 2 \
and len_meas_second == 4:
break
else:
time.sleep(constants.RETRY_WAIT_SECS)
else:
time.sleep(constants.RETRY_WAIT_SECS)
cls._start_time = start_time
cls._end_time = end_time
