def custom_metric(client):
custom_metric_descriptor = create_custom_metric(
client, PROJECT_RESOURCE, METRIC_RESOURCE, METRIC_KIND)
# wait until metric has been created, use the get call to wait until
# a response comes back with the new metric with 10 retries.
custom_metric = None
retry_count = 0
while not custom_metric and retry_count < 10:
time.sleep(1)
retry_count += 1
custom_metric = get_custom_metric(
client, PROJECT_RESOURCE, METRIC_RESOURCE)
# make sure we get the custom_metric
assert custom_metric
yield custom_metric
# cleanup
delete_metric_descriptor(client, custom_metric_descriptor['name'])
def test_custom_metric(cloud_config, client):
PROJECT_RESOURCE = "projects/{}".format(cloud_config.project)
# Use a constant seed so psuedo random number is known ahead of time
random.seed(1)
pseudo_random_value = random.randint(0, 10)
# Reseed it
random.seed(1)
INSTANCE_ID = "test_instance"
METRIC_KIND = "GAUGE"
custom_metric_descriptor = create_custom_metric(
client, PROJECT_RESOURCE, METRIC_RESOURCE, METRIC_KIND)
# wait until metric has been created, use the get call to wait until
# a response comes back with the new metric
custom_metric = None
while not custom_metric:
time.sleep(1)
custom_metric = get_custom_metric(
client, PROJECT_RESOURCE, METRIC_RESOURCE)
write_timeseries_value(client, PROJECT_RESOURCE,
METRIC_RESOURCE, INSTANCE_ID,
METRIC_KIND)
# Sometimes on new metric descriptors, writes have a delay in being
# read back. Use eventually_consistent to account for this.
@eventually_consistent.call
def _():
response = read_timeseries(client, PROJECT_RESOURCE, METRIC_RESOURCE)
value = int(
response['timeSeries'][0]['points'][0]['value']['int64Value'])
# using seed of 1 will create a value of 1
assert value == pseudo_random_value
delete_metric_descriptor(client, custom_metric_descriptor['name'])
def test_custom_metric(client):
PROJECT_RESOURCE = "projects/{}".format(PROJECT)
# Use a constant seed so psuedo random number is known ahead of time
random.seed(1)
pseudo_random_value = random.randint(0, 10)
# Reseed it
random.seed(1)
INSTANCE_ID = "test_instance"
METRIC_KIND = "GAUGE"
custom_metric_descriptor = create_custom_metric(client, PROJECT_RESOURCE,
METRIC_RESOURCE,
METRIC_KIND)
# wait until metric has been created, use the get call to wait until
# a response comes back with the new metric
custom_metric = None
while not custom_metric:
time.sleep(1)
custom_metric = get_custom_metric(client, PROJECT_RESOURCE,
METRIC_RESOURCE)
write_timeseries_value(client, PROJECT_RESOURCE, METRIC_RESOURCE,
INSTANCE_ID, METRIC_KIND)
# Sometimes on new metric descriptors, writes have a delay in being
# read back. Use eventually_consistent to account for this.
@eventually_consistent.call
def _():
response = read_timeseries(client, PROJECT_RESOURCE, METRIC_RESOURCE)
value = int(
response['timeSeries'][0]['points'][0]['value']['int64Value'])
# using seed of 1 will create a value of 1
assert value == pseudo_random_value
delete_metric_descriptor(client, custom_metric_descriptor['name'])