def testCreateAllModels(self):
host = os.environ.get("TAURUS_HTM_SERVER", "127.0.0.1")
apikey = os.environ.get("TAURUS_APIKEY", "taurus")
# Resize metrics down to a much smaller random sample of the original
# so as to not overload the system under test. We need only to test that
# everything returned goes through the right channels.
metrics = {
key:value
for (key, value)
in random.sample(metric_utils.getMetricsConfiguration().items(), 3)
}
with patch("taurus.metric_collectors.metric_utils.getMetricsConfiguration",
return_value=metrics,
spec_set=metric_utils.getMetricsConfiguration):
createdModels = metric_utils.createAllModels(host, apikey)
allModels = metric_utils.getAllModels(host, apikey)
for model in createdModels:
self.addCleanup(requests.delete,
"https://%s/_metrics/custom/%s" % (host, model["name"]),
auth=(apikey, ""),
verify=False)
remoteModel = metric_utils.getOneModel(host, apikey, model["uid"])
self.assertDictEqual(remoteModel, model)
self.assertIn(model, allModels)
def testCreateAllModels(self):
host = os.environ.get("TAURUS_HTM_SERVER", "127.0.0.1")
apikey = os.environ.get("TAURUS_APIKEY", "taurus")
# Resize metrics down to a much smaller random sample of the original
# so as to not overload the system under test. We need only to test that
# everything returned goes through the right channels.
metrics = {
key: value
for (key, value) in random.sample(
metric_utils.getMetricsConfiguration().items(), 3)
}
with patch(
"taurus.metric_collectors.metric_utils.getMetricsConfiguration",
return_value=metrics,
spec_set=metric_utils.getMetricsConfiguration):
createdModels = metric_utils.createAllModels(host, apikey)
allModels = metric_utils.getAllModels(host, apikey)
for model in createdModels:
self.addCleanup(requests.delete,
"https://%s/_metrics/custom/%s" %
(host, model["name"]),
auth=(apikey, ""),
verify=False)
remoteModel = metric_utils.getOneModel(host, apikey, model["uid"])
self.assertDictEqual(remoteModel, model)
self.assertIn(model, allModels)
def testGetOneModel(self, requestsMock):
# We'll mock out only the minimal response that would be required to
# satisfy the requirements of metric_utils.getAllModels(), and then
# assert that the returned result is the json-decoded response from the
# mocked out API.
requestsMock.get.return_value = Mock(status_code=200,
text='[{"parameters":"True"}]')
result = metric_utils.getOneModel("localhost", "taurus", "foo")
requestsMock.get.assert_called_once_with("https://localhost/_models/foo",
verify=ANY, auth=("taurus", ""))
self.assertEqual(result, {"parameters":"True"})
def testGetOneModel(self, requestsMock):
# We'll mock out only the minimal response that would be required to
# satisfy the requirements of metric_utils.getAllModels(), and then
# assert that the returned result is the json-decoded response from the
# mocked out API.
requestsMock.get.return_value = Mock(status_code=200,
text='[{"parameters":"True"}]')
result = metric_utils.getOneModel("localhost", "taurus", "foo")
requestsMock.get.assert_called_once_with(
"https://localhost/_models/foo", verify=ANY, auth=("taurus", ""))
self.assertEqual(result, {"parameters": "True"})
def main():
logging_support.LoggingSupport.initTool()
try:
options = _parseArgs()
g_log.info("Running %s with options=%r", sys.argv[0], options)
if options["unmonitorAll"]:
models = metric_utils.getAllModels(
host=options["htmServer"],
apiKey=options["apiKey"])
else:
models = tuple(
metric_utils.getOneModel(
host=options["htmServer"],
apiKey=options["apiKey"],
modelId=modelId)
for modelId in options["modelIds"]
)
# Save model objects to file for use by monitor_metrics
with open(options["modelsFilePath"], "w") as outFile:
json.dump(models, outFile, indent=4)
if not models:
g_log.info("No models to unmonitor")
return
g_log.info("Unmonitoring %d models", len(models))
for i, model in enumerate(models, 1):
modelId = model["uid"]
metric_utils.unmonitorMetric(
host=options["htmServer"],
apiKey=options["apiKey"],
modelId=modelId)
g_log.info("Unmonitored metric=%s (%d of %d)",
modelId, i, len(models))
g_log.info("Unmonitored %d models", len(models))
except SystemExit as e:
if e.code != 0:
g_log.exception("unmonitor_metrics failed")
raise
except Exception:
g_log.exception("unmonitor_metrics failed")
raise
def main():
logging_support.LoggingSupport.initTool()
try:
options = _parseArgs()
g_log.info("Running %s with options=%r", sys.argv[0], options)
if options["unmonitorAll"]:
models = metric_utils.getAllModels(host=options["htmServer"],
apiKey=options["apiKey"])
else:
models = tuple(
metric_utils.getOneModel(host=options["htmServer"],
apiKey=options["apiKey"],
modelId=modelId)
for modelId in options["modelIds"])
# Save model objects to file for use by monitor_metrics
with open(options["modelsFilePath"], "w") as outFile:
json.dump(models, outFile, indent=4)
if not models:
g_log.info("No models to unmonitor")
return
g_log.info("Unmonitoring %d models", len(models))
for i, model in enumerate(models, 1):
modelId = model["uid"]
metric_utils.unmonitorMetric(host=options["htmServer"],
apiKey=options["apiKey"],
modelId=modelId)
g_log.info("Unmonitored metric=%s (%d of %d)", modelId, i,
len(models))
g_log.info("Unmonitored %d models", len(models))
except SystemExit as e:
if e.code != 0:
g_log.exception("unmonitor_metrics failed")
raise
except Exception:
g_log.exception("unmonitor_metrics failed")
raise
def testCreateAllModels(self):
host = os.environ.get("TAURUS_HTM_SERVER", "127.0.0.1")
apikey = os.environ.get("TAURUS_APIKEY", "taurus")
# Resize metrics down to a much smaller random sample of the original
# so as to not overload the system under test. We need only to test that
# everything returned goes through the right channels.
metricsConfig = {
key:value
for (key, value)
in random.sample(metric_utils.getMetricsConfiguration().items(), 3)
}
expectedMetricNames = []
for resVal in metricsConfig.itervalues():
for metricName in resVal["metrics"]:
expectedMetricNames.append(metricName)
self.addCleanup(requests.delete,
"https://%s/_metrics/custom/%s" % (host, metricName),
auth=(apikey, ""),
verify=False)
self.assertGreater(len(expectedMetricNames), 0)
with patch("taurus.metric_collectors.metric_utils.getMetricsConfiguration",
return_value=metricsConfig,
spec_set=metric_utils.getMetricsConfiguration):
createdModels = metric_utils.createAllModels(host, apikey)
self.assertEqual(len(createdModels), len(expectedMetricNames))
for model in createdModels:
remoteModel = metric_utils.getOneModel(host, apikey, model["uid"])
self.assertIn(remoteModel["name"], expectedMetricNames)
# Verify that the model is either in "ACTIVE" or the transient
# "PENDNG DATA" or "CREATE PENDING" states
self.assertIn(remoteModel["status"], [1, 2, 8])