def test_create_gauge(self):
""" check creating gauge using api functions """
# Create a metrics with no metric instances
mf = pmp.create_gauge(self.gauge_metric_name, self.gauge_metric_help,
[])
self.assertIsInstance(mf, pmp.MetricFamily)
self.assertEqual(len(mf.metric), 0)
# Create it with metrics
mf = pmp.create_gauge(self.gauge_metric_name, self.gauge_metric_help,
self.gauge_metric_data)
self.assertIsInstance(mf, pmp.MetricFamily)
self.assertEqual(mf.name, self.gauge_metric_name)
self.assertEqual(mf.help, self.gauge_metric_help)
self.assertEqual(mf.type, self.gauge_metric_type)
mf_ = pmp.create_gauge(self.gauge_metric_name, self.gauge_metric_help,
self.gauge_metric_data)
self.assertIsInstance(mf_, pmp.MetricFamily)
self.assertEqual(mf, mf_)
for m in mf_.metric:
self.assertEqual(m.timestamp_ms, 0)
# Create another with timestamp
mf_ = pmp.create_gauge(
self.gauge_metric_name,
self.gauge_metric_help,
self.gauge_metric_data,
timestamp=True,
)
self.assertIsInstance(mf_, pmp.MetricFamily)
for m in mf_.metric:
self.assertNotEqual(m.timestamp_ms, 0)
self.assertNotEqual(mf, mf_)
# Create Gauge with const_labels
mf_ = pmp.create_gauge(
self.gauge_metric_name,
self.gauge_metric_help,
self.gauge_metric_data,
const_labels=self.const_labels,
)
self.assertIsInstance(mf_, pmp.MetricFamily)
# Check that const_label is present in the LabelPair associated
# with each metric instance.
for m in mf_.metric:
labels = [lp.name for lp in m.label]
self.assertIn("app", labels)
self.assertNotEqual(mf, mf_)
def test_gauge_format_binary(self):
g = Gauge(name=self.gauge_metric_name, doc=self.gauge_metric_help)
# Add data to the collector
for labels, values in self.gauge_metric_data:
g.set_value(labels, values)
f = binary.BinaryFormatter()
result = f.marshall_collector(g)
self.assertIsInstance(result, pmp.MetricFamily)
# Construct the result to expected to receive when the gauge
# collector is marshalled.
expected_result = pmp.create_gauge(self.gauge_metric_name,
self.gauge_metric_help,
self.gauge_metric_data)
self.assertEqual(result, expected_result)
######################################################################
# Check metric with constant labels
g = Gauge(
name=self.gauge_metric_name,
doc=self.gauge_metric_help,
const_labels=self.const_labels,
)
# Add data to the collector
for labels, values in self.gauge_metric_data:
g.set_value(labels, values)
f = binary.BinaryFormatter()
result = f.marshall_collector(g)
self.assertIsInstance(result, pmp.MetricFamily)
# Construct the result to expected to receive when the gauge
# collector is marshalled.
expected_result = pmp.create_gauge(
self.gauge_metric_name,
self.gauge_metric_help,
self.gauge_metric_data,
const_labels=self.const_labels,
)
self.assertEqual(result, expected_result)
######################################################################
# Check metric with timestamps
with unittest.mock.patch.object(pmp.utils,
"_timestamp_ms",
return_value=TEST_TIMESTAMP):
g = Gauge(name=self.gauge_metric_name, doc=self.gauge_metric_help)
# Add data to the collector
for labels, values in self.gauge_metric_data:
g.set_value(labels, values)
f = binary.BinaryFormatter(timestamp=True)
result = f.marshall_collector(g)
self.assertIsInstance(result, pmp.MetricFamily)
# Construct the result to expected to receive when the gauge
# collector is marshalled.
expected_result = pmp.create_gauge(
self.gauge_metric_name,
self.gauge_metric_help,
self.gauge_metric_data,
timestamp=True,
)
self.assertEqual(result, expected_result)
def main():
# Define some labels that we want added to all metrics. These labels are
# independent of the instance labels that define a metric as unique.
# These could be used to add hostname, app name, etc.
const_labels = {'host': 'examplehost', 'app': 'my_app'}
# Create a Counter metric to track logged in users. This counter has
# 5 separate instances.
# We'll make use of the optional const_labels argument to add extra
# constant labels.
# We will also add a timestamp to the metric instances.
# We will request that the labels be sorted.
cm = pmp.create_counter('logged_users_total',
'Logged users in the application.', (
({
'country': "sp",
"device": "desktop"
}, 520),
({
'country': "us",
"device": "mobile"
}, 654),
({
'country': "uk",
"device": "desktop"
}, 1001),
({
'country': "de",
"device": "desktop"
}, 995),
({
'country': "zh",
"device": "desktop"
}, 520),
),
timestamp=True,
const_labels=const_labels,
ordered=True)
# Create a Gauge metric, similar to the counter above.
gm = pmp.create_gauge('logged_users_total',
'Logged users in the application.', (
({
'country': "sp",
"device": "desktop"
}, 520),
({
'country': "us",
"device": "mobile"
}, 654),
({
'country': "uk",
"device": "desktop"
}, 1001),
({
'country': "de",
"device": "desktop"
}, 995),
({
'country': "zh",
"device": "desktop"
}, 520),
),
timestamp=True,
const_labels=const_labels,
ordered=True)
# Now lets create a Summary and Histogram metric object. These forms
# of metrics are slightly more complicated.
#
# Remember, the collection of metrics and the management of Summary
# Quantiles and Histogram Buckets are outside the scope of
# functionality provided by this package.
#
# The following examples assume they are taking the data values from
# a management library that can also emit the sum and count fields
# expected for both Summary and Histogram metrics.
# Create a Summary metric. The values for a summary are slightly
# different to a Counter or Gauge. They are composed of a dict representing
# the various quantile values of the metric. The count and sum are
# expected to be present in this dict.
sm = pmp.create_summary('request_payload_size_bytes',
'Request payload size in bytes.', (
({
'route': '/'
}, {
0.5: 4.0,
0.9: 5.2,
0.99: 5.2,
'sum': 25.2,
'count': 4
}),
({
'route': '/data'
}, {
0.5: 4.0,
0.9: 5.2,
0.99: 5.2,
'sum': 25.2,
'count': 4
}),
),
timestamp=True,
const_labels=const_labels,
ordered=True)
# Create a Histogram metric. The values for a histogram are slightly
# different to a Counter or Gauge. They are composed of a dict representing
# the various bucket values of the metric. The cumulative count and sum
# values are expected to be present in this dict.
#
# Libraries manageing buckets typically have add a POS_INF upper bound to
# catch values beyond the largest bucket bound. Simulate this behavior in
# the data below.
POS_INF = float("inf")
hm = pmp.create_histogram('request_latency_seconds',
'Request latency in seconds.', (
({
'route': '/'
}, {
5.0: 3,
10.0: 2,
15.0: 1,
POS_INF: 0,
'count': 6,
'sum': 46.0
}),
({
'route': '/data'
}, {
5.0: 3,
10.0: 2,
15.0: 1,
POS_INF: 0,
'count': 6,
'sum': 46.0
}),
),
timestamp=True,
const_labels=const_labels,
ordered=True)
# Serialize a sequence of metrics into a payload suitable for network
# transmission.
input_metrics = (cm, gm, sm, hm)
payload = pmp.encode(*input_metrics)
assert isinstance(payload, bytes)
# De-serialize the payload into a sequence of MetricsFamily objects.
recovered_metrics = pmp.decode(payload)
# Confirm that the round trip re-produced the same number of metrics
# and that the metrics are identical.
assert len(recovered_metrics) == len(input_metrics)
for recovered_metric, input_metric in zip(recovered_metrics,
input_metrics):
assert recovered_metric == input_metric
for metric in input_metrics:
print(metric)
def test_encode_decode(self):
''' check encode and decode functions '''
# check passing no metrics raises an exception
input_metrics = None
with self.assertRaises(Exception) as ctx:
payload = pmp.encode(input_metrics)
self.assertIn('Expected metrics to be instances of MetricFamily, got ',
str(ctx.exception))
cm = pmp.create_counter(self.counter_metric_name,
self.counter_metric_help,
self.counter_metric_data)
self.assertIsInstance(cm, pmp.MetricFamily)
gm = pmp.create_gauge(self.gauge_metric_name, self.gauge_metric_help,
self.gauge_metric_data)
self.assertIsInstance(gm, pmp.MetricFamily)
sm = pmp.create_summary(self.summary_metric_name,
self.summary_metric_help,
self.summary_metric_data)
self.assertIsInstance(sm, pmp.MetricFamily)
hm = pmp.create_histogram(self.histogram_metric_name,
self.histogram_metric_help,
self.histogram_metric_data)
self.assertIsInstance(hm, pmp.MetricFamily)
# check encode can take a single valid metric
input_metrics = [cm]
payload = pmp.encode(input_metrics[0])
self.assertIsInstance(payload, bytes)
# check round-trip
metrics = pmp.decode(payload)
self.assertIsInstance(metrics, list)
self.assertEqual(len(metrics), len(input_metrics))
for m_in, m_out in zip(input_metrics, metrics):
self.assertEqual(m_in, m_out)
# check passing invalid type raises an exception
input_metrics = ['a']
with self.assertRaises(Exception) as ctx:
payload = pmp.encode(*input_metrics)
self.assertIn('Expected metrics to be instances of MetricFamily, got ',
str(ctx.exception))
# check parser can take multiple valid metrics
input_metrics = (cm, gm, sm, hm)
payload = pmp.encode(*input_metrics)
self.assertIsInstance(payload, bytes)
# check round-trip
metrics = pmp.decode(payload)
self.assertIsInstance(metrics, list)
self.assertEqual(len(metrics), len(input_metrics))
for m_in, m_out in zip(input_metrics, metrics):
self.assertEqual(m_in, m_out)
# check decoding empty bytes returns empty list
metrics = pmp.decode(b'')
self.assertIsInstance(metrics, list)
self.assertEqual(len(metrics), 0)