def _format_summary(self, summary: MetricTupleType, name: str,
const_labels: LabelsType) -> pmp.Metric:
'''
:param summary: a 2-tuple containing labels and a dict representing
the summary value. The dict contains keys for each quantile as
well as the sum and count fields.
:param labels: a dict of labels for a metric.
:param const_labels: a dict of constant labels to be associated with
the metric.
'''
summary_labels, summary_value_dict = summary
# typing check, no runtime behaviour.
summary_value_dict = cast(SummaryDictType, summary_value_dict)
labels = self._unify_labels(summary_labels, const_labels, ordered=True)
pb_labels = self._create_labels(labels)
pb_quantiles = []
for k, v in summary_value_dict.items():
if not isinstance(k, str):
pb_quantile = pmp.Quantile(quantile=k, value=v)
pb_quantiles.append(pb_quantile)
pb_summary = pmp.Summary(sample_count=summary_value_dict['count'],
sample_sum=summary_value_dict['sum'],
quantile=pb_quantiles)
pb_metric = pmp.Metric(label=pb_labels, summary=pb_summary)
if self.timestamp:
pb_metric.timestamp_ms = self._get_timestamp()
return pb_metric
def _format_histogram(self, histogram: MetricTupleType, name: str,
const_labels: LabelsType) -> pmp.Metric:
'''
:param histogram: a 2-tuple containing labels and a dict representing
the histogram value. The dict contains keys for each bucket as
well as the sum and count fields.
:param labels: a dict of labels for a metric.
:param const_labels: a dict of constant labels to be associated with
the metric.
'''
histogram_labels, histogram_value_dict = histogram
# typing check, no runtime behaviour.
histogram_value_dict = cast(HistogramDictType, histogram_value_dict)
labels = self._unify_labels(histogram_labels,
const_labels,
ordered=True)
pb_labels = self._create_labels(labels)
pb_buckets = []
for k, v in histogram_value_dict.items():
if not isinstance(k, str):
pb_bucket = pmp.Bucket(cumulative_count=v, upper_bound=k)
pb_buckets.append(pb_bucket)
pb_histogram = pmp.Histogram(
sample_count=histogram_value_dict['count'],
sample_sum=histogram_value_dict['sum'],
bucket=pb_buckets)
pb_metric = pmp.Metric(label=pb_labels, histogram=pb_histogram)
if self.timestamp:
pb_metric.timestamp_ms = self._get_timestamp()
return pb_metric
def _format_gauge(self, gauge: MetricTupleType, name: str,
const_labels: LabelsType) -> pmp.Metric:
'''
:param gauge: a 2-tuple containing labels and the gauge value.
:param labels: a dict of labels for a metric.
:param const_labels: a dict of constant labels to be associated with
the metric.
'''
gauge_labels, gauge_value = gauge
labels = self._unify_labels(gauge_labels, const_labels, ordered=True)
pb_labels = self._create_labels(labels)
pb_gauge = pmp.Gauge(value=gauge_value)
pb_metric = pmp.Metric(label=pb_labels, gauge=pb_gauge)
if self.timestamp:
pb_metric.timestamp_ms = self._get_timestamp()
return pb_metric
def _format_counter(self,
# counter: Tuple[LabelsType, ValueType],
counter: MetricTupleType,
name: str,
const_labels: LabelsType) -> pmp.Metric:
'''
:param counter: a 2-tuple containing labels and the counter value.
:param labels: a dict of labels for a metric.
:param const_labels: a dict of constant labels to be associated with
the metric.
'''
counter_labels, counter_value = counter
labels = self._unify_labels(counter_labels, const_labels, ordered=True)
pb_labels = self._create_labels(labels)
pb_counter = pmp.Counter(value=counter_value)
pb_metric = pmp.Metric(label=pb_labels, counter=pb_counter)
if self.timestamp:
pb_metric.timestamp_ms = self._get_timestamp()
return pb_metric
def _create_protobuf_object(self,
data,
metrics,
metric_type,
const_labels={},
ts=False):
pb_metrics = []
for i in metrics:
labels = [pmp.LabelPair(name=k, value=v) for k, v in i[0].items()]
c_labels = [
pmp.LabelPair(name=k, value=v)
for k, v in const_labels.items()
]
labels.extend(c_labels)
if metric_type == pmp.COUNTER:
metric = pmp.Metric(counter=pmp.Counter(value=i[1]),
label=labels)
elif metric_type == pmp.GAUGE:
metric = pmp.Metric(gauge=pmp.Gauge(value=i[1]), label=labels)
elif metric_type == pmp.SUMMARY:
quantiles = []
for k, v in i[1].items():
if not isinstance(k, str):
q = pmp.Quantile(quantile=k, value=v)
quantiles.append(q)
metric = pmp.Metric(summary=pmp.Summary(
quantile=quantiles,
sample_sum=i[1]['sum'],
sample_count=i[1]['count']),
label=labels)
elif metric_type == pmp.HISTOGRAM:
buckets = []
for k, v in i[1].items():
if not isinstance(k, str):
bucket = pmp.Bucket(cumulative_count=v, upper_bound=k)
buckets.append(bucket)
metric = pmp.Metric(summary=pmp.Histogram(
buckets=buckets,
histogram_sum=i[1]['sum'],
histogram_count=i[1]['count']),
label=labels)
else:
raise TypeError("Not a valid metric")
if ts:
metric.timestamp_ms = int(
datetime.datetime.now(
tz=datetime.timezone.utc).timestamp() * 1000)
pb_metrics.append(metric)
valid_result = pmp.MetricFamily(name=data['name'],
help=data['doc'],
type=metric_type,
metric=pb_metrics)
return valid_result