mirror of
https://github.com/open-telemetry/opentelemetry-python-contrib.git
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Add conversion to TimeSeries methods (#207)
This commit is contained in:
Shovnik Bhattacharya
committed by
GitHub
GitHub
parent
8b323e935d
commit
f71bc2b79e
@ -5,3 +5,5 @@
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((#180)[https://github.com/open-telemetry/opentelemetry-python-contrib/pull/180])
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- Add Exporter constructor validation methods
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((#206)[https://github.com/open-telemetry/opentelemetry-python-contrib/pull/206])
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- Add conversion to TimeSeries methods
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((#207)[https://github.com/open-telemetry/opentelemetry-python-contrib/pull/207])
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@ -12,8 +12,13 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import re
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from typing import Dict, Sequence
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from opentelemetry.exporter.prometheus_remote_write.gen.remote_pb2 import (
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WriteRequest,
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)
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from opentelemetry.exporter.prometheus_remote_write.gen.types_pb2 import (
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Label,
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Sample,
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@ -24,6 +29,15 @@ from opentelemetry.sdk.metrics.export import (
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MetricsExporter,
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MetricsExportResult,
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)
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from opentelemetry.sdk.metrics.export.aggregate import (
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HistogramAggregator,
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LastValueAggregator,
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MinMaxSumCountAggregator,
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SumAggregator,
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ValueObserverAggregator,
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)
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logger = logging.getLogger(__name__)
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class PrometheusRemoteWriteMetricsExporter(MetricsExporter):
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@ -55,6 +69,14 @@ class PrometheusRemoteWriteMetricsExporter(MetricsExporter):
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self.tls_config = tls_config
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self.proxies = proxies
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self.converter_map = {
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MinMaxSumCountAggregator: self._convert_from_min_max_sum_count,
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SumAggregator: self._convert_from_sum,
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HistogramAggregator: self._convert_from_histogram,
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LastValueAggregator: self._convert_from_last_value,
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ValueObserverAggregator: self._convert_from_value_observer,
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}
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@property
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def endpoint(self):
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return self._endpoint
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@ -142,50 +164,145 @@ class PrometheusRemoteWriteMetricsExporter(MetricsExporter):
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def shutdown(self) -> None:
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raise NotImplementedError()
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def convert_to_timeseries(
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def _convert_to_timeseries(
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self, export_records: Sequence[ExportRecord]
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) -> Sequence[TimeSeries]:
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timeseries = []
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for export_record in export_records:
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aggregator_type = type(export_record.aggregator)
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converter = self.converter_map.get(aggregator_type)
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if converter:
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timeseries.extend(converter(export_record))
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else:
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logger.warning(
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"%s aggregator is not supported, record dropped",
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aggregator_type,
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)
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return timeseries
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def _convert_from_sum(
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self, sum_record: ExportRecord
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) -> Sequence[TimeSeries]:
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return [
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self._create_timeseries(
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sum_record,
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sum_record.instrument.name + "_sum",
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sum_record.aggregator.checkpoint,
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)
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]
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def _convert_from_min_max_sum_count(
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self, min_max_sum_count_record: ExportRecord
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) -> Sequence[TimeSeries]:
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timeseries = []
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for agg_type in ["min", "max", "sum", "count"]:
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name = min_max_sum_count_record.instrument.name + "_" + agg_type
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value = getattr(
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min_max_sum_count_record.aggregator.checkpoint, agg_type
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)
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timeseries.append(
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self._create_timeseries(min_max_sum_count_record, name, value)
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)
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return timeseries
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def _convert_from_histogram(
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self, histogram_record: ExportRecord
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) -> Sequence[TimeSeries]:
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timeseries = []
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for bound in histogram_record.aggregator.checkpoint.keys():
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bound_str = "+Inf" if bound == float("inf") else str(bound)
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value = histogram_record.aggregator.checkpoint[bound]
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timeseries.append(
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self._create_timeseries(
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histogram_record,
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histogram_record.instrument.name + "_histogram",
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value,
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extra_label=("le", bound_str),
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)
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)
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return timeseries
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def _convert_from_last_value(
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self, last_value_record: ExportRecord
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) -> Sequence[TimeSeries]:
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return [
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self._create_timeseries(
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last_value_record,
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last_value_record.instrument.name + "_last",
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last_value_record.aggregator.checkpoint,
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)
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]
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def _convert_from_value_observer(
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self, value_observer_record: ExportRecord
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) -> Sequence[TimeSeries]:
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timeseries = []
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for agg_type in ["min", "max", "sum", "count", "last"]:
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timeseries.append(
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self._create_timeseries(
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value_observer_record,
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value_observer_record.instrument.name + "_" + agg_type,
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getattr(
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value_observer_record.aggregator.checkpoint, agg_type
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),
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)
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)
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return timeseries
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# TODO: Implement convert from quantile once supported by SDK for Prometheus Summaries
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def _convert_from_quantile(
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self, summary_record: ExportRecord
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) -> Sequence[TimeSeries]:
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raise NotImplementedError()
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def convert_from_sum(self, sum_record: ExportRecord) -> TimeSeries:
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raise NotImplementedError()
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def convert_from_min_max_sum_count(
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self, min_max_sum_count_record: ExportRecord
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) -> TimeSeries:
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raise NotImplementedError()
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def convert_from_histogram(
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self, histogram_record: ExportRecord
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) -> TimeSeries:
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raise NotImplementedError()
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def convert_from_last_value(
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self, last_value_record: ExportRecord
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) -> TimeSeries:
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raise NotImplementedError()
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def convert_from_value_observer(
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self, value_observer_record: ExportRecord
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) -> TimeSeries:
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raise NotImplementedError()
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def convert_from_quantile(
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self, summary_record: ExportRecord
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) -> TimeSeries:
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raise NotImplementedError()
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# pylint: disable=no-member
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def create_timeseries(
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self, export_record: ExportRecord, name, value: float
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def _create_timeseries(
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self,
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export_record: ExportRecord,
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name: str,
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value: float,
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extra_label: (str, str) = None,
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) -> TimeSeries:
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raise NotImplementedError()
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timeseries = TimeSeries()
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seen = set()
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def create_sample(self, timestamp: int, value: float) -> Sample:
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raise NotImplementedError()
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def add_label(label_name: str, label_value: str):
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# Label name must contain only alphanumeric characters and underscores
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label_name = re.sub("[^\\w_]", "_", label_name)
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if label_name not in seen:
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label = Label()
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label.name = label_name
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label.value = label_value
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timeseries.labels.append(label)
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seen.add(label_name)
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else:
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logger.warning(
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"Duplicate label with name %s and value %s",
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label_name,
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label_value,
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)
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def create_label(self, name: str, value: str) -> Label:
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raise NotImplementedError()
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# The __name__ label is required by PromQL as its value appears as the metric_name
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add_label("__name__", name)
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if extra_label:
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add_label(extra_label[0], extra_label[1])
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if export_record.resource.attributes:
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for (
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label_name,
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label_value,
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) in export_record.resource.attributes.items():
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add_label(label_name, str(label_value))
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if export_record.labels:
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for [label_name, label_value] in export_record.labels:
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add_label(label_name, label_value)
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sample = Sample()
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sample.timestamp = int(
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export_record.aggregator.last_update_timestamp / 1000000
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)
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sample.value = value
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timeseries.samples.append(sample)
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return timeseries
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def build_message(self, timeseries: Sequence[TimeSeries]) -> bytes:
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raise NotImplementedError()
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@ -13,10 +13,28 @@
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# limitations under the License.
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import unittest
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from logging import Logger
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from unittest.mock import MagicMock, Mock, patch
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from opentelemetry.exporter.prometheus_remote_write import (
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PrometheusRemoteWriteMetricsExporter,
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)
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from opentelemetry.exporter.prometheus_remote_write.gen.types_pb2 import (
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Label,
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Sample,
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TimeSeries,
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)
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from opentelemetry.sdk.metrics import Counter
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from opentelemetry.sdk.metrics.export import ExportRecord, MetricsExportResult
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from opentelemetry.sdk.metrics.export.aggregate import (
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HistogramAggregator,
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LastValueAggregator,
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MinMaxSumCountAggregator,
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SumAggregator,
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ValueObserverAggregator,
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)
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from opentelemetry.sdk.resources import Resource
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from opentelemetry.sdk.util import get_dict_as_key
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class TestValidation(unittest.TestCase):
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@ -115,44 +133,228 @@ class TestValidation(unittest.TestCase):
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class TestConversion(unittest.TestCase):
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# Initializes test data that is reused across tests
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def setUp(self):
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pass
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self.exporter = PrometheusRemoteWriteMetricsExporter(
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endpoint="/prom/test_endpoint"
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)
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# Ensures conversion to timeseries function works with valid aggregation types
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def test_valid_convert_to_timeseries(self):
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pass
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test_records = [
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ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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SumAggregator(),
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Resource({}),
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),
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ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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MinMaxSumCountAggregator(),
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Resource({}),
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),
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ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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HistogramAggregator(),
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Resource({}),
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),
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ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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LastValueAggregator(),
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Resource({}),
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),
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ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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ValueObserverAggregator(),
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Resource({}),
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),
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]
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for record in test_records:
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record.aggregator.update(5)
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record.aggregator.take_checkpoint()
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data = self.exporter._convert_to_timeseries(test_records)
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self.assertIsInstance(data, list)
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self.assertEqual(len(data), 13)
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for timeseries in data:
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self.assertIsInstance(timeseries, TimeSeries)
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# Ensures conversion to timeseries fails for unsupported aggregation types
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def test_invalid_convert_to_timeseries(self):
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pass
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data = self.exporter._convert_to_timeseries(
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[ExportRecord(None, None, None, Resource({}))]
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)
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self.assertIsInstance(data, list)
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self.assertEqual(len(data), 0)
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# Ensures sum aggregator is correctly converted to timeseries
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def test_convert_from_sum(self):
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pass
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sum_record = ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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SumAggregator(),
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Resource({}),
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)
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sum_record.aggregator.update(3)
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sum_record.aggregator.update(2)
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sum_record.aggregator.take_checkpoint()
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expected_timeseries = self.exporter._create_timeseries(
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sum_record, "testname_sum", 5.0
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)
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timeseries = self.exporter._convert_from_sum(sum_record)
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self.assertEqual(timeseries[0], expected_timeseries)
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# Ensures sum min_max_count aggregator is correctly converted to timeseries
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def test_convert_from_min_max_sum_count(self):
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pass
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min_max_sum_count_record = ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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MinMaxSumCountAggregator(),
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Resource({}),
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)
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min_max_sum_count_record.aggregator.update(5)
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min_max_sum_count_record.aggregator.update(1)
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min_max_sum_count_record.aggregator.take_checkpoint()
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expected_min_timeseries = self.exporter._create_timeseries(
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min_max_sum_count_record, "testname_min", 1.0
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)
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expected_max_timeseries = self.exporter._create_timeseries(
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min_max_sum_count_record, "testname_max", 5.0
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)
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expected_sum_timeseries = self.exporter._create_timeseries(
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min_max_sum_count_record, "testname_sum", 6.0
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)
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expected_count_timeseries = self.exporter._create_timeseries(
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min_max_sum_count_record, "testname_count", 2.0
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)
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timeseries = self.exporter._convert_from_min_max_sum_count(
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min_max_sum_count_record
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)
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self.assertEqual(timeseries[0], expected_min_timeseries)
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self.assertEqual(timeseries[1], expected_max_timeseries)
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self.assertEqual(timeseries[2], expected_sum_timeseries)
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self.assertEqual(timeseries[3], expected_count_timeseries)
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# Ensures histogram aggregator is correctly converted to timeseries
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def test_convert_from_histogram(self):
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pass
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histogram_record = ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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HistogramAggregator(),
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Resource({}),
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)
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histogram_record.aggregator.update(5)
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histogram_record.aggregator.update(2)
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histogram_record.aggregator.update(-1)
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histogram_record.aggregator.take_checkpoint()
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expected_le_0_timeseries = self.exporter._create_timeseries(
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histogram_record, "testname_histogram", 1.0, ("le", "0")
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)
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expected_le_inf_timeseries = self.exporter._create_timeseries(
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histogram_record, "testname_histogram", 2.0, ("le", "+Inf")
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)
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timeseries = self.exporter._convert_from_histogram(histogram_record)
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self.assertEqual(timeseries[0], expected_le_0_timeseries)
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self.assertEqual(timeseries[1], expected_le_inf_timeseries)
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# Ensures last value aggregator is correctly converted to timeseries
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def test_convert_from_last_value(self):
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pass
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last_value_record = ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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LastValueAggregator(),
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Resource({}),
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)
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last_value_record.aggregator.update(1)
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last_value_record.aggregator.update(5)
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last_value_record.aggregator.take_checkpoint()
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expected_timeseries = self.exporter._create_timeseries(
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last_value_record, "testname_last", 5.0
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)
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timeseries = self.exporter._convert_from_last_value(last_value_record)
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self.assertEqual(timeseries[0], expected_timeseries)
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# Ensures value observer aggregator is correctly converted to timeseries
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def test_convert_from_value_observer(self):
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pass
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value_observer_record = ExportRecord(
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Counter("testname", "testdesc", "testunit", int, None),
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None,
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ValueObserverAggregator(),
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Resource({}),
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)
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value_observer_record.aggregator.update(5)
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value_observer_record.aggregator.update(1)
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value_observer_record.aggregator.update(2)
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value_observer_record.aggregator.take_checkpoint()
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expected_min_timeseries = self.exporter._create_timeseries(
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value_observer_record, "testname_min", 1.0
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)
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expected_max_timeseries = self.exporter._create_timeseries(
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value_observer_record, "testname_max", 5.0
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)
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expected_sum_timeseries = self.exporter._create_timeseries(
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value_observer_record, "testname_sum", 8.0
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)
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expected_count_timeseries = self.exporter._create_timeseries(
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value_observer_record, "testname_count", 3.0
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)
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expected_last_timeseries = self.exporter._create_timeseries(
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value_observer_record, "testname_last", 2.0
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)
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timeseries = self.exporter._convert_from_value_observer(
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value_observer_record
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)
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self.assertEqual(timeseries[0], expected_min_timeseries)
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self.assertEqual(timeseries[1], expected_max_timeseries)
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self.assertEqual(timeseries[2], expected_sum_timeseries)
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self.assertEqual(timeseries[3], expected_count_timeseries)
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self.assertEqual(timeseries[4], expected_last_timeseries)
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# Ensures quantile aggregator is correctly converted to timeseries
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# TODO: Add test once method is implemented
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def test_convert_from_quantile(self):
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pass
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# TODO: Add test_convert_from_quantile once method is implemented
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# Ensures timeseries produced contains appropriate sample and labels
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def test_create_timeseries(self):
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pass
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def create_label(name, value):
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label = Label()
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label.name = name
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label.value = value
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return label
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sum_aggregator = SumAggregator()
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sum_aggregator.update(5)
|
||||
sum_aggregator.take_checkpoint()
|
||||
export_record = ExportRecord(
|
||||
Counter("testname", "testdesc", "testunit", int, None),
|
||||
get_dict_as_key({"record_name": "record_value"}),
|
||||
sum_aggregator,
|
||||
Resource({"resource_name": "resource_value"}),
|
||||
)
|
||||
|
||||
expected_timeseries = TimeSeries()
|
||||
expected_timeseries.labels.append(create_label("__name__", "testname"))
|
||||
expected_timeseries.labels.append(
|
||||
create_label("resource_name", "resource_value")
|
||||
)
|
||||
expected_timeseries.labels.append(
|
||||
create_label("record_name", "record_value")
|
||||
)
|
||||
|
||||
sample = expected_timeseries.samples.add()
|
||||
sample.timestamp = int(sum_aggregator.last_update_timestamp / 1000000)
|
||||
sample.value = 5.0
|
||||
|
||||
timeseries = self.exporter._create_timeseries(
|
||||
export_record, "testname", 5.0
|
||||
)
|
||||
self.assertEqual(timeseries, expected_timeseries)
|
||||
|
||||
|
||||
class TestExport(unittest.TestCase):
|
||||
|
||||
Reference in New Issue
Block a user