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Refactor metric format (#2658)
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* Refactor metric format

Fixes #2646

* Do not overwrite pb2_scope_metrics

* Refactor for loops

* Add multiple scope test case

* Fix interfaces

* Fix docs

* Update exporter/opentelemetry-exporter-prometheus/src/opentelemetry/exporter/prometheus/__init__.py

Co-authored-by: Aaron Abbott <aaronabbott@google.com>

* Fix lint

* Remove resource check

* Remove instrumentation_scope check

* Group metrics by instrumentation scopes in the SDK

* Remove label_keyss

* Use strings instead of mocks

* Return generator instead of a list

* Fix lint

* Rename variables

Co-authored-by: Aaron Abbott <aaronabbott@google.com>
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@ocelotl @aabmass
ocelotl and aabmass authored May 12, 2022
1 parent a821311 commit e8fbb08
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189 changes: 107 additions & 82 deletions ...r-otlp-proto-grpc/src/opentelemetry/exporter/otlp/proto/grpc/_metric_exporter/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
from logging import getLogger
from os import environ
from typing import Optional, Sequence
from grpc import ChannelCredentials, Compression
Expand Down Expand Up @@ -40,9 +40,10 @@
from opentelemetry.sdk._metrics.export import (
MetricExporter,
MetricExportResult,
MetricsData,
)

logger = logging.getLogger(__name__)
_logger = getLogger(__name__)


class OTLPMetricExporter(
Expand Down Expand Up @@ -79,103 +80,127 @@ def __init__(
)

def _translate_data(
self, data: Sequence[Metric]
self, data: MetricsData
) -> ExportMetricsServiceRequest:
sdk_resource_scope_metrics = {}

for metric in data:
resource = metric.resource
scope_map = sdk_resource_scope_metrics.get(resource, {})
if not scope_map:
sdk_resource_scope_metrics[resource] = scope_map

scope_metrics = scope_map.get(metric.instrumentation_scope)

if not scope_metrics:
if metric.instrumentation_scope is not None:
scope_map[metric.instrumentation_scope] = pb2.ScopeMetrics(
scope=InstrumentationScope(
name=metric.instrumentation_scope.name,
version=metric.instrumentation_scope.version,
)
)
else:
scope_map[
metric.instrumentation_scope
] = pb2.ScopeMetrics()

scope_metrics = scope_map.get(metric.instrumentation_scope)
resource_metrics_dict = {}

pbmetric = pb2.Metric(
name=metric.name,
description=metric.description,
unit=metric.unit,
)
if isinstance(metric.point, Gauge):
pt = pb2.NumberDataPoint(
attributes=self._translate_attributes(metric.attributes),
time_unix_nano=metric.point.time_unix_nano,
)
if isinstance(metric.point.value, int):
pt.as_int = metric.point.value
else:
pt.as_double = metric.point.value
pbmetric.gauge.data_points.append(pt)
elif isinstance(metric.point, Histogram):
pt = pb2.HistogramDataPoint(
attributes=self._translate_attributes(metric.attributes),
time_unix_nano=metric.point.time_unix_nano,
start_time_unix_nano=metric.point.start_time_unix_nano,
count=sum(metric.point.bucket_counts),
sum=metric.point.sum,
bucket_counts=metric.point.bucket_counts,
explicit_bounds=metric.point.explicit_bounds,
)
pbmetric.histogram.aggregation_temporality = (
metric.point.aggregation_temporality
)
pbmetric.histogram.data_points.append(pt)
elif isinstance(metric.point, Sum):
pt = pb2.NumberDataPoint(
attributes=self._translate_attributes(metric.attributes),
start_time_unix_nano=metric.point.start_time_unix_nano,
time_unix_nano=metric.point.time_unix_nano,
)
if isinstance(metric.point.value, int):
pt.as_int = metric.point.value
else:
pt.as_double = metric.point.value
# note that because sum is a message type, the fields must be
# set individually rather than instantiating a pb2.Sum and setting
# it once
pbmetric.sum.aggregation_temporality = (
metric.point.aggregation_temporality
for resource_metrics in data.resource_metrics:

resource = resource_metrics.resource

# It is safe to assume that each entry in data.resource_metrics is
# associated with an unique resource.
scope_metrics_dict = {}

resource_metrics_dict[resource] = scope_metrics_dict

for scope_metrics in resource_metrics.scope_metrics:

instrumentation_scope = scope_metrics.scope

# The SDK groups metrics in instrumentation scopes already so
# there is no need to check for existing instrumentation scopes
# here.
pb2_scope_metrics = pb2.ScopeMetrics(
scope=InstrumentationScope(
name=instrumentation_scope.name,
version=instrumentation_scope.version,
)
)
pbmetric.sum.is_monotonic = metric.point.is_monotonic
pbmetric.sum.data_points.append(pt)
else:
logger.warn("unsupported datapoint type %s", metric.point)
continue

scope_metrics.metrics.append(
pbmetric,
)

scope_metrics_dict[instrumentation_scope] = pb2_scope_metrics

for metric in scope_metrics.metrics:
pb2_metric = pb2.Metric(
name=metric.name,
description=metric.description,
unit=metric.unit,
)

if isinstance(metric.data, Gauge):
for data_point in metric.data.data_points:
pt = pb2.NumberDataPoint(
attributes=self._translate_attributes(
data_point.attributes
),
time_unix_nano=data_point.time_unix_nano,
)
if isinstance(data_point.value, int):
pt.as_int = data_point.value
else:
pt.as_double = data_point.value
pb2_metric.gauge.data_points.append(pt)

elif isinstance(metric.data, Histogram):
for data_point in metric.data.data_points:
pt = pb2.HistogramDataPoint(
attributes=self._translate_attributes(
data_point.attributes
),
time_unix_nano=data_point.time_unix_nano,
start_time_unix_nano=(
data_point.start_time_unix_nano
),
count=data_point.count,
sum=data_point.sum,
bucket_counts=data_point.bucket_counts,
explicit_bounds=data_point.explicit_bounds,
)
pb2_metric.histogram.aggregation_temporality = (
metric.data.aggregation_temporality
)
pb2_metric.histogram.data_points.append(pt)

elif isinstance(metric.data, Sum):
for data_point in metric.data.data_points:
pt = pb2.NumberDataPoint(
attributes=self._translate_attributes(
data_point.attributes
),
start_time_unix_nano=(
data_point.start_time_unix_nano
),
time_unix_nano=data_point.time_unix_nano,
)
if isinstance(data_point.value, int):
pt.as_int = data_point.value
else:
pt.as_double = data_point.value
# note that because sum is a message type, the
# fields must be set individually rather than
# instantiating a pb2.Sum and setting it once
pb2_metric.sum.aggregation_temporality = (
metric.data.aggregation_temporality
)
pb2_metric.sum.is_monotonic = (
metric.data.is_monotonic
)
pb2_metric.sum.data_points.append(pt)
else:
_logger.warn(
"unsupported datapoint type %s", metric.point
)
continue

pb2_scope_metrics.metrics.append(pb2_metric)

return ExportMetricsServiceRequest(
resource_metrics=get_resource_data(
sdk_resource_scope_metrics,
resource_metrics_dict,
pb2.ResourceMetrics,
"metrics",
)
)

def export(
self,
metrics: Sequence[Metric],
metrics_data: MetricsData,
timeout_millis: float = 10_000,
**kwargs,
) -> MetricExportResult:
# TODO(#2663): OTLPExporterMixin should pass timeout to gRPC
return self._export(metrics)
return self._export(metrics_data)

def shutdown(self, timeout_millis: float = 30_000, **kwargs) -> None:
pass
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