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Refactor metric format #2658

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Merged
ocelotl merged 16 commits into from
May 12, 2022
Merged

Refactor metric format #2658

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e01077a
Refactor metric format
ocelotl May 9, 2022
a828fc1
Do not overwrite pb2_scope_metrics
ocelotl May 11, 2022
4b8f7f4
Refactor for loops
ocelotl May 11, 2022
951810d
Add multiple scope test case
ocelotl May 11, 2022
002c74e
Fix interfaces
ocelotl May 11, 2022
bc370f2
Fix docs
ocelotl May 11, 2022
d6d3db6
Update exporter/opentelemetry-exporter-prometheus/src/opentelemetry/e…
ocelotl May 12, 2022
a58bdad
Fix lint
ocelotl May 12, 2022
71ba28b
Remove resource check
ocelotl May 12, 2022
436dfff
Remove instrumentation_scope check
ocelotl May 12, 2022
6af9b2b
Group metrics by instrumentation scopes in the SDK
ocelotl May 12, 2022
9e13d7a
Remove label_keyss
ocelotl May 12, 2022
93f853b
Use strings instead of mocks
ocelotl May 12, 2022
5dee4fb
Return generator instead of a list
ocelotl May 12, 2022
2175593
Fix lint
ocelotl May 12, 2022
4d534a4
Rename variables
ocelotl May 12, 2022
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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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