Merge branch 'main' into logname_change

Author: DylanRussell

.github/workflows/ci.yaml

@@ -13,7 +13,6 @@ jobs:
     strategy:
       matrix:
         py:
-          - { version: "3.8", tox: "38" }
           - { version: "3.9", tox: "39" }
           - { version: "3.10", tox: "310" }
           - { version: "3.11", tox: "311" }

docs/contributing.md

@@ -55,17 +55,17 @@ tox -e dev
 
 ### Running tests
 
-This project supports python versions 3.5 to 3.9. To run tests, use `tox`:
+This project supports python versions 3.9 to 3.13. To run tests, use `tox`:
 
 ```sh
 # List all tox environments
 tox -l
 
-# Run python3.8 exporter tests
-tox -e py38-ci-test-exporter
+# Run python3.12 exporter tests
+tox -e py312-ci-test-exporter
 
-# Run all python3.8 tests in parallel
-tox -f py38-test -pauto
+# Run all python3.12 tests in parallel
+tox -f py312-test -pauto
 
 # All checks that run in continuous integration use the "ci" factor, which
 # makes it easy to test without submitting a PR. To run all of them in

opentelemetry-exporter-gcp-logging/setup.cfg

@@ -14,14 +14,13 @@ classifiers =
     License :: OSI Approved :: Apache Software License
     Programming Language :: Python
     Programming Language :: Python :: 3
-    Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
     Programming Language :: Python :: 3.10
     Programming Language :: Python :: 3.11
     Programming Language :: Python :: 3.12
 
 [options]
-python_requires = >=3.8
+python_requires = >=3.9
 package_dir=
     =src
 packages=find_namespace:

opentelemetry-exporter-gcp-monitoring/setup.cfg

@@ -14,15 +14,14 @@ classifiers =
     License :: OSI Approved :: Apache Software License
     Programming Language :: Python
     Programming Language :: Python :: 3
-    Programming Language :: Python :: 3.8
     Programming Language :: Python :: 3.9
     Programming Language :: Python :: 3.10
     Programming Language :: Python :: 3.11
     Programming Language :: Python :: 3.12
     Programming Language :: Python :: 3.13
 
 [options]
-python_requires = >=3.8
+python_requires = >=3.9
 package_dir=
     =src
 packages=find_namespace:

opentelemetry-exporter-gcp-monitoring/src/opentelemetry/exporter/cloud_monitoring/__init__.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import logging
+import math
 import random
 from dataclasses import replace
 from time import time_ns
@@ -212,11 +213,7 @@ def _get_metric_descriptor(
         elif isinstance(data, Histogram):
             descriptor.metric_kind = MetricDescriptor.MetricKind.CUMULATIVE
         elif isinstance(data, ExponentialHistogram):
-            logger.warning(
-                "Unsupported metric data type %s, ignoring it",
-                type(data).__name__,
-            )
-            return None
+            descriptor.metric_kind = MetricDescriptor.MetricKind.CUMULATIVE
         else:
             # Exhaustive check
             _: NoReturn = data
@@ -235,6 +232,8 @@ def _get_metric_descriptor(
             )
         elif isinstance(first_point, HistogramDataPoint):
             descriptor.value_type = MetricDescriptor.ValueType.DISTRIBUTION
+        elif isinstance(first_point, ExponentialHistogramDataPoint):
+            descriptor.value_type = MetricDescriptor.ValueType.DISTRIBUTION
         elif first_point is None:
             pass
         else:
@@ -265,7 +264,9 @@ def _get_metric_descriptor(
     @staticmethod
     def _to_point(
         kind: "MetricDescriptor.MetricKind.V",
-        data_point: Union[NumberDataPoint, HistogramDataPoint],
+        data_point: Union[
+            NumberDataPoint, HistogramDataPoint, ExponentialHistogramDataPoint
+        ],
     ) -> Point:
         if isinstance(data_point, HistogramDataPoint):
             mean = (
@@ -283,6 +284,55 @@ def _to_point(
                     ),
                 )
             )
+        elif isinstance(data_point, ExponentialHistogramDataPoint):
+            # Adapted from https://github.com/GoogleCloudPlatform/opentelemetry-operations-go/blob/v1.8.0/exporter/collector/metrics.go#L582
+
+            # Calculate underflow bucket (zero count + negative buckets)
+            underflow = data_point.zero_count
+            if data_point.negative.bucket_counts:
+                underflow += sum(data_point.negative.bucket_counts)
+
+            # Create bucket counts array: [underflow, positive_buckets..., overflow=0]
+            bucket_counts = [underflow]
+            if data_point.positive.bucket_counts:
+                bucket_counts.extend(data_point.positive.bucket_counts)
+            bucket_counts.append(0)  # overflow bucket is always empty
+
+            # Determine bucket options
+            if not data_point.positive.bucket_counts:
+                # If no positive buckets, use explicit buckets with bounds=[0]
+                bucket_options = Distribution.BucketOptions(
+                    explicit_buckets=Distribution.BucketOptions.Explicit(
+                        bounds=[0.0],
+                    )
+                )
+            else:
+                # Use exponential bucket options
+                # growth_factor = 2^(2^(-scale))
+                growth_factor = math.pow(2, math.pow(2, -data_point.scale))
+                # scale = growth_factor^(positive_bucket_offset)
+                scale = math.pow(growth_factor, data_point.positive.offset)
+                num_finite_buckets = len(bucket_counts) - 2
+
+                bucket_options = Distribution.BucketOptions(
+                    exponential_buckets=Distribution.BucketOptions.Exponential(
+                        num_finite_buckets=num_finite_buckets,
+                        growth_factor=growth_factor,
+                        scale=scale,
+                    )
+                )
+
+            mean = (
+                data_point.sum / data_point.count if data_point.count else 0.0
+            )
+            point_value = TypedValue(
+                distribution_value=Distribution(
+                    count=data_point.count,
+                    mean=mean,
+                    bucket_counts=bucket_counts,
+                    bucket_options=bucket_options,
+                )
+            )
         else:
             if isinstance(data_point.value, int):
                 point_value = TypedValue(int64_value=data_point.value)
@@ -350,10 +400,6 @@ def export(
                         continue
 
                     for data_point in metric.data.data_points:
-                        if isinstance(
-                            data_point, ExponentialHistogramDataPoint
-                        ):
-                            continue
                         labels = {
                             _normalize_label_key(key): str(value)
                             for key, value in (