add group_by to sample processor

.generated-info

@@ -1,4 +1,4 @@
 {
-  "spec_repo_commit": "b75095c",
-  "generated": "2025-07-31 10:46:07.850"
+  "spec_repo_commit": "b14c9da",
+  "generated": "2025-07-31 15:32:05.142"
 }

.generator/schemas/v2/openapi.yaml

@@ -27480,6 +27480,16 @@ components:
       description: The `sample` processor allows probabilistic sampling of logs at
         a fixed rate.
       properties:
+        group_by:
+          description: Optional list of fields to group events by. Each group will
+            be sampled independently
+          example:
+          - service
+          - host
+          items:
+            type: string
+          minItems: 1
+          type: array
         id:
           description: The unique identifier for this component. Used to reference
             this component in other parts of the pipeline (for example, as the `input`

src/datadog_api_client/v2/model/observability_pipeline_config_processor_item.py

@@ -61,6 +61,9 @@ def __init__(self, **kwargs):
         :param metrics: Configuration for generating individual metrics.
         :type metrics: [ObservabilityPipelineGeneratedMetric]
 
+        :param group_by: Optional list of fields to group events by. Each group will be sampled independently
+        :type group_by: [str], optional
+
         :param percentage: The percentage of logs to sample.
         :type percentage: float, optional
 
@@ -91,9 +94,6 @@ def __init__(self, **kwargs):
         :param target: Path where enrichment results should be stored in the log.
         :type target: str
 
-        :param group_by: A list of fields used to group log events for merging.
-        :type group_by: [str]
-
         :param merge_strategies: List of merge strategies defining how values from grouped events should be combined.
         :type merge_strategies: [ObservabilityPipelineReduceProcessorMergeStrategy]
 

src/datadog_api_client/v2/model/observability_pipeline_sample_processor.py

@@ -21,6 +21,9 @@
 
 class ObservabilityPipelineSampleProcessor(ModelNormal):
     validations = {
+        "group_by": {
+            "min_items": 1,
+        },
         "rate": {
             "inclusive_minimum": 1,
         },
@@ -33,6 +36,7 @@ def openapi_types(_):
         )
 
         return {
+            "group_by": ([str],),
             "id": (str,),
             "include": (str,),
             "inputs": ([str],),
@@ -42,6 +46,7 @@ def openapi_types(_):
         }
 
     attribute_map = {
+        "group_by": "group_by",
         "id": "id",
         "include": "include",
         "inputs": "inputs",
@@ -56,13 +61,17 @@ def __init__(
         include: str,
         inputs: List[str],
         type: ObservabilityPipelineSampleProcessorType,
+        group_by: Union[List[str], UnsetType] = unset,
         percentage: Union[float, UnsetType] = unset,
         rate: Union[int, UnsetType] = unset,
         **kwargs,
     ):
         """
         The ``sample`` processor allows probabilistic sampling of logs at a fixed rate.
 
+        :param group_by: Optional list of fields to group events by. Each group will be sampled independently
+        :type group_by: [str], optional
+
         :param id: The unique identifier for this component. Used to reference this component in other parts of the pipeline (for example, as the ``input`` to downstream components).
         :type id: str
 
@@ -81,6 +90,8 @@ def __init__(
         :param type: The processor type. The value should always be ``sample``.
         :type type: ObservabilityPipelineSampleProcessorType
         """
+        if group_by is not unset:
+            kwargs["group_by"] = group_by
         if percentage is not unset:
             kwargs["percentage"] = percentage
         if rate is not unset: