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Regenerate client from commit fde9d6e of spec repo
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‎.generator/schemas/v1/openapi.yaml‎

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@@ -6182,6 +6182,76 @@ components:
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type: string
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x-enum-varnames:
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- ARITHMETIC_PROCESSOR
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LogsArrayMapProcessor:
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description: |-
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The array-map processor transforms each element of a source array by running
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sub-processors against it and writing results to a target array.
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Sub-processors reference the current element via `$sourceElem` (read) and
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`$targetElem` (write). Parent log attributes are read via plain paths as usual.
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Supported sub-processor types: `attribute-remapper`, `string-builder-processor`,
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`arithmetic-processor`, `category-processor`.
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`is_enabled` on sub-processors is ignored; sub-processor execution is gated
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entirely by the parent array-map's `is_enabled` flag.
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properties:
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is_enabled:
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default: false
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description: Whether or not the processor is enabled.
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type: boolean
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name:
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description: Name of the processor.
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type: string
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preserve_source:
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default: true
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description: |-
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When `false` and `source != target`, the source attribute is removed after
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processing. Cannot be `false` when `source == target`.
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type: boolean
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processors:
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description: |-
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Sub-processors applied to each element. Allowed types: `attribute-remapper`,
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`string-builder-processor`, `arithmetic-processor`, `category-processor`.
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items:
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$ref: "#/components/schemas/LogsArrayMapSubProcessor"
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type: array
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source:
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description: |-
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Attribute path of the source array. Elements are read-only via `$sourceElem`
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inside sub-processors.
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example: detail.resource.s3BucketDetails
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type: string
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target:
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description: |-
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Attribute path of the output array. Sub-processors write to `$targetElem`
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(or `$targetElem.<field>`) to build each output element.
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example: ocsf.resources
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type: string
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type:
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$ref: "#/components/schemas/LogsArrayMapProcessorType"
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required:
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- source
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- target
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- processors
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- type
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type: object
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LogsArrayMapProcessorType:
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default: array-map-processor
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description: Type of logs array-map processor.
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enum:
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- array-map-processor
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example: array-map-processor
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type: string
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x-enum-varnames:
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- ARRAY_MAP_PROCESSOR
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LogsArrayMapSubProcessor:
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description: |-
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A sub-processor used inside an array-map processor.
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Allowed types: `attribute-remapper`, `string-builder-processor`,
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`arithmetic-processor`, `category-processor`.
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oneOf:
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- $ref: "#/components/schemas/LogsAttributeRemapper"
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- $ref: "#/components/schemas/LogsStringBuilderProcessor"
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- $ref: "#/components/schemas/LogsArithmeticProcessor"
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- $ref: "#/components/schemas/LogsCategoryProcessor"
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LogsArrayProcessor:
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description: |-
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A processor for extracting, aggregating, or transforming values from JSON arrays within your logs.
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- $ref: "#/components/schemas/LogsDecoderProcessor"
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- $ref: "#/components/schemas/LogsSchemaProcessor"
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- $ref: "#/components/schemas/LogsExcludeAttributeProcessor"
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- $ref: "#/components/schemas/LogsArrayMapProcessor"
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LogsQueryCompute:
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description: Define computation for a log query.
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properties:

‎docs/datadog_api_client.v1.model.rst‎

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:members:
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:show-inheritance:
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datadog\_api\_client.v1.model.logs\_array\_map\_processor module
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----------------------------------------------------------------
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.. automodule:: datadog_api_client.v1.model.logs_array_map_processor
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:members:
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:show-inheritance:
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datadog\_api\_client.v1.model.logs\_array\_map\_processor\_type module
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----------------------------------------------------------------------
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.. automodule:: datadog_api_client.v1.model.logs_array_map_processor_type
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:members:
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:show-inheritance:
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datadog\_api\_client.v1.model.logs\_array\_map\_sub\_processor module
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---------------------------------------------------------------------
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.. automodule:: datadog_api_client.v1.model.logs_array_map_sub_processor
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:members:
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:show-inheritance:
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datadog\_api\_client.v1.model.logs\_array\_processor module
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-----------------------------------------------------------
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‎examples/v1/logs-pipelines/CreateLogsPipeline_104735144.py‎

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"""
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Create a pipeline with Array Map Processor with preserve_source false returns "OK" response
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"""
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from datadog_api_client import ApiClient, Configuration
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from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
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from datadog_api_client.v1.model.logs_array_map_processor import LogsArrayMapProcessor
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from datadog_api_client.v1.model.logs_array_map_processor_type import LogsArrayMapProcessorType
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from datadog_api_client.v1.model.logs_attribute_remapper import LogsAttributeRemapper
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from datadog_api_client.v1.model.logs_attribute_remapper_type import LogsAttributeRemapperType
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from datadog_api_client.v1.model.logs_filter import LogsFilter
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from datadog_api_client.v1.model.logs_pipeline import LogsPipeline
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body = LogsPipeline(
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filter=LogsFilter(
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query="source:python",
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),
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name="testPipelineArrayMapNoPreserve",
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processors=[
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LogsArrayMapProcessor(
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type=LogsArrayMapProcessorType.ARRAY_MAP_PROCESSOR,
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is_enabled=True,
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name="map and remove source",
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source="items",
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target="out",
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preserve_source=False,
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processors=[
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LogsAttributeRemapper(
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type=LogsAttributeRemapperType.ATTRIBUTE_REMAPPER,
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sources=[
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"$sourceElem.id",
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],
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target="$targetElem.uid",
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),
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],
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),
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],
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tags=[],
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)
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configuration = Configuration()
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with ApiClient(configuration) as api_client:
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api_instance = LogsPipelinesApi(api_client)
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response = api_instance.create_logs_pipeline(body=body)
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print(response)

‎examples/v1/logs-pipelines/CreateLogsPipeline_1185292896.py‎

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"""
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Create a pipeline with Array Map Processor using category sub-processor returns "OK" response
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"""
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from datadog_api_client import ApiClient, Configuration
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from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
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from datadog_api_client.v1.model.logs_array_map_processor import LogsArrayMapProcessor
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from datadog_api_client.v1.model.logs_array_map_processor_type import LogsArrayMapProcessorType
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from datadog_api_client.v1.model.logs_category_processor import LogsCategoryProcessor
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from datadog_api_client.v1.model.logs_category_processor_category import LogsCategoryProcessorCategory
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from datadog_api_client.v1.model.logs_category_processor_type import LogsCategoryProcessorType
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from datadog_api_client.v1.model.logs_filter import LogsFilter
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from datadog_api_client.v1.model.logs_pipeline import LogsPipeline
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body = LogsPipeline(
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filter=LogsFilter(
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query="source:python",
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),
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name="testPipelineArrayMapCategory",
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processors=[
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LogsArrayMapProcessor(
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type=LogsArrayMapProcessorType.ARRAY_MAP_PROCESSOR,
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is_enabled=True,
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name="categorize items",
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source="items",
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target="out",
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processors=[
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LogsCategoryProcessor(
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type=LogsCategoryProcessorType.CATEGORY_PROCESSOR,
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target="$targetElem.level",
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categories=[
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LogsCategoryProcessorCategory(
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filter=LogsFilter(
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query="@$sourceElem.status:error",
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),
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name="error",
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),
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LogsCategoryProcessorCategory(
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filter=LogsFilter(
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query="*",
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),
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name="info",
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),
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],
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),
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],
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),
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],
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tags=[],
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)
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configuration = Configuration()
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with ApiClient(configuration) as api_client:
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api_instance = LogsPipelinesApi(api_client)
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response = api_instance.create_logs_pipeline(body=body)
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print(response)

‎examples/v1/logs-pipelines/CreateLogsPipeline_2402034476.py‎

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"""
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Create a pipeline with Array Map Processor using arithmetic sub-processor returns "OK" response
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"""
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from datadog_api_client import ApiClient, Configuration
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from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
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from datadog_api_client.v1.model.logs_arithmetic_processor import LogsArithmeticProcessor
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from datadog_api_client.v1.model.logs_arithmetic_processor_type import LogsArithmeticProcessorType
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from datadog_api_client.v1.model.logs_array_map_processor import LogsArrayMapProcessor
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from datadog_api_client.v1.model.logs_array_map_processor_type import LogsArrayMapProcessorType
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from datadog_api_client.v1.model.logs_filter import LogsFilter
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from datadog_api_client.v1.model.logs_pipeline import LogsPipeline
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body = LogsPipeline(
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filter=LogsFilter(
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query="source:python",
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),
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name="testPipelineArrayMapArithmetic",
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processors=[
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LogsArrayMapProcessor(
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type=LogsArrayMapProcessorType.ARRAY_MAP_PROCESSOR,
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is_enabled=True,
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name="double counts",
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source="items",
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target="out",
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processors=[
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LogsArithmeticProcessor(
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type=LogsArithmeticProcessorType.ARITHMETIC_PROCESSOR,
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expression="$sourceElem.count * 2",
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target="$targetElem.doubled",
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),
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],
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),
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],
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tags=[],
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)
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configuration = Configuration()
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with ApiClient(configuration) as api_client:
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api_instance = LogsPipelinesApi(api_client)
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response = api_instance.create_logs_pipeline(body=body)
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print(response)

‎examples/v1/logs-pipelines/CreateLogsPipeline_3996915493.py‎

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"""
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Create a pipeline with Array Map Processor returns "OK" response
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"""
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from datadog_api_client import ApiClient, Configuration
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from datadog_api_client.v1.api.logs_pipelines_api import LogsPipelinesApi
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from datadog_api_client.v1.model.logs_array_map_processor import LogsArrayMapProcessor
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from datadog_api_client.v1.model.logs_array_map_processor_type import LogsArrayMapProcessorType
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from datadog_api_client.v1.model.logs_attribute_remapper import LogsAttributeRemapper
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from datadog_api_client.v1.model.logs_attribute_remapper_type import LogsAttributeRemapperType
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from datadog_api_client.v1.model.logs_filter import LogsFilter
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from datadog_api_client.v1.model.logs_pipeline import LogsPipeline
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from datadog_api_client.v1.model.logs_string_builder_processor import LogsStringBuilderProcessor
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from datadog_api_client.v1.model.logs_string_builder_processor_type import LogsStringBuilderProcessorType
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body = LogsPipeline(
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filter=LogsFilter(
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query="source:python",
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),
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name="testPipelineArrayMap",
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processors=[
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LogsArrayMapProcessor(
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type=LogsArrayMapProcessorType.ARRAY_MAP_PROCESSOR,
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is_enabled=True,
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name="map items",
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source="items",
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target="out",
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preserve_source=True,
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processors=[
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LogsAttributeRemapper(
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type=LogsAttributeRemapperType.ATTRIBUTE_REMAPPER,
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sources=[
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"$sourceElem.id",
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],
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target="$targetElem.uid",
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preserve_source=True,
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),
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LogsStringBuilderProcessor(
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type=LogsStringBuilderProcessorType.STRING_BUILDER_PROCESSOR,
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template="item-%{$sourceElem.id}",
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target="$targetElem.label",
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),
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],
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),
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],
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tags=[],
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)
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configuration = Configuration()
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with ApiClient(configuration) as api_client:
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api_instance = LogsPipelinesApi(api_client)
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response = api_instance.create_logs_pipeline(body=body)
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print(response)

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