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feat: add the cohort and auto tuning configuration to the batch's RuntimeConfig #26159

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Jun 25, 2024
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,13 @@ module V1
# @!attribute [rw] repository_config
# @return [::Google::Cloud::Dataproc::V1::RepositoryConfig]
# Optional. Dependency repository configuration.
# @!attribute [rw] autotuning_config
# @return [::Google::Cloud::Dataproc::V1::AutotuningConfig]
# Optional. Autotuning configuration of the workload.
# @!attribute [rw] cohort
# @return [::String]
# Optional. Cohort identifier. Identifies families of the workloads having
# the same shape, e.g. daily ETL jobs.
class RuntimeConfig
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
Expand Down Expand Up @@ -508,6 +515,31 @@ class GkeNodePoolAutoscalingConfig
end
end

# Autotuning configuration of the workload.
# @!attribute [rw] scenarios
# @return [::Array<::Google::Cloud::Dataproc::V1::AutotuningConfig::Scenario>]
# Optional. Scenarios for which tunings are applied.
class AutotuningConfig
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods

# Scenario represents a specific goal that autotuning will attempt to achieve
# by modifying workloads.
module Scenario
# Default value.
SCENARIO_UNSPECIFIED = 0

# Scaling recommendations such as initialExecutors.
SCALING = 2

# Adding hints for potential relation broadcasts.
BROADCAST_HASH_JOIN = 3

# Memory management for workloads.
MEMORY = 4
end
end

# Configuration for dependency repositories
# @!attribute [rw] pypi_repository_config
# @return [::Google::Cloud::Dataproc::V1::PyPiRepositoryConfig]
Expand Down