If the pipeline type is pyspark, we use PySpark executors for pipeline and block executions. You can customize the compute resource of PySpark executor by updating the instance types of emr_config in project’s metadata.yaml file.

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Example:

emr_config:
  ec2_key_name: "xxxxx"
  master_instance_type: "r5.2xlarge"
  slave_instance_type: "r5.2xlarge"

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Spark compute resource manager

Manage your Spark compute resources and track Spark pipeline execution metrics. Learn more