Data Science Model Deployments and Cloud Computing on GCP - PySpark Serverless Autoscaling Properties

The video tutorial explains how Dataproc Serverless can dynamically scale resources for Spark workloads using dynamic resource allocation. It covers five key properties for controlling Spark job scaling: dynamic allocation, initial executors, minimum executors, maximum executors, and executor allocation ratio. The tutorial provides default values and ranges for these properties, emphasizing the importance of understanding them for efficient workload management. The video concludes with a brief overview of the next steps in deploying a serverless Spark job.

What is the default behavior of auto scaling in Serverless Dataproc?

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What are the five properties that can be set at the time of job submission in Spark?

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What is the maximum number of executors that can be set for scaling the workload up?

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Explain the significance of the Executor Allocation Ratio property.

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What is the default value for the Executor Allocation Ratio property?

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