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

The video tutorial explains how Dataproc Serverless dynamically scales resources for Spark workloads using dynamic resource allocation. It covers five key properties for controlling 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 the first four. The video concludes with a brief introduction to the next tutorial on deploying a serverless job.

What is the default behavior of Dataproc Serverless when handling Spark workloads?

• It uses static resource allocation.

• It requires manual scaling of resources.

• It does not support scaling.

• It dynamically scales resources using Spark's dynamic resource allocation.

Which property indicates whether dynamic resource allocation is enabled for a Spark job?

• Initial number of executors

• Executor allocation ratio

• Dynamic allocation enabled

• Maximum number of executors

What is the default maximum number of executors for scaling a Spark workload?

• 500

• 1000

• 2

• 2000

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

• 0.5

• 1

• 0.3

• 0

How does a value of 1 for the Executor Allocation Ratio affect scaling?

• It provides maximum scale-up capability and parallelism.

• It sets scaling to the minimum value.

• It limits scaling to half the maximum value.

• It disables scaling.