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 Dataproc Serverless when submitting a Spark workload?

• It requires manual scaling of resources.

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

• It does not support scaling.

• It only scales down resources.

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

• Initial number of executors

• Maximum number of executors

• Dynamic allocation enabled

• Executor allocation ratio

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

• 500

• 1500

• 1000

• 2000

What is the default value for the executor allocation ratio?

• 1.0

• 0.7

• 0.3

• 0.5

How does an executor allocation ratio of 1 affect a Spark workload?

• It limits the scale-up capability.

• It provides maximum scale-up capability and parallelism.

• It sets the scale-up capability to half.

• It disables scaling.