Spark Programming in Python for Beginners with Apache Spark 3 - Spark Execution Modes and Cluster Managers

The video tutorial explains how Spark applications are executed on both local machines and clusters. It covers the roles of the driver and executors, and how Spark can run locally using a local cluster manager with multithreading. The tutorial also discusses running Spark with interactive tools like Spark shell and notebooks in client mode, where the driver runs locally but connects to a cluster. Finally, it explains cluster mode, where both the driver and executors run on the cluster, suitable for long-running jobs.

What is the role of the local cluster manager when running Spark applications on a local machine?

• It simulates a distributed architecture using multiple threads.

• It only manages the driver process.

• It requires a real cluster to function.

• It connects to a remote cluster for execution.

How does configuring Spark with a single local thread affect the application?

• It runs with multiple executors.

• It becomes a multithreaded application.

• The driver handles all tasks without parallel execution.

• It requires a cluster manager.

In client mode, where does the Spark driver process run?

• On the client machine

• On the cluster

• On a remote server

• In the cloud

What happens to the executors if the client machine is logged off in client mode?

• Executors are transferred to another client.

• Executors die due to the absence of the driver.

• Executors switch to cluster mode.

• Executors continue running independently.

Which mode is suitable for long-running Spark jobs?

• Interactive mode

• Local mode

• Client mode

• Cluster mode