Spark Programming in Python for Beginners with Apache Spark 3 - Summarizing Spark Execution Models - When to Use What?

The video tutorial explains the Spark client-server execution model, focusing on cluster managers and execution modes. It covers the decision-making process for choosing between local and YARN cluster managers, and discusses different cluster setups, including on-premise and cloud options. The tutorial also explains the two execution modes supported by Spark: client mode and cluster mode. Finally, a practical demonstration of these execution modes is provided, showcasing the driver and executors in action.

What is the primary use of the local cluster manager in Spark?

• Running applications on a real cluster

• Working in an IDE or notebook on a local machine

• Managing resources in a cloud environment

• Executing applications in cluster mode

Which cluster manager is commonly used for running Spark applications on a real cluster?

• YARN

• Mesos

• Standalone

• Local

What is a characteristic of on-premise cluster setups?

• They do not use YARN

• They rely on Google Data Proc

• They use cloud error distribution

• They are primarily cloud-based

When is it appropriate to use client mode on a YARN cluster?

• When submitting applications to a real cluster

• When working with a notebook or Spark shell

• When managing resources in a cloud environment

• When executing applications in cluster mode

What is the main advantage of using cluster mode in Spark?

• It allows for local testing

• It simplifies resource management

• It is ideal for notebook environments

• It is suitable for real cluster execution