Spark Programming in Python for Beginners with Apache Spark 3 - Spark Distributed Processing Model - How Your Program Ru

This video explains how Apache Spark executes applications on a distributed cluster using a master-slave architecture. Each Spark application creates a driver (master) and executors (slaves) to process tasks. The Spark engine requests containers from the cluster manager to run these processes. The video uses examples to illustrate how multiple applications run independently, each with its own driver and executors, highlighting Spark's capability as a distributed computing platform.

What is the primary focus of this video tutorial?

• Understanding Spark's data storage

• Learning about Spark's user interface

• Exploring Spark's distributed processing model

• Discussing Spark's security features

In Spark's architecture, what is the role of the driver?

• To store data

• To handle user authentication

• To manage executors and coordinate tasks

• To execute tasks directly

What does the Spark engine request from the cluster manager to start a driver process?

• A data node

• A container

• A network connection

• A storage unit

How do Spark applications A1 and A2 operate on the cluster?

• They depend on each other for execution

• They require manual intervention to start

• They run independently with their own drivers and executors

• They share the same driver and executors

What is the key architectural model that every Spark application follows?

• Client-server model

• Peer-to-peer model

• Master-slave architecture

• Monolithic architecture