In a Databricks cluster, there’s a division of labor between two key node types: driver node and worker nodes. Here’s how they differ:
Driver Node:
The Brain of the Operation: Consider the driver node the mastermind of your cluster. It’s responsible for:
Coordinating tasks: The driver receives your code (from notebooks or libraries) and breaks it down into smaller tasks.
Managing SparkContext: It acts as the interface between your code and the Apache Spark framework that runs on the cluster.
Monitoring and Communication: The driver node keeps track of the worker nodes, monitors their progress, and communicates results.
Worker Nodes:
The Workhorses: Worker nodes are the workhorses of the cluster. They handle the actual computations:
Running Executors: Each worker node runs an executor process, which executes the smaller tasks assigned by the driver node.
Distributing Work: The workload is distributed across all the worker nodes in the cluster for parallel processing.
Data Storage: Worker nodes may also store data locally for faster processing during tasks.
Analogy:Think of the driver node as a conductor in an orchestra. It interprets the music (your code) and directs the musicians (worker nodes) to play their parts (tasks) in a coordinated way. The worker nodes are the skilled musicians who execute the music (process the data) as instructed by the conductor.
Additional Points:
A cluster typically has one driver node and zero or more worker nodes (minimum one for Spark jobs).
By default, the driver node uses the same instance type as the worker nodes, but you can configure them differently based on needs.