Understanding Spark Application Concepts

 

What makes a Spark job tick—from code to cluster ?

Apache Spark follows a distributed computing model that allows it to process massive datasets efficiently by splitting work across multiple machines.

To use Spark effectively, you must understand how its core components work together.

 

Key Components of a Spark Application

Each Spark application consists of multiple layers, all working in harmony to execute distributed tasks:

  • Driver Program –

    • The central brain of any Spark application.

    Responsibilities:

  • Initializes the SparkSession
  • Converts user code into a DAG (Directed Acyclic Graph)
  • Manages task scheduling and execution
  • Collects results from worker nodes

    • Think of it as the project manager for your entire data job.

     

  • Cluster Manager –

    • Handles resource allocation across your cluster.

    Spark supports various cluster managers:

  • Standalone (built-in)
  • YARN (Hadoop)
  • Apache Mesos
  • Kubernetes

    • The Driver contacts the Cluster Manager to request resources (Executors), which are then launched across worker nodes.

     

  • Worker Nodes & Executors –

    • These are the engines that do the actual work.

  • Executors run the individual tasks
  • Store intermediate results in memory for faster processing
  • Communicate status and results back to the Driver

    • Each Worker Node may run multiple Executors depending on the resources assigned.

     

  • Stages & Tasks

    • Spark decomposes your job into stages and tasks for distributed execution.

  • Stage: A collection of tasks that operate on different data partitions
  • Task: The smallest unit of work; each task processes a single data partition

    • View these clearly in the Spark UI, especially after running actions like .show () or .count ().

     

    Summary: Spark Execution Flow

    Let’s put it all together:

  • Job Submission – The user submits code to the Driver (via PySpark , Scala, etc.)
  • DAG Creation – Spark constructs a DAG based on the transformations
  • Stage & Task Scheduling – The DAG is divided into stages, which are broken into tasks
  • Task Execution by Executors – Tasks are distributed and executed on Worker Nodes
  • Result Collection – Executors return results to the Driver, which compiles the final output

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    💡 Did You Know?

    •   Each Spark application has its own Driver and Executors, isolated from others.

    •   Spark allows dynamic allocation of Executors based on workload—great for resource efficiency.

    •   You can set number of partitions manually to optimize performance on very large datasets.

    📚 Study Notes

    •   A Spark job flows through: Driver → DAG → Stages → Tasks → Executors

    •   Executors do the heavy lifting; Driver manages the brainwork

    •   Spark's lazy execution optimizes workflows before launching any task

    •   Cluster Manager assigns resources depending on the environment (cloud/on-premise)

     

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