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PySparkSnowflakeDatabricks

PySpark Architecture — Driver, Executor, and Cluster Modes

Imagine you are running a PySpark job that processes millions of retail transactions daily. How does your single Python script run efficiently across a cluster of machines? The secret lies in PySpark architecture.

PySpark, as a Python API for Apache Spark, works in a distributed computing environment, meaning it splits your data and computation across multiple nodes (machines) for parallel processing. Understanding its architecture is key to writing efficient Spark jobs.

Core Components of PySpark Architecture​

1. Driver​

  • The Driver is the master program that runs your PySpark application.
  • Responsibilities:
    • Maintains SparkContext (entry point for Spark functionality).
    • Converts Python code into DAG (Directed Acyclic Graph) of tasks.
    • Coordinates Executors across the cluster.

Think of the Driver as the orchestra conductor, ensuring all nodes work in sync.

2. Executor​

  • Executors are the worker processes running on each node of the cluster.
  • Responsibilities:
    • Execute tasks assigned by the Driver.
    • Store data in memory or disk for caching and shuffling.
    • Report task progress and results back to the Driver.

Analogy: Executors are the musicians performing the music directed by the conductor.

3. Cluster Manager​

  • The Cluster Manager allocates resources to your Spark application.
  • Spark supports multiple cluster managers:
    • Standalone — Spark’s built-in manager.
    • YARN — Common in Hadoop ecosystems.
    • Mesos — For fine-grained resource sharing.
    • Kubernetes — Modern containerized deployment.

Cluster Modes in PySpark​

PySpark applications can run in different cluster deployment modes:

ModeDescription
LocalRuns Spark on a single machine — good for testing and learning.
ClientDriver runs on the client machine, Executors on cluster nodes.
ClusterDriver runs on one of the worker nodes, ideal for production.

Choosing the right cluster mode affects performance, reliability, and resource usage.

Real-Life Example​

At ShopVerse Retail, a nightly sales ETL job was running slower than expected.

  • Issue: Driver was running in Client mode on a small laptop.
  • Solution: Switched to Cluster mode on a YARN-managed Spark cluster.
  • Result: Job completed in 30 minutes instead of 2 hours, thanks to proper resource distribution across Executors.

Key Takeaways​

  • Driver: Orchestrates tasks, maintains SparkContext, and creates DAGs.
  • Executor: Performs computation, stores intermediate data, reports back.
  • Cluster Manager: Allocates resources for Spark jobs (Standalone, YARN, Mesos, Kubernetes).
  • Cluster Modes: Local, Client, Cluster — choose based on job size and production needs.
  • Understanding architecture helps optimize Spark jobs for performance and scalability.

Next, we’ll cover Installing PySpark & Setting Up Environment, so you can get hands-on and start running your first Spark jobs in a fully configured environment.