PySpark Architecture (Driver- Executor) two ways to describe

by | Jun 6, 2024 | Pyspark | 0 comments

PySpark, as part of the Apache Spark ecosystem, follows a master-slave architecture(Or Driver- Executro Architecture) and provides a structured approach to distributed data processing.

Here’s a breakdown of the PySpark architecture with diagrams to illustrate the key components and their interactions.

1. Overview of PySpark Architecture

The architecture of PySpark involves the following main components:

  • Driver Program: The main control program that manages the entire Spark application. When the driver program executes, it calls the original program of the app and generates a Spark Context.
  • Cluster Manager: Manages the resources and schedules tasks on the cluster. Examples include YARN, Mesos, or Spark’s standalone cluster manager.
  • Workers: Execute tasks on the cluster. Each worker runs one or more executors.
  • Executors: Run the tasks assigned by the driver on the worker nodes and store the data partitions.

2. Diagram of PySpark Architecture

Here’s a visual representation of the PySpark architecture:

+-------------------------------------------+
| Driver |
| +-------------------------------------+ |
| | SparkContext | |
| | | |
| | +-------------------------------+ | |
| | | Cluster Manager | | |
| | | | | |
| | | +------------+ +----------+ | | |
| | | | Worker 1 | | Worker 2 | | | |
| | | | +----------+| |+--------+| | | |
| | | | | Executor || || Executor|| | | |
| | | | | || || || | | |
| | | +------------+ +----------+ | | |
| | | | | |
| | +-------------------------------+ | |
| +-------------------------------------+ |
+-------------------------------------------+

3. Components Explained

  • Driver Program: The entry point for the Spark application. It contains the SparkContext, which is the main interface for interacting with Spark. Spark Context includes all the basic functions. You can assume Spark Context as a gateway to all Spark’s functionality. The driver is responsible for:
    • Creating RDDs, DataFrames, Datasets.
    • Defining transformations and actions.
    • Managing the lifecycle of Spark applications.
  • Cluster Manager: Manages the cluster resources and schedules tasks. The SparkContext connects to the cluster manager to negotiate resources and submit tasks. The cluster manager works with the Spark Context and also manages the execution of various jobs inside the cluster. The cluster manager can be:
    • Standalone: Spark’s built-in cluster manager.
    • YARN: Hadoop’s resource manager.
    • Mesos: A distributed systems kernel.
  • Workers: Nodes in the cluster that execute the tasks. Each worker node hosts one or more executors.
  • Executors: Run on worker nodes and are responsible for:
    • Executing code assigned by the driver.
    • Storing data for in-memory processing and disk storage.
    • Reporting the status and results of computations back to the driver.

4. Detailed Diagram with Data Flow

Here’s a more detailed diagram showing the data flow and interaction between components:

+---------------------------+                +-----------------------+
| Driver | | Cluster Manager |
| | | |
| +---------------------+ | | +------------------+ |
| | SparkContext | | | | Resource Manager | |
| +---------+-----------+ | | +--------+---------+ |
| | | | | |
| v | | v |
| +-------------------+ | | +------------------+ |
| | DAG Scheduler |<-------------------->| Task Scheduler | |
| +---------+---------+ | | +--------+---------+ |
| | | | | |
| v | | v |
| +----------+------------+ | | +------------------+ |
| | Task Scheduler |<-------------------->| Worker Manager | |
| +----------+------------+ | | +------------------+ |
| | | | |
| v | +-----------------------+
| +----------+------------+
| | Executors |
| +-----------------------+
| |
+---------------------------+

|
v
+----------------------------+
| Worker Nodes |
| |
| +----------------------+ |
| | Executor 1 | |
| +----------------------+ |
| | Executor 2 | |
| +----------------------+ |
| |
+----------------------------+

Detailed Component Descriptions

  • Driver Program:
    • SparkContext: Initializes Spark application, connects to cluster manager, and creates RDDs.
    • DAG Scheduler: Translates logical plans into a physical execution plan, creating a Directed Acyclic Graph (DAG) of stages.
    • Task Scheduler: Schedules tasks to run on executors, handles retries on failure.
  • Cluster Manager:
    • Resource Manager: Manages cluster resources and assigns them to applications.
    • Task Scheduler: Assigns tasks to executors based on available resources.
  • Worker Nodes:
    • Executors: Run the tasks, store the intermediate results in memory or disk, and communicate results back to the driver.

Data Flow

  1. Submit Application: The driver program is submitted to the cluster.
  2. Initialize SparkContext: SparkContext connects to the cluster manager.
  3. Resource Allocation: Cluster manager allocates resources to the application.
  4. Task Scheduling: Driver schedules tasks through the DAG scheduler and task scheduler.
  5. Execution: Executors on worker nodes execute the tasks.
  6. Data Storage: Intermediate results are stored in memory or on disk.
  7. Completion: Executors return the results to the driver, which processes and provides the final output.

This architecture allows PySpark to efficiently process large-scale data in a distributed environment, leveraging the power of parallel computation and fault tolerance.

In Different Way:-

Here’s a breakdown of PySpark architecture using diagrams:

1. High-Level Overview:

+--------------------+         +--------------------+         +---------------------+
|       Driver       |         | Cluster Manager     |         | Worker Nodes (N)    |
+--------------------+         +--------------------+         +---------------------+
     |                     |         | (YARN, Mesos,       |         | (Executor Processes) |
     |                     |         | Standalone)        |         |                     |
     | Submits application |         +--------------------+         |                     |
     | and coordinates    |                                 |                     |
     | tasks              |                                 |   Spark Tasks       |
+--------------------+         +--------------------+         +---------------------+
     | (SparkContext)   |         |                     |         | (on each Executor) |
     |                     |         |                     |         |                     |
     |-----------------|         |                     |         |-----------------|
     |  Libraries (SQL,  |         |                     |         |  Data Processing   |
     |  MLlib, Streaming) |         |                     |         |   (RDDs, DataFrames) |
     |-----------------|         |                     |         |-----------------|
  • Driver: The program running your PySpark application. It submits the application to the cluster manager, coordinates tasks, and interacts with Spark libraries.
  • Cluster Manager: Manages resources in the cluster, allocating resources (machines) to applications like PySpark. Examples include YARN (Hadoop), Mesos, or Spark’s standalone mode.
  • Worker Nodes: Machines in the cluster that run Spark applications. Each node has an Executor process that executes Spark tasks.

2. Data Processing Flow:

+--------------------+         +--------------------+         +---------------------+
|       Driver       |         | Cluster Manager     |         | Worker Nodes (N)    |
+--------------------+         +--------------------+         +---------------------+
     | Submits job     |         |                     |         | (Executor Processes) |
     | (transforms)     |         |                     |         |                     |
     |-----------------|         |                     |         |-----------------|
     |  SparkContext   |         |                     |         |  RDD Operations   |
     |-----------------|         |                     |         |   (map, filter, etc) |
     |  Transform Data  |         |                     |         |   (on each partition) |
     |  (RDDs)          |         |                     |         |-----------------|
     |-----------------|         |                     |         |  Shuffle & Aggregation |
     |   Shuffle Data   |         |                     |         |   (if needed)        |
     |   (if needed)     |         |                     |         |-----------------|
     |-----------------|         |                     |         |   Write Results    |
     |   Save Results   |         +--------------------+         |   (to storage)     |
     +--------------------+                                 +---------------------+
  • The driver submits a Spark job with transformations to be applied to the data.
  • SparkContext in the driver translates the job into tasks for each partition of the data.
  • Executor processes on worker nodes execute these tasks on their assigned data partitions.
  • Shuffling (data exchange) might occur between executors if operations require data from different partitions (e.g., joins).
  • Finally, the results are written to storage or used for further processing.

3. Spark Libraries:

+--------------------+
|       Driver       | (imports libraries)
+--------------------+
     |
     |-----------------|
     |  SparkContext   |
     |-----------------|
     |  Spark SQL      |
     |  (DataFrame/SQL) |
     |-----------------|
     |  MLlib          |
     |  (Machine Learning)|
     |-----------------|
     |  Spark Streaming |
     |  (Real-time)    |
     |-----------------|
  • PySpark provides various libraries accessible through the SparkContext:
    • Spark SQL: Enables SQL-like operations on DataFrames and Datasets.
    • MLlib: Offers machine learning algorithms and tools for building and deploying models.
    • Spark Streaming: Allows processing of continuous data streams.

These diagrams provide a visual representation of PySpark’s architecture, highlighting the key components and data processing flow. As you delve deeper into PySpark, these visuals can serve as a foundation for understanding its functionalities.

Written by HintsToday Team

Related Posts

Are Dataframes in PySpark Lazy evaluated?

Yes, DataFrames in PySpark are lazily evaluated, similar to RDDs. Lazy evaluation is a key feature of Spark's processing model, which helps optimize the execution of transformations and actions on large datasets. What is Lazy Evaluation? Lazy evaluation means that...

read more

BDL Ecosystem-HDFS and Hive Tables

Big Data Lake: Data Storage HDFS is a scalable storage solution designed to handle massive datasets across clusters of machines. Hive tables provide a structured approach for querying and analyzing data stored in HDFS. Understanding how these components work together...

read more

Get the latest news

Subscribe to our Newsletter

0 Comments

Submit a Comment

Your email address will not be published. Required fields are marked *