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Apache Spark’s architecture is designed to enable distributed data processing across clusters. Understanding how Spark’s components work together is essential for building efficient applications.

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Core Components

Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext object in your main program (called the driver program).

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Driver Program

The driver program runs the main() function of your application and creates the SparkContext. It is responsible for:
  • Converting your program into tasks
  • Scheduling tasks on executors
  • Coordinating task execution across the cluster

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Cluster Manager

To run on a cluster, the SparkContext connects to several types of cluster managers which allocate resources across applications:
  • Standalone - A simple cluster manager included with Spark that makes it easy to set up a cluster
  • Hadoop YARN - The resource manager in Hadoop 3
  • Kubernetes - An open-source system for automating deployment, scaling, and management of containerized applications

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Executors

Once connected, Spark acquires executors on nodes in the cluster. Executors are processes that:
  • Run computations for your application
  • Store data in memory or disk storage
  • Stay up for the duration of the whole application
  • Run tasks in multiple threads

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Tasks

The SparkContext sends tasks to the executors to run. Tasks are units of work sent to one executor.

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Architecture Principles

There are several important aspects of this architecture:
Each application gets its own executor processes, which stay up for the duration of the whole application and run tasks in multiple threads. This has the benefit of isolating applications from each other, on both the scheduling side (each driver schedules its own tasks) and executor side (tasks from different applications run in different JVMs).
However, this also means that data cannot be shared across different Spark applications (instances of SparkContext) without writing it to an external storage system.
Spark is agnostic to the underlying cluster manager. As long as it can acquire executor processes, and these communicate with each other, it is relatively easy to run it even on a cluster manager that also supports other applications.

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Network Considerations

The driver program must listen for and accept incoming connections from its executors throughout its lifetime. This means:
  • The driver must be network addressable from the worker nodes
  • The driver should be run close to the worker nodes, preferably on the same local area network
You can configure the driver port using spark.driver.port in the network configuration section.
If you’d like to send requests to the cluster remotely, it’s better to open an RPC to the driver and have it submit operations from nearby than to run a driver far away from the worker nodes.

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Execution Model

When you run a Spark application:
  1. Application Submission - Submit your application using spark-submit
  2. Resource Allocation - The cluster manager allocates resources and launches executors
  3. Code Distribution - Spark sends your application code (JAR or Python files) to the executors
  4. Task Execution - SparkContext sends tasks to executors to run
  5. Result Collection - Results are returned to the driver program

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Jobs, Stages, and Tasks

Spark organizes computation into a hierarchy:
  • Job - A parallel computation consisting of multiple tasks that gets spawned in response to a Spark action (e.g. save, collect)
  • Stage - Each job gets divided into smaller sets of tasks called stages that depend on each other (similar to the map and reduce stages in MapReduce)
  • Task - A unit of work sent to one executor

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Monitoring

Each driver program has a web UI, typically on port 4040, that displays information about:
  • Running tasks
  • Executors
  • Storage usage
Access the UI by navigating to http://<driver-node>:4040 in a web browser. 
# Example: Access the Spark UI locally
curl http://localhost:4040

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Terminology Reference

TermMeaning
ApplicationUser program built on Spark. Consists of a driver program and executors on the cluster.
Application jarA jar containing your Spark application. Should never include Hadoop or Spark libraries.
Driver programThe process running the main() function of the application and creating the SparkContext.
Cluster managerAn external service for acquiring resources on the cluster (e.g. standalone manager, YARN, Kubernetes).
Deploy modeDistinguishes where the driver process runs. In “cluster” mode, the framework launches the driver inside of the cluster. In “client” mode, the submitter launches the driver outside of the cluster.
Worker nodeAny node that can run application code in the cluster.
ExecutorA process launched for an application on a worker node, that runs tasks and keeps data in memory or disk storage across them.
TaskA unit of work that will be sent to one executor.
JobA parallel computation consisting of multiple tasks that gets spawned in response to a Spark action.
StageEach job gets divided into smaller sets of tasks called stages that depend on each other.

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Next Steps

RDD Programming

Learn how to work with Resilient Distributed Datasets

Job Scheduling

Understand how Spark schedules and allocates resources

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