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What are the differences between the Spark Driver vs Executor? As a data engineer with several years of experience working with Apache Spark, I have had the opportunity to gain a deep understanding of the Spark architecture and its various components. In particular, I have worked extensively with Spark components and their configurations to improve the performance of the Spark jobs. In this article, I will explain the difference between Spark Driver and executor and their roles in running Spark applications/jobs.

The Spark Driver and Executor are key components of the Apache Spark architecture but have different roles and responsibilities. Hence, it is crucial to understand the difference between Spark Driver and Executor and what role each component plays in running your Spark or PySpark jobs.

Spark Driver vs Executor

What is Spark Driver & What Role Does it Play?

The Apache Spark Driver is the program that declares the SparkContext, which is responsible for converting the user program into a series of tasks that can be distributed across the cluster. It also coordinates the execution of tasks and communicates with the Cluster Manager to allocate resources for the application. In short, the Spark Driver plays a crucial role in managing the overall execution of a Spark application.

You need to specify the driver configurations when submitting the spark application using spark-submit command. Following are some of them you can use based on your needs. For a complete list, refer to Spark official documentation.

  • --driver-memory: Specifies the amount of memory to allocate for the driver (e.g., “1g” for 1 gigabyte).
  • --driver-cores: Specifies the number of cores to allocate for the driver.
  • --driver-class-path: Specifies the classpath for the driver.
  • --driver-java-options: Specifies extra Java options for the driver.
  • --driver-library-path: Specifies the library path for the driver.
  • --conf spark.driver.maxResultSize: Limits the maximum size of results that Spark will return to the driver.
  • --conf spark.driver.host: Specifies the hostname or IP address the driver runs on.

What is Spark Executor & Why is it needed?

Apache Spark Executor is a process that is responsible for running the tasks in parallel on worker nodes in the cluster. The Spark driver program launches the Executor process, and it runs on the worker nodes to execute the tasks the driver assigns. Hence, it is critical to understand the difference between Spark Driver and Executor.

The Executor is a key component of the Spark runtime architecture because it is responsible for processing the data and executing the code in parallel on the worker nodes. The Executor runs the user-defined Spark code, which can be written in various programming languages such as Scala, Java, Python or R, and it performs the necessary calculations and transformations on the data using the RDD (Resilient Distributed Dataset) API or the DataFrame API.

The Executor is designed to be fault-tolerant and resilient to failures, which is essential for handling large-scale data processing workloads. If an executor fails due to hardware or software issues, the Spark framework automatically re-launches the Executor on a different worker node and re-runs the failed tasks to ensure that the processing is not interrupted.

The number of Executors and their configuration parameters, such as memory, cores, and parallelism, can be adjusted based on the specific requirements of the Spark application and the resources available in the cluster. The optimal configuration can help to improve the performance and scalability of the Spark application and allow it to process large amounts of data efficiently.

Following are some of the executor configurations you can use. Again, for a complete list, refer to Spark official documentation.

  • --executor-memory: Specifies the amount of memory to allocate per executor (e.g., “1g” for 1 gigabyte).
  • --num-executors: Specifies the number of executors to launch.
  • --executor-cores: Specifies the number of cores to allocate for each executor.
  • --conf spark.executor.extraClassPath: Specifies extra classpath entries for executors.
  • --conf spark.executor.extraJavaOptions: Specifies extra Java options for executors.
  • --conf spark.executor.extraLibraryPath: Specifies extra library path entries for executors.
  • --conf spark.yarn.executor.memoryOverhead: Specifies the amount of non-heap memory to be allocated per executor.
  • --conf spark.executor.memoryFraction: Specifies the fraction of the heap space that is allocated for Spark’s memory management.
  • --conf spark.dynamicAllocation.enabled: Enables or disables dynamic allocation of executors.

Difference Between Spark Driver vs Executor

So now you understand the role Spark Driver and Executor play in running your Spark or PySpark applications, let’s see the differences in their roles or tasks they perform.

AspectSpark DriverSpark Executor
ResponsibilityManages the overall execution of a Spark ApplicationExecutes tasks on worker nodes as directed by Driver
ExistenceOne per Spark ApplicationMultiple Executors per Spark Application
LifecycleStarts when a Spark Application is submittedCreated when a SparkContext is created
TasksSchedules tasks to Executors for executionExecutes individual tasks assigned by the Driver
CommunicationCommunicates with Cluster Manager (e.g., YARN, Mesos)Communicates with the Driver for task assignments
Memory ManagementManages the overall memory for the Spark ApplicationManages its own memory space for task execution
Fault ToleranceEnsures fault tolerance by keeping track of tasksNo fault tolerance for individual Executor failures
Spark Driver vs Executor

Spark Driver:

  • Manages the overall execution of a Spark application.
  • There is only one Driver per Spark application.
  • Responsible for coordinating tasks, scheduling, and interacting with the Cluster Manager.
  • Initiates SparkContext, which represents a connection to a Spark cluster.
  • Monitors the execution progress and ensures fault tolerance.

Spark Executor:

  • Executes tasks on worker nodes as directed by the Driver.
  • Multiple Executors run concurrently in a Spark application.
  • Created when a SparkContext is created and runs until the application is terminated.
  • Manages its own memory and executes individual tasks assigned by the Driver.
  • Communicates with the Driver for task assignments and reports task status.

Conclusion

In short, the difference between Spark Driver and Executor is that Spark Driver manages the overall execution of the Spark application. At the same time, the Executor is responsible for executing the individual tasks that make up the application.

Naveen Nelamali

Naveen Nelamali (NNK) is a Data Engineer with 20+ years of experience in transforming data into actionable insights. Over the years, He has honed his expertise in designing, implementing, and maintaining data pipelines with frameworks like Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. Naveen journey in the field of data engineering has been a continuous learning, innovation, and a strong commitment to data integrity. In this blog, he shares his experiences with the data as he come across. Follow Naveen @ LinkedIn and Medium