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Difference Between Spark Worker 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 that deal with billions of data. In this article, I will explain the difference between Spark worker and executor and their roles in running Spark applications/jobs.

Spark Worker vs Executor

Spark Worker vs Executor Introduction

Apache Spark is a distributed computing framework that enables processing large-scale data sets across many nodes in a cluster. In Spark, the main unit of work is a task, which is a unit of computation that operates on a partition of a dataset. Tasks are executed by executors, which are processes that run on worker nodes in the cluster.

Key Takeaways of Spark Worker:

  • A Spark Worker is a node in the Spark cluster that manages resources and hosts Executors. It is responsible for coordinating and overseeing the execution of tasks on that particular node.
  • A Worker Node in a Spark cluster is a physical or virtual machine that is part of the distributed computing environment.
  • Each Worker Node hosts one or more Spark Executors, and it is responsible for managing resources on that node.
  • The Spark Worker communicates with the Cluster Manager to receive resource allocations and reports resource usage.
  • It provides the execution environment for Executors, managing their lifecycle on the node.

Key Takeaways of Spark Executor:

  1. A Spark Executor is a Java process responsible for running computations and storing data for a Spark application. Executors execute individual tasks assigned by the Driver on worker nodes.
  2. There can be multiple Executors running concurrently in a Spark application, each handling a portion of the workload.
  3. Executors are created when a SparkContext is initiated and persist until the application terminates.
  4. Executors communicate with the Driver for task assignments and report task status upon completion.

What role does Spark Executor play?

An executor is a process that is responsible for executing tasks assigned to it by the Spark driver. Each executor runs in its own Java Virtual Machine (JVM) and has a fixed amount of resources allocated to it, such as CPU cores and memory. Executors are launched at the beginning of a Spark application and remain active until the application terminates.

The primary role of an executor is to run tasks assigned to it by the Spark driver. When a Spark job is submitted, the driver divides the work into smaller tasks and assigns them to the executors. Each executor can run multiple tasks concurrently, which helps to improve the overall performance of the Spark application.

Executors also play a critical role in managing data in Spark. Each executor has a cache that stores frequently accessed data, which helps to reduce the number of times data needs to be read from disk. Additionally, executors can spill data to disk when their memory is full, which helps to prevent out-of-memory errors.

In Spark, executors can be dynamically adjusted based on the workload of the application. For example, if the application is running low on resources, Spark can dynamically add more executors to the cluster to help distribute the workload. Conversely, if the application is running with too many resources, Spark can remove some of the executors to free up resources.

Another important role of executors is fault tolerance. In Spark, if an executor fails, Spark can automatically reassign the failed tasks to another executor in the cluster. This helps to ensure that the Spark application can continue running even if there are failures in the cluster.

Following are some of the executor configurations you can use. For a complete list, refer to Spark’s 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.

What role does Spark Worker play?

A Spark worker is responsible for executing tasks that are assigned to it by the Spark driver. The worker is typically run on a separate node in a cluster, and multiple workers can run concurrently on different nodes. The worker is responsible for managing the processing resources on its node, which includes CPU, memory, and disk space. It also communicates with other workers and the driver to coordinate the processing of data.

The Spark worker is designed to be fault-tolerant, which means that it can recover from failures gracefully. If a worker node fails, the tasks it was executing are automatically rescheduled to run on another worker node. This ensures that the processing of data continues uninterrupted even if a worker node fails. The Spark worker is also designed to be resource-aware, which means that it can dynamically adjust its resource usage based on the workload it is processing. For example, if a worker is processing a large data set, it may allocate more memory to the task to improve performance. Similarly, if a task requires more CPU resources, the worker can allocate more CPU resources to the task.

The Spark worker is responsible for executing a variety of tasks, including data transformations, aggregations, and machine learning algorithms. These tasks are typically expressed as a series of operations on distributed data sets known as RDDs (Resilient Distributed Datasets). RDDs are the fundamental data structure in Spark, and they are designed to be fault-tolerant and easily parallelizable. The Spark worker processes these RDDs in parallel across multiple nodes in the cluster to achieve high performance.

In addition to executing tasks, the Spark worker also plays a role in data storage and retrieval. The worker is responsible for caching data that is frequently accessed by tasks to improve performance. It also retrieves data from external storage systems, such as Hadoop Distributed File System or Amazon S3, when needed.

Following are some of the configurations you can set for worker nodes.

  • spark.worker.cores: Specifies the total number of cores available for Spark Workers on a node.
  • spark.worker.memory: Sets the total memory available for Spark Workers on a node.
  • spark.worker.cleanup.enabled: Enables or disables cleaning up worker directories.
  • spark.worker.cleanup.appDataTtl: Sets the time-to-live for application data in the worker directory.
  • Specifies the directory for Spark Worker to store application files.
  • spark.worker.ui.reverseProxy: Configures whether to enable reverse proxy for the worker web UI.
  • spark.worker.ui.reverseProxyUrl: Sets the reverse proxy URL for the worker web UI.
  • spark.worker.recoveryMode: Specifies whether to enable worker recovery after driver failure.
  • spark.worker.resourceFile: Specifies a resource file to be downloaded with Spark tasks.
  • spark.worker.cleanup.logFiles: Controls whether to clean up worker log files.
  • spark.worker.log.level: Sets the logging level for Spark Worker.

Spark Worker vs Executor Differences

Following are the differences of Spark worker vs executor

AspectSpark ExecutorSpark Worker
DefinitionAn individual process executing tasks for Spark.A node in the Spark cluster hosting Executors.
QuantityMultiple Executors per Spark application.Typically one Worker per node in the Spark cluster.
CreationCreated when SparkContext is initiated.Created when the Spark application is submitted.
Task ExecutionExecutes individual tasks assigned by the Driver.Manages and monitors Executors, doesn’t execute tasks.
Resource AllocationManages its own memory and CPU resources.Allocates resources to Executors on the node.
CommunicationCommunicates with the Driver for task assignments.Communicates with the Cluster Manager for resources.
Fault ToleranceNo inherent fault tolerance for Executor failures.Monitors Executor health and reports to Cluster Manager.
EnvironmentRuns tasks in a controlled environment.Provides the execution environment for Executors.
Dynamic AllocationCan be configured for dynamic allocation of Executors.Dynamically allocates resources to Executors on the node.
Spark Executor vs Worker


In conclusion, the difference between Spark worker vs executor is that a worker provides resources to the Spark application, while an executor is responsible for executing tasks assigned to it by the Spark driver. Both executors and workers play critical roles in the performance, scalability, and fault-tolerance of Spark applications. As a data engineer, I have found that a deep understanding of Spark architecture and its components is essential for designing and implementing robust and scalable data processing pipelines.

Happy Learning !!

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