What Are Workers, Executors, and Cores in a Spark Standalone Cluster?

When working with a Spark standalone cluster, understanding the roles of Workers, Executors, and Cores is crucial for designing efficient cluster operations. Below is a detailed explanation of each component:

Contents hide

1 Workers

2 Executors

2.1 Key Functions of Executors:

3 Cores

3.1 Example of Cores Allocation:

4 Summary

5 About Editorial Team

6 You Might Also Like:

Workers

In a Spark standalone cluster, a Worker is a node that runs the application code in a distributed manner. Each Worker node has the resources (like CPU, memory, disk) to execute parts of the tasks assigned by the Spark driver. The Worker node communicates with the Spark Master to get task assignments.

Executors

Executors are distributed agents that are responsible for running the tasks of a Spark job. They are launched on Worker nodes. Each Executor is a Java process that runs in the Worker node and includes multiple components such as task threads. Executors perform the actual data processing required by your Spark application. They maintain data in memory across different stages of the job execution, which reduces I/O overhead and speeds up processing.

Key Functions of Executors:

Execute code assigned to them by the driver.
Store data for high performance (e.g., caching results of operations).
Report the status of computation and data transfer to the driver node.

Cores

Cores refer to the computational capability of a Worker node. They determine the parallelism for task execution. Each task in Spark is a unit of work that will be assigned to a core for execution. The more cores available, the higher the number of tasks that can run simultaneously.

Example of Cores Allocation:

Suppose you have a Worker node with 4 cores and a Spark application that has 8 tasks to execute. Here, 4 tasks can run in parallel on the Worker node, while the remaining tasks will wait until a core becomes free.

Summary

In summary, a Spark standalone cluster is composed of multiple Worker nodes, each hosting Executors. These Executors perform the real computation and their parallelism is defined by the number of available cores.


Worker Node 1: 
    Executor 1: Uses Core 1, Core 2
    Executor 2: Uses Core 3, Core 4

Worker Node 2:
    Executor 3: Uses Core 1, Core 2
    Executor 4: Uses Core 3, Core 4

This setup allows a scalable and efficient execution of Spark applications by dividing the workload across multiple nodes and cores.

About Editorial Team

Our Editorial Team is made up of tech enthusiasts who are highly skilled in Apache Spark, PySpark, and Machine Learning. They are also proficient in Python, Pandas, R, Hive, PostgreSQL, Snowflake, and Databricks. They aren't just experts; they are passionate teachers. They are dedicated to making complex data concepts easy to understand through engaging and simple tutorials with examples.