The main difference between Spark submit client and cluster mode lies in where the Spark application runs.
- Spark submit client: This mode runs the application on the machine where the
spark-submit
command is executed. This is suitable for small applications or for testing purposes. - Cluster mode: This mode runs the application on a cluster of machines, such as a Hadoop cluster or a standalone Spark cluster. This mode is suitable for large applications that require more resources or for production deployments.
Here's a breakdown of the key differences:
Spark Submit Client:
- Execution: Runs on the local machine where the
spark-submit
command is executed. - Resource Management: Manages resources on the local machine.
- Suitable for: Small applications, testing, or when limited resources are available.
- Example: Running a simple Spark application on your laptop for analysis.
Cluster Mode:
- Execution: Runs on a cluster of machines.
- Resource Management: Manages resources across the cluster.
- Suitable for: Large applications, production deployments, and when more resources are needed.
- Example: Running a complex machine learning model on a Hadoop cluster for real-time predictions.
Here's a table summarizing the key differences:
Feature | Spark Submit Client | Cluster Mode |
---|---|---|
Execution | Local machine | Cluster |
Resource Management | Local machine | Cluster |
Suitable for | Small applications, testing | Large applications, production |
Understanding these differences will help you choose the appropriate mode for your Spark application based on your needs and resources.