Spark Cluster Overview: A Comprehensive Guide
Introduction
Spark is a powerful in-memory data processing engine that enables efficient and scalable data analysis. When running Spark on a cluster, it’s essential to understand the various components involved in managing the cluster, submitting applications, and monitoring tasks. In this article, we’ll delve into the details of Spark’s cluster management, application submission, and task scheduling.
Spark Component Description
A Spark application is a separate process that runs on the cluster, managed by SparkContext objects (drivers). These drivers are responsible for executing the main application. The overall architecture of Spark involves several management components, including Spark’s own manager, Mesos, or Yarn, which allocate resources to the application.
Once the application is up and running, Spark can access the cluster nodes, providing computing and data storage for applications. The SparkContext sends the application to the node for execution, and finally, it sends tasks to the actuator to run.
Key Considerations
There are several important aspects to note in the life cycle of a Spark application:
- Process Isolation: Each application is a separate process, isolated from other applications by a good scheduler (driver scheduling tasks) and management controller (different applications of scheduled tasks). This means that SparkContext instances cannot be shared, and data cannot be accessed by other applications during operation.
- Cluster Manager Awareness: Spark is not directly aware of the underlying cluster manager. The process can be obtained as long as the actuator, and these processes can communicate with each other, even into other managers (e.g., Mesos or Yarn).
- Application Listening: The application must listen for incoming connections from the actuator during operation. Therefore, the application must be published in an addressable working node.
- Task Scheduling: Because the program schedules tasks in a clustered environment, it should run in the neighboring working node, preferably within the LAN. If you want to send a request to a remote cluster, it’s best to open a driver through the RPC method and submit the operation in the neighboring node.
Type Cluster Manager
Spark currently supports three managers:
- Standalone: It is a simple cluster manager internal to Spark, which can be very simple to build a cluster.
- Apache Mesos: It is a common cluster management that can run Hadoop MapReduce and service applications.
- Hadoop YARN: It is Hadoop 2.x’s Explorer.
Applications Submitted
Applications can be submitted to any type of cluster using the spark-submit scripts.
Monitor
Each application has published a monitoring web page, usually on port 4040. This displays information about the task being performed, application, and hard disk status. You can access this page by typing in the browser http://drive-node:4040.
Task Scheduling
Spark provides cross-application (if multiple calculations on the same SparkContext) control on resource allocation (cluster manager level) and applications. This allows for efficient and scalable data analysis.
Conclusion
In this article, we’ve explored the Spark cluster overview, including the management components, application submission, and task scheduling. By understanding these aspects, you can efficiently run Spark on a cluster and achieve scalable data analysis.