Which entity does the driver code communicate with to manage the Spark cluster?

The driver code communicates with the Cluster Manager to manage the Spark cluster. The Cluster Manager is responsible for resource allocation, scheduling tasks, and maintaining the overall state of the cluster. It orchestrates the execution of jobs across the various node workers in the cluster by coordinating how resources are allocated and managing the lifecycle of applications.

The role of the driver is to compile the application code into Spark jobs and communicate with the Cluster Manager to request resources for the tasks that need to be executed. The driver effectively acts as the main control point of the Spark application, initiating and monitoring tasks and handling the communication between the various components.

While other entities mentioned, such as the Master Node and SparkContext, play important roles, they do not directly govern resource management in the cluster as the Cluster Manager does. The Master Node can refer to a specific instance of a Cluster Manager in Spark Standalone mode, but in a broader context, the Cluster Manager covers all environments, including standalone, Mesos, and YARN. The SparkContext serves as the entry point for interacting with various Spark functionalities, but it is the Cluster Manager that ultimately communicates with the underlying cluster infrastructure to handle job execution. Meanwhile, DataFrames are high-level abstractions for distributed data processing in Spark and do not