Which Spark component manages the coordination of various tasks across the cluster?

The driver is the main component in Apache Spark that manages the coordination of tasks across the cluster. It is responsible for converting user programs into tasks that can be distributed to the executors. The driver maintains the application's state and keeps track of the various tasks that are scheduled and executed across the cluster nodes.

The driver performs several critical functions, including:

1. Job Scheduling: It schedules jobs and stages based on dependencies, ensuring that tasks are executed in the correct order.
2. Task Management: It monitors the execution of tasks, handling successful completions and failures, and rescheduling tasks when necessary.
3. Resource Allocation: The driver communicates with the cluster manager to allocate resources across the available worker nodes, balancing the load and optimizing overall performance.

In contrast, the executor is responsible for executing the tasks that the driver assigns but does not manage the coordination itself. The worker nodes are the physical or virtual machines where the executors run, and a manager, while it might refer to something like the cluster manager, doesn’t specifically manage task coordination like the driver does. Thus, the driver is essential in orchestrating the entire process of distributed computing in Spark.