Which Spark component is responsible for handling memory management?

The executor is the component in Spark that is primarily responsible for handling memory management. Each Spark application is composed of one or more executors that run on the worker nodes in the cluster. Executors manage their own memory and resources for the tasks they are executing, including allocating memory for storage and computation.

In Spark, when data is processed, it is often cached in the executor's memory for efficient reuse. The memory management strategy includes determining how much memory to allocate for caching, managing memory used for computations, and handling garbage collection. Executors also communicate the memory status back to the driver program to optimize resource allocation across tasks.

This role is crucial for optimizing the performance of Spark applications, as well-managed memory can significantly reduce the time taken for data processing and minimize the need for disk I/O. Other components, such as the driver program, cluster manager, and application master, have different responsibilities, such as scheduling tasks, managing resources across the cluster, and monitoring application execution, but they do not actively manage memory in the same way executors do.