Task scheduling: The Spark task scheduler is responsible for arranging and scheduling the execution order of tasks in Spark jobs and allocating resources to ensure tasks are completed in the most optimal way.
Resource Allocation Management: The Spark task scheduler is responsible for managing and allocating resources in the cluster, such as memory, CPU, etc., to ensure that tasks can be completed on time.
Fault recovery: The Spark task scheduler can monitor potential failures that may occur during task execution and take appropriate measures to recover, ensuring tasks can continue to execute.
Task optimization: The Spark task scheduler can optimize tasks by combining tasks, reordering tasks, localizing data, etc., to improve task execution efficiency and performance.
Monitoring and logging: The Spark task scheduler is capable of monitoring various metrics and log information during task execution, helping users understand the status of task execution, and promptly identify and resolve issues.
