We argue for breaking data-parallel jobs in compute clusters into tiny tasks that each complete in hundreds of milliseconds. Tiny tasks avoid the need for complex skew mitigation techniques: by breaking a large job into millions of tiny tasks, work will be evenly spread over available resources by the scheduler. Furthermore, tiny tasks alleviate long wait times seen in today’s clusters for interactive jobs: even large batch jobs can be split into small tasks that finish quickly. We demonstrate a 5.2x improvement in response times due to the use of smaller tasks.

In current data-parallel computing frameworks, high task launch overheads and scalability limitations prevent users from running short tasks. Recent research has addressed many of these bottlenecks; we discuss remaining challenges and propose a task execution framework that can efficiently support tiny tasks.