Job Scheduling in Hadoop
When Hadoop started out, it was designed mainly for running large batch jobs such as web indexing and log mining. Users submitted jobs to a queue, and the cluster ran them in order. However, as organizations placed more data in their Hadoop clusters and developed more computations they wanted to run, another use case became attractive: sharing a MapReduce cluster between multiple users. The benefits of sharing are tremendous: with all the data in one place, users can run queries that they may never have been able to execute otherwise, and costs go down because system utilization is higher than building a separate Hadoop cluster for each group. However, sharing requires support from the Hadoop job scheduler to provide guaranteed capacity to production jobs and good response time to interactive jobs while allocating resources fairly between users.
This July, the scheduler in Hadoop became a pluggable component and opened the door for innovation in this space. The result was two schedulers for multi-user workloads: the Fair Scheduler, developed at Facebook, and the Capacity Scheduler, developed at Yahoo.
