[MAPREDUCE-93] Job Tracker should prefer input-splits from overloaded racks

• Type: Bug
• Status: Open
• Priority: Major
• Resolution: Unresolved
• Assignee: Devaraj Das
• Reporter: Runping Qi
• Created: 09/Oct/07 14:08
• Updated: 20/Jun/09 07:50

Description

Currently, when the Job Tracker assigns a mapper task to a task tracker and there is no local split to the task tracker, the
job tracker will find the first runable task in the mast task list and assign the task to the task tracker.
The split for the task is not local to the task tracker, of course. However, the split may be local to other task trackers.
Assigning the that task, to that task tracker may decrease the potential number of mapper attempts with data locality.
The desired behavior in this situation is to choose a task whose split is not local to any task tracker.
Resort to the current behavior only if no such task is found.

In general, it will be useful to know the number of task trackers to which each split is local.
To assign a task to a task tracker, the job tracker should first try to pick a task that is local to the task tracker and that has minimal number of task trackers to which it is local. If no task is local to the task tracker, the job tracker should try to pick a task that has minimal number of task trackers to which it is local.

It is worthwhile to instrument the job tracker code to report the number of splits that are local to some task trackers.
That should be the maximum number of tasks with data locality. By comparing that number with the the actual number of
data local mappers launched, we can know the effectiveness of the job tracker scheduling.

When we introduce rack locality, we should apply the same principle.

Issue Links

• relates to: HADOOP-2560 Processing multiple input splits per mapper task
• relates to: HADOOP-2119 JobTracker becomes non-responsive if the task trackers finish task too fast