Hi all,
I have been using the Hadoop Fair Scheduler for some experiments on a
100 node cluster with 2 map slots per node (hence, a total of 200 map
slots).
In one of my experiments, all the map tasks finish within a heartbeat
interval of 3 seconds. I noticed that the maximum number of
concurrently
active map slots on my cluster never exceeds 100, and hence, the
cluster utilization during my experiments never exceeds 50% even when
large jobs with more than a 1000 maps are being executed.
A look at the Fair Scheduler code (in particular, the assignTasks
function) revealed the reason.
As per my understanding, with the implementation in Hadoop 0.20.0, a
TaskTracker is not assigned more than 1 map and 1 reduce task per
heart beat.
In my experiments, in every heart beat, each TT has 2 free map slots
but is assigned only 1 map task, and hence, the utilization never goes
beyond 50%.
Of course, this (degenerate) case does not arise when map tasks take
more than one 1 heart beat interval to finish. For example, I repeated
the experiments with maps tasks taking close to 15 s to finish and
noticed close to 100 % utilization when large jobs were executing.
Why does the Fair Scheduler not assign more than one map task to a TT
per heart beat? Is this done to spread the load uniformly across the
cluster?
I looked at assignTasks function in the default Hadoop scheduler
(JobQueueTaskScheduler.java), and it does assign more than 1 map task
per heart beat to a TT.
It will be easy to change the Fair Scheduler to assign more than 1 map
task to a TT per heart beat (I did that and achieved 100% utilization
even with small map tasks). But I am wondering, if doing so will
violate some fairness properties.
Thanks,
Abhishek
