Thanks Sudhakara for your reply.
I did my experminets by varing number of reducers and made it double in
each experiments .I have a qustion regarding to the response time.Suppose
there is 6 cluster nodes and in first experminet i have 3 reducers and it
gets doubled (6 ) in second experiment and
Hi Samanesh,
Increasing the reducer for a job would not help as you excepting. In most
of MR jobs more then 60% time will spent in mapper phase(it depends upon
what type of operation performing on data in map and reducer phase).
Increasing the number of reduces increases the framework overhead,
Sudhakara,thanks again for your information.
Actually the reason i am focused on response time is i am going to modify
hadoop to skip the sort phase in mapTask and run a sample like wordCount
example on modified hadoop (skipped sort in map task) and compare its
performance with unmodified hadoop
Hi All,
I am doing some experiments by running WordCount example on hadoop.
I have a cluster with 7 nodes .I want to run WordCount example with
3mappers and 3 reducers and compare the response time with another
experiments when number of mappers and reducers increased to 6 and 12 and
so on.
For
Thanks Sudhakara for your reply.
So if number of mappers depends on the data size ,maybe the best way to do
my experiments is to increase the number of reducers based on the number of
estimated blocks in data file.Actually i want to know how response time is
changed by changing the number of
Hi Samanesh,
You can experiment with
1. By varying number reducer(mapred.reduce.tasks)
(Configure these parameters depends to you system capacity) .
mapred.tasktracker.map.tasks.maximum
mapred.tasktracker.reduce.tasks.maximum
Tasktrackers have a fixed number of slots for map tasks and for
