Assume the block size is 128M, and your mapper each finishes within half
minute, then there is not too much logic in your mapper, as it can finish
processing 128M around 30 seconds. If your reducers cannot finish with 1 week,
then something is wrong.
So you may need to find out following:
1) How many mappers generated in your MR job?2) Are they all finished? (Check
them in the jobtracker through web or command line)3) How many reducers in this
job?4) Are reducers starting? What stage are they in?
Copying/Sorting/Reducing?5) If in the reducing stage, check the userlog of
reducers. Is your code running now?
All these information you can find out from the Job Tracker web UI.
Yong
Date: Thu, 12 Dec 2013 09:03:29 +0800
Subject: Re: issue about Shuffled Maps in MR job summary
From: justlo...@gmail.com
To: user@hadoop.apache.org
hi,
suppose i have 5-worknode cluster,each worknode can allocate 40G mem ,and i
do not care map task,be cause the map task in my job finished within half a
minuter,as my observe the real slow task is reduce, i allocate 12G to each
reduce task,so each worknode can support 3 reduce parallel,and the whole
cluster can support 15 reducer,and i run the job with all 15 reducer, and i do
not know if i increase reducer number from 15 to 30 ,each reduce allocate 6G
MEM,that will speed the job or not ,the job run on my product env, it run
nearly 1 week,it still not finished
On Wed, Dec 11, 2013 at 9:50 PM, java8964 <java8...@hotmail.com> wrote:
The whole job complete time depends on a lot of factors. Are you sure the
reducers part is the bottleneck?
Also, it also depends on how many Reducer input groups it has in your MR job.
If you only have 20 reducer groups, even you jump your reducer count to 40,
then the epoch of reducers part won't have too much change, as the additional
20 reducer task won't get data to process.
If you have a lot of reducer input groups, and your cluster does have capacity
at this time, and your also have a lot idle reducer slot, then increase your
reducer count should decrease your whole job complete time.
Make sense?
Yong
Date: Wed, 11 Dec 2013 14:20:24 +0800
Subject: Re: issue about Shuffled Maps in MR job summary
From: justlo...@gmail.com
To: user@hadoop.apache.org
i read the doc, and find if i have 8 reducer ,a map task will output 8
partition ,each partition will be send to a different reducer, so if i increase
reduce number ,the partition number increase ,but the volume on network traffic
is same,why sometime ,increase reducer number will not decrease job complete
time ?
On Wed, Dec 11, 2013 at 1:48 PM, Vinayakumar B <vinayakuma...@huawei.com> wrote:
It looks simple, J
Shuffled Maps= Number of Map Tasks * Number of Reducers
Thanks and Regards,
Vinayakumar B
From: ch huang [mailto:justlo...@gmail.com]
Sent: 11 December 2013 10:56
To: user@hadoop.apache.org
Subject: issue about Shuffled Maps in MR job summary
hi,maillist:
i run terasort with 16 reducers and 8 reducers,when i double reducer
number, the Shuffled maps is also double ,my question is the job only run 20
map tasks (total input file is 10,and each file is 100M,my block size is 64M,so
split is 20) why i need shuffle 160 maps in 8 reducers run and 320 maps in 16
reducers run?how to caculate the shuffle maps number?
16 reducer summary output:
Shuffled Maps =320
8 reducer summary output:
Shuffled Maps =160
