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Posted to user@pig.apache.org by prasenjit mukherjee <pm...@quattrowireless.com> on 2010/01/26 16:57:40 UTC
distributing hdfs put
Hi Mridul,
Thanks your approach works fine. This is how my current pig script
looks like :
define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
grp_r1 = GROUP r1 BY filename PARALLEL 5;
r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
r3 = STREAM r2 through CMD;
store r3 INTO '/op/s3fetch_debug_log';
And here is my s3fetch.py :
for word in sys.stdin:
word=word.rstrip()
str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
(input_str,out_err) = os.popen4(str);
for line in out_err.readlines():
sys.stdout.write('\t'+word+'::\t'+line)
So, the job starts fine and I see that my hadoop directory ( /ip/data/.)
starts filling up with s3 files. But after sometime it gets stuck. I see
lots of failed/restarted jobs in the jobtracker. And the number of files
dont increase in /ip/data.
Could this be happening because of parallel hdfs writes ( via hadoop fs -cp
<> <> ) making primary-name-node a blocking server ?
Any help is greatly appreciated.
-Thanks,
Prasen
On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
<mr...@yahoo-inc.com>wrote:
>
> If each line from your file has to be processed by a different mapper -
> other than by writing a custom slicer, a very dirty hack would be to :
> a) create N number of files with one line each.
> b) Or, do something like :
> input_lines = load 'my_s3_list_file' as (location_line:chararray);
> grp_op = GROUP input_lines BY location_line PARALLEL $NUM_MAPPERS_REQUIRED;
> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
>
>
> The preferred way, as Dmitriy mentioned, would be to use a custom Slicer
> ofcourse !
>
> Regards,
> Mridul
>
>
> prasenjit mukherjee wrote:
>
>> I want to use Pig to paralelize processing on a number of requests. There
>> are ~ 300 request which needs to be processed. Each processing consist of
>> following :
>> 1. Fetch file from s3 to local
>> 2. Do some preprocessing
>> 3. Put it into hdfs
>>
>> My input is a small file with 300 lines. The problem is that pig seems to
>> be
>> always creating a single mapper, because of which the load is not properly
>> distributed. Any way I can enforce splitting of smaller input files as
>> well
>> ? Below is the pig output which tends to indicate that there is only 1
>> mapper. Let me know if my understanding is wrong.
>>
>> 2010-01-24 05:31:53,148 [main] INFO
>>
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
>> - MR plan size before optimization: 1
>> 2010-01-24 05:31:53,148 [main] INFO
>>
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
>> - MR plan size after optimization: 1
>> 2010-01-24 05:31:55,006 [main] INFO
>>
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
>> - Setting up single store job
>>
>> Thanks
>> -Prasen.
>>
>
>
Re: distributing hdfs put
Posted by Ashutosh Chauhan <as...@gmail.com>.
You can set it through Pig as well as you have mentioned. Advantage is
that instead of setting permanently to high value through
hadoop-site.xml (which will then affect all subsequent hadoop jobs of
your cluster) through Pig you can set it on per job basis.
Ashutosh
On Wed, Jan 27, 2010 at 21:55, prasenjit mukherjee
<pm...@quattrowireless.com> wrote:
> Not sure I understand. Are you saying that pig takes -D<> parameters
> directly. Will the following work :
>
> "pig -Dmapred.task.timeout=0 -f myfile.pig"
>
>
> On Thu, Jan 28, 2010 at 11:08 AM, Amogh Vasekar <am...@yahoo-inc.com> wrote:
>
>> Hi,
>> You should be able to pass this as a cmd line argument using -D ... If you
>> want to change it for all jobs on your own cluster, it would be in
>> mapred-site.
>>
>> Amogh
>>
>>
>> On 1/28/10 11:03 AM, "prasenjit mukherjee" <pm...@quattrowireless.com>
>> wrote:
>>
>> Thanks Amogh for your quick response. Changing this property only on
>> master's hadoop-site.xml will do or I need to do it on all the slaves as
>> well ?
>>
>> Any way I can do this from PIG ( or I guess I am asking too much here :) )
>>
>> On Thu, Jan 28, 2010 at 10:57 AM, Amogh Vasekar <am...@yahoo-inc.com>
>> wrote:
>>
>> > Yes, parameter is mapred.task.timeout in mS.
>> > You can also update status / output to stdout after some time chunks to
>> > avoid this :)
>> >
>> > Amogh
>> >
>> >
>> > On 1/28/10 10:52 AM, "prasenjit mukherjee" <
>> pmukherjee@quattrowireless.com>
>> > wrote:
>> >
>> > Now I see. The tasks are failing with the following error message :
>> >
>> > *Task attempt_201001272359_0001_r_000000_0 failed to report status for
>> 600
>> > seconds. Killing!*
>> >
>> > Looks like hadoop kills/restarts jobs which takes more than 600 seconds.
>> > Is
>> > there any way I can increase it to some very high number ?
>> >
>> > -Thanks,
>> > Prasenjit
>> >
>> >
>> >
>> > On Tue, Jan 26, 2010 at 9:55 PM, Dmitriy Ryaboy <dv...@gmail.com>
>> > wrote:
>> > >
>> > > Do you know why the jobs are failing? Take a look at the logs. I
>> > > suspect it may be due to s3, not hadoop.
>> > >
>> > > -D
>> > >
>> > > On Tue, Jan 26, 2010 at 7:57 AM, prasenjit mukherjee
>> > > <pm...@quattrowireless.com> wrote:
>> > > > Hi Mridul,
>> > > > Thanks your approach works fine. This is how my current pig
>> script
>> > > > looks like :
>> > > >
>> > > > define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
>> > > > r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
>> > > > grp_r1 = GROUP r1 BY filename PARALLEL 5;
>> > > > r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
>> > > > r3 = STREAM r2 through CMD;
>> > > > store r3 INTO '/op/s3fetch_debug_log';
>> > > >
>> > > > And here is my s3fetch.py :
>> > > > for word in sys.stdin:
>> > > > word=word.rstrip()
>> > > > str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
>> > > > s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
>> > > > sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
>> > > > (input_str,out_err) = os.popen4(str);
>> > > > for line in out_err.readlines():
>> > > > sys.stdout.write('\t'+word+'::\t'+line)
>> > > >
>> > > >
>> > > >
>> > > > So, the job starts fine and I see that my hadoop directory (
>> > /ip/data/.)
>> > > > starts filling up with s3 files. But after sometime it gets stuck. I
>> > see
>> > > > lots of failed/restarted jobs in the jobtracker. And the number of
>> > files
>> > > > dont increase in /ip/data.
>> > > >
>> > > > Could this be happening because of parallel hdfs writes ( via hadoop
>> fs
>> > -cp
>> > > > <> <> ) making primary-name-node a blocking server ?
>> > > >
>> > > > Any help is greatly appreciated.
>> > > >
>> > > > -Thanks,
>> > > > Prasen
>> > > >
>> > > > On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
>> > > > <mr...@yahoo-inc.com>wrote:
>> > > >
>> > > >>
>> > > >> If each line from your file has to be processed by a different
>> mapper
>> > -
>> > > >> other than by writing a custom slicer, a very dirty hack would be to
>> :
>> > > >> a) create N number of files with one line each.
>> > > >> b) Or, do something like :
>> > > >> input_lines = load 'my_s3_list_file' as (location_line:chararray);
>> > > >> grp_op = GROUP input_lines BY location_line PARALLEL
>> > $NUM_MAPPERS_REQUIRED;
>> > > >> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
>> > > >>
>> > > >>
>> > > >> The preferred way, as Dmitriy mentioned, would be to use a custom
>> > Slicer
>> > > >> ofcourse !
>> > > >>
>> > > >> Regards,
>> > > >> Mridul
>> > > >>
>> > > >>
>> > > >> prasenjit mukherjee wrote:
>> > > >>
>> > > >>> I want to use Pig to paralelize processing on a number of
>> requests.
>> > There
>> > > >>> are ~ 300 request which needs to be processed. Each processing
>> > consist of
>> > > >>> following :
>> > > >>> 1. Fetch file from s3 to local
>> > > >>> 2. Do some preprocessing
>> > > >>> 3. Put it into hdfs
>> > > >>>
>> > > >>> My input is a small file with 300 lines. The problem is that pig
>> > seems
>> > to
>> > > >>> be
>> > > >>> always creating a single mapper, because of which the load is not
>> > properly
>> > > >>> distributed. Any way I can enforce splitting of smaller input files
>> > as
>> > > >>> well
>> > > >>> ? Below is the pig output which tends to indicate that there is
>> only
>> > 1
>> > > >>> mapper. Let me know if my understanding is wrong.
>> > > >>>
>> > > >>> 2010-01-24 05:31:53,148 [main] INFO
>> > > >>>
>> > > >>>
>> >
>> >
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
>> > > >>> - MR plan size before optimization: 1
>> > > >>> 2010-01-24 05:31:53,148 [main] INFO
>> > > >>>
>> > > >>>
>> >
>> >
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
>> > > >>> - MR plan size after optimization: 1
>> > > >>> 2010-01-24 05:31:55,006 [main] INFO
>> > > >>>
>> > > >>>
>> >
>> >
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
>> > > >>> - Setting up single store job
>> > > >>>
>> > > >>> Thanks
>> > > >>> -Prasen.
>> > > >>>
>> > > >>
>> > > >>
>> > > >
>> >
>> >
>>
>>
>
Re: distributing hdfs put
Posted by prasenjit mukherjee <pm...@quattrowireless.com>.
So I changed the property in hadoop-site.xml and it works great. Thanks
to you guys for your help. s3->hdfs-put time got reduced by over 90%.
On Thu, Jan 28, 2010 at 11:25 AM, prasenjit mukherjee <
pmukherjee@quattrowireless.com> wrote:
> Not sure I understand. Are you saying that pig takes -D<> parameters
> directly. Will the following work :
>
> "pig -Dmapred.task.timeout=0 -f myfile.pig"
>
>
>
> On Thu, Jan 28, 2010 at 11:08 AM, Amogh Vasekar <am...@yahoo-inc.com>wrote:
>
>> Hi,
>> You should be able to pass this as a cmd line argument using -D ... If you
>> want to change it for all jobs on your own cluster, it would be in
>> mapred-site.
>>
>> Amogh
>>
>>
>> On 1/28/10 11:03 AM, "prasenjit mukherjee" <
>> pmukherjee@quattrowireless.com> wrote:
>>
>> Thanks Amogh for your quick response. Changing this property only on
>> master's hadoop-site.xml will do or I need to do it on all the slaves as
>> well ?
>>
>> Any way I can do this from PIG ( or I guess I am asking too much here :) )
>>
>> On Thu, Jan 28, 2010 at 10:57 AM, Amogh Vasekar <am...@yahoo-inc.com>
>> wrote:
>>
>> > Yes, parameter is mapred.task.timeout in mS.
>> > You can also update status / output to stdout after some time chunks to
>> > avoid this :)
>> >
>> > Amogh
>> >
>> >
>> > On 1/28/10 10:52 AM, "prasenjit mukherjee" <
>> pmukherjee@quattrowireless.com>
>> > wrote:
>> >
>> > Now I see. The tasks are failing with the following error message :
>> >
>> > *Task attempt_201001272359_0001_r_000000_0 failed to report status for
>> 600
>> > seconds. Killing!*
>> >
>> > Looks like hadoop kills/restarts jobs which takes more than 600
>> seconds.
>> > Is
>> > there any way I can increase it to some very high number ?
>> >
>> > -Thanks,
>> > Prasenjit
>> >
>> >
>> >
>> > On Tue, Jan 26, 2010 at 9:55 PM, Dmitriy Ryaboy <dv...@gmail.com>
>> > wrote:
>> > >
>> > > Do you know why the jobs are failing? Take a look at the logs. I
>> > > suspect it may be due to s3, not hadoop.
>> > >
>> > > -D
>> > >
>> > > On Tue, Jan 26, 2010 at 7:57 AM, prasenjit mukherjee
>> > > <pm...@quattrowireless.com> wrote:
>> > > > Hi Mridul,
>> > > > Thanks your approach works fine. This is how my current pig
>> script
>> > > > looks like :
>> > > >
>> > > > define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
>> > > > r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
>> > > > grp_r1 = GROUP r1 BY filename PARALLEL 5;
>> > > > r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
>> > > > r3 = STREAM r2 through CMD;
>> > > > store r3 INTO '/op/s3fetch_debug_log';
>> > > >
>> > > > And here is my s3fetch.py :
>> > > > for word in sys.stdin:
>> > > > word=word.rstrip()
>> > > > str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
>> > > > s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
>> > > > sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
>> > > > (input_str,out_err) = os.popen4(str);
>> > > > for line in out_err.readlines():
>> > > > sys.stdout.write('\t'+word+'::\t'+line)
>> > > >
>> > > >
>> > > >
>> > > > So, the job starts fine and I see that my hadoop directory (
>> > /ip/data/.)
>> > > > starts filling up with s3 files. But after sometime it gets stuck. I
>> > see
>> > > > lots of failed/restarted jobs in the jobtracker. And the number of
>> > files
>> > > > dont increase in /ip/data.
>> > > >
>> > > > Could this be happening because of parallel hdfs writes ( via hadoop
>> fs
>> > -cp
>> > > > <> <> ) making primary-name-node a blocking server ?
>> > > >
>> > > > Any help is greatly appreciated.
>> > > >
>> > > > -Thanks,
>> > > > Prasen
>> > > >
>> > > > On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
>> > > > <mr...@yahoo-inc.com>wrote:
>> > > >
>> > > >>
>> > > >> If each line from your file has to be processed by a different
>> mapper
>> > -
>> > > >> other than by writing a custom slicer, a very dirty hack would be
>> to :
>> > > >> a) create N number of files with one line each.
>> > > >> b) Or, do something like :
>> > > >> input_lines = load 'my_s3_list_file' as (location_line:chararray);
>> > > >> grp_op = GROUP input_lines BY location_line PARALLEL
>> > $NUM_MAPPERS_REQUIRED;
>> > > >> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
>> > > >>
>> > > >>
>> > > >> The preferred way, as Dmitriy mentioned, would be to use a custom
>> > Slicer
>> > > >> ofcourse !
>> > > >>
>> > > >> Regards,
>> > > >> Mridul
>> > > >>
>> > > >>
>> > > >> prasenjit mukherjee wrote:
>> > > >>
>> > > >>> I want to use Pig to paralelize processing on a number of
>> requests.
>> > There
>> > > >>> are ~ 300 request which needs to be processed. Each processing
>> > consist of
>> > > >>> following :
>> > > >>> 1. Fetch file from s3 to local
>> > > >>> 2. Do some preprocessing
>> > > >>> 3. Put it into hdfs
>> > > >>>
>> > > >>> My input is a small file with 300 lines. The problem is that pig
>> > seems
>> > to
>> > > >>> be
>> > > >>> always creating a single mapper, because of which the load is not
>> > properly
>> > > >>> distributed. Any way I can enforce splitting of smaller input
>> files
>> > as
>> > > >>> well
>> > > >>> ? Below is the pig output which tends to indicate that there is
>> only
>> > 1
>> > > >>> mapper. Let me know if my understanding is wrong.
>> > > >>>
>> > > >>> 2010-01-24 05:31:53,148 [main] INFO
>> > > >>>
>> > > >>>
>> >
>> >
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
>> > > >>> - MR plan size before optimization: 1
>> > > >>> 2010-01-24 05:31:53,148 [main] INFO
>> > > >>>
>> > > >>>
>> >
>> >
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
>> > > >>> - MR plan size after optimization: 1
>> > > >>> 2010-01-24 05:31:55,006 [main] INFO
>> > > >>>
>> > > >>>
>> >
>> >
>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
>> > > >>> - Setting up single store job
>> > > >>>
>> > > >>> Thanks
>> > > >>> -Prasen.
>> > > >>>
>> > > >>
>> > > >>
>> > > >
>> >
>> >
>>
>>
>
Re: distributing hdfs put
Posted by prasenjit mukherjee <pm...@quattrowireless.com>.
Not sure I understand. Are you saying that pig takes -D<> parameters
directly. Will the following work :
"pig -Dmapred.task.timeout=0 -f myfile.pig"
On Thu, Jan 28, 2010 at 11:08 AM, Amogh Vasekar <am...@yahoo-inc.com> wrote:
> Hi,
> You should be able to pass this as a cmd line argument using -D ... If you
> want to change it for all jobs on your own cluster, it would be in
> mapred-site.
>
> Amogh
>
>
> On 1/28/10 11:03 AM, "prasenjit mukherjee" <pm...@quattrowireless.com>
> wrote:
>
> Thanks Amogh for your quick response. Changing this property only on
> master's hadoop-site.xml will do or I need to do it on all the slaves as
> well ?
>
> Any way I can do this from PIG ( or I guess I am asking too much here :) )
>
> On Thu, Jan 28, 2010 at 10:57 AM, Amogh Vasekar <am...@yahoo-inc.com>
> wrote:
>
> > Yes, parameter is mapred.task.timeout in mS.
> > You can also update status / output to stdout after some time chunks to
> > avoid this :)
> >
> > Amogh
> >
> >
> > On 1/28/10 10:52 AM, "prasenjit mukherjee" <
> pmukherjee@quattrowireless.com>
> > wrote:
> >
> > Now I see. The tasks are failing with the following error message :
> >
> > *Task attempt_201001272359_0001_r_000000_0 failed to report status for
> 600
> > seconds. Killing!*
> >
> > Looks like hadoop kills/restarts jobs which takes more than 600 seconds.
> > Is
> > there any way I can increase it to some very high number ?
> >
> > -Thanks,
> > Prasenjit
> >
> >
> >
> > On Tue, Jan 26, 2010 at 9:55 PM, Dmitriy Ryaboy <dv...@gmail.com>
> > wrote:
> > >
> > > Do you know why the jobs are failing? Take a look at the logs. I
> > > suspect it may be due to s3, not hadoop.
> > >
> > > -D
> > >
> > > On Tue, Jan 26, 2010 at 7:57 AM, prasenjit mukherjee
> > > <pm...@quattrowireless.com> wrote:
> > > > Hi Mridul,
> > > > Thanks your approach works fine. This is how my current pig
> script
> > > > looks like :
> > > >
> > > > define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
> > > > r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
> > > > grp_r1 = GROUP r1 BY filename PARALLEL 5;
> > > > r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
> > > > r3 = STREAM r2 through CMD;
> > > > store r3 INTO '/op/s3fetch_debug_log';
> > > >
> > > > And here is my s3fetch.py :
> > > > for word in sys.stdin:
> > > > word=word.rstrip()
> > > > str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
> > > > s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
> > > > sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
> > > > (input_str,out_err) = os.popen4(str);
> > > > for line in out_err.readlines():
> > > > sys.stdout.write('\t'+word+'::\t'+line)
> > > >
> > > >
> > > >
> > > > So, the job starts fine and I see that my hadoop directory (
> > /ip/data/.)
> > > > starts filling up with s3 files. But after sometime it gets stuck. I
> > see
> > > > lots of failed/restarted jobs in the jobtracker. And the number of
> > files
> > > > dont increase in /ip/data.
> > > >
> > > > Could this be happening because of parallel hdfs writes ( via hadoop
> fs
> > -cp
> > > > <> <> ) making primary-name-node a blocking server ?
> > > >
> > > > Any help is greatly appreciated.
> > > >
> > > > -Thanks,
> > > > Prasen
> > > >
> > > > On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
> > > > <mr...@yahoo-inc.com>wrote:
> > > >
> > > >>
> > > >> If each line from your file has to be processed by a different
> mapper
> > -
> > > >> other than by writing a custom slicer, a very dirty hack would be to
> :
> > > >> a) create N number of files with one line each.
> > > >> b) Or, do something like :
> > > >> input_lines = load 'my_s3_list_file' as (location_line:chararray);
> > > >> grp_op = GROUP input_lines BY location_line PARALLEL
> > $NUM_MAPPERS_REQUIRED;
> > > >> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
> > > >>
> > > >>
> > > >> The preferred way, as Dmitriy mentioned, would be to use a custom
> > Slicer
> > > >> ofcourse !
> > > >>
> > > >> Regards,
> > > >> Mridul
> > > >>
> > > >>
> > > >> prasenjit mukherjee wrote:
> > > >>
> > > >>> I want to use Pig to paralelize processing on a number of
> requests.
> > There
> > > >>> are ~ 300 request which needs to be processed. Each processing
> > consist of
> > > >>> following :
> > > >>> 1. Fetch file from s3 to local
> > > >>> 2. Do some preprocessing
> > > >>> 3. Put it into hdfs
> > > >>>
> > > >>> My input is a small file with 300 lines. The problem is that pig
> > seems
> > to
> > > >>> be
> > > >>> always creating a single mapper, because of which the load is not
> > properly
> > > >>> distributed. Any way I can enforce splitting of smaller input files
> > as
> > > >>> well
> > > >>> ? Below is the pig output which tends to indicate that there is
> only
> > 1
> > > >>> mapper. Let me know if my understanding is wrong.
> > > >>>
> > > >>> 2010-01-24 05:31:53,148 [main] INFO
> > > >>>
> > > >>>
> >
> >
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> > > >>> - MR plan size before optimization: 1
> > > >>> 2010-01-24 05:31:53,148 [main] INFO
> > > >>>
> > > >>>
> >
> >
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> > > >>> - MR plan size after optimization: 1
> > > >>> 2010-01-24 05:31:55,006 [main] INFO
> > > >>>
> > > >>>
> >
> >
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
> > > >>> - Setting up single store job
> > > >>>
> > > >>> Thanks
> > > >>> -Prasen.
> > > >>>
> > > >>
> > > >>
> > > >
> >
> >
>
>
Re: distributing hdfs put
Posted by Amogh Vasekar <am...@yahoo-inc.com>.
Hi,
You should be able to pass this as a cmd line argument using -D ... If you want to change it for all jobs on your own cluster, it would be in mapred-site.
Amogh
On 1/28/10 11:03 AM, "prasenjit mukherjee" <pm...@quattrowireless.com> wrote:
Thanks Amogh for your quick response. Changing this property only on
master's hadoop-site.xml will do or I need to do it on all the slaves as
well ?
Any way I can do this from PIG ( or I guess I am asking too much here :) )
On Thu, Jan 28, 2010 at 10:57 AM, Amogh Vasekar <am...@yahoo-inc.com> wrote:
> Yes, parameter is mapred.task.timeout in mS.
> You can also update status / output to stdout after some time chunks to
> avoid this :)
>
> Amogh
>
>
> On 1/28/10 10:52 AM, "prasenjit mukherjee" <pm...@quattrowireless.com>
> wrote:
>
> Now I see. The tasks are failing with the following error message :
>
> *Task attempt_201001272359_0001_r_000000_0 failed to report status for 600
> seconds. Killing!*
>
> Looks like hadoop kills/restarts jobs which takes more than 600 seconds.
> Is
> there any way I can increase it to some very high number ?
>
> -Thanks,
> Prasenjit
>
>
>
> On Tue, Jan 26, 2010 at 9:55 PM, Dmitriy Ryaboy <dv...@gmail.com>
> wrote:
> >
> > Do you know why the jobs are failing? Take a look at the logs. I
> > suspect it may be due to s3, not hadoop.
> >
> > -D
> >
> > On Tue, Jan 26, 2010 at 7:57 AM, prasenjit mukherjee
> > <pm...@quattrowireless.com> wrote:
> > > Hi Mridul,
> > > Thanks your approach works fine. This is how my current pig script
> > > looks like :
> > >
> > > define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
> > > r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
> > > grp_r1 = GROUP r1 BY filename PARALLEL 5;
> > > r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
> > > r3 = STREAM r2 through CMD;
> > > store r3 INTO '/op/s3fetch_debug_log';
> > >
> > > And here is my s3fetch.py :
> > > for word in sys.stdin:
> > > word=word.rstrip()
> > > str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
> > > s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
> > > sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
> > > (input_str,out_err) = os.popen4(str);
> > > for line in out_err.readlines():
> > > sys.stdout.write('\t'+word+'::\t'+line)
> > >
> > >
> > >
> > > So, the job starts fine and I see that my hadoop directory (
> /ip/data/.)
> > > starts filling up with s3 files. But after sometime it gets stuck. I
> see
> > > lots of failed/restarted jobs in the jobtracker. And the number of
> files
> > > dont increase in /ip/data.
> > >
> > > Could this be happening because of parallel hdfs writes ( via hadoop fs
> -cp
> > > <> <> ) making primary-name-node a blocking server ?
> > >
> > > Any help is greatly appreciated.
> > >
> > > -Thanks,
> > > Prasen
> > >
> > > On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
> > > <mr...@yahoo-inc.com>wrote:
> > >
> > >>
> > >> If each line from your file has to be processed by a different mapper
> -
> > >> other than by writing a custom slicer, a very dirty hack would be to :
> > >> a) create N number of files with one line each.
> > >> b) Or, do something like :
> > >> input_lines = load 'my_s3_list_file' as (location_line:chararray);
> > >> grp_op = GROUP input_lines BY location_line PARALLEL
> $NUM_MAPPERS_REQUIRED;
> > >> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
> > >>
> > >>
> > >> The preferred way, as Dmitriy mentioned, would be to use a custom
> Slicer
> > >> ofcourse !
> > >>
> > >> Regards,
> > >> Mridul
> > >>
> > >>
> > >> prasenjit mukherjee wrote:
> > >>
> > >>> I want to use Pig to paralelize processing on a number of requests.
> There
> > >>> are ~ 300 request which needs to be processed. Each processing
> consist of
> > >>> following :
> > >>> 1. Fetch file from s3 to local
> > >>> 2. Do some preprocessing
> > >>> 3. Put it into hdfs
> > >>>
> > >>> My input is a small file with 300 lines. The problem is that pig
> seems
> to
> > >>> be
> > >>> always creating a single mapper, because of which the load is not
> properly
> > >>> distributed. Any way I can enforce splitting of smaller input files
> as
> > >>> well
> > >>> ? Below is the pig output which tends to indicate that there is only
> 1
> > >>> mapper. Let me know if my understanding is wrong.
> > >>>
> > >>> 2010-01-24 05:31:53,148 [main] INFO
> > >>>
> > >>>
>
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> > >>> - MR plan size before optimization: 1
> > >>> 2010-01-24 05:31:53,148 [main] INFO
> > >>>
> > >>>
>
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> > >>> - MR plan size after optimization: 1
> > >>> 2010-01-24 05:31:55,006 [main] INFO
> > >>>
> > >>>
>
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
> > >>> - Setting up single store job
> > >>>
> > >>> Thanks
> > >>> -Prasen.
> > >>>
> > >>
> > >>
> > >
>
>
Re: distributing hdfs put
Posted by prasenjit mukherjee <pm...@quattrowireless.com>.
Thanks Amogh for your quick response. Changing this property only on
master's hadoop-site.xml will do or I need to do it on all the slaves as
well ?
Any way I can do this from PIG ( or I guess I am asking too much here :) )
On Thu, Jan 28, 2010 at 10:57 AM, Amogh Vasekar <am...@yahoo-inc.com> wrote:
> Yes, parameter is mapred.task.timeout in mS.
> You can also update status / output to stdout after some time chunks to
> avoid this :)
>
> Amogh
>
>
> On 1/28/10 10:52 AM, "prasenjit mukherjee" <pm...@quattrowireless.com>
> wrote:
>
> Now I see. The tasks are failing with the following error message :
>
> *Task attempt_201001272359_0001_r_000000_0 failed to report status for 600
> seconds. Killing!*
>
> Looks like hadoop kills/restarts jobs which takes more than 600 seconds.
> Is
> there any way I can increase it to some very high number ?
>
> -Thanks,
> Prasenjit
>
>
>
> On Tue, Jan 26, 2010 at 9:55 PM, Dmitriy Ryaboy <dv...@gmail.com>
> wrote:
> >
> > Do you know why the jobs are failing? Take a look at the logs. I
> > suspect it may be due to s3, not hadoop.
> >
> > -D
> >
> > On Tue, Jan 26, 2010 at 7:57 AM, prasenjit mukherjee
> > <pm...@quattrowireless.com> wrote:
> > > Hi Mridul,
> > > Thanks your approach works fine. This is how my current pig script
> > > looks like :
> > >
> > > define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
> > > r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
> > > grp_r1 = GROUP r1 BY filename PARALLEL 5;
> > > r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
> > > r3 = STREAM r2 through CMD;
> > > store r3 INTO '/op/s3fetch_debug_log';
> > >
> > > And here is my s3fetch.py :
> > > for word in sys.stdin:
> > > word=word.rstrip()
> > > str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
> > > s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
> > > sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
> > > (input_str,out_err) = os.popen4(str);
> > > for line in out_err.readlines():
> > > sys.stdout.write('\t'+word+'::\t'+line)
> > >
> > >
> > >
> > > So, the job starts fine and I see that my hadoop directory (
> /ip/data/.)
> > > starts filling up with s3 files. But after sometime it gets stuck. I
> see
> > > lots of failed/restarted jobs in the jobtracker. And the number of
> files
> > > dont increase in /ip/data.
> > >
> > > Could this be happening because of parallel hdfs writes ( via hadoop fs
> -cp
> > > <> <> ) making primary-name-node a blocking server ?
> > >
> > > Any help is greatly appreciated.
> > >
> > > -Thanks,
> > > Prasen
> > >
> > > On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
> > > <mr...@yahoo-inc.com>wrote:
> > >
> > >>
> > >> If each line from your file has to be processed by a different mapper
> -
> > >> other than by writing a custom slicer, a very dirty hack would be to :
> > >> a) create N number of files with one line each.
> > >> b) Or, do something like :
> > >> input_lines = load 'my_s3_list_file' as (location_line:chararray);
> > >> grp_op = GROUP input_lines BY location_line PARALLEL
> $NUM_MAPPERS_REQUIRED;
> > >> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
> > >>
> > >>
> > >> The preferred way, as Dmitriy mentioned, would be to use a custom
> Slicer
> > >> ofcourse !
> > >>
> > >> Regards,
> > >> Mridul
> > >>
> > >>
> > >> prasenjit mukherjee wrote:
> > >>
> > >>> I want to use Pig to paralelize processing on a number of requests.
> There
> > >>> are ~ 300 request which needs to be processed. Each processing
> consist of
> > >>> following :
> > >>> 1. Fetch file from s3 to local
> > >>> 2. Do some preprocessing
> > >>> 3. Put it into hdfs
> > >>>
> > >>> My input is a small file with 300 lines. The problem is that pig
> seems
> to
> > >>> be
> > >>> always creating a single mapper, because of which the load is not
> properly
> > >>> distributed. Any way I can enforce splitting of smaller input files
> as
> > >>> well
> > >>> ? Below is the pig output which tends to indicate that there is only
> 1
> > >>> mapper. Let me know if my understanding is wrong.
> > >>>
> > >>> 2010-01-24 05:31:53,148 [main] INFO
> > >>>
> > >>>
>
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> > >>> - MR plan size before optimization: 1
> > >>> 2010-01-24 05:31:53,148 [main] INFO
> > >>>
> > >>>
>
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> > >>> - MR plan size after optimization: 1
> > >>> 2010-01-24 05:31:55,006 [main] INFO
> > >>>
> > >>>
>
> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
> > >>> - Setting up single store job
> > >>>
> > >>> Thanks
> > >>> -Prasen.
> > >>>
> > >>
> > >>
> > >
>
>
Re: distributing hdfs put
Posted by Amogh Vasekar <am...@yahoo-inc.com>.
Yes, parameter is mapred.task.timeout in mS.
You can also update status / output to stdout after some time chunks to avoid this :)
Amogh
On 1/28/10 10:52 AM, "prasenjit mukherjee" <pm...@quattrowireless.com> wrote:
Now I see. The tasks are failing with the following error message :
*Task attempt_201001272359_0001_r_000000_0 failed to report status for 600
seconds. Killing!*
Looks like hadoop kills/restarts jobs which takes more than 600 seconds. Is
there any way I can increase it to some very high number ?
-Thanks,
Prasenjit
On Tue, Jan 26, 2010 at 9:55 PM, Dmitriy Ryaboy <dv...@gmail.com> wrote:
>
> Do you know why the jobs are failing? Take a look at the logs. I
> suspect it may be due to s3, not hadoop.
>
> -D
>
> On Tue, Jan 26, 2010 at 7:57 AM, prasenjit mukherjee
> <pm...@quattrowireless.com> wrote:
> > Hi Mridul,
> > Thanks your approach works fine. This is how my current pig script
> > looks like :
> >
> > define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
> > r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
> > grp_r1 = GROUP r1 BY filename PARALLEL 5;
> > r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
> > r3 = STREAM r2 through CMD;
> > store r3 INTO '/op/s3fetch_debug_log';
> >
> > And here is my s3fetch.py :
> > for word in sys.stdin:
> > word=word.rstrip()
> > str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
> > s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
> > sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
> > (input_str,out_err) = os.popen4(str);
> > for line in out_err.readlines():
> > sys.stdout.write('\t'+word+'::\t'+line)
> >
> >
> >
> > So, the job starts fine and I see that my hadoop directory ( /ip/data/.)
> > starts filling up with s3 files. But after sometime it gets stuck. I see
> > lots of failed/restarted jobs in the jobtracker. And the number of
files
> > dont increase in /ip/data.
> >
> > Could this be happening because of parallel hdfs writes ( via hadoop fs
-cp
> > <> <> ) making primary-name-node a blocking server ?
> >
> > Any help is greatly appreciated.
> >
> > -Thanks,
> > Prasen
> >
> > On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
> > <mr...@yahoo-inc.com>wrote:
> >
> >>
> >> If each line from your file has to be processed by a different mapper -
> >> other than by writing a custom slicer, a very dirty hack would be to :
> >> a) create N number of files with one line each.
> >> b) Or, do something like :
> >> input_lines = load 'my_s3_list_file' as (location_line:chararray);
> >> grp_op = GROUP input_lines BY location_line PARALLEL
$NUM_MAPPERS_REQUIRED;
> >> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
> >>
> >>
> >> The preferred way, as Dmitriy mentioned, would be to use a custom
Slicer
> >> ofcourse !
> >>
> >> Regards,
> >> Mridul
> >>
> >>
> >> prasenjit mukherjee wrote:
> >>
> >>> I want to use Pig to paralelize processing on a number of requests.
There
> >>> are ~ 300 request which needs to be processed. Each processing
consist of
> >>> following :
> >>> 1. Fetch file from s3 to local
> >>> 2. Do some preprocessing
> >>> 3. Put it into hdfs
> >>>
> >>> My input is a small file with 300 lines. The problem is that pig seems
to
> >>> be
> >>> always creating a single mapper, because of which the load is not
properly
> >>> distributed. Any way I can enforce splitting of smaller input files as
> >>> well
> >>> ? Below is the pig output which tends to indicate that there is only 1
> >>> mapper. Let me know if my understanding is wrong.
> >>>
> >>> 2010-01-24 05:31:53,148 [main] INFO
> >>>
> >>>
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> >>> - MR plan size before optimization: 1
> >>> 2010-01-24 05:31:53,148 [main] INFO
> >>>
> >>>
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> >>> - MR plan size after optimization: 1
> >>> 2010-01-24 05:31:55,006 [main] INFO
> >>>
> >>>
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
> >>> - Setting up single store job
> >>>
> >>> Thanks
> >>> -Prasen.
> >>>
> >>
> >>
> >
Re: distributing hdfs put
Posted by prasenjit mukherjee <pm...@quattrowireless.com>.
Now I see. The tasks are failing with the following error message :
*Task attempt_201001272359_0001_r_000000_0 failed to report status for 600
seconds. Killing!*
Looks like hadoop kills/restarts jobs which takes more than 600 seconds. Is
there any way I can increase it to some very high number ?
-Thanks,
Prasenjit
On Tue, Jan 26, 2010 at 9:55 PM, Dmitriy Ryaboy <dv...@gmail.com> wrote:
>
> Do you know why the jobs are failing? Take a look at the logs. I
> suspect it may be due to s3, not hadoop.
>
> -D
>
> On Tue, Jan 26, 2010 at 7:57 AM, prasenjit mukherjee
> <pm...@quattrowireless.com> wrote:
> > Hi Mridul,
> > Thanks your approach works fine. This is how my current pig script
> > looks like :
> >
> > define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
> > r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
> > grp_r1 = GROUP r1 BY filename PARALLEL 5;
> > r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
> > r3 = STREAM r2 through CMD;
> > store r3 INTO '/op/s3fetch_debug_log';
> >
> > And here is my s3fetch.py :
> > for word in sys.stdin:
> > word=word.rstrip()
> > str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
> > s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
> > sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
> > (input_str,out_err) = os.popen4(str);
> > for line in out_err.readlines():
> > sys.stdout.write('\t'+word+'::\t'+line)
> >
> >
> >
> > So, the job starts fine and I see that my hadoop directory ( /ip/data/.)
> > starts filling up with s3 files. But after sometime it gets stuck. I see
> > lots of failed/restarted jobs in the jobtracker. And the number of
files
> > dont increase in /ip/data.
> >
> > Could this be happening because of parallel hdfs writes ( via hadoop fs
-cp
> > <> <> ) making primary-name-node a blocking server ?
> >
> > Any help is greatly appreciated.
> >
> > -Thanks,
> > Prasen
> >
> > On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
> > <mr...@yahoo-inc.com>wrote:
> >
> >>
> >> If each line from your file has to be processed by a different mapper -
> >> other than by writing a custom slicer, a very dirty hack would be to :
> >> a) create N number of files with one line each.
> >> b) Or, do something like :
> >> input_lines = load 'my_s3_list_file' as (location_line:chararray);
> >> grp_op = GROUP input_lines BY location_line PARALLEL
$NUM_MAPPERS_REQUIRED;
> >> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
> >>
> >>
> >> The preferred way, as Dmitriy mentioned, would be to use a custom
Slicer
> >> ofcourse !
> >>
> >> Regards,
> >> Mridul
> >>
> >>
> >> prasenjit mukherjee wrote:
> >>
> >>> I want to use Pig to paralelize processing on a number of requests.
There
> >>> are ~ 300 request which needs to be processed. Each processing
consist of
> >>> following :
> >>> 1. Fetch file from s3 to local
> >>> 2. Do some preprocessing
> >>> 3. Put it into hdfs
> >>>
> >>> My input is a small file with 300 lines. The problem is that pig seems
to
> >>> be
> >>> always creating a single mapper, because of which the load is not
properly
> >>> distributed. Any way I can enforce splitting of smaller input files as
> >>> well
> >>> ? Below is the pig output which tends to indicate that there is only 1
> >>> mapper. Let me know if my understanding is wrong.
> >>>
> >>> 2010-01-24 05:31:53,148 [main] INFO
> >>>
> >>>
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> >>> - MR plan size before optimization: 1
> >>> 2010-01-24 05:31:53,148 [main] INFO
> >>>
> >>>
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
> >>> - MR plan size after optimization: 1
> >>> 2010-01-24 05:31:55,006 [main] INFO
> >>>
> >>>
org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
> >>> - Setting up single store job
> >>>
> >>> Thanks
> >>> -Prasen.
> >>>
> >>
> >>
> >
Re: distributing hdfs put
Posted by Dmitriy Ryaboy <dv...@gmail.com>.
Do you know why the jobs are failing? Take a look at the logs. I
suspect it may be due to s3, not hadoop.
-D
On Tue, Jan 26, 2010 at 7:57 AM, prasenjit mukherjee
<pm...@quattrowireless.com> wrote:
> Hi Mridul,
> Thanks your approach works fine. This is how my current pig script
> looks like :
>
> define CMD `s3fetch.py` SHIP('/root/s3fetch.py');
> r1 = LOAD '/ip/s3fetch_input_files' AS (filename:chararray);
> grp_r1 = GROUP r1 BY filename PARALLEL 5;
> r2 = FOREACH grp_r1 GENERATE FLATTEN(r1);
> r3 = STREAM r2 through CMD;
> store r3 INTO '/op/s3fetch_debug_log';
>
> And here is my s3fetch.py :
> for word in sys.stdin:
> word=word.rstrip()
> str='/usr/local/hadoop-0.20.0/bin/hadoop fs -cp
> s3n://<s3-credentials>@bucket/dir-name/'+word+' /ip/data/.';
> sys.stdout.write('\n\n'+word+ ':\t'+str+'\n')
> (input_str,out_err) = os.popen4(str);
> for line in out_err.readlines():
> sys.stdout.write('\t'+word+'::\t'+line)
>
>
>
> So, the job starts fine and I see that my hadoop directory ( /ip/data/.)
> starts filling up with s3 files. But after sometime it gets stuck. I see
> lots of failed/restarted jobs in the jobtracker. And the number of files
> dont increase in /ip/data.
>
> Could this be happening because of parallel hdfs writes ( via hadoop fs -cp
> <> <> ) making primary-name-node a blocking server ?
>
> Any help is greatly appreciated.
>
> -Thanks,
> Prasen
>
> On Mon, Jan 25, 2010 at 8:58 AM, Mridul Muralidharan
> <mr...@yahoo-inc.com>wrote:
>
>>
>> If each line from your file has to be processed by a different mapper -
>> other than by writing a custom slicer, a very dirty hack would be to :
>> a) create N number of files with one line each.
>> b) Or, do something like :
>> input_lines = load 'my_s3_list_file' as (location_line:chararray);
>> grp_op = GROUP input_lines BY location_line PARALLEL $NUM_MAPPERS_REQUIRED;
>> actual_result = FOREACH grp_op GENERATE MY_S3_UDF(group);
>>
>>
>> The preferred way, as Dmitriy mentioned, would be to use a custom Slicer
>> ofcourse !
>>
>> Regards,
>> Mridul
>>
>>
>> prasenjit mukherjee wrote:
>>
>>> I want to use Pig to paralelize processing on a number of requests. There
>>> are ~ 300 request which needs to be processed. Each processing consist of
>>> following :
>>> 1. Fetch file from s3 to local
>>> 2. Do some preprocessing
>>> 3. Put it into hdfs
>>>
>>> My input is a small file with 300 lines. The problem is that pig seems to
>>> be
>>> always creating a single mapper, because of which the load is not properly
>>> distributed. Any way I can enforce splitting of smaller input files as
>>> well
>>> ? Below is the pig output which tends to indicate that there is only 1
>>> mapper. Let me know if my understanding is wrong.
>>>
>>> 2010-01-24 05:31:53,148 [main] INFO
>>>
>>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
>>> - MR plan size before optimization: 1
>>> 2010-01-24 05:31:53,148 [main] INFO
>>>
>>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.MultiQueryOptimizer
>>> - MR plan size after optimization: 1
>>> 2010-01-24 05:31:55,006 [main] INFO
>>>
>>> org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.JobControlCompiler
>>> - Setting up single store job
>>>
>>> Thanks
>>> -Prasen.
>>>
>>
>>
>