A particular query that I run fails with the following error:

***
Job 18: Map: 2  Reduce: 1   Cumulative CPU: 3.67 sec   HDFS Read: 0 HDFS
Write: 0 SUCCESS
Exception in thread "main"
org.apache.hadoop.mapreduce.counters.LimitExceededException: Too many
counters: 121 max=120
 ...
***

Googling suggests that I should increase "mapreduce.job.counters.limit".
And that the number of counters a job uses
has an effect on the memory used by the JobTracker, so I shouldn't increase
this number too high.

Is there a rule of thumb for what this number should be as a function of
JobTracker memory? That is should I be cautious and
increase by 5 at a time, or could I just double it?

Cheers,

Krishna

Hi,

These happens when operators are used in queries (Hive Operators). Hive creates 4 counters per operator, max upto 1000, plus a few additional counters like file read/write, partitions and tables. Hence the number of counter required is going to be dependent upon the query.

Using "EXPLAIN EXTENDED" and "grep -ri operators | wc -l" print out the used numbers of operators. Use this value to tweak the MR settings carefully.

Praveen has a good explanation 'bout counters online:
http://www.thecloudavenue.com/2011/12/limiting-usage-counters-in-hadoop.html

Rule of thumb for Hive:
count of operators * 4 + n (n for file ops and other stuff).

cheers,
 Alex
On Jan 2, 2013, at 10:35 AM, Krishna Rao <[EMAIL PROTECTED]> wrote:

> A particular query that I run fails with the following error:
>
> ***
> Job 18: Map: 2  Reduce: 1   Cumulative CPU: 3.67 sec   HDFS Read: 0 HDFS
> Write: 0 SUCCESS
> Exception in thread "main"
> org.apache.hadoop.mapreduce.counters.LimitExceededException: Too many
> counters: 121 max=120
> ...
> ***
>
> Googling suggests that I should increase "mapreduce.job.counters.limit".
> And that the number of counters a job uses
> has an effect on the memory used by the JobTracker, so I shouldn't increase
> this number too high.
>
> Is there a rule of thumb for what this number should be as a function of
> JobTracker memory? That is should I be cautious and
> increase by 5 at a time, or could I just double it?
>
> Cheers,
>
> Krishna

--
Alexander Alten-Lorenz
http://mapredit.blogspot.com
German Hadoop LinkedIn Group: http://goo.gl/N8pCF

I ended up increasing the counters limit to 130 which solved my issue.

Do you know of any good sources to learn how to decipher hive's EXPLAIN?

Cheers,

Krishna
On 2 January 2013 11:20, Alexander Alten-Lorenz <[EMAIL PROTECTED]> wrote:

> Hi,
>
> These happens when operators are used in queries (Hive Operators). Hive
> creates 4 counters per operator, max upto 1000, plus a few additional
> counters like file read/write, partitions and tables. Hence the number of
> counter required is going to be dependent upon the query.
>
> Using "EXPLAIN EXTENDED" and "grep -ri operators | wc -l" print out the
> used numbers of operators. Use this value to tweak the MR settings
> carefully.
>
> Praveen has a good explanation 'bout counters online:
>
> http://www.thecloudavenue.com/2011/12/limiting-usage-counters-in-hadoop.html
>
> Rule of thumb for Hive:
> count of operators * 4 + n (n for file ops and other stuff).
>
> cheers,
>  Alex
>
>
> On Jan 2, 2013, at 10:35 AM, Krishna Rao <[EMAIL PROTECTED]> wrote:
>
> > A particular query that I run fails with the following error:
> >
> > ***
> > Job 18: Map: 2  Reduce: 1   Cumulative CPU: 3.67 sec   HDFS Read: 0 HDFS
> > Write: 0 SUCCESS
> > Exception in thread "main"
> > org.apache.hadoop.mapreduce.counters.LimitExceededException: Too many
> > counters: 121 max=120
> > ...
> > ***
> >
> > Googling suggests that I should increase "mapreduce.job.counters.limit".
> > And that the number of counters a job uses
> > has an effect on the memory used by the JobTracker, so I shouldn't
> increase
> > this number too high.
> >
> > Is there a rule of thumb for what this number should be as a function of
> > JobTracker memory? That is should I be cautious and
> > increase by 5 at a time, or could I just double it?
> >
> > Cheers,
> >
> > Krishna
>
> --
> Alexander Alten-Lorenz
> http://mapredit.blogspot.com
> German Hadoop LinkedIn Group: http://goo.gl/N8pCF
>
>