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Pig, mail # user - How can I split the data with more reducers?

Haitao Yao 2012-09-16, 02:08
Haitao Yao 2012-09-16, 02:50
Dmitriy Ryaboy 2012-09-16, 08:41
Haitao Yao 2012-09-16, 09:05
Haitao Yao 2012-09-16, 09:18
Dmitriy Ryaboy 2012-09-17, 05:01
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Re: How can I split the data with more reducers?
Haitao Yao 2012-09-17, 08:53
Thank you very much for the reply. I've checked the latest heapdump again,and you're right: the OOME is not caused by split, but DefaultDataBag. Sorry for the misleading, I've got too many heap dumps and I ' confused.

Here's  the latest screen shot of the heap dump:

There's a lot of DefaultDataBag.

I've tried your suggestion: set pig.exec.mapPartAgg true; set pig.exec.mapPartAgg.minReduction 3; But the reducer count is still 1.
The pig version I'm using is 0.9.2.

BTW, is "the latest trunk" stable enough for production deployment? If not, does 0.10.0 provide this optimization rule? When will  0.11 release?

Thank you again.

Haitao Yao
weibo: @haitao_yao
Skype:  haitao.yao.final

On 2012-9-17, at 下午1:01, Dmitriy Ryaboy wrote:

> Ok, then it's not POSplit that's holding the memory -- it does not
> participate in any of the reduce stages, according the the plan you
> attached.
> To set parallelism, you can hardcode it on every operation that causes
> an MR boundary, with the exception of "group all"  and "limit" since
> those by definition require a single reducer. So, you can alter your
> script to explicitly request parallelism to be greater than what is
> estimated: "join .. parallel $P", "group by .. parallel $P", "order
> ... parallel $P", etc.
> I would recommend two things:
> 1) Make sure you are running the latest trunk, and have enabled
> in-memory aggregation ( set pig.exec.mapPartAgg true; set
> pig.exec.mapPartAgg.minReduction 3 ). I just made some significant
> improvements to Distinct's Initial phase (not requiring it to register
> with SpillableMemoryManager at all), and also improved in-mem
> aggregation performance.
> 2) It seems like you are doing a lot of "group, distinct the group,
> count" type operations. If you do have a distinct group that is very
> large, loading it all into ram is bound to cause problems. When the
> size of distinct sets is expected to be fairly high, we usually
> recommend a different pattern for count(distinct x):
> Instead of :
> results = foreach (group data by country) {
>  distinct_ids = distinct data.id;
>  generate group as country, COUNT(distinct_ids) as num_dist,
> COUNT(data) as total;
> }
> Do the following:
> results_per_id = foreach (group data by (country, id))
>  generate flatten(group) as (country, id), COUNT(data) as num_repeats;
> results = foreach (group results_per_id by country)
>  generate group as country, COUNT(results_per_id) as num_dist,
> SUM(results_per_id.num_repeats) as total;
> This will introduce an extra MR step, but it's much more scalable when
> you get into millions of distincts in a single dimension.
> D
> On Sun, Sep 16, 2012 at 2:18 AM, Haitao Yao <[EMAIL PROTECTED]> wrote:
>> The map output of the first MR job is over 500MB, and only 1 reducer processes it. So OutOfMemoryError is caused.
>> After set the child memory to 1GB, the first job succeeded. But most of our jobs does not need that much memory. 512MB is enough if I can set the reducer to more than 1.
>> Haitao Yao
>> weibo: @haitao_yao
>> Skype:  haitao.yao.final
>> On 2012-9-16, at 下午5:05, Haitao Yao wrote:
>>> here's the explain result compressed.(The apache mail server does not allow big attachments.)
>>> <explain.tar.gz>
>>> Haitao Yao
>>> weibo: @haitao_yao
>>> Skype:  haitao.yao.final
>>> On 2012-9-16, at 下午4:41, Dmitriy Ryaboy wrote:
>>>> Still would like to see the script or the explain plan..
>>>> D
>>>> On Sat, Sep 15, 2012 at 7:50 PM, Haitao Yao <[EMAIL PROTECTED]> wrote:
>>>>> No, I also thought it is a mapper , but It surely is a reducer. all the mappers succeeded and the reducer failed.
>>>>> Haitao Yao
>>>>> weibo: @haitao_yao
>>>>> Skype:  haitao.yao.final
>>>>> On 2012-9-16, at 上午10:08, Haitao Yao wrote:
>>>>>> Hi,
>>>>>>     I 'v encountered a problem: the job failed because of POSplit retained too much memory in the reducer. How can I specify more reducers for the spill?
Dmitriy Ryaboy 2012-09-17, 09:07
Haitao Yao 2012-09-17, 09:26
Dmitriy Ryaboy 2012-09-16, 02:39