The point is that sequentially the GC gets to remove stale entries so
simplistically only 3000 records are in memory at any one time, in
parallel processing all 90000 can be in memory at the same time.

Sent from my iPad

On 6 Aug 2011, at 21:34, Shoeb Bhinderwala <shoeb.bhinderw...@gmail.com> wrote:

> You didn't understand my problem. The exact same code throws out of
> memory when I change map to pmap.
>
> My monthly data is evenly divided into 30 sets. For e.g total monthly
> data = 90000 records, daily data size for each day = 3000 records. I
> am trying to achieve performance gain by processing the daily data in
> parallel.
>
> On Aug 6, 2:18 pm, Sunil S Nandihalli <sunil.nandiha...@gmail.com>
> wrote:
>> Just  a guess. If your daily data is huge you will be loading the data for
>> only one day when using map and you will be loading the data for multiple
>> days (equal to number of parallel threads) .. and may be this is the cause
>> of the problem.
>> Sunil.
>>
>> On Sat, Aug 6, 2011 at 11:40 PM, Shoeb Bhinderwala <
>>
>>
>>
>>
>>
>>
>>
>> shoeb.bhinderw...@gmail.com> wrote:
>>> Problem summary: I am running out of memory using pmap but the same code
>>> works with regular map function.
>>
>>> My problem is that I am trying to break my data into sets and process them
>>> in parallel. My data is for an entire month and I am breaking it into 30/31
>>> sets - one for each day. I run a function for each daily set of data using
>>> pmap, something like:
>>
>>> (defn process-monthly-data
>>>   [grp-id month year]
>>>   (doall (pmap
>>>     #(process-daily-data grp-id % month year)
>>>     (range 31)))
>>
>>> (defn process-daily-data
>>>   [grp-id day month year]
>>>   (
>>>      ;load and process daily data …
>>>   ))
>>
>>> When I run my function using regular map it works fine, but when I change
>>> it to pmap I get an OutOfMemoryException.
>>
>>> What am I doing wrong?
>>
>>> -- Shoeb
>>
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