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From Gourav Sengupta <gourav.sengu...@gmail.com>
Subject Re: SparkSQL with large result size
Date Mon, 02 May 2016 18:02:55 GMT
Hi,

I have worked on 300GB data by querying it  from CSV (using SPARK CSV)  and
writing it to Parquet format and then querying parquet format to query it
and partition the data and write out individual csv files without any
issues on a single node SPARK cluster installation.

Are you trying to cache in the entire data? What is that you are trying to
achieve in your used case?

Regards,
Gourav

On Mon, May 2, 2016 at 5:59 PM, Ted Yu <yuzhihong@gmail.com> wrote:

> That's my interpretation.
>
> On Mon, May 2, 2016 at 9:45 AM, Buntu Dev <buntudev@gmail.com> wrote:
>
>> Thanks Ted, I thought the avg. block size was already low and less than
>> the usual 128mb. If I need to reduce it further via parquet.block.size, it
>> would mean an increase in the number of blocks and that should increase the
>> number of tasks/executors. Is that the correct way to interpret this?
>>
>> On Mon, May 2, 2016 at 6:21 AM, Ted Yu <yuzhihong@gmail.com> wrote:
>>
>>> Please consider decreasing block size.
>>>
>>> Thanks
>>>
>>> > On May 1, 2016, at 9:19 PM, Buntu Dev <buntudev@gmail.com> wrote:
>>> >
>>> > I got a 10g limitation on the executors and operating on parquet
>>> dataset with block size 70M with 200 blocks. I keep hitting the memory
>>> limits when doing a 'select * from t1 order by c1 limit 1000000' (ie, 1M).
>>> It works if I limit to say 100k. What are the options to save a large
>>> dataset without running into memory issues?
>>> >
>>> > Thanks!
>>>
>>
>>
>

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