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From Deepak Gopalakrishnan <dgk...@gmail.com>
Subject Re: Mapper side join with DataFrames API
Date Fri, 04 Mar 2016 12:02:42 GMT
Have added this to SO, can you guys share any thoughts ?

http://stackoverflow.com/questions/35795518/spark-1-6-spills-to-disk-even-when-there-is-enough-memory
<http://www.google.com/url?q=http%3A%2F%2Fstackoverflow.com%2Fquestions%2F35795518%2Fspark-1-6-spills-to-disk-even-when-there-is-enough-memory&sa=D&sntz=1&usg=AFQjCNEzDJqylz5aF0998u08RGlf5YF1-g>

On Thu, Mar 3, 2016 at 7:06 AM, Deepak Gopalakrishnan <dgkris@gmail.com>
wrote:

> Hello,
>
> I'm using 1.6.0 on EMR
>
> On Thu, Mar 3, 2016 at 12:34 AM, Yong Zhang <java8964@hotmail.com> wrote:
>
>> What version of Spark you are using?
>>
>> I am also trying to figure out how to do the map side join in Spark.
>>
>> In 1.5.x, there is a broadcast function in the Dataframe, and it caused
>> OOM for me simple test case, even one side of join is very small.
>>
>> I am still trying to find out the root cause yet.
>>
>> Yong
>>
>> ------------------------------
>> Date: Wed, 2 Mar 2016 15:38:29 +0530
>> Subject: Re: Mapper side join with DataFrames API
>> From: dgkris@gmail.com
>> To: michael@databricks.com
>> CC: user@spark.apache.org
>>
>>
>> Thanks for the help guys.
>>
>> Just to ask a part of my question in a little different way.
>>
>> I have attached my screenshots here. There is so much of memory that is
>> unused and yet there is a spill ( as in screenshots). Any idea why ?
>>
>> Thanks
>> Deepak
>>
>> On Wed, Mar 2, 2016 at 5:14 AM, Michael Armbrust <michael@databricks.com>
>> wrote:
>>
>> Its helpful to always include the output of df.explain(true) when you
>> are asking about performance.
>>
>> On Mon, Feb 29, 2016 at 6:14 PM, Deepak Gopalakrishnan <dgkris@gmail.com>
>> wrote:
>>
>> Hello All,
>>
>> I'm trying to join 2 dataframes A and B with a
>>
>> sqlContext.sql("SELECT * FROM A INNER JOIN B ON A.a=B.a");
>>
>> Now what I have done is that I have registeredTempTables for A and B
>> after loading these DataFrames from different sources. I need the join to
>> be really fast and I was wondering if there is a way to use the SQL
>> statement and then being able to do a mapper side join ( say my table B is
>> small) ?
>>
>> I read some articles on using broadcast to do mapper side joins. Could I
>> do something like this and then execute my sql statement to achieve mapper
>> side join ?
>>
>> DataFrame B = sparkContext.broadcast(B);
>> B.registerTempTable("B");
>>
>>
>> I have a join as stated above and I see in my executor logs the below :
>>
>> 16/02/29 17:02:35 INFO TaskSetManager: Finished task 198.0 in stage 7.0
>> (TID 1114) in 20354 ms on localhost (196/200)
>> 16/02/29 17:02:35 INFO ShuffleBlockFetcherIterator: Getting 200 non-empty
>> blocks out of 200 blocks
>> 16/02/29 17:02:35 INFO ShuffleBlockFetcherIterator: Started 0 remote
>> fetches in 0 ms
>> 16/02/29 17:02:35 INFO ShuffleBlockFetcherIterator: Getting 1 non-empty
>> blocks out of 128 blocks
>> 16/02/29 17:02:35 INFO ShuffleBlockFetcherIterator: Started 0 remote
>> fetches in 0 ms
>> 16/02/29 17:03:03 INFO Executor: Finished task 199.0 in stage 7.0 (TID
>> 1115). 2511 bytes result sent to driver
>> 16/02/29 17:03:03 INFO TaskSetManager: Finished task 199.0 in stage 7.0
>> (TID 1115) in 27621 ms on localhost (197/200)
>>
>> *16/02/29 17:07:06 INFO UnsafeExternalSorter: Thread 124 spilling sort
>> data of 256.0 KB to disk (0  time so far)*
>>
>>
>> Now, I have around 10G of executor memory and my memory faction should be
>> the default ( 0.75 as per the documentation) and my memory usage is < 1.5G(
>> obtained from the Storage tab on Spark dashboard), but still it says
>> spilling sort data. I'm a little surprised why this happens even when I
>> have enough memory free.
>> Any inputs will be greatly appreciated!
>> Thanks
>> --
>> Regards,
>> *Deepak Gopalakrishnan*
>> *Mobile*:+918891509774
>> *Skype* : deepakgk87
>> http://myexps.blogspot.com
>>
>>
>>
>>
>>
>> --
>> Regards,
>> *Deepak Gopalakrishnan*
>> *Mobile*:+918891509774
>> *Skype* : deepakgk87
>> http://myexps.blogspot.com
>>
>>
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>>
>
>
>
> --
> Regards,
> *Deepak Gopalakrishnan*
> *Mobile*:+918891509774
> *Skype* : deepakgk87
> http://myexps.blogspot.com
>
>


-- 
Regards,
*Deepak Gopalakrishnan*
*Mobile*:+918891509774
*Skype* : deepakgk87
http://myexps.blogspot.com

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