Hello Guys, No help yet. Can someone tell me with a reply to the above question in SO ?
Thanks Deepak On Fri, Mar 4, 2016 at 5:32 PM, Deepak Gopalakrishnan <dgk...@gmail.com> wrote: > 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 <dgk...@gmail.com> > wrote: > >> Hello, >> >> I'm using 1.6.0 on EMR >> >> On Thu, Mar 3, 2016 at 12:34 AM, Yong Zhang <java8...@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: dgk...@gmail.com >>> To: mich...@databricks.com >>> CC: u...@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 <mich...@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 <dgk...@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 >>> >>> >>> --------------------------------------------------------------------- To >>> unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional >>> commands, e-mail: user-h...@spark.apache.org >>> >> >> >> >> -- >> Regards, >> *Deepak Gopalakrishnan* >> *Mobile*:+918891509774 >> *Skype* : deepakgk87 >> http://myexps.blogspot.com >> >> > > > -- > Regards, > *Deepak Gopalakrishnan* > *Mobile*:+918891509774 > *Skype* : deepakgk87 > http://myexps.blogspot.com > > -- Regards, *Deepak Gopalakrishnan* *Mobile*:+918891509774 *Skype* : deepakgk87 http://myexps.blogspot.com
