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