John Engelhart John Engelhart - 7 months ago 143
Scala Question

Apache Spark Handling Skewed Data

I have two tables I would like to join together. One of them has a very bad skew of data. This is causing my spark job to not run in parallel as a majority of the work is done on one partition.

I have heard and read and tried to implement salting my keys to increase the distribution. at 12:45 seconds is exactly what I would like to do.

Any help or tips would be appreciated. Thanks!


Yes you should use salted keys on the larger table (via randomization) and then replicate the smaller one / cartesian join it to the new salted one:

Here are a couple of suggestions:

Tresata skew join RDD

python skew join:

The tresata library looks like this:

import com.tresata.spark.skewjoin.Dsl._  // for the implicits   

// skewjoin() method pulled in by the implicits
rdd1.skewJoin(rdd2, defaultPartitioner(rdd1, rdd2),