What should be the optimal value for spark.sql.shuffle.partitions or how do we increase partitions when using Spark SQL?

Question

I am using Spark SQL actually hiveContext.sql() which uses group by queries and I am running into OOM issues. So thinking of increasing value of spark.sql.shuffle.partitions from 200 default to 1000 but it is not helping.

I believe this partition will share data shuffle load so more the partitions less data to hold. I am new to Spark. I am using Spark 1.4.0 and I have around 1TB of uncompressed data to process using hiveContext.sql() group by queries.

Answer 1

If you're running out of memory on the shuffle, try setting spark.sql.shuffle.partitions to 2001.

Spark uses a different data structure for shuffle book-keeping when the number of partitions is greater than 2000:

private[spark] object MapStatus {

  def apply(loc: BlockManagerId, uncompressedSizes: Array[Long]): MapStatus = {
    if (uncompressedSizes.length > 2000) {
      HighlyCompressedMapStatus(loc, uncompressedSizes)
    } else {
      new CompressedMapStatus(loc, uncompressedSizes)
    }
  }
...

I really wish they would let you configure this independently.

By the way, I found this information in a Cloudera slide deck.