performance - What is the difference between spark.sql.shuffle.partitions and spark.default.parallelism?

Question

What's the difference between spark.sql.shuffle.partitions and spark.default.parallelism?

I have tried to set both of them in SparkSQL, but the task number of the second stage is always 200.

Answer 1

From the answer here, spark.sql.shuffle.partitions configures the number of partitions that are used when shuffling data for joins or aggregations.

spark.default.parallelism is the default number of partitions in RDDs returned by transformations like join, reduceByKey, and parallelize when not set explicitly by the user. Note that spark.default.parallelism seems to only be working for raw RDD and is ignored when working with dataframes.

If the task you are performing is not a join or aggregation and you are working with dataframes then setting these will not have any effect. You could, however, set the number of partitions yourself by calling df.repartition(numOfPartitions) (don't forget to assign it to a new val) in your code.

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To change the settings in your code you can simply do:

sqlContext.setConf("spark.sql.shuffle.partitions", "300")
sqlContext.setConf("spark.default.parallelism", "300")

Alternatively, you can make the change when submitting the job to a cluster with spark-submit:

./bin/spark-submit --conf spark.sql.shuffle.partitions=300 --conf spark.default.parallelism=300