spark.sql.shuffle.partitions 和 spark.default.parallelism 的区别

原文链接： http://www.cnblogs.com/lestatzhang/p/10611324.html

 

在关于spark任务并行度的设置中，有两个参数我们会经常遇到，spark.sql.shuffle.partitions 和 spark.default.parallelism, 那么这两个参数到底有什么区别的？

首先，让我们来看下它们的定义

Property Name	Default	Meaning
spark.sql.shuffle.partitions	200	Configures the number of partitions to use when shuffling data for joins or aggregations.
spark.default.parallelism	For distributed shuffle operations like reduceByKey and join, the largest number of partitions in a parent RDD. For operations like parallelize with no parent RDDs, it depends on the cluster manager: - Local mode: number of cores on the local machine - Mesos fine grained mode: 8 - Others: total number of cores on all executor nodes or 2, whichever is larger	Default number of partitions in RDDs returned by transformations like join, reduceByKey, and parallelize when not set by user.

看起来它们的定义似乎也很相似，但在实际测试中，

• spark.default.parallelism只有在处理RDD时才会起作用，对Spark SQL的无效。
• spark.sql.shuffle.partitions则是对sparks SQL专用的设置

 

转载于:https://www.cnblogs.com/lestatzhang/p/10611324.html