spark通过合理设置spark.default.parallelism参数提高执行效率

spark中有partition的概念（和slice是同一个概念，在spark1.2中官网已经做出了说明），一般每个partition对应一个task。在我的测试过程中，如果没有设置spark.default.parallelism参数，spark计算出来的partition非常巨大，与我的cores非常不搭。我在两台机器上（8cores *2 +6g * 2）上，spark计算出来的partition达到2.8万个，也就是2.9万个tasks，每个task完成时间都是几毫秒或者零点几毫秒，执行起来非常缓慢。在我尝试设置了
spark.default.parallelism 后，任务数减少到10，执行一次计算过程从minute降到20second。

参数可以通过spark_home/conf/spark-default.conf配置文件设置。

eg.

spark.master                       spark://master:7077
spark.default.parallelism          10
spark.driver.memory                2g
spark.serializer                   org.apache.spark.serializer.KryoSerializer
spark.sql.shuffle.partitions       50

 

下面是官网的相关描述：

 

from:http://spark.apache.org/docs/latest/configuration.html

Property Name	Default	Meaning
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.

from:http://spark.apache.org/docs/latest/tuning.html

Level of Parallelism

Clusters will not be fully utilized unless you set the level of parallelism for each operation high enough. Spark automatically sets the number of “map” tasks to run on each file according to its size (though you can control it through optional parameters to 

SparkContext.textFile

,
etc), and for distributed “reduce” operations, such as 

groupByKey

 and 

reduceByKey

, it uses the largest parent RDD’s number of partitions. You can pass the level of parallelism
as a second argument (see the 

spark.PairRDDFunctions

 documentation),
or set the config property

spark.default.parallelism

 to change the default. In general, we recommend 2-3 tasks per CPU core in your cluster.