Apache Spark Performance Tuning – Degree of Parallelism - spark 性能调优之partition
一般说来，spark并行任务量取决于分区数目。
Spark Partition Principles
The general principles to be followed when tuning partition for Spark application are as follows:
- Too few partitions – Cannot utilize all cores available in the cluster.
- Too many partitions – Excessive overhead in managing many small tasks.
- Reasonable partitions – Helps us to utilize the cores available in the cluster and avoids excessive overhead in managing small tasks
cited by https://dzone.com/articles/apache-spark-performance-tuning-degree-of-parallel
Understanding Spark Data Partitionsi
在Spark配置中有两个参数是用来设spartition的:spark.default.parallelism , spark.sql.shuffle.partitions
On considering the event timeline to understand those 200 shuffled partition tasks, there are tasks with more scheduler delay and less computation time. It indicates that 200 tasks are not necessary here and can be tuned to decrease the shuffle partition to reduce scheduler burden.
On considering the event timeline to understand those 200 shuffled partition tasks, there are tasks with more scheduler delay and less computation time. It indicates that 200 tasks are not necessary here and can be tuned to decrease the shuffle partition to reduce scheduler burde
The Stages view in Spark UI indicates that most of the tasks are simply launched and terminated without any computation, as shown in the below diagram:
Spark Partition Tuning
Let us first decide the number of partitions based on the input dataset size. The rule of thumb to decide the partition size while working with HDFS is 128 MB.
As our input dataset size is about 1.5 GB (1500 MB) and going with 128 MB per partition, the number of partitions will be:
Total input dataset size / partition size => 1500 / 128 = 11.71 = ~12 partitions.
This is equal to the Spark default parallelism (spark.default.parallelism) value. The metrics based on default parallelism are shown in the above section.
Now, let us perform a test by reducing the partition size and increasing the number of partitions.
Consider partition size as 64 MB.
Number of partitions = Total input dataset size / partition size => 1500 / 64 = 23.43 = ~23 partitions.