You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Kostas Sakellis (JIRA)" <ji...@apache.org> on 2014/11/26 23:11:12 UTC
[jira] [Created] (SPARK-4630) Dynamically determine optimal number
of partitions
Kostas Sakellis created SPARK-4630:
--------------------------------------
Summary: Dynamically determine optimal number of partitions
Key: SPARK-4630
URL: https://issues.apache.org/jira/browse/SPARK-4630
Project: Spark
Issue Type: Improvement
Components: Spark Core
Reporter: Kostas Sakellis
Partition sizes play a big part in how fast stages execute during a Spark job. There is a direct relationship between the size of partitions to the number of tasks - larger partitions, fewer tasks. For better performance, Spark has a sweet spot for how large partitions should be that get executed by a task. If partitions are too small, then the user pays a disproportionate cost in scheduling overhead. If the partitions are too large, then task execution slows down due to gc pressure and spilling to disk.
To increase performance of jobs, users often hand optimize the number(size) of partitions that the next stage gets. Factors that come into play are:
Incoming partition sizes from previous stage
number of available executors
available memory per executor (taking into account spark.shuffle.memoryFraction)
Spark has access to this data and so should be able to automatically do the partition sizing for the user. This feature can be turned off/on with a configuration option.
To make this happen, we propose modifying the DAGScheduler to take into account partition sizes upon stage completion. Before scheduling the next stage, the scheduler can examine the sizes of the partitions and determine the appropriate number tasks to create. Since this change requires non-trivial modifications to the DAGScheduler, a detailed design doc will be attached before proceeding with the work.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org