Affects Version/s: None
Fix Version/s: None
Component/s: Spark Core
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.