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| author | Josh Rosen <joshrosen@databricks.com> | 2015-05-20 16:37:11 -0700 |
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| committer | Josh Rosen <joshrosen@databricks.com> | 2015-05-20 16:37:11 -0700 |
| commit | 7956dd7ab03e1542d89dd94c043f1e5131684199 (patch) | |
| tree | a753324eb6f10972f914ad5fbab29d97b88c8e26 /python | |
| parent | 3c434cbfd0d6821e5bcf572be792b787a514018b (diff) | |
| download | spark-7956dd7ab03e1542d89dd94c043f1e5131684199.tar.gz spark-7956dd7ab03e1542d89dd94c043f1e5131684199.tar.bz2 spark-7956dd7ab03e1542d89dd94c043f1e5131684199.zip | |
[SPARK-7698] Cache and reuse buffers in ExecutorMemoryAllocator when using heap allocation
When on-heap memory allocation is used, ExecutorMemoryManager should maintain a cache / pool of buffers for re-use by tasks. This will significantly improve the performance of the new Tungsten's sort-shuffle for jobs with many short-lived tasks by eliminating a major source of GC.
This pull request is a minimum-viable-implementation of this idea. In its current form, this patch significantly improves performance on a stress test which launches huge numbers of short-lived shuffle map tasks back-to-back in the same JVM.
Author: Josh Rosen <joshrosen@databricks.com>
Closes #6227 from JoshRosen/SPARK-7698 and squashes the following commits:
fd6cb55 [Josh Rosen] SoftReference -> WeakReference
b154e86 [Josh Rosen] WIP sketch of pooling in ExecutorMemoryManager
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