Github user JoshRosen commented on the pull request:
https://github.com/apache/spark/pull/2740#issuecomment-58717116
I tried a small experiment to test this out:
```python
import os
from pyspark import SparkContext, SparkConf
conf = SparkConf().set("spark.executor.memory", "2g")
sc = SparkContext(conf=conf)
mb = 1000000
def inflateDataSize(x):
return bytearray(os.urandom(1 * mb))
sc.parallelize(range(1000), 10).map(inflateDataSize).cache().count()
```
Prior to this patch, the Python worker's memory consumption would steadily
grow while it attempted to batch together 100 MB of data per task, whereas now
the memory usage remains constant because we emit smaller batches more often
(since the objects are big).
Thanks for updating the docs. This looks good to me, so I'm going to merge
it into master.
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