Hello,
For the need of my application, I need to periodically "shuffle" the data
across nodes/partitions of a reasonably-large dataset. This is an expensive
operation but I only need to do it every now and then. However it seems that
I am doing something wrong because as the iterations go the memory usage
increases, causing the job to spill onto HDFS, which eventually gets full. I
am also getting some "Lost executor" errors that I don't get if I don't
repartition.
Here's a basic piece of code which reproduces the problem:
data = sc.textFile("ImageNet_gist_train.txt",50).map(parseLine).cache()
data.count()
for i in range(1000):
data=data.repartition(50).persist()
# below several operations are done on data
What am I doing wrong? I tried the following but it doesn't solve the issue:
for i in range(1000):
data2=data.repartition(50).persist()
data2.count() # materialize rdd
data.unpersist() # unpersist previous version
data=data2
Help and suggestions on this would be greatly appreciated! Thanks a lot!
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