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Sprint’s Official 990M support page.Q:

Why is my PySpark RDD size smaller than what’s printed?

I’m writing a Spark function that concatenates the values of a series of RDDs. My function takes a list of RDDs, and a list of partitions for the RDDs. I’m doing a pretty straightforward transformation where I group all the RDDs together in one RDD, and then join them back together.
I’ve tested my code and it seems to work as intended. My problem is that my RDD size is always the smallest of the RDDs in the list, and I’m not entirely sure why.
For example, if I call my_func(rdd1, rdd2) on two RDDs that are 2x as large as rdd1, I get a smaller RDD than what’s printed in the PySpark Log.
My goal here is to make my code more extensible. I can’t simply say that my_func should take rdd1.getNumPartitions() because then it will break when called on two RDDs that are exactly the same size.
Here’s a simplified example that shows the issue:
import pyspark.sql.functions as F

rdd1 = sc.parallelize([1, 2, 3, 4, 5, 6, 7, 8])
rdd2 = sc.parallelize([1, 2, 3, 4, 5, 6, 7, 8])

my_func(rdd1, rdd2)
print(rdd1.collect())

I expect the output to be [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8], but it actually is [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8].
I’ve also noticed that I can write the RDDs to a single dataframe, and the dataframe’s size is correct. I’m guessing that the RDDs are actually being garbage collected after they’re written. Is that correct?

A:

You should probably always provide the list of partitions to your my_

 

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