Spark fastest way for creating RDD of numpy arrays

Question

My spark application is using RDD's of numpy arrays.
At the moment, I'm reading my data from AWS S3, and its represented as a simple text file where each line is a vector and each element is seperated by space, for example:

1 2 3
5.1 3.6 2.1
3 0.24 1.333

I'm using numpy's function loadtxt() in order to create a numpy array from it.
However, this method seems to be very slow and my app is spending too much time(I think) for converting my dataset to a numpy array.

Can you suggest me a better way for doing it? For example, should I keep my dataset as a binary file?, should I create the RDD in another way?

Some code for how I create my RDD:

data = sc.textFile("s3_url", initial_num_of_partitions).mapPartitions(readData)

readData function:

 def readPointBatch(iterator):
     return [(np.loadtxt(iterator,dtype=np.float64)]

Answer 1

It would be a little bit more idiomatic and slightly faster to simply map with numpy.fromstring as follows:

import numpy as np.

path = ...
initial_num_of_partitions = ...

data = (sc.textFile(path, initial_num_of_partitions)
   .map(lambda s: np.fromstring(s, dtype=np.float64, sep=" ")))

but ignoring that there is nothing particularly wrong with your approach. As far as I can tell, with basic configuration, it is roughly twice a slow a simply reading the data and slightly slower than creating dummy numpy arrays.

So it looks like the problem is somewhere else. It could be cluster misconfiguration, cost of fetching data from S3 or even unrealistic expectations.