Now that we have seen some of the functionality, let's explore further. We can use a similar script to count the word occurrences in a file, as follows:

import pyspark
if not 'sc' in globals():
    sc = pyspark.SparkContext()
text_file = sc.textFile("Spark File Words.ipynb")
counts = text_file.flatMap(lambda line: line.split(" ")) \
             .map(lambda word: (word, 1)) \
             .reduceByKey(lambda a, b: a + b)
for x in counts.collect():
    print x

We have the same preamble to the coding. Then we load the text file into memory.

Once the file is loaded, we split each line into words. Use a lambda function to tick off each occurrence of a word. The code is truly creating a new record for each word occurrence. If a word appears in the stream, a record with the count of 1 is added for that word and for every other instance the word appears, new records with the same count of 1 are added. The idea is that this process could be split over multiple processors, where each...