# Part 2: Parallel map/reduce¶

The concurrent.futures.ProcessPoolExecutor provides an excellent mechanism for the parallelisation of map/reduce style calculations. The standard map can be almost directly replaced with a ProcessPoolExecutor.map and the reduce function can be used as-is. Note than the reduction will not be parallelised as it is, in general, a serial operation.

As we have seen though, some changes are needed such as putting the code in an if __name__ == "__main__" block. The largest remaining difference is how ProcessPoolExecutor treats lambda functions.

## ProcessPoolExecutor doesn’t support lambda functions¶

One of the annoying limitations of the current version of multiprocessing (the underlying module for ProcessPoolExecutor) is that it does not support anonymous (lambda) functions. The mapping function has to be created using the def name(args) syntax. The reason is because Python currently doesn’t pickle functions correctly (i.e. Python cannot convert the code of a function to a binary array of data that can be transmitted to the worker copies of the script. In contrast, Python can correctly pickle most argument types, so can send arguments to the workers).

### Exercise 1¶

Edit your countlines.py script that you wrote for Part 1 so that you use concurrent.futures to parallelise the counting of lines. Note that you will not be able to use lambda in the pool.map function.

If you get stuck or want some inspiration, a possible answer is given here.

### Exercise 2¶

Below are two functions. The first counts the number of times every word in a file appears in that file, returning the result as a dictionary (the key is the word, the value is the number of times it appears). The second function combines (reduces) two dictionaries together.

import re

def count_words(filename):
"""
Count the number of times every word in the file filename
is contained in this file.

Args:
filename (str): the filename to count the words in

Returns:
dict: a mapping of word to count
"""

all_words = {}

with open(filename) as f:
for line in f:
words = line.split()

for word in words:
#lowercase the word and remove all
#characters that are not [a-z] or hyphen
word = word.lower()
match = re.search(r"([a-z\-]+)", word)

if match:
word = match.groups()[0]

if word in all_words:
all_words[word] += 1
else:
all_words[word] = 1

return all_words

def reduce_dicts(dict1, dict2):
"""
Combine (reduce) the passed two dictionaries to return
a dictionary that contains the keys of both, where the
values are equal to the sum of values for each key
"""

# explicitly copy the dictionary, as otherwise
# we risk modifying 'dict1'
combined = {}

for key in dict1:
combined[key] = dict1[key]

for key in dict2:
if key in combined:
combined[key] += dict2[key]
else:
combined[key] = dict2[key]

return combined


Use the above two functions to write a parallel Python script called countwords.py that counts how many times each word used by Shakespeare appears in all of his plays, e.g. by using the command line call

python countwords.py shakespeare


Have your script print out every word that appears more than 2000 times across all of the plays. The words should be printed out in alphabetical order, and printed together with the number of times that they are used.

If you get stuck or want some inspiration, a possible answer is given here.