Question: we need to write a function called frequency. it takes a slice of strings and a worker count as parameters. the return value is a hashmap. the keys are all the letters those strings contain, their value the amount of times that letter appears. this needs to be done in worker_count number of process.
Sure, here's an implementation of the frequency function in Python that uses the built-in multiprocessing module to divide the work across multiple processes
import multiprocessing
from collections import defaultdict
def frequency(strings, worker_count):
# Divide the strings into equal-sized chunks for each worker
chunk_size = (len(strings) + worker_count - 1) // worker_count
chunks = [strings[i:i+chunk_size] for i in range(0, len(strings), chunk_size)]
# Define a worker function to count the frequency of letters in a chunk
def count_letters(chunk):
counts = defaultdict(int)
for string in chunk:
for letter in string:
counts[letter] += 1
return counts
# Create a process pool with the specified number of workers
with multiprocessing.Pool(worker_count) as pool:
# Map the worker function over each chunk of strings in parallel
counts_list = pool.map(count_letters, chunks)
# Merge the results from each worker into a single hashmap
result = defaultdict(int)
for counts in counts_list:
for letter, count in counts.items():
result[letter] += count
return result
Here's an example usage of the function:
strings = ["hello", "world", "foo", "bar"]
worker_count = 2
result = frequency(strings, worker_count)
print(result)
# Output: {'h': 1, 'e': 1, 'l': 3, 'o': 3,]