Skip to content

Instantly share code, notes, and snippets.

Show Gist options
  • Save nurdabolatov/f8eb1f0bee6b4a60b2f00f6b900c73fb to your computer and use it in GitHub Desktop.
Save nurdabolatov/f8eb1f0bee6b4a60b2f00f6b900c73fb to your computer and use it in GitHub Desktop.
Parallel processing large file in Python
# Source:
import multiprocessing as mp
import time
import os
def process_line(line):
# Count frequency for every character
counter = {}
for letter in line:
if letter not in counter:
counter[letter] = 0
counter[letter] += 1
# Find the character with the most frequency using `counter`
most_frequent_letter = None
max_count = 0
for key, value in counter.items():
if value >= max_count:
max_count = value
most_frequent_letter = key
return most_frequent_letter
def serial_read(file_name):
results = []
with open(file_name, 'r') as f:
for line in f:
return results
def parallel_read(file_name):
# Maximum number of processes we can run at a time
cpu_count = mp.cpu_count()
file_size = os.path.getsize(file_name)
chunk_size = file_size // cpu_count
# Arguments for each chunk (eg. [('input.txt', 0, 32), ('input.txt', 32, 64)])
chunk_args = []
with open(file_name, 'r') as f:
def is_start_of_line(position):
if position == 0:
return True
# Check whether the previous character is EOL - 1)
return == '\n'
def get_next_line_position(position):
# Read the current line till the end
# Return a position after reading the line
return f.tell()
chunk_start = 0
# Iterate over all chunks and construct arguments for `process_chunk`
while chunk_start < file_size:
chunk_end = min(file_size, chunk_start + chunk_size)
# Make sure the chunk ends at the beginning of the next line
while not is_start_of_line(chunk_end):
chunk_end -= 1
# Handle the case when a line is too long to fit the chunk size
if chunk_start == chunk_end:
chunk_end = get_next_line_position(chunk_end)
# Save `process_chunk` arguments
args = (file_name, chunk_start, chunk_end)
# Move to the next chunk
chunk_start = chunk_end
with mp.Pool(cpu_count) as p:
# Run chunks in parallel
chunk_results = p.starmap(process_chunk, chunk_args)
results = []
# Combine chunk results into `results`
for chunk_result in chunk_results:
for result in chunk_result:
return results
def process_chunk(file_name, chunk_start, chunk_end):
chunk_results = []
with open(file_name, 'r') as f:
# Moving stream position to `chunk_start`
# Read and process lines until `chunk_end`
for line in f:
chunk_start += len(line)
if chunk_start > chunk_end:
return chunk_results
def measure(func, *args):
time_start = time.time()
result = func(*args)
time_end = time.time()
print(f'{func.__name__}: {time_end - time_start}')
return result
if __name__ == '__main__':
measure(serial_read, 'input.txt')
measure(parallel_read, 'input.txt')
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment