How to read a large file - line by line?

python

file-io

performance-optimization

multiprocessing

byAnton Shumikhin·Sep 9, 2024

To tackle large files line by line in Python, engage with the handy open() function enclosed within the with statement. This strategy ensures a stingy memory usage and deals with the resource management automatically:

with open('largefile.txt') as file:
    for line in file:
        # Insert your line processing logic here, be creative!

Following this pattern leaves a light footprint on your memory, as it returns one line at a time, stopping short of loading the entire file into memory.

How to calculate string similarity within large files

Homing in on operation specifics, when you're tasked with processing text, computing string similarity can be extremely key. In cases where a fine sieve has to pan out duplicate or near-duplicate lines, Levenshtein distance serves well. Here you go, the integration of it right in the file reading loop:

from Levenshtein import distance

with open('largefile.txt') as file:
    previous_line = None
    for current_line in file:
        if previous_line is not None:
            sim_score = distance(previous_line, current_line)
            # Use sim_score to identify twins or doppelgangers here
        previous_line = current_line

Polishing your file reading techniques

As a juggler of bloated files, pigeonholing I/O operations and system capacities isn't an option. Python's got your back with Buffered I/O enabled by default, which softens the blow of disk access overhead. If binary files are your puzzle or if newline characters are giving you lines on your forehead, embrace the 'rb' opening mode or open the file with 'r', newline=None respectively. Here's a pro tip: dodge the temptation of file.readlines(), it's a leech on your memory.

To nitro-boost performance, multiprocessing can chop the file into chunks and process them neck and neck. However, balance the number of processes and your machine's stamina:

# Pseudocode for multiprocessing, because many hands make light work
from multiprocessing import Pool

def process_lines(chunk_of_lines):
    for line in chunk_of_lines:
        # Light up line processing
    # Release the results

if __name__ == '__main__':
    # Serve chunks of the file to separate processes
    chunks = prepare_file_chunks('largefile.txt')
    with Pool() as pool:
        results = pool.map(process_lines, chunks)
        # Congregate the results

Shaking up the use case mix, memory-mapped files can be a game-changer, letting you toy around with file bytes as if they're romping in memory.

Prepping up for exceptions and optimizing your game

Clasping onto the with statement like a life jacket during file operations lends a hand in sailing smooth through exceptions and ensures that files are dutifully closed, even if an error pops up to play spoilsport. If you're shunning the with approach, don't forget to punctuate with file.close().

Finding your sweet spot in the setup comes with trial and more trials. Juggle your worker counts in multiprocessing scenarios to find what fits your groove. Also, to handle file reading positions with extra care, work with file methods like seek() and tell().

Universal newline mode to the rescue

Python 3 endows the gifting of universal newline support (newline=None) in which every variant of the end-of-line sequence is neatly translated to '\n', making your code a globetrotter:

with open('largefile.txt', 'r', newline=None) as file:
    for line in file:
        line = line.rstrip('\n')  # To ensure '\n' is the only line terminator calling the shots.
        # Voyage further into processing