Reference library

Concurrency & performance

asyncio, threading, multiprocessing, and profiling-friendly performance patterns.

4 matches

Build a Python Performance Profiler That Generates Readable Reports

Use cProfile and pstats to profile Python functions and print a sorted performance report showing the top time-consuming calls.

profiling cprofile pstats

Python

import cProfile
import pstats
import io
from pathlib import Path

def slow_function():
    total = 0
    for i in range(500_000):
        total += i ** 2
    return total

def fast_function():
    total = sum(i * i for i in range(500_000))
    return total

def profile_functions():
    profiler = cProfile.Profile()
  …

44 0 Open

Concurrency & performance medium

How to Parse JSON Files in Parallel with Python ThreadPoolExecutor

Load and transform JSON records from multiple files concurrently using ThreadPoolExecutor for faster I/O-bound parsing.

threadpool json concurrency

Python

import time
from concurrent.futures import ThreadPoolExecutor
import json

def load_json_file(path):
    with open(path, 'r') as f:
        return json.load(f)

def transform_record(record):
    record['full_name'] = f"{record.pop('first_name', '')} {record.pop('last_name', '')}".strip()
    record['score'] = int(reco…

17 0 Open

Concurrency & performance medium

How to Profile CPU Hot Path in Python with cProfile and sort_stats cumtime

Profile a Python function's CPU usage by running cProfile, sorting stats by cumulative time, and printing a readable report to stdout.

cprofile profiling performance

Python

import cProfile
import pstats
import io


def slow_function():
    total = 0
    for i in range(100_000):
        total += i * i
    return total


def fast_function():
    return sum(i for i in range(100))


def main():
    slow_function()
    fast_function()


if __name__ == "__main__":
    profiler = cProfile.Profi…

13 0 Open

Concurrency & performance medium

Profile Memory Usage with tracemalloc Snapshot Diff in Python

Use tracemalloc to take two memory snapshots, compute a diff, and print the top changes (size and count) by line number.

tracemalloc memory-profile performance

Python

import tracemalloc

def profile_memory():
    tracemalloc.start()
    
    # Allocate some objects to track
    data = [i * 2 for i in range(10000)]
    text = "x" * 5000
    nested = {"key": [1, 2, 3], "value": (4, 5)}
    
    # Take first snapshot
    snapshot1 = tracemalloc.take_snapshot()
    
    # Free some mem…

11 0 Open

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