90% reduction in memory usage for large files.
from concurrent.futures import ProcessPoolExecutor
import asyncio async def fetch_api_data(endpoint: str) -> dict: # Simulated network I/O await asyncio.sleep(1) return "endpoint": endpoint, "status": "success" async def main(): async with asyncio.TaskGroup() as tg: task1 = tg.create_task(fetch_api_data("/users")) task2 = tg.create_task(fetch_api_data("/orders")) print(task1.result(), task2.result()) asyncio.run(main()) Use code with caution. 4. Memory Optimization via Generators and Iterators 90% reduction in memory usage for large files
Ideal for I/O-bound operations with high concurrency requirements (web scraping, API serving, chat applications).
pdf = PdfReader(open("huge.pdf", "rb")) (loads entire file into RAM). The Modern Feature: Use PdfReader(open("huge.pdf", "rb"), strict=False, lazy_loading=True) Memory Optimization via Generators and Iterators Ideal for
Use try-except-else with three fallbacks:
Detailed setup guides for (like Ruff and pre-commit). Powerful Python: The Most Impactful Patterns, Features and
Powerful Python: The Most Impactful Patterns, Features and Development Strategies Modern Python Provides is an advanced guide by Aaron Maxwell
┌─────────────────────────────────┐ │ Async Orchestration │ └────────────────┬────────────────┘ │ ┌─────────────────────────┴─────────────────────────┐ ▼ ▼ ┌─────────────────────────────────┐ ┌─────────────────────────────────┐ │ Task Groups │ │ Subinterpreters │ │ - Structured concurrency │ │ - True multi-core execution │ │ - Unified error handling │ │ - Independent GIL per sub │ │ - Concurrent I/O bound │ │ - CPU-bound parallel processing│ └─────────────────────────────────┘ └─────────────────────────────────┘