Is the bottleneck CPU-bound or I/O-bound?

CPU-bound (computation-heavy):
├── Independent iterations? → multiprocessing.Pool / ProcessPoolExecutor
├── Shared state needed? → multiprocessing with Manager or shared memory
├── NumPy/Pandas operations? → Vectorization first, then consider numba/dask
└── Large data chunks? → chunked processing with Pool.map

I/O-bound (network, disk, database):
├── Many independent requests? → asyncio with aiohttp/aiofiles
├── Legacy sync code? → ThreadPoolExecutor
├── Mixed sync/async? → asyncio.to_thread()
└── Database queries? → Connection pooling + async drivers

Data-parallel (array/matrix ops):
├── NumPy arrays? → Vectorize, avoid Python loops
├── Pandas DataFrames? → Use built-in vectorized methods
├── Large datasets? → Dask for out-of-core parallelism
└── GPU available? → Consider CuPy or JAX

results = []
for item in items:
    results.append(expensive_computation(item))

from concurrent.futures import ProcessPoolExecutor

with ProcessPoolExecutor() as executor:
    results = list(executor.map(expensive_computation, items))

import requests

def fetch_all(urls):
    return [requests.get(url).json() for url in urls]

import asyncio
import aiohttp

async def fetch_all(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_one(session, url) for url in urls]
        return await asyncio.gather(*tasks)

async def fetch_one(session, url):
    async with session.get(url) as response:
        return await response.json()

result = []
for i in range(len(a)):
    row = []
    for j in range(len(b)):
        row.append(a[i] * b[j])
    result.append(row)

import numpy as np
result = np.outer(a, b)

import asyncio
from concurrent.futures import ProcessPoolExecutor

async def hybrid_pipeline(data, urls):
    loop = asyncio.get_event_loop()

    # CPU-bound in process pool
    with ProcessPoolExecutor() as pool:
        processed = await loop.run_in_executor(pool, cpu_heavy_fn, data)

    # I/O-bound with async
    results = await asyncio.gather(*[fetch(url) for url in urls])

    return processed, results