安装pip install pypeln，基本元素如下：
这个是基于多进程。
import pypeln as pl
import time
from random import random
def slow_add1(x):
time.sleep(random()) # <= some slow computation
return x + 1
def slow_gt3(x):
time.sleep(random()) # <= some slow computation
return x > 3
data = range(10) # [0, 1, 2, ..., 9]
stage = pl.process.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.process.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
顾名思义，基于多线程。
import pypeln as pl
import time
from random import random
def slow_add1(x):
time.sleep(random()) # <= some slow computation
return x + 1
def slow_gt3(x):
time.sleep(random()) # <= some slow computation
return x > 3
data = range(10) # [0, 1, 2, ..., 9]
stage = pl.thread.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.thread.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
协程，异步io。
import pypeln as pl
import asyncio
from random import random
async def slow_add1(x):
await asyncio.sleep(random()) # <= some slow computation
return x + 1
async def slow_gt3(x):
await asyncio.sleep(random()) # <= some slow computation
return x > 3
data = range(10) # [0, 1, 2, ..., 9]
stage = pl.task.map(slow_add1, data, workers=3, maxsize=4)
stage = pl.task.filter(slow_gt3, stage, workers=2)
data = list(stage) # e.g. [5, 6, 9, 4, 8, 10, 7]
IO 密集型应用CPU等待IO时间远大于CPU 自身运行时间，太浪费；常见的 IO 密集型业务包括：浏览器交互、磁盘请求、网络爬虫、数据库请求等。
Python 世界对于 IO 密集型场景的并发提升有 3 种方法：多进程、多线程、异步 IO(asyncio)。理论上讲asyncio是性能最高的，原因如下：
1.进程、线程会有CPU上下文切换
2.进程、线程需要内核态和用户态的交互，性能开销大；而协程对内核透明的,只在用户态运行
3.进程、线程并不可以无限创建，最佳实践一般是 CPU*2；而协程并发能力强，并发上限理论上取决于操作系统IO多路复用(Linux下是 epoll)可注册的文件描述符的极限
下面是一个数据库访问的测试：
内存：
串行：75M
多进程：1.4G
多线程：150M
asyncio：120M
