Python 协程

迭代器

• 可迭代 (Iterable)：直接作用于for循环变量
• 迭代器 (Iterator)：直接作用于for循环变量，并且可以用next调用
• 鉴别，可用isinstancle()

生成器

• 不用for，占内存小，一边循环一边计算——时间换空间

• next函数调用，到最后一个，报StopIteration异常

• 生成：

  1. 直接使用

  g = (x * x for x in range(10)) # 中括号是列表生成器，小括号是生成器
  

  1. 函数包含yield，则叫生成器，next调用函数，遇yield返回

  def odd():
      print('step 1')
      yield 1
      print('step 2')
      yield(3)
      print('step 3')
      yield(5)
      
      
  g = odd()
  one = next(g)
  print(one)
  two = next(g)
  print(two)
  three = next(g)
  print(three)
  

  Step 1
  1
  Step 2
  2
  Step 3
  3

  注意：此时g是一个generator, next调用从上次yield后开始

  1. for循环调用生成器

  def fib(max):
      n, a, b = 0, 0, 1
      while n < max:
          yield b
          a, b = b, a+b
          n += 1
      return 'Done'
  
  
  g = fib(5)
  
  for i in g:
      print(i)
  

  1
  1
  2
  3
  5

协程

• 定义：为非抢占式多任务产生子程序，可以暂停执行——像generator一样

• 关键字：yield和send

  def simple_coroutine(a):
      print('-> start')
  
      b = yield a
      print('-> recived', a, b)
  
      c = yield a + b
      print('-> recived', a, b, c)
  
  
  # runc
  sc = simple_coroutine(5)
  
  aa = next(sc)  # 预激
  print(aa)
  bb = sc.send(6)  # 5, 6
  print(bb)
  cc = sc.send(7)  # 5, 6, 7
  print(cc)
  

  -> start
  5
  -> recived 5 6
  11
  -> recived 5 6 7

  分析：动脑子

• 协程终止：向上冒泡，发送哨符值，让协程退出

• yield from：相当于加一个通道（协程与主线程间）

  def gen():
      for c in 'AB':
          yield c
  
  
  print(list(gen()))
  
  
  def gen_new():
      yield from 'AB'
  
  
  print(list(gen_new()))
  

• 委派生成器：包含yield from的生成器函数

  from collections import namedtuple
  
  ResClass = namedtuple('Res', 'count average')
  
  
  # 子生成器
  def averager():
      total = 0.0
      count = 0
      average = None
  
      while True:
          term = yield
          if term is None:
              break
          total += term
          count += 1
          average = total / count
  
      return ResClass(count, average)
  
  
  # 委派生成器
  def grouper(storages, key):
      while True:
          # 获取averager()返回的值
          storages[key] = yield from averager()
  
  
  # 客户端代码
  def client():
      process_data = {
          'boys_2': [39.0, 40.8, 43.2, 40.8, 43.1, 38.6, 41.4, 40.6, 36.3],
          'boys_1': [1.38, 1.5, 1.32, 1.25, 1.37, 1.48, 1.25, 1.49, 1.46]
      }
  
      storages = {}
      for k, v in process_data.items():
          # 获得协程
          coroutine = grouper(storages, k)
  
          # 预激协程
          next(coroutine)
  
          # 发送数据到协程
          for dt in v:
              coroutine.send(dt)
  
          # 终止协程
          coroutine.send(None)
      print(storages)
  
  # run
  client()
  

  {'boys_2': Res(count=9, average=40.422222222222224), 'boys_1': Res(count=9, average=1.3888888888888888)}

  解释：

  1. client()函数开始，for k, v 循环内，每次创建一个新的grouper实例coroutine
  2. next(coroutine)预激协程，进入while True循环，调用averager()，yield from处暂停
  3. 内层for dt in v 结束后，grouper实例仍暂停，所以storages[key]的赋值还未完成
  4. coroutine.send(None)后，term变为None，averager子生成器中止，抛出StopIteration，并将返回的数据包含在异常对象的value中，yield from 直接抓取 StopItration ，将异常对象的 value 赋值给 storages[key]

asyncio

• 步骤：创建消息循环（解决异步IO，有中转：相当于信箱，消息queue）-> 导入协程-> 关闭

  import threading
  import asyncio
  
  
  @asyncio.coroutine
  def hello():
      print('Hello world! (%s)' % threading.currentThread())
      print('Starting......(%s)' % threading.currentThread())
      yield from asyncio.sleep(3)
      print('Done......(%s)' % threading.currentThread())
      print('Hello again! (%s)' % threading.currentThread())
  
  
  loop = asyncio.get_event_loop()
  tasks = [hello(), hello()]
  loop.run_until_complete(asyncio.wait(tasks))
  loop.close()
  
  

async & await

• 更简洁，不用装饰器

  import threading
  import asyncio
  
  
  async def hello():
      print('Hello world! (%s)' % threading.currentThread())
      print('Starting......(%s)' % threading.currentThread())
      await asyncio.sleep(3)
      print('Done......(%s)' % threading.currentThread())
      print('Hello again! (%s)' % threading.currentThread())
  
  
  loop = asyncio.get_event_loop()
  tasks = [hello(), hello()]
  loop.run_until_complete(asyncio.wait(tasks))
  loop.close()
  
  

aiohttp

• 介绍：

  • 用asyncio和coroutine配合——http是io操作

• 例：

  import asyncio
  from aiohttp import web
  
  
  async def index(request):
      await asyncio.sleep(0.5)
      return web.Response(body=b'<h1>Index</h1>')
  
  
  async def hello(request):
      await asyncio.sleep(0.5)
      text = '<h1>hello, %s!</h1>' % request.match_info['name']
      return web.Response(body=text.encode('utf-8'))
  
  
  async def init(loop):
      app = web.Application(loop=loop)
      app.router.add_route('GET', '/', index)
      app.router.add_route('GET', '/hello/{name}', hello)
      srv = await loop.create_server(app.make_handler(), '127.0.0.1', 8000)
      print('Server started at http://127.0.0.1:8000...')
      return srv
  
  loop = asyncio.get_event_loop()
  loop.run_until_complete(init(loop))
  loop.run_forever()
  

  注：查+理解

concurrent.futures

• 类似线程池

• 用multiprocessing实现真正并行计算——运行多个解释器

• concurrent.furtures.Executor

  • ThreadPoolExecutor
  • ProcessPoolExecutor

• 例子：

  from concurrent.futures import ThreadPoolExecutor
  import time
  
  
  def return_future(msg):
      time.sleep(3)
      return msg
  
  
  # 创建一个线程池
  pool = ThreadPoolExecutor(max_workers=2)
  
  # 往线程池加入2个task
  f1 = pool.submit(return_future, 'hello')
  f2 = pool.submit(return_future, 'world')
  
  print(f1.done())
  time.sleep(3)
  print(f2.done())
  
  print(f1.result())
  print(f2.result())
  

• map(fn, *iterables, timeout=None):

  map和submit用一个就行

  import time
  import re
  import os
  import datetime
  from concurrent import futures
  
  data = ['1', '2']
  
  
  def wait_on(argument):
      print(argument)
      time.sleep(2)
      return "ok"
  
  
  ex = futures.ThreadPoolExecutor(max_workers=2)
  for i in ex.map(wait_on, data):
      print(i)
  
  

• Future

  • future实例由Executor.submit创建

  from concurrent.futures import ThreadPoolExecutor as Pool
  from concurrent.futures import as_completed
  import requests
  
  URLS = ['http://qq.com', 'http://sina.com', 'http://www.baidu.com', ]
  
  
  def task(url, timeout=10):
      return requests.get(url, timeout=timeout)
  
  
  with Pool(max_workers=3) as executor:
      future_tasks = [executor.submit(task, url) for url in URLS]
  
      for f in future_tasks:
          if f.running():
              print('%s is running' % str(f))
  
      for f in as_completed(future_tasks):
          try:
              ret = f.done()
              if ret:
                  f_ret = f.result()
                  print('%s, done, result: %s, %s' % (str(f), f_ret.url, len(f_ret.content)))
          except Exception as e:
              f.cancel()
              print(str(e))