爬虫之性能相关

在编写爬虫时，性能的消耗主要在IO请求中，当单进程单线程模式下请求URL时必然会引起等待，从而使得请求整体变慢。

1、同步执行

import requests

def fetch_async(url):
    response = requests.get(url)
    return response

url_list = ['http://www.github.com', 'http://www.bing.com']

for url in url_list:
    fetch_async(url)

2、多线程执行

• 线程池不能太多，因为线程的上下文切换，浪费时间，会降低整体效率；

• 每个线程发出请求之后就阻塞，等待返回数据，这中间的时间线程处于空闲状态；

from concurrent.futures import ThreadPoolExecutor
import requests

def fetch_async(url):
    response = requests.get(url)
    return response

url_list = ['http://www.github.com', 'http://www.bing.com']
pool = ThreadPoolExecutor(5)
for url in url_list:
    pool.submit(fetch_async, url)
pool.shutdown(wait=True)

3、多线程+回调函数执行

• 优点：请求成功返回之后调用回调函数，降低耦合

from concurrent.futures import ThreadPoolExecutor
import requests

def fetch_async(url):
    response = requests.get(url)
    return response

def callback(future):
    print(future.result())

url_list = ['http://www.github.com', 'http://www.bing.com']
pool = ThreadPoolExecutor(5)
for url in url_list:
    v = pool.submit(fetch_async, url)
    v.add_done_callback(callback)
pool.shutdown(wait=True)

4、多进程执行

• 每个进程发出请求之后就阻塞，等待返回数据，这中间的时间进程处于空闲状态；

from concurrent.futures import ProcessPoolExecutor
import requests

def fetch_async(url):
    response = requests.get(url)
    return response

url_list = ['http://www.github.com', 'http://www.bing.com']
pool = ProcessPoolExecutor(5)
for url in url_list:
    pool.submit(fetch_async, url)
pool.shutdown(wait=True)

5、多进程+回调函数执行

from concurrent.futures import ProcessPoolExecutor
import requests

def fetch_async(url):
    response = requests.get(url)
    return response

def callback(future):
    print(future.result())

url_list = ['http://www.github.com', 'http://www.bing.com']
pool = ProcessPoolExecutor(5)
for url in url_list:
    v = pool.submit(fetch_async, url)
    v.add_done_callback(callback)
pool.shutdown(wait=True)

多线程和多进程的区别：

• IO密集型操作，使用多线程，因为不调用CPU

• 计算密集型操作，使用多进程，调用CPU

• 线程之间共用资源，可以节省资源空间

• 进程之间不共享资源，比较占用资源空间

• 因为GIL锁的原因，如果用多线程进行计算型操作，每次一个进程同一时间只能有一个线程被CPU调用，效率不高；

通过上述代码均可以完成对请求性能的提高，对于多线程和多进程的缺点是在IO阻塞时会造成了线程和进程的浪费，所以异步IO会是首选：

• asyncio可以实现单线程并发IO操作。如果仅用在客户端，发挥的威力不大。如果把asyncio用在服务器端，例如Web服务器，由于HTTP连接就是IO操作，因此可以用单线程+coroutine实现多用户的高并发支持。

• asyncio实现了TCP、UDP、SSL等协议，aiohttp则是基于asyncio实现的HTTP框架。

6、async io（异步IO）示例一

• asyncio 不支持HTTP请求，只支持TCP

import asyncio

@asyncio.coroutine
def func1():
    print('before...func1......')
    yield from asyncio.sleep(5)
    print('end...func1......')

tasks = [func1(), func1()]

loop = asyncio.get_event_loop()
loop.run_until_complete(asyncio.gather(*tasks))
loop.close()

7、async io（异步IO）示例二

• 把发送的数据封装成HTTP请求的方式

import asyncio


@asyncio.coroutine
def fetch_async(host, url='/'):
    print(host, url)
    reader, writer = yield from asyncio.open_connection(host, 80)

    request_header_content = """GET %s HTTP/1.0
Host: %s

""" % (url, host,)
    request_header_content = bytes(request_header_content, encoding='utf-8')

    writer.write(request_header_content)
    yield from writer.drain()
    text = yield from reader.read()
    print(host, url, text)
    writer.close()

tasks = [
    fetch_async('www.cnblogs.com', '/wupeiqi/'),
    fetch_async('dig.chouti.com', '/pic/show?nid=4073644713430508&lid=10273091')
]

loop = asyncio.get_event_loop()
results = loop.run_until_complete(asyncio.gather(*tasks))
loop.close()

8、asyncio + aiohttp

• aiohttp帮我们封装了HTTP数据包
• 两个模块组合实现异步IO

import aiohttp
import asyncio

@asyncio.coroutine
def fetch_async(url):
    print(url)
    response = yield from aiohttp.request('GET', url)
    # data = yield from response.read()
    # print(url, data)
    print(url, response)
    response.close()

tasks = [fetch_async('http://www.google.com/'), fetch_async('http://www.chouti.com/')]

event_loop = asyncio.get_event_loop()
results = event_loop.run_until_complete(asyncio.gather(*tasks))
event_loop.close()

9、asyncio + requests

• requests帮我们封装了HTTP数据包

import asyncio
import requests

@asyncio.coroutine
def fetch_async(func, *args):
    loop = asyncio.get_event_loop()
    future = loop.run_in_executor(None, func, *args)
    response = yield from future
    print(response.url, response.content)

tasks = [
    fetch_async(requests.get, 'http://www.cnblogs.com/wupeiqi/'),
    fetch_async(requests.get, 'http://dig.chouti.com/pic/show?nid=4073644713430508&lid=10273091')
]

loop = asyncio.get_event_loop()
results = loop.run_until_complete(asyncio.gather(*tasks))
loop.close()

10、gevent + requests

• Python内部socket在发送完数据后等待接收数据，是阻塞的，monkey.patch_all()之后，就会把内部所有的socket换成gevent封装的异步IO操作；

• gevent是第三方库，通过greenlet实现协程，greenlet可以实现协程，不过每一次都要人为的去指向下一个该执行的协程，显得太过麻烦。

• 当一个greenlet遇到IO操作时，比如访问网络，就自动切换到其他的greenlet，等到IO操作完成，再在适当的时候切换回来继续执行。由于IO操作非常耗时，经常使程序处于等待状态，有了gevent为我们自动切换协程，就保证总有greenlet在运行，而不是等待IO。

• 协程存在的意义：对于多线程应用，CPU通过切片的方式来切换线程间的执行，线程切换时需要耗时（保存状态，下次继续）。协程，则只使用一个线程，在一个线程中规定某个代码块执行顺序。

import gevent

import requests
from gevent import monkey

monkey.patch_all()


def fetch_async(method, url, req_kwargs):
    print(method, url, req_kwargs)
    response = requests.request(method=method, url=url, **req_kwargs)
    print(response.url, response.content)

# ##### 发送请求 #####
gevent.joinall([
    gevent.spawn(fetch_async, method='get', url='https://www.python.org/', req_kwargs={}),
    gevent.spawn(fetch_async, method='get', url='https://www.yahoo.com/', req_kwargs={}),
    gevent.spawn(fetch_async, method='get', url='https://github.com/', req_kwargs={}),
])

# ##### 发送请求（协程池控制最大协程数量） #####
# from gevent.pool import Pool
# pool = Pool(5)最多同时5个协程
# gevent.joinall([
#     pool.spawn(fetch_async, method='get', url='https://www.python.org/', req_kwargs={}),
#     pool.spawn(fetch_async, method='get', url='https://www.yahoo.com/', req_kwargs={}),
#     pool.spawn(fetch_async, method='get', url='https://www.github.com/', req_kwargs={}),
# ])

11、grequests

• gevent + requests组合成一个模块

import grequests


request_list = [
    grequests.get('http://httpbin.org/delay/1', timeout=0.001),
    grequests.get('http://fakedomain/'),
    grequests.get('http://httpbin.org/status/500')
]


# ##### 执行并获取响应列表 #####
# response_list = grequests.map(request_list)
# print(response_list)


# ##### 执行并获取响应列表（处理异常） #####
# def exception_handler(request, exception):
# print(request,exception)
#     print("Request failed")

# response_list = grequests.map(request_list, exception_handler=exception_handler)
# print(response_list)

12、Twisted示例

from twisted.web.client import getPage, defer
from twisted.internet import reactor

def one_done(arg):
　　print('finished...')

def all_done(arg):
    reactor.stop()

def callback(contents):
    print(contents)

deferred_list = [] # 列表里是一些特殊对象，封装了已经向URL发送请求的对象

url_list = ['http://www.bing.com', 'http://www.baidu.com', ]
for url in url_list:
    deferred = getPage(bytes(url, encoding='utf8'))#发送HTTP请求
    deferred.addCallback(callback)#执行回调函数
    deferred_list.append(deferred)

dlist = defer.DeferredList(deferred_list)
dlist.addBoth(all_done)#给每个对象添加回调函数

reactor.run()#检测是否有执行完成的请求，每完成一个执行一次one_done,等所有的请求都回来，执行all_done(),这是个死循环，需要all_done来停止它

13、Tornado

from tornado.httpclient import AsyncHTTPClient
from tornado.httpclient import HTTPRequest
from tornado import ioloop

COUNT = 0
def handle_response(response):
    """
    处理返回值内容（需要维护计数器，来停止IO循环），调用 ioloop.IOLoop.current().stop()
    """
    global COUNT
    COUNT -= 1
    if response.error:
        print("Error:", response.error)
    else:
        print(response.body)
    if COUNT == 0:
        ioloop.IOLoop.current().stop()

def func():
    url_list = [
        'http://www.baidu.com',
        'http://www.bing.com',
    ]
    global COUNT
    COUNT = len(url_list)
    for url in url_list:
        print(url)
        http_client = AsyncHTTPClient()
        http_client.fetch(HTTPRequest(url), handle_response)

ioloop.IOLoop.current().add_callback(func)
ioloop.IOLoop.current().start() # 也是个死循环，需要自定义一个停止条件，一个简单的计数器

14、Twisted更多

from twisted.internet import reactor
from twisted.web.client import getPage
import urllib.parse


def one_done(arg):
    print(arg)
    reactor.stop()

post_data = urllib.parse.urlencode({'check_data': 'adf'})
post_data = bytes(post_data, encoding='utf8')
headers = {b'Content-Type': b'application/x-www-form-urlencoded'}
response = getPage(bytes('http://dig.chouti.com/login', encoding='utf8'),
                   method=bytes('POST', encoding='utf8'),
                   postdata=post_data,
                   cookies={},
                   headers=headers)
response.addBoth(one_done)

reactor.run()

总结：以上选择使用的优先级为：

grequests(gevent+requests) --> Twisted --> Tornado --> asyncio

以上均是Python内置以及第三方模块提供异步IO请求模块，使用简便大大提高效率，而对于异步IO请求的本质则是【非阻塞Socket】+【IO多路复用】

15、自定义异步IO模块

• IO多路复用：监听多个socket对象（while循环），谁有变化就处理谁，利用这个特性，可以开发出很多操作，比如异步IO模块；

• 异步IO：当进程执行到一个IO（等待外部数据）的时候，不等待，直到数据接收成功，再回来处理，其实就是回调；

• 利用非阻塞的socket+IO多路复用，可以实现伪并发；

•  精简版

import select
import socket
import time


class HttpRequest(object):
    """封装请求和相应的基本数据"""
    def __init__(self, sock, host, callback):
        self.sock = sock
        self.callback = callback
        self.host = host

    def fileno(self):
        """请求sockect对象的文件描述符，用于select监听"""
        return self.sock.fileno()

class HttpResponse:
    def __init__(self,recv_data):
        self.recv_data = recv_data
        self.header_dict = {}
        self.body = None
        self.initialize()

    def initialize(self):
        # 把响应头和响应体分开
        headers, body = self.recv_data.split(b'

', 1)
        self.body = body
        header_list = headers.split(b'
')
        for head in header_list:
            head = str(head,encoding='utf-8')
            v = head.split(':',1)
            if len(v) == 2:
                self.header_dict[v[0]] = v[1]
            elif len(v) == 1:
                self.header_dict['method'] = v[0]


class AsyncRequest(object):
    def __init__(self):
        self.conn = []  # 检测是否有数据返回
        self.connections = []#检测是否已经链接成功

    def add_request(self, host, callback,):
        """创建一个要请求"""
        try:
            sk = socket.socket()
            sk.setblocking(False)
            sk.connect((host, 80))
        except BlockingIOError as e:
            pass
        # print('已经向远程发送连接的请求')
        req = HttpRequest(sk, host, callback)
        self.connections.append(req)
        self.conn.append(req)

    def run(self):
        """事件循环，用于检测请求的socket是否已经就绪，从而执行相关操作"""
        while True:
            rlist, wlist, elist = select.select(self.conn, self.connections, self.conn, 0.05)

            for w in wlist:
                # 已经连接成功远程服务器，开始向远程发送请求数据
                print(w.host,'连接成功。。。')
                data = "GET / HTTP/1.0
Host:%s

"%(w.host,)
                w.sock.sendall(bytes(data,encoding='utf-8'))
                # 连接成功，发送请求之后，移除监听对象
                self.connections.remove(w)

            for r in rlist:
                sock = r.sock
                recv_data = bytes()
                while True: # 服务端返回的数据可能很多，需要循环接收
                    try:
                        data = sock.recv(8096)
                        recv_data += data
                        r.write(recv_data)
                    except Exception as e:
                        break
                # print(recv_data)
                response = HttpResponse(recv_data)
                r.callback(r.host,response)
                sock.close() # 接收完成，关闭链接
                self.conn.remove(r) # 移除监听对象

            # 如果接收数据的对象列表为空，说明所有接收数据完成，结束循环
            if len(self.conn) == 0:
                break


if __name__ == '__main__':
    def callback_1(host,response):
        print(host,'保存到文件',response.header_dict,response.body)

    def callback_2(host,response):
        print(host,'保存到数据库',response.header_dict,response.body)

    obj = AsyncRequest()
    url_list = [
        {'host': 'www.cnblogs.com','callback': callback_1},
        {'host': 'www.baidu.com','callback': callback_2},
        {'host': 'www.zhihu.com', 'callback': callback_2},
    ]
    for item in url_list:
        obj.add_request(**item)

    obj.run()

• 增强版

import select
import socket
import time


class AsyncTimeoutException(TimeoutError):
    """
    请求超时异常类
    """

    def __init__(self, msg):
        self.msg = msg
        super(AsyncTimeoutException, self).__init__(msg)


class HttpContext(object):
    """封装请求和相应的基本数据"""

    def __init__(self, sock, host, port, method, url, data, callback, timeout=5):
        """
        sock: 请求的客户端socket对象
        host: 请求的主机名
        port: 请求的端口
        port: 请求的端口
        method: 请求方式
        url: 请求的URL
        data: 请求时请求体中的数据
        callback: 请求完成后的回调函数
        timeout: 请求的超时时间
        """
        self.sock = sock
        self.callback = callback
        self.host = host
        self.port = port
        self.method = method
        self.url = url
        self.data = data

        self.timeout = timeout

        self.__start_time = time.time()
        self.__buffer = []

    def is_timeout(self):
        """当前请求是否已经超时"""
        current_time = time.time()
        if (self.__start_time + self.timeout) < current_time:
            return True

    def fileno(self):
        """请求sockect对象的文件描述符，用于select监听"""
        return self.sock.fileno()

    def write(self, data):
        """在buffer中写入响应内容"""
        self.__buffer.append(data)

    def finish(self, exc=None):
        """在buffer中写入响应内容完成，执行请求的回调函数"""
        if not exc:
            response = b''.join(self.__buffer)
            self.callback(self, response, exc)
        else:
            self.callback(self, None, exc)

    def send_request_data(self):
        content = """%s %s HTTP/1.0
Host: %s

%s""" % (
            self.method.upper(), self.url, self.host, self.data,)

        return content.encode(encoding='utf8')


class AsyncRequest(object):
    def __init__(self):
        self.fds = []
        self.connections = []

    def add_request(self, host, port, method, url, data, callback, timeout):
        """创建一个要请求"""
        client = socket.socket()
        client.setblocking(False)
        try:
            client.connect((host, port))
        except BlockingIOError as e:
            pass
            # print('已经向远程发送连接的请求')
        req = HttpContext(client, host, port, method, url, data, callback, timeout)
        self.connections.append(req)
        self.fds.append(req)

    def check_conn_timeout(self):
        """检查所有的请求，是否有已经连接超时，如果有则终止"""
        timeout_list = []
        for context in self.connections:
            if context.is_timeout():
                timeout_list.append(context)
        for context in timeout_list:
            context.finish(AsyncTimeoutException('请求超时'))
            self.fds.remove(context)
            self.connections.remove(context)

    def running(self):
        """事件循环，用于检测请求的socket是否已经就绪，从而执行相关操作"""
        while True:
            r, w, e = select.select(self.fds, self.connections, self.fds, 0.05)

            if not self.fds:
                return

            for context in r:
                sock = context.sock
                while True:
                    try:
                        data = sock.recv(8096)
                        if not data:
                            self.fds.remove(context)
                            context.finish()
                            break
                        else:
                            context.write(data)
                    except BlockingIOError as e:
                        break
                    except TimeoutError as e:
                        self.fds.remove(context)
                        self.connections.remove(context)
                        context.finish(e)
                        break

            for context in w:
                # 已经连接成功远程服务器，开始向远程发送请求数据
                if context in self.fds:
                    data = context.send_request_data()
                    context.sock.sendall(data)
                    self.connections.remove(context)

            self.check_conn_timeout()


if __name__ == '__main__':
    def callback_func(context, response, ex):
        """
        :param context: HttpContext对象，内部封装了请求相关信息
        :param response: 请求响应内容
        :param ex: 是否出现异常（如果有异常则值为异常对象；否则值为None）
        :return:
        """
        print(context, response, ex)

    obj = AsyncRequest()
    url_list = [
        {'host': 'www.google.com', 'port': 80, 'method': 'GET', 'url': '/', 'data': '', 'timeout': 5,
         'callback': callback_func},
        {'host': 'www.baidu.com', 'port': 80, 'method': 'GET', 'url': '/', 'data': '', 'timeout': 5,
         'callback': callback_func},
        {'host': 'www.bing.com', 'port': 80, 'method': 'GET', 'url': '/', 'data': '', 'timeout': 5,
         'callback': callback_func},
    ]
    for item in url_list:
        print(item)
        obj.add_request(**item)

    obj.running()