爬取豆瓣音乐Top250(多线程、多进程)

发表于 分类于 Waline： 本文字数： 9.6k 阅读时长 ≈ 16 分钟

If you stay positive, you have a shot at a sliver lining. ——《乌云背后的幸福线》

山重水复疑无路，柳暗花明又一村。

站点分析

  本次抓取目标是豆瓣音乐Top250排行榜，目标站点为 https://music.douban.com/top250 ，打开之后便可以查看榜单。

  将网页滑到最下方，发现有分页，点击切换到第2页，观察URL的变化。发现页面的URL变成了 https://music.douban.com/top250?start=25 ，如下图：

  比之前的URL多了一个start参数,初步推断这是一个偏移量的参数。再点击下一页，start参数变成了50。多次切换页码offset都有改变，由此得出规律，start代表偏移量值。也就是说Top250我们只需要分开请求10次即可，而10次的参数分别设置为0、25、50…225即可。
  使用Chrome浏览器开发者模式查看源代码。选中任意一个条目，发现其详情页链接在class属性为ngb的a节点。

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all_music_url = tree.xpath('//*[@class="nbg"]/@href')

  进入专辑页面，需要抓取的信息都在这里，不过有的信息使用XPath不太好获取，所以对于这些不好提取的信息使用了正则表达式。如流派的内容：

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styles = re.findall(r'<span class="pl">流派:</span>&nbsp;(.*?)<br />', html, re.S)
    if len(styles) == 0:
        style = '未知'
    else:
        style = styles[0].strip()

完整代码

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import re
import csv
import requests
from lxml import etree
from datetime import datetime
from fake_useragent import UserAgent


# 开始时间
# start_time = datetime.now()

# 随机请求头
headers = {
    "User-Agent": UserAgent(verify_ssl=False).random,    # 随机UA
    "Connection": "close"    # 关闭长链接
}


# 获取所有音乐详情页链接
def get_music_url(url):
    html = requests.get(url, headers=headers).text
    tree = etree.HTML(html)
    all_music_url = tree.xpath('//*[@class="nbg"]/@href')
    for music_url in all_music_url:
        get_music_info(music_url)


# 解析详情页
def get_music_info(music_url):
    html = requests.get(music_url, headers=headers).text
    tree = etree.HTML(html)
    name = tree.xpath('//*[@id="wrapper"]/h1/span/text()')[0]    # 音乐名称
    author = tree.xpath('//*[@id="info"]/span/span/a/text()')[0]    # 演唱者
    styles = re.findall(r'<span class="pl">流派:</span>&nbsp;(.*?)<br />', html, re.S)    # 流派
    if len(styles) == 0:
        style = '未知'
    else:
        style = styles[0].strip()
    time = re.findall(r'<span class="pl">发行时间:</span>&nbsp;(.*?)<br />', html, re.S)[0].strip()    # 发行时间
    publishers = re.findall(r'<span class="pl">出版者:</span>&nbsp;(.*?)<br />', html, re.S)    # 出版者
    score = tree.xpath('//*[@class="ll rating_num"]/text()')[0]
    if len(publishers) == 0:
        publisher = '未知'
    else:
        publisher = publishers[0].strip()
    music_info = {
        "name": name,
        "author": author,
        "style": style,
        "time": time,
        "publisher": publisher,
        "score": score
    }
    print(music_info)
    save_to_csv(filename, music_info)


# 储存到CSV文件
def save_to_csv(filename, music_info):
    with open(filename, 'a', encoding='utf-8') as f:
        fieldnames = ["name", "author", "style", "time", "publisher", "score"]
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writerow(music_info)


if __name__ == "__main__":
    urls = ['https://music.douban.com/top250?start={}'.format(str(i)) for i in range(0, 250, 25)]
    filename = 'musicTop250.csv'
    # 创建musicTop250.csv文件，并添加表头信息
    with open(filename, 'a', encoding='utf-8') as f:
        fieldnames = ["name", "author", "style", "time", "publisher", "score"]
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writeheader()
    for url in urls:
        get_music_url(url)

    # 结束时间
    # end_time = datetime.now()
    # print(f'用时：{(end_time - start_time).seconds}秒')

多线程版

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import re
import csv
import requests
from lxml import etree
from fake_useragent import UserAgent
from threading import Thread, Lock
from datetime import datetime


# 开始时间
start_time = datetime.now()

# 线程锁
lock = Lock()

# 随机请求头
headers = {
    "User-Agent": UserAgent(verify_ssl=False).random,    # 随机UA
    "Connection": "close"    # 关闭长链接
}


# 从URL列表中获取URl
def get_url():
    global urls
    lock.acquire()
    if len(urls) == 0:
        lock.release()
        return ""
    lock.release()
    return urls.pop(0)


# 获取所有音乐详情页链接
def get_music_url(url, thread_name):
    html = requests.get(url, headers=headers).text
    tree = etree.HTML(html)
    all_music_url = tree.xpath('//*[@class="nbg"]/@href')
    for music_url in all_music_url:
        get_music_info(music_url, thread_name)


# 解析详情页
def get_music_info(music_url, thread_name):
    html = requests.get(music_url, headers=headers).text
    tree = etree.HTML(html)
    name = tree.xpath('//*[@id="wrapper"]/h1/span/text()')[0]    # 音乐名称
    author = tree.xpath('//*[@id="info"]/span/span/a/text()')[0]    # 演唱者
    styles = re.findall(r'<span class="pl">流派:</span>&nbsp;(.*?)<br />', html, re.S)    # 流派
    if len(styles) == 0:
        style = '未知'
    else:
        style = styles[0].strip()
    time = re.findall(r'<span class="pl">发行时间:</span>&nbsp;(.*?)<br />', html, re.S)[0].strip()    # 发行时间
    publishers = re.findall(r'<span class="pl">出版者:</span>&nbsp;(.*?)<br />', html, re.S)    # 出版者
    score = tree.xpath('//*[@class="ll rating_num"]/text()')[0]
    if len(publishers) == 0:
        publisher = '未知'
    else:
        publisher = publishers[0].strip()
    music_info = {
        "name": name,
        "author": author,
        "style": style,
        "time": time,
        "publisher": publisher,
        "score": score
    }
    print(thread_name, music_info)
    # save_to_csv(filename, music_info)


# 储存到CSV文件
def save_to_csv(filename, music_info):
    with open(filename, 'a', encoding='utf-8') as f:
        fieldnames = ["name", "author", "style", "time", "publisher", "score"]
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writerow(music_info)


class MusicSpiderThread(Thread):
    def __init__(self, name):
        Thread.__init__(self)
        self.name = name

    def run(self):
        while True:
            url = get_url()
            if url != "":
                get_music_url(url, self.name)
            else:
                break


if __name__ == "__main__":
    urls = ['https://music.douban.com/top250?start={}'.format(str(i)) for i in range(0, 250, 25)]
    # filename = 'musicTop250.csv'
    # with open(filename, 'a', encoding='utf-8') as f:
    #     fieldnames = ["name", "author", "style", "time", "publisher", "score"]
    #     writer = csv.DictWriter(f, fieldnames=fieldnames)
    #     writer.writeheader()
    print(len(urls))
    threads = []
    for i in range(1, 5): # 开启4个线程
        thread = MusicSpiderThread('thread-{}'.format(i))
        threads.append(thread)

    for thread in threads:
        thread.start()

    for thread in threads:
        thread.join()

    # 结束时间
    end_time = datetime.now()
    print(f'用时：{(end_time-start_time).seconds}秒')

多进程版

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import re
import csv
import requests
from lxml import etree
from datetime import datetime
from multiprocessing import Pool
from fake_useragent import UserAgent

# 开始时间
start_time = datetime.now()

# 随机请求头
headers = {
    "User-Agent": UserAgent(verify_ssl=False).random,    # 随机UA
    "Connection": "close"    # 关闭长链接
}


# 获取所有音乐详情页链接
def get_music_url(url):
    html = requests.get(url, headers=headers).text
    tree = etree.HTML(html)
    all_music_url = tree.xpath('//*[@class="nbg"]/@href')
    for music_url in all_music_url:
        get_music_info(music_url)


# 解析详情页
def get_music_info(music_url):
    html = requests.get(music_url, headers=headers).text
    tree = etree.HTML(html)
    name = tree.xpath('//*[@id="wrapper"]/h1/span/text()')[0]    # 音乐名称
    author = tree.xpath('//*[@id="info"]/span/span/a/text()')[0]    # 演唱者
    styles = re.findall(r'<span class="pl">流派:</span>&nbsp;(.*?)<br />', html, re.S)    # 流派
    if len(styles) == 0:
        style = '未知'
    else:
        style = styles[0].strip()
    time = re.findall(r'<span class="pl">发行时间:</span>&nbsp;(.*?)<br />', html, re.S)[0].strip()    # 发行时间
    publishers = re.findall(r'<span class="pl">出版者:</span>&nbsp;(.*?)<br />', html, re.S)    # 出版者
    score = tree.xpath('//*[@class="ll rating_num"]/text()')[0]
    if len(publishers) == 0:
        publisher = '未知'
    else:
        publisher = publishers[0].strip()
    music_info = {
        "name": name,
        "author": author,
        "style": style,
        "time": time,
        "publisher": publisher,
        "score": score
    }
    print(music_info)
    # save_to_csv(filename, music_info)


# 储存到CSV文件
def save_to_csv(filename, music_info):
    with open(filename, 'a', encoding='utf-8') as f:
        fieldnames = ["name", "author", "style", "time", "publisher", "score"]
        writer = csv.DictWriter(f, fieldnames=fieldnames)
        writer.writerow(music_info)


if __name__ == "__main__":
    urls = ['https://music.douban.com/top250?start={}'.format(str(i)) for i in range(0, 250, 25)]
    # filename = 'musicTop250.csv'
    # with open(filename, 'a', encoding='utf-8') as f:
    #     fieldnames = ["name", "author", "style", "time", "publisher", "score"]
    #     writer = csv.DictWriter(f, fieldnames=fieldnames)
    #     writer.writeheader()
    # for url in urls:
    #     get_music_url(url)

    pool = Pool(processes=4)    # 开启4个进程
    pool.map(get_music_url, urls)

    # 结束时间
    end_time = datetime.now()
    print(f'用时：{(end_time - start_time).seconds}秒')

用时对比

  为了测试，并没有把抓取到的结构化数据存储到文件，而是直接输出到控制台。在同一台机器上运行了上面多线程与多进程的代码，多线程开启4个线程用时104秒，多进程开启4个进程用时123秒。感觉不是很快，可能跟我这边的网络不太好有关系。豆瓣这边快速抓取一定数量后，会提示你IP异常，然后让你登录，对此，你可以将登录后的cookies放到headers中。

写在最后

  对于多线程多进程爬虫效率固然可观，但缺点也很明显，你需要考虑更多的细节，并且多线程和多进程的程序并不好调试，操作不好的话容易造成死锁或脏数据。对于一些小型站点个人感觉没必要使用多线程或多进程，对于大型站点或者你实在想要高速爬取，推荐使用框架。