【Python爬虫4】并发并行下载
来源：互联网 发布：jsp和php 编辑：程序博客网 时间：2024/06/05 11:00
- 1一百万个网站
- 1用普通方法解析Alexa列表
- 2复用爬虫代码解析Alexa列表
- 2串行爬虫
- 3并发并行爬虫
- 0并发并行工作原理
- 1多线程爬虫
- 2多进程爬虫
- 4性能对比
这篇将介绍使用多线程和多进程这两种方式并发并行下载网页，并将它们与串行下载的性能进行比较。
1一百万个网站
亚马逊子公司Alexa提供了最受欢迎的100万个网站列表（http://www.alexa.com/topsites ），我们也可以通过http://s3.amazonaws.com/alexa-static/top-1m.csv.zip 直接下载这一列表的压缩文件，这样就不用去提取Alexa网站的数据了。
1.1用普通方法解析Alexa列表
提取数据的4个步骤：
- 下载.zip文件；
- 从.zip文件中提取出CSV文件；
- 解析CSV文件；
- 遍历CSV文件中的每一行，从中提取出域名数据。
# -*- coding: utf-8 -*-import csvfrom zipfile import ZipFilefrom StringIO import StringIOfrom downloader import Downloaderdef alexa(): D = Downloader() zipped_data = D('http://s3.amazonaws.com/alexa-static/top-1m.csv.zip') urls = [] # top 1 million URL's will be stored in this list with ZipFile(StringIO(zipped_data)) as zf: csv_filename = zf.namelist()[0] for _, website in csv.reader(zf.open(csv_filename)): urls.append('http://' + website) return urlsif __name__ == '__main__': print len(alexa())下载得到的压缩数据是使用StringIO封装之后，才传给ZipFile，是因为ZipFile需要一个相关的接口，而不是字符串。由于这个zip文件只包含一个文件，所以直接选择第一个文件即可。然后在域名数据前添加http://协议，附加到URL列表中。
1.2复用爬虫代码解析Alexa列表
要复用上述功能，需要修改scrape_callback接口。
# -*- coding: utf-8 -*-import csvfrom zipfile import ZipFilefrom StringIO import StringIOfrom mongo_cache import MongoCacheclass AlexaCallback: def __init__(self, max_urls=1000): self.max_urls = max_urls self.seed_url = 'http://s3.amazonaws.com/alexa-static/top-1m.csv.zip' def __call__(self, url, html): if url == self.seed_url: urls = [] #cache = MongoCache() with ZipFile(StringIO(html)) as zf: csv_filename = zf.namelist()[0] for _, website in csv.reader(zf.open(csv_filename)): if 'http://' + website not in cache: urls.append('http://' + website) if len(urls) == self.max_urls: break return urls这里添加了一个新的输入参数max_urls，用于设定从Alexa文件中提取的URL数量。如果真要下载100万个网页，那要消耗11天的时间，所以这里只设置为1000个URL。
2串行爬虫
# -*- coding: utf-8 -*-from link_crawler import link_crawlerfrom mongo_cache import MongoCachefrom alexa_cb import AlexaCallbackdef main(): scrape_callback = AlexaCallback() cache = MongoCache() #cache.clear() link_crawler(scrape_callback.seed_url, scrape_callback=scrape_callback, cache=cache, timeout=10, ignore_robots=True)if __name__ == '__main__': main()time python ...
3并发并行爬虫
为了加快下载网页速度，我们用多进程和多线程将串行下载扩展成并发下载，并将delay标识最小时间间隔为1秒，以免造成服务器过载，或导致IP地址封禁。
3.0并发并行工作原理
并行是基于多处理器多核而言的，让多个处理器多核真正同时跑多个程序或多个进程。而并发是单个处理器而言的，同一时刻每个处理器只会执行一个进程，然后在不同进程间快速切换，宏观上给人以多个程序同时运行的感觉，但微观上单个处理器还是串行工作的。同理，在一个进程中，程序的执行也是不同线程间进行切换的，每个线程执行程序的的不同部分。这就意味着当一个线程等待网页下载时，进程可以切换到其他线程执行，避免浪费处理器时间。因此，为了充分利用计算机中的所有资源尽可能快地下载数据，我们需要将下载分发到多个进程和线程中。
3.1多线程爬虫
我们可以修改第一篇文章链接爬虫队列结构的代码，修改为多个线程中启动爬虫循环process_queue()，以便并发下载这些链接。
import timeimport threadingimport urlparsefrom downloader import DownloaderSLEEP_TIME = 1def threaded_crawler(seed_url, delay=5, cache=None, scrape_callback=None, user_agent='Wu_Being', proxies=None, num_retries=1, max_threads=10, timeout=60): """Crawl this website in multiple threads """ # the queue of URL's that still need to be crawled #crawl_queue = Queue.deque([seed_url]) crawl_queue = [seed_url] # the URL's that have been seen seen = set([seed_url]) D = Downloader(cache=cache, delay=delay, user_agent=user_agent, proxies=proxies, num_retries=num_retries, timeout=timeout) def process_queue(): while True: try: url = crawl_queue.pop() except IndexError: # crawl queue is empty break else: html = D(url) if scrape_callback: try: links = scrape_callback(url, html) or [] except Exception as e: print 'Error in callback for: {}: {}'.format(url, e) else: for link in links: link = normalize(seed_url, link) # check whether already crawled this link if link not in seen: seen.add(link) # add this new link to queue crawl_queue.append(link) # wait for all download threads to finish threads = [] while threads or crawl_queue: # the crawl is still active for thread in threads: if not thread.is_alive(): # remove the stopped threads threads.remove(thread) while len(threads) < max_threads and crawl_queue: # can start some more threads thread = threading.Thread(target=process_queue) thread.setDaemon(True) # set daemon so main thread can exit when receives ctrl-c thread.start() threads.append(thread) # all threads have been processed # sleep temporarily so CPU can focus execution on other threads time.sleep(SLEEP_TIME)def normalize(seed_url, link): """Normalize this URL by removing hash and adding domain """ link, _ = urlparse.urldefrag(link) # remove hash to avoid duplicates return urlparse.urljoin(seed_url, link)上面代码在循环会不断创建线程，直到达到线程池threads的最大值。在爬取过程中，如果当前列队没有更多可以爬取的URL时，该线程会提前停止。 例如当前有两个线程以及两个待下载的URL，当第一个线程完成下载时，待爬取队列为空，则该线程退出。第二个线程稍后也完成了下载，但又发现了另一个待下载的URL。此时，thread循环注意到还有URL需要下载，并且线程数未达到最大值，因些创建一个新的下载线程。
# -*- coding: utf-8 -*-import sysfrom threaded_crawler import threaded_crawlerfrom mongo_cache import MongoCachefrom alexa_cb import AlexaCallbackdef main(max_threads): scrape_callback = AlexaCallback() cache = MongoCache() #cache.clear() threaded_crawler(scrape_callback.seed_url, scrape_callback=scrape_callback, cache=cache, max_threads=max_threads, timeout=10)if __name__ == '__main__': max_threads = int(sys.argv[1]) main(max_threads)$time python 3threaded_test.py 5
上面使用了5个线程，因此下载速度几乎是串行版本的5倍。
3.2多进程爬虫
对于有多核的中央处理器，则可以启动多进程。
# -*- coding: utf-8 -*-import sysfrom process_crawler import process_crawlerfrom mongo_cache import MongoCachefrom alexa_cb import AlexaCallbackdef main(max_threads): scrape_callback = AlexaCallback() cache = MongoCache() cache.clear() process_crawler(scrape_callback.seed_url, scrape_callback=scrape_callback, cache=cache, max_threads=max_threads, timeout=10) ##process_crawlerif __name__ == '__main__': max_threads = int(sys.argv[1]) main(max_threads)下面代码首先获取中央处理器内核个数，然后启动相应的进程个数，在每进程启动多个线程爬虫。之前的爬虫队列是存储在本地内存中，其他进程都无法处理这一爬虫，为了解决这一问题，需要把爬虫队列转移到MongoDB当中。单独存储队列，意味着即使是不同服务器上的爬虫也能够协同处理同一个爬虫任务。我们可以使用更加健壮的队列，比如专用的消息传输工具Celery，这里我们利用MongoDB实现的队列代码。在threaded_crawler需要做如下修改：
- 内建的队列换成基于MongoDB的新队列MongoQueue；
- 由于队列内部实现中处理重复URL的问题，因此不再需要seen变量；
- 在URL处理结束后调用complete()方法，用于记录该URL已经被成功解析。
import timeimport urlparseimport threadingimport multiprocessingfrom mongo_cache import MongoCachefrom mongo_queue import MongoQueuefrom downloader import DownloaderSLEEP_TIME = 1### process_crawler(scrape_callback.seed_url, scrape_callback=scrape_callback, cache=cache, max_threads=max_threads, timeout=10)def process_crawler(args, **kwargs): #args:number of args, kwargs:args list num_cpus = multiprocessing.cpu_count() #pool = multiprocessing.Pool(processes=num_cpus) print 'Starting {} processes...'.format(num_cpus) ###################### processes = [] for i in range(num_cpus): p = multiprocessing.Process(target=threaded_crawler, args=[args], kwargs=kwargs)### threaded_crawler #parsed = pool.apply_async(threaded_link_crawler, args, kwargs) p.start() processes.append(p) # wait for processes to complete for p in processes: p.join()def threaded_crawler(seed_url, delay=5, cache=None, scrape_callback=None, user_agent='wu_being', proxies=None, num_retries=1, max_threads=10, timeout=60): """Crawl using multiple threads """ # the queue of URL's that still need to be crawled crawl_queue = MongoQueue() ###################### crawl_queue.clear() ###################### crawl_queue.push(seed_url) ###################### D = Downloader(cache=cache, delay=delay, user_agent=user_agent, proxies=proxies, num_retries=num_retries, timeout=timeout) def process_queue(): while True: # keep track that are processing url try: url = crawl_queue.pop() ###################### except KeyError: # currently no urls to process break else: html = D(url) if scrape_callback: try: links = scrape_callback(url, html) or [] except Exception as e: print 'Error in callback for: {}: {}'.format(url, e) else: for link in links: ############# # add this new link to queue###################### crawl_queue.push(normalize(seed_url, link))###################### crawl_queue.complete(url) ###################### # wait for all download threads to finish threads = [] while threads or crawl_queue: ###################### for thread in threads: if not thread.is_alive(): threads.remove(thread) while len(threads) < max_threads and crawl_queue.peek(): ####################### # can start some more threads thread = threading.Thread(target=process_queue) thread.setDaemon(True) # set daemon so main thread can exit when receives ctrl-c thread.start() threads.append(thread) time.sleep(SLEEP_TIME)def normalize(seed_url, link): """Normalize this URL by removing hash and adding domain """ link, _ = urlparse.urldefrag(link) # remove hash to avoid duplicates return urlparse.urljoin(seed_url, link)在MongoQueue定义了三种状态：
- OUTSTANDING：添加一人新URL时；
- PROCESSING：队列中取出准备下载时；
- COMPLETE：完成下载时。
由于大部分线程都在从队列准备取出未完成处理的URL，比如处理的URL线程被终止的情况。所以在该类中使用了timeout参数，默认为300秒。在repaire()方法中，如果某个URL的处理时间超过了这个timeout值，我们就认定处理过程出现了错误，URL的状态将被重新设为OUTSTANDING，以便再次处理。
from datetime import datetime, timedeltafrom pymongo import MongoClient, errorsclass MongoQueue: """ >>> timeout = 1 >>> url = 'http://example.webscraping.com' >>> q = MongoQueue(timeout=timeout) >>> q.clear() # ensure empty queue >>> q.push(url) # add test URL >>> q.peek() == q.pop() == url # pop back this URL True >>> q.repair() # immediate repair will do nothin >>> q.pop() # another pop should be empty >>> q.peek() >>> import time; time.sleep(timeout) # wait for timeout >>> q.repair() # now repair will release URL Released: test >>> q.pop() == url # pop URL again True >>> bool(q) # queue is still active while outstanding True >>> q.complete(url) # complete this URL >>> bool(q) # queue is not complete False """ # possible states of a download OUTSTANDING, PROCESSING, COMPLETE = range(3) def __init__(self, client=None, timeout=300): """ host: the host to connect to MongoDB port: the port to connect to MongoDB timeout: the number of seconds to allow for a timeout """ self.client = MongoClient() if client is None else client self.db = self.client.cache self.timeout = timeout def __nonzero__(self): """Returns True if there are more jobs to process """ record = self.db.crawl_queue.find_one( {'status': {'$ne': self.COMPLETE}} ) return True if record else False def push(self, url): """Add new URL to queue if does not exist """ try: self.db.crawl_queue.insert({'_id': url, 'status': self.OUTSTANDING}) except errors.DuplicateKeyError as e: pass # this is already in the queue def pop(self): """Get an outstanding URL from the queue and set its status to processing. If the queue is empty a KeyError exception is raised. """ record = self.db.crawl_queue.find_and_modify( query={'status': self.OUTSTANDING}, update={'$set': {'status': self.PROCESSING, 'timestamp': datetime.now()}} ) if record: return record['_id'] else: self.repair() raise KeyError() def peek(self): record = self.db.crawl_queue.find_one({'status': self.OUTSTANDING}) if record: return record['_id'] def complete(self, url): self.db.crawl_queue.update({'_id': url}, {'$set': {'status': self.COMPLETE}}) def repair(self): """Release stalled jobs """ record = self.db.crawl_queue.find_and_modify( query={ 'timestamp': {'$lt': datetime.now() - timedelta(seconds=self.timeout)}, 'status': {'$ne': self.COMPLETE} }, update={'$set': {'status': self.OUTSTANDING}} ) if record: print 'Released:', record['_id'] def clear(self): self.db.crawl_queue.drop()4性能对比
此外，下载的带宽是有限的，最终添加新线程将无法加快的下载速度。因此要想获得更好性能的爬虫，就需要在多台服务器上分布式部署爬虫，并且所有服务器都要指向同一个MongoDB队列实例中。
Wu_Being 博客声明：本人博客欢迎转载，请标明博客原文和原链接！谢谢！
【Python爬虫系列】《【Python爬虫4】并发并行下载》http://blog.csdn.net/u014134180/article/details/55506994
Python爬虫系列的GitHub代码文件：https://github.com/1040003585/WebScrapingWithPython
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