1为链接爬虫添加缓存支持2磁盘缓存1用磁盘缓存的实现2缓存测试3节省磁盘空间4清理过期数据5用磁盘缓存的缺点3数据库缓存1NoSQL是什么2安装MongoDB3MongoDB概述4MongoDB缓存实现5压缩存储6缓存测试7MongoDB缓存完整代码
上篇文章，我们学习了如何提取网页中的数据，以及将提取结果存到表格中。如果我们还想提取另一字段，则需要重新再下载整个网页，这对我们这个小型的示例网站问题不大，但对于数百万个网页的网站而言来说就要消耗几个星期的时间。所以，我们可以先对网页进行缓存，就使得每个网页只下载一次。
1为链接爬虫添加缓存支持
我们将downloader重构一类，这样参数只需在构造方法中设置一次，就能在后续多次复用，在URL下载之前进行缓存检查，并把限速功能移到函数内部。在Downloader类的call特殊方法实现了下载前先检查缓存，如果已经定义该URL缓存则再检查下载中是否遇到了服务端错误，如果都没问题表明缓存结果可用，否则都需要正常下载该URL存到缓存中。downloader方法返回添加了HTTP状态码，以便缓存中存储错误机校验。如果不需要限速或缓存的话，你可以直接调用该方法，这样就不会通过call方法调用了。class Downloader: def __init__(self, delay=5, user_agent='Wu_Being', PRoxies=None, num_retries=1, cache=None): self.throttle = Throttle(delay) self.user_agent = user_agent self.proxies = proxies self.num_retries = num_retries self.cache = cache def __call__(self, url): result = None if self.cache: try: result = self.cache[url] except KeyError: # url is not available in cache pass else: if self.num_retries > 0 and 500 <= result['code'] < 600: # server error so ignore result from cache and re-download result = None if result is None: # result was not loaded from cache so still need to download self.throttle.wait(url) proxy = random.choice(self.proxies) if self.proxies else None headers = {'User-agent': self.user_agent} result = self.download(url, headers, proxy=proxy, num_retries=self.num_retries) if self.cache: # save result to cache self.cache[url] = result return result['html'] def download(self, url, headers, proxy, num_retries, data=None): print 'Downloading:', url ... return {'html': html, 'code': code}class Throttle: def __init__(self, delay): ... def wait(self, url): ...为了支持缓存功能，链接爬虫代码也需用一些微调，包括添加cache参数、移除限速以及将download函数替换为新的类。
from downloader import Downloaderdef link_crawler(... cache=None): crawl_queue = [seed_url] seen = {seed_url: 0} # track how many URL's have been downloaded num_urls = 0 rp = get_robots(seed_url) #cache.clear() ############################### D = Downloader(delay=delay, user_agent=user_agent, proxies=proxies, num_retries=num_retries, cache=cache) while crawl_queue: url = crawl_queue.pop() depth = seen[url] # check url passes robots.txt restrictions if rp.can_fetch(user_agent, url): html = D(url) ###def __call__(self, url): links = [] ...def normalize(seed_url, link): ...def same_domain(url1, url2): ...def get_robots(url): ...def get_links(html): ..."""if __name__ == '__main__': link_crawler('http://example.webscraping.com', '/(index|view)', delay=0, num_retries=1, user_agent='BadCrawler') link_crawler('http://example.webscraping.com', '/(index|view)', delay=0, num_retries=1, max_depth=1, user_agent='GoodCrawler')"""现在，这个支持缓存的网络爬虫的基本架构已经准备好了，下面就要开始构建实际的缓存功能了。
2磁盘缓存
| 操作系统 | 文件系统 | 非法文件名字符 | 文件名最大长度 |
| linux | Ext3/Ext4 | /和/0 | 255个字节 |
| OS X | HFS Plus | :和/0 | 255个UTF-16编码单元 |
| Windows | NTFS | /、/、?、:、*、"、>、<和` | ` |
为了保证在不同文件系统中，我们的文件路径都是安全的，就需要把除数字、字母和基本符号的其他字符替换为下划线。
>>> import re>>> url="http://example.webscraping.com/default/view/australia-1">>> re.sub('[^/0-9a-zA-Z/-,.;_ ]','_',url)'http_//example.webscraping.com/default/view/australia-1'此外，文件名及其目录长度需要限制在255个字符以内。
>>> filename=re.sub('[^/0-9a-zA-Z/-,.;_ ]','_',url)>>> filename='/'.join(segment[:255] for segment in filename.split('/'))>>> print filenamehttp_//example.webscraping.com/default/view/australia-1>>> print '#'.join(segment[:5] for segment in filename.split('/'))http_##examp#defau#view#austr>>> 还有一种边界情况，就是URL以斜杠结尾。这样分割URL后就会造成一个非法的文件名。例如： - http://example.webscraping.com/index/ - http://example.webscraping.com/index/1
对于第一个URL可以在后面添加index.html作为文件名，所以可以把index作为目录名，1为子目录名，index.html为文件名。
>>> import urlparse>>> components=urlparse.urlsplit('http://exmaple.scraping.com/index/')>>> print componentsSplitResult(scheme='http', netloc='exmaple.scraping.com', path='/index/', query='', fragment='')>>> print components.path/index/>>> path=components.path>>> if not path:... path='/index.html'... elif path.endswith('/'):... path+='index.html'... >>> filename=components.netloc+path+components.query>>> filename'exmaple.scraping.com/index/index.html'>>> 2.1用磁盘缓存的实现
现在可以把URL到目录和文件名完整映射逻辑结合起来，就形成了磁盘缓存的主要部分。该构造方法传入了用于设定缓存位置的参数，然后在url_to_path方法中应用了前面讨论的文件名限制。
from link_crawler import link_crawlerclass DiskCache: def __init__(self, cache_dir='cache', ...): """ cache_dir: the root level folder for the cache """ self.cache_dir = cache_dir ... def url_to_path(self, url): """Create file system path for this URL """ components = urlparse.urlsplit(url) # when empty path set to /index.html path = components.path if not path: path = '/index.html' elif path.endswith('/'): path += 'index.html' filename = components.netloc + path + components.query # replace invalid characters filename = re.sub('[^/0-9a-zA-Z/-.,;_ ]', '_', filename) # restrict maximum number of characters filename = '/'.join(segment[:255] for segment in filename.split('/')) return os.path.join(self.cache_dir, filename) #拼接当前目录和文件名为完整目录 def __getitem__(self, url): ... def __setitem__(self, url, result): ... def __delitem__(self, url): ... def has_expired(self, timestamp): ... def clear(self): ...if __name__ == '__main__': link_crawler('http://example.webscraping.com/', '/(index|view)', cache=DiskCache())现在我们还缺少根据文件名存取数据的方法，就是Downloader类result=cache[url]和cache[url]=result的接口方法：__getitem__()和__setitem__()两个特殊方法。
import pickleclass DiskCache: def __init__(self, cache_dir='cache', expires=timedelta(days=30), compress=True): ... def url_to_path(self, url): ... def __getitem__(self, url): ... def __setitem__(self, url, result): """Save data to disk for this url """ path = self.url_to_path(url) folder = os.path.dirname(path) if not os.path.exists(folder): os.makedirs(folder) with open(path, 'wb') as fp: fp.write(pickle.dumps(result))在__setitem__()中，我们使用url_to_path()方法将URL映射为安全文件名，在必要情况下还需要创建目录。这里使用的pickle模块会把输入转化为字符串（序列化），然后保存到磁盘中。
import pickleclass DiskCache: def __init__(self, cache_dir='cache', expires=timedelta(days=30), compress=True): ... def url_to_path(self, url): ... def __getitem__(self, url): """Load data from disk for this URL """ path = self.url_to_path(url) if os.path.exists(path): with open(path, 'rb') as fp: return pickle.loads(fp.read()) else: # URL has not yet been cached raise KeyError(url + ' does not exist') def __setitem__(self, url, result): ...在__getitem__()中，还是先用url_to_path()方法将URL映射为安全文件名。然后检查文件是否存在，如果存在则加载内容，并执行反序列化，恢复其原始数据类型；如果不存在，则说明缓存中还没有该URL的数据，此时会抛出KeyError异常。
2.2缓存测试
可以在python命令前加time计时。我们可以发现，如果是在本地服务器的网站，当缓存为空时爬虫实际耗时0m58.710s，第二次运行全部从缓存读取花了0m0.221s,快了265多倍。如果是爬取远程服务器的网站的数据时，将会耗更多时间。
wu_being@Ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ time python 2disk_cache_Nozip127.py Downloading: http://127.0.0.1:8000/places/Downloading: http://127.0.0.1:8000/places/default/index/1...Downloading: http://127.0.0.1:8000/places/default/view/Afghanistan-1real 0m58.710suser 0m0.684ssys 0m0.120swu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ time python 2disk_cache_Nozip127.py real 0m0.221suser 0m0.204ssys 0m0.012s2.3节省磁盘空间
为节省缓存占用空间，我们可以对下载的HTML文件进行压缩处理，使用zlib压缩序列化字符串即可。
fp.write(zlib.compress(pickle.dumps(result)))从磁盘加载后解压的代码如下：
return pickle.loads(zlib.decompress(fp.read()))压缩所有网页之后，缓存占用大小2.8 MB下降到821.2 KB，耗时略有增加。
wu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ time python 2disk_cache.py Downloading: http://127.0.0.1:8000/places/Downloading: http://127.0.0.1:8000/places/default/index/1...Downloading: http://127.0.0.1:8000/places/default/view/Afghanistan-1real 1m0.011suser 0m0.800ssys 0m0.104swu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ wu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ time python 2disk_cache.py real 0m0.252suser 0m0.228ssys 0m0.020swu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ 2.4清理过期数据
本节中，我们将为缓存数据添加过期时间，以便爬虫知道何时需要重新下载网页。在构造方法中，我们使用timedelta对象将默认过期时间设置为30天，在__set__方法中把当前时间戳保存在序列化数据中，在__get__方法中对比当前时间和缓存时间，检查是否过期。
from datetime import datetime, timedeltaclass DiskCache: def __init__(self, cache_dir='cache', expires=timedelta(days=30), compress=True): """ cache_dir: the root level folder for the cache expires: timedelta of amount of time before a cache entry is considered expired compress: whether to compress data in the cache """ self.cache_dir = cache_dir self.expires = expires self.compress = compress def __getitem__(self, url): """Load data from disk for this URL """ path = self.url_to_path(url) if os.path.exists(path): with open(path, 'rb') as fp: data = fp.read() if self.compress: data = zlib.decompress(data) result, timestamp = pickle.loads(data) if self.has_expired(timestamp): raise KeyError(url + ' has expired') return result else: # URL has not yet been cached raise KeyError(url + ' does not exist') def __setitem__(self, url, result): """Save data to disk for this url """ path = self.url_to_path(url) folder = os.path.dirname(path) if not os.path.exists(folder): os.makedirs(folder) data = pickle.dumps((result, datetime.utcnow())) if self.compress: data = zlib.compress(data) with open(path, 'wb') as fp: fp.write(data) ... def has_expired(self, timestamp): """Return whether this timestamp has expired """ return datetime.utcnow() > timestamp + self.expires为了测试时间功能，我们可以将其缩短为5秒，如下操作：
""" Dictionary interface that stores cached values in the file system rather than in memory. The file path is formed from an md5 hash of the key. """>>> from disk_cache import DiskCache>>> cache=DiskCache()>>> url='http://www.baidu.com'>>> result={'html':'<html>...','code':200}>>> cache[url]=result>>> cache[url]{'code': 200, 'html': '<html>...'}>>> cache[url]['html']==result['html']True>>> >>> from datetime import timedelta>>> cache2=DiskCache(expires=timedelta(seconds=5))>>> url2='https://www.baidu.sss'>>> result2={'html':'<html>..ss.','code':500}>>> cache2[url2]=result2>>> cache2[url2]{'code': 200, 'html': '<html>...'}>>> cache2[url2]{'code': 200, 'html': '<html>...'}>>> cache2[url2]{'code': 200, 'html': '<html>...'}>>> cache2[url2]{'code': 200, 'html': '<html>...'}>>> cache2[url2]Traceback (most recent call last): File "<stdin>", line 1, in <module> File "disk_cache.py", line 57, in __getitem__ raise KeyError(url + ' has expired')KeyError: 'http://www.baidu.com has expired'>>> cache2.clear()2.5用磁盘缓存的缺点
由于受制于文件系统的限制，之前我们将URL映射为安全文件名，然而这样又会引发一些问题： - 有些URL会被映射为相同的文件名。比如URL：.../count.asp?a+b,.../count.asp?a*b。 - URL截断255个字符的文件名也可能相同。因为URL可以超过2000下字符。
使用URL哈希值为文件名可以带来一定的改善。这样也有一些问题： - 每个卷和每个目录下的文件数量是有限制的。FAT32文件系统每个目录的最大文件数65535，但可以分割到不同目录下。 - 文件系统可存储的文件总数也是有限的。ext4分区目前支持略多于1500万个文件，而一个大型网站往往拥有超过1亿个网页。
要想避免这些问题，我们需要把多个缓存网页合并到一个文件中，并使用类似B+树的算法进行索引。但我们不会自己实现这种算法，而是在下一节中介绍已实现这类算法的数据库。
3数据库缓存
爬取时，我们可能需要缓存大量数据，但又无须任何复杂的连接操作，因此我们将选用NoSQL数据库，这种数据库比传统的关系型数据库更容易扩展。在本节中，我们将选用目前非常流行的MongoDB作为缓存数据库。
3.1NoSQL是什么
NoSQL全称为Not Only SQL，是一种相对较新的数据库设计方式。传统的关系模型使用是固定模式，并将数据分割到各个表中。然而，对于大数据集的情况，数据量太大使其难以存放在单一服务器中，此时就需要扩展到多台服务器。不过，关系模型对于这种扩展的支持并不够好，因为在查询多个表时，数据可能在不同的服务器中。相反，NoSQL数据库通常是无模式的，从设计之初就考虑了跨服务器无缝分片的问题。在NoSQL中，有多种方式可以实现该目标，分别是：- 列数据存储（如HBase）； - 键值对存储（如Redis）； - 图形数据库（如Neo4j）； - 面向文档的数据库（如MongoDB）。
3.2安装MongoDB
MongoDB可以从https://www.mongodb.org/downloads 下载。然后安装其Python封装库：
pip install pymongo检测安装是否成功，在本地启动MongoDB服务器：
wu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ mongod -dbpath MongoD2017-01-17T21:20:46.224+0800 [initandlisten] MongoDB starting : pid=1978 port=27017 dbpath=MongoD 64-bit host=ubuntukylin642017-01-17T21:20:46.224+0800 [initandlisten] db version v2.6.102017-01-17T21:20:46.224+0800 [initandlisten] git version: nogitversion2017-01-17T21:20:46.225+0800 [initandlisten] OpenSSL version: OpenSSL 1.0.2g 1 Mar 20162017-01-17T21:20:46.225+0800 [initandlisten] build info: Linux lgw01-12 3.19.0-25-generic #26~14.04.1-Ubuntu SMP Fri Jul 24 21:16:20 UTC 2015 x86_64 BOOST_LIB_VERSION=1_582017-01-17T21:20:46.225+0800 [initandlisten] allocator: tcmalloc2017-01-17T21:20:46.225+0800 [initandlisten] options: { storage: { dbPath: "MongoD" } }2017-01-17T21:20:46.269+0800 [initandlisten] journal dir=MongoD/journal2017-01-17T21:20:46.270+0800 [initandlisten] recover : no journal files present, no recovery needed2017-01-17T21:20:49.126+0800 [initandlisten] preallocateIsFaster=true 33.722017-01-17T21:20:51.932+0800 [initandlisten] preallocateIsFaster=true 32.72017-01-17T21:20:55.729+0800 [initandlisten] preallocateIsFaster=true 32.362017-01-17T21:20:55.730+0800 [initandlisten] preallocateIsFaster check took 9.459 secs2017-01-17T21:20:55.730+0800 [initandlisten] preallocating a journal file MongoD/journal/prealloc.02017-01-17T21:20:58.042+0800 [initandlisten] File Preallocator Progress: 608174080/1073741824 56%2017-01-17T21:21:03.290+0800 [initandlisten] File Preallocator Progress: 744488960/1073741824 69%2017-01-17T21:21:08.043+0800 [initandlisten] File Preallocator Progress: 954204160/1073741824 88%2017-01-17T21:21:18.347+0800 [initandlisten] preallocating a journal file MongoD/journal/prealloc.12017-01-17T21:21:21.166+0800 [initandlisten] File Preallocator Progress: 639631360/1073741824 59%2017-01-17T21:21:26.328+0800 [initandlisten] File Preallocator Progress: 754974720/1073741824 70%...然后，在Python中，使用MongoDB的默认端口尝试连接MongoDB：
>>> from pymongo import MongoClient>>> client=MongoClient('localhost',27017)3.3MongoDB概述
下面是MongoDB示例代码：
>>> from pymongo import MongoClient>>> client=MongoClient('localhost',27017)>>> url='http://www.baidu.com/view/China-47'>>> html='...<html>...'>>> db=client.cache>>> db.webpage.insert({'url':url,'html':html})ObjectId('587e2cb26b00c10b956e0be9')>>> db.webpage.find_one({'url':url}){u'url': u'http://www.baidu.com/view/China-47', u'_id': ObjectId('587e2cb26b00c10b956e0be9'), u'html': u'...<html>...'}>>> db.webpage.find({'url':url})<pymongo.cursor.Cursor object at 0x7fcde0ca60d0>>>> db.webpage.find({'url':url}).count()1当插入同一条记录时，MongoDB会欣然接受并执行这次操作，但通过查找发现记录没更新。
>>> db.webpage.insert({'url':url,'html':html})ObjectId('587e2d546b00c10b956e0bea')>>> db.webpage.find({'url':url}).count()2>>> db.webpage.find_one({'url':url}){u'url': u'http://www.baidu.com/view/China-47', u'_id': ObjectId('587e2cb26b00c10b956e0be9'), u'html': u'...<html>...'}为了存储最新的记录，并避免重复记录，我们将ID设置为URL，并执行upsert操作。该操作表示当记录存在时则更新记录，否则插入新记录。
>>> >>> new_html='<...>...'>>> db.webpage.update({'_id':url},{'$set':{'html':new_html}},upsert=True){'updatedExisting': True, u'nModified': 1, u'ok': 1, u'n': 1}>>> db.webpage.find_one({'_id':url}){u'_id': u'http://www.baidu.com/view/China-47', u'html': u'<...>...'}>>> db.webpage.find({'_id':url}).count()1>>> db.webpage.update({'_id':url},{'$set':{'html':new_html}},upsert=True){'updatedExisting': True, u'nModified': 0, u'ok': 1, u'n': 1}>>> db.webpage.find({'_id':url}).count()1>>> MongoDB官方文档：http://docs.mongodb.org/manual/
3.4MongoDB缓存实现
现在我们已经准备好创建基于MongoDB的缓存了，这里使用了和之前的DiskCache类相同的接口。我们在下面构造方法中创建了timestamp索引，在达到给定的时间戳之后，MongoDB的这一便捷功能可以自动过期删除记录。
import picklefrom datetime import datetime, timedeltafrom pymongo import MongoClientclass MongoCache: def __init__(self, client=None, expires=timedelta(days=30)): """ client: mongo database client expires: timedelta of amount of time before a cache entry is considered expired """ # if a client object is not passed # then try connecting to mongodb at the default localhost port self.client = MongoClient('localhost', 27017) if client is None else client #create collection to store cached webpages, # which is the equivalent of a table in a relational database self.db = self.client.cache self.db.webpage.create_index('timestamp', expireAfterSeconds=expires.total_seconds()) def __getitem__(self, url): """Load value at this URL """ record = self.db.webpage.find_one({'_id': url}) if record: return record['result'] else: raise KeyError(url + ' does not exist') def __setitem__(self, url, result): """Save value for this URL """ record = {'result': result, 'timestamp': datetime.utcnow()} self.db.webpage.update({'_id': url}, {'$set': record}, upsert=True)下面我们来测试一下这个MongoCache类，我们用默认0时间间隔timedelta()对象进行测试，此时记录创建后应该会马上会被删除，但实际却没有。这是因为MongoDB运行机制造成的，MongoDB后台运行了一个每分钟检查一次过期记录的任务。所以我们可以再等一分钟，就会发现缓存过期机制已经运行成功了。
>>> from mongo_cache import MongoCache>>> from datetime import timedelta>>> cache=MongoCache(expires=timedelta())>>> result={'html':'.....'}>>> cache[url]=result>>> cache[url]{'html': '.....'}>>> cache[url]{'html': '.....'}>>> import time>>> import time;time.sleep(60)>>> cache[url]Traceback (most recent call last): File "<stdin>", line 1, in <module> File "mongo_cache.py", line 62, in __getitem__ raise KeyError(url + ' does not exist')KeyError: 'http://www.baidu.com/view/China-47 does not exist'>>> 3.5压缩存储
import pickleimport zlibfrom bson.binary import Binaryclass MongoCache: def __getitem__(self, url): """Load value at this URL """ record = self.db.webpage.find_one({'_id': url}) if record: #return record['result'] return pickle.loads(zlib.decompress(record['result'])) else: raise KeyError(url + ' does not exist') def __setitem__(self, url, result): """Save value for this URL """ #record = {'result': result, 'timestamp': datetime.utcnow()} record = {'result': Binary(zlib.compress(pickle.dumps(result))), 'timestamp': datetime.utcnow()} self.db.webpage.update({'_id': url}, {'$set': record}, upsert=True)3.6缓存测试
wu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ time python 3mongo_cache.py Downloading: http://127.0.0.1:8000/places/Downloading: http://127.0.0.1:8000/places/default/index/1Downloading: http://127.0.0.1:8000/places/default/index/2...Downloading: http://127.0.0.1:8000/places/default/view/Algeria-4Downloading: http://127.0.0.1:8000/places/default/view/Albania-3Downloading: http://127.0.0.1:8000/places/default/view/Aland-Islands-2Downloading: http://127.0.0.1:8000/places/default/view/Afghanistan-1real 0m59.239suser 0m1.164ssys 0m0.108swu_being@ubuntukylin64:~/GitHub/WebScrapingWithPython/3.下载缓存$ time python 3mongo_cache.py real 0m0.695suser 0m0.408ssys 0m0.044s可以看出，用数据库缓存的读取时间是磁盘缓存的两倍，但成功地避免了磁盘缓存的缺点。
3.7MongoDB缓存完整代码
try: import cPickle as pickleexcept ImportError: import pickleimport zlibfrom datetime import datetime, timedeltafrom pymongo import MongoClientfrom bson.binary import Binaryfrom link_crawler import link_crawlerclass MongoCache: """ Wrapper around MongoDB to cache downloads >>> cache = MongoCache() >>> cache.clear() >>> url = 'http://example.webscraping.com' >>> result = {'html': '...'} >>> cache[url] = result >>> cache[url]['html'] == result['html'] True >>> cache = MongoCache(expires=timedelta()) >>> cache[url] = result >>> # every 60 seconds is purged http://docs.mongodb.org/manual/core/index-ttl/ >>> import time; time.sleep(60) >>> cache[url] Traceback (most recent call last): ... KeyError: 'http://example.webscraping.com does not exist' """ def __init__(self, client=None, expires=timedelta(days=30)): """ client: mongo database client expires: timedelta of amount of time before a cache entry is considered expired """ # if a client object is not passed # then try connecting to mongodb at the default localhost port self.client = MongoClient('localhost', 27017) if client is None else client #create collection to store cached webpages, # which is the equivalent of a table in a relational database self.db = self.client.cache self.db.webpage.create_index('timestamp100s', expireAfterSeconds=expires.total_seconds()) #timestamp def __contains__(self, url): try: self[url] except KeyError: return False else: return True def __getitem__(self, url): """Load value at this URL """ record = self.db.webpage.find_one({'_id': url}) if record: #return record['result'] return pickle.loads(zlib.decompress(record['result'])) else: raise KeyError(url + ' does not exist') def __setitem__(self, url, result): """Save value for this URL """ #record = {'result': result, 'timestamp': datetime.utcnow()} record = {'result': Binary(zlib.compress(pickle.dumps(result))), 'timestamp100s': datetime.utcnow()} #timestamp self.db.webpage.update({'_id': url}, {'$set': record}, upsert=True) def clear(self): self.db.webpage.drop() print 'drop() successful'if __name__ == '__main__': #link_crawler('http://example.webscraping.com/', '/(index|view)', cache=MongoCache()) #link_crawler('http://127.0.0.1:8000/places/', '/places/default/(index|view)/', cache=MongoCache()) link_crawler('http://127.0.0.1:8000/places/', '/places/default/(index|view)/', cache=MongoCache(expires=timedelta(seconds=100)))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3】在下载的本地缓存做爬虫》http://blog.csdn.net/u014134180/article/details/55506984 Python爬虫系列的GitHub代码文件：https://github.com/1040003585/WebScrapingWithPython
