def run(self):
if log.isEnabledFor(logging.DEBUG):
log.debug("邮件服务线程启动")
try:
while True:
time.sleep(SMTP_SEND_INTERVAL)
# 准备发送的内容
msg = MIMEText(self.get_email_content(), 'plain', 'utf-8')
msg['from'] = SMTP_FROM_ADDR
msg['to'] = SMTP_TO_ADDR
msg['Subject'] = Header(self.get_email_header(),
'utf-8').encode()
# 发送
smtp_server = smtplib.SMTP(SMTP_SERVER_HOST, SMTP_SERVER_PORT)
smtp_server.login(SMTP_FROM_ADDR, SMTP_SERVER_PASSWORD)
smtp_server.sendmail(SMTP_FROM_ADDR, [SMTP_TO_ADDR],
msg.as_string())
smtp_server.quit()
# 更新最后一次发送时间
self.lastSendTime = datetime.datetime.now()
except Exception as e:
if log.isEnabledFor(logging.ERROR):
log.error("邮件发送失败")
log.exception(e)
self.status = 'error'
def run(self):
try:
while True:
raw_data = None
token = None
thread_name = None
data = self.cache_queue.get_data_from_user_list_cache_queue()
if QUEUE_ELEM_HTML in data:
raw_data = data[QUEUE_ELEM_HTML]
if QUEUE_ELEM_TOKEN in data:
token = data[QUEUE_ELEM_TOKEN]
if QUEUE_ELEM_THREAD_NAME in data:
thread_name = data[QUEUE_ELEM_THREAD_NAME]
if raw_data is not None and token is not None:
token_list = self.parse_user_list(raw_data, token)
if token_list is not None:
if log.isEnabledFor(logging.DEBUG):
log.debug('[' + thread_name + ']开始分析用户“' + token +
'”的关注列表')
self.user_token_cache_queue.add_token_into_cache_queue(
token_list)
except Exception as e:
if log.isEnabledFor(logging.ERROR):
log.exception(e)
self.status = 'error'
def run(self):
if log.isEnabledFor(logging.DEBUG):
log.debug('用户信息抓取线程' + self.thread_name + '启动')
try:
self.user_info_scrape()
except Exception as e:
if log.isEnabledFor(logging.ERROR):
log.exception(e)
self.status = 'error'
def validate_proxy_ip(self, proxy_ip_info):
if proxy_ip_info is None:
return False
# 构造代理信息
proxy_ip = proxy_ip_info[proxyCore.PROXY_IP]
proxy_port = proxy_ip_info[proxyCore.PROXY_PORT]
proxy_protocol = proxy_ip_info[proxyCore.PROXY_PROTOCOL].lower()
proxy = {proxy_protocol: proxy_ip + ':' + proxy_port}
# 使用代理进行连接
self.session.headers = header
self.session.proxies = proxy
retry_time = 0
while retry_time < NETWORK_RECONNECT_TIMES:
try:
response = self.session.get(url, timeout=CONNECT_TIMEOUT)
# 解析返回的当前使用的IP并判断是否有效
match_list = re.findall(r'[0-9]+(?:\.[0-9]+){3}', response.text)
if len(match_list) > 0:
current_ip = match_list.pop()
if current_ip is not None and current_ip == proxy_ip:
if log.isEnabledFor(logging.DEBUG):
log.debug("获取到一个可用的代理IP")
return True
else:
retry_time += 1
except Exception:
retry_time += 1
return False
def run(self):
try:
while True:
raw_data = None
token = None
thread_name = None
data = self.cache_queue.get_data_from_user_info_cache_queue()
if QUEUE_ELEM_HTML in data:
raw_data = data[QUEUE_ELEM_HTML]
if QUEUE_ELEM_TOKEN in data:
token = data[QUEUE_ELEM_TOKEN]
if QUEUE_ELEM_THREAD_NAME in data:
thread_name = data[QUEUE_ELEM_THREAD_NAME]
if raw_data is not None and token is not None:
user_info = self.parse_user_information(raw_data, token)
if user_info is not None:
if log.isEnabledFor(logging.DEBUG):
log.debug('[' + thread_name + "]搜索到一个用户:" +
user_info[USER_NAME])
# 使用布隆过滤器标记
self.bloom_filter.mark_value(user_info[USER_URL_TOKEN])
self.db_connection.add_user_info(
self.convert_user_info(user_info))
# 封装代分析用户关注列表的token信息
token_info = {
USER_URL_TOKEN: user_info[USER_URL_TOKEN],
USER_FOLLOWING_COUNT:
user_info[USER_FOLLOWING_COUNT],
USER_FOLLOWER_COUNT: user_info[USER_FOLLOWER_COUNT]
}
# print(token_info)
self.user_token_cache_queue.add_token_into_analysed_cache_queue(
[token_info])
except Exception as e:
if log.isEnabledFor(logging.ERROR):
log.exception(e)
self.status = 'error'
def __init__(self):
# 初始化配置参数
self.config_init()
# 初始化数据库模块
self.DBConnectModule = DBConnector.DBConnectModule()
# 初始化 BloomFilter 模块
self.bloomFilterModule = BloomFilter.BloomFilter()
# 初始化用户Token缓存
self.userTokenCacheQueue = UserList.UserTokenCacheQueue(
self.DBConnectModule)
# 初始化待分析网页缓存
self.cacheQueue = DataParser.CacheQueue()
# 初始化数据获取模块
self.dataFetchModule = DataFetch.DataFetchModule(IS_PROXY_ENABLE)
# 初始化数据解析模块
self.dataParseModule = DataParser.DataParseModule(
self.DBConnectModule, self.userTokenCacheQueue, self.cacheQueue,
self.bloomFilterModule)
# 初始化邮件服务模块
if IS_EMAIL_NOTIFICATION_ENABLE is True:
self.emailService = EmailService.EmailService(self.DBConnectModule)
# 初始化用户信息爬取线程
self.user_info_scrape_thread_list = []
for thread_count in range(USER_INFO_SCRAPE_THREAD_NUM):
thread_name = 'Info-Thread' + str(thread_count)
user_info_scrape_thread = UserInfoScrapeThread(
thread_name, self.dataFetchModule, self.userTokenCacheQueue,
self.cacheQueue, self.bloomFilterModule)
self.user_info_scrape_thread_list.append(user_info_scrape_thread)
# 初始化用户列表爬取线程
self.user_list_scrape_thread_list = []
for thread_count in range(USER_LIST_SCRAPE_THREAD_NUM):
thread_name = 'List-Thread' + str(thread_count)
user_list_scrape_thread = UserListScrapeThread(
thread_name, self.DBConnectModule, self.dataFetchModule,
self.userTokenCacheQueue, self.cacheQueue)
self.user_info_scrape_thread_list.append(user_list_scrape_thread)
# 若有起始token则放入
if start_token != '':
self.userTokenCacheQueue.add_token_into_cache_queue([start_token])
if log.isEnabledFor(logging.DEBUG):
log.debug("爬虫核心模块初始化完毕")
def user_info_scrape(self):
# 为该线程绑定 session
self.data_fetch_module.thread_bind_session(self.thread_name)
if log.isEnabledFor(logging.DEBUG):
log.debug('用户信息爬取线程[' + self.thread_name + ']开始运行')
while True:
# 从未分析 token 缓存列表中获取一个可用的token
while True:
token = self.user_token_cache_queue.get_token_from_cache_queue(
)
if token is not None:
if self.is_token_available(token) is False:
break
else:
time.sleep(0.5)
# 抓取 token 对应用户的个人信息，并保存
response = self.data_fetch_module.fetch_data_of_url(
self.generate_user_info_url(token), self.thread_name)
# 判断返回的数据是否有效，若有效再继续对数据进行分析
if response is not None:
if response == 'reuse':
# 将该 token 放回队列
self.user_token_cache_queue.add_token_into_cache_queue(
[token])
else:
# 添加到待分析队列
self.cache_queue.add_data_into_user_info_cache_queue({
DataParser.QUEUE_ELEM_HTML:
response.text,
DataParser.QUEUE_ELEM_TOKEN:
token,
DataParser.QUEUE_ELEM_THREAD_NAME:
self.thread_name
})
# 爬取时间间隔
time.sleep(SCRAPE_TIME_INTERVAL)
def __init__(self, db_connection):
self.db_connection = db_connection
self.analysed_cache_queue = queue.Queue(MAX_ANALYSED_CACHE_QUEUE_SIZE)
self.cache_queue = queue.Queue(MAX_CACHE_QUEUE_SIZE)
# 配置未分析用户信息列表
token_total = self.db_connection.get_user_token_num()
if token_total > 0:
temp_list = self.get_token_from_db(REMAIN_CACHE_QUEUE_SIZE)
for token in temp_list:
self.cache_queue.put(token)
# 配置已分析用户信息列表
token_total = self.db_connection.get_analysed_token_num()
if token_total > 0:
temp_list = self.get_analysed_token_from_db(
REMAIN_ANALYSED_CACHE_QUEUE_SIZE)
for token in temp_list:
self.analysed_cache_queue.put(token)
if log.isEnabledFor(logging.DEBUG):
log.debug('用户 Token 缓存列表初始化完毕')
def send_message(email_content):
# 准备发送的内容
now = datetime.datetime.now()
header = SMTP_EMAIL_HEADER + '[' + str(now.month) + '-' + str(now.day) + ' ' + \
str(now.hour) + ':' + str(now.minute) + ':' + str(now.second) + ']'
msg = MIMEText(email_content, 'plain', 'utf-8')
msg['from'] = SMTP_FROM_ADDR
msg['to'] = SMTP_TO_ADDR
msg['Subject'] = Header(header, 'utf-8').encode()
# 发送
try:
smtp_server = smtplib.SMTP(SMTP_SERVER_HOST, SMTP_SERVER_PORT)
smtp_server.login(SMTP_FROM_ADDR, SMTP_SERVER_PASSWORD)
smtp_server.sendmail(SMTP_FROM_ADDR, [SMTP_TO_ADDR],
msg.as_string())
smtp_server.quit()
except Exception as e:
if log.isEnabledFor(logging.ERROR):
log.error("邮件发送失败")
log.exception(e)
print(e)
def start_user_info_data_parse_thread(self):
self.user_info_data_parse_thread.start()
if log.isEnabledFor(logging.DEBUG):
log.debug("用户信息数据解析线程启动")
def parse_user_information(html_string, user_token):
if html_string is None:
return None
# 提取 json 数据
bs_object = BeautifulSoup(html_string, 'html.parser')
data_string = bs_object.find('div', attrs={'id': 'data'})
if data_string is None:
return None
else:
data_string = data_string['data-state']
# 字符串处理
# 对转义 html 字符进行处理
data_string = html.unescape(data_string)
# 去除夹杂的 html 标签
data_string = BeautifulSoup(data_string, 'html.parser').text
# 转换为 json 对象
try:
# 防止解析到的 JSON 格式错误而引发异常
json_data = json.loads(data_string)
except ValueError:
if log.isEnabledFor(logging.DEBUG):
log.debug('[error]解析到错误的 json 数据')
return None
# 提取实体
if JSON_ENTITIES not in json_data:
return None
entities = json_data[JSON_ENTITIES]
# 提取各个用户信息
if JSON_USERS not in entities:
return None
users = entities[JSON_USERS]
# 提取目标用户
if user_token not in users:
return None
user = users[user_token]
# 提取目标用户的个人信息
avatar_url_template = None
url_token = None
name = None
headline = None
locations = []
business = None
employments = []
educations = []
description = None
sina_weibo_url = None
gender = None
following_count = None
follower_count = None
answer_count = None
question_count = None
voteup_count = None
if USER_AVATAR_URL_TEMPLATE in user:
avatar_url_template = user[USER_AVATAR_URL_TEMPLATE]
if USER_URL_TOKEN in user:
url_token = user[USER_URL_TOKEN]
if USER_NAME in user:
name = user[USER_NAME]
if USER_HEADLINE in user:
headline = user[USER_HEADLINE]
if USER_LOCATIONS in user:
for location in user[USER_LOCATIONS]:
locations.append(location['name'])
if USER_BUSINESS in user:
business = user[USER_BUSINESS]['name']
if USER_EMPLOYMENTS in user:
for employment in user[USER_EMPLOYMENTS]:
elem = {}
if 'job' in employment:
job = employment['job']['name']
elem.update({'job': job})
if 'company' in employment:
company = employment['company']['name']
elem.update({'company': company})
employments.append(elem)
if USER_EDUCATIONS in user:
for education in user[USER_EDUCATIONS]:
if 'school' in education:
school = education['school']['name']
educations.append(school)
if USER_DESCRIPTION in user:
description = user[USER_DESCRIPTION]
if USER_SINAWEIBO_URL in user:
sina_weibo_url = user[USER_SINAWEIBO_URL]
if USER_GENDER in user:
gender = user[USER_GENDER]
if USER_FOLLOWING_COUNT in user:
following_count = user[USER_FOLLOWING_COUNT]
if USER_FOLLOWER_COUNT in user:
follower_count = user[USER_FOLLOWER_COUNT]
if USER_ANSWER_COUNT in user:
answer_count = user[USER_ANSWER_COUNT]
if USER_QUESTION_COUNT in user:
question_count = user[USER_QUESTION_COUNT]
if USER_VOTE_UP_COUNT in user:
voteup_count = user[USER_VOTE_UP_COUNT]
# 构造用户信息实体
user_info = {
USER_AVATAR_URL_TEMPLATE: avatar_url_template,
USER_URL_TOKEN: url_token,
USER_NAME: name,
USER_HEADLINE: headline,
USER_LOCATIONS: locations,
USER_BUSINESS: business,
USER_EMPLOYMENTS: employments,
USER_EDUCATIONS: educations,
USER_DESCRIPTION: description,
USER_SINAWEIBO_URL: sina_weibo_url,
USER_GENDER: gender,
USER_FOLLOWING_COUNT: following_count,
USER_FOLLOWER_COUNT: follower_count,
USER_ANSWER_COUNT: answer_count,
USER_QUESTION_COUNT: question_count,
USER_VOTE_UP_COUNT: voteup_count
}
return user_info
def start_user_list_data_parse_thread(self):
self.user_list_data_parse_thread.start()
if log.isEnabledFor(logging.DEBUG):
log.debug('用户列表数据解析线程启动')
def start_spider(self):
# 启动定时邮件线程
if IS_EMAIL_NOTIFICATION_ENABLE is True:
self.emailService.start_email_notification_service()
# 启动数据解析线程
self.dataParseModule.start_user_info_data_parse_thread()
self.dataParseModule.start_user_list_data_parse_thread()
# 启动用户信息爬取线程
for user_info_scrape_thread in self.user_info_scrape_thread_list:
user_info_scrape_thread.start()
# 启动用户列表爬取线程
for user_list_scrape_thread in self.user_list_scrape_thread_list:
user_list_scrape_thread.start()
if IS_EMAIL_NOTIFICATION_ENABLE is True:
self.emailService.send_message("爬虫启动成功")
# 工作线程检测并重启
while True:
# 检测邮件服务线程
if IS_EMAIL_NOTIFICATION_ENABLE is True:
if self.emailService.get_email_notification_service_status(
) == 'error':
self.emailService.restart_email_notification_service()
if log.isEnabledFor(logging.ERROR):
log.error('邮件服务线程重新启动')
# 检测用户信息解析线程
if self.dataParseModule.get_user_info_data_parse_thread_status(
) == 'error':
self.dataParseModule.restart_user_info_data_parse_thread()
if log.isEnabledFor(logging.ERROR):
log.error('用户信息解析线程重新启动')
# 检测用户列表解析线程
if self.dataParseModule.get_user_list_data_parse_thread_status(
) == 'error':
self.dataParseModule.restart_user_list_data_parse_thread()
if log.isEnabledFor(logging.ERROR):
log.error('用户信息解析线程重新启动')
# 检测用户信息爬取线程
for thread in self.user_info_scrape_thread_list:
if thread.status == 'error':
thread_name = thread.thread_name
self.user_info_scrape_thread_list.remove(thread)
new_thread = UserInfoScrapeThread(thread_name,
self.DBConnectModule,
self.dataFetchModule,
self.userTokenCacheQueue,
self.cacheQueue)
self.user_info_scrape_thread_list.append(new_thread)
new_thread.start()
if log.isEnabledFor(logging.ERROR):
log.error('用户信息爬取线程[' + thread_name + ']重新启动')
# 检测用户列表爬取线程
for thread in self.user_list_scrape_thread_list:
if thread.status == 'error':
thread_name = thread.thread_name
self.user_list_scrape_thread_list.remove(thread)
new_thread = UserListScrapeThread(thread_name,
self.DBConnectModule,
self.dataFetchModule,
self.userTokenCacheQueue,
self.cacheQueue)
self.user_list_scrape_thread_list.append(new_thread)
new_thread.start()
if log.isEnabledFor(logging.ERROR):
log.error('用户列表爬取线程[' + thread_name + ']重新启动')
# 检测间隔
time.sleep(180)
def user_list_scrape(self):
# 为该线程绑定 session
self.data_fetch_module.thread_bind_session(self.thread_name)
if log.isEnabledFor(logging.DEBUG):
log.debug('用户列表爬取线程[' + self.thread_name + ']开始运行')
while True:
# 从已分析 token 缓存列表中获取一个可用的token
while True:
token_info = self.user_token_cache_queue.get_token_form_analysed_cache_queue(
)
if token_info is not None:
break
time.sleep(0.5)
# 分析正在关注列表
if ANALYSE_FOLLOWING_LIST is True:
# 计算页码范围
following_page_size = 1
if DataParser.USER_FOLLOWING_COUNT in token_info:
following_page_size = self.calculate_max_page(
token_info[DataParser.USER_FOLLOWING_COUNT])
if 0 < FOLLOWING_PAGE_MAX < following_page_size:
following_page_size = FOLLOWING_PAGE_MAX
# 开始分析
cur_page = 1
while cur_page <= following_page_size:
# 获取数据
following_list_response = self.data_fetch_module.fetch_data_of_url(
self.generate_following_list_url(
token_info[DataParser.USER_URL_TOKEN], cur_page),
self.thread_name)
# 判断返回的数据是否有效，若有效再对数据进行分析
if following_list_response is not None:
if following_list_response == 'reuse':
# 重新分析该页的列表
continue
else:
# 添加到分析队列
self.cache_queue.add_data_into_user_list_cache_queue(
{
DataParser.QUEUE_ELEM_HTML:
following_list_response.text,
DataParser.QUEUE_ELEM_TOKEN:
token_info[DataParser.USER_URL_TOKEN],
DataParser.QUEUE_ELEM_THREAD_NAME:
self.thread_name
})
cur_page += 1
time.sleep(SCRAPE_TIME_INTERVAL)
# 分析关注者列表
if ANALYSE_FOLLOWER_LIST is True:
# 计算页码范围
follower_page_size = 1
if DataParser.USER_FOLLOWER_COUNT in token_info:
follower_page_size = self.calculate_max_page(
token_info[DataParser.USER_FOLLOWER_COUNT])
if follower_page_size > FOLLOWER_PAGE_MAX > 0:
follower_page_size = FOLLOWER_PAGE_MAX
# 开始分析
cur_page = 1
while cur_page <= follower_page_size:
# 获取数据
follower_list_response = self.data_fetch_module.fetch_data_of_url(
self.generate_follower_list_url(
token_info[DataParser.USER_URL_TOKEN], cur_page),
self.thread_name)
# 判断返回的数据是否有效，若有效再继续对数据进行分析
if follower_list_response is not None:
if follower_list_response == 'reuse':
# 重新分析该页的列表
continue
else:
# 添加到待分析队列
self.cache_queue.add_data_into_user_list_cache_queue(
{
DataParser.QUEUE_ELEM_HTML:
follower_list_response.text,
DataParser.QUEUE_ELEM_TOKEN:
token_info[DataParser.USER_URL_TOKEN],
DataParser.QUEUE_ELEM_THREAD_NAME:
self.thread_name
})
cur_page += 1
time.sleep(SCRAPE_TIME_INTERVAL)
