# Define your item pipelines here
#
# Don‘t forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html
import os
import time
from twisted.enterprise import adbapi
from pymongo import MongoClient

class XbiqugePipeline(object):
    conn = MongoClient(‘mongodb://admin:admin@localhost:27017/admin‘)
    db = conn.novels #建立数据库novels的连接对象db
    name_novel = ‘‘
    url_firstchapter = ‘‘
    name_txt = ‘‘
    start_time=time.time()

    #定义类初始化动作
    def __init__(self):

        return

    #爬虫开始
    def open_spider(self, spider):

        return

    def get_collection(self,name_collection):  #获取数据集cursor对象
        myset = self.db[name_collection]
        return myset

    def process_item(self, item, spider):
        #if self.name_novel == ‘‘:
        self.name_novel = item[‘name‘]
        self.url_firstchapter = item[‘url_firstchapter‘]
        self.name_txt = item[‘name_txt‘]
        myset = self.db[self.name_novel]
        myset.insert_one(dict(item))
#        if self.name_novel != ‘‘:
#            exec(‘self.db.‘+ self.name_novel + ‘.insert_one(dict(item))‘)
        return item

    #从数据库取小说章节内容写入txt文件
    def content2txt(self,dbname,firsturl,txtname):
        myset = self.db[dbname]
        record_num = myset.find().count() #获取小说章节数量
        print("小说总章节数:",record_num)
        counts=record_num
        url_c = firsturl
        start_time=time.time()  #获取提取小说内容程序运行的起始时间
        f = open(txtname+".txt", mode=‘w‘, encoding=‘utf-8‘)   #写方式打开小说名称加txt组成的文件
        for i in range(counts):  #括号中为counts
#-----------使用count()方法获得的返回整型值作为是否获得数据的判断依据-------------
#            record_m_count=myset.find({"url": url_c},{"content":1,"_id":0}).count()
#            if record_m_count == 0:
#               print("数据集中没有找到章节内容。\n出错url:",url_c)
#               break
#--------------------------------------------------------------------------------

#-----------使用next()方法读取迭代器数据，并使用try except捕获未获得数据的错误-----
            try:
                record_m=myset.find({"url": url_c},{"content":1,"_id":0}).next()
            #except Exception as e:
            except StopIteration:
                print("数据集中没有获得章节内容。\n出错url:",url_c)
                break   #跳出for循环，终止小说文件生成
#--------------------------------------------------------------------------------
            record_content_c2a0 = ‘‘

#------------使用for循环读取迭代器数据模式---------------------------------
#            record_i = myset.find({"url": url_c},{"content":1,"_id":0})
#            for record_m in record_i:
#                record_content_c2a0 = record_m["content"]  #获取小说章节内容
#---------------------------------------------------------------------------
            record_content_c2a0 = record_m["content"]

            #record_content=record_content_c2a0.replace(u‘\xa0‘, u‘‘)  #消除特殊字符\xc2\xa0
            record_content=record_content_c2a0
            #print(record_content)
            f.write(‘\n‘)
            f.write(record_content + ‘\n‘)
            f.write(‘\n\n‘)
            url_ct = myset.find({"url": url_c},{"next_page":1,"_id":0})  #获取下一章链接的查询对象
            for item_url in url_ct:
                url_c = item_url["next_page"]  #下一章链接地址赋值给url_c，准备下一次循环。
                #print("下一页",url_c)
        f.close()
        print("文件生成用时:",time.time()-start_time)
        print("小说爬取总用时:",time.time()-self.start_time)
        print(txtname + ".txt" + " 文件已生成！")
        return

    #爬虫结束，调用content2txt方法，生成txt文件
    def close_spider(self,spider):
        if self.name_novel !=‘‘ and self.url_firstchapter != ‘‘ and self.name_txt != ‘‘:
            self.content2txt(self.name_novel,self.url_firstchapter,self.name_txt)
        return

# -*- coding: utf-8 -*-
import scrapy
from xbiquge.items import XbiqugeItem
from xbiquge.pipelines import XbiqugePipeline

class SancunSpider(scrapy.Spider):
    name = ‘sancun‘
    allowed_domains = [‘www.xbiquge.la‘]
    #start_urls = [‘http://www.xbiquge.la/10/10489/‘]
    url_ori= "https://www.xbiquge.la"
    url_firstchapter = "https://www.xbiquge.la/10/10489/4534454.html"
    name_txt = "./novels/三寸人间"
    url_chapters = url_firstchapter[0:32]
    pipeline=XbiqugePipeline()
    novelcollection=pipeline.get_collection(name) #获取小说数据集cursor对象，mongodb的数据集（collection）相当于mysql的数据表table
    #--------------------------------------------
    #如果next_page的值是小说目录页面url，则把包含目录页面的记录删除，以免再次抓取时，出现多>个目录页面url，使得无法获得最新内容。
    if novelcollection.find({"next_page":url_chapters}).count() != 0 :
        print("包含目录页面url的记录数:",novelcollection.find({"next_page":url_chapters}).count())
        novelcollection.remove({"next_page":url_chapters})
        print("已删除包含目录页面url的记录。")
    #--------------------------------------------
    novelcounts=novelcollection.find().count()
    novelurls=novelcollection.find({},{"_id":0,"id":1,"url":1})
    item = XbiqugeItem()
    item[‘id‘] = novelcounts         #id置初值为colletion的记录总数
    item[‘name‘] = name
    item[‘url_firstchapter‘] = url_firstchapter
    item[‘name_txt‘] = name_txt

    def start_requests(self):
        start_urls = [self.url_chapters]
        print("小说目录url:",start_urls)
        for url in start_urls:
            yield scrapy.Request(url=url, callback=self.parse)

    def parse(self, response):    #网页提取数据，并与mongodb数据集比较，没有相同的数据才从网页抓取。
        count_bingo=0   #数据集中已有记录的条数
        dl = response.css(‘#list dl dd‘)     #提取章节链接相关信息
        for dd in dl:
            count_iterator = 0
            self.url_c = self.url_ori + dd.css(‘a::attr(href)‘).extract()[0]   #组合形成小说的各章节链接
            #print("网页提取url:", self.url_c)
            self.novelurls=self.novelcollection.find({},{"_id":0,"id":1,"url":1})   #通过重新赋值迭代器来重置迭代器指针，使for循环能够从头遍历迭代器。
            for url in self.novelurls:
                #print("mongodb提取url:", url)
                if url["url"]==self.url_c:      #如果数据集中找到与网页提取的url值相同，则跳出循环
                    count_bingo += 1
                    count_iterator += 1
                    break
            if count_iterator != 0 :            #如果有命中结果，则继续下一个循环，不执行爬取动作
               continue
            print("爬取url:",self.url_c)
            #yield scrapy.Request(self.url_c, callback=self.parse_c,dont_filter=True)
            yield scrapy.Request(self.url_c, callback=self.parse_c)    #以生成器模式（yield）调用parse_c方法获得各章节链接、上一页链接、下一页链接和章节内容信息。
            #print(self.url_c)
        print("数据集已有记录数count_bingo:",count_bingo)

    def parse_c(self, response):
        self.item[‘id‘] += 1
        self.item[‘url‘] = response.url
        self.item[‘preview_page‘] = self.url_ori + response.css(‘div .bottem1 a::attr(href)‘).extract()[1]
        self.item[‘next_page‘] = self.url_ori + response.css(‘div .bottem1 a::attr(href)‘).extract()[3]
        title = response.css(‘.con_top::text‘).extract()[4]
        contents = response.css(‘#content::text‘).extract()
        text=‘‘
        for content in contents:
            text = text + content
        #print(text)
        self.item[‘content‘] = title + "\n" + text.replace(‘\15‘, ‘\n‘)     #各章节标题和内容组合成content数据，\15是^M的八进制表示，需要替换为换行符。
        yield self.item     #以生成器模式（yield）输出Item对象的内容给pipelines模块。

        if self.item[‘url‘][32:39] == self.item[‘next_page‘][32:39]: #同一章有分页的处理
            self.url_c = self.item[‘next_page‘]
            yield scrapy.Request(self.url_c, callback=self.parse_c)