用scrapy爬取ttlsa博文相關數據存儲至mysql

運維生存時間這個博客內容仍是比較詳盡的,對與運維技術人員的我來講,是偶爾溜達進來的地方,從中也學習到很多知識，感謝博主的奉獻!

這段時間我就經過scrapy來收集下此博客內文章的相關數據,供之後須要從中提取我認爲值得看的文章做爲數據依據.

今天,要作的事就是把數據先抓取出來,後期再將其數據存儲起來.

首先經過命令
scrapy genspider ttlsa www.ttlsa.com建立一個蜘蛛程序應用名爲ttlsa

其次在ttlsa.py下編寫以下代碼.

# -*- coding: utf-8 -*-
import re
from urllib import parse
from datetime import datetime

import scrapy
from scrapy.http import Request
from ScrapyProject.utils.common import get_object_id

'''
獲取ttlsa文章相關數據
'''

class TtlsaSpider(scrapy.Spider):
    name = 'ttlsa'
    allowed_domains = ['www.ttlsa.com']
    start_urls = ['http://www.ttlsa.com/']

    def parse(self, response):
        post_nodes = response.css("article")
        for node in post_nodes:
            front_img_url = node.css("figure:nth-child(1) > a:nth-child(1) > img:nth-child(1)::attr(src)").extract_first("")
            #create_time = node.css("div:nth-child(3) > span:nth-child(3) > span:nth-child(1)::text").extract_first("")
            url = node.css("figure:nth-child(1) > a:nth-child(1)::attr(href)").extract_first("")
            url = parse.urljoin(response.url,url)
            if  url != "http://www.ttlsa.com/":
                yield Request(url=url,meta={"front_img_url": front_img_url}, callback=self.parse_detail)

        next_page = response.css(".next ::attr(href)").extract_first("")
        if next_page:
            yield Request(url=parse.urljoin(response.url,next_page),callback=self.parse)

    def parse_detail(self,response):
        front_img_url = response.meta.get("front_img_url", "")
        try:
            create_time = response.css(".spostinfo ::text").extract()[3]
            pattern = ".*?(\d+/\d+/\d+)"
            m = re.match(pattern, create_time)
            create_time = datetime.strptime(m[1], "%d/%m/%Y").date()
        except IndexError:
            create_time = datetime.now().date()
        title = response.css(".entry-title::text").extract_first("")
        #評論數
        comment_nums = response.css("div.entry-content li.comment a::text").extract_first("0")
        comment_nums=comment_nums.replace("發表評論", "0")
        #點贊數
        praise_nums = response.css("a.dingzan .count::text").extract_first("0").strip()
        #tags
        tags = ",".join(response.css("ul.wow li a::text").extract())
        content = response.css(".single-content").extract()

        from ScrapyProject.items import TtlsaItem
        ttlsa_item = TtlsaItem()
        ttlsa_item["title"] = title
        ttlsa_item["comment_nums"] = comment_nums
        ttlsa_item["praise_nums"] = praise_nums
        ttlsa_item["tags"] = tags
        ttlsa_item["content"] = content
        ttlsa_item["create_time"] = create_time
        ttlsa_item["front_img_url"] = [front_img_url]
        ttlsa_item["url"] = response.url
        ttlsa_item["url_object_id"] = get_object_id(response.url)

        #使用yield,將會跳轉到pipelines裏執行相關類中,須要在settings.py中開啓而且設置正確的ITEM_PIPELINES
        yield ttlsa_item

items.py

class TtlsaItem(scrapy.Item):
    title = scrapy.Field()
    comment_nums = scrapy.Field()
    praise_nums = scrapy.Field()
    tags = scrapy.Field()
    content = scrapy.Field()
    create_time = scrapy.Field()
    front_img_url = scrapy.Field()
    #記錄下載的圖片本地路徑
    front_img_path = scrapy.Field()
    url=scrapy.Field()
    #由於url是固定長度,因此咱們但願能獲取一個固定長度的url對象值,供之後重複收集數據以斷定是添加仍是更新
    url_object_id=scrapy.Field()

pipeline.py

class TtlsaPipeline(object):
    def process_item(self,item,spider):
        return item

settings.py

# Configure item pipelines
# See https://doc.scrapy.org/en/latest/topics/item-pipeline.html
ITEM_PIPELINES = {
   'ScrapyProject.pipelines.TtlsaPipeline': 300,  #注意這裏的數字，越小越優先執行
}

經過代碼調試功能,即可以看到咱們已經獲取到咱們想要的數據了.

而且在pipeline內打上斷點,也能從pipeline中獲取到數據了.

使用scrapy自帶的pipeline下載圖片,而且將其下載到本地,而且將圖片路徑保存到item中

1.重寫pipeline

from scrapy.pipelines.images import ImagesPipeline,DropItem

class TtlsaImagesPipeline(ImagesPipeline):
     def item_completed(self, results, item, info):
        image_paths = [x['path'] for ok, x in results if ok]
        if not image_paths:
            raise DropItem("Item contains no images")
        item[' front_img_path '] = image_paths
        return item

2.設置settings.py

ITEM_PIPELINES = {
    ' ScrapyProject.pipelines. TtlsaImagesPipeline ': 1,
}
IMAGES_URLS_FIELD = "front_img_url"
project_dir=os.path.abspath(os.path.dirname(__file__))
IMAGES_STORE = os.path.join(project_dir,"images")

3.在項目錄下新建一個images目錄,如圖:

這樣,我在抓取網站圖片後，就能夠將其下載到項目的images目錄下了.

而且能夠看到咱們下載的圖片路徑存儲到item[‘front_img_url’]中了.

對與此字段url_object_id=scrapy.Field()咱們可使用hashlib庫來實現.
utils/common.py

import hashlib

def get_object_id(url):
    md5 = hashlib.md5()
    md5.update(url.encode('utf-8'))
    return md5.hexdigest()

if __name__ == '__main__':
    print(get_object_id("http://www.baidu.com"))

這時候咱們在ttlsa.py中這樣改寫.
from ScrapyProject.utils.common import get_object_id
ttlsa_item["url_object_id"] = get_object_id(response.url)

好了,該獲取的數據都已經獲取了,下面咱們將其數據存儲起來,供之後分析.

存儲這塊,我考略將其分2部分,第一部分存儲到文件,另外一部分存儲到mysql

1.存儲到文件
1.1修改pipelines.py

class JsonWithEncodingPipeline(object):
    #自定義json文件的導出
    def __init__(self):
        self.file = codecs.open('ttlsa.json', 'w', encoding="utf-8")

    def process_item(self, item, spider):
        lines = json.dumps(dict(item), ensure_ascii=False) + "\n"
        self.file.write(lines)
        return item

    def spider_closed(self, spider):
        self.file.close()

1.2 修改settings.py
ITEM_PIPELINES = {
'ScrapyProject.pipelines.TtlsaImagesPipeline': 1,
'ScrapyProject.pipelines.JsonWithEncodingPipeline':2,
}
這樣咱們就能夠將數據存儲到ttlsa.json文件中了.

2.存儲到mysql中
2.1設計存儲mysql庫ttlsa_spider表article


讓咱們編寫一個MysqlPipeline,讓其抓取的數據存儲到mysql中吧.

import MySQLdb
class MysqlPipeline(object):
    def __init__(self):
        self.conn = MySQLdb.connect('localhost', 'root', 'root', 'ttlsa_spider', charset="utf8", use_unicode=True)
        self.cursor = self.conn.cursor()

    def process_item(self,item,spider):
        insert_sql = """
            insert into article(title,url,url_object_id,comment_nums,praise_nums,tags,content,create_time,
            front_img_url,front_img_path)
             values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)
        """
        self.cursor.execute(insert_sql,(item["title"], item["url"], item["url_object_id"], int(item["comment_nums"]),
                                        int(item["praise_nums"]), item["tags"], item["content"], item["create_time"],
                                        item["front_img_url"], item["front_img_path"]))
        self.conn.commit()

注意:咱們將評論數,佔贊數強制轉換成int類型了.

修改settings.py
ITEM_PIPELINES = {
'ScrapyProject.pipelines.TtlsaImagesPipeline': 1,
#'ScrapyProject.pipelines.JsonWithEncodingPipeline': 2,
'ScrapyProject.pipelines.MysqlPipeline': 3,

}

Debug跑下看看可有什麼問題.

經過不斷按F8,數據源源不斷的流進數據庫了,哈哈。

可是有一個問題，那就是當咱們的數據量很大，大量的向數據庫寫入的時候,可能會致使數據庫出現異常,這時咱們應該使用異步的方式向數據庫插入數據.下面我將使用異步插入的方式來重寫pipeline.

首先們將數據庫的配置文件寫入到settings.py中.
#MYSQL
MYSQL_HOST="127.0.0.1"
MYSQL_USER="root"
MYSQL_PWD="4rfv%TGB^"
MYSQL_DB="ttlsa_spider"

後面咱們若是想使用settings.py文件裏定義的變量，能夠在pipeline.py文件中的定義的類中使用from_settings(cls,settings)這個方法來獲取.

from twisted.enterprise import adbapi
class MysqlTwsitedPipeline(object):
    def __init__(self, dbpool):
        self.dbpool = dbpool

    @classmethod
    def from_settings(cls, settings):
        dbparms = {
            'host': settings["MYSQL_HOST"],
            'db': settings["MYSQL_DB"],
            'user': settings["MYSQL_USER"],
            'passwd': settings["MYSQL_PWD"],
            'charset': 'utf8',
            'use_unicode': True
        }
        dbpool=adbapi.ConnectionPool("MySQLdb", cp_min=10, cp_max=20, **dbparms)
        return cls(dbpool)

    def process_item(self,item,spider):
        """
        使用twisted將mysql插入變成異步執行
        """
        query = self.dbpool.runInteraction(self.doInsert,item)
        query.addErrback(self.handle_error,item,spider) #處理異步寫入錯誤

    def handle_error(self,failurer,item,spider):
        if failurer:
            print(failurer)

    def doInsert(self,cursor,item):
        insert_sql = """
             insert into article(title,url,url_object_id,comment_nums,praise_nums,tags,content,create_time,
             front_img_url,front_img_path)
              values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)
         """
        cursor.execute(insert_sql, (item["title"], item["url"], item["url_object_id"], int(item["comment_nums"]),
                                         int(item["praise_nums"]), item["tags"], item["content"], item["create_time"],
                                         item["front_img_url"], item["front_img_path"]))

再將MysqlTwsitedPipeline類寫入到settings.py文件中.
ITEM_PIPELINES = {
'ScrapyProject.pipelines.TtlsaImagesPipeline': 1,
#'ScrapyProject.pipelines.JsonWithEncodingPipeline': 2,
'ScrapyProject.pipelines.MysqlTwsitedPipeline': 3,
}

調試代碼.

好了，數據又源源不斷的寫到數據庫中了.

再看下與數據庫鏈接的數目:

數了一下，有12個。也就是在鏈接池中的數量是由cp_min=10,cp_max=20定義的.

到此,數據便存儲到mysql中了.

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