1 Introduction
Hello everyone, I'm Ango!
As we all know, the most popular crawler framework for Python is Scrapy, which is mainly used to crawl website structural data
Today I recommend a simpler, lightweight, and powerful crawler framework: feapder
project address:
2. Introduction and Installation
Similar to Scrapy, feapder supports lightweight crawler, distributed crawler, batch crawler, crawler alarm mechanism and other functions
The three built-in crawlers are as follows:
-
AirSpider
Lightweight crawlers, suitable for crawlers with simple scenarios and small amounts of data
-
Spider
Distributed crawler, based on Redis, suitable for massive data, and supports functions such as breakpoint continuous crawling, automatic data storage and other functions
-
BatchSpider
Distributed batch crawlers, mainly used for crawlers that require periodic collection
Before the actual combat, we install the corresponding dependency library in the virtual environment
# 安装依赖库
pip3 install feapder
复制代码3. Let's fight
We use the simplest AirSpider to crawl some simple data
Target website: aHR0cHM6Ly90b3BodWIudG9kYXkvIA==
The detailed implementation steps are as follows (5 steps)
3-1 Create a crawler project
First, we use the "feapder create -p" command to create a crawler project
# 创建一个爬虫项目
feapder create -p tophub_demo
复制代码3-2 Create a crawler AirSpider
Go to the spiders folder from the command line and use the "feapder create -s" command to create a crawler
cd spiders
# 创建一个轻量级爬虫
feapder create -s tophub_spider 1
复制代码in
-
1 is the default, which means to create a lightweight crawler AirSpider
-
2 represents the creation of a distributed crawler Spider
-
3 represents the creation of a distributed batch crawler BatchSpider
3-3 Configure database, create data table, create mapping item
Taking Mysql as an example, first we create a data table in the database
# 创建一张数据表
create table topic( id int auto_increment primary key, title varchar(100) null comment '文章标题', auth varchar(20) null comment '作者', like_count int default 0 null comment '喜欢数', collection int default 0 null comment '收藏数', comment int default 0 null comment '评论数');
复制代码Then, open the settings.py file in the project root directory to configure the database connection information
# settings.py
MYSQL_IP = "localhost"
MYSQL_PORT = 3306
MYSQL_DB = "xag"
MYSQL_USER_NAME = "root"
MYSQL_USER_PASS = "root"
复制代码Finally, create a mapping Item (optional)
Go to the items folder and use the "feapder create -i" command to create a file that maps to the database
PS: Since AirSpider does not support automatic data storage, this step is not necessary
3-4 Write crawler and data analysis
The first step is to initialize the database with "MysqlDB"
from feapder.db.mysqldb import MysqlDB
class TophubSpider(feapder.AirSpider):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.db = MysqlDB()
复制代码In the second step, in the start_requests method, specify the address of the main link to be crawled, and use the keyword "download_midware" to configure a random UA
import feapder
from fake_useragent import UserAgent
def start_requests(self):
yield feapder.Request("https://tophub.today/", download_midware=self.download_midware)
def download_midware(self, request):
# 随机UA
# 依赖：pip3 install fake_useragent
ua = UserAgent().random
request.headers = {'User-Agent': ua}
return request
复制代码The third step is to crawl the title and link address of the home page
Use feapder's built-in method xpath to parse the data
def parse(self, request, response):
# print(response.text)
card_elements = response.xpath('//div[@class="cc-cd"]')
# 过滤出对应的卡片元素【什么值得买】
buy_good_element = [card_element for card_element in card_elements if
card_element.xpath('.//div[@class="cc-cd-is"]//span/text()').extract_first() == '什么值得买'][0]
# 获取内部文章标题及地址
a_elements = buy_good_element.xpath('.//div[@class="cc-cd-cb nano"]//a')
for a_element in a_elements:
# 标题和链接
title = a_element.xpath('.//span[@class="t"]/text()').extract_first()
href = a_element.xpath('.//@href').extract_first()
# 再次下发新任务，并带上文章标题
yield feapder.Request(href, download_midware=self.download_midware, callback=self.parser_detail_page,
title=title)
复制代码The fourth step is to crawl the details page data
In the previous step, a new task is issued, and the callback function is specified through the keyword "callback", and finally the data analysis is performed on the details page in the parser_detail_page
def parser_detail_page(self, request, response):
"""
解析文章详情数据
:param request:
:param response:
:return:
"""
title = request.title
url = request.url
# 解析文章详情页面，获取点赞、收藏、评论数目及作者名称
author = response.xpath('//a[@class="author-title"]/text()').extract_first().strip()
print("作者：", author, '文章标题:', title, "地址：", url)
desc_elements = response.xpath('//span[@class="xilie"]/span')
print("desc数目:", len(desc_elements))
# 点赞
like_count = int(re.findall('\d+', desc_elements[1].xpath('./text()').extract_first())[0])
# 收藏
collection_count = int(re.findall('\d+', desc_elements[2].xpath('./text()').extract_first())[0])
# 评论
comment_count = int(re.findall('\d+', desc_elements[3].xpath('./text()').extract_first())[0])
print("点赞：", like_count, "收藏:", collection_count, "评论:", comment_count)
复制代码3-5 Data storage
Use the database object instantiated above to execute SQL and insert data into the database.
# 插入数据库
sql = "INSERT INTO topic(title,auth,like_count,collection,comment) values('%s','%s','%s','%d','%d')" % (
title, author, like_count, collection_count, comment_count)
# 执行
self.db.execute(sql)
复制代码4. Finally
This article talks about the simplest crawler AirSpider in feapder through a simple example
Regarding the use of advanced functions of feapder, I will explain in detail through a series of examples later.
I have uploaded all the code in the article to the background of the official account, and replied to the keyword " airspider " in the background to get the complete source code
If you think the article is not bad, please like , share, and leave a message , because this will be the strongest motivation for me to continue to output more high-quality articles!