Structure of a spider

本文介绍了一种针对大规模网站资源下载的爬虫系统优化方案,该系统支持每日抓取超过一百万页面,并通过改进MySQL负载及采用多进程任务分配等技术手段提高效率。

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    Recently I am trying to rewrite a spider of a website to download the resources on it, which supports to crawl the website daily more than 1 million pages and thousands of download, each connection cost about 10 second, and while I hold 10 processors, I could deal with 0.8 million pages per day. I wanna do all the things in python, but also MySQL is used. I always think a lot before I code, but every time when I am coding, I come out a better idea to finish the job. So I don't think too much this time, I just need to satisfy the need, and do less.

    The last version cost too much CPU time in MySQL, so here comes the new structure to low down the MySQL load, and I use a master to hold a Queue, and maintain the faults, multi-processor to get job from master, when they finish the job, they reply to the MySQL rather than reply to the master.


G.01 polling structure

    Besides, the polling pool need more new items, another part of the system is the crawl more pages which needs to be monitor. Another spider goes from some root pages, and go down the tree nodes by getting inside the links, also MySQL is used to persist data.

### Spider Technology or Framework in IT Field In the context of information technology (IT), spider technology refers to automated programs that crawl through websites, collecting data by following links from one page to another. These spiders are also known as web crawlers or bots. They systematically browse the internet, often used for indexing content on search engines like Google[^5]. The primary purpose of these technologies includes gathering large amounts of data efficiently and effectively. A well-known example is **Scrapy**, which is a Python-based framework designed specifically for this task. Scrapy allows users to define rules about how pages should be traversed and parsed into structured formats such as JSON or CSV files. Another prominent mention relates to Sikuli—a tool leveraging visual recognition techniques rather than traditional URL parsing methods employed within typical crawling frameworks [^1]. It uses screenshots instead of HTML elements but still falls under broader definitions encompassing automation tools capable of interacting programmatically with graphical user interfaces across applications beyond just browsers alone. For those interested primarily in implementing their own custom solutions without relying upon third-party libraries altogether; there exist several approaches involving lower-level languages where more control over network requests can be achieved directly via sockets programming combined alongside regular expressions processing raw HTTP responses manually – though generally discouraged due its complexity compared against higher level abstractions provided modern packages today offer out-of-box functionalities saving significant development time while reducing potential errors introduced during implementation phases significantly too! Below demonstrates basic usage patterns associated when working inside environments utilizing either approach mentioned above: #### Example Code Using Scrapy Framework ```python import scrapy class MySpider(scrapy.Spider): name = 'example' allowed_domains = ['example.com'] start_urls = ['http://www.example.com'] def parse(self, response): self.log('Visited %s' % response.url) # Extract items here... ``` #### Visual Representation Of Crawling Process With ASCII Art Below: ``` Website Root Node / | \ Page_A Page_B ... External Link | | Sublink_X Sublink_Y ```
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