python scrapy 教程

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这是一个介绍python轻量级爬虫scrapy的文档,文档中使用vagrant、docker、viturlbox、git搭建了一个虚拟开发环境,搭建方法介绍很详细、很简单,完美支持windows。然后在这个环境下集成了文本中所有代码。 文章还主要介绍了爬虫的使用,包括HTML、xpath概念、简单基础爬虫、如何爬取APP、如何快速高并发爬取、分布式爬虫等。内容全面,层层深入,可以学习到很多知识。
Learning Scrap Table of contents Learning Scrap Credits about the author About the reviewer Support files, eBooks, discount offers, and more Why subscribe? Free access for packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Errata Piracy uestions 1. Introducing Scrap Hello scrap More reasons to love Scrape about this book: aim and usage The importance of mastering automated data scraping Developing robust, quality applications, and providing realistic schedules Developing quality minimum viable products quickly Scraping gives you scale: Google couldn't use forms Discovering and integrating into your ecosystem Being a good citizen in a world full of spiders What Scrap is not Summary 2. Understanding HTML and XPath HTML, the doM tree representation, and the XPath The urL The html document The tree representation What you see on the screen Selecting HTML elements with XPath Useful XPath expressions Using Chrome to get XPath expressions Examples of common tasks Anticipating changes Summary 3. Basic crawling Installing Scrap Macos Windows Linux Ubuntu or debian linux Red hat or CentoS Linux From the latest source Upgrading Scrap Vagrant: this book's official way to run examples UR2IM-the fundamental scraping process The URL The request and the response The items A Scrap project Defining items Writing spiders Populating an item Saving to files Cleaning up-item loaders and housekeeping fields Creating contracts Extracting more urls Two-direction crawling with a spider TwO-direction crawling with a CrawlSpider Summary 4. From Scrap to a Mobile App Choosing a mobile application framework Creating a database and a collection Populating the database with Scrapy Creating a mobile application Creating a database access service Setting up the user interface Mapping data to the User Interface Mappings between database fields and User Interface controls Testing Sharing and exporting your mobile app Summary 5. Quick Spider recipes d SIger that logs A spider that uses JSON APIs and AjAX pages Passing arguments between responses A30-times faster property spider A spider that crawls based on an Excel file Summary 6. Deploying to scrapinghub Signing up, signing in, and starting a project Deploying our spiders and scheduling runs Accessing our items Scheduling recurring crawls Summary 7. Configuration and Management Using Scrap settings Essential settings analysis ogging Stats Telnet Example 1-using telnet Performance Stopping crawls early Http caching and working offline Example 2 worKing offline Dy using the cache Crawling style Feeds Downloading media er meala Example 3-downloading images Amazon Web services USing proxies and crawlers Example 4-using - proxies and Crawlera's clever proxy Further settings Project-related settings Extending Scrap settings Fine-tuning downloading Autothrottle extension settings Memory UsageExtension settings ogging and debugging Summary 8. Programming Scrap Scrapy is a Twisted application Deferreds and deferred chains Understanding Twisted and nonblocking 1/ Python tale Overview of Scrap architecture Example 1-a very simple pipeline Signals Example 2-an extension that measures throughput and latencies Extending beyond middlewares Summary 9. Pipeline recipes Using REST APIS Using treg a pipeline that writes to Elasticsearch A pipeline that geocodes using the google geocoding APl Enabling geoindexing on Elasticsearch Interfacing databases with standard Python clients A pipeline that writes to MySQL Interfacing services using Twisted-specific clients A pipeline that reads/writes to Redis Interfacing CPU-intensive, blacking, or legacy functionality A pipeline that performs CPU-intensive or blocking operations a pipeline that uses binaries or scripts Summary 10. Understanding Scrapy's Performance Scrapy's engine-an intuitive approach Cascading queuing systems Identifying the bottleneck Scrapy's performance model Getting component utilization using telnet Our benchmark system The standard performance model Solving performance problems Case#1-saturated cpu Case #2-blocking code Case#3-“ garbage” on the downloader Case #4-overflow due to many or large responses Case #5-overflow due to limited/excessive item concurrency Case#6-the downloader doesnt have enough to do Troubleshooting flow Summary 11. Distributed Crawling with Scrapyd and Real-Time Analytics How does the title of a property affect the price? crapy Overview of our distributed system Changes to our spider and middleware Sharded-index crawling Batching crawl URLS Getting start URLS from settings Deploy your proiect to scrapyd servers Creating our custom monitoring command Calculating the shift with apache spark streaming Running a distributed crawl System performance The key take-away Summary A. Installing and troubleshooting prerequisite software Installing prerequisites he system Installation in a nutshell Installing on linux Installing on Windows or Mac Install Vagrant How to access the terminal Install VirtualBox and git Ensure that Virtual Box supports 64-bit images Enable ssh client for windows Download this book's code and set up the system System setup and operations FAQ What do i download and how much time does it take? What should i do if vagrant freezes? How do i shut down/resume the vm quickly? How do i fully reset the VM? How do i resize the virtual machine? How do i resolve any port conflicts. On Linux using Docker natively On Windows or Mac using a VM How do i make it work behind a corporate proxy? How do I connect with the Docker provider vM? How much CPu/memory does each server use? How can I see the size of docker container images How can I reset the system if Vagrant doesn't respond? There's a problem I can't work around, what can i do ngex

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