Scrapy Web Scraping Software

Scrapy: An open source and collaborative framework for extracting the data you need from websites. In a fast, simple, yet extensible way. By the way, if you are interested in scraping Tweets, you should definitely read this article. Alternatively, you can set up your own web scraping server using the open-source software Scrapyd. Scrapy is a sophisticated platform for performing web scraping with Python. The architecture of the tool is designed to meet the needs of professional projects. For example, Scrapy contains an integrated pipeline for processing scraped data.

In this Python Scrapy tutorial, you will learn how to write a simple webscraper in Python using the Scrapy framework. The Data Blogger website will be used as an example in this article.

Scrapy: An open source and collaborative framework for extracting the data you need from websites. In a fast, simple, yet extensible way.

By the way, if you are interested in scraping Tweets, you should definitely read this article.

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Introduction

In this tutorial we will build the webscraper using only Scrapy + Python 3 (or Python 2) and no more! The tutorial has both Python 2 and Python 3 support. The possibilities are endless. Beware that some webscrapes are not legal! For example, although it is possible, it is not allowed to use Scrapy or any other webscraper to scrape LinkedIn (https://www.linkedin.com/). However, LinkedIn lost in one case in 2017.

Content + Link extractor

The purpose of Scrapy is to extract content and links from a website. This is done by recursively following all the links on the given website.

Step 1: Installing Scrapy

According to the website of Scrapy, we just have to execute the following command to install Scrapy:

Step 2: Setting up the project

Now we will create the folder structure for your project. For the Data Blogger scraper, the following command is used. You can change datablogger_scraper to the name of your project.

Step 3: Creating an Object

The next thing to do, is to create a spider that will crawl the website(s) of interest. The spider needs to know what data is crawled. This data can be put into an object. In this tutorial we will crawl internal links of a website. A link is defined as an object having a source URL and a destination URL. The source URL is the URL on which the link can be found. It also has a destination URL to which the link is navigating to when it is clicked. A link is called an internal link if both the source URL and destination URL are on the website itself.

Scrape Object Implementation

The object is defined in items.py and for this project, items.py has the following contents:

Notice that you can define any object you would like to crawl! For example, you can specify an object Game Console (with properties “vendor”, “price” and “release date”) when you are scraping a website about Game Consoles. If you are scraping information about music from multiple websites, you could define an object with properties like “artist”, “release date” and “genre”. On LinkedIn you could scrape a “Person” with properties “education”, “work” and “age”.

Step 4: Creating the Spider

Now we have encapsulated the data into an object, we can start creating the spider. First, we will navigate towards the project folder. Then, we will execute the following command to create a spider (which can then be found in the spiders/ directory):

Spider Implementation

Now, a spider is created (spiders/datablogger.py). You can customize this file as much as you want. I ended up with the following code:

A few things are worth mentioning. The crawler extends the CrawlSpider object, which has a parse method for scraping a website recursively. In the code, one rule is defined. This rule tells the crawler to follow all links it encounters. The rule also specifies that only unique links are parsed, so none of the links will be parsed twice! Furthermore, the canonicalize property makes sure that links are not parsed twice.

LinkExtractor

The LinkExtractor is a module with the purpose of extracting links from web pages.

Step 5: Executing the Spider

Go to the root folder of your project. Then execute the following command:

This command then runs over your website and generates a CSV file to store the data into. In my case, I got a CSV file named links.csv with the following content:

Conclusion

It is relatively easy to write your own spider with Scrapy. You can specify the data you want to scrape in an object and you can specify the behaviour of your crawler. If you have any questions, feel free to ask them in the comments section!

Scrapy web scraperMonday, February 01, 2021

A web scraper (also known as web crawler) is a tool or a piece of code that performs the process to extract data from web pages on the Internet. Various web scrapers have played an important role in the boom of big data and make it easy for people to scrape the data they need.

Among various web scrapers, open-source web scrapers allow users to code based on their source code or framework, and fuel a massive part to help scrape in a fast, simple but extensive way. We will walk through the top 10 open source web scrapers in 2020.

1. Scrapy

Language: Python

Scrapy is the most popular open-source and collaborative web scraping tool in Python. It helps to extract data efficiently from websites, processes them as you need, and store them in your preferred format(JSON, XML, and CSV). It’s built on top of a twisted asynchronous networking framework that can accept requests and process them faster. With Scrapy, you’ll be able to handle large web scraping projects in an efficient and flexible way.

Advantages:

  • Fast and powerful
  • Easy to use with detailed documentation
  • Ability to plug new functions without having to touch the core
  • A healthy community and abundant resources
  • Cloud environment to run the scrapers

2. Heritrix

Language: JAVA

Heritrix is a JAVA based open source scarper with high extensibility and designed for web archiving. It highly respects the robot.txt exclusion directives and Meta robot tags and collects data at a measured, adaptive pace unlikely to disrupt normal website activities. It provides a web-based user interface accessible with a web browser for operator control and monitoring of crawls.

Advantages:

  • Replaceable pluggable modules
  • Web-based interface
  • Respect to the robot.txt and Meta robot tags
  • Excellent extensibility

3. Web-Harvest

Language: JAVA

Web-Harvest is an open-source scraper written in Java. It can collect useful data from specified pages. In order to do that, it mainly leverages techniques and technologies such as XSLT, XQuery, and Regular Expressions to operate or filter content from HTML/XML based web sites. It could be easily supplemented by custom Java libraries to augment its extraction capabilities.

Advantages:

  • Powerful text and XML manipulation processors for data handling and control flow
  • The variable context for storing and using variables
  • Real scripting languages supported, which can be easily integrated within scraper configurations

4. MechanicalSoup

Language: Python

MechanicalSoup is a Python library designed to simulate the human’s interaction with websites when using a browser. It was built around Python giants Requests (for http sessions) and BeautifulSoup (for document navigation). It automatically stores and sends cookies, follows redirects, and follows links and submits forms. If you try to simulate human behaviors like waiting for a certain event or click certain items rather than just scraping data, MechanicalSoup is really useful.

Advantages:

  • Ability to simulate human behavior
  • Blazing fast for scraping fairly simple websites
  • Support CSS & XPath selectors

Web Scraping

5. Apify SDK

Language: JavaScript

Apify SDK is one of the best web scrapers built in JavaScript. The scalable scraping library enables the development of data extraction and web automation jobs with headless Chrome and Puppeteer. With its unique powerful tools like RequestQueue and AutoscaledPool, you can start with several URLs and recursively follow links to other pages and can run the scraping tasks at the maximum capacity of the system respectively.

Advantages:

  • Scrape with largescale and high performance
  • Apify Cloud with a pool of proxies to avoid detection
  • Built-in support of Node.jsplugins like Cheerio and Puppeteer

6. Apache Nutch

Language: JAVA

Apache Nutch, another open-source scraper coded entirely in Java, has a highly modular architecture, allowing developers to create plug-ins for media-type parsing, data retrieval, querying and clustering. Being pluggable and modular, Nutch also provides extensible interfaces for custom implementations.

Advantages:

  • Highly extensible and scalable
  • Obey txt rules
  • Vibrant community and active development
  • Pluggable parsing, protocols, storage, and indexing

7. Jaunt

Language: JAVA

Jaunt, based on JAVA, is designed for web-scraping, web-automation and JSON querying. It offers a fast, ultra-light and headless browser which provides web-scraping functionality, access to the DOM, and control over each HTTP Request/Response, but does not support JavaScript.

Advantages:

  • Process individual HTTP Requests/Responses
  • Easy interfacing with REST APIs
  • Support for HTTP, HTTPS & basic auth
  • RegEx-enabled querying in DOM & JSON

8. Node-crawler

Language: JavaScript

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Node-crawler is a powerful, popular and production web crawler based on Node.js. It is completely written in Node.js and natively supports non-blocking asynchronous I/O, which provides a great convenience for the crawler's pipeline operation mechanism. At the same time, it supports the rapid selection of DOM, (no need to write regular expressions), and improves the efficiency of crawler development.

Advantages:

  • Rate control
  • Different priorities for URL requests
  • Configurable pool size and retries
  • Server-side DOM & automatic jQuery insertion with Cheerio (default) or JSDOM

9. PySpider

Language: Python

PySpider is a powerful web crawler system in Python. It has an easy-to-use Web UI and a distributed architecture with components like scheduler, fetcher, and processor. It supports various databases, such as MongoDB and MySQL, for data storage.

Advantages:

  • Powerful WebUI with a script editor, task monitor, project manager, and result viewer
  • RabbitMQ, Beanstalk, Redis, and Kombu as the message queue
  • Distributed architecture

10. StormCrawler

Language: JAVA

StormCrawler is a full-fledged open-source web crawler. It consists of a collection of reusable resources and components, written mostly in Java. It is used for building low-latency, scalable and optimized web scraping solutions in Java and also is perfectly suited to serve streams of inputs where the URLs are sent over streams for crawling.

Advantages:

Scrapy Web Scraping Python

  • Highly scalable and can be used for large scale recursive crawls
  • Easy to extend with additional libraries
  • Great thread management which reduces the latency of crawl

Open source web scrapers are quite powerful and extensible but are limited to developers. There are lots of non-coding tools like Octoparse, making scraping no longer only a privilege for developers. If you are not proficient with programming, these tools will be more suitable and make scraping easy for you.

日本語記事:2020年オープンソースWebクローラー10選
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Artículo en español:10 Mejores Web Scraper de Código Abierto en 2020
También puede leer artículos de web scraping en el Website Oficial

Scrapy Software

Author: Yina

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