Top 17 Open-source Web Scrapping Frameworks
Open-source web scraping frameworks are software tools that provide a set of functionalities and APIs for extracting data from websites. They are typically used by developers, data scientists, and researchers to automate the process of gathering structured data from the web.
Some common use cases for open-source web scraping frameworks include:
- Data Mining and Research: Open-source web scraping frameworks allow users to collect large amounts of data from websites for research purposes, enabling analysis, visualization, and insights.
- Competitive Intelligence: Companies can use web scraping frameworks to gather data on competitors, such as pricing information, product details, or customer reviews, to gain a competitive edge.
- Market Research: By scraping websites, businesses can collect data on market trends, customer preferences, or product availability, helping them make informed decisions.
- Lead Generation: Web scraping frameworks can extract contact information, customer reviews, or job postings, which can be valuable for lead generation and sales prospecting.
- Content Aggregation: Blogs, news websites, and content platforms can use web scraping frameworks to gather articles, blog posts, or news headlines from various sources to create curated content.
The audience for open-source web scraping frameworks includes developers, data analysts, data scientists, researchers, and anyone who requires structured data from the web.
Benefits of using open-source web scraping frameworks include:
- Flexibility: These frameworks offer customizable options to meet specific scraping requirements, allowing users to define rules and patterns for data extraction.
- Automation: Web scraping frameworks automate the process of gathering data from websites, saving time and effort compared to manual data collection.
- Scalability: Open-source web scraping frameworks can handle scraping tasks at scale, enabling the extraction of data from a large number of websites in an efficient manner.
- Cost-Effectiveness: By utilizing open-source frameworks, users can avoid the need for expensive proprietary software, reducing costs associated with web scraping.
- Community Support: These frameworks often have active communities of developers, providing support, documentation, and continuous development of new features.
It's important to note that when using web scraping frameworks, users should be mindful of the legal and ethical implications of scraping websites, respecting the terms of service, and ensuring compliance with data privacy regulations.
Here are the best open-source web scrapping framework that you can download, install and use for free.
1- Webster (Node.js)
Webster is a dependable web crawling and scraping framework written in Node.js. It is capable of crawling websites and extracting structured data, including content rendered by client-side JavaScript and AJAX requests.
zhuyingda2- Scrapy
Scrapy is a powerful web crawling and scraping framework used for extracting structured data from websites. It is versatile and can be used for various purposes such as data mining, monitoring, and automated testing. Scrapy is maintained by Zyte (formerly Scrapinghub) and has contributions from many other contributors.
scrapy3- Crawless
Crawlee handles the entire process of crawling and scraping, allowing you to effortlessly build dependable scrapers quickly.
With Crawlee, your crawlers will mimic human behavior, easily bypassing modern bot protections even with the default settings. It provides you with the necessary tools to crawl the web for links, extract data, and save it to your preferred storage location, whether it be on disk or in the cloud. Additionally, Crawlee offers customizable options to meet the specific requirements of your project.
apify4- Autoscraper (Python)
AutoScraper is a Python library designed for automatic web scraping. It simplifies the scraping process by learning the rules from a provided URL or HTML content and a list of sample data to scrape. The library then returns similar elements, allowing users to scrape similar content or exact elements from new pages using the learned object.
alirezamika5- Rod (Go)
Rod is a high-level driver for web automation and scraping, based on the DevTools Protocol. It offers both high-level and low-level functionality, allowing senior developers to customize or build their own version of Rod using the low-level packages and functions.
Features
- Chained context design, intuitive to timeout or cancel the long-running task
- Auto-wait elements to be ready
- Debugging friendly, auto input tracing, remote monitoring headless browser
- Thread-safe for all operations
- Automatically find or download browser
- High-level helpers like WaitStable, WaitRequestIdle, HijackRequests, WaitDownload, etc
- Two-step WaitEvent design, never miss an event (how it works)
- Correctly handles nested iframes or shadow DOMs
- No zombie browser process after the crash (how it works)
- CI enforced 100% test coverage
go-rod6- Grab (Python)
Grab Framework Project Web Scraping Framework is a web scraping framework that is focused on flexibility and simplicity. It allows users to easily extract data from websites by defining rules and patterns. Grab offers a wide range of features and supports various protocols and authentication methods. It is built on top of the lxml library and provides a convenient API for web scraping tasks.
lorien7- Trafilatura (Python)
Trafilatura is a Python package and command-line tool for web crawling, scraping, and extraction of text, metadata, and comments. It aims to eliminate noise in text quality and includes author and date information. The tool is robust, fast, and useful for quantitative research in various fields, including corpus linguistics and natural language processing.
Features
- Web crawling and text discovery:
- Focused crawling and politeness rules
- Support for sitemaps (TXT, XML) and feeds (ATOM, JSON, RSS)
- URL management (blacklists, filtering and de-duplication)
- Seamless and parallel processing, online and offline:
- URLs, HTML files or parsed HTML trees usable as input
- Efficient and polite processing of download queues
- Conversion of previously downloaded files
- Robust and efficient extraction:
- Main text (with LXML, common patterns and generic algorithms: jusText, fork of readability-lxml)
- Metadata (title, author, date, site name, categories and tags)
- Formatting and structural elements: paragraphs, titles, lists, quotes, code, line breaks, in-line text formatting
- Comments (if applicable)
- Output formats:
- Text (minimal formatting or Markdown)
- CSV (with metadata, tab-separated values)
- JSON (with metadata)
- XML (with metadata, text formatting and page structure) and TEI-XML
- Optional add-ons:
- Language detection on extracted content
- Graphical user interface (GUI)
- Speed optimizations
adbar8- Rvest (R)
rvest is a package that helps with web scraping using R, specifically designed to work with magrittr. It allows users to easily perform common web scraping tasks and is inspired by libraries like beautiful soup and RoboBrowser. When scraping multiple pages, it is recommended to use rvest in conjunction with polite to ensure compliance with robots.txt and avoid excessive requests.
tidyverse9- Geziyor (Go)
Geziyor is an exceptionally fast web crawling and web scraping framework. It excels at crawling websites and extracting structured data from them. Geziyor proves to be highly valuable for numerous purposes including data mining, monitoring, and automated testing.
Features
- JS Rendering
- 5.000+ Requests/Sec
- Caching (Memory/Disk/LevelDB)
- Automatic Data Exporting (JSON, CSV, or custom)
- Metrics (Prometheus, Expvar, or custom)
- Limit Concurrency (Global/Per Domain)
- Request Delays (Constant/Randomized)
- Cookies, Middlewares, robots.txt
- Automatic response decoding to UTF-8
- Proxy management (Single, Round-Robin, Custom)
geziyor10- Colly (Go)
Colly is a scraping framework for Gophers that offers a clean API, fast performance, request management, cookie handling, and support for sync/async/parallel scraping. It allows easy extraction of structured data from websites for various applications such as data mining, processing, and archiving.
Features
- Clean API
- Fast (>1k request/sec on a single core)
- Manages request delays and maximum concurrency per domain
- Automatic cookie and session handling
- Sync/async/parallel scraping
- Caching
- Automatic encoding of non-unicode responses
- Robots.txt support
- Distributed scraping
- Configuration via environment variables
- Extensions
gocolly11- Ferret (Go)
ferret is a web scraping system that simplifies data extraction from the web for various purposes. It provides a declarative language, supports both static and dynamic web pages, is embeddable, extensible, and fast.
MontFerret12- PHPScraper (PHP)
PHPScraper is a versatile web-utility for PHP. Its primary objective is to streamline the process of extracting information from websites, allowing you to focus on accomplishing tasks without getting caught up in the complexities of selectors, data structure preparation, and conversion.
spekulatius13- Tinking (Google Chrome Extension)
Tinking is a Chrome extension that enables data extraction from websites without coding. Users can create scraping recipes by simply selecting elements on a page with their mouse.
baptisteArno14- Dataflow kit (Go)
Dataflow kit (DFK) is a web scraping framework for Gophers that extracts data from web pages using CSS selectors. It consists of a web scraping pipeline with components for downloading, parsing, and encoding data. The fetch service downloads web page content using either a base fetcher or a Chrome fetcher, while the parse service extracts data based on rules specified in a configuration JSON file.
Dataflow kit benefits:
- Scraping of JavaScript generated pages;
- Data extraction from paginated websites;
- Processing infinite scrolled pages.
- Sсraping of websites behind login form;
- Cookies and sessions handling;
- Following links and detailed pages processing;
- Managing delays between requests per domain;
- Following robots.txt directives;
- Saving intermediate data in Diskv or Mongodb. Storage interface is flexible enough to add more storage types easily;
- Encode results to CSV, MS Excel, JSON(Lines), XML formats;
- Dataflow kit is fast. It takes about 4-6 seconds to fetch and then parse 50 pages.
- Dataflow kit is suitable to process quite large volumes of data. Our tests show the time needed to parse appr. 4 millions of pages is about 7 hours.
slotix15- NScrape (.Net)
NScrape is a web scraping framework for .NET that simplifies the process by handling the tedious tasks, allowing you to focus on the actual scraping. It suggests using the HTML Agility Pack for scraping, but also supports string functions and regular expressions if preferred.
darrylwhitmore16- Scrapple (Python)
Scrapple is a framework for creating web scrapers and web crawlers using a key-value based configuration file. It abstracts the process of designing web content extractors by focusing on what to extract rather than how to do it.
The user-specified configuration file contains selector expressions and attributes to be selected, and Scrapple handles the extraction process. It also provides a command line interface and a web interface for easy use.
AlexMathew17- Spidr
Spidr is a Ruby web spidering library that is fast and easy to use. It can spider a site, multiple domains, certain links, or infinitely. It follows various types of links, supports black-listing or white-listing URLs based on different criteria, and provides callbacks for customization.
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