The Top-10 Open-Source Web Scraping Tools in 2023. Best Open Source Web Scrapersl Tools in 2023

A comparison of the best open source web scrapers available in 2023 can help you figure out which one you should try.

Although all of them come with amazing features, it is best to choose one and focus all of your efforts on it instead of trying to work with multiple scrapers at the same time.

Scrapy – Winner!
Heritrix
Pyspider
Web-Harvest
Apify SDK
MechanicalSoup
Node-crawler
Apache Nutch
Jaunt
Crawler4j

The most popular web scraping framework in 2022 is Scrapy. There are a number of reasons behind the popularity of Scrapy.

It was written in Python, which is one of the most popular programming languages in the world.

Python is also the most popular programming language among web scrapers developers.

With Scrapy, you can develop high-performing web crawlers and scrapers.

Speed has never been an issue with Scrapy, so you can develop your scraper quickly and begin crawling the web for data extraction straightaway.

Another plus point of Scrapy is that it can efficiently handle large web scraping projects for you.

It also gives you the option to store data in your preferred format, including JSON and CSV.

Advantages

Detailed documentation to facilitate users
Endless resources
A healthy community of developers that are always ready to offer help

Heritrix is the perfect tool for anyone who wishes to preserve the information currently available across the World Wide Web.

It is a JavaScript-based open-source web scraper that gives you the option to monitor crawls.

Most web scrapers do not respect the robot.txt exclusion directives and end up disrupting the normal functioning of a website.

Heritrix has a web-based UI that you can access from a browser.

Advantages

High extensibility
Web-based UI accessible from a browser for operator control
Respects the robot.txt exclusion directives

Pyspider is another Python-based web scraping framework that can be used for writing web crawlers as well as for coding powerful scrapers.

Scraper Python. Scrapy at a glance

Scrapy (/ˈskreɪpaɪ/) is an application framework for crawling web sites and extracting structured data which can be used for a wide range of useful applications, like data mining, information processing or historical archival.

Even though Scrapy was originally designed for, it can also be used to extract data using APIs (such as) or as a general purpose web crawler.

Walk-through of an example spider

In order to show you what Scrapy brings to the table, we’ll walk you through an example of a Scrapy Spider using the simplest way to run a spider.

Here’s the code for a spider that scrapes famous quotes from website, following the pagination:

Put this in a text file, name it to something like quotes_spider.py and run the spider using the

When this finishes you will have in the quotes.jsonl file a list of the quotes in JSON Lines format, containing text and author, looking like this:

{ "author" : "Jane Austen" , "text" : " \u201c The person, be it gentleman or lady, who has not pleasure in a good novel, must be intolerably stupid. \u201d " } { "author" : "Steve Martin" , "text" : " \u201c A day without sunshine is like, you know, night. \u201d " } { "author" : "Garrison Keillor" , "text" : " \u201c Anyone who thinks sitting in church can make you a Christian must also think that sitting in a garage can make you a car.

What just happened?

When you ran the command scrapy runspider quotes_spider.py , Scrapy looked for a Spider definition inside it and ran it through its crawler engine.

start_urls attribute (in this case, only the URL for quotes in humor category) and called the default callback method parse , passing the response object as an argument. In the parse callback, we loop through the quote elements using a CSS Selector, yield a Python dict with the extracted quote text and author, look for a link to the next page and schedule another request using the same parse method as callback.

Here you notice one of the main advantages about Scrapy: requests are. This means that Scrapy doesn’t need to wait for a request to be finished and processed, it can send another request or do other things in the meantime. This also means that other requests can keep going even if some request fails or an error happens while handling it.

While this enables you to do very fast crawls (sending multiple concurrent requests at the same time, in a fault-tolerant way) Scrapy also gives you control over the politeness of the crawl through. You can do things like setting a download delay between each request, limiting amount of concurrent requests per domain or per IP, and eventhat tries to figure out these automatically.

Note

This is usingto generate the JSON file, you can easily change the export format (XML or CSV, for example) or the storage backend (FTP or Amazon S3 , for example). You can also write anto store the items in a database.

Scrap. 8 documentation ¶

Scrapy is a fast high-levelandframework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.

Getting help

Having trouble? We’d like to help!

Try the FAQ – it’s got answers to some common questions.

Ask or search questions in StackOverflow using the scrapy tag .

Ask or search questions in the Scrapy subreddit .

Search for questions on the archives of the.

Ask a question in the #scrapy IRC channel ,

Report bugs with Scrapy in our issue tracker .

Join the Discord community Scrapy Discord .

First steps

Understand what Scrapy is and how it can help you.

Get Scrapy installed on your computer.

Write your first Scrapy project.

Basic concepts

Learn about the command-line tool used to manage your Scrapy project.

Write the rules to crawl your websites.

Extract the data from web pages using XPath.

Test your extraction code in an interactive environment.

Define the data you want to scrape.

Populate your items with the extracted data.

Post-process and store your scraped data.

Output your scraped data using different formats and storages.

Understand the classes used to represent HTTP requests and responses.

Convenient classes to extract links to follow from pages.

Learn how to configure Scrapy and see all.

See all available exceptions and their meaning.

Built-in services

Learn how to use Python’s builtin logging on Scrapy.

Collect statistics about your scraping crawler.

Send email notifications when certain events occur.

Inspect a running crawler using a built-in Python console.

Solving specific problems

Get answers to most frequently asked questions.

Learn how to debug common problems of your Scrapy spider.

Learn how to use contracts for testing your spiders.

Get familiar with some Scrapy common practices.

Tune Scrapy for crawling a lot domains in parallel.

Learn how to scrape with your browser’s developer tools.

Read webpage data that is loaded dynamically.

Learn how to find and get rid of memory leaks in your crawler.

Download files and/or images associated with your scraped items.

Deploying your Scrapy spiders and run them in a remote server.

Adjust crawl rate dynamically based on load.

Check how Scrapy performs on your hardware.

Learn how to pause and resume crawls for large spiders.

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Web scraper cloud. A CLOUD BASED WEB SCRAPER

Introduction

In this article, we will guide you through the process of building a web scraper and setting it up to run autonomously on the cloud. It's important to understand what web scraping is before we delve into deployment. According to Wikipedia, web scraping is the process of extracting data from websites. There are various reasons one might want to extract data from a website, such as for analytical purposes or personal use. The use case will depend on the specific needs and goals of the individual or organization. If you're interested in learning more about web scraping, you can check out the Wikipedia article linked here:. It provides a comprehensive overview of the topic.

There are several techniques for web scraping that can be implemented using a variety of programming languages. In this article, we will be using the Python programming language. Don't worry if you're not familiar with Python, as we will be explaining each step in detail. If you do have a basic understanding of Python syntax, this should be a fairly easy process.

Our web scraper will be tasked with extracting news articles from a specific news website. The main reason for creating an autonomous web scraper is to extract data that is constantly being updated, such as news articles. This allows us to easily gather and analyze the latest information from a particular site. So, let's get started and build our web scraper!

Disclaimer: before scraping any website be sure to read their user terms and conditions. Some sites may take legal action if you don't follow usage guidelines.

Platforms and services

In this section, we will provide an overview of the platforms and services we will be using to create a cloud-based web scraper as an example. We will briefly explain the purpose and function of each platform or service to give you a better understanding of how they will be used in the process.

IBM cloud platform: this will be our cloud platform of choice, for the reason being that you can access several services without having to provide credit card information. For our example we'll get to work with :
- Cloud functions service: this service will allow us to execute our web scraper on the cloud.
- Cloudant: a non-relational, distributed database service. We'll use this to store the data we scrape.
Docker container platform: this platform we'll allow us to containerize our web scraper in a well defined environment with all necessary dependencies. This action allows our web scraper to work on any given platform that supports docker containers. In our example our docker container will be used by the ibm cloud functions service .
Github: we'll use Github for version control and also to link to our docker container. Linking our docker container to a github repository containing our web scraper will automatically initiate a new build for our docker container image. The new image will carry all changes made to the repository's content.
Cloud phantomjs platform: this platform will help render the web pages from the HTTP requests we'll make on the cloud. Once a page is rendered the response is returned as HTML.
Rapid API platform: this platform will help manage our API calls to the cloud phantomjs platform and also provide an interface that shows execution statistics.

Web Crawler. 15 Best FREE Website Crawler Tools & Software (2023 Update)

A web crawler is an internet bot that browses WWW (World Wide Web). It is sometimes called as spiderbot or spider. The main purpose of it is to index web pages.

Web crawlers enable you to boost your SEO ranking visibility as well as conversions. It can find broken links, duplicate content, missing page titles, and recognize major problems involved in SEO. There is a vast range of web crawler tools that are designed to effectively crawl data from any website URLs. These apps help you to improve website structure to make it understandable by search engines and improve rankings.

Following is a handpicked list of Top Web Crawler with their popular features and website links to download web crawler apps. The list contains both open source(free) and commercial(paid) software.

Best Web Crawler Tools & Software (Free / Paid)

#1)

is a website crawler tool that analyzed pages & structure of your website in order to identify technical SEO issues. Fixing these issues helps to improve your search performance. Apart from this service, it also offers tools for SEO, market research, SMM and advertising.

Features:

It will test for Metadata, HTTP/HTTPS, Directives, Status codes, Duplicate content, Page response time, Internal linking, Image sizes, Structured data, Site structure, etc
Provides easy to use interface
It helps you to analyze log file.
This application has a dashboard that enables you to view website issues with ease.
Enables you to audit your website without any hassle.

is a web crawling tool that can monitor your website performance. It enables you to share tasks and issues with your team members.

Features:

It can check the security problems of your website.
Offers intuitive dashboard.
This application can perform white label SEO.
Hexometer can optimize for SERP (Search Engine Results Page).
This software can be integrated with Telegram, Slack, Chrome, Gmail, etc.
It helps you to keep track of your website changes.

is a website SEO checker that helps you to improve SEO ratings. It provides on-page SEO audit report that can be sent to clients.

Features:

This web crawler tool can scan internal and external links on your website.
It helps you to test the speed of your site.
You can visualize the structure of a web page with ease.
Sitechecker.pro also allows you to check indexing issues on landings pages.
It enables you to prevent hackers from attack.

is an app that enables you to perform real-time SEO monitoring and auditing. This application can be used without installing any software.

Features:

It helps you to structure your site with segments.
You can monitor your website changes.
It offers various APIs like Google Search Console and Analytics.
It provides a user-friendly dashboard.
It helps you to collaborate with your clients or colleagues.

is a website crawler tool that provides website analysis and optimization facilities. It helps you to make your site works seamlessly. This application enables you to find out the most visited pages of your website.

Features:

Provides site optimization reports that help you to boost your business productivity.
You can customize this tool according to your desire.
Easy to configure your site settings.

Best Website scraper. 10 Best Web Scraping Tools for Digital Marketers

Data extraction and structurization is a commonly used process for marketers. However, it also requires a great amount of time and effort, and after a few days, the data can change, and all that amount of work will be irrelevant. That’s where web scraping tools come into play.

If you start googling web scraping tools, you will find hundreds of solutions: free and paid options, API and visual web scraping tools, desktop and cloud-based options; for SEO, price scraping, and many more. Such variety can be quite confusing.

We made this guide for the best web scraping tools to help you find what fits your needs best so that you can easily scrape information from any website for your marketing needs.

Quick Links

What Does a Web Scraper Do?

A web scraping tool is software that simplifies the process of data extraction from websites or advertising campaigns. Web scrapers use bots to extract structured data and content: first, they extract the underlying HTML code and then store data in a structured database as a CSC file, an Excel spreadsheet, SQL database, and other formats.

You can use web scraping tools in many ways; for example:

Perform keyword and PPC research.
Analyze your competitors for SEO purposes.
Collect competitors’ prices and special offers.
Crawl social trends (mentions and hashtags).
Extract emails from online business directories, for example, Yelp.
Collect companies’ information.
Scrape retailer websites for the best prices and discounts.
Scrape jobs postings.

There are dozens of other ways of implementing web scraping features, but let’s focus on how marketers can profit from automated data collection.

Web Scraping for Marketers

Web scraping can supercharge your marketing tactics in many ways, from finding leads to analyzing how people react to your brand on social media. Here are some ideas on how you can use these tools.

Web scraping for lead generation

If you need to extend your lead portfolio, you may want to contact people who fit your customer profile. For example, if you sell software for real estate agents, you need those agents’ email addresses and phone numbers. Of course, you can browse websites and collect their details manually, or you can save time and scrape them with a tool.

A web scraper can automatically collect the information you need: name, phone number, website, email, location, city, zip code, etc. We recommend starting scraping with Yelp and Yellowpages.

Now, you can build your email and phone lists to contact your prospects.

Web scraping for market research

With web scraping tools, you can scrape valuable data about your industry or market.For example, you can scrape data from marketplaces such as Amazon and collect valuable information, including product and delivery details, pricing, review scores, and more.

Using this data, you can generate insights into positioning and advertising your products effectively.

For example, if you sell smartphones, scrape data from a smartphone reseller catalog to develop your pricing, shipment conditions, etc. Additionally, by analyzing consumers’ reviews, you can understand how to position your products and your business in general.

Web scraping for competitor research

You may browse through your competitors’ websites and gather information manually, but what if there are dozens of them that each have hundreds or thousands of web pages? Web scraping will save you a lot of time, and with regular scraping, you will always be up-to-date.

You can regularly scrape entire websites, including product catalogs, pricing, reviews, blog posts, and more, to make sure you are riding the wave.

Web scraping can be incredibly useful for PPC marketers to get an insight into competitors’ advertising activities. You can scrape competitors’ Search, Image, Display, and HTML ads. You’ll get all of the URLs, headlines, texts, images, country, popularity, and more in just a few minutes.

Web scraping for knowing your audience

Knowing what your audience thinks and what they talk about is priceless. That’s how you can understand their issues, values, and desires to create new ideas and develop existing products.

Web scraping tools can help here too. For example, you can scrape trending topics, hashtags, location, and personal profiles of your followers to get more information about your ideal customer personas, including their interests and what they care and talk about. You may also create a profile network to market to specific audience segments.

Web scraping for SEO

Web scraping is widely used for SEO purposes. Here are some ideas about what you can do:

Analyze robots.txt и sitemap.xml.

Web Crawler Python. Build a Python Web Crawler from scratch

Why would anyone want to collect more data when there is so much already? Even though the magnitude of information is alarmingly large, you often find yourself looking for data that is unique to your needs.

For example, what would you do if you wanted to collect info on the history of your favorite basketball team or your favorite ice cream flavor?

Enterprise data collection is essential in the day-to-day life of a data scientist because the ability to collect actionable data on trends of the modern-day means possible business opportunities.

In this tutorial, you’ll learn about web crawling via a simple online store.

HTML anatomy refresher

Let’s review basic HTML anatomy. Nearly all websites on the Internet are built using the combination of HTML and CSS code (including JavaScript, but we won’t talk about it here).

Below is a sample HTML code with some critical parts annotated.

The HTML code on the web will be a bit more complicated than this, however. It will be nearly impossible to just look at the code and figure out what it’s doing. For this reason, we will learn about more sophisticated tools to make sense of massive HTML pages, starting with XPath syntax.

XPath with lxml

The whole idea behind web scraping is to use automation to extract information from the massive sea of HTML tags and their attributes. One of the tools, among many, to use in this process is using XPath.

XPath stands for XML path language. XPath syntax contains intuitive rules to locate HTML tags and extract information from their attributes and texts. For this section, we will practice using XPath on the HTML code you saw in the above picture:

sample_html = """ Harry Potter29.99 Learning XML 39.95 """

To start using XPath to query this HTML code, we will need a small library:

pip install lxml

LXML allows you to read HTML code as a string and query it using XPath. First, we will convert the above string to an HTML element using thefromstringfunction:

from lxml import html source = html.fromstring(sample_html) >>> source >>> type(source) lxml.html.HtmlElement

Now, let’s write our first XPath code. We will select the bookstore tag first:

>>> source.xpath("//bookstore") >

As you can see, we get a list of two book tags. Now, let’s see how to choose an immediate child of a tag. For example, let’s select the title tag that comes right inside the book tag:

>>> source.xpath("//book/title") >

We only have a single element, which is the first title tag. We didn’t choose the second tag because it is not an immediate child of the second book tag. But we can replace the single forward slash with a double one to choose both title tags:

>>> source.xpath("//book//title") , >

Now, let’s see how to choose the text inside a tag:

>>> source.xpath("//book/title/text()")

Here, we are selecting the text inside the first title tag. As you can see, we can also specify which of the title tags we want using brackets notation. To choose the text inside that tag, just follow it with a forward slash and atext()function.

Finally, we look at how to locate tags based on their attributes likeid,class,href,or any other attribute inside. Below, we will choose the title tag with the name class:

>>> source.xpath("//title") >

As expected, we get a single element. Here are a few examples of choosing other tags using attributes:

>>> source.xpath("//*") # choose any element with id 'main' > >>> source.xpath("//title") # choose a title tag with 'lang' attribute of 'en'. , >

I suggest you look at this page to learn more about XPath.