Introduction​

In this article, we will build a web application that scrapes LinkedIn for job postings using Crawlee and Streamlit.

We will create a LinkedIn job scraper in Python using Crawlee for Python to extract the company name, job title, time of posting, and link to the job posting from dynamically received user input through the web application.

By the end of this tutorial, you’ll have a fully functional web application that you can use to scrape job postings from LinkedIn.

Let's begin.

As scraping developers, we sometimes need to extract authentication data like temporary keys to perform our tasks. However, it is not as simple as that. Usually, it is in HTML or XHR network requests, but sometimes, the auth data is computed. In that case, we can either reverse-engineer the computation, which takes a lot of time to deobfuscate scripts or run the JavaScript that computes it. Normally, we use a browser, but that is expensive. Crawlee provides support for running browser scraper and Cheerio Scraper in parallel, but that is very complex and expensive in terms of compute resource usage. JSDOM helps us run page JavaScript with fewer resources than a browser and slightly higher than Cheerio.

This article will discuss a new approach that we use in one of our Actors to obtain the authentication data from TikTok ads creative center generated by browser web applications without actually running the browser but instead of it, using JSDOM.

Dynamic websites that use JavaScript for content rendering and backend interaction often create challenges for web scraping. The traditional approach to solving this problem is browser emulation, but it's not very efficient in terms of resource consumption.

In this article, we'll explore an alternative method based on in-depth site analysis and the use of an HTTP client. We'll go through the entire process from analyzing a dynamic website to implementing an efficient web crawler using the Crawlee for Python framework.

Hello, Crawlee Devs, and welcome back to another tutorial on the Crawlee Blog. This tutorial will teach you how to scrape infinite-scrolling websites using Crawlee for Python.

For context, infinite-scrolling pages are a modern alternative to classic pagination. When users scroll to the bottom of the webpage instead of choosing the next page, the page automatically loads more data, and users can scroll more.

As a big sneakerhead, I'll take the Nike shoes infinite-scrolling website as an example, and we'll scrape thousands of sneakers from it.

Crawlee for Python has some amazing initial features, such as a unified interface for HTTP and headless browser crawling, automatic retries, and much more.

Introduction​

Greetings! I'm Max, a Python developer from Ukraine, a developer with expertise in web scraping, data analysis, and processing.

My journey in web scraping started in 2016 when I was solving lead generation challenges for a small company. Initially, I used off-the-shelf solutions such as Import.io and Kimono Labs. However, I quickly encountered limitations such as blocking, inaccurate data extraction, and performance issues. This led me to learn Python. Those were the glory days when requests and lxml/beautifulsoup were enough to extract data from most websites. And if you knew how to work with threads, you were already a respected expert :)

As a freelancer, I've built small solutions and large, complex data mining systems for products over the years.

Today, I want to discuss the realities of web scraping with Python in 2024. We'll look at the mistakes I sometimes see and the problems you'll encounter and offer solutions to some of them.

Let's get started.

Just take requests and beautifulsoup and start making a lot of money...

No, this is not that kind of article.

Testimonial from early adopters

“Crawlee for Python development team did a great job in building the product, it makes things faster for a Python developer.”

~ Maksym Bohomolov

We launched Crawlee in August 2022 and got an amazing response from the JavaScript community. With many early adopters in its initial days, we got valuable feedback, which gave Crawlee a strong base for its success.

Today, Crawlee built-in TypeScript has nearly 13,000 stars on GitHub, with 90 open-source contributors worldwide building the best web scraping and automation library.

Since the launch, the feedback we’ve received most often [1][2][3] has been to build Crawlee in Python so that the Python community can use all the features the JavaScript community does.

With all these requests in mind and to simplify the life of Python web scraping developers, we’re launching Crawlee for Python today.

The new library is still in beta, and we are looking for early adopters.

Crawlee for Python has some amazing initial features, such as a unified interface for HTTP and headless browser crawling, automatic retries, and much more.

Hello Crawlee community,

We are back with another blog, this time explaining how Crawlee rotates proxies and prevents crawlers from getting blocked.

Proxies vary in quality, speed, reliability, and cost. There are a few types of proxies, such as datacenter and residential proxies. Datacenter proxies are cheaper but, on the other hand, more prone to getting blocked, and vice versa with residential proxies.

It is hard for developers to decide which proxy to use while scraping data. We might get blocked if we use datacenter proxies for low-cost scraping, but residential proxies are sometimes too expensive for bigger projects. Developers need a system that can manage both costs and avoid getting blocked. To manage this, we recently introduced tiered proxies in Crawlee. Let’s take a look at it.

In this blog, we'll guide you through the process of using Vite and Crawlee to build a website that recommends Netflix shows based on their categories and genres. To do that, we will first scrape the shows and categories from Netflix using Crawlee, and then visualize the scraped data in a React app built with Vite. By the end of this guide, you'll have a functional web show recommender that can provide Netflix show suggestions.

Hey, crawling masters!

Welcome to another post on the Crawlee blog; this time, we are going to compare Scrapy, one of the oldest and most popular web scraping libraries in the world, with Crawlee, a relative newcomer. This article will answer your questions about when to use Scrapy and help you decide when it would be better to use Crawlee instead. This article will be the first in a series comparing the various technical aspects of Crawlee with Scrapy.

Introduction:​

Scrapy is an open-source Python-based web scraping framework that extracts data from websites. With Scrapy, you create spiders, which are autonomous scripts to download and process web content. The limitation of Scrapy is that it does not work very well with JavaScript rendered websites, as it was designed for static HTML pages. We will do a comparison later in the article about this.

Crawlee is also an open-source library that originated as Apify SDK. Crawlee has the advantage of being the latest library in the market, so it already has many features that Scrapy lacks, like autoscaling, headless browsing, working with JavaScript rendered websites without any plugins, and many more, which we are going to explain later on.

Introduction​

Amazon is one of the largest and most complex websites, which means scraping it is pretty challenging. Thankfully, the Crawlee library makes things a little easier, with utilities like JSON file outputs, automatic scaling, and request queue management.

In this guide, we'll be extracting information from Amazon product pages using the power of TypeScript in combination with the Cheerio and Crawlee libraries. We'll explore how to retrieve and extract detailed product data such as titles, prices, image URLs, and more from Amazon's vast marketplace. We'll also discuss handling potential blocking issues that may arise during the scraping process.