Electron VS cheerio

Repository: Electron

Electron GitHub Repository

The team at Electron have been faithfully shipping new releases almost every single month. I think they had Christmas off 🤔. This popular framework has developers writing cross-platform desktop applications using JavaScript, HTML and CSS. The latest update depreciates some process events, and added new modules, APIs, methods, and more. Read into all the changes in the Electron release notes. This month, Electron also introduced a new formal RFC process.

VS Code has been crashing at launch in Wayland since more than eight months ago:

https://github.com/electron/electron/issues/37531

So we talked a lot about the Atomic Design Principle, but you could just use that in any system and start creating. You could have Angular components, React Components, and Vue Components. But if you notice these don't easily work Everwhere. So the solution is to use Web Components because the modern browser can already understand these, and any Front-End framework can then utilize these components. You can use Electron for desktop (Slack, VSCode), PWA for both Android and iOS, and across all browsers Can I Use.

I'm noticing a significant boost in performance, crisper text, and better power savings. The only shortcoming is that the window which Figma will run on will lose its shadow. This is due to a technical limitation with frameless windows on Linux.

For the longest time, building desktop apps was a daunting task to web developers. That is, until technologies like Electron made creating these apps more approachable to a wider audience. Today, we’ve got a wide array of native applications built with solutions like Electron, Tauri, Capacitor, and many more. While these are great solutions, sometimes configuration can be tricky and the applications we create can become somewhat bloated in terms of memory usage.

Electron patch for version 27: https://github.com/electron/electron/pull/39823

I opted to use cheerio, a nifty HTML parsing library to clean-up the HTML before returning it from the function. Using cheerio I:

We're going to use cheerio to parse the content from websites ("web scraping").

Cheerio is your ticket to the world of server-side magic, allowing you to manipulate HTML and XML documents with jQuery-like syntax. It’s perfect for web scraping, data extraction, or just making sense of the mess that is web content. With Cheerio, you get to play around with the DOM, use CSS selectors, and basically do all the cool things you'd do in the browser, but server-side.

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.

Used libraries Axios - it is a promise HTTP clients to make requests to the specified URL. Cheerio- it is a library for parsing and manipulating HTML that is commonly used here for extracting data from downloaded HTML content. Apify SDK- it is for building Apify Actors, that is utilized for initializing actor environments, getting input data, and pushing extracted data to the dataset.

Nice. I've used Cheerio for this in the past: https://github.com/cheeriojs/cheerio?tab=readme-ov-file#sele...

For this article, I will be using the TypeScript Starter template as shown in the screenshot above. This comes with Nodejs, Cheerio, Axios

> I'm not sure why Python web scraping is so popular compared to Node.js web scraping

Take this with a grain of salt, since I am fully cognizant that I'm the outlier in most of these conversations, but Scrapy is A++ the no-kidding best framework for this activity that has been created thus far. So, if there was scrapyjs maybe I'd look into it, but there's not (that I'm aware of) so here we are. This conversation often comes up in any such "well, I just use requests & ..." conversation and if one is happy with main.py and a bunch of requests invocations, I'm glad for you, but I don't want to try and cobble together all the side-band stuff that Scrapy and its ecosystem provide for me in a reusable and predictable way

Also, often those conversations conflate the server side language with the "scrape using headed browser" language which happens to be the same one. So, if one is using cheerio <https://github.com/cheeriojs/cheerio> then sure node can be a fine thing - if the blog post is all "fire up puppeteer, what can go wrong?!" then there is the road to ruin of doing battle with all kinds of detection problems since it's kind of a browser but kind of not

I, under no circumstances, want the target site running their JS during my crawl runs. I fully accept responsibility for reproducing any XHR or auth or whatever to find the 3 URLs that I care about, without downloading every thumbnail and marketing JS and beacon and and and. I'm also cognizant that my traffic will thus stand out since it uniquely does not make the beacon and marketing calls, but my experience has been that I get the ban hammer less often with my target fetches than trying to pretend to be a browser with a human on the keyboard/mouse but is not

Web scraping is a powerful technique used to extract data from websites. In this tutorial, we'll explore how to perform web scraping using Node.js, Axios for making HTTP requests,Cheerio for parsing HTML content and also json2csv for converting json data to csv. We'll scrape product data from a sample website, "https://scrapeme.live/shop/".

Web scraping, while immensely useful, often requires developers to navigate a sea of tools and libraries, each with its own quirks and intricacies. Whether it's JSDOM, Cheerio, Playwright, or even just plain old vanilla JS in the DevTools console, moving between these platforms can be a challenge.