cheerio VS husky

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.

While looking for a way to implement capo.js in BigQuery to understand how pages in HTTP Archive are ordered, I came across the Cheerio library, which is a jQuery-like interface over an HTML parser.

Cheerio is a JavaScript tool for parsing HTML and XML in Node.js. It provides APIs for traversing and manipulating the DOM of a webpage.

Dive deeper into the world of Husky with the official documentation. Explore the capabilities of Standard Version through its comprehensive documentation. Unravel the mysteries of semantic-release with its detailed documentation. Discover the versatility of bumpp with its comprehensive documentation.

Let’s walk through the steps for a one-time setup to configure husky pre-commit and pre-push hooks, ESLint with code styles conventions, prettier code formatter, and lint-staged. Husky automatically runs a script on each commit or push. This is useful for linting files to enforce code styles that keeps the entire code base following conventions.

We were already using lint-staged and have a pre-commit hook in place using Husky in our project for linter and prettier. So it made sense to add a check here.

Now, let's talk about Husky. It's a wonderful tool that enables you to run scripts on any Git hooks. We'll add a pre-commit hook to run ESLint and Stylelint checks before committing. This ensures that we don't commit code with errors.

In all my projects I use husky, lint-staged and commitlint.

One of the greatest things about a monorepo is that you can centralise a lot of tooling configuration. For example, chuck a .prettierrc in the root of your monorepo and you'll never have arguments about code style again. Put a few base tsconfigs in the there to extend, and suddenly TypeScript behaves with beautiful consistency across all your packages. Add formatting, linting and husky as dependencies in the root of your workspace and you no longer need to work through upgrading everything individually when a new version comes out.