Playwright VS cheerio

We start with a project that was bootstrapped with npx create-next-app. For the E2E test we use Playwright and set it up as described in the testing guide provided by Next.js.

Playwright is a powerful tool developed by Microsoft, it allows developers to write reliable end-to-end tests and perform browser automation tasks with ease. What sets Playwright apart is its ability to work seamlessly across multiple browsers (Chrome, Firefox, and WebKit), it provides a consistent and efficient way to interact with web pages, extract data, and automate repetitive tasks. Moreover, it supports various programming languages such as Node.js, Python, Java, and .NET, that’s making it a versatile choice for web scraping projects. Whether you're scraping public data for analysis, building a web crawler, or automating manual workflows, Playwright has you covered.

The consensus I could gather is either use playwright or use a workaround to solve it in the puppeteer layer. The root cause of the bug is a websocket size limitation on the CDP protocol for chromium.

With the advent of tools like Puppeteer and now Playwright, end-to-end testing has become much easier and more reliable. For anyone who's used Selenium in the past, you know what I'm talking about. Puppeteer has opened the way in terms of E2E tooling, but Playwright has taken it to the next level and made it easier to await for certain selectors or conditions to be fulfilled (via locators), thus making tests more reliable and less flaky. Also, it's a game changer that it introduced a test-runner - this made the integration between the headless browser and the actual test code much smoother.

In this tutorial, our main focus will be on Playwright web scraping. So what is Playwright? It’s a handy framework created by Microsoft. It's known for making web interactions more streamlined and works reliably with all the latest browsers like WebKit, Chromium, and Firefox. You can also run tests in headless or headed mode and emulate native mobile environments like Google Chrome for Android and Mobile Safari.

// playwright.config.ts import { defineConfig } from "@playwright/test"; /** * See https://playwright.dev/docs/test-configuration. */ export default defineConfig({ testDir: "./src/pages", reporter: "list", use: { baseURL: "http://localhost:5432/", }, timeout: process.env.CI ? 10000 : 4000, // ... more options });

Issues with Playwright

Fast and reliable end-to-end testing for modern web apps | Playwright

This means you cannot place test files outside of this directory, which was brought up as a question on Github some time ago. Initially, I thought it would be nice to add another folder in the repo called "scripts", but Playwright does not allow multiple testDir values.

4.Playwright: Playwright is a browser automation library by Microsoft. Key Features: Supports Chromium, Firefox, and WebKit. Provides cross-browser testing capabilities. Allows automating web, mobile, and desktop applications

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.