httpbin VS cheerio

We proceed to define routes to https://httpbin.org/ to test our setup. Let's call the route to /get:

https://httpbin.org/ is an excellent tool for learning (and testing) the basics. Take a few minutes to explore the endpoints they provide. You can also use different response codes to test your apps error handling, etc

export FABI_PROD="http://httpbin.org/" export FABI_LOCAL="http://0.0.0.0:8000/"

use anyhow; use embedded_svc::http::client::Client; use embedded_svc::wifi::{AuthMethod, ClientConfiguration, Configuration}; use esp_idf_hal::peripherals::Peripherals; use esp_idf_svc::eventloop::EspSystemEventLoop; use esp_idf_svc::http::client::{Configuration as HttpConfig, EspHttpConnection}; use esp_idf_svc::nvs::EspDefaultNvsPartition; use esp_idf_svc::wifi::{BlockingWifi, EspWifi}; fn main() -> anyhow::Result<()> { esp_idf_sys::link_patches(); // Configure Wifi let peripherals = Peripherals::take().unwrap(); let sysloop = EspSystemEventLoop::take()?; let nvs = EspDefaultNvsPartition::take()?; let mut wifi = BlockingWifi::wrap( EspWifi::new(peripherals.modem, sysloop.clone(), Some(nvs))?, sysloop, )?; wifi.set_configuration(&Configuration::Client(ClientConfiguration { ssid: "SSID".into(), bssid: None, auth_method: AuthMethod::None, password: "PASSWORD".into(), channel: None, }))?; // Start Wifi wifi.start()?; // Connect Wifi wifi.connect()?; // Wait until the network interface is up wifi.wait_netif_up()?; // Print Out Wifi Connection Configuration while !wifi.is_connected().unwrap() { // Get and print connection configuration let config = wifi.get_configuration().unwrap(); println!("Waiting for station {:?}", config); } println!("Wifi Connected, Intiatlizing HTTP"); // HTTP Configuration // Create HTTPS Connection Handle let httpconnection = EspHttpConnection::new(&HttpConfig { use_global_ca_store: true, crt_bundle_attach: Some(esp_idf_sys::esp_crt_bundle_attach), ..Default::default() })?; // Create HTTPS Client let mut httpclient = Client::wrap(httpconnection); // HTTP Request Submission // Define URL let url = "https://httpbin.org/get"; // Prepare request let request = httpclient.get(url)?; // Log URL and type of request println!("-> GET {}", url); // Submit Request and Store Response let response = request.submit()?; // HTTP Response Processing let status = response.status(); println!("<- {}", status); match response.header("Content-Length") { Some(data) => { println!("Content-Length: {}", data); } None => { println!("No Content-Length Header"); } } match response.header("Date") { Some(data) => { println!("Date: {}", data); } None => { println!("No Date Header"); } } Ok(()) }

has_domain: whether the matched route references an upstream with a domain, e.g., http://httpbin.org, or not, e.g., 192.168.0.1

But where does the data go now? By default, the form data is sent to the URL of the page containing the form — the current page, actually. Generally, all the data should be sent to a server to store and be handled. We can set up a server like Next.js, but it's a big separate topic to discover. You can learn more about it in our tutorial. In this post, I suggest concentrating on uploading staff and using httpbin — a simple OSS HTTP Request & Response Service.

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.