A Complete Guide To Incremental Static Regeneration (ISR) With Next.js

Incremental Static Regeneration (ISR) is a new evolution of the Jamstack, allowing you to update static content instantly without needing a full rebuild of your site. The hybrid approach of Next.js allows you to use ISR for e-commerce, marketing pages, blog posts, ad-backed media, and more.

A year ago, Next.js 9.3 released support for Static Site Generation (SSG) making it the first hybrid framework. I’d been a happy Next.js user for about a few years at this point, but this release made Next.js my new default solution. After working with Next.js extensively, I joined Vercel to help companies like Tripadvisor and Washington Post as they adopt and scale Next.js.

In this article, I’d like to explore a new evolution of the Jamstack: Incremental Static Regeneration (ISR). Below you’ll find a guide to ISR — including use cases, demos and tradeoffs.

The Problem With Static-Site Generation

The idea behind the Jamstack is appealing: pre-rendered static pages which can be pushed to a CDN and globally available in seconds. Static content is fast, resilient to downtime, and immediately indexed by crawlers. But there are some issues.

If you’ve adopted the Jamstack architecture while building a large-scale static site, you might be stuck waiting hours for your site to build. If you double the number of pages, the build time also doubles. Let’s consider Target.com. Is it possible to statically generate millions of products with every deployment?

The Problem with Static-Site Generation: Because build-times scale linearly with the number of pages, you might be stuck waiting for hours for your site to build. (Large preview)

Even if every page was statically generated in an unrealistic 1ms, it would still take hours to rebuild the entire site. For large web applications, choosing complete static-site generation is a non-starter. Large-scale teams need a more flexible, personalized, hybrid solution.

Content Management Systems (CMS)

For many teams, their site’s content is decoupled from the code. Using a Headless CMS allows content editors to publish changes without involving a developer. However, with traditional static sites, this process can be slow.

Consider an e-commerce store with 100,000 products. Product prices change frequently. When a content editor changes the price of headphones from $100 to $75 as part of a promotion, their CMS uses a webhook to rebuild the entire site. It’s not feasible to wait hours for the new price to be reflected.

Long builds with unnecessary computation might also incur additional expenses. Ideally, your application is intelligent enough to understand which products changed and incrementally update those pages without needing a full rebuild.

#next #react #javascript #jamstack

What is GEEK

Buddha Community

Google analytics Setup with Next JS, React JS using Router Events - 14

In this video, we are going to implement Google Analytics to our Next JS application. Tracking page views of an application is very important.

Google analytics will allow us to track analytics information.

Frontend: https://github.com/amitavroy/video-reviews
API: https://github.com/amitavdevzone/video-review-api
App link: https://video-reviews.vercel.app

You can find me on:
Twitter: https://twitter.com/amitavroy7​
Discord: https://discord.gg/Em4nuvQk

#next js #js #react js #react #next #google analytics

A Complete Guide To Incremental Static Regeneration (ISR) With Next.js

Incremental Static Regeneration (ISR) is a new evolution of the Jamstack, allowing you to update static content instantly without needing a full rebuild of your site. The hybrid approach of Next.js allows you to use ISR for e-commerce, marketing pages, blog posts, ad-backed media, and more.

A year ago, Next.js 9.3 released support for Static Site Generation (SSG) making it the first hybrid framework. I’d been a happy Next.js user for about a few years at this point, but this release made Next.js my new default solution. After working with Next.js extensively, I joined Vercel to help companies like Tripadvisor and Washington Post as they adopt and scale Next.js.

In this article, I’d like to explore a new evolution of the Jamstack: Incremental Static Regeneration (ISR). Below you’ll find a guide to ISR — including use cases, demos and tradeoffs.

The Problem With Static-Site Generation

The idea behind the Jamstack is appealing: pre-rendered static pages which can be pushed to a CDN and globally available in seconds. Static content is fast, resilient to downtime, and immediately indexed by crawlers. But there are some issues.

If you’ve adopted the Jamstack architecture while building a large-scale static site, you might be stuck waiting hours for your site to build. If you double the number of pages, the build time also doubles. Let’s consider Target.com. Is it possible to statically generate millions of products with every deployment?

The Problem with Static-Site Generation: Because build-times scale linearly with the number of pages, you might be stuck waiting for hours for your site to build. (Large preview)

Even if every page was statically generated in an unrealistic 1ms, it would still take hours to rebuild the entire site. For large web applications, choosing complete static-site generation is a non-starter. Large-scale teams need a more flexible, personalized, hybrid solution.

Content Management Systems (CMS)

For many teams, their site’s content is decoupled from the code. Using a Headless CMS allows content editors to publish changes without involving a developer. However, with traditional static sites, this process can be slow.

Consider an e-commerce store with 100,000 products. Product prices change frequently. When a content editor changes the price of headphones from $100 to $75 as part of a promotion, their CMS uses a webhook to rebuild the entire site. It’s not feasible to wait hours for the new price to be reflected.

Long builds with unnecessary computation might also incur additional expenses. Ideally, your application is intelligent enough to understand which products changed and incrementally update those pages without needing a full rebuild.

#next #react #javascript #jamstack

Laravel API and React Next JS frontend development - 28

In this video, I wanted to touch upon the functionality of adding Chapters inside a Course. The idea was to not think much and start the development and pick up things as they come.

There are places where I get stuck and trying to find answers to it up doing what every developer does - Google and get help. I hope this will help you understand the flow and also how developers debug while doing development.

App url: https://video-reviews.vercel.app
Github code links below:
Next JS App: https://github.com/amitavroy/video-reviews
Laravel API: https://github.com/amitavdevzone/video-review-api

You can find me on:
Twitter: https://twitter.com/amitavroy7​
Discord: https://discord.gg/Em4nuvQk

#next js #api #react next js #next #frontend #development

The Complete Guide to Next.js Authentication with AWS

In this video you’ll learn how to implement authentication in a Nextjs app using AWS Amplify and Amazon Cognito. We will cover everything from SSR authentication, SSR redirects, protected routes, client-side redirects, and authenticating a request in an API route.

The tutorial that goes along with this video is located here: https://dev.to/dabit3/the-complete-guide-to-next-js-authentication-2aco

The code for this video is located here: https://github.com/dabit3/next.js-authentication-aws

#aws #next.js #next

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

• J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7, Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer