normalizrplus

normalizr

Install

Install from the NPM repository using yarn or npm:

yarn add normalizr

npm install normalizr

Motivation

Many APIs, public or not, return JSON data that has deeply nested objects. Using data in this kind of structure is often very difficult for JavaScript applications, especially those using Flux or Redux.

Solution

Normalizr is a small, but powerful utility for taking JSON with a schema definition and returning nested entities with their IDs, gathered in dictionaries.

Documentation

• Introduction
  • Build Files
• Quick Start
• API
  • normalize
  • denormalize
  • schema
• Using with JSONAPI

Examples

• Normalizing GitHub Issues
• Relational Data
• Interactive Redux

Quick Start

Consider a typical blog post. The API response for a single post might look something like this:

{
  "id": "123",
  "author": {
    "id": "1",
    "name": "Paul"
  },
  "title": "My awesome blog post",
  "comments": [
    {
      "id": "324",
      "commenter": {
        "id": "2",
        "name": "Nicole"
      }
    }
  ]
}

We have two nested entity types within our article: users and comments. Using various schema, we can normalize all three entity types down:

import { normalize, schema } from 'normalizrplus';

// Define a users schema
const user = new schema.Entity('users');

// Define your comments schema
const comment = new schema.Entity('comments', {
  commenter: user
});

// Define your article
const article = new schema.Entity('articles', {
  author: user,
  comments: [comment]
});

const normalizedData = normalize(originalData, article);

Now, normalizedData will be:

{
  result: "123",
  entities: {
    "articles": {
      "123": {
        id: "123",
        author: "1",
        title: "My awesome blog post",
        comments: [ "324" ]
      }
    },
    "users": {
      "1": { "id": "1", "name": "Paul" },
      "2": { "id": "2", "name": "Nicole" }
    },
    "comments": {
      "324": { id: "324", "commenter": "2" }
    }
  }
}

Dependencies

None.

Credits

Normalizr was originally created by Dan Abramov and inspired by a conversation with Jing Chen. Since v3, it was completely rewritten and maintained by Paul Armstrong. It has also received much help, enthusiasm, and contributions from community members.