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A tiny in-memory javascript database with indexing and sql like filters.A small javascript in memory database with indexing and filters.

Home Page: https://www.npmjs.com/package/pixiedb

License: MIT License

TypeScript 99.66% JavaScript 0.34%

database in-memory-database in-memory-storage javascript-database js-database small-database

pixiedb's Introduction

PixieDB

A tiny in-memory javascript database with indexing and sql like filters.

PixieDb perform all operations (insert, delete, get) in log(n) time.

Warning

Please keep in mind that PixieDb is still in under active development.

Usage

import { PixieDb } from "pixiedb";

const products = [
    { id: 1, name: "Apple", price: 5, category: "Fruit" },
    { id: 2, name: "Banana", price: 10, category: "Fruit" },
    { id: 3, name: "Grapes", price: 6, category: "Fruit" },
    { id: 4, name: "Orange", price: 8, category: "Fruit" },
    { id: 5, name: "Potato", price: 18, category: "Vegetable" },
    { id: 6, name: "Milk", price: 7, category: "Dairy" },
    // ...
]

// provide unique key, data and indexes for better performance
// 3rd param data is optional can be load after using the load method
const pd = new PixieDb('id', ["price", "category"], products) 
// or
const pd = new PixieDb<Product>('id', ["price", "category"]) // pass type if using typescript
pd.load(products) // to load data later

const byId = pd.select().eq("id", 2).single()
console.log(byId); // { id: 2, name: "Banana", price: 10, category: "Fruit" }

// can also pass array of fields to select method to pick only those fields/properties
const fruitBelow10 = pd.select(["id", "name", "price"]).eq("category", "Fruit").lte("price", 10).orderBy(["name", ["price", "desc"]]).range(2, 3).data()
console.log(fruitBelow10); // [{ id: 3, name: "Grapes", price: 6 }, ...]

const updatedBanana = pd.where().eq("name", "Banana").update({price: 100})
// [{ id: 2, name: "Banana", price: 100, category: "Fruit" }, ...]

// delete all docs where name equals "Apple"
const deletedApples = pd.where().eq("name", "Apple").delete()
// [{ id: 1, name: "Apple", price: 5, category: "Fruit"}, ...]

Installation

# using npm
npm install pixiedb

# using pnpm
pnpm add pixiedb

# using yarn
yarn add pixiedb

# using bun
bun add pixiedb

Docs

PixieDb

This is a class which create an PixieDb instance to use.

// pass type/interface if using typescript
const pd = new PixieDb<Product>('id', ["price", "category"]) 

// or with data
const pd = new PixieDb<Product>('id', ["price", "category"], products)

Methods

load

Used to import data without cloning (so don't mutate the data or clone before load). Pass true as second parameter to clear the previous data and indexes state. (default: false).

pd.load(products)
// or
pd.load(products, true)
// remove previous data and index state

get

Get single doc/row using key (primary key/unique id). Returns doc/row if present else undefined.

pd.get(2)
// { id: 2, name: "Banana", price: 10, category: "Fruit" }

select

Get single doc/row using key (primary key/unique id). Returns doc/row if present else undefined.

pd.select().eq("category", "Fruit").gte("price", 6).data()
// [{ id: 2, name: "Banana", price: 10, category: "Fruit" }, { id: 3, name: "Grapes", price: 6, category: "Fruit" }, ...]

pd.select(["id", "name", "price"]).eq("category", "Fruit").lte("price", 6).data()
// [{ id: 1, name: "Apple", price: 5 }, ...]

pd.select().eq("category", "Fruit").between("price", [6, 10]).data()
// [{ id: 2, name: "Banana", price: 10, category: "Fruit" }, { id: 3, name: "Grapes", price: 6, category: "Fruit" }, { id: 4, name: "Orange", price: 8, category: "Fruit" }, ...]

where

used to perform delete/update with complex filtering

// this will delete and return all the docs according to the filters
pd.where().eq("category", "Fruit").gte("price", 6).delete()
// [{ id: 2, name: "Banana", price: 10, category: "Fruit" }, { id: 3, name: "Grapes", price: 6, category: "Fruit" }, ...]

pd.where().eq("category", "Fruit").between("price", [6, 10]).update({price: 11})
// [{ id: 2, name: "Banana", price: 11, category: "Fruit" }, { id: 3, name: "Grapes", price: 11, category: "Fruit" }, { id: 4, name: "Orange", price: 11, category: "Fruit" }, ...]

data

Get all docs/rows ordered respect to primary key/unique id. Pass false to get all without clone (don't modify). default: true

pd.data()
// [{ id: 1, name: "Apple", price: 5, category: "Fruit" }, ...]

count

Get all docs/rows ordered respect to primary key/unique id. Pass false to get all without clone (don't modify). default: true

pd.select().count()
// 6

pd.select().eq("category", "Fruit").between("price", [6, 10]).count()
// 4

close

to close/quit/terminate the database and remove all data/indexes and fire "Q" ("quit") event. Pass true to not emit events. default: false

pd.close()
// or
pd.close(true) // doesn't fire event

toJson

return JSON of all data (without cloning), key and index names.

pd.toJSON()
// { key: "id", indexes: ["price", "category", {name: "id", unique: true}], data: [{ id: 1, name: "Apple", price: 10, category: "Fruit" }, ...]

// this will call the above toJSON method
JSON.stringify(pd)

Roadmap

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