@convenience/store

@convenience/store

a simple native-node data store that is convenient.

Convenience Store exists because I got tired of writing of simple test data stores that did not persist. I found myself at a point were I needed MongoDB or Postgres but something better then an in memory javascript object. When working on a new project, I would need some place simple to put, get and iterate some objects and have it persist to disk. So I decided to write this library. Now storing objects and getting them is as easy as this:

Install

$ npm i @convenience/store

Application: app.js

const { Store, ORDER } = require('@convenience/store')
const Path = require('path')

const path = Path.join(__dirname, 'data')
const store = new Store(path) // create an instance of the store

const item = { active: true, name: 'first', id: 1 }
if(!store.existsBucket('items')) { // does bucket exist
  store.createBucket('items', item) // create a bucket with an example item to enforce a schema
}

store.create('items', item) // add item to the 'items' bucket
store.create('items', { active: true, name: 'second', id: 2 }) // add another item to the 'items' bucket

const update = Object.assign(item, { active: false })
store.update('items', 1, update) // update the first item in the 'items' bucket

const first = store.get('items', 1) // get item from 'items' bucket by id
const exists = store.exists('items', 1) // does item exists in'items' bucket by id

const all = store.getItems('items') // get first items from 'items' bucket in the order they were added (oldest > newest)
const reverse = store.getItems('items', { order: ORDER.DESCENDING }) // get items from 'items' bucket in newest > oldest order
const page = store.getItems('items', { offset: 10, take: 5 }) // pagination!
const active = store.filterItems('items', (i) => i.active) // filtering! this gets the active items back

store.delete('items', 1) // delete item from 'items' bucket by id

store.compress('items') // save disk space by removing open space left by updates and deleted files in a bucket.

This is pretty basic usage. Check out the API docs for more detailed usage.

Some of the features include:

• Optional password based encryption to files
• Optional compression to files
• Fast Avro serialization for data schema protection and performance
• Optional and extensible Caching mechanisms
• 100% unit test coverage

Looks great! Right? Well it is not perfect. It's not for production (probably) and it has some shortcomings. The are:

• No sorting by data fields. Building and maintaining sort indexes was out of scope of the purpose of this project, and can get ugly.
• Everything is synchronous, so performance might be meh. You don't need to close a connection becuase all the file operations are synchronous and atomic. This should keep things semi-transactional. It may or may not be acid (I have not tested it), but it is probably close enough.
• All lists (indexes) need to fit into memory.
• No support for clustering. If you web app has more than one instance then the stores will be different on each instance. Though if you wrapped it in a lightweight single instance micro-service.... ;P

Terminology

• store: Represents a place to store items. It is bound to a directory where it writes files to persist data.
• bucket: A store has buckets to put items in, they are named and are bound to a particular Avro schema (items are stored in a .data file per bucket)
• list: The an index of the items that are stored in a bucket (lists are stored in a .list file per list).