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prevalence

v0.0.8

Published

Promise based, co/koa-friendly prevalence component.

Readme

Promise based, co/koa-friendly prevalence component.

npm travis license js-standard-style

Think of it a super extensible, super fast, in-memory database that will restore it self from a history of commands.

Sample code

The sample code below two blog posts and then prints them, illustrating

  • command registration via register()
  • command execution via execute()
  • querying via query()
'use strict'

let path = require('path')
let co = require('co')
let prevalence = require('prevalence')

let repo = prevalence({
  path: path.resolve(__dirname, '../tmp/sample.journal'),
  model: {
    posts: {}
  }
})
.register('add-post', function * (model, post) {
  model.posts[post.id] = post
  return post
})

var log = console.log

co(function * () {
  yield repo.execute('add-post', {id: 'post#1', subject: 'Lorem'})
  yield repo.execute('add-post', {id: 'post#2', subject: 'Ipsum'})

  let posts = yield repo.query(function * (model) {
    let posts = []
    for (let id of Object.getOwnPropertyNames(model.posts)) {
      posts.push(model.posts[id])
    }
    return posts
  })

  for (let post of posts){
    log('%s: %s', post.id, post.subject)
  }
})

Events

let repo = prevalence({...})
  .on('init', () => console.log('One time restore of journal complete'))
  .on('command', (name, arg, result) => {...})
  .on('error', e => console.log(e.stack))

Overview

Prevalence acts as a simple in-memory database with very controlled access patterns.

  • Queries may inspect the data model (in parallel controlled by a read lock)
  • Commands may alter the data model (serially controlled by a write lock)
  • Commands must be described (with name and a handler) and invocations are logged with name and argument to a durable history (journal) so that the command can be re-executed on startup, thus restoring the model to the last known state.
  • Results from queries and commands are by default serialized (i.e. deep copied), eliminating some nasty race conditions.

Check out wikipedia for a more formal definition of the prevalence pattern. Note that this implementation deviates on some key points. Most notably the support for snapshots is dropped.