jhollingworth

#Translating Marty to Redux

Some people have asked how I've translated a Marty app to Redux. Once you get your head around how Redux works I found concepts in Marty translated easily. Below is side-by-side comparison of the two libraries to help you along the way.

Also if you're looking for some devtools, check out redux-devtools.

##Constants

Marty

sebmarkbage

setProps - depends on reading the last reconciled props from the current reconciled state of the app, at the time of the call. It also depends on an object that doesn't necessarily need to be there outside reconciliation. This is unlike setState, which is state that needs to be there. setState is queued up and merged at the time of reconciliation. Not at the time of the call. setState has a side-effect but is not a stateful nor mutative API.

isMounted - reads the current state of the tree, which may be stale if you're in a batch or reconciliation.

getDOMNode/findDOMNode - Reads the currently flushed node. This currently relies on the state of the system and that everything has flushed at this time. We could potentially do a forced render but that would still rely on the state of the system allowing us to synchronously being able to force a rerender of the system. Note: in 0.14, refs directly to DOM node will resolve to the DOM node. This allow you to get access to a node at the time of its choos

acdlite

import Flummox from 'flummox';
import { Map } from 'immutable';

// Instead of a constructor, just wrap flux creation inside a function
export default function createFlux() {
  const flux = new Flummox();
  
  // Actions are the same. Just pass an object instead of a class.
  const thingActions = flux.createActions('things', {
    incrementSomething(amount) {

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t