Simply put, destructuring in Clojure is a way extract values from a datastructure and bind them to symbols, without having to explicitly traverse the datstructure. It allows for elegant and concise Clojure code.
package akkahttptest | |
import akka.actor.ActorSystem | |
import akka.http.Http | |
import akka.stream.FlowMaterializer | |
import akka.http.server._ | |
import akka.http.marshalling.PredefinedToResponseMarshallers._ | |
import akka.stream.scaladsl.{HeadSink, Source} | |
object Proxy extends App { |
The MIT License (MIT) | |
Copyright (c) 2014 Tomas Kafka | |
Permission is hereby granted, free of charge, to any person obtaining a copy | |
of this software and associated documentation files (the "Software"), to deal | |
in the Software without restriction, including without limitation the rights | |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
copies of the Software, and to permit persons to whom the Software is | |
furnished to do so, subject to the following conditions: |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
Mini projects by Maxime Euzière (xem), subzey, Martin Kleppe (aemkei), Mathieu Henri (p01), Litterallylara, Tommy Hodgins (innovati), Veu(beke), Anders Kaare, Keith Clark, Addy Osmani, bburky, rlauck, cmoreau, maettig, thiemowmde, ilesinge, adlq, solinca, xen_the,...
(For more info and other projects, visit http://xem.github.io)
(Official Slack room: http://jsgolf.club / join us on http://register.jsgolf.club)
- 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.
- Models and Issues in Data Stream Systems
- 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
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
//This shows an updated websocket example for play2.2.0 utilizing Concurrent.broadcast vs Enumerator.imperative, which | |
// is now deprecated. | |
object Application extends Controller { | |
def index = WebSocket.using[String] { request => | |
//Concurernt.broadcast returns (Enumerator, Concurrent.Channel) | |
val (out,channel) = Concurrent.broadcast[String] |
Locate the section for your github remote in the .git/config
file. It looks like this:
[remote "origin"]
fetch = +refs/heads/*:refs/remotes/origin/*
url = [email protected]:joyent/node.git
Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/*
to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:
#!/bin/bash | |
#------------------------------------------------------------------------------ | |
# Name: sbtmkdirs | |
# Version: 1.5 | |
# Purpose: Create an SBT project directory structure with a few simple options. | |
# Author: Alvin Alexander, http://alvinalexander.com | |
# License: Creative Commons Attribution-ShareAlike 2.5 Generic | |
# http://creativecommons.org/licenses/by-sa/2.5/ | |
#------------------------------------------------------------------------------ |