staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

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.

---

agrlh

Building and setting up netatalk 3.1.2 on Debian

1. Build

Install dependencies

sudo apt-get install automake libtool build-essential pkg-config checkinstall git-core avahi-daemon libavahi-client-dev libssl-dev libdb5.1-dev db-util db5.1-util libgcrypt11 libgcrypt11-dev libcrack2-dev libpam0g-dev libdbus-1-dev libdbus-glib-1-dev libglib2.0-dev libwrap0-dev systemtap-sdt-dev libacl1-dev libldap2-dev

Download and unpack netatalk 3.1.2

jrudolph

import java.io.{ FileOutputStream, File }
import scala.io.Source
import scala.util.matching.Regex

object UpdateCopyrightHeaders extends App {
  val encoding = "utf-8"
  val copyrightLines: Seq[String] = "Copyright (C) 1894-2014 Example Corp. <http://www.example.com>".split("\n")
  val srcDir = new File("src/")

  def updateCopyright(file: File): Unit = {

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

vmarquez

import java.util.concurrent.atomic.AtomicReference
import java.util.concurrent.CountDownLatch
import scala.concurrent.Future
import scala.concurrent.ExecutionContext
import ExecutionContext.Implicits.global

object TxMapTest {
  /*
   *  Example Usage
   *  We want to show two threads working with the same data source having both of their effects succeed

dacr

package controllers

import play.api._
import play.api.mvc._
import play.api.libs.concurrent.Execution.Implicits._
import play.api.libs.concurrent.Akka
import play.api.Play.current
import scala.concurrent._
import scala.concurrent.duration._

dgageot

package main;

import com.google.common.base.Strings;
import com.google.common.io.ByteStreams;
import com.sun.jersey.spi.container.ContainerRequest;
import com.sun.jersey.spi.container.ContainerResponse;
import com.sun.jersey.spi.container.ContainerResponseFilter;

import javax.ws.rs.core.MediaType;
import javax.ws.rs.core.Response;

dgageot

package net.gageot;

import org.junit.Test;

import static org.fest.assertions.Assertions.assertThat;

public class CodeStoryStatusTest extends PhantomJsTest {
  @Override
  public String defaultUrl() {
    return "http://status.code-story.net";

spullara

package webserver;

import org.webbitserver.WebServers;

public class App {
  public static void main(String[] args) {
    WebServers.createWebServer(8080).add((req, res, con) -> {
      res.content("Hello, world!").end();
    }).start();
  }

mslinn

package controllers

import play.api._
import libs.ws.WS
import play.api.mvc._
import com.micronautics.paypal.{TransactionProcessor, PaypalTransactions}
import java.net.URLEncoder
import concurrent.{Await, ExecutionContext}
import concurrent.duration.Duration
import java.util.concurrent.TimeUnit