Ketan Khairnar ketankhairnar

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

AKKA Test Patterns for Java

NOTE: These patterns assume you have a working understanding of Akka's JavaTestKit

Testing Actors In Complete Isolation

AKKA is all about building hierarchies of actors to represent a system. I've found this is a great way to decompose, design, and if needed distribute a system. This means your system becomes a hierarchy of actors, where parent actors create and manage their child actors, and child actors either carry out some unit of work, and/or themselves become parent actors.

But this can be a bit of a pain when it comes to writing tests for your actors. For example, say we have an actor, actorA, who in turn creates one or more child actors. I could have actorA create its child actor(s) as part of its instantiation. This is attractive especially if I know the exact child actors its going to create.

General Background and Overview

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

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.

Introduction to scalaz-stream

Every application ever written can be viewed as some sort of transformation on data. Data can come from different sources, such as a network or a file or user input or the Large Hadron Collider. It can come from many sources all at once to be merged and aggregated in interesting ways, and it can be produced into many different output sinks, such as a network or files or graphical user interfaces. You might produce your output all at once, as a big data dump at the end of the world (right before your program shuts down), or you might produce it more incrementally. Every application fits into this model.

The scalaz-stream project is an attempt to make it easy to construct, test and scale programs that fit within this model (which is to say, everything). It does this by providing an abstraction around a "stream" of data, which is really just this notion of some number of data being sequentially pulled out of some unspecified data source. On top of this abstraction, sca

	import scala.collection.mutable.ListBuffer
	import akka.actor.{Actor,ActorRef}
	import akka.actor.Actor._
	import akka.routing.{ Listeners, Listen }

	//Represents a domain event
	trait Event

	//A builder to create domain entities
	trait EntityBuilder[Entity] {

	// ###########################################################
	//
	// Demonstrates how to supervise an Akka consumer actor.
	//
	// The consumer consumes messages from a file endpoint:
	// - successful message processing by the consumer will
	// positively acknowledge the message receipt, causing
	// the file endpoint to delete the file.
	// - an exception during message processing will cause a
	// supervisor to restart the consumer. Before restart,

	{
	"variables": {
	"aws_access_key": "", // This helps me connect to AWS
	"aws_secret_key": ""
	},
	"builders": [{
	"type": "amazon-ebs",
	"access_key": "{{user `aws_access_key`}}",
	"secret_key": "{{user `aws_secret_key`}}",
	"region": "us-east-1",

	package demo;

	import static java.lang.reflect.Modifier.*;

	import java.util.Arrays;
	import java.util.Set;

	import org.springframework.beans.BeansException;
	import org.springframework.beans.factory.annotation.Value;
	import org.springframework.beans.factory.config.BeanExpressionResolver;

	#!/bin/bash
	MAC="$(curl -s http://169.254.169.254/latest/meta-data/network/interfaces/macs/)"
	VPCCIDR="$(curl -s http://169.254.169.254/latest/meta-data/network/interfaces/macs/"$MAC"/vpc-ipv4-cidr-block)"
	VPCNET="${VPCCIDR%%/*}"
	VPCBASE="$(echo "$VPCNET" \| cut -d"." -f1-3)"
	VPCDNS="$VPCBASE"'.2'
	echo "$VPCDNS"