Andy Petrella andypetrella

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

What is Scala?

Créé par Martin en 2003 -- Son nom vient de l'anglais Scalable language Scala intègre les paradigmes de programmation orientée objet et de programmation fonctionnelle, avec un typage statique

Fonctionnel basique

a) Immutability

model	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21	6	160	110	3.9	2.62	16.46	0	1	4	4
Mazda RX4 Wag	21	6	160	110	3.9	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.32	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.44	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.46	20.22	1	0	3	1
Duster 360	14.3	8	360	245	3.21	3.57	15.84	0	0	3	4
Merc 240D	24.4	4	146.7	62	3.69	3.19	20	1	0	4	2
Merc 230	22.8	4	140.8	95	3.92	3.15	22.9	1	0	4	2

##Building and using Spark Notebook for MapR

The spark-notebook is a useful browser-based REPL that can be used to explore data and build visualizations. This guide will illustrate the MapR-specific requirements for building, and using the spark notebook on MapR clusters.

###Building Checkout the source:

$ git clone https://github.com/andypetrella/spark-notebook.git

	package foo.bar

	import spray.routing._
	import spray.http._
	import spray.http.StatusCodes.Forbidden


	// See https://developer.mozilla.org/en-US/docs/HTTP/Access_control_CORS

	case class Origin(origin: String) extends HttpHeader {

	case class Banque(nom:String) {banque =>

	def accepte(m:Montant) =
	CompteBuilder(banque, m)

	}

	implicit def stringToBanque(s:String) = Banque(s)

	implicit def intWithDevise(i:Int) = new {

	```{r setup, echo=FALSE, results='hide'}
	chunkref <- local({
	function(chunklabel) {
	sprintf('[%s](#%s)', chunklabel, chunklabel )
	}
	})

	secref <- local({
	function(seclabel) {
	sprintf('[%s](#%s)', seclabel, seclabel )

	import scala.collection.immutable
	import scala.collection.immutable.Queue
	import akka.actor._

	/**
	* Rough, initial cut of a trait to mixin when an Actor requires
	* initalization, where the initialization is long and arduous (for
	* example, data initialization related). This strategy allows the
	* implementing actor to delegate the work to another Actor, on a
	* separate, dedicated Dispatcher, and not block any other related

	module Bob (responseFor) where
	import Data.Char (isSpace, isLower, isAlpha)
	import Control.Applicative ((<$>), (<*>), (<\|>))
	data Prompt = Silence \| Yell \| Question \| Other
	deriving (Show, Eq)

	data P a = P { pCanParse :: Char -> Bool
	, pParse :: Char -> Either (P a) (Maybe a)
	, pParseEOF :: Maybe a
	}

	import scalaz.Applicative
	import scalaz.concurrent.Task

	/**
	* This `Applicative[Task]` runs tasks in parallel, by defining
	* `ap` and `apply2` in terms of `mapBoth`. This differs from the
	* default `Applicative[Task]`, where effects are sequenced
	* deterministically, in left to right order.
	*/
	val T = new Applicative[Task] {