THE IMPROBABLE STORY OF: FP AND ENTREPRENEURSHIP 

WHO ARE WE? 

XAVIER TORDOIR 

ANDY PETRELLA 

Find your ways Pick some and dig Mémoire: polishing phase STUDYING =:= FUTURE[OPTION[JOB]] Grab the basis 

JOB =:= (WORK => OPTION[EXPERIENCE]) => OPTION[MONEY] Working will probably raise your experience Based on this experience, you might get some money Experience might not be enough It will depend on the kind of experience 

IT'S NOT THAT EASY Proposal: Job =:= ( (Work, Explore, Learn) => Option[ Expertize ]) => Option[(Money, () => Future[Option[Job]] )] Still some work *sight* But never stop learning, nor exploring Expertize is like experience, yet rarer Money, the unfortunate obligation Create your secret sauce, a lazy call to Job 

WORKING IN IT Everything is distributed Clues for answers are in data Answer questions 

MORE AND MORE DATA Industrial data energy grid production lines finance transport ... 

MORE AND MORE DATA People data web 1.0 : few control content web 2.0 : lots of end-users manual feed web x.y : lots of captors per user 

MORE AND MORE DATA Why do we collect data? We always miss some at some point something of value is encoded collaborative consiousness 

MOBILE? REALLY? Providing content = aggregate distributed sources BUT in small quantity (network is the bottleneck) Yes data is available from anywhere 

IT IS A COMMODITY ...even development Everything done is distributed Software (SaaS): user management, social login product support online billing/payment access to data sources Infrastructure (IaaS): storage, servers... 

A TECH STARTUP Managing distributed resources is key! Gathers distributed datasources Calls distributed services collects raw data does some aggregation Must be able to scale resources and costs 

FP? Failure management Asynchronous calls FP describe the result you want, not how you get it Threads, cores, servers, datacenters If FP gets a global adoption it will be because it solves issues related to the distributed environment. Complexity of distribution 

FP IN THE INDUSTRY? Examples of popular languages with OO and FP javascript ruby Java 8 scala 

AWAIT[_] 

EVAL[EXPRESSION] SCALA val getQueryParameter[T] = (request:Request) => (name:String) => request.query.parameters.get(name).as[T] abstract class Request[T](val body:Body[T]) extends Query with Header { def parser:BodyParser[T] val content:Option[T] = parser(body) def send(to:Service) = ??? } case class HttpRequest[T]( method:Method, body:Body)(implicit parser:BodyParser[T]) extends Request(body) { // ... } 

PLAY! FRAMEWORK 2 Async HTTP Event driven/Concurrency using Actor Non-blocking IO (reactive: Iteratee/ CPS) Stateless object Dummy extends Controller { // return async'ly the # of users w/ name start'g w/ q"char" def countAs = Action.async { request => val char = request.queryString("char") val all:Future[Seq[User]] = users.all // or WS.url Ok(all.map(_.count(_.name startsWith "a"))) } } 

AKKA Message passing style Messages are immutable Actors are mutable (stateful) Thread safety by confinement Location transparency class UserRepo extends Actor { var users = Set.empty def receive = { case Add(user:User) => users = users + user case All => sender ! users case Get(id:String) => user = users.find(_.id == id) sender ! user } } object AkkaApp extends App { //... val repo = system.actorOf(Props[UserRepo]) // add repo ! Add(User(1L, "") //get val user = repo ? Get(1L) } 

SPARK Tends to resolve Big Data solution flaws Provides an abstraction on distributed data Using an extended functional style (not only MR) Represents a distributed computation as a Graph In-Memory! val sc = new SparkContext(/*...*/) lines = sc.textFile("hdfs://...") errors = lines.filter(_.startsWith("ERROR")) errors.filter(_.contains("HDFS")) .map(_.split('\t')(3)) .collect()