The improbable story of:






Studying =:= Future[Option[Job]]

Grab the basis
Find your ways
Pick some and dig
Mémoire: polishing phase

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

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


Asynchronous calls
FP describe the result you want, not how you get it
Failure management
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




val getQueryParameter[T] = 
  (request:Request) =>
    (name:String) =>
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](
    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)


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( startsWith "a")))



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)
      sender ! user
object AkkaApp extends App {

  val repo = system.actorOf(Props[UserRepo])

  // add
  repo ! Add(User(1L, "")

  val user = repo ? Get(1L)


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
val sc = new SparkContext(/*...*/)
lines = sc.textFile("hdfs://...")
errors = lines.filter(_.startsWith("ERROR"))

FP |+| Entrepreneurship

By andy petrella

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