Given by Xavier Tordoir and my self for the students of University of Liège. This slide is a poor export of this one: http://slides.com/xa_ndy/fp-and-entrepreneurship

1. THE IMPROBABLE STORY
OF:
FP AND
ENTREPRENEURSHIP

2. WHO ARE WE?

3. XAVIER TORDOIR

4. ANDY PETRELLA

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

6. 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

7. 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

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

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

10. 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

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

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

13. 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...

14. 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

15. 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

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

17. AWAIT[_]

18. 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) {
// ...
}

19. 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")))
}
}

20. 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)
}

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