scalaliftoff2009.pdf

Scala – The Next 5 Years

Martin Odersky

The last 6 years

2003 Scala first used in classroom at EPFL.
2004 First public release,
Jamie & Jon at Sygneca are probably first
outside professional users.
2005 Scalable component abstractions paper.
2006 2.x version released, David Pollak
gets interested.
2007 First JavaOne talk.
2008 First Scala LiftOff Unconference.
2009 JavaOne talks on Scala almost fill a full day.

Even more rewarding:

We have a fantastic user community:
Friendly, helpful, and intelligent.

A big Thank You to our contributors. We could not have
done it without you.

A scripting language?

Scala is a regular contender at
JavaOne’s ScriptBowl. var capital = Map( "US" → "Washington",
"France" → "paris",
Syntax is lightweight "Japan" → "tokyo" )
and concise, due to:
capital += "Russia" → "Moskow"
– semicolon inference,
– type inference, for ( (country, city) ← capital )
– lightweight classes, capital += country → city.capitalize
– extensible API’s,
– closures as assert ( capital("Japan") == "Tokyo" )
control abstractions.

Average reduction in LOC wrt Java: ≥ 2
due to concise syntax and better abstraction capabilities.
Explorative programming with a read-eval-print-loop (REPL)

The Java of the future?
It has basically everything Java has now.
(sometimes in different form)
It has closures.
(proposed for Java 7, but rejected)
It has traits and pattern matching.
(I would not be surprised to see them in Java 8, 9 or 10)
It compiles to .class files, is completely interoperable and runs about as
fast as Java

object App {
  def main(args: Array[String]) {
    if (args exists (_.toLowerCase == "‐help"))
      printUsage()
    else
      process(args)
  }
}

A language for concurrent programming?

It has powerful concurrency
// asynchronous message send
abstractions such as actors.
actor ! message

It’s used in heavy duty
applications: kestrel message // message receive
queue, lift web framework. receive {
  case msgpat1 => action1
It makes functional programming   …
with immutable data easy.
  case msgpatn => actionn
}

A composition language?

• New approach to module
systems: trait Analyzer { this: Backend =>
component = class or trait   …
}
composition via mixins
• Components connect with trait Backend extends Analyzer
              with Optimization
– parameters,               with Generation {
– abstract members (both
  val global: Main
types and values),   import global._
– self types
  type OutputMedium <: Writable
• gives dependency injection
for free }

How can it be all these things?

Q. Is there not a danger that a language being all of these
things becomes too complex?

A. Normally, yes. However, Scala is deep where other
languages are broad.

Cuts down on boilerplate.

Concentrates on powerful abstraction methods, so that
users can write powerful libraries.

The Essence of Scala

Key design principles of Scala:

1. Concentrate on scalability: The same constructions
should work for small as well as large programs.

2. Unify object-oriented and functional programming.

Where do we go from here?

• Summer/fall 2009: Scala 2.8, with:
– new collections
– package objects
– named and default parameters
– @specialized for faster generics
– improved tool support for IDEs and REPL.

• The next 5 years:

Concurrency // Parallelism
(on all levels: primitives, abstractions, types, tools)

Old Scala collections
• Pre 2.8 collections have grown gradually. They lack a uniform
structure.
• Examples:

scala.Seq
scala.RandomAccessSeq.Mutable
scala.collection.mutable.Set

• Operations like map need to be redefined for every collection type:

List(1, 2, 3).map(inc) : List[Int]
Array(1, 2, 3).map(inc): Array[Int]
Set(1, 2, 3).map(inc) : Set[Int]

New Scala collections

• Uniform structure:
scala.collection.immutableList
scala.collection.immutable.Set
scala.collection.mutable.Vector
• Every operation is implemented only once.
• Selection of building blocks in package
scala.collection.generic.

Package Objects

Problem: Everything has to live inside an object or class.
=> No top-level value definitions, type aliases, implicits.

Package objects make packages more like objects.

They also let you define your own ``Predefs’’.

package object scala {
  type List[+T] = scala.collection.immutable.List
  val List = scala.collection.immutable.List
  ...
}

Named and default arguments
def newTable(size: Int = 100, load: Float = 0.5f) =
new HashMap[String, String]{
override val initialSize = size
override val loadFactor = (load * 1000).toInt
}
}
newTable(50, 0.75f)
newTable()
newTable(load = 0.5f)
newTable(size = 20, load = 0.5f)

Defaults can be arbitrary expressions, not just constants.
Default evaluation is dynamic.

Copy method
Problem: Case classes are great for pattern matching,
but how do you transform data?
Often need to update (functionally) some specific field(s)
of a date structure defined by case classes.
Solution: Selective copy method.

case class Tree[+T](elem: T, left: Tree[T], right: Tree[T])

def incElem(t: Tree[Int]) = t copy (elem = t.elem + 1)

• Problem: How to define “copy”?

Defining copy

• Problem: A class with n parameters allows 2n possible
combinations of parameters to copy.
• Default parameters to the rescue.

case class Tree[+T](elem: T, left: Tree[T], right: Tree[T]) {
  // generated automatically:
  def copy[U](elem: U = this.elem,
              left: Tree[U] = this.left,
              right: Tree[U] = this.right) =
    Branch(elem, left, right)
}

Using copy
scala> case class Tree[T](elem: T, left: Tree[T], right: Tree[T])
defined class Tree

scala> val t = Tree(1, null, null)
t: Tree[Int] = Tree(1,null,null)

scala> val u = t.copy(left = Tree(2, null, null))
u: Tree[Int] = Tree(1,Tree(2,null,null),null)

scala> val v = u.copy(elem = 3, right = Tree(4, null, null))
v: Tree[Int] = Tree(3,Tree(2,null,null),Tree(4,null,null))

Faster Generics

• Problem: Generics cause a performance hit because
they require a uniform representation: everything needs
to be boxed.
trait Function1[‐T, +U] { def apply(x: T): U }
• Example:
def app(x: Int, fn: Int => Int) = fn(x)
app(7, x => x * x)

becomes: trait Function1 { def apply(x: Object): Object }
class anonfun extends Function1 {
  def apply(x: Object): Object =
    Int.box(apply(Int.unbox(x)))
  def apply(x: Int): Int = x * x
}
def app(x: Int, fn: Function1) =
  Int.unbox(fn(Int.box(x)))
app(7, new anonfun)

Solution: @specialized

• We should specialize functions that take primitive
parameter types.
• If we had the whole program at our disposal, this would
be easy: Just do it like C++ templates.
• The problem is that we don’t.
• Idea: specialize opportunistically, when types are known.

If Function1 is @specialized ...
trait Function1[@specialized ‐T, @specialized +U] {
  def apply(x: T): U }
def app(x: Int, fn: Int => Int) = fn(x)
app(7, x => x * x)

... the compiler generates instead:
trait Function1 {
  def apply(x: Object): Object
  def applyIntInt(x: Int): Int =
    Int.unbox(apply(Int.box(x)))
  // specializations for other primitive types
} No boxing!
class anonfun extends (Int => Int) {
  def apply(x: Object): Object =
    Int.box(applyIntInt(Int.unbox(x)))
  override def applyIntInt(x: Int): Int = x * x
}
def app(x: Int, fn: Function1) =
fn.applyIntInt(x)
app(7, anonfun)

Better tools

• REPL with command completion (courtesy Paul Phillips).

• Reworked IDE/compiler interface (Miles Sabin, Martin
Odersky, Iulian Dragos).

Developed originally for Eclipse, but should be reusable for
other IDEs such as Netbeans, VisualStudio.

New control abstractions:

• Continations plugin
– allow to unify receive and react in actors.
– give a clean basis for encpsulating nio.
– let you do lots of other control abstractions.
• Break (done in library)
• Trampolining tail calls (done in library)
import scala.util.control.Breaks._
breakable {
for (x <- elems) {
println(x * 2)
if (x > 0) break
}
}

Slipping break back into Scala.

More features

• Extended swing library.
• Performance improvements, among others for
– structural type dispatch,
– actors,
– vectors, sets and maps.
• Standardized compiler plugin architecture.

How to get 2.8

Can use trunk today (bleeding edge).

Stable preview planned for July.

Final version planned for September or October.

New challenges

Concurrency:

How to make concurrent activities play well together?

Parallelism:

How to extract better performance from today’s and
tomorrow’s parallel processors (multicores, gpus, etc).

The two do not necessarily have the same solutions!

What can be done?

(1) Better abstractions:

Actors
Transactions
Join patterns
Stream processing with single-assignment variables
Data parallelism

(2) Better Analyses. Distinguish between:

pure: no side effects
thread-local: no interaction
transactional: atomic interaction
unrestricted access (danger!)

Need alias tracking and effect systems.
Problem: can we make these lightweight enough?

(3) Better support for static types.

Need to overcome the “type wall”.

Static typing is a key ingredient for reliable concurrent
programs, yet programs are annoyingly hard to get right.

Need better tools: Type browsers, type debuggers.

Need better presentations: Users might not always want
to see the most general type!

Why Scala?

I believe Scala is well placed as a basis for concurrent
and parallel programming, because

– It runs natively on the JVM.
– It supports functional programming.
– It has great syntactic flexibility.

• Btw we are not the only ones to have recognized this:
– The X10, PPL, Fortress are al using Scala in some role or other.

Learning Scala

• To get started:
• First steps in Scala, by Bill Venners
published in Scalazine at
www.artima.com

• Scala for Java Refugees by Daniel
Spiewack (great blog series)

• To continue:
3+1 books published; more in the pipeline.

How to find out more

Track it on
scala-lang.org:

Thank You

scalaliftoff2009.pdf

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scalaliftoff2009.pdf