Slides of the keynote given at the Programming Language Evolution Workshop and the Scala Symposium, July 28th, Uppsala, Sweden.

1. The Evolution Of Scala
Martin Odersky
EPFL and Typesafe

2. 10 Years of Scala

3. Pre History
1980s Modula-2, Oberon
1990-95 Functional Programming
1995-98 Pizza
1998-99 GJ, javac
2000-02 Functional Nets, Funnel
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4. 4
Minimal programming language based on type members and functional nets (a
variant of join calculus)
Analogous to Pict (Pierce and Turner 2001) for Pi-calculus.

5. A Minimal Language
• Idea of Funnel: Show that we can build a general
programming language that can be understood as thin
syntactic sugar over a core calculus.
– General: OO, functional + imperative, concurrent
– Core calculus: Functional nets
– Sugar: Records, Lambdas, Type members.
• Wrote some programs (including parts of the Funnel
library) in Funnel.
• Quickly became apparent that encodings suck:
– Confusing for beginners
– Boring to do them over and over again for experts
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6. Motivation for Scala
• Grew out of Funnel
• Wanted to show that we can do a practical combination
of OOP and FP.
• What got dropped:
– Concurrency was relegated to libraries
– No tight connection between language and core calculus
(fragments were studied in the νObj paper and others.)
• What got added:
– Native object and class model, Java interop, XML literals (!).
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7. Why a New Language?
• The OO dogma ruled then: Encapsulate
mutable data with methods.
– Infamous example: Java beans.
– There was no place for functional
programming in this.
• New at the time: Webservices that
process immutable (semi-)structured
data.
– Service sees the data “from the outside”.
– Functional programming supports that view,
e.g. using pattern matching, recursion.
• Rationale given: Would be good to have
a new FP language for webservices
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8. 8
Really, Why a new Language?
The work on Scala was motivated by
two hypotheses:
Hypothesis 1: A general-purpose
language needs to be scalable; the
same concepts should describe small
as well as large parts.
Hypothesis 2: Scalability can be
achieved by unifying and generalizing
functional and object-oriented
programming concepts.

9. How That Worked Out
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(from:James Iry: A Brief, Incomplete, and
Mostly Wrong History of Programming
Languages)

10. Scala and Pizza
• Pizza (Odersky and Wadler 96) was another language
on the JVM that added functional elements to Java:
– algebraic datatypes and pattern matching
– function values
– generics
• Scala was more ambitious:
– More innovation on the OOP side
– More functional, e.g. immutable values, by-name parameters,
– Better integration of functional/oop, e.g. case classes.
– Not backwards compatible with Java
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11. Java Features Not kept in Scala
public
static
void
Enumerations
Annotation Syntax
Wildcard types
Raw types
Primitive types
Array types
Definite assignment rules
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Statements:
break
continue
synchronized
assert
for (C-style)
try (resource)
super(...)
Expressions:
primitive operators
cast syntax
conditional x ? y : z
array selection a[i]

12. Scala Beginnings
2003: First internal use
– to teach “Functional and Logic Programming Course” at EPFL.
(2nd year, ~ 150 participants),
– despite being not really ready for the task.
2004: Official announcement of Scala 1.0
– First vocal outside users: Miles Sabin, John Pretty @ Sygneca
– Together with Iulian Dragos and myself these are probably the
only people who have used Scala continuously for 10 years.
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13. Scala Reloaded
2006: Scala 2.0 released
– Compiler written in Scala
– Followed the cake-pattern described “Scalable Component
Abstractions [Odersky&Zenger 05].
A few new features:
– Semicolon inference (!)
– Generalization of implicits and traits
– Automatically added empty parameter lists ()
Additions in 2.1, 2.2:
– Qualified access: private[C], protected[C]
– Multi-line string literals: ”””this is a line
and this is another”””
– Procedure syntax: def sort(xs: Array[T]) {...}
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14. Scala Reloaded
2006: Scala 2.0 released
– Compiler written in Scala
– Followed the cake-pattern described “Scalable Component
Abstractions [Odersky&Zenger 05].
A few new features:
– Semicolon inference (!)
– Generalization of implicits and traits
– Automatically added empty parameter lists ()
Additions in 2.1, 2.2:
– Qualified access: private[C], protected[C]
– Multi-line string literals: ”””this is a line
and this is another”””
– Procedure syntax: def sort(xs: Array[T]) {...}
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15. Learning from Experience
Scala 1.x had
– Parameterless methods supporting the uniform access principle.
def length: Int = ...
– Partially applied functions that are always eta-expanded:
def sum(f: Int => Int)(bounds: Range) = ...
val sumSquares = sum(x => x*x)
The combination of these two was a source of common
pitfalls:
println(“abc”.length) // prints: <function>
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16. Avoiding the Pitfalls
1. Auto-add () for references f is to nullary functions
def f() = ...
2. Eta-expand only if
– expected type is a function
or
– missing parameters are specified with `_’
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17. The Growth Year
2007: Scala 2.3-2.7 add lots of new features:
Extractors object Email { def unapply ... }
case Email(name, domain) => ...
Tuples (1, “a”, true)
Assignment operators +=, *=, ...
“_” notation for functions (_ + 1)
Early initialization object Foo extends {
val x = 3
} with SomeTrait
Lazy values lazy val rest = f()
Higher-kinded types class Functor[F[_]] { ... }
Structural types { val key: String }
Existential types Map[T, T] forSome { type T }
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18. Why The Rapid Growth?
• People asked for it
– “If Scala only had this one new feature, I could use it in my
organization”
• People volunteered to do it
– Lots of thoughtful suggestions on the mailing list.
– PhD students were keen to see their thesis work applied.
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19. Community Formation
2007: Lift web framework launched.
2008: First Scala liftoff unconference (50 particants)
– Twitter goes public with Scala, hype starts
2009: More Scala liftoffs.
2010-14: Scala Days
– 2010 EPFL 180 participants
– 2011 Stanford 280
– 2012 London 400
– 2013 New York 500 Scala Workshop Montellier
– 2014 Berlin 800 Scala Symposium Uppsala
Lots of other meetups and conferences
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20. Scala 2.8 and 2.9: Consolidation
2010: Scala 2.8, with
– New collections with bitrot prevention.
– Fixed leaky array model.
– New semantics of nested packages.
– Better type inference for implicit resolution
– Lots of bug-fixes
2011: Scala 2.9, with
– Parallel collections
– Special trait DelayedInit, used in App
– Trait Dynamic, to interface with dynamic languages
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21. Scala 2.10: Differentiation
2012: Scala 2.10, with
• New features, added through the Scala Improvement
Process (SIPs):
– Value classes class Meter(x: Long)
extends AnyVal
– Implicit classes implicit class StringOps(s: String)
– String interpolation s”you have $n new calls”
• Experimental features
– Macros def await(x: Future[T]) = macro ...
– Reflection
These are only enabled when compiling with –Xexperimental
• Language imports require explicit enabling of some features
available previously.
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22. Scala Improvement Process
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23. Design Tradeoffs
The Scala way: Provide few constructs of maximal generality.
Implicit conversions
> implicit classes
> extension methods
where “>” means more general.
Implicit conversions are very powerful
But they can be misused,
in particular if there are too many of them.
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24. General Problem
• Scala is geared for orthogonality and expressiveness
• I believe that in the end, that’s the most productive
combination.
• But there are challenges.
– Some combinations of language features might be less desirable
than others.
– How to avoid feature misuse?
• Idea: Have a mechanism that demands that some
problematic features are explicitly imported (Haskell
uses something similar).
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25. SIP 18: Language Imports
• Say you have:
object letsSimulateJS {
implicit def foo(x: String): Int =
Integer.parseInt(x)
}
• Compiling gives:
warning: there were 1 feature warnings; re-run with -
feature for details
one warning found
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26. SIP 18: Language Imports
Say you have:
object letsSimulateJS {
implicit def foo(x: String): Int =
Integer.parseInt(x)
}
Compiling with –feature gives:
letsSimulateJS.scala:8: warning: implicit conversion method foo should be
enabled
by making the implicit value language.implicitConversions visible.
This can be achieved by adding the import clause 'import
scala.language.implicitConversions'
or by setting the compiler option -language:implicitConversions.
See the Scala docs for value scala.language.implicitConversions for a discussion
why the feature should be explicitly enabled.
implicit def foo(x: String): Int = Integer.parseInt(x)
^ 26

27. Turning off the Warnings
You turn off the warning by bringing the identifier
scala.language.implicitConversions
into scope, usually, using an import:
import language.implicitConversions
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28. Features Controlled by SIP-18
From language:
– Implicit Conversions
– Dynamic
– Postfix Operators
– Dynamic dispatch on structural types
– Existential types
– Higher-kinded types
From language.experimental
– Macros
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29. Now: Scala 2.11
• Smaller:
– broke out parts of libraries into separate modules
• Faster
– Better incremental compilation
• Stronger:
– Lots of bug fixes, tooling improvements
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30. Now: Scala.JS
Why a Scala for Javascript?
– JS is becoming ubiquitous.
– Desire to use the same language on
client and server.
– But not everybody likes Javascript or
dynamic languages.
Scala.JS profits from Scala’s tradition of
interoperating with a host language through very general
abstractions.
Can combine JS DOM and Scala collections.
For the young age of the project, very mature and well-
received.
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31. Invariants
In all this evolution, what stays constant?
What are some of the essential traits that make Scala what
it is?
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32. 1st Invariant: A Scalable Language
• Instead of providing lots of features in the language,
have the right abstractions so that they can
be provided in libraries.
• This has worked quite well so far.
• It implicitly trusts programmers and library designers to
“do the right thing”, or at least the community to sort
things out.
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33. Libraries on top of Scala
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SBT
Chisel Spark
Spray
Kafka
Akka
ScalaTest
Squeryl
Specs
shapeless
Scalaz
Slick

34. Growable = Good?
In fact, it’s a double edged sword.
– DSLs can fracture the user
community (“The Lisp curse”)
– Besides, no language is liked
by everyone, no matter whether
its a DSL or general purpose.
– Host languages get the blame
for the DSLs they embed.
Growable is great for experimentation.
But it demands conformity and discipline for large scale
production use.
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35. • Scala’s core is its type system.
• Most of the advanced types concepts are about
flexibility, less so about safety.
2nd Invariant: It’s about the Types
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Flexibility / Ease of Use
Safety
Scala
Trend in Type-systems
Goals of PL design

36. Stunted Evolution
null - “The Million Dollar Mistake”
• Why does Scala not have null-safety?
• We had plans to do it
you can see the traces in the stdlib with marker trait NotNull.
• But by then everybody was using already Option.
• So NPEs are actually quite rare in Scala code.
• Don’t want two ways to do the same thing.
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37. What’s Next?
• Scala 2.12 will be a fairly conservative evolution of 2.11
• Main feature: Java 8 interop.
– Scala and Java lambdas can understand each other
– SAM method convention added to Scala
– Should make use of Java 8 streams
– Default methods for traits?
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38. And After That?
Main Goals: Make the language and its libraries
• simpler to understand,
• more robust,
• better performing
Want to continue to make it the language of choice for
smart kids.
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39. Scala “Aida”
Will concentrate on the standard library.
– Reduce reliance on inheritance
– Make all default collections immutable (e.g. scala.Seq will be an
alias of scala.immutable.Seq)
– Other small cleanups that are possible with a rewriting step (e.g.
rename mapValues)
Projects which might make it if they mature fast enough:
– scala.meta, the new, simplified approach to macros and
reflection.
– Collection fusion in the style of ScalaBlitz
– Better specialization through miniboxing.
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40. Scala “Don Giovanni”
Concentrates on the language
• Simple foundations:
– A single fundamental concept - type members – can give precise
meaning to generics, existential types, and higher-kinded types.
– Intersection and union types.
– Theoretical foundations given by minimal core calculus (DOT).
• Cleaned-up syntax:
– Trait parameters instead of early definition syntax
– XML string interpolation instead of XML literals
– Procedure syntax is dropped.
– Simplified and unified type syntax for all forms of information
elision, forSome syntax is eliminated.
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41. Scala “Don Giovanni”
• Removing puzzlers:
– Result types mandatory for implicit definitions.
– Inherited explicit result types take precedence over locally-
inferred ones.
– String “+” needs explicit enabling.
– Avoid surprising behavior of auto-tupling.
• Backwards compatibility:
– A migration tool will upgrade sources automatically.
– Should work for almost all commonly used code.
– Will not generally work for code using –Xexperimental
– But we aim to have features that can support analogous
functionality.
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42. The Growth Year, Revisited
Extractors object Email { def unapply ... } ✔
case Email(name, domain) => ...
Tuples (1, “a”, true) ✔
Assignment operators +=, *=, ++= ✔
Annotations @volatile, @deprecated ✔
“_” notation for functions (_ + 1) ✔
Early initialization object Foo extends { ✗
val x = 3
} with SomeTrait
Higher-kinded types class Functor[F[_]] { ... } ≈
Structural types { val key: String } ≈
Lazy values lazy val rest = f() ✔
Existential types Map[T, T] forSome { type T } ✗
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43. Conclusion
• Languages are not cast in stone; they evolve whether
you like it or not.
• Community matters
• Community will take a language where you never
expected it to go.
In the end languages are as much social phenomena as
technical ones.
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