The Evolution Of Scala презентация

The Evolution Of Scala Martin Odersky EPFL and Typesafe

10 Years of Scala

Pre History 1980s Modula-2, Oberon 1990-95 Functional Programming 1995-98 Pizza 1998-99 GJ, javac 2000-02 Functional Nets, Funnel

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

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 (!).

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

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.

How That Worked Out

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

Java Features Not kept in Scala public static void Enumerations Annotation Syntax Wildcard types Raw types Primitive types Array types Definite assignment rules

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.

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]) {...}

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]) {...}

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>

Avoiding the Pitfalls Auto-add () for references f is to nullary functions def f() = ... Eta-expand only if expected type is a function or missing parameters are specified with `_’

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 }

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.

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

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

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.

Scala Improvement Process

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.

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).

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

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) ^ one warning found

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

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

Now: Scala 2.11 Smaller: broke out parts of libraries into separate modules Faster Better incremental compilation Stronger: Lots of bug fixes, tooling improvements

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.

Invariants In all this evolution, what stays constant? What are some of the essential traits that make Scala what it is?

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.

Libraries on top of Scala

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.

2nd Invariant: It’s about the Types Scala’s core is its type system. Most of the advanced types concepts are about flexibility, less so about safety.

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.

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?

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.

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.

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.

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.

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 } ✗

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.