![... and its usage
import
java.util.ArrayList;
...
Person[]
people;
Person[]
minors;
Person[]
adults;
{
ArrayList<Person>
minorsList
=
new
ArrayList<Person>();
ArrayList<Person>
adultsList
=
new
ArrayList<Person>();
... in Java:
for
(int
i
=
0;
i
<
people.length;
i++)
(people[i].age
<
18
?
minorsList
:
adultsList)
.add(people[i]);
minors
=
minorsList.toArray(people);
adults
=
adultsList.toArray(people);
}
A function value
An infix method call
... in Scala: val
people:
Array[Person]
val
(minors,
adults)
=
people
partition
(_.age
<
18)
A simple pattern match 9](https://image.slidesharecdn.com/baseodersky-111219121336-phpapp01/85/Martin-Odersky-What-s-next-for-Scala-9-320.jpg)
![Going Parallel
... in Java: ?
... in Scala: val
people:
Array[Person]
val
(minors,
adults)
=
people.par
partition
(_.age
<
18)
10](https://image.slidesharecdn.com/baseodersky-111219121336-phpapp01/85/Martin-Odersky-What-s-next-for-Scala-10-320.jpg)
![Liszt as Virtualized Scala
val // calculating scalar convection (Liszt)
val Flux = new Field[Cell,Float] AST
val Phi = new Field[Cell,Float]
val cell_volume = new Field[Cell,Float]
val deltat = .001
...
untilconverged {
for(f <- interior_faces) {
val flux = calc_flux(f)
Flux(inside(f)) -= flux
Flux(outside(f)) += flux
}
for(f <- inlet_faces) { Optimisers Generators
Flux(outside(f)) += calc_boundary_flux(f)
} …
for(c <- cells(mesh)) {
Phi(c) += deltat * Flux(c) /cell_volume Schedulers
(c)
} …
for(f <- faces(mesh))
Flux(f) = 0.f Hardware
}
DSL Library GPU, Multi-Core, etc
52](https://image.slidesharecdn.com/baseodersky-111219121336-phpapp01/85/Martin-Odersky-What-s-next-for-Scala-52-320.jpg)
Martin Odersky: What's next for Scala
- 1. Scala Next SF Scala meetup Dec 8th, 2011
- 2. Scala Today 2
- 3. Some adoption vectors: • Web platforms • Trading platforms • Financial modeling • Simulation Fast to first product, scalable afterwards 3
- 4. Github vs. Stack Overflow RedMonk: Revisiting the Dataists Programming Language Rankings Typesafe Confidential 4
- 5. Commercial Adoption • Scala jobs tripled in last year • Now at estimated 100,000 developers Typesafe Confidential 5
- 6. (Only 17 months ago!) Scala New collections 2.8: Package objects Context bounds Better implicits ... 6
- 7. Parallel collections DelayedInit and App Scala Faster REPL Progress on IDEs: 2.9: Eclipse, IntelliJ, Neatbeans, ENSIME Better docs Lots of bug fixes 7
- 8. Parallel Collections • Use Java 7 Fork Join framework • Split work by number of Processors • Each Thread has a work queue that is split exponentially. Largest on end of queue • Granularity balance against scheduling overhead • On completion threads “work steals” from end of other thread queues 8
- 9. ... and its usage import java.util.ArrayList; ... Person[] people; Person[] minors; Person[] adults; { ArrayList<Person> minorsList = new ArrayList<Person>(); ArrayList<Person> adultsList = new ArrayList<Person>(); ... in Java: for (int i = 0; i < people.length; i++) (people[i].age < 18 ? minorsList : adultsList) .add(people[i]); minors = minorsList.toArray(people); adults = adultsList.toArray(people); } A function value An infix method call ... in Scala: val people: Array[Person] val (minors, adults) = people partition (_.age < 18) A simple pattern match 9
- 10. Going Parallel ... in Java: ? ... in Scala: val people: Array[Person] val (minors, adults) = people.par partition (_.age < 18) 10
- 11. General Collection Hierarchy Remove this layer in 2.10? GenTraversable GenIterable Traversable GenSeq Iterable ParIterable Seq ParSeq 11
- 12. Going Distributed • Can we get the power of parallel collections to work on 10’000s of computers? • Hot technologies: MapReduce (Google’s and Hadoop) • But not everything is easy to fit into that mold • Sometimes 100’s of map-reduce steps are needed. • Distributed collections retain most operations, provide a powerful frontend for MapReduce computations. • Scala’s uniform collection model is designed to also accommodate parallel and distributed. • Projects at Google (Cascade), Berkeley (Spark), EPFL. 12
- 13. Scala Eclipse IDE next: Play web framework 2.0 Akka 2.0 Scala 2.10 13
- 14. Scala Now in RC2 Final expected before the end of the year. Eclipse IDE 14
- 15. Goals reliable (no crashes/lock ups) responsive (never wait when typing) work with large projects/files – Scala compiler (80k LOC), 4-5000 LOC/file – advanced use of the type system: path-dependent types, self-types, mix-ins
- 16. Features Keep it simple – highlight errors as you type – completions (including implicits) – hyperlinking – project builder (+ dependent projects) Support mixed Java-Scala projects – all features should work between Java/Scala sources JUnit Test Runner should pick up tests More stuff based on external libraries – (some) refactoring, code formatter, mark occurrences, structured selections, show inferred semi-colons
- 17. Features (3) based on external libraries – (some) refactoring – code formatter – mark occurrences – structured selections – show inferred semi-colons
- 18. @jonifreeman Joni Freeman Latest Scala Eclipse plugin works surprisingly well! Even manages our mixed Java/Scala project. Kudos to the team! #scala @esorribas Eduardo Sorribas The latest beta of the Scala IDE for eclipse is much better. I'm starting to like it. @jannehietamaki Janne Hietamäki After years of misery, the Eclipse Scala plugin actually seems to work quite well.
- 19. Architecture Use the full-blown Scala compiler for: – interactive error highlight, completion, hyperlinking – turning Scala symbols into Java model elements Weave the JDT compiler when it needs help – JDT was NOT meant to be extended
- 20. Why rely on scalac? – reuse (type-checker == 1-2 person years) – consistency – compiler plugins Why not? – SPEED – (very) tight dependency on the Scala version
- 21. asynchronous interruptible targeted stop after type-checking Presentation Compiler
- 24. Result is communicated through a SyncVar
- 25. • All compiler activity happens on PC thread • compile loaded files when work queue is empty (in the background) • Check work queue when type checker reaches safe-points in the AST • Drop everything when a file is changed (AskReload)
- 27. 1 type-checker run / instance --> 100s of type-check runs / minute – memory leaks – side-effects/state – out-of-order and targeted type-checking needed to improve the compiler – 2.9.x, 2.10 (trunk) – what about 2.8? 2.8.2, 2.8.3-SNAPSHOT
- 28. New: Play Framework 2.0 • Play Framework is an open source web application framework, inspired by Ruby on Rails, for Java and Scala • Play Framework 2.0 retains full Java support while moving to a Scala core and builds on key pieces of the Typesafe Stack, including Akka middleware and SBT • Play will be integrated in TypeSafe stack 2.0 • Typesafe will contribute to development and provide commercial support and maintenance.
- 29. Roadmap May 2011 Oct 2011 Q1 2012 Q3 2012 Typesafe Typesafe Typesafe Typesafe Stack 1.0 Stack 1.1 Stack 2.0 Stack 2.x Scala 2.9.0 Scala 2.9.1 Scala 2.9.x Scala 2.10 Akka 1.1 Akka 1.2 Akka 2.0 Akka 2.x Play 2.0 Play 2.x Slick (DB)
- 30. 1. New reflection framework Scala 2. 3. Reification type Dynamic 2.10: 4. More IDE improvements: find- references, debugger, worksheet. 5. Faster builds 6. SIPs: string interpolation, simpler implicits. ETA: Early 2012. 30
- 31. New in Scala 2.10: Dynamic Type Dynamic bridges the gap between static and dynamic typing. Method calls get translated to applyDynamic Great for interfacing with dynamic languages (e.g. JavaScript) class JS extends Dynamic { def applyDynamic(methName: String, args: Any*): Any = { println("apply dynamic "+methName+args.mkString("(", ",", ")")) } } val x = new JS x.foo(1) // à x.applyDynamic( foo , 1) x.bar // à x.applyDynamic( bar ) 31
- 32. Proposed for Scala 2.10: SIP 11: String interpolation Idea: Instead of Bob is + n + years old write: s Bob is $n years old which gets translated to new StringContext( Bob is , years old ).s(n) Here, s is a library-defined method for string interpolation. 32
- 33. This can be generalized to other string processors besides s: xml <body> <a href = some link > ${linktext} </a> </body> scala scala.concurrent.transaction.withinTransaction { (implicit currentTransaction: Transaction) => $expr } 33
- 34. Proposed for Scala 2.10: SIP 12: Uncluttering control Should be able to write: if x < 0 then –x else x while x > 0 do { println(x); x -‐= 1 } for x <-‐ xs do println(x) for x <-‐ xs yield x * x 34
- 35. Proposed for Scala 2.10: SIP 13: Implicit classes Variation: Add @inline to class def to get speed of extension methods. 35
- 36. New in Scala 2.10: Reflection Previously: Needed to use Java reflection, no runtime info available on Scala s types. Now you can do: 36
- 37. (Bare-Bones) Reflection in Java Why not add some meaningful operations? Need to write essential parts of a compiler (hard). Need to ensure that both compilers agree (almost impossible). Want to know whether type A conforms to B? Write your own Java compiler! 37
- 38. How to do Better? • Problem is managing dependencies between compiler and reflection. • Time to look at DI again. Dependency Injection • Idea: Avoid hard dependencies to specific classes. • Instead of calling specific classes with new, have someone else do the wiring. 38
- 39. Using Guice for Dependency Injection (Example by Jan Kriesten) 39
- 40. ... plus some Boilerplate 40
- 41. Dependency Injection in Scala Components are classes or traits Requirements are abstract values Wiring by implementing requirement values But what about cyclic dependencies? 41
- 42. The Cake Pattern Components are traits Wiring by mixin composition Requirements are types of this 42
- 43. Cake Pattern in the Compiler The Scala compiler uses the cake pattern for everything Here s a schema: (In reality there are about ~20 slices in the cake.) 43
- 44. Towards Better Reflection Can we unify the core parts of the compiler and reflection? Compiler Reflection Different requirements: Error diagnostics, file access, classpath handling - but we are close! 44
- 45. Compiler Architecture Problem: This exposes way too much detail! reflect.internal.Universe nsc.Global (scalac) reflect.runtime.Mirror 45
- 46. Complete Reflection Architecture reflect.api.Universe / Cleaned-up facade: reflect.mirror reflect.internal.Universe Full implementation: nsc.Global (scalac) reflect.runtime.Mirror 46
- 47. How to Make a Facade The Facade Interfaces are not enough! The Implementation 47
- 48. Conclusion Scala is a very regular language when it comes to composition: 1. Everything can be nested: – classes, methods, objects, types 2. Everything can be abstract: – methods, values, types 3. The type of this can be declared freely, can thus express dependencies 4. This gives great flexibility for SW architecture, allows us to attack previously unsolvable problems. 48
- 49. Going further: Parallel DSLs Mid term, research project: How do we keep tomorrow s computers loaded? – How to find and deal with 10000+ threads in an application? – Parallel collections and actors are necessary but not sufficient for this. Our bet for the mid term future: parallel embedded DSLs. – Find parallelism in domains: physics simulation, machine learning, statistics, ... Joint work with Kunle Olukuton, Pat Hanrahan @ Stanford. EPFL side funded by ERC. 49
- 50. EPFL / Stanford Research Scientific Virtual Personal Data Applications Engineering Worlds Robotics informatics Domain Physics Probabilistic Machine Specific Rendering Scripting Learning Languages (Liszt) (RandomT) (OptiML) Domain Embedding Language (Scala) Polymorphic Embedding Staging Static Domain Specific Opt. DSL Infrastructure Parallel Runtime (Delite, Sequoia, GRAMPS) Dynamic Domain Spec. Opt. Task & Data Parallelism Locality Aware Scheduling Hardware Architecture Heterogeneous OOO Cores SIMD Cores Threaded Cores Specialized Cores Hardware Programmable Scalable Isolation & On-chip Pervasive Hierarchies Coherence Atomicity Networks Monitoring 50
- 51. Example: Liszt - A DSL for Physics Simulation Combustion Turbulence Fuel injection Transition Thermal • Mesh-based • Numeric Simulation • Huge domains Turbulence – millions of cells • Example: Unstructured Reynolds-averaged Navier Stokes (RANS) solver 51
- 52. Liszt as Virtualized Scala val // calculating scalar convection (Liszt) val Flux = new Field[Cell,Float] AST val Phi = new Field[Cell,Float] val cell_volume = new Field[Cell,Float] val deltat = .001 ... untilconverged { for(f <- interior_faces) { val flux = calc_flux(f) Flux(inside(f)) -= flux Flux(outside(f)) += flux } for(f <- inlet_faces) { Optimisers Generators Flux(outside(f)) += calc_boundary_flux(f) } … for(c <- cells(mesh)) { Phi(c) += deltat * Flux(c) /cell_volume Schedulers (c) } … for(f <- faces(mesh)) Flux(f) = 0.f Hardware } DSL Library GPU, Multi-Core, etc 52
- 53. Follow us on twitter: @typesafe akka.io scala-lang.org typesafe.com scala-lang.org 53