Scala Next
SF Scala meetup Dec 8th, 2011
2
Scala Today
3
Some adoption vectors:
• Web platforms
• Trading platforms
• Financial modeling
• Simulation
Fast to first product, scalable afterwards
Github vs. Stack Overflow
RedMonk: “Revisiting the Dataists Programming Language Rankings”
4Typesafe Confidential
Commercial Adoption
• Scala jobs tripled in last year
• Now at estimated 100,000 developers
5Typesafe Confidential
6
Scala 2.8:
(Only 17 months ago!)
New collections
Package objects
Context bounds
Better implicits
...
7
Scala 2.9:
Parallel collections
DelayedInit and App
Faster REPL
Progress on IDEs: Eclipse, IntelliJ, Neatbeans, ENSIME
Better docs
Lots of bug fixes
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 usageimport java.util.ArrayList;
...
Person[] people;
Person[] minors;
Person[] adults;
{ ArrayList<Person> minorsList = new ArrayList<Person>();
ArrayList<Person> adultsList = new ArrayList<Person>();
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);
}
... in Java:
... in Scala: val people: Array[Person]val (minors, adults) = people partition (_.age < 18)
A simple pattern match
An infix method call
A function value
10
Going Parallel
?... in Java:
... in Scala: val people: Array[Person]val (minors, adults) = people.par partition (_.age < 18)
General Collection Hierarchy
GenTraversable
GenIterable
GenSeq
Traversable
Iterable
Seq
ParIterable
ParSeq
11
Remove this layer in 2.10?
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 next:
Eclipse IDE
Play web framework 2.0
Akka 2.0
Scala 2.10
14
Scala Eclipse IDE
Now in RC2
Final expected before the end of the year.
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
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
Features (3)
based on external libraries– (some) refactoring
– code formatter
– mark occurrences
– structured selections
– show inferred semi-colons
@jonifreemanJoni Freeman
Latest Scala Eclipse plugin works surprisingly well! Even manages our mixed Java/Scala project. Kudos to the team! #scala
@esorribasEduardo Sorribas
The latest beta of the Scala IDE for eclipse is much better. I'm starting to like it.
@jannehietamakiJanne Hietamäki
After years of misery, the Eclipse Scala plugin actually seems to work quite well.
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
Why rely on scalac?
– reuse (type-checker == 1-2 person years)
– consistency
– compiler plugins
Why not?
– SPEED
– (very) tight dependency on the Scala version
Presentation Compiler
asynchronousinterruptibletargetedstop after type-checking
Result is communicated through a SyncVar
• 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)
26
Implementation
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
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.
Roadmap
Typesafe Stack 1.0
Typesafe Stack 1.1
Typesafe Stack 2.0
Typesafe Stack 2.x
May 2011 Oct 2011 Q1 2012 Q3 2012
Scala 2.9.0Akka 1.1
Scala 2.9.1Akka 1.2
Scala 2.9.xAkka 2.0Play 2.0
Scala 2.10Akka 2.xPlay 2.x
Slick (DB)
30
Scala 2.10:
1. New reflection framework
2. Reification
3. type Dynamic4. More IDE improvements: find-
references, debugger, worksheet.
5. Faster builds
6. SIPs: string interpolation, simpler implicits.
ETA: Early 2012.
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)
31
class JS extends Dynamic { def applyDynamic(methName: String, args: Any*): Any = { println("apply dynamic "+methName+args.mkString("(", ",", ")")) } } val x = new JS x.foo(1) // x.applyDynamic(“foo”, 1) x.bar // x.applyDynamic(“bar”)
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
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
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
Proposed for Scala 2.10: SIP 13: Implicit classes
Variation: Add @inline to class def to get speed of extension methods.
35
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
(Bare-Bones) Reflection in Java
37
Want to know whether type A conforms to B?
Write your own Java compiler!
Why not add some meaningful operations?
Need to write essential parts of a compiler (hard).
Need to ensure that both compilers agree (almost impossible).
How to do Better?
• Problem is managing dependencies between compiler and reflection.
• Time to look at DI again.
38
Dependency Injection
• Idea: Avoid hard dependencies to specific classes.• Instead of calling specific classes with new, have someone else do
the wiring.
Using Guice for Dependency Injection
39
(Example by Jan Kriesten)
... plus some Boilerplate
40
Dependency Injection in Scala
41
Components are classes or traits
Requirements are abstract values
Wiring by implementing
requirement valuesBut what about cyclic dependencies?
The Cake Pattern
42
Requirements are types of this
Components are traits
Wiring by mixin composition
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
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
Compiler Architecture
45
reflect.internal.Universe
nsc.Global (scalac) reflect.runtime.Mirror
Problem: This exposes way too much detail!
Complete Reflection Architecture
Cleaned-up facade:
Full implementation:
46
reflect.internal.Universe
nsc.Global (scalac) reflect.runtime.Mirror
reflect.api.Universe /reflect.mirror
How to Make a Facade
47
The Facade
The Implementation
Interfaces are not enough!
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
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
EPFL / Stanford Research
Domain Embedding Language (Scala)
Virtual Worlds
Personal Robotics
Datainformatics
ScientificEngineering
Physics(Liszt)
ScriptingProbabilistic(RandomT)
Machine Learning(OptiML)
Rendering
Parallel Runtime (Delite, Sequoia, GRAMPS)
Dynamic Domain Spec. Opt. Locality Aware Scheduling
StagingPolymorphic Embedding
Applications
DomainSpecific
Languages
HeterogeneousHardware
DSLInfrastructure
Task & Data Parallelism
Hardware Architecture
OOO CoresOOO Cores SIMD CoresSIMD Cores Threaded CoresThreaded Cores Specialized CoresSpecialized Cores
Static Domain Specific Opt.
ProgrammableHierarchies
ProgrammableHierarchies
Scalable CoherenceScalable
CoherenceIsolation & Atomicity
Isolation & Atomicity
On-chipNetworksOn-chip
NetworksPervasive MonitoringPervasive Monitoring
50
Fuel injection
Transition Thermal
Turbulence
Turbulence
Combustion
Example: Liszt - A DSL for Physics Simulation
• Mesh-based• Numeric Simulation• Huge domains
– millions of cells
• Example: Unstructured Reynolds-averaged Navier Stokes (RANS) solver
51
Liszt as Virtualized Scala
val // calculating scalar convection (Liszt)
val Flux = new Field[Cell,Float]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) { Flux(outside(f)) += calc_boundary_flux(f) } for(c <- cells(mesh)) { Phi(c) += deltat * Flux(c)
/cell_volume(c) } for(f <- faces(mesh)) Flux(f) = 0.f}
AST
Hardware
DSL Library
Optimisers Generators
…
…
Schedulers
GPU, Multi-Core, etc
52
53
Follow us on twitter: @typesafe
scala-lang.org
typesafe.com
akka.io
scala-lang.org
Top Related