JDays Lviv 2014: Java8 vs Scala: Difference points & innovation stream

JAVA8 / SCALA
Difference points & innovation streams
Ruslan Shevchenko. <ruslan@shevchenko.kiev.ua>
https://github.com/rssh
@rssh1

JAVA / SCALA
• Java & Scala signiﬁcantly different
• Exists innovations: scala => java & java=>scala
• What language I must learn ?
• All of them !

SCALA JAVA8
public class Person
{
private String firstName;
private String lastName;
String getFirstName()
{ return firstName; }
void setFirstName(String v)
{ firstName = v; }
………………….
int hashCode() {
if (firstName==null) {
if (secondName==null) {
return 0;
} else {
return secondName.hashCode();
}
} else{
if (secondName==null) {
}
}
}
boolean equals() { …. }
}
case class Person
(
firstName: String
lastName: String
)

SCALA JAVA8
public class Person
extends DTOBase
{
public String firstName;
public String lastName;
}
case class Person
(
firstName: String
lastName: String
)

SCALA => JAVA
Similar:
lambda-expressions
traits / default methods
collections API with hight-order functions

LAMBDA EXPRESSIONS
list.sort((x,y)-> {
int cmp = x.lastName.compareTo(y.lastName);
return cmp!=0 ? cmp : x.firstName.compareTo(y.firstName)
}
list.sort((x,y) => {
val cmp = x.lastName.compareTo(y.lastName)
if (cmp!=0) cmp else x.firstName.compareTo(y.lastName)
}
Java
Scala

LAMBDA EXPRESSIONS
var (maxFirstLen, maxSecondLen) = (0,0)
list.foreach{
x => maxFirstLen = max(maxFirstLen, x.ﬁrstName.length)
maxSecondLen = max(maxSecondLen, x.secondName.length)
}
Java
Scala
Closures can’t modify environment context
…………………….

TRAITS/DEFAULT METHODS
trait AsyncInput[T]
{
def onReceive(acceptor:T=>()): Unit
def read: Future[T] = {
Promise p = Promise[T]()
onReceive(p.complete(_))
p.future
}
}
interface AsyncInput<T>
{
void onReceive(Acceptor<T> acceptor)
default void read(): Future<T> {
ﬁnal CompletableFuture<T> promise =
new CompletableFuture<>();
onReceive( x -> promise.complete(x) );
return promise;
}
}
Scala Java

trait LoggedAsyncInput[T]
{
this:AsyncInput =>
override def onReceive(acceptor:T => ()) =
super.onReceive(x => {
println(s“received:${x}”)
acceptor(x) })
}
Scala
Java
aspects ? …

trait LoggedAsyncInput[T]
{
override def onReceive(acceptor:T => ()) =
super.onReceive(x => {
println(s“received:${x}”)
acceptor(x) })
}
Scala
Java
aspects ? …
trait MyToString
{
override def toString = s”[${super.toString}]”
}

Java default interface:
dispatch across class/interface hierarchy
Scala traits
building hierarchy with process of linearization

STREAMING COLLECTIONS
persons.stream().filter(
x -> x.firstName.equals(”Jon”)
).collect(Collectors.toList())
persons.filter(_.firstName == “Jon”)
Java
Scala

STREAMING COLLECTIONS
persons.stream().ﬁlter(
Java
Reason - existing API
Don’t want to mix old and new API in one
Operation composition without reiterating.

PARALLEL
persons.parallelStream().filter(
persons.par.filter(_.firstName == “Jon”)
Java
Scala

SQL
..??***8dc
persons.ﬁlter(_.ﬁrstName === “Jon”)
Scala (slick)
Java ?
?

SQL
..??***8dc
persons.filter(_.firstName === “Jon”)
Scala (slick)
Java (http://jinq.org)
dbStream(em,Person.class).filter(
x -> x.firstName.equals(“Jon”)
).list

SQL (INSIDE ?)
persons.ﬁlter(_.ﬁrstName === “Jon”).toList
Scala (slick)
TableQuery[Expr[Person]]
Expr[String], Expr[StringConstant] => Expr[Boolean]

SQL (INSIDE ?)
persons.ﬁlter(_.ﬁrstName === “Jon”).toList
Scala (slick)
TableQuery[Expr[Person]]
Expr[String], Expr[StringConstant] => Expr[Boolean]
Query[Expr[T]], Expr[Boolean] => Query[Expr[T]]
Query { … generateSql( .. ) }

SQL (INSIDE)
..??***8dc
).list
DbStream<Person>
Person => Boolean

SQL (INSIDE)
..??***8dc
).list
Person => Boolean
for sql generation we need:
analyse bytecode
symbolic interpretation
collect trace
generate sql
// runtime-only, not very fast

SQL (INSIDE)
// runtime-only, not very fast
complex, state-of-the-art technology
all work is in runtime
unable to check function correctness in compile-time
Scala (slick)
relatively simple
compile-time analysis, runtime generation
veriﬁcation in compile time

JAVA => SCALA: SAM
trait AsyncInputOutput[T]
{
def onSend(generator: ()=>T): Unit
}
interface AsyncInputOutput<T>
{
void onSend(Generator<T> generator)
………………
}
Scala Java

SAM
• SAM-type =Type with Single Abstract Method
• Method require SAM-type => we can pass lambda-
expression to one.
• In scala:
• 2.11 — with -Xexperimental
• 2.12 — by default

SAM
trait AsyncInputOutput[T]
{
Function1.class
Function1.class
def onSend(generator: ()=>T): Unit
}
interface AsyncInputOutput<T>
{
Acceptor.class
Generator.class
void onSend(Generator<T> generator)
………………
}
Scala: JVM Java
JIT inlining impossible Jit inlining is possible

SCALA=>JAVA; JAVA=>SCALA
• Java use ‘additional’ streaming API [not ideal, better
than before]
• Scala library can’t utilize SAM conversion [not ideal,
better than before]
• So, we have place for something 3-rd ?
• Evolution: all ‘ideal’ shifts ….

FUTURE EVOLUTION
• 2 Groups
• which will be integrated in java 9,10, 11, .. if exists.
• [ FORTRAN 90 is object-oriented. Do you know this (?)]
• case classes, type inheritance.
• which represent essential different aspect, not present in java

CASE CLASSES
..??***8dc
case class Person(firstName: String, lastName: String)
p match {
case Person(“Jon”,”Galt” ) => “Hi, who are you ?”
case Person(firstName, lastName) =>
s”Hi, ${firstName}, ${lastName}”
case _ => “You are not person”
}
ML-style pattern matching, 1973
scala,
kotlin,
ceylon,
swift

FUTURE EVOLUTION
• essential different aspect, not present in java:
• internal DSL
• ﬂexible syntax
• call by name
• macros
• Variance
• strong typing
• implicit context

FLEXIBLE SYNTAX
..??***8dc
def +++(x:Int, y:Int) = x*x*y*y
1 to 100 == 1.to(100)
future(1) та future{1}
def until(cond: =>Boolean)(body: => Unit): Unit

CALL BY NAME
..??***8dc
def dountil(cond: =>Boolean)(body: => Unit): Unit =
{
var quit = false;
while(!quit) {
body
quit = !cond
}
}
First introduced in Algol 68
var x = 0
dountil(x != 10)(x+=1)

OWN SYNTAX
..??***8dc
object Do
{
def apply(body: =>Unit) = new DoBody(body)
}
class DoBody(body: => Unit)
{
def until(cond: =>Boolean): Unit =
{ body; while(!cond) body }
}
Do { x = x+1 } until (x<10)

BASIC DSL:)
..??***8dc
object Lunar extends Baysick {
def main(args:Array[String]) = {
10 PRINT "Welcome to Baysick Lunar Lander v0.9"
20 LET ('dist := 100)
30 LET ('v := 1)
40 LET ('fuel := 1000)
50 LET ('mass := 1000)
60 PRINT "You are drifting towards the moon."
70 PRINT "You must decide how much fuel to burn."
80 PRINT "To accelerate enter a positive number"
90 PRINT "To decelerate a negative"
100 PRINT "Distance " % 'dist % "km, " % "Velocity " % 'v % "km/s, " % "Fuel " % 'fuel
110 INPUT 'burn
120 IF ABS('burn) <= 'fuelTHEN 150
130 PRINT "You don't have that much fuel"
140 GOTO 100
ogus.me/2009/03/26/baysick-a-scala-dsl-implementing-basic/

..??***8dccollection.foreach(x => doSomething)
‘FOR’ SPECIAL SYNTAX
..??***8dcfor( x <- collection) doSomething
=
..??***8dc
for(x <- fun1 if (x.isGood);
y <- fun2(x) ) yield z(x,y)
=
..??***8dc
fun1.withFilter(_.isGood).
ﬂatMap(x =>
fun2.map(y=>z(x,y)))

..??***8dccollection.foreach(x => doSomething)
..??***8dcfor( x <- collection) doSomething
=
..??***8dc
for(x <- collection)
yield something =
..??***8dccollection.map( x => something)

..??***8dc
=
..??**8dc
for(r <- rows;
c <- cell(r) ) ….
rows.ﬂatMap(r =>
cell.map(c =>….))
..??***8dcfor(x <- c if p(x)) …. =
..??***8dcc.withFilter(x->p(x)) …

FOR-SYNTAX
• own foreach: possible to use ‘for’
• ‘Monadic interfaces’
• //foreach, map, ﬂatMap, withFilter
• // scala-virtualized (not in standard)
• deﬁne own function for all syntax constructions

IMPLICIT
implicit def f(x): y
Use x as y
add ‘own’ methods to existing classes
pass context
deﬁne type-guided expressions

FUTURE: MONADIC
..??***8dc
for( x <- Future{ calculate x };
y <- Future{ calculate y } ) yield x(x,y)
Future[T]
foreach — do something after competition
map
ﬂatMap — future composition

MACROS
..??***8dc
object Log {
def apply(msg: String): Unit = macro applyImpl
def applyImpl(c: Context)(msg: c.Expr[String]):c.Expr[Unit] =
{
import c.universe._
val tree = q"""if (Log.enabled) {
Log.log(${msg})
}
"""
c.Expr[Unit](tree)
}
Log(msg)
if (Log.enabled) {
Log.log(msg)
}

MACROS
Reduce boilerplate code
Fast code generation for HPC
Deep AST transformations
example: async

ASYNC AS MACROS
..??***8dc
async {
val x = async{ long-running-code-x }
val y = async{ long-running-code-y }
val z = await(x) + await(y)
}
Rewritten as state machine without blocking
Implemented as library (without language change)
async: T => Future[T ]
await: Future[T] =>T

MACROS
async/await
jscala (generate javascript)
miniboxing (analog @specialized)

JAVA/SCALA
• Java - stable domain, mapped to classes and objects.
• Scala - in complex area, where new level of
abstractions needed.
• In the beginning of PL
evolution, but totally new dimension

THANKS FOR ATTENTION
Ruslan Shevchenko <ruslan@shevchenko.kiev.ua>
@rssh1
https://github.com/rssh

JDays Lviv 2014: Java8 vs Scala: Difference points & innovation stream

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JDays Lviv 2014: Java8 vs Scala: Difference points & innovation stream