5. How I Write Programs
• Pre-Scala:
– Make it work
– Make it work well
– Make it work fast
• With Scala:
– Make it work and work well
– Make it work fast
7. Case Classes
case class Person(firstName: String = "Jamie",
lastName: String = "Allen")
val jamieDoe = Person(lastName = "Doe")
res0: Person = Person(Jamie,Doe)
• Data Transfer Objects (DTOs) done right
• By default, class arguments are immutable & public
• Should never be extended
• Provide equals(), copy(), hashCode() and toString()
implementations
• Don’t have to use new keyword to create instances
• Named Parameters and Default arguments give us Builder pattern
semantics
8. Lazy Definitions
lazy val calculatedValue = piToOneMillionDecimalPoints()
• Excellent for deferring expensive operations
until they are needed
• Reducing initial footprint
• Resolving ordering issues
• Implemented with a guard field and
synchronization, ensuring it is created when
necessary
9. Imports
import scala.collection.immutable.Map
class Person(val fName: String, val lName: String) {
import scala.collection.mutable.{Map => MMap}
val cars: MMap[String, String] = MMap()
...
}
• Can be anywhere in a class
• Allow for selecting multiple classes from a package or
using wildcards
• Aliasing
• Order matters!
10. Objects
object Bootstrapper extends App { Person.createJamieAllen }
object Person {
def createJamieAllen = new Person("Jamie", "Allen")
def createJamieDoe = new Person("Jamie", "Doe")
val aConstantValue = "A constant value”
}
class Person(val firstName: String, val lastName: String)
• Singletons within a JVM process
• No private constructor histrionics
• Companion Objects, used for factories and constants
11. The apply() method
Array(1, 2, 3)
res0: Array[Int] = Array(1, 2, 3)
res0(1)
res1: Int = 2
• In companion objects, it defines default
behavior if no method is called on it
• In a class, it defines the same thing on an
instance of the class
12. Tuples
def firstPerson = (1, Person(firstName = “Barbara”))
val (num: Int, person: Person) = firstPerson
• Binds you to an implementation
• Great way to group values without a DTO
• How to return multiple values, but wrapped in
a single instance that you can bind to specific
values
14. Pattern Matching Examples
name match {
case "Lisa" => println("Found Lisa”)
case Person("Bob") => println("Found Bob”)
case "Karen" | "Michelle" => println("Found Karen or Michelle”)
case Seq("Dave", "John") => println("Got Dave before John”)
case Seq("Dave", "John", _*) => println("Got Dave before John”)
case ("Susan", "Steve") => println("Got Susan and Steve”)
case x: Int if x > 5 => println("got value greater than 5: " + x)
case x => println("Got something that wasn't an Int: " + x)
case _ => println("Not found”)
}
• A gateway drug for Scala
• Extremely powerful and readable
• Not compiled down to lookup/table switch unless
you use the @switch annotation,
17. Referential Transparency
// Transparent
val example1 = "jamie".reverse
val example2 = example1.reverse
println(example1 + example2) // eimajjamie
// Opaque
val example1 = new StringBuffer("Jamie").reverse
val example2 = example1.reverse
println(example1 append example2) // jamiejamie
• An expression is transparent if it can be replaced by its
VALUE without changing the behavior of the program
• In math, all functions are referentially transparent
18. Scala Collections
val myMap = Map(1 -> "one", 2 -> "two", 3 -> "three")
val mySet = Set(1, 4, 2, 8)
val myList = List(1, 2, 8, 3, 3, 4)
val myVector = Vector(1, 2, 3...)
• You have the choice of mutable or immutable
collection instances, immutable by default
• Rich implementations, extremely flexible
19. Rich Collection Functionality
val numbers = 1 to 20 // Range(1, 2, 3, ... 20)
numbers.head // Int = 1
numbers.tail // Range(2, 3, 4, ... 20)
numbers.take(5) // Range(1, 2, 3, 4, 5)
numbers.drop(5) // Range(6, 7, 8, ... 20)
• There are many methods available to you in
the Scala collections library
• Spend 5 minutes every day going over the
ScalaDoc for one collection class
20. Higher Order Functions
val names = List("Barb", "May", "Jon")
names map(_.toUpperCase)
res0: List[java.lang.String] = List(BARB, MAY, JON)
• Really methods in Scala
• Applying closures to collections
21. Higher Order Functions
val names = List("Barb", "May", "Jon")
names map(_.toUpperCase)
res0: List[java.lang.String] = List(BARB, MAY, JON)
names flatMap(_.toUpperCase)
res1: List[Char] = List(B, A, R, B, M, A, Y, J, O, N)
names filter (_.contains("a"))
res2: List[java.lang.String] = List(Barb, May)
val numbers = 1 to 20 // Range(1, 2, 3, ... 20)
numbers.groupBy(_ % 3)
res3: Map[Int, IndexedSeq[Int]] = Map(1 -> Vector(1, 4, 7, 10, 13,
16, 19), 2 -> Vector(2, 5, 8, 11, 14, 17, 20), 0 -> Vector(3, 6, 9,
12, 15, 18))
22. For Comprehensions
val myNums = 1 to 20
for (i <- myNums) yield i + 1
myNums map(_ + 1)
for {
i <- myNums
j <- 1 to i
} yield i * j
myNums flatMap(i => 1 to i map (j => i * j))
• Used for composing higher-order functions
• As you chain higher-order functions, you may
find it easier to reason about them this way
23. Parallel Collections
scala> 1 to 1000000
res0: scala.collection.immutable.Range.Inclusive = Range(1, 2, 3,...
scala> res0.par
res1: s.c.parallel.immutable.ParRange = ParRange(1, 2, 3,...
scala> res1 map(_ + 1)
res2: s.c.parallel.immutable.ParSeq[Int] = ParVector(2, 3, 4,...
scala> res2.seq
res3: s.c.immutable.Range = Range(2, 3, 4,...
• You can easily parallelize the application of a function literal to your
collection by calling the par() method on a collection instance
• Uses JSR166 under the covers to fork/join for you
• Use the seq() method on the parallel collection to return to a
non-parallel instance
24. Partial Functions
class MyActor extends Actor {
def receive = {
case s: String => println("Got a String: " + s)
case i: Int => println("Got an Int: " + i)
case x => println("Got something else: " + x)
}
}
• A simple match without the match keyword
• The receive block in Akka actors is an excellent
example
• Is characterized by what "isDefinedAt" in the
case statements
25. Currying
def product(i: Int)(j: Int) = i * j
val doubler = product(2)_
doubler(3) // Int = 6
doubler(4) // Int = 8
val tripler = product(3)_
tripler(4) // Int = 12
tripler(5) // Int = 15
• Take a function that takes n parameters as separate argument lists
• “Curry” it to create a new function that only takes one parameter
• Fix on a value and use it to apply a specific implementation of a
product with semantic value
• Have to be defined explicitly as such in Scala
• The _ is what explicitly marks this as curried
27. Actors
import akka.actor._
class MyActor extends Actor {
def receive = {
case x => println(“Got value: “ + x)
}
}
• Based on concepts from Erlang/OTP
• Akka is replacing the core language actors
• Concurrency paradigm using networks of
independent objects that only communicate
via messaging and mailboxes
29. Futures
import scala.concurrent._
val costInDollars = Future {
webServiceProxy.getCostInDollars.mapTo[Int]
}
costInDollars map (myPurchase.setCostInDollars(_))
• Allows you to write asynchronous code, which
can be more performant than blocking
• Are not typed, hence the mapTo call above
30. Futures in Sequence
val customerPurchases = for (
costUSD <- Future{ proxy.getCostInDollars.mapTo[Int]}
totalPurchase <- Future{ proxy.addToTotal(costUSD).mapTo[Int]}
} yield ((customerId -> totalPurchase))
• Scala’s for comprehensions allow you to
compose higher-order functions, including
Futures
• By sequencing the expressions on multiple
lines, you can order dependencies
31. Futures in Parallel
val costUSD = Future{proxy.getCostInUSD(cost).mapTo[Int]}
val costCAD = Future{proxy.getCostInCAD(cost).mapTo[Int]}
val combinedCosts = for {
cUSD <- costUSD
cCAD <- costCAD
} yield (cUSD, cCAD)
val costs = for (
(costUSD, costCAD) <-
Future{proxy.getCostInUSD(cost).mapTo[Int]} zip
Future{proxy.getCostInCAD(cost).mapTo[Int]}
} yield (costUSD, costCAD)
• Define the futures separately and then compose
• Alternatively, the zip method allows you to
parallelize futures execution within a for
comprehension
34. Implicit Conversions
case class Person(firstName: String, lastName: String)
implicit def PersonToInt(p: Person) = p.toString.head.toInt
val me = Person("Jamie", "Allen")
val weird = 1 + me
res0: Int = 81
• Looks for definitions at compile time that will
satisfy type incompatibilities
• Modern IDEs will warn you with an underline
when they are in use
• Limit scope as much as possible (see Josh
Suereth's NE Scala 2011)
35. Implicit Parameters
def executeFutureWithTimeout(f: Future)(implicit t: Timeout)
implicit val t: Timeout = Timeout(20, TimeUnit.MILLISECONDS)
executeFutureWithTimeout(Future {proxy.getCustomer(id)})
• Allow you to define default parameter values
that are only overridden if you do so explicitly
• Handy to avoid code duplication
36. Implicit Classes
implicit class Person(name: String)
class Person(name: String)
implicit final def Person(name: String): Person = new Person(name)
• New to Scala 2.10
• Create extension methods to existing types
• Desugars at compile time into a class
definition with an implicit conversion
38. Type Inference
• Declaring a variable/value
• Return types of methods/functions
• See Daniel Spiewak's Philly ETE 2011 talk
• Good idea to show types on public interfaces
• Specify types when you want to type certainty
39. Type Classes I
case class Customer(id: Long, firstName: String, lastName: String)
trait CustomerOrderById extends Ordering[Customer] {
def compare(x: Customer, y: Customer): Int = { ... }
}
implicit object CustomerIdSort extends CustomerOrderById
val customers = List(Customer(1, "Jamie", "Allen"), Customer(5,
"John", "Doe"), Customer(2, "Jane", "Smith"))
val sortedCustomers = customers.sorted(CustomerIdSort)
sortedCustomers: List[Customer] = List(Customer(1,Jamie,Allen),
Customer(2,Jane,Smith), Customer(5,John,Doe))
• Allow you to layer in varying implementations
of behavior without changing an existing
inheritance structure
40. Type Classes II
case class Dog(name: String)
case class Ferret(name: String)
case class Cat(name: String)
abstract class OkayPets[T]
object OkayPets {
implicit object OkayDog extends OkayPets[Dog]
implicit object OkayFerret extends OkayPets[Ferret]
}
def getPet[T](t: T)(implicit p: OkayPets[T]) = t
val myDog = getPet(Dog("Sparky")) // Works
val myCat = getPet(Cat("Sneezy")) // Fails at compile time
• Allows you to generalize types that are
acceptable parameters for methods
41. Higher Kinded Types
Map[A, B] // Type constructor, not a type!
val myMap = Map[Int, String]() // Now it’s a type!
• Use other types to construct a new type
• Also called type constructors
42. Algebraic Data Types
sealed abstract class DayOfTheWeek
case object Sunday extends DayOfTheWeek
case object Monday extends DayOfTheWeek
...
case object Saturday extends DayOfTheWeek
val nextDay(d: DayOfTheWeek): DayOfTheWeek = d match {
case Sunday => Monday
case Monday => Tuesday
...
case Saturday => Sunday
}
}
• Allow you to model the world in finite terms, such as enumerations, but
also define behavior around them, with all of the power of case classes
• A finite number of possible subtypes, enforced by the "sealed" keyword
(must be defined in the same source file)
44. Macros
• New to Scala 2.10
• Macros are used for generating code at
compile time, similar to LISP macros
• Does not have compiler pragmas such as
#ifdef
• Are implemented as "hygenic" macros at the
point you call reify() – identifiers cannot
be closed over in a macro definition
45. ScalaLogging Macro
def debug(message: String): Unit = macro LoggerMacros.debug
private object LoggerMacros {
def debug(c: LoggerContext)(message: c.Expr[String]) = c.universe.reify(
if (c.prefix.splice.underlying.isDebugEnabled)
c.prefix.splice.underlying.debug(message.splice)
)
}
import com.typesafe.scalalogging.Logging
class MyClass extends Logging {
logger.debug("This won't occur if debug is not defined")
}
• Existing log libraries allow us to define logging statements and then
determine whether they result in output at runtime
• ScalaLogging allows a user to use a logging facility but decide at compile
time whether or not to include the logging statement based on log level.
48. Concepts and Arrows
val myIntToStringArrow: Int => String = _.toString
myIntToStringArrow(1100)
res0: String = 1100
• Concepts are types
• Arrows are functions that convert one concept
to another
49. Morphism
val number = 1000
val numericString = number.toString
• Morphisms change one value in a category to
another in the same category, from one type
to another where types are the category
• Simplified, it converts a type with one
property to a type with another property
• Must be pure, not side-effecting
50. Functor
val numbers = List(1, 2, 3, 4)
val numericStrings = numbers.map(_.toString)
• Functors are transformations from one
category to another that preserve morphisms
• Simplified, converts a type from one to
another while maintaining the conversion of a
type with one property to a type with another
property
51. Monad
val customerPurchases = for (
costUSD <- proxy.getCostInDollars
totalPurchase <- proxy.addToTotal(costUSD)
} yield ((customerId -> totalPurchase))
• Very ephemeral concept
• Must meet the laws of a monad to be one
• Combine functor applications because they can be
bound together, sequencing operations on the
underlying types
• flatMap() is the method the Scala compiler uses to
bind monads
53. Credits
• Sources
– Fast Track to Scala courseware by Typesafe
– Scala in Depth, by Josh Suereth
– DSLs in Action, Debasish Ghosh
– Wikipedia
– Runar Bjarnason's NE Scala 2011 talk
– Daniel Sobral's blog
– Brendan McAdams' blog
• Contributors
– Dave Esterkin, Chariot Solutions
– Josh Suereth, Typesafe
Editor's Notes
When you walk out of this room, your head should hurt. But that’s a GOOD thing – the problem is not that these concepts are difficult to grasp, but that you have to understand the vocabulary to even sit in the room with someone discussing them. I want to provide you with a reference point from which you can do your own investigation of what these concepts mean.
Ask why we don’t need the “new” keyword with case classes
Very fragile
Constant, Constructor, Or, Sequence, Sequence with wildcard, tuple, typed with guard, bound variable wildcard, wildcardBy definition, a lookup/table switch on the JVM can only be an int or enumerated type. Tell the story about your implementation of a jump table using hashes of class definitions – 5000 of them, had to do some delegation due to max method size on the JVM, but was able to perform the deepest match in ~300ns
Very powerful programming paradigmInverts imperative logic - apply your idempotent function to your dataThis is NOT monads, functors and the like, despite what you will hear in the communityAt it’s essence, functional programming is functions, referential transparency and immutability ONLY
How many times have you been bitten by someone altering the contents of your collection?Can happen with closing over state very easily, or sending state to another method/function without considering whether or not it can be changed after it is sent.
Map is key to valueSet does not allow dups and doesn’t care about orderSequence allows dups and maintains orderList provides Lisp-like cons semantics, but is a linked list and can be slow, must prependVector is a bitmapped vector trie, organizing data into 32-element arrays. Very performant, holds 2.15 billion elements only seven levels deep
map, filter and flatMap in ScalaYou have the choice as to how to organize your code
Map is key to valueSet does not allow dups and doesn’t care about orderSequence allows dups and maintains orderList provides Lisp-like cons semantics, but is a linked list and can be slow, must prependVector is a bitmapped vector trie, organizing data into 32-element arrays. Very performant, holds 2.15 billion elements only seven levels deep
Functions are automatically curry-able in ML and Haskell, but has to be explicitly defined with multiple parameter lists in Scala
This is ALL you need to know. There is a ton of goodness in Akka that make performing actor-based work much simpler and reasonable than it has been in the past, as well as a codifying of best practices. Please check out the documentation if you are interested.
Don't use them until you understand them! And limit their scope when you do so nobody shoots their foot off.
Implicits will seem like voodoo at first. Exist in other languages, like C type coercion
Very powerful programming paradigmInverts imperative logic - apply your idempotent function to your dataThis is NOT monads, functors and the like, despite what you will hear in the communityAt it’s essence, functional programming is functions, referential transparency and immutability ONLY
Global type inferencing is found in ML, for example
Using Java's interfaces requires you to specify the inheritance structure in your code. What if you can't because you're using a library? What if you want to make the way your code handles situations orthogonal to it's inheritance structure
Using Java's interfaces requires you to specify the inheritance structure in your code. What if you can't because you're using a library? What if you want to make the way your code handles situations orthogonal to it's inheritance structure
This is the understandable reaction of most developers when they first engage the people who like CT. Or when they read their first blog post about how monads are like burritos or some other metaphor. I'm convinced what we need to do is understand that there is a whole vocabulary that must be learned in order for you to know what CT is.
Morphism is chewy brownie to a hard brownie
Would convert a brownie to a cookie, and a chewy brownie to a chewy cookie and hard brownie into hard cookie, but also chewy cookies into hard cookies just like the brownie because the morphism is preserved
They are not containers! They are not collections. Having a flatMap method does not mean that your type is monadic.Like a collection with flatMap. you won't know what they are by looking at code at first. Monads are ephemeral - they have to meet the laws of monads. Left and right identity as well as binding.
Is the language trying to support too many paradigms at the expense of usability? Should a language be responsible for providing convention as well as capability? I think not. You can start by using Scala as a DSL for Java and make your code more concise, more readable and more correct. As your abilities with the language grows, try expanding what you're doing, but keep in mind your limitations.
They are not containers! They are not collections. Having a flatMap method does not mean that your type is monadic.Like a collection with flatMap. you won't know what they are by looking at code at first. Monads are ephemeral - they have to meet the laws of monads. Left and right identity as well as binding.