Kotlin Sequences

Kotlin sequences are an ordered collection of elements that are potentially unbounded in size. The values are evaluated lazily. They are great at representing collection where the size isn’t known in advance like reading lines from a file. Java 8 and Scala both have the concept of streams which is the same idea, Kotlin has chosen to use sequence to avoid naming conflicts when running on a Java 8 jvm.

The api documentation is here

I haven’t seen a lot of example usage so here a couple of examples that I am keeping for reference.

Simple Arithmetic and Geometric Progressions

val nums = generateSequence(1) {it + 1} // sequence starting at 1 incrementing by 1
val powersOf2 = generateSequence(1) {it * 2} // sequence of powers of 2

// Take creates a new sequence (so values are not yet evaluated)
// toList() causes the 10 elements in the sequence to be evaluated
println(nums.take(10).toList()) // prints [1,2,3,4,5,6,7,8,9,10]

Map and Filters

Map, fold and filter functions can be applied like for any other collection and are only evaluated when the value is evaluated

val squares = generateSequence(1) {it + 1}.map {it * it}
val oddSquares = squares.filter {it % 2 != 0}

println(oddSquares.take(5).toList()) // prints [1, 9, 25, 49, 81]

Mapping Java Readers to a Sequence

val reader:java.io.BufferedReader = ...
val lines = generateSequence {reader.readLine()}.takeWhile {it != null}

Gives you a nice little collection that you can you forEach, map or fold operations on but you don’t have to read the whole file into memory upfront.

Advanced Examples

Kotlin lazy evaluation of values is a bit limited in that evaluation can only be done on the previous element in the sequence. It’s fine if the next element is a simple computation on the previous element but can be quite difficult if you need to know a number of previous elements.

Fibonacci Sequence

The Fibonacci numbers is a sequence of numbers where the next value is found by adding together the previous to values. An easy way to do this in Kotlin is to start with a sequence of Pairs that represent the two previous values so its available to next element calculation. Then apply a map to the sequence to only have the first element in each Pair as the resulting sequence.

val fibonacci = generateSequence(1 to 1) {it.second to it.first+it.second}.map {it.first}
println(fibonacci.take(10).toList()) // prints [1, 1, 2, 3, 5, 8, 13, 21, 34, 55]


A prime is defined as a number that is only divisible by 1 and itself (not including 1) and all numbers can be defined as a product of their primes.

To implement this as a sequence of primes a clean way is to define the next prime as the next integer that is not divisible by any of the previous numbers in the stream. This could be solved using the Pairs approach as the fibonacci example but here is alternative recursive style approach.

Option 1 – Pairs Approach

val primes = generateSequence(2 to generateSequence(3) {it + 2}) {
  val currSeq = it.second.iterator()
  val nextPrime = currSeq.next()
  nextPrime to currSeq.asSequence().filter { it % nextPrime != 0}
}.map {it.first}
println(primes.take(10).toList()) // prints [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]

Option 2 – Recursive Approach

The recursive approach might seem a bit overkill for this example but it does provide a useful lazy plus operator for other problems.

Define a plus operator on the Sequence that allows adding a sequence generator function that lazily evaluated. This allows the calculation for the next element of the sequence to be recursively defined without being eagerly evaluated. This function is not specific to the primes example and can be used for any similar case.

public operator fun <T> Sequence<T>.plus(otherGenerator: () -> Sequence<T>) =
  object : Sequence<T> {
    private val thisIterator: Iterator<T> by lazy { this@plus.iterator() }
    private val otherIterator: Iterator<T> by lazy { otherGenerator().iterator() }
    override fun iterator() = object:Iterator<T> {
      override fun next(): T =
        if (thisIterator.hasNext())

      override fun hasNext(): Boolean = thisIterator.hasNext() || otherIterator.hasNext()

So now to get all primes you can define a recursively defined sequence where the a number is prime if it is not divisible by any previous prime in the sequence.

fun primesFilter(from: Sequence<Int>): Sequence<Int> = from.iterator().let {
  val current = it.next()
  sequenceOf(current) + { primesFilter(it.asSequence().filter { it % current != 0 }) }

val primes = primesFilter(generateSequence(2) {it + 1})
println(primes.take(10).toList()) // prints [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]

Game Development with Kotlin and Libgdx

I’ve been using libgdx for a couple of simple games. Its a great platform as it provides cross-platform game development and is compatible with any JVM language. I’ve been using Kotlin and it works fantastically with Libgdx. So you get the power of a modern language that doesn’t add much bulk to an Android app and still have access to a great toolkit. The one caveat with using Kotlin (or any other JVM language) on LibGdx is that you can’t target HTML 5 as a target platform as GWT operates on the Java source code (not the byte code). You can still target desktop, android and iOS so its not much of a limitation for me.

I’m not going to give a tutorial as there are already great ones out there but I’ll keep a collection of useful links I discover as I go.

Some useful links to get you started:


Example projects written in Kotlin-Libgdx:


Scala vs Kotlin

It’s been a long time since I’ve updated this blog. Over the year I’ve moved away from Scala as my preferred language and towards Kotlin. I’ve found Kotlin a refreshing approach as its borrowed a lot of the good things I liked about Scala but kept it simple and practical by avoiding a lot of the gotchas and ambiguity that can exist in Scala.

Here is a collection of things I like about Scala and Kotlin and also a comparison of how these features are accomplished in each language.

Type Declaration and Inference

Something I love about both these languages is they both have static typing with type inference. This gives you the power of compile time type checking with out the declarative boiler plate. Largely it works the same in both languages. Both languages also have preference to immutable type declaration as well with the optional type declaration being placed after the variable name.

Example, the below code is the same in both languages:

Declare a immutable variable named age of type Int:

val age = 1

Declare a mutable variable of type String:

var greeting = "Hello"

Both languages support lambda functions as first class citizens that can be assigned to variables or passed as function parameters.


val double = (i: Int) => { i * 2 }


val double = {i: Int -> i * 2}

Data / Case Class

Both Scala and Kotlin have a similar concept of a data class which can be use to represent a data model object.

Scala’s Approach

Scala calls this a case class and it can be defined like:

case class Person(name: String, age: Int)

This gives you the following main advantages over a normal class:

  • Has an apply method (You don’t need to use the ‘new’ word to construct instances)
  • Accessor methods are defined for each property (If property are defined as var then setters are also defined)
  • toString, equal and hashCode is sensibly defined
  • copy function
  • Has an unapply method (which allows use in match expressions)

Kotlin’s Approach

Kotlin calls this a data class and it’s defined like:

data class Person(val name: String, val age: Int)

Key Features

  • Accessor methods are defined for each property (If property are defined as var then setters are also defined). This is not unique to data class and works on any class in Kotlin.
  • Sensibly defined toString, equal and hashCode
  • copy function
  • component1..componentN functions. Similar use to unapply.
  • Implements JavaBean getter and setters defined so native Java frameworks (Hibernate, Jackson) without change

Kotlin doesn’t need a special ‘apply’ method as it doesn’t require a ‘new’ keyword to instantiate class constructors. So this is a standard constructor definition like any other class.


Generally data and case classes are similar.

This example usage works the same in Kotlin or Scala:

val jack = Person("Jack", 1)
val olderJack = jack.copy(age = 2)

Generally I’ve found data and case classes interchangeable in day to day use. Kotlin does enforce some restriction on extending a data class with inheritance but its done for good reasons when you consider the implementations of equals and componentN functions and prevents the gotcha moments.

The Scala case classes can be more powerful in a match statements compared to Kotlin’s handling of data classes in ‘when’ statements which is something I miss.

Kotlin approach works a lot better when being used from existing Java frameworks as it will look like a normal java bean.

Both languages support supplying parameters by name and allow for default values.

Null Safety / Optionality

Scala’s Approach

Scala’s approach to null safety is the option monad. Simply an option can be one of two concrete types; Some(x) or None.

val anOptionInt: Option[Int] = Some(1)


val anOptionInt: Option[Int] = None

You can operate on the option using functions on the option class like “isDefined” and “getOrElse” (to provide a default value) but more commonly you would use monad operations like map, foreach or fold which will treat the option as a collection containing 0 or 1 elements.

For example to sum two Optionally defined Ints you could do:

val n1Option: Option[Int] = Some(1)
val n2Option: Option[Int] = Some(2)
val sum = for (n1 <- n1Option; n2 <- n2Option) yield { n1 + n2 }

The variable sum will have the value Some(3). This is leveraging Scala’s for comprehension which can be foreach or a flat map function depending on the use of the yield keyword.

Another example of chaining could be:

case class Person(name: String, age: Option[Int])
val person:Option[Person] = Some(Person("Jack",Some(1)))
for (p <- person; age <- p.age) {
 println(s"The person is aged $age")

This will print “The person is aged 1”

Kotlin’s Approach

Kotlin’s approach borrows from groovy style syntax and is very practical in every day use. In Kotlin all types are non-nullable and must be explicitly declared nullable using ‘?’ if it can contain null.

The same example could be written

val n1: Int? = 1
val n2: Int? = 2
val sum = if (n1 != null && n2 != null) n1 + n2 else null

This is much closer to Java syntax except Kotlin will enforce compile time checks so its not possible to use a nullable variable without checking it is not null first so you won’t fear NullPointerExceptions. Its also not possible to assign a null to a variable declared as non-nullable. The compiler is quite smart in checking branch logic so you don’t have the situation of over guarding that you see in Java where the same variable is checked for null multiple times.

An equivalent Kotlin code for the second example of chaining is:

data class Person(val name: String, val age: Int?)
val person:Person? = Person("Jack", 1)
if (person?.age != null) {
  println("The person is aged ${person?.age}")

An alternative is also available using “let” which could replace the if block with

person?.age?.let {
  println("The person is aged $it")


I really prefer the Kotlin approach. It’s a lot easier to read and understand what’s going on and multiple levels of nesting is easy to handle. The scala approach has symmetry in that other monads can be acted on the same as option can (e.g. futures) which some people like but I’ve found it can get complicated really fast once there is a little bit of nesting. There are also a lot of gotcha’s with for comprehension as under the covers they are maps or flat maps but you don’t get the compile time warnings if you do something wrong like mix monads or do a pattern match without covering alternative paths which leads to runtime exceptions that are cryptic.

Kotlin’s approach also bridges the gap when integrating with Java code as they can default to nullable types where as Scala still has to support null as a concept without null safety protection.

Functional Collections

Scala of course supports many functional goodies. Kotlin is a little more restrictive but the basics are covered.

There isn’t much difference in the basic fold and map functions.


val numbers = 1 to 10
val doubles = numbers.map {_ * 2}
val sumOfSquares = doubles.fold(0) {_ + _}


val numbers = 1..10
val doubles = numbers.map {it * 2}
val sumOfSquares = doubles.fold(0) {x,y -> x+y}

Both support the concept of lazy evaluated sequences. For example printing first 10 even squares.


val numbers = Stream.from(1) // all natural numbers
val squares = numbers.map {x => x * x}
val evenSquares = squares.filter {_%2 == 0}


val numbers = sequence(1) {it + 1} // all natural numbers
val squares = numbers.map {it * it}
val evenSquares = squares.filter {it%2 == 0}

Implicits Conversion vs Extension Functions

This is an area where Scala and Kotlin diverge a little.

Scala’s Approach

Scala has a a concept of implicit conversion that allows you to add extra behaviour to a class by automatically converting to another class when needed. An example of this

object Helpers {
 implicit class IntWithTimes(x: Int) {
   def times[A](f: => A): Unit = {
    for (i <- 1 to x) {

Then later in the code you can do:

import Helpers._

This will print “Hello” 5 times. How this works is when you try to use the “times” function which doesn’t exist on the Int the object will be automatically boxed into an IntWithTimes object and the times function will executed on that.

Kotlin’s Approach

Kotlin has the concept of extension functions that can be used to accomplish a similar job. In the Kotlin approach you define a normal function but prefix the function name with a type to extend.

fun Int.times(f: ()->Unit) {
  for (i in 1..this) {

5.times {println("Hello")}


Kotlin approach fits the use case that I generally would use this Scala capability for and has the advantage of being a little simpler to understand.

Scala Features Not Present in Kotlin that I won’t Miss

One of the best part of the Kotlin language for me is not the features it has but more the features from Scala that are not in Kotlin

  • Call by name – This destroys readability. If a function is being passed its a lot easier when its visible that its a function pointer in a basic review of the code. I don’t see any advantage this gives over passing explicit lambdas
  • Implicit parameters – This is something I’ve really hated. It leads to situation of code changing drastically based on a change of import statement. It makes it really hard to tell what values will be passed to a function without good IDE support
  • Overloaded FOR comprehension – To me this is a clunk to get around the problem with dealing with multiple monads
  • The mess with optional syntax on infix and postfix operators – Kotlin is little more prescriptive and means that the code is less ambiguous to read and not as easy for simple typo to become a non-obvious error
  •  Operator Overload to the Max – Kotlin allows basic operator overloads for the basic operators (+, – etc.) but Scala allows any bunch of characters to be used and it seems to have been embraced by library developers. Am I really meant to remember difference between “~%#>” and “~+#>”?
  • Slow compile times

Receiving Mail in Scala

On recent project I had the need to read emails from my app so I thought I’d share a sample. This problem fits neatly into the construct of an Akka Actor.

import javax.mail.internet.MimeMessage
import javax.mail.{Folder, MessagingException, NoSuchProviderException, Session}

import actor.MailReceiverActor.Check
import akka.actor.{Props, Actor}

class MailReceiverActor(host: String, port: Int, user: String, password: String, inboxName: String) extends Actor {

  override def receive: Receive = {
    case Check =>
      val props = System.getProperties
      props.setProperty("mail.store.protocol", "imaps")
      val session = Session.getDefaultInstance(props, null)
      val store = session.getStore("imaps")
      try {
        store.connect(host, port, user, password)
        val inbox = store.getFolder(inboxName)
        inbox.getMessages map {
          case message:MimeMessage => processMessage(message) // define this function to handle the message
          case _ => // do nothing or log that you only process MimeMessages
      } catch {
        case e @ (_:NoSuchProviderException|_:MessagingException) => // log the error
      } finally {

object MailReceiverActor {
  case object Check

  def props(host:String, port:Int, user:String, password:String, inboxName:String) = {
    Props(new MailReceiverActor(host, port, user, password, inboxName))


Then to use this Actor you can just create a schedule for how often the mail will be checked. In play you can place this in the onStart of Global.

val system = Akka.system(app)
val mailActor = system.actorOf(MailReceiverActor.props(host, userId, user, password, inboxName))

implicit val executionContext = // user defined or use the default play context
system.scheduler.schedule(5.seconds, 20.minutes, mailActor, MailReceiverActor.Check)

Registration and Login with Play 2.3 Revisted (Silhoutte)

My early attempts for play login/rego were using SecureSocial. SecureSocial looked promising. It was providing good support for the features I wanted and getting a simple implementation up and going worked relatively easily.

Its only when I start going past the initial simple implementation that it started to give me real problems. The version for Play 2.3 is still immature and there isn’t enough documentation on the interfaces that you need to implement. Customising the user services to get user login and registration working with an existing backend takes too long and leads to hard to debug errors. In conclusion, it just isn’t worth the headache. The library makes life harder.

So I’ve now moved to Play Silhoutte. It is actually a fork of SecureSocial. The author behind it forked it for a lot of the same reasons that I dislike SecureSocial. Silhoutte is more modular, simpler to customise and has good documentation. There is also plenty of activator seed projects that prove how it works and how to customise it.

The Silhoutte Slick Seed provides a good example project that shows how to integrate with your own backend.

Scala Streams Examples

A couple of Scala streams examples for reference

Recursive Definition

The basic fibonacci example defines each element in the stream as the sum of the previous two elements in the stream.

def fibFrom(a: Int, b: Int): Stream[Int] = a #:: fibFrom(b, a + b)
val fibs = fibFrom(1,1)

Using From with Mapping or Filter

Use from for a basic sequence and then apply a mapping function on the sequence. The mapping function is evaluated lazily. In this case a stream of all squares.

Stream.from(1).map(x => x * x)

Mapping or Filter Function Recursively

A prime is defined as a number that is only divisible by 1 and itself (not including 1) and all numbers can be defined as a product of their primes. This gives an clean way of define the next prime as next integer that is not divisible by any of the previous primes in the stream. You can apply a filter function to the tail and its only evaluated lazily.

def primeStream(s: Stream[Int]): Stream[Int] = s.head #:: primeStream(s.tail filter(_ % s.head != 0))
val primes = primeStream(Stream.from(2))

Converting Java Readers to a Stream

This is a handy way to convert a Java style reading into Stream so it can be process with map and fold functions easily.

val reader: BufferedReader = ..
val lines = Stream.continually(reader.readLine()).takeWhile(_ != null)

Login and Registration in Play 2.3 with SecureSocial

A quick and easy way to setup login and registration in Play is using the SecureSocial play plugin.

The SecureSocial plugin provides out of the box:

  • Twitter (OAuth1)
  • Facebook (OAuth2)
  • GitHub (OAuth2)
  • Google (OAuth2)
  • LinkedIn (OAuth1 and OAuth2)
  • Foursquare (OAuth2)
  • Instagram (OAuth2)
  • VK (OAuth2)
  • XING (OAuth1)
  • Username/Password with signup and reset password functionality.

The main thing I was looking for was just simple username / password with signup so all the extra social integration is really a bonus.

There is currently no stable version of SecureSocial for Play 2.3 and as far as I can tell there isn’t any documentation on how to get SecureSocial working using the current milestone release of v3.0. The configuration has changed a lot since the v2 release so here are my notes mostly based on how the sample apps are built.

Prereq : Configure play mailer plugin

See https://github.com/playframework/play-mailer/tree/2.3.1

Include SecureSocial as a dependency

Add the dependency to the sbt build file

libraryDependencies ++= Seq(
  //...existing dependencies
  &quot;ws.securesocial&quot; %% &quot;securesocial&quot; % &quot;3.0-M1&quot;

Create a custom UserService

Create a custom UserService to map your internal user model to the SecureSocial model. The InMemoryUserService provides a good starter for testing. This is based on the sample in SecureSocial project InMemoryUserService.scala. This gives you an implementation that can be replaced with a database backed implementation.

// a simple User class that can have multiple identities
case class MyUser(main: BasicProfile, identities: List[BasicProfile])
import securesocial.core._
import securesocial.core.providers.{MailToken, UsernamePasswordProvider}
import securesocial.core.services.{SaveMode, UserService}

import scala.concurrent.Future

class InMemoryUserService extends UserService[MyUser] with common.Logger {
  var users = Map[(String, String), MyUser]()
  private var tokens = Map[String, MailToken]()

  def find(providerId: String, userId: String): Future[Option[BasicProfile]] = {
    logger.debug(s&quot;findByUserId $userId, users = $users&quot;)

    val result = for (
      user &lt;- users.values;
      basicProfile &lt;- user.identities.find(su =&gt; su.providerId == providerId &amp;&amp; su.userId == userId)
    ) yield {

  def findByEmailAndProvider(email: String, providerId: String): Future[Option[BasicProfile]] = {
    logger.debug(s&quot;findByEmail $email, users = $users&quot;)

    val someEmail = Some(email)
    val result = for (
      user &lt;- users.values;
      basicProfile &lt;- user.identities.find(su =&gt; su.providerId == providerId &amp;&amp; su.email == someEmail)
    ) yield {

  private def findProfile(p: BasicProfile) = {
    logger.debug(s&quot;findByProfile $p, users = $users&quot;)

    users.find {
      case (key, value) if value.identities.exists(su =&gt; su.providerId == p.providerId &amp;&amp; su.userId == p.userId) =&gt; true
      case _ =&gt; false

  private def updateProfile(user: BasicProfile, entry: ((String, String), MyUser)): Future[MyUser] = {
    logger.debug(s&quot;updateProfile $user, $entry, users = $users&quot;)

    val identities = entry._2.identities
    val updatedList = identities.patch(identities.indexWhere(i =&gt; i.providerId == user.providerId &amp;&amp; i.userId == user.userId), Seq(user), 1)
    val updatedUser = entry._2.copy(identities = updatedList)
    users = users + (entry._1 -&gt; updatedUser)

  def save(user: BasicProfile, mode: SaveMode): Future[MyUser] = {
    logger.debug(s&quot;save $user, users = $users, mode = $mode&quot; )

    mode match {
      case SaveMode.SignUp =&gt;
        val newUser = MyUser(user, List(user))
        users = users + ((user.providerId, user.userId) -&gt; newUser)
      case SaveMode.LoggedIn =&gt;
        // first see if there is a user with this BasicProfile already.
        findProfile(user) match {
          case Some(existingUser) =&gt;
            updateProfile(user, existingUser)

          case None =&gt;
            val newUser = MyUser(user, List(user))
            users = users + ((user.providerId, user.userId) -&gt; newUser)

      case SaveMode.PasswordChange =&gt;
        findProfile(user).map { entry =&gt; updateProfile(user, entry) }.getOrElse(
          // this should not happen as the profile will be there
          throw new Exception(&quot;missing profile)&quot;)

  def link(current: MyUser, to: BasicProfile): Future[MyUser] = {
    logger.debug(s&quot;link $current, to $to, users = $users&quot; )

    if (current.identities.exists(i =&gt; i.providerId == to.providerId &amp;&amp; i.userId == to.userId)) {
    } else {
      val added = to :: current.identities
      val updatedUser = current.copy(identities = added)
      users = users + ((current.main.providerId, current.main.userId) -&gt; updatedUser)

  def saveToken(token: MailToken): Future[MailToken] = {
    logger.debug(s&quot;saveToken $token, users = $users&quot; )

    Future.successful {
      tokens += (token.uuid -&gt; token)

  def findToken(token: String): Future[Option[MailToken]] = {
    logger.debug(s&quot;findToken $token, users = $users&quot; )

    Future.successful { tokens.get(token) }

  def deleteToken(uuid: String): Future[Option[MailToken]] = {
    logger.debug(s&quot;deleteToken $uuid, users = $users&quot; )

    Future.successful {
      tokens.get(uuid) match {
        case Some(token) =&gt;
          tokens -= uuid
        case None =&gt; None

  def deleteExpiredTokens() {
    logger.debug(s&quot;deleteExpiredTokens&quot; )

    tokens = tokens.filter(!_._2.isExpired)

  override def updatePasswordInfo(user: MyUser, info: PasswordInfo): Future[Option[BasicProfile]] = {
    logger.debug(s&quot;updatePasswordInfo $user, users = $users&quot; )

    Future.successful {
      for (
        found &lt;- users.values.find(_ == user);
        identityWithPasswordInfo &lt;- found.identities.find(_.providerId == UsernamePasswordProvider.UsernamePassword)
      ) yield {
        val idx = found.identities.indexOf(identityWithPasswordInfo)
        val updated = identityWithPasswordInfo.copy(passwordInfo = Some(info))
        val updatedIdentities = found.identities.patch(idx, Seq(updated), 1)
        val updatedEntry = found.copy(identities = updatedIdentities)
        users = users + ((updatedEntry.main.providerId, updatedEntry.main.userId) -&gt; updatedEntry)

  override def passwordInfoFor(user: MyUser): Future[Option[PasswordInfo]] = {
    logger.debug(s&quot;passwordInfoFor $user, users = $users&quot; )

    Future.successful {
      for (
        found &lt;- users.values.find(u =&gt; u.main.providerId == user.main.providerId &amp;&amp; u.main.userId == user.main.userId);
        identityWithPasswordInfo &lt;- found.identities.find(_.providerId == UsernamePasswordProvider.UsernamePassword)
      ) yield {

Update the controllers to use Secure Social

This involves changing your controllers from objects to classes that take the RuntimeEnvironment as a contructor parameter. The controllers will now need to extend SecureSocial instead of controller. Any actions that are to be secured should return SecuredAction instead of Action.

class Application(override implicit val env: RuntimeEnvironment[MyUser]) extends securesocial.core.SecureSocial[MyUser] {

  def index = SecuredAction {
    Ok(views.html.index(&quot;Your new application is ready.&quot;))

Update the routes

Update the routes to include the controllers as classes and include the Secure Social routes for login and registration in your play application. This is now a lot easier than the previous version as the default routes can be added with one line. Note the ‘@’ sign needed for the controllers as they are now classes that need to be instantiated.

GET         /                                     @controllers.Application.index
// all secure social routes for login and registration
-&gt;          /auth                                 securesocial.Routes

Add to the Global.scala

Update Global.scala (this should be in default package unless you explicitly configured to be somewhere else) to create SecureSocial runtime and inject into the controller classes on request.

package app

import java.lang.reflect.Constructor

import actor.SyncActorGuardian
import common.Logger
import play.api.libs.concurrent.Akka
import play.api.{Application, GlobalSettings}
import securesocial.core.RuntimeEnvironment
import securesocial.core.providers.UsernamePasswordProvider
import services.{LoginEventListener, MyUser, InMemoryUserService}

import scala.collection.immutable.ListMap

object Global extends GlobalSettings with Logger {
   * The runtime environment
  object SecureSocialRuntimeEnvironment extends RuntimeEnvironment.Default[MyUser] {
    //override lazy val routes = new CustomRoutesService()
    override lazy val userService: InMemoryUserService = new InMemoryUserService()
    override lazy val eventListeners = List(new LoginEventListener())
    override lazy val providers = ListMap(
      include(new UsernamePasswordProvider[MyUser](userService, avatarService, viewTemplates, passwordHashers))
      // ... other providers

   * An implementation that checks if the controller expects a RuntimeEnvironment and
   * passes the instance to it if required.
  override def getControllerInstance[A](controllerClass: Class[A]): A = {
    val instance = controllerClass.getConstructors.find { c =&gt;
      val params = c.getParameterTypes
      params.length == 1 &amp;&amp; params(0) == classOf[RuntimeEnvironment[MyUser]]
    }.map {

Configure Secure Social

The configuration guide is still valid for the current version http://securesocial.ws/guide/configuration.html. I’m just configuring the user password provider.

securesocial {
  userpass {