09 December 2011

Pragmatic IO - part 3

A follow-up to my previous posts about IO.

Why types and laws matter

It's embarrassing to write post after post only to find out I keep being wrong :-). While the technique I described in my last post (using StreamT) works ok, I actually don't have to use it. The traverse technique explained in EIP works perfectly, provided that:

  • we write a proper Traverse instance using foldLeft (as explained in the first part of this post)

  • we trampoline the State to avoid Stack overflows due to a long chain of flatMaps during the traversal

  • we use the right types!

Get the types right

That's the point I want to insist on today. Last evening I read the revisited WordCount example in Scalaz-seven which is like the canonical example of using the EIP ideas. Two words in the comments struck my mind: "compose" and "fuse". Indeed, one very important thing about EIP is the ability to compose Applicatives so that their actions should "fuse" during a traversal. As if they were executed in the same "for" loop!

So, when I wrote in my previous post that I needed to traverse the stream of lines several times to get the results, something had to be wrong somewhere. The types were wrong!

  1. instead of traversing with State[S, *] where * =:= IO[B], I should traverse with State[S, IO[*]]
  2. what I get back is a State[S, IO[Seq[B]]], instead of a State[S, Seq[IO[B]]
  3. this matters because passing in an initial state then returns IO[Seq[B]] instead of Seq[IO[B]]

Exactly what I want, without having to sequence anything.

Use the laws

Not only I get what I want, but also it is conceptually right as the result of an important traverse law:

  traverse(f compose g) == traverse(f) compose traverse(g)

That "fusion" law guarantees that composing 2 effects can be fused, and executed in only one traversal. It's worth instantiating f and g to make that more concrete:

  // what to do with the line and line number
  def importLine(i: Int, l: String): (Int, IO[Unit]) =
    (i, storeLine(l) >>= println("imported line "+i))

  // `g` keeps track of the line number
  val g : String => State[Int, String] =
    (s: String) => state((i: Int) => (i+1, s))

  // `f` takes the current line/line number and do the import/reporting
  val f : State[Int, String] => State[Int, IO[Unit]] =
    st => state((i: Int) => importLine(st(i)))

  // `f compose g` fuses both actions
  val f_g: String => State[Int, IO[Unit]] = f compose g

I'm really glad that I was able to converge on established principles. Why couldn't I see this earlier?

Scala and Scalaz-seven might help

In retrospect, I remember what led me astray. When I realized that I had to use a State transformer with a Trampoline, I just got scared by the Applicative instance I had to provide. Its type is:

   * Here we want M1 to `Trampoline` and M2 to be `IO`
  implicit def StateTMApplicative[M1[_]: Monad, S, M2[_] : Applicative] =
    Applicative.applicative[({type l[a] = StateT[M1, S, M2[a]]})#l]

I also need some Pure and Apply instances in scope:

  implicit def StateTMApply[M1[_]: Monad, S, M2[_] : Applicative] =
    new Apply[({type l[a]=StateT[M1, S, M2[a]]})#l] {
      def apply[A, B](f: StateT[M1, S, M2[A => B]], a: StateT[M1, S, M2[A]]) =
        f.flatMap(ff => => aa <*> ff))

  implicit def StateTMPure[M1[_] : Pure, S, M2[_] : Pure] =
    new Pure[({type l[a]=StateT[M1, S, M2[a]]})#l] {
      def pure[A](a: => A) = stateT((s: S) => (s, a.pure[M2]).pure[M1])

Once it's written, it may not seem so hard but I got very confused trying to get there. How can it be made easier? First, we could have better type annotations for partial type application, like:

  // notice the * instead of the "type l" trick
  implicit def StateTMApplicative[M1[_]: Monad, S, M2[_] : Applicative] =
    Applicative.applicative[StateT[M1, S, M2[*]]]

  implicit def StateTMApply[M1[_]: Monad, S, M2[_] : Applicative] =
    new Apply[StateT[M1, S, M2[*]]] {
      def apply[A, B](f: StateT[M1, S, M2[A => B]], a: StateT[M1, S, M2[A]]) =
        f.flatMap(ff => => aa <*> ff))

  implicit def StateTMPure[M1[_] : Pure, S, M2[_] : Pure] =
    new Pure[StateT[M1, S, M2[*]]] {
      def pure[A](a: => A) = stateT((s: S) => (s, a.pure[M2]).pure[M1])

And with a better type inference, the first definition could be even be (we can always dream :-)):

  implicit def StateTMApplicative[M1[_]: Monad, S, M2[_] : Applicative]:
    Applicative[StateT[M1, S, M2[*]]] = Applicative.applicative

Which means that it may even be removed, just import Applicative.applicative!

Actually Scalaz-seven might help by:

  • providing those instances out-of-the box

  • even better, provide combinators to create those instances easily. That's what the compose method does

  • give even better type inference. Look at the traverseU method here, no type annotations Ma!

Now that the fundamentals are working ok for my application, I can go back to adding features, yay!

08 December 2011

Pragmatic IO - part 2

A follow-up to my previous post about IO.

Learning by being wrong, very wrong

I didn't think that diving into the functional IO world would have me think so hard about how to use some FP concepts.

Mind the law

The technique I described at the end of my previous post didn't really work. It was wrong on many accounts. First of all, my
Traverse instance was buggy because I was reversing the traversed Stream. This is why I had inverted messages in the console. During the traversal, starting from the left, I needed to append each traversed element to the result, not prepend it:

   * WRONG: because each new element `b` must be appended to the result.
   * it should be bs :+ b instead
  def SeqLeftTraverse: Traverse[Seq] = new Traverse[Seq] {
    def traverse[F[_]: Applicative, A, B](f: A => F[B], as: Seq[A]): F[Seq[B]] =
      as.foldl[F[Seq[B]]](Seq[B]().pure) { (ys, x) =>
        implicitly[Apply[F]].apply(f(x) map ((b: B) => (bs: Seq[B]) => b +: bs), ys)

It is important to mention here that proper testing should have caught this. There are some laws that applicative traversals must satisfy (see EIP, section 5. One of them is that seq.traverse(identity) == seq. This is obviously not the case when I returned a reverted sequence.

Not at the right time

Then I realized something also which was also worrying. On big files, my application would do a lot, then, report its activity to the console. This would definitely not have happened in a simple for loop with unrestricted effects!

So I set out to find a better implementation for my file reading / records saving. The solution I'm going to present here is just one way of doing it, given my way of constraining the problem. There are others, specifically the use of Iteratees.

My goal

This is what I want to do: I want to read lines from a file, transform them into "records" and store them in a database. Along the way, I want to inform the user of the progress of the task, based on the number of lines already imported.

More precisely, I want 2 classes with the following interfaces:

  • the Source class is capable of reading lines and do something on each line, possibly based on some state, like the number of lines already read.
    The Source class should also be responsible for closing all the opened resources whether things go wrong or not

  • the Extractor class should provide a function declaring what to do with the current line and state. Ideally it should be possible to create that function by composing independent actions: storing records in the db and / or printing messages on the console

  • and, by the way, I don't want this to go out-of-memory or stack overflow at run-time just because I'm importing a big file :-)

Mind you, it took me quite a while to arrive there. In retrospect, I could have noticed that:

  • all the processing has to take place inside the Source.readLines method. Otherwise there's no way to close the resources properly (well unless you're using Iteratees, what Paul Chiusano refers to as "Consumer" processing)

  • the signature of the function passed to Source.readLines has to be String => State[IO[A]], meaning that for each line, we're doing an IO action (like saving it to the database) but based on a State

  • the end result of readLines has to be IO[Seq[A]] which is an action returning the sequence of all the return values when all the IO actions have been performed

Considering all that, I ended up with the following signatures:

   * @param  filePath the file to read from
   * @f      a function doing an IO action for each line, based on a State
   * @init   the initial value of the State
   * @return an IO action reading all the lines and returning the sequence of computed values
   def readLinesIO[S, A](filePath: String)(f: String => State[S, IO[A]])(init: S): IO[Seq[A]]

and the Extractor code goes:

  // the initial state
  val counter = new LogarithmicCounter(level = 100, scale = 10)

  // a function to notify the user when we've reached a given level
  val onLevel            = (i: Int) => printTime("imported from "+filePath+": "+i+" lines")
  // a function to create and store each line
  def onCount(l: String) = (i: Int) => createAndStoreRecord(Line(file, l), creator)

  // for each line, apply the actions described as a State[LogarithmicCounter, IO[A]] object
  readLinesIO(file.getPath)(l => counter.asState(onLevel, onCount(l)))(counter)

I like that interface because it keeps things separated and generic enough:

  • reading the lines and opening/closing the resources goes to one class: Source
  • defining how the state evolves when an action is done goes to the LogarithmicCounter class
  • defining what to do exactly with each line goes in one class, the Extractor class

Now, the tricky implementation question is: how do you implement readLinesIO?

What not to do

Definitely the wrong way to do it is to use a "regular" traverse on the stream of input lines. Even with the trampolining trick described in my previous post.

Indeed traverse is going to fold everything once to go from Stream[String] to State[S, Seq[IO[B]]] then, with the initial State value, to Seq[IO[B]] which then has to be sequenced into IO[Seq[B]]. This is guaranteed to do more work than necessary. More than what a single for loop would do.

The other thing not to do is so silly that I'm ashamed to write it here. Ok, I'll write it. At least to avoid someone else the same idea. I had an implicit conversion from A to IO[A],...

That's a very tempting thing to do and very convenient at first glance. Whenever a function was expecting an IO[A], I could just pass a without having to write a.pure[IO]. The trouble is, some actions were never executed! I don't have a specific example to show, but I'm pretty sure that, at some point, I had values of type IO[IO[A]]. In that case, doing unsafePerformIO, i.e. "executing" the IO action only returns another action to execute and doesn't do anything really!

Learn your FP classes

I must admit I've been reluctant to go into what was advised on the Scalaz mailing-list: "go with StreamT", "use Iteratees". Really? I just want to do IO! Can I learn all that stuff later? I finally decided to go with the first option and describe it here in detail.

StreamT is a "Stream transformer". It is the Scala version of the ListT done right in Haskell. That looks a bit scary at first but it is not so hard. It is an infinite list of elements which are not values but computations. So it more or less has the type Seq[M[A]] where M is some kind of effect, like changing a State or doing IO.

My first practical question was: "how do I even build that thing from a Stream of elements?"

Unfolding a data structure

Unfolding is the way. In the Scala world we are used to "folding" data structures into elements: get the sum of the elements of a list, get the maximum element in a tree. We less often do the opposite: generate a data structure from one element and a function. One example of this is generating the list of Fibonacci numbers out of an initial pair (0, 1) and a function computing the next "Fibonacci step".

With the StreamT class in Scalaz, you do this:

  /** our record-creation function */
  def f[B]: String => M[B]

   * unfolding the initial Stream to a StreamT where we apply
   * a `State` function `f` to each element
  StreamT.unfoldM[M, B, Seq[A]](seq: Seq[A]) { (ss: Seq[A]) =>
    if (ss.isEmpty)  (None: Option[(B, Seq[A])]).pure[M]
    else              f(ss.head).map((b: B) => Some((b, ss.tail)))

The code above takes an initial sequence seq (the Stream of lines to read) and:

  • if there are no more elements, you return None.pure[M], there's nothing left to do. In my specific use case that'll be State[LogarithmicCounter, None]

  • if there is an element in the Stream, f is applied to that element, that is, we create a record from the line and store it in the database (that's the b:B parameter, which is going to be an IO action)

  • because M is the State monad, when we apply f, this also computes the next state to keep track of how many rows we've imported so far

  • then the rest of the stream of lines to process, ss.tail, is also returned and unfoldM will use that to compute the next "unfolding" step

One very important thing to notice here is that we haven't consumed any element at that stage. We've only declared what we plan to do with each element of the input stream.

Running the StreamT

In the StreamT object in Scalaz there is this method call runStreamT taking in:

  • a StreamT[State[S, *], A], so that's a StreamT where the state may change with each element of the Stream
  • an initial state s0
  • and returning a StreamT[Id, A]

Note: State[S, *] is a non-existing notation for ({type l[a]=State[S, a]})#l

That doesn't see to be game-changing, but this does a useful thing for us. runStreamT "threads" the State through all elements, starting with the initial value. In our case that means that we're going to create IO actions, where each created action depends on the current state (the number of lines read so far).

The cool thing is that so far we've described transformations to apply: Stream[String] to StreamT[State, IO[B]] to StreamT[Id, IO[B]] but we still haven't executed anything!

UnsafePerformIO at last

Then, I coded an unsafePerformIO method specialized for Stream[Id, IO[A]] to execute all the IO actions. The method itself is not difficult to write but we need to make sure it's tail-recursive:

  /** execute all the IO actions on a StreamT when it only contains IO actions */
  def unsafePerformIO: Seq[A] = toSeq(streamT, Seq[A]())

   * execute a StreamT containing `IO` actions.
   * Each IO action is executed and the result goes into a list of results
  private def toSeq[A](streamT: StreamT[Id, IO[A]], result: Seq[A]): Seq[A] =
    streamT.uncons match {
      case Some((head, tail)) => toSeq(tail, result :+ head.unsafePerformIO)
      case None               => result
Hidding everything under the rug

Let's assemble the pieces of the puzzle now. With an implicit conversion, it is possible to transform any Seq to a StreamT performing some action on its elements:

    * for this to work, M[_] has to be a "PointedFunctor", i.e. have "pure" and
    * "fmap" methods. It is implemented using the `unfoldM` method code seen above
   seq.toStreamT(f: A => M[B]): StreamT[M, B]

Then, we can traverse a whole sequence with a composition of a State and IO actions:


Where traverseStateIO is using the toStreamT transformation and is defined as:

   * traverse a stream with a State and IO actions by using a Stream transformer
  def traverseStateIO[S, B](f: A => State[S, IO[B]])(init: S): Seq[B] =
    StreamT.runStreamT(seq.toStreamT[State[S, *], IO[B]](f), init).unsafePerformIO

[you'll get bonus points for anyone providing a Traverse[Stream] instance based on StreamT - if that exists]

Finally the Source.readLines method is implemented as:

   * this method reads the lines of a file and apply stateful actions.
   * @return an IO action doing all the reading and return a value for each processed line
  def readLinesIO[S, A](path: String)(f: String => State[S, IO[A]])(init: S): IO[Seq[A]] =

  def readLines[S, A](path: String)(f: String => State[S, IO[A]])(init: S): Seq[A] = {
    // store the opened resource
    var source: Option[] = None
    def getSourceLines(src: = { source = Some(src); getLines(src) }

    try {
      // read the lines and execute the actions
    } finally { }

And the client code, the Extractor class does:

  val counter = new LogarithmicCounter(level = 100, scale = 10)

  // notify the user when we've reached a given level
  val onLevel            = (i: Int) => printTime("imported from "+filePath+": "+i+" lines")
  // create and store a record for each new line
  def onCount(l: String) = (i: Int) => createAndStoreRecord(Line(file, l), creator)

  // an `IO` action doing the extraction
  readLinesIO(file.getPath)(l => counter.asState(onLevel, onCount(l)))(counter)


I've had more than one WTF moment when trying to mix-in State, IO and resources management together. I've been very tempted to go back to vars and unrestricted effects :-).

Yet, I got to learn useful abstractions like StreamT and I've seen that it was indeed possible to define generic, composable and functional interfaces between components. I also got the impression that there lots of different situations where we are manually passing state around which is error-prone and which can be done generically by traverse-like methods.

05 December 2011

Pragmatic IO

This post is my exploration of the use of the IO type in a small Scala application.

A short IO introduction

Scala is a pragmatic language when you start learning Functional Programming (FP). Indeed there are some times when you can't apply the proper FP techniques just because you don't know them yet. In those cases you can still resort to variables and side-effects to your heart's content. One of these situations is IO (input/output).

Let's take an example: you need to save data in a database (not a rare thing :-)). The signature of your method will certainly look like that:

  /** @return the unique database id of the saved user */
  def save(user: User): Int

This method is not referentially transparent since it changes the state of the "outside world": if you call it twice, you'll get a different result. This kind of thing is very troubling for a Functional Programmer, that doesn't make him sleep well at night. And anyway, in a language like Haskell, where each function is pure you can't implement it. So how do you do?

The neat trick is to return an action saying "I'm going to save the user" instead of actually saving it:

  def save(user: User): IO[Int]

At first, it seems difficult to do anything from there because we just have an IO[Int], not a real Int. If we want to display the result on the screen, what do we do?

We use IO as a Monad, with the flatMap operation, to sequence the 2 actions, into a new one:

  /** create an IO action to print a line on the screen */
  def printLine(line: Any): IO[Unit] = println(line).pure[IO]

  /** save a user and print the new id on the screen */
                                           // or save(user) >>= printLine
  def saveAndPrint(user: User): IO[Unit] = save(user).flatMap(id => printLine(id))

Then finally when we want to execute the actions for real, we call unsafePerformIO in the main method:

  def main(args: Array[String]) {

That's more or less the Haskell way to do IO in Scala but nothing forces us to do so. The natural question which arises is: what should we do?

IO or not IO

Before starting this experiment, I fully re-read this thread on the Scala mailing-list, and the answer is not clear for many people apparently. Martin Odersky doesn't seemed convinced that this is the way to go. He thinks that there should be a more lightweight and polymorphic way to get effect checking and Bob Harper doesn't see the point.

Surely the best way to form my own judgement was to try it out by myself (as encouraged by Runar). One thing I know for sure is that, the moment I started printing out files in specs2, I got an uneasy feeling that something could/would go wrong. Since then,
IO has been on my radar of things to try out.

Luckily, these days, I'm developing an application which is just the perfect candidate for that. This application:

  • reads log files
  • stores the records in a MongoDB database
  • creates graphs to analyze the data (maximums, average, distribution of some variables)

Perfect but,... where do I start :-)?

Theory => Practice

That was indeed my first question. Even in a small Swing application like mine the effects were interleaved in many places:

  • when I opened the MongoDB connection, I was printing some messages on the application console to inform the user about the db name, the MongoDB version,...
  • the class responsible for creating the reports was fetching its own data, effectively doing IO
  • datastore queries are cached, and getting some records (an IO action) required to get other data (the date of the latest import, another IO action) to decide if we could reuse the cached ones instead

The other obvious question was: "where do I call unsafePerformIO?". This in an interactive application, I can't put just one
unsafePerformIO in the main method!

The last question was: "OMG, I started to put IO in a few places, now it's eating all of my application, what can I do?" :-))

Segregating the effects

Here's what I ended up doing. First of all let's draw a diagram of the main components of my application before refactoring:

   +-------+    extract/getReport       +------ IO +   store      +------ IO +
   + Gui   + <------------------------> + Process  + -----------> + Store    +
   +-------+     Query/LogReport        +----------+              +----------+
                                             | getReport               ^
                                             v                         |
                                        +------ IO +     getRecords    |
                                        + Reports  + ------------------+

Simple app, I told you.

The first issue was that the Reports class (actually a bit more than just one class) was fetching records and building reports at the same time. So if I decided to put IO types on the store I would drag them in any subsequent transformation (marked as IO on the diagram).

The next issue was that the Process class was also doing too much: reading files and storing records.

Those 2 things led me to this refactoring and "architectural" decision, IO annotations would only happen on the middle layer:

                                        +------- IO +            store
                               read     + Extractor + -----------------------------+
                               files    +-----------+          |                   |
                                             ^                 |                   |
                                             | extract         v                   v
   +-------+    extract/getReport       +----------+      +------ IO +        +----------+
   + Gui   + <------------------------> + Process  +      + Status   +        + Store    +
   +-------+     Query/LogReport        +----------+      +----------+        +----------+
                                             | getReport       ^                   ^
                                             v                 |                   |
                                        +------ IO +           |    getRecords     |
                                        + Reporter + ------------------------------+
                                             | createReport
                                        + Reports  +
  • all the IO actions are being segregrated to special classes. For example extracting lines for log files creates a big composite IO action to read the lines, to store the lines as a record and to report the status of the extraction. This is handled by the Extractor class

  • the Status class handles all the printing on the the console. It provides methods like:

       * print a message with a timestamp, execute an action,
       * and print an end message with a timestamp also.
       * This method used by the `Extractor`, `Reporter` and `Process` classes.
       def statusIO[T](before: String, action: IO[T], after: String): IO[T]`
  • the Reports class only deals with the aggregation of data from records coming from the Store. However it is completely ignorant of this fact, so testing becomes easier (true story, that really helped!)

  • the Store does not have any IO type. In that sense my approach is not very pure, since nothing prevents "someone" from directly calling the store from the middle layer without encapsulating the call in an IO action. I might argue that this is pragmatic. Instead of sprinkling IO everywhere I started by defining a layer isolating the IO world (the data store, the file system) from the non-IO world (the reports, the GUI. Then, if I want, I can extend the IO marking the rest of the application if I want to. That being said I didn't do it because I didn't really see the added-value at that stage. A name for this approach could be "gradual effecting" (the equivalent of "gradual typing")

  • the Process class is not marked as IO because all the methods provided by that class are actually calling
    unsafePerformIO to really execute the actions. This is the only place in the application where this kind of call occurs and the GUI layer could be left unchanged

All of that was not too hard, but was not a walk in the park either. Some details of the implementation were hard to come up with.

Under the bonnet: syntactic tricks

First of all, what was easy?

"Monadic" sequencing

Sequencing IO actions is indeed easy. I've used the for comprehension:

  def statusIO[T](before: String, action: IO[T], after: String): IO[T] = {
    for {
      _      <- printTime(before)
      result <- action
      _      <- printTime(after)
    } yield result
"Semi-column" sequencing

But also the >>=| operator in Scalaz to just sequence 2 actions when you don't care about the first one:

  def statusIO[T](before: String)(action: IO[T]): IO[T] = printTime(before) >>=| action
"Conditional" sequencing

And, for the fun of it, I implemented a "conditional flatMap operator", >>=?:

  action1 >>=? (condition, action2)

In the code above the action1 is executed and, if the condition is true, we execute action2 and forget about the result of action1, otherwise we keep the result of action1.

"if else" with a twist

Not completely related to IO I also liked the ?? operator. Say I want to execute an action only if a file exists:

   if (file.exists) createRecords(file)
   else             ()

The else line really feels ugly. This kind of "default value" should be inferred from somewhere. Indeed! This "default value" is the Zero of a given type in Scalaz, so it is possible to write:

   file.exists ?? createRecords(file)

It just works (tm) but it's worth knowing where that zero values really comes from:

  1. createRecords(file) returns IO[Int] (the last created id - that's a simplification of the real program)

  2. there is a way to create a Monoid from another Monoid + an Applicative:

    // from scalaz.Monoid
    /** A monoid for sequencing Applicative effects. */
    def liftMonoid[F[_], M](implicit m: Monoid[M], a: Applicative[F]): Monoid[F[M]] =
      new Monoid[F[M]] {
        val zero: F[M] = a.pure(
        def append(x: F[M], y: => F[M]): F[M] =
          a.liftA2(x, y, (m1: M, m2: M) => m.append(m1, m2))
  3. in this case IO has an Applicative, M is Int so it has a Monoid hence IO[Int] defines a Monoid where the zero is IO(0). I had to open up the debugger to check that precisely :-)

Under the bonnet: it just works,... not

The next piece of implementation I was really happy with was the "logarithmic reporting". This is a feature I implemented using vars at first, which I wanted to make pure in my quest for IO.

What I want is to extract log lines and notify the user when a bunch of lines have been imported (so that he doesn't get too bored). But I don't know how many lines there are in a given file. It could be 100 but it could be 30.000 or 100.000. So I thought that a "logarithmic" counter would be nice. With that counter, I notify the user every 100, 1000, 10.000, 100.000 lines.

The LogarithmicCounter works by creating a State object encapsulating 2 actions to do, one on each tick, one when a
level is reached:

    // create and store a line on each `tick`
    def onCount(line: String) = (i: Int) => createAndStoreRecord(line, creator)

    // and notify the user when we've reached a given level
    val onLevel = (i: Int) => printTime("imported from "+filePath+": "+i+" lines")

    /** @return a State[LogarithmicCounter, IO[Unit]] */
    val readLine = (line: String) => counter.asState(onLevel, onCount(line))

The readLine method is used in a traversal of the lines returned by a FileReader:

    (lines traverseState readLine): State[LogarithmicCounter, Stream[IO[Unit]]]

Pass it an initial counter and you get back a Stream of IO actions which you can then sequence to get an IO[Stream[Unit]]:

    // initial traversal with a function returning a `State`
    val traversed: State[LogarithmicCounter, Stream[IO[Unit]]] = lines traverseState readLine

    // feed in the initial values: count = 1, level = 100 to get the end `State`
    val traversedEndState: Stream[IO[Unit]] = traversed ! new LogarithmicCounter

    // finally get a global action which will execute a stream of
    // record-storing actions and printing actions
    val toStore: IO[Stream[Unit]] = traversedEndState.sequence

Some readers of this blog may recognize one usage of the Essence of the Iterator Pattern - EIP and that's indeed what it is (my first real use case, yes!). traverseState is just a way to use the more traditional traverse method but hiding the ugly type annotations.

This kind of type-directed development is nice. You add some type here and there and you let the compiler guide you to which method to apply in order to get the desired results:

  1. after the traversal I get back a State
  2. if I want to get the final state, the Stream of IO, I need to feed in some initial values, that's the '!' method
  3. if I want to get an action equivalent to the stream of actions, I need the sequence method which has exactly the type signature doing what I want

I was about to call it a day when I actually tried my application,... StackOverflow! What?!?

Trampolining to the rescue

It turns out that traversing a "big" sequence with a State is not so easy. First of all, State is an Applicative because it is also a Monad (please read the earlier EIP post for the details). So basically this amounts to chaining a lot of flatMap operations which blows up the stack.

Fortunately for me, Runar has implemented a generic solution for this kind of issue, like, less than 2 months ago! I leave you to his excellent post for a detailed explanation but the gist of it is to use continuations to describe computations and store them on the heap instead of letting calls happen on the stack. So instead of using State[S, A] I use StateT[Trampoline, S, A] where each computation (S, A) returned by the State monad is actually encapsulated in a Trampoline to be executed on the heap.

The application of this idea was not too easy at first and Runar helped me with a code snippet (thanks!). Eventually I managed to keep everything well hidden behind the traverseState function. The first thing I did was to "trampoline" the function passed to traverseState:

   * transform a function into its "trampolined" version
   * val f:             T => State[S, A]              = (t: T) => State[S, A]
   * val f_trampolined: T => StateT[Trampoline, S, A] = f.trampolined
  implicit def liftToTrampoline[T, S, A](f: T => State[S, A]) = new LiftToTrampoline(f)

  class LiftToTrampoline[T, S, A](f: T => State[S, A]) {
    def trampolined = (t: T) => stateT((s: S) => suspend(st.apply(s)))

So the traverseState function definition becomes:

  // with the full ugly type annotation
  def traverseState(f: T => State[S, B]) =
    seq.traverse[({type l[a]=StateT[Trampoline, S, a]})#l, B](f.trampolined)

However I can't leave things that like that because traverseState then returns a StateT[Trampoline, S, B] when the client of the function expects a State[S, B]. So I added an untrampolined method to recover a State from a "trampolined" one:

  /** @return a normal State from a "trampolined" one */
  implicit def fromTrampoline[S, A](st: StateT[Trampoline, S, A]) = new FromTrampoline(st)

  class FromTrampoline[S, A](st: StateT[Trampoline, S, A]) {
    def untrampolined: State[S, A] = state((s: S) => st(s).run)

The end result is not so bad. The "trampoline" trick is hidden as an implementation detail and I don't get StackOverflows anymore. Really? Not really,...

The subtleties of foldRight and foldLeft

I was still getting a StackOverflow error but not in the same place as before (#&$^@!!!). It was in the traversal function itself, not in the chaining of flatMaps. The reason for that one was that the Traverse instance for a Stream in Scalaz is using foldRight (or foldr):

  implicit def StreamTraverse: Traverse[Stream] = new Traverse[Stream] {
    def traverse[F[_]: Applicative, A, B](f: A => F[B], as: Stream[A]): F[Stream[B]] =
      as.foldr[F[Stream[B]]](Stream.empty.pure) { (x, ys) =>
        implicitly[Apply[F]].apply(f(x) map ((a: B) => (b: Stream[B]) => a #:: b), ys)

and foldr is recursively intensive. It basically says: foldRight(n) = f(n, foldRight(n-1)) whereas foldl is implemented with a for loop and a variable to accumulate the result.

The workaround for this situation is simple: just provide a Traverse instance using foldLeft. But then you can wonder: "why is traverse even using foldRight in the first place?". The answer is in my next bug! After doing the modifications above I didn't get a SOE anymore but the output in the console was like:

  imported from test.log: 10000 lines [12:33:30]
  imported from test.log: 1000 lines  [12:33:30]
  imported from test.log: 100 lines   [12:33:30]

Cool, I have invented a Time Machine, Marty! That one left me puzzled for a while but I found the solution if not the explanation. The "left-folding" Traverse instance I had left in scope was being used by the sequence method to transform a Stream[IO] into an IO[Stream]. Changing that to the standard "right-folding" behaviour for a Stream traversal was ok. So there is a difference (meaning that something is not associative somewhere,...)


The main conclusion from this experiment is that tagging methods with IO made me really think about where are the effects of my application. It also encouraged functional programming techniques such as traverse, sequence and al.

I must however say that I was surprised on more than one account:

  • I stumbled upon a whole new class of bugs: non-execution. My effects were not executed improperly, they were not executed at all because I forgot to call unsafePerformIO!

  • I was not expecting to be needing an optimisation which had just been added to Scalaz, for something which I thought was a casual traversal

  • there are still some FP mysteries to me. For example I don't know yet how to traverse a full Stream

  • I also don't get why my messages were inverted on the console. I tried different experiments, with a Seq instead of a Stream, with Identity or Option as an Applicative instead of IO and I could only reproduce this specific behavior with Stream and IO

Anyway, I would say that it was overall worth using the IO type, at least for the architectural clarification and the better testing. It also brought back the warm, fuzzy feeling that things are under control. On the other hand refactoring to IO took me longer than expected and required more knowledge than just using vars and regular I/O. But that should really be accounted for in the 'investment for the future' column.


There are certainly many stones left unturned in that post for programmers new to Functional Programming and Scalaz. I do apologize for that (this post is certainly long enough :-)) and I encourage those readers to ask questions on the Scalaz mailing-list.

11 November 2011

Practical uses for Unboxed Tagged Types

Another blog post to show that the esoteric type system tricks you read about Haskell or Scala actually have real uses.

Groovy vs Scala for log analysis

These days I'm implementing a non-critical application which deals with:

  1. importing performance log files
  2. parsing them and storing some structured data corresponding to each line
  3. creating some graphs to show the maximum execution times, the cumulated maximum execution times, the events inside a given time range, and so on,...

This, in itself, is a very interesting exercise for me since I had coded a similar application more than 3 years ago in Groovy. After deciding that the Groovy implementation was slow and cumbersome, I decided to give it a go with Scala (and MongoDB for the backend :-)).

I've been really amazed to see that many of the things I learnt for free, on my spare time, were applicable in the case of that application, to yield much better code. This post shows one of these techniques: "Unboxed Tagged Types".

Unboxed what?

Two months ago, I saw this enigmatic tweet by @milessabin. I followed the link, read the code and thought: "oh nice, I see, at least Miles is having fun playing with Scala's type system". But I wasn't really able to see what that thing could be used for.

Then, this week, developing my log analysis application, I became midly annoyed about one specific messy point.

There is time and,... time

The log records I'm getting from the log files are all timestamped with a number of millis which are what Java's Date.getTime returns when you ask for it. That is to say, the number of milliseconds, as a Long, elapsed from January, 1st, 1970, 00:00:00.000 GMT (the so-called EPOCH time by people thinking that the world started in the seventies).

Not very user friendly. Usually you would like to display that as readable date, using the java.text.SimpleDateFormat class for example. So in Scala, you are very tempted to write code like that:

  val hhmmFormat = new SimpleDateFormat("hh:mm")

  implicit def toTimeDisplay(t: Long) = new TimeDisplay(t)

  case class TimeDisplay(time: Long) {
    def hhmm = hhmmFormat.format(new Date(time))

  > val startTime: Long = 12398234093458L
  > startTime.hhmm
  res0: java.lang.String = 12:54

Then you move on, there's so much to do. In particular I wanted to be able to specify a time range to select exactly the events occuring during that time. The most straightforward way to do that is to give a "start time" and an "end time":

    * DaytimeRange(0, 2*60*60*1000) is 00:00 -> 02:00
   case class DaytimeRange(start: Long, end: Long)

Here starts the ugliness. When I want to check if a startTime given by the log file is included in the DaytimeRange I have to do a conversion to make sure I'm using the proper Longs: the number of milliseconds since the start of the day, not the milliseconds since the EPOCH time!

Similarly, if I blindly try to reuse the hhmm method defined above, I need to make sure I apply that to a number of milliseconds corresponding to an EPOCH time and not just since the beginning of the day.

That's the recipe for disaster,...

Twitter forever

Fortunately the answer was right there, in my Twitter timeline (well in my memory of the timeline to be more precise :-)): use "Unboxed newtypes".

It all fits in a few lines of code but makes everything incredibly clear. First we define "Tagged types":

  type Tagged[U] = { type Tag = U }
  type @@[T, U] = T with Tagged[U]

Then we declare that there are 2 different types of time:

  trait Day
  trait Epoch

And we declare that a given Long will either represent the number of millis since 1970 or since the beginning of the day:

  type Epochtime = Long @@ Epoch
  type Daytime   = Long @@ Day

Daytime simply means that we have a Long value, with an additional Day type.

Finally, we provide 2 functions to create instances of those types from Longs:

  def daytime(i: java.lang.Long): Daytime     = i.asInstanceOf[Daytime]
  def epochtime(i: java.lang.Long): Epochtime = i.asInstanceOf[Epochtime]

with a method which explicitly converts EPOCH millis to "day" millis:

  def epochtimeToDaytime(time: Long): Daytime = {
    val calendar = Calendar.getInstance
    calendar.setTime(new Date(time))
    daytime(((calendar.get(HOUR_OF_DAY)* 60 +
              calendar.get(MINUTE))    * 60 +
              calendar.get(SECOND))    * 1000 +

Using the new toys

We can use the Daytime type for our DaytimeRange class:

  case class DaytimeRange(start: Daytime, end: Daytime)

There's no risk that we now accidentally create a DaytimeRange instance with Longs which do not represent elapsed millis since the beginning of the day. The compiler reminds us to write code like:

  /** @return the number of millis from a string representing hours and minutes */
  def hhmmToDaytime(s: String): Daytime = ...

  DaytimeRange(hhmmToDaytime("10:00"), hhmmToDaytime("10:20"))

And if we want to create a DaytimeRange instance from 2 startTimes found in the log file:

  DaytimeRange(epochtimeToDaytime(s1), epochtimeToDaytime(s2))

Similarly, we can use the Epochtime for the hhmm display

  implicit def toEpochtimeDisplay(t: Epochtime) = new EpochtimeDisplay(t)

  case class EpochtimeDisplay(time: Epochtime) {
    // here new Date expects a Long, but this is ok because Epochtime *is* a Long
    def hhmm = hhmmFormat.format(new Date(time))

We can safely reuse this code to display a DaytimeRange instance:

  case class DaytimeRange(start: Daytime, end: Daytime) {

    // the developer *has* to think about which kind of time he's handling
    def show = daytimeToEpochtime(start).hhmm + " -> " + daytimeToEpochtime(end).hhmm


Final comments

  • It's practical

This technique is very pratical because it avoids making silly mistakes with Longs representing different concepts while still keeping the ability to use them as Long objects without having to "Unbox" them. Indeed we could also have created a case class like:

  case class Daytime(time: Long)

But then we would have had to "unbox" the time value everytime we wanted to do an addition or a comparison.

  • WTF?

I had a compiler puzzler when with my first implementation:

  case class DaytimeRange(start: Daytime, end: Daytime)
  found   : Double
  required: AnyRef

  Note: an implicit exists from scala.Double => java.lang.Double, but methods inherited from Object are
  rendered ambiguous.  This is to avoid a blanket implicit which would convert any scala.Double to any AnyRef.
  You may wish to use a type ascription: `x: java.lang.Double`.

Go figure what that means in this context,... After much head scratching, I found a workaround:

  type Epochtime = java.lang.Long @@ Epoch
  type Daytime   = java.lang.Long @@ Day

  def daytime(i: java.lang.Long): Daytime     = i.asInstanceOf[Daytime]
  def epochtime(i: java.lang.Long): Epochtime = i.asInstanceOf[Epochtime]

I used java.lang.Long instead of scala.Long because it looks like we need to get AnyRef objects while scala.Long is only AnyVal. But the compiler message is still very obscure in that case.

  • This is not a unit system!

Because Epochtime and Daytime are still Longs, it is still possible to add them and make a mess!

  • Kudos to @retronym too

You'll see that Unboxed Tagged Types are also part of the next scalaz7. Jason came up with the @@ type alias and is using the tag types to distinguish "multiplicative" monoids from "additive" monoids. Or conjonctive vs disjonctive. This means that given a Monoid[Boolean], we can specify if it does an AND or if it does an OR. Scalaz is becoming the ultimate toolbox,...

26 October 2011

Scala collections are awesome

This is just a small post to show how incredibly easy it is to use scala concurrency constructs to speed-up your work.

Doing the job

This question occured to me as a new enhancement was requested for specs2. In specs2, the processing of examples was very sequential:

  1. all the examples are executed concurrently
  2. then they are reported to the console

The concurrent execution of examples makes it generally fast enough that the reporting appears fast on the screen. However, if examples take a long time to execute, you might think that sbt is stuck with nothing whatsoever being reported back to you. You might even think that the compilation is still going on!

I changed that in specs2 and now the examples are reported as soon as they are executed. But I also thought: "this question must happen all the time, for all sorts of tasks". This is why I'm going to demonstrate how straightforward it is to speed-up your computations or your scripts with just a few annotations and imports.

Let's say I have some jobs to execute, each of them taking a random time:

  def job(i: Int) = {
    Thread.sleep(50 * random.nextInt(i))
    println("executed "+i)

And I have a function to report the results:

  def report(i: Int) = println("  reported "+i) 


The naive approach for executing and reporting the jobs would be:

  def naive(n: Int) = (1 to n).map(job).foreach(report)

  executed 1
  executed 2
  executed 3
    reported 1
    reported 2
    reported 3

Unfortunately this is slow since all the jobs are executed in sequence, then reported.


If we want to make a better use of our laptop's cores, we can write:

  def parallel(n: Int) = (1 to n)

This is better, because now the jobs are executed in parallel, but not satisfying because the reporting now comes out of order:

  executed 3
  executed 1
  executed 2
    reported 3
    reported 1
    reported 2

Fast and right

The trick to have best of both worlds is to use futures and seq:

  import actors._
  import Futures._

  def best(n: Int) = (1 to n) => report(f()))

In the code above, we execute concurrently each job in a Future. Then we force the collection to be evaluated sequentially again with seq and we report each result with f(), using the Future.apply method to wait until the value is available:

  executed 3
  executed 1
    reported 1
  executed 2
    reported 2
    reported 3


With a few minor modifications to the first code version we managed to do exactly what we wanted. Having this kind of power in the toolbox feels great, and I hope this post contributes a bit more to showing that Scala is also a practical language (because there's nothing wrong with being academic :-)).


Before I get flamed down in the comments, here an interesting update :-).

As soon as I published this post, I started wondering if the best behavior had anything to do with parallel collections at all. It turns out that using futures only gives good results as well:

  def futures(n: Int) = (1 to n).map(future(job)).foreach(f => report(f()))

  executed 2
  executed 1
    reported 1
  executed 3
    reported 2
    reported 3

However, if the input collection is a view, the results are completely different:

  def futures(n: Int) = (1 to n) => report(f()))

  executed 1
    reported 1
  executed 2
    reported 2
  executed 3
    reported 3

Hence the use of par is really necessary, in this scenario, to get a fully parallel execution.

12 October 2011

Counting words - part 2

In this small post, I'm going to show how I used Functional Reactive Programming (FRP) to improve the GUI part of the small application presented in the previous post.

The best article to read on the subject is Deprecating the Observer Pattern (DOP). This article explains what are the drawbacks of using a listener-based approach to components communication in a user interface and proposes FRP as an alternative.

Be reactive

What I've been using is a simplified yet powerful version of the code described in DOP, using the Reactive library by Naftoli Gugenheim. In this library you have 2 essential concepts: EventStreams and Signals:

  • EventStream[T] is a collection of values of type T happening at certain moments in time
  • Signal[T] is a value of type T which can change from time to time

They are actually 2 sides of the same coin (more or less) as explained in this video: from an EventStream you can get a Signal by holding the last emitted value and from a Signal you can get an EventStream by getting all the changes.

The great thing about an EventStream (let's focus on that one for now) is that you can manipulate it like a collection of objects:

  • you can filter it to get only the events you're interested in
  • you can map the events to other events
  • you can flatMap with a function returning another EventStream and so on,...

But how is this helpful for GUI programming?

With GUI components

The hard reality of Swing GUI components is that they are really mutable at heart. Once you compose a GUI with components (say a Frame) containing other components (say a TextField), then, when anything happens in your application, you mutate the innermost components heavily (by changing the text color for example). When you add publish/subscribe mechanisms on top of that, you add even more developer-managed mutability since you need to add-remove listeners to the whole graph of components. It is also not very easy to understand how events "flow" between components.

The way I see the usage of FRP with GUIs is:

  • components are seen as either creating values to propagate (like a button action) or consuming values (like a text field change with the new value). Hence they have a role of an EventStream source or of an EventStream sink (sometimes both)

  • those components explicitely declare the abstract type of streamed events they're expecting. For a given TextField this might be something like NewSelectedFile whether the selection comes from a FileChooser or from a simple TextField

  • the event streams can be merged, filtered, mapped functionally, with no side-effect so that the logic of events routing is very composable and explicit

Let's see that more precisely in the context of my simple application.

An example

In my WordCount application, the first thing the user does is to select a file to count. Then she is supposed to click on the "Count" button to start the count before the results are displayed. In terms of graphical components I have:

    val openFileMenuItem = OpenFileMenuItem("./src/test/resources")
    val countAction      = CountAction()

What you don't see above is that those 2 custom GUI components have been declared as extending EventStream[T] (simplified code for the explanation):

    OpenFileMenuItem ... with EventStream[File]
    CountAction      ... with EventStream[Unit]

This means that when you open a file using the OpenFileMenuItem you're providing new File events which other components can react on, and when you invoke the CountAction you, well,... you just pressed the button, there's no meaningful value to convey, so the Unit type is appropriate here (the clients just want to know that something happened).

Then we can compose those 2 EventStreams:

  val filePath: Signal[String]  ="")
  val doCount: EventStream[Any] = filePath.distinct.change | countAction

First we do a bit of filtering, because we just need the file path as a Signal[String] (using hold to transform the stream to a signal, with an empty initial value).
Then we declare that we need to do a count whether there's a distinct change in the file path value, or (|), if the user pressed countAction button.

How do we "consume" this doCount stream of events? We flatMap it to another stream of events providing the results of the counting:

 val results: EventStream[Results] =
   doCount flatMap { doIt => WordCounter.count( }

For each doCount event we flatMap it (actually we forget about it,...) and we use the current value of the filePath to count the number of words.

The expression computeValue.inBackground computes a value using the SwingUtils.invokeLater method to avoid computations being done on the event dispatch thread (this might cause grey screens). The inBackground method returns an EventStream[T] to signal consumers that the value is ready.

Since the result of counting is an EventStream[Results] I can then "plug" it into the GUI component doing the display:

  val resultsPanel = ResultsPanel(results)

And that's it. The ResultsPanel component doesn't care where the values come from, who created them. It is also interesting to see how the ResultsPanel declares its sub-components:

  object ResultsPanel {
    def apply(results: EventStream[Results]) = {
      new ResultsPanel(TotalWordsNumbers(results),

There are 3 sub-components and they use different parts of the Results event stream, so we use the map function to restrict exactly the stream to what is needed:

  • TotalWordsNumbers uses the full Results object to display the total words count (the one we really want), the references word count and the full text word count (to check that the count is ok)

  • ReferencesTable just needs the references

  • ErrorMessageBox needs the error message so we just map that

Finally, how is the event stream used in the component itself? Let's look at the ErrorMessageBox component:

  case class ErrorMessageBox(message: EventStream[String]) extends TextField with Observing {
    foreground =
    font = new Font("Courier", Font.PLAIN, 15);
    editable = false

    message.foreach { m => text = m }

The important line is the last one where we declare that for each message event, we change the text attribute of the TextField to the new value m.

Some implementation notes

If you read the actual code, you'll find quite a few differences with the real implementation:

  • the inBackground mechanism is enhanced with an additional actionProgress eventStream which fires events before and after the action to execute. This is used to change the main frame cursor to a waiting watch when the computation takes some time

  • I found useful to introduce a trait called Trigger for EventStream[()]

  • I also added an EventStreamSourceProxy[T] extends EventStream[T] trait which can be mixed in any GUI class. This trait uses an internal EventSource[T] which is the implementation of the EventStream[T] and can be used to fire events. For example:

    // When the action is executed (a file is selected) we fire an event and
    // the whole `OpenFileMenuItem` component acts as an `EventStream[File]`.
    case class OpenFileMenuItem(start: String) extends MenuItem("Open") with EventStreamSourceProxy[File] {
      action = new Action("Open") {
        def apply() {

  • I had some difficult debugging time with the Observing trait. If you place it on an object that's going to be garbage collected, your components will not be notified of new events. This happened to me as I placed it on a class used for an implicit conversion, in order to get a new shinyMethod (tm):

    implicit def toComponent(a: A): ImplicitComponent = new ImplicitComponent(a)
    class ImplicitComponent(a: A) extends Observing {
      def shinyMethod = a

Features ideas

This is something which amazed me: just having to think about event streams made me rethink the application functionalities. How? When I started thinking about what would trigger a word count I realized that:

  • there is no reason why the user should have to click the "Count" button when the file is selected, we can do the count and display the results right away

  • thinking about event stream as a flux made me realize that the file can be polled regularly for changes and the results displayed whenever there's a change

Changing my program to incorporate those 2 ideas was soooo easy:

  1. the first idea is reflected by the doCount event stream definition given above, we just say that we want to count whenever there's a file selection or a count action

    val doCount: EventStream[Any] = filePath.distinct.change | countAction
  2. creating a file poller is easy using the Timer class from the reactive library

    class FilePoller(path: Signal[String], delay: Long = 500) extends Trigger {
      private var previousLastModified = new File(
      val timer = new Timer(0, delay, {t => false}) foreach { tick =>
        def newLastModified = new File(
        if (newLastModified > previousLastModified || newLastModified == 0) {
          previousLastModified = newLastModified

    The `FilePoller` uses a `path` signal regularly. If the underlying file is modified, a notification event is triggered.
  3. then the final version of doCount becomes:

    lazy val filePoller                = new FilePoller(filePath)
    lazy val doCount: EventStream[Any] = filePath.distinct.change | countAction | filePoller

I don't know about you but I really find nice that the abstractions in my implementation give me hints about what the application could do! For me this is a good sign that FRP is really well-suited for the job of GUI programming.

11 October 2011

Counting words

In this 3 parts post I want to show:

  1. how standard, out-of-the-box, Scala helped me to code a small application
  2. how Functional Reactive Programming brings a real improvement on how the GUI is built
  3. how to replace Parser Combinators with the Parboiled library to enhance error reporting

You can access the application project via github.

I can do that in 2 hours!

That's more or less what I told my wife as she was explaining one small problem she had. My wife is studying psychology and she has lots of essays to write, week after week. One small burden she's facing is keeping track of the number of words she writes because each essay must fit in a specific number of words, say 4000 +/- 10%. The difficulty is that quotations and references must not be counted. So she cannot check the file properties in Word or Open Office and she has to keep track manually.

For example, she may write: "... as suggested by the Interpretation of Dreams (Freud, 1905, p.145) ...". The reference "(Freud, 1905, p.145)" must not be counted. Or, "Margaret Malher wrote: "if the infant has an optimal experience of the symbiotic union with the mother, then the infant can make a smooth psychological differentiation from the mother to a further psychological expansion beyond the symbiotic state." (Malher cited in St. Clair, 2004, p.92)" (good luck with that :-)). In that case the quotation is not counted either and we must only count 3 words.

Since this counting is a bit tedious and has to be adjusted each time she does a revision of her essay, I proposed to automate this check. I thought "a few lines of Parser Combinators should be able to do the trick, 2 hours max". It actually took me a bit more (tm) to:

  • write a parser robust enough to accommodate for all sorts of variations and irregularities. For example, pages can be written as "p.154" or "pp.154-155", years can also be written "[1905] 1962" where 1905 is the first edition, and so on

  • use scala-swing to display the results: number of words, references table, file selection

  • write readers to extract the text from .docx or .odt files

Let's see now how Scala helped me with those 3 tasks.

Parsing the text

The idea behind parser combinators is very powerful. Instead of building a monolithic parser with lots of sub-routines and error-prone tracking of character indices, you describe the grammar of the text to parse by combining smaller parsers in many different ways.

In Scala, to do this, you need to extend one of the Parsers traits. The one I've choosen is RegexParsers. This is a parser which is well suited for unstructured text. If you were to parse something more akin to a programming language you might prefer StdTokenParsers which already define keywords, numeric/string literals, identifiers,...

I'm now just going to comment on a few points regarding the TextParsing trait which is parsing the essay text. If you want to understand how parser combinators work in detail, please read the excellent blog post by Daniel Spiewak: The Magic behind Parser Combinators.

The main definition for this parser is:

  def referencedText: Parser[Results] =
    rep((references | noRefParenthesised | quotation | words | space) <~ opt(punctuation)) ^^ reduceResults

This means that I expect the text to be:

  • a repetition (the rep combinator) of a parser

  • the repeated parser is an alternation (written |) of references, parenthetised text, quotations, words or spaces. For each of these "blocks" I'm able to count the number of words. For example, a reference will be 0 and parenthetised text will be the number of words between the parentheses

  • there can be a following punctuation sign (optional, using the opt combinator), but we don't really care about it, so it can be discarded (hence the <~ combinator, instead of ~ which sequences 2 parsers)

Then I have a function called reduceResults taking the result of the parsing of each repeated parser, to create the final Result, which is a case class providing:

  • the number of counted words
  • the references in the text
  • the quotations in the text

Using the RegexParser trait is very convenient. For example, if I want to specify how to parse "Pages" in a reference: (Freud, 1905, p.154), I can sequence 2 parsers built from regular expressions:

  val page = "p\\.*\\s*".r ~ "\\d+".r
  • appending .r to a string returns a regular expression (of type Regex)
  • there is an implicit conversion method in the RegexParsers trait, called regex from a Regex to a Parser[String]
  • I can sequence 2 parsers using the ~ operator

The page parser above can recognize page numbers like p.134 or p.1 but it will also accept p134. You can argue that this is not very well formatted, and my wife will agree with you. However she certainly doesn't want to see the count of words being wrong or fail just because she forgot a dot! The plan here is to display what was parsed so that she can eventually fix some incorrect references, not written according to the academia standards. We'll see, in part 3 of this series how we can use another parsing library to manage those errors, without breaking the parsing.

One more important thing to mention about the use of the RegexParsers trait is the skipWhitespace method. If it returns true (the default), any regex parser will discard space before any string matching a regular expression. This is convenient most of the time but not here where I need to preserve spaces to be able to count words accurately.

To finish with the subject of Parsing you can have a look at the TextParsingSpec specification. This specification features a ParserMatchers trait to help with testing your parsers. It also uses the Auto-Examples feature of specs2 to use the text of the example directly as a description:

  "Pages"                                                                           ^
  { page must succeedOn("p.33") }                                                   ^
  { page must succeedOn("p33") }                                                    ^
  { pages must succeedOn("pp.215-324") }                                            ^
  { pages must succeedOn("pp.215/324") }                                            ^
  { pages("pp. 215/324") must haveSuccessResult(===(Pages("pp. 215/324"))) }        ^

Displaying the results

The next big chunk of this application is a Swing GUI. The Scala standard distribution provides a scala-swing library adding some syntactic sugar on top of regular Swing components. If you want to read more about Scala and Swing you can have a look at this presentation.

The main components of my application are:

  • a menu bar with 2 buttons to: select a file, do the count
  • a field to display the currently selected file
  • a results panel showing: the number of counted words and the document references

wordcount application
count example, note that the parsing is not perfect since the word counts do not add up!

If you have a look at the code you will see that this translates to:

  • an instance of SimpleSwingApplication defining a top method and including all the embedded components: a menu bar, a panel with the selected file and results
  • the subcomponents themselves: the menu items, the count action, the results panel
  • the "reactions" which is a PartialFunction listening to the events created by some components, SelectionChanged for example, and triggering the count or displaying the results

I was pretty happy to see that much of the verbosity of Swing programming is reduced with Scala:

  • you don't need to create xxxListeners for everything
  • there are components providing both a Panel and a LayoutManager with the appropriate syntax to display the components: FlowPanel, BoxPanel, BorderPanel
  • thanks to scala syntax you can write action = new Action(...) instead of setAction(new Action(...))

This is nice but I think that there is a some potential for pushing this way further and create more reusable out-of-the-box components. For example, I've created an OpenFileMenuItem which is a MenuItem with an Action to open a FileChooser. Also, something like a pervasive LabeledField with just a label and some text would very useful to have in a standard library.

I also added a bit of syntactic sugar to have actions executed on a worker thread, instead of the event dispatch thread (to avoid grey screens), using the SwingUtilities.invokeLater method. For example: myAction.inBackground will be executed on a separate thread.

Eventually, I was able to code up the GUI of the application pretty fast. The only thing which I didn't really like was the Publish/React pattern. It felt a bit messy. The next part of this series will show how Functional Reactive Programming with the reactive library helped me write cleaner code.

Reading the file

I anticipated this part to be a tad difficult. My first experiments of text parsing were using a simple text file and I knew that having the user (my wife, remember,...) copy and paste her text to another file just for counting would be a deal-breaker. So I tried to read .odt and .docx files directly. This was actually much easier than anything I expected!

Both formats are zipped xml files. Getting the content of those files is just a matter of:

  • reading the ZipFile entries and find the file containing the text

    val rootzip = new ZipFile(doc.path)
  • loading the xml as a NodeSeq

  • find the nodes containing the actual text of the document and transform them to text

    // for a Word document text is under <p><t> tags
    (xml \\ "p") map (p => (p \\ "t").map(_.text) mkString "") mkString "\n"

For further details you can read the code here.


That's it, parser combinators + scala-swing + xml = a quick app solving a real-world problem. In the next posts we'll try to make this even better!

24 June 2011

The Essence of the Iterator Pattern

"The Essence of the Iterator Pattern"(EIP) is the paper I liked the most last year. It gave me a brand new look over something which I had be using for years: the for loop.

In this post I'll try to present some ideas of that paper and show how to implement the solution described by the authors using Scalaz-like code. A minimum previous exposure to functional programming, functors and monads will definitely help!

What's in a for loop?

That was really what hooked me. What do you mean "what's in a for loop"?. Is there anything magic in that construct I've been using for years?

The introduction of EIP shows an example of a for loop to iterate on elements (not the C-like for used with an index). I'm transmogrifying it here to Scala but the idea remains the same:

  val basket: Basket[Fruit] = Basket(orange, apple)
var count = 0

val juices = Basket[Juice]()
for (fruit <- basket) {
count = count + 1

We start from a "container" of fruits: Basket. It could actually be anything, a List, a Tree, a Map... Then the for loop actually does 3 things:

  1. it returns a container having the same "shape"; juices is still a Basket
  2. it accumulates some kind of measure. Here, the number of fruits in the count variable
  3. it maps the elements to other elements: pressing the fruits to get some juice

And this for loop is actually not the most complex:

  • the count variable could influence the mapping of elements: juices.add(
  • we could have several variables depending on each other: cumulative = cumulative + count
  • the mapping could also influence a "measure" variable: liquid = liquid +

The purpose of EIP is to show that the "essence" of what happens in the for loop above can be abstracted by an Applicative Traversal. And the authors go on showing that given this Applicative abstraction, we get an incredible modularity for programming.

The Applicative typeclass

How can an Applicative traversal be better than a for loop, and what does that even mean?? EIP has a lot of sentences and expressions which can be hard to grasp if you don't have a strong functional programming / Haskell background. Let's try to dissect that slowly and start with the formal definitions anyway.

What is a Functor?

The first thing we need to talk about is the Functor typeclass:

  trait Functor[F[_]] {
def fmap[A, B](f: A => B): F[A] => F[B]

One way of interpreting a Functor is to describe it as a computation of values of type A. For example List[A] is a computation returning several values of type A (non-deterministic computation), Option[A] is for computations that you may or may not have, Future[A] is a computation of a value of type A that you will get later, and so on. Another way of picturing it is as some kind of "container" for values of type A.

Saying that those computations are Functors is essentially showing that we can have a very useful way of combining them with regular functions. We can apply a function to the value that is being computed. Given a value F[A] and a function f, we can apply that function to the value with fmap. For example, fmap is a simple map for a List or an Option.

Pointed Functor

By the way, how do you even create a value of type F[A]? One way to do that is to say that F[_] is Pointed:

  trait Pointed[F[_]] {
def point[A](a: => A): F[A]

That is, there is a point function taking a (call by name) value of type A and returning a F[A]. For example, a regular List is Pointed just by using the constructor for Lists:

  object PointedList extends Pointed[List] {
def point[A](a: => A) = List(a)

Then combining the 2 capabilities, pointed and functor, gives you a PointedFunctor:

  trait PointedFunctor[F[_]] {
val functor: Functor[F]
val pointed: Pointed[F]

def point[A](a: => A): F[A] = pointed.point(a)

def fmap[A, B](f: A => B): F[A] => F[B] = functor.fmap(f)

The PointedFunctor trait is merely the aggregation of a Pointed and a Functor.

What about Applicative then? We're getting to it, the last missing piece is Applic.


Applic is another way to combine a "container" with a function.

Instead of using fmap to apply the function to the computed value we suppose that the function is itself a computed value inside the container F (F[A => B]) and we provide a method applic to apply that function to a value F[A]:

  trait Applic[F[_]] {
def applic[A, B](f: F[A => B]): F[A] => F[B]

Let's take an example. Say I have a way to compute the price of a Fruit when the market is open:

  def pricer(market: Market): Option[Fruit => Double]

If the market is closed, pricer returns None, because we don't know what are the prices. Otherwise it returns a pricing function. Now if I have a grow function possibly returning a Fruit:

  def grow: Option[Fruit]

Then, using the Applic instance, you can price the Fruit:

  val price: Option[Double] = applic(pricer(market)).apply(grow)

The price will necessarily be an Option because you may not have a pricer nor a Fruit to price. And a bit of renaming and pimping reveals why we're using the term "Applicative":

  val pricingFunction = pricer(market)
val fruit = grow

val price: Option[Double] = pricingFunction ⊛ fruit

In a way we're just doing a normal function application, but we're just doing it inside the Applicative container. Now we have all the pieces to build the Applicative functor that EIP is talking about.

Applicative Functor

An Applicative Functor is the aggregation of an Applic and a PointedFunctor:

  trait Applicative[F[_]] {
val pointedFunctor: PointedFunctor[F]
val applic: Applic[F]

def functor: Functor[F] = new Functor[F] {
def fmap[A, B](f: A => B) = pointedFunctor fmap f
def pointed: Pointed[F] = new Pointed[F] {
def point[A](a: => A) = pointedFunctor point a

def fmap[A, B](f: A => B): F[A] => F[B] = functor.fmap(f)
def point[A](a: => A): F[A] = pointed.point(a)
def apply[A, B](f: F[A => B]): F[A] => F[B] = applic.applic(f)

Let's see how that can be implemented for a List. fmap and point are straightforward:

   def fmap[A, B](f: A => B): F[A] => F[B] = (l: List[A]) => l map f
def point[A](a: => A): F[A] = List(a)

apply turns out to be more interesting because there are 2 ways to implement it, both of them being useful:

  1. apply a list of functions to each element and gather the results in a List:

    def apply[A, B](f: F[A => B]): F[A] => F[B] = (l: List[A]) =>
    for { a <- l; func <- f } yield func(a)
  2. zip the list of functions to the list of elements to apply each function to each element

    def apply[A, B](f: F[A => B]): F[A] => F[B] = (l: List[A]) =>
    (l zip f) map (p => p._2 apply p._1)

There is even a third way to use List as an Applicative by using the fact that List is a Monoid. But more on that later, for now we still have to see how all of this relates to the for loop...

Traversing the structure

When we do a for loop, we take a "structure" containing some elements and we "traverse" it to return:

  • that same structure containing other elements
  • a value computed from the structure elements
  • some combination of above

Gibbons & Oliveira argue that any kind of for loop can be represented as the following traverse operation:

  trait Traversable[T[_]] {
def traverse[F[_] : Applicative, A, B](f: A => F[B]): T[A] => F[T[B]]

That is, if the container/structure of type T has this traverse function using an Applicative F, then we can do whatever we would do with a for loop on it.

To get a better feel for this traverse function, we're going to implement the Traversable trait for a binary tree and then we'll see how can we loop on that tree.

A Binary Tree

For all the other examples in this post, we're going to use a very simple binary tree:

  sealed trait BinaryTree[A]
case class Leaf[A](a: A) extends BinaryTree[A]
case class Bin[A](left: BinaryTree[A], right: BinaryTree[A]) extends BinaryTree[A]

On the other hand, the first shot at the Traversable implementation is barely readable!

 def BinaryTreeIsTraversable[A]: Traversable[BinaryTree] = new Traversable[BinaryTree] {

def createLeaf[B] = (n: B) => (Leaf(n): (BinaryTree[B]))
def createBin[B] = (nl: BinaryTree[B]) =>
(nr: BinaryTree[B]) => (Bin(nl, nr): BinaryTree[B])

def traverse[F[_] : Applicative, A, B](f: A => F[B]):
BinaryTree[A] => F[BinaryTree[B]] = (t: BinaryTree[A]) => {
val applicative = implicitly[Applicative[F]]
t match {
case Leaf(a) => applicative.apply(applicative.point(createLeaf[B]))(f(a))
case Bin(l, r) =>
applicative.apply(applicative.apply(applicative.point(createBin[B]))(traverse[F, A, B](f).apply(l))).
apply(traverse[F, A, B](f).apply(r))

This is a shame because the corresponding Haskell code is so concise:

  instance Traversable Tree where
traverse f (Leaf x) = pure Leaf ⊛ f x
traverse f (Bin t u) = pure Bin ⊛ traverse f t ⊛ traverse f u

A bit of pimping to the rescue and we can improve the situation:

def traverse[F[_] : Applicative, A, B](f: A => F[B]): BinaryTree[A] => F[BinaryTree[B]] = (t: BinaryTree[A]) => {
t match {
case Leaf(a) => createLeaf[B] ∘ f(a)
case Bin(l, r) => createBin[B] ∘ (l traverse f) <*> (r traverse f)

Informally the traverse method applies the function f to each node and "reconstructs" the tree by using the apply method (<*>) of the Applicative functor.

That's certainly still some Ancient Chinese to you (as it was for me) so we'd better see the traverse method at work now. But we need to take another detour :-)

Applicative Monoid

One simple thing we might want to do, when iterating on a BinaryTree, is to get the content of that tree in a List. To do that, we're going to use the 3rd way to use List as an Applicative, as mentioned earlier. It turns out indeed that any Monoid (what is it?) gives rise to an Applicative instance but in a way that's a bit surprising.

  /** Const is a container for values of type M, with a "phantom" type A */
case class Const[M, +A](value: M)

implicit def ConstIsPointed[M : Monoid] = new Pointed[({type l[A]=Const[M, A]})#l] {
def point[A](a: => A) = Const[M, A](implicitly[Monoid[M]].z)

implicit def ConstIsFunctor[M : Monoid] = new Functor[({type l[A]=Const[M, A]})#l] {
def fmap[A, B](f: A => B) = (c: Const[M, A]) => Const[M, B](c.value)

implicit def ConstIsApplic[M : Monoid] = new Applic[({type l[A]=Const[M, A]})#l] {
def applic[A, B](f: Const[M, A => B]) = (c: Const[M, A]) => Const[M, B](implicitly[Monoid[M]].append(f.value, c.value))

implicit def ConstIsPointedFunctor[M : Monoid] = new PointedFunctor[({type l[A]=Const[M, A]})#l] {
val functor = Functor.ConstIsFunctor
val pointed = Pointed.ConstIsPointed

implicit def ConstIsApplicative[M : Monoid] = new Applicative[({type l[A]=Const[M, A]})#l] {
val pointedFunctor = PointedFunctor.ConstIsPointedFunctor
val applic = Applic.ConstIsApplic

In the code above, Const is the Applicative instance for a given Monoid. Const contains values of type T where T is a Monoid and we progressively establish what are the properties that Const must satisfy to be Applicative.

  • it must first be Pointed. Informally, the point method puts the neutral element of the Monoid in a Const instance

  • then it must be a Functor. Here the fmap function doesn't do anything but changing the type of Const from Const[M, A] to Const[M, B]

  • finally it must be an Applic where the apply method of Applic uses the append method of the Monoid to "add" 2 values and return the result in a Const instance.

There is unfortunately a lot of typing vodoo thing here:

  • the type declaration for Const is Const[A, +B]. It has a type parameter B which is actually not represented by a value in the Const class! It is a phantom type. But it is actually indispensable to match the type declarations of the typeclasses

  • the type F that is supposed to be Applicative is... ({type l[A] = Const[T, A]})#l. Ouch, this deserves some explanation!

What we want is not so hard. The type Const[A, B] has 2 type parameters. We need a way to fix A to be T and get the resulting type which will have only one type parameter. The expression above is the most concise way to get this desired type:

  • { type l = SomeType } is an anonymous type with a type member called l. We can access that type l in Scala by using #: { type l = SomeType }#l

  • Then, in { type l[A] = SomeType[T, A] }#l, l is a higher-kinded type, having a type variable A (actually SomeType[T, A] where T is fixed)

That was a really long detour for a mere for loop, isn't it? Now... profit!

Contents of a BinaryTree...

We're going to use the Traversable instance for the BinaryTree and the List Monoid Applicative to get the contents of a BinaryTree:

  import Applicative._

val f = (i: Int) => List(i)
val tree = Bin(Leaf(1), Leaf(2))

(tree.traverse[...](f)).value must_== List(1, 2)

Simple, for each element of the tree, we put it in a List then we let the List Monoid do its magic and aggregate all the results as we traverse the tree. The only difficulty here is the limits of Scala type inference. The ... stands for type annotations that the compiler requires:

  tree.traverse[Int, ({type l[A]=Const[List[Int], A]})#l](f)

Not pretty :-(

Update: As pointed out by Ittay Dror in the comments, List[Int] is not an applicative by itself and we need to put this list into a Const value to make it usable by the traverse function.

This is actually done by an implicit conversion method, liftConst, provided by the Applicative object:
  implicit def liftConst[A, B, M : Monoid](f: A => M): A => Const[M, B] = 
(a: A) => Const[M, B](f(a))

Profit time

Not everything is lost! We can encapsulate a bit the complexity in this case. We can extract part of the code above and create a contents method which will work on any of Traversable instance (assume I'm pimping the following examples so that I can write tree.method instead of method(tree)):

  val tree: BinaryTree[Int] = Bin(Leaf(1), Leaf(2))
tree.contents must_== List(1, 2)

This is based on the following definition:

  def contents[A]: T[A] => List[A] = {
val f = (a: A) => Const[List[A], Any](List(a))
(ta: T[A]) => traverse[({type l[U]=Const[List[A], U]})#l, A, Any](f).apply(ta).value

It also turns out that the contents function is a specialized version of something even more generic, the reduce function, working with any Monoid:

  def contents[A]: T[A] => List[A] = reduce((a: A) => List(a))

def reduce[A, M : Monoid](reducer: A => M): T[A] => M = {
val f = (a: A) => Const[M, Any](reducer(a))
(ta: T[A]) => traverse[({type l[A]=Const[M, A]})#l, A, Any](f).apply(ta).value

The reduce function can traverse any Traversable structure with a function mapping each element to a Monoid element. We've used it to get the contents of the tree but can as easily get the number of elements:

  def count[A]: T[A] => Int = reduce((a: A) => 1)

tree.count must_== 2

Can it get simpler than this :-)? Actually in that case it can! Since we don't need (a: A) at all we can use reduceConst:

  def reduceConst[A, M : Monoid](m: M): T[A] => M = reduce((a: A) => m)

def count[A]: T[A] => Int = reduceConst(1)

It's like a Scala standard reduce on steroids because instead you don't need to provide a binary operation, you just need a Monoid instance.

.... and shape of a BinaryTree

We've addressed the question of doing some kind of accumulation based on the elements in the tree, now we're going to "map" them.

Monads are Applicatives too!

The following map method can be derived from the traverse method (note that no type annotations are necessary in that case, yes!):

  def map[A, B](mapper: A => B) = (ta: T[A]) => traverse((a: A) => Ident(mapper(a))).apply(ta).value

Here we're traversing with an Applicative which is very simple, the Ident class:

  case class Ident[A](value: A)

The Ident class is a simple wrapper around a value, nothing more. That simple class is an Applicative. But how?

Easy. Ident is actually a Monad and we can construct an Applicative instance from every Monad. This comes from the fact that a Monad is both a PointedFunctor and an Applic:

  trait Monad[F[_]] {
val pointed: Pointed[F]
val bind: Bind[F]

def functor: Functor[F] = new Functor[F] {
def fmap[A, B](f: A => B): F[A] => F[B] = (fa: F[A]) =>
bind.bind((a: A) => pointed.point(f(a))).apply(fa)

def pointedFunctor: PointedFunctor[F] = new PointedFunctor[F] {
val functor = Monad.this.functor
val pointed = Monad.this.pointed

def applic: Applic[F] = new Applic[F] {
def applic[A, B](f: F[A => B]) = a =>
bind.bind[A => B, B](ff => functor.fmap(ff)(a))(f)

def applicative: Applicative[F] = new Applicative[F] {
val pointedFunctor = Monad.this.pointedFunctor
val applic = Monad.this.applic

And the Ident class is trivially a Monad (having a pointed and a bind member):

  implicit def IdentIsMonad = new Monad[Ident] {

val pointed = new Pointed[Ident] {
def point[A](a: => A): Ident[A] = Ident(a)
val bind = new Bind[Ident] {
def bind[A, B](f: A => Ident[B]): Ident[A] => Ident[B] =
(i: Ident[A]) => f(i.value)


We can use our brand new map function now: Int) => i.toString) must_== Bin(Leaf("1"), Leaf("2"))

We can even use it to get the "shape" of our container and discard all the elements:

  tree.shape must_== Bin(Leaf(()), Leaf(()))

The shape method just maps each element to ().

Decompose / Compose

I recap. We implemented a very generic way to iterate over a structure, any kind of structure (as long as it's Traversable), containing elements, any kind of element, with a function which does an "application", any kind of application. Among the possible "applications", we've seen 2 examples: collecting and mapping which are the essential operations that we usually do in a for loop.

Specifically we were able to get the contents of a tree and its shape. Is there a way to compose those 2 operations into a decompose operation that would get both the content and the shape at once? Our first attempt might be:

  def decompose[A] = (t: T[A]) => (shape(t), contents(t))

tree.decompose must_== (Bin(Leaf(()), Leaf(())), List(1, 2))

This works but it is pretty naive because this requires 2 traversals of the tree. Is that possible to do just one?

Applicative products

This is indeed possible by noticing the following: the product of 2 Applicatives is still an Applicative.

Proof, proof. We define Product as:

  case class Product[F1[_], F2[_], A](first: F1[A], second: F2[A]) {
def tuple = (first, second)

I spare you the full definition of Product as an Applicative to just focus on the Applic instance:

  implicit def ProductIsApplic[F1[_] : Applic, F2[_] : Applic] =
new Applic[({type l[A]=Product[F1, F2, A]})#l] {
val f1 = implicitly[Applic[F1]]
val f2 = implicitly[Applic[F2]]

def applic[A, B](f: Product[F1, F2, A => B]) = (c: Product[F1, F2, A]) =>
Product[F1, F2, B](f1.applic(f.first).apply(c.first),

That's not too complicated, you just have to follow the types. What's more troubling is the amount of type annotations which are necessary to implement decompose. Ideally we would like to write:

  def decompose[A] = traverse((t: T[A]) => shape(t) ⊗ contents(t))

Where is an operation taking 2 Applicatives and returning their product. Again the lack of partial type application for Const muddies the whole (upvote SI-2712 please!):

val shape   = (a: A) => Ident(())
val content = (a: A) => Const[List[A], Unit](List(a))

val product = (a: A) => (shape(a).⊗[({type l[T] = Const[List[A], T]})#l](content(a)))

implicit val productApplicative =
ProductIsApplicative[Ident, ({type l1[U] = Const[List[A], U]})#l1]

(ta: T[A]) => { val (Ident(s), Const(c)) =
traverse[({type l[V] = Product[Ident, ({type l1[U] = Const[List[A], U]})#l1, V]})#l, A, Unit](product).
(s, c)

We can improve the code sligthly by moving the implicit definition for productApplicative inside the Applicative companion object:

  object Applicative {
implicit def ProductWithListIsApplicative[A[_] : Applicative, B] =
ProductIsApplicative[A, ({type l1[U] = Const[List[B], U]})#l1]

Then no implicit val productApplicative is necessary and the Applicative imports will be all we need.

Collection and dispersal

There is another way to do things "in parallel" while traversing the structure. The collect method that we're going to build will do 2 things:

  • it will accumulate some kind of state, based on the elements that we meet

  • it will map each element to another kind of element

So, as we're iterating, we can do a regular mapping while computing some kind of measure. But before that, we need to take a little detour (again?? Yes, again) with the State monad.

The State monad

The State Monad is defined by:

  trait State[S, +A] {
def apply(s: S): (S, A)

It is basically:

  • an object keeping some previous "state", of type S
  • a method to extract a meaningful value from this "state", of type A

  • this method computes a new "state", of type S

For example, a simple counter for the number of elements in a List[Int] can be implemented by:

  val count = state((n: Int) => (n+1, ()))

It takes the previous "count" number n and returns the new state n+1 and the extracted value (() here, because we don't need to extract anything special).

The State type above is a Monad. I encourage you to read "Learn You a Haskell" to get a better understanding on the subject. I will just show here that the flatMap (or bind) method of the Monad typeclass is central in putting that State to work:

  val count = (s: String) => state((n: Int) => (n+1, s + n))

(count("a-") flatMap count flatMap count).apply(0) must_== (3, "a-012")

The count function takes the latest computed string and returns a State where we increment the current "state" by 1 and we have a new String as the result, where the current count is appended. So when we start with the string "a-" and we flatMap count 2 times, we get (3, "a-012") where 3 is the number of times we've applied the n+1 function and "a-012" the result of appending to the current string.

By the way, why do we need to apply(0)?

When we do all the flatMaps, we actually store "stateful computations". And they are executed only once we provide the initial state: 0!

Collecting elements

Let's now define a collect operation on Traversable which will help us to count:

  def collect[F[_] : Applicative, A, B](f: A => F[Unit], g: A => B) = {
val applicative = implicitly[Applicative[F]]
import applicative._

val application = (a: A) => point((u: Unit) => g(a)) <*> f(a)

This collect operation, defined in EIP, is different from the collect operation on Scala collections which is the equivalent of filter + map. The collect of EIP is using 2 functions:

  • f: A => F[Unit] which collects data from each element "effectfully" (that is, possibly keeping state)

  • g: A => B which maps each element to something else

So we could say that the EIP collect is a bit like fold + map. Knowing this, we can use collect to count elements and do some mapping:

  val count = (i: Int) => state((n: Int) => (n+1, ()))
val map = (i: Int) => i.toString

tree.collect[({type l[A]=State[Int, A]})#l, String](count, map).apply(0) must_==
(2, Bin(Leaf("1"), Leaf("2")))

Here again the type annotations are obscuring the intent a bit and if type inference was perfect we would just read:

  val count = (i: Int) => state((n: Int) => (n+1, ()))
val map = (i: Int) => i.toString

tree.collect(count, map).apply(0) must_== (2, Bin(Leaf("1"), Leaf("2")))

I don't know about you, but I find this a bit magical. With the Applicative and Traversable abstractions, we can assemble our program based on 2 independent functions possibly developed and tested elsewhere.

Dispersing elements

The next utility function proposed by EIP is the disperse function. Its signature is:

  def disperse[F[_] : Applicative, A, B, C](f: F[B], g: A => B => C): T[A] => F[T[C]]

What does it do?

  • f is the Applicative context that's going to evolve when we traverse the structure, but regardless of what the elements of type A are
  • g is a function which, for each element of type A says what to do with the current context value, B, and map that element back to the structure

Please, please, a concrete example!

Say I want to mark each element of a BinaryTree with its "number" in the Traversal (the "label"). Moreover I want to use the element name to be able to qualify this label:

  // a BinaryTree of Doubles
val tree: BinaryTree[Double] = Bin(Leaf(1.1), Bin(Leaf(2.2), Leaf(3.3)))

// the "label" state returning integers in sequence
val labelling: State[Int, Int] = state((n: Int) => (n+1, n+1))

// for each element in the tree, and its label,
// produce a String with the name and label
val naming: Double => Int => String = (p1: Double) => (p2: Int) => p1+" node is "+p2

// testing by applying an initial state (label `0`) and
// taking the second element of the pair `(last label, resulting tree)`
tree.disperse[elided for sanity](labelling, naming).apply(0)._2 must_==
Bin(Leaf("1.1 node is 1"), Bin(Leaf("2.2 node is 2"), Leaf("3.3 node is 3")))

Note that the naming function above is curried. A more familiar way to write it would be:

  val naming: (Double, Int) => String = (p1: Double, p2: Int) => p1+" node is "+p2

But then you would have to curry that function to be able to use it with the disperse function:

  tree.disperse[...](labelling, naming.curried)

The implementation of disperse is:

  def disperse[F[_] : Applicative, A, B, C](f: F[B], g: A => B => C) = {
val applicative = implicitly[Applicative[F]]
import applicative._

val application = (a: A) => point(g(a)) <*> f

It is using the very capabilities of the applicative functor, the point method and the <*> application.

An overview of traversals

We've seen in the 2 examples above that we get different, specialized, versions of the traverse function by constraining how mapping and Applicative effects occur. Here's a tentative table for classifying other specialized versions of the traverse function:

function map element create state mapped depend on state state depend on element
collect X X X
disperse X X X
measure X X
traverse X X X X
reduce X X
reduceConst X
map X

The only function we haven't shown before is measure. It is mapping and accumulating state but this accumulation does not depend on the current element. Here's an example:

  val crosses = state((s: String) => (s+"x", ()))
val map = (i: Int) => i.toString

tree.measure(crosses, map).apply("") must_==
("xxx", Bin(Leaf("1"), Bin(Leaf("2"), Leaf("3"))))

Other than not looking very useful, the code above is also lying! It is not possible to have a measure function accepting a State monad without having to provide the usual ugly type annotations. So the actual example is:

  tree.measureState(crosses, map).apply("") must_== 
("xxx", Bin(Leaf("1"), Bin(Leaf("2"), Leaf("3"))))

where measureState is a specialization of the measure method to States. I think that one take-away of this post is that it might be beneficial to specialize a few generic functions in Scala , like traverse, collect... for Const and State in order to avoid type annotations.

Composing traversals

There's another axis of composition that we haven't exploited yet.

In a for loop, without thinking about it, you may write:

  for (a <- as) {
val currentSize = a.size
total += currentSize

In the body of that for loop, you have statements with dependency on each other. In an Applicative traversal, this translates to the Sequential composition of Applicatives. From 2 Applicatives, we can create a third one which is their Sequential composition. More precisely, this means that if F1[_] and F2[_] are Applicatives then F1[F2[_]] is an Applicative as well. You want the demonstration? Ok, go.

First, we introduce a utility function on ApplicFunctors:

  def liftA2[A, B, C](function: A => B => C): F[A] => F[B] => F[C] = 
fa => applic.applic(functor.fmap(function)(fa))

liftA2 allows to lift a regular function of 2 arguments to a function working on the Applicative arguments. This is using the fact that an ApplicFunctor is a Functor so we can apply function: A => B => C to the "a in the box", to get a F[B => C] "in the box". And then, an ApplicFunctor is an Applic, so we can "apply" F[B] to get a F[C]

Armed with this function, we can write the applic method for F1[F2[_]]:

  implicit val f1ApplicFunctor = implicitly[ApplicFunctor[F1]]
implicit val f2ApplicFunctor = implicitly[ApplicFunctor[F2]]

val applic = new Applic[({type l[A]=F1[F2[A]]})#l] {
def applic[A, B](f: F1[F2[A => B]]) = (c: F1[F2[A]]) => {
f1ApplicFunctor.liftA2((ff: F2[A => B]) => f2ApplicFunctor.apply(ff))(f).apply(c)

It's not so easy to get an intuition for what the code above is doing except that saying that we're using previous definitions to allow a F1[F2[A => B]] to be applied to F1[F2[A]].

In mere mortal terms, this means that if we do an Applicative computation inside a loop and if we reuse that computation in another Applicative computation, we still get an Applicative computation. The EIP illustration of this principle is a crazy function, the assemble function.

The assemble function

The assemble function takes the shape of a Traversable and a list of elements. If there are enough elements it returns Some[Traversable] filled with all the elements (+ the reminder), otherwise it returns None (and an empty list). Let's see it in action:

        // the "shape" to fill
val shape: BinaryTree[Unit] = Bin(Leaf(()), Leaf(()))

// we assemble the tree with an exact list of elements
shape.assemble(List(1, 2)) must_== (List(), Some(Bin(Leaf(1), Leaf(2))))

// we assemble the tree with more elements
shape.assemble(List(1, 2, 3)) must_== (List(3), Some(Bin(Leaf(1), Leaf(2))))

// we assemble the tree with not enough elements
shape.assemble(List(1)) must_== (List(), None)

What's the implementation of the assemble function? The implementation uses 2 Monads (which are also Applicatives as we know now):

  • the State[List[Int], _] Monad is going to keep track of what we've already consumed
  • the Option[_] Monad is going to provide, or not, an element to put in the structure
  • the composition of those 2 monads is State[List[Int], Option[_]] (our F1[F2[_]] in the ApplicFunctor definitions above

So we just need to traverse the BinaryTree with one function:

def takeHead: State[List[B], Option[B]] = state { s: List[B] =>
s match {
case Nil => (Nil, None)
case x :: xs => (xs, Some(x))

The takeHead function is a State instance where each state application removes the first element of the list of elements if possible, and returns it in an Option.
This is why the result of the assemble function, once we apply it to a list of elements, is of type (List[Int], Option[BinaryTree[Int]]).

A recursive implementation

Just for the fun of comparison, I'm going to write a recursive version doing the same thing:

  def assemble(es: List[Int], s: BinaryTree[Unit]) : (List[Int], Option[BinaryTree[Int]]) = {
(es, s) match {
case (Nil, _) => (es, None)
case (e :: rest, Leaf(())) => (rest, Some(Leaf(e)))
case (_, Bin(left, right)) => {
assemble(es, left) match {
case (l, None) => (l, None)
case (Nil, Some(l)) => (Nil, None)
case (rest, Some(l)) => assemble(rest, right) match {
case (r, None) => (r, None)
case (finalRest, Some(r)) => (finalRest, Some(Bin(l, r)))
assemble(List(1, 2, 3), shape) must_== (List(3), Some(Bin(Leaf(1), Leaf(2))))

It works, but it makes my head spin!

A classical for-loop implementation

By the way, what would be the real for loop version of that functionality? That one is not so easy to come up with because AFAIK there's no easy way to iterate on a BinaryTree to get a similar BinaryTree with just a for loop! So, for the sake of the argument, we're going to do something similar with just a List structure:

  def assemble[T](es: List[T], shape: List[Unit]) = {
var elements = es
var list: Option[List[T]] = None
for (u <- shape) {
if (!elements.isEmpty) {
list match {
case None => list = Some(List(elements.first))
case Some(l) => list = Some(l :+ elements.first)
elements = elements.drop(1)
} else {
list = None
(elements, list)
assemble(List(1, 2, 3), List((), ())) must_== (List(3), Some(List(1, 2)))

Contrast and compare with:

  List((), ()).assemble(List(1, 2, 3)) must_== (List(3), Some(List(1, 2)))

where you just define List as a Traversable:

  implicit def ListIsTraversable[A]: Traversable[List] = new Traversable[List] {

def traverse[F[_] : Applicative, A, B](f: A => F[B]): List[A] => F[List[B]] =
(l: List[A]) => {
val applicative = implicitly[Applicative[F]]
l match {
case Nil => applicative.point(List[B]())
case a :: rest =>
((_:B) :: (_: List[B])).curried ∘ f(a) <*> (rest traverse f)


The Applicative composition is indeed very powerful, but we're going to see that there are other ways to compose functions and use them with Traversables.

Monadic composition

This paragraph is exploring the fine relationships between applicative composition and monadic composition when doing traversals. We've seen that Applicative instances can be composed and that Monads can be Applicative. But Monads can also be composed using the so-called Kleisli composition. If we have:

  val f: B => M[C]
val g: A => M[B]


  val h: A => M[C] = f ∎ g // is also a function from a value to a Monad

If we have 2 "monadic" functions f and g, we can then compose them, in the Kleisli sense, and use the composed version for a traversal. Indeed we can, but does this traversal have "nice properties"? Specifically, do we have:

  traverse(f ∎ g) == traverse(f) ∎ traverse(g)

The answer is... it depends.

Monad commutativity

EIP shows that, if the Monad is commutative, then this will always be true. What is a commutative Monad you ask?

A Monad is commutative if for all mx: M[X] and my: M[Y] we have:

    val xy = for {
x <- mx
y <- my
} yield (x, y)

val yx = for {
y <- my
x <- mx
} yield (x, y)

xy == yx

This is not the case with the State Monad for example:

   val mx = state((n: Int) => (n+1, n+1))
val my = state((n: Int) => (n+1, n+1))

xy.apply(0) must_== (2, (1, 2))
yx.apply(0) must_== (2, (2, 1))

Monadic functions commutativity

Another slightly different situation is when we have a non-commutative Monad but commutative functions:

  val plus1  = (a: A) => state((n: Int) => (n+1, a))
val plus2 = (a: A) => state((n: Int) => (n+2, a))
val times2 = (a: A) => state((n: Int) => (n*2, a))

Here plus1 and times2 are not commutative:

  (0 + 1) * 2 != (0 * 2) + 1

However it is obvious that plus1 and plus2 are commutative. What does that mean when we do a traversal?

If we traverse a simple List of elements using monadic composition we get:

  • List(1, 2, 3).traverse(times2 ∎ plus1) === 22
  • List(1, 2, 3).traverse(times2) ∎ List(1, 2, 3).traverse(plus1) === 32

We get different results. However, when f and g commute we get the same result:

  • List(1, 2, 3).traverse(plus2 ∎ plus1) === 10
  • List(1, 2, 3).traverse(plus2) ∎ List(1, 2, 3).traverse(plus1) === 10

Applicative composition vs Monadic composition

Another question we can ask ourselves is: if we consider the monadic functions as applicative functions (because each Monad is Applicative), do we get the nice "distribution" property we're after? The answer is yes, even when the functions are not commutative:

  • List(1, 2, 3).traverse(times2 ⊡ plus1) === 4
  • List(1, 2, 3).traverse(times2) ⊡ List(1, 2, 3).traverse(plus1) === 4

Well... more or less. The real situation is a bit more complex. List(1, 2, 3).traverse(times2 ⊡ plus1) returns a State[Int, State[Int, List[Int]]] while the second expression returns a State[Int, List[State[Int, Int]] so what I'm hiding here is some more manipulations to be able to query the final result with some kind of join.


You wouldn't believe it but I've only shown here half of the ideas presented in EIP!

To finish off this post here's 3 take-away points that I've learned while writing it:

  • functional programming is also about mastering some of these higher-level control structures like Applicative. Once you master them, your toolbox expands considerably in power (just consider the assemble example)

  • Scalaz is an incredible library but somewhat obscure to the beginner. For this post I've rewritten all the typeclasses I needed to have, and lots of examples (using specs2 of course). That gave me a much better understanding of the Scalaz functionality. You may consider doing the same to learn Scalaz (my code is available on github)

  • Scala is lacking behind Haskell in terms of type inference and it's a real pain for higher-order, generic programming. This can be sometimes encapsulated away by specializing generic functions to very common types (like traverseState instead of traverse). Again, please upvote SI-2712!

Finally, I want to mention that there are many other Haskell functional pearls waiting to be transliterated to Scala. I mean, it's a shame that we don't have yet any equivalent for "Learn you a Haskell" or "Typeclassopedia" in the Scala world. I hope that my post, like this other one by Debasish Ghosh, will also contribute to bridge the gap.