Is Scala Like EJB2?

In this post and then in this followup, Stephen Colebourne (@jodastephen) states his criticisms of the Scala language and how it feels to him similar to the same feeling of when he was working with EJB2. A colleague of mine also tweeted the following:

"This is why I will never use Scala: def ++ [B >: A, That] (that: TraversableOnce[B])(implicit bf: CanBuildFrom[List[A], B, That]) : That"

Here's my thoughts and experience with the above.

I started out as a C and C++ developer, but joined the Java set right at the very beginning. I've been fortunate enough to have been working in Java since 1998. Or unfortunate, if you include the early days of the Servlet API & JSP, EJB1 and then EJB2! About 2007 I really started to feel the need for a different language, one that would allow me to express my ideas more concisely and without writing so much boilerplate code. I looked at Groovy, but found it too easy to keep dropping back and writing Java. I then learned Ruby. This taught me how expressive a good language can be and how powerful closures can be. I dabbled with a few more languages before finally finding Scala at the beginning of 2009. These days I mostly code a mix of Scala, Java and Groovy. Next challenge is Clojure.

Changing Mindset

Now, I'm an engineer at heart and my typical approach is to not only understand how to use something but also (and usually at the same time) understand how and why it works. This approach always served me well when learning languages like Java and Ruby. These languages have a fairly small surface area and a relatively simple type system. It's easy to read up on them, understand how to use them and understand exactly how they work. It's easy to look at the standard and third-party libraries and see how these work as well. For me, Scala was different and involved me making a fundamental shift in mindset about how I learned the language.

When I started out I approached Scala in the same way I did other languages: I read a book and tried to build some test applications while at the same time delving into the library and language to see how they worked. This was daunting! I can fully appreciate Stephen's position that it feels a bit like EJB2. Let's put that in context. EJB2 was a horrid mess of unnecessary complexity and complication. It aimed to simplify building enterprise apps involving persistent entities and business services, but it just added way more complexity and mind effort than it saved.

Now, back to Scala. It's a big language with a very extensive and powerful type system. If you attack trying to understand it as a whole I can see how it could be mistaken as another EJB2: there seems to be loads of complexity, a lot of which you can't see the reason for and a lot of stuff just requires so much mind effort to understand. Much of it initially seems unecessary.

It was after my initial failure to understand how the Scala language and libraries actually were built and worked that I took a step back and reconsidered my learning style. I could already see some value to the language and I wanted to explore more, but how? I then made a fundamental change in my approach. I decided that it was okay for me to start using the language without necessarily understanding exactly how all the details worked. As I used the language more I would gradually try to understand the how and why, one small step at a time.

I started learning to write Scala code. When I wanted to achieve something I researched how to do it until I was familiar with the use of a particular language feature or method. If there was some syntax, implementation detail or concept that I didn't get at that point I just noted it down as something to come back to. Now after a year or so writing Scala I look back at what I didn't initially understand and it mostly makes sense. I still occasionally encounter things that don't make total sense initially, so I just learn enough to use them and then come back later to learn the why or how. It's a system that seems to work incredibly well.

When you attack looking at and learning Scala in this more gradual way, first understanding how to use it then gradually, and incrementally, delving more deeply into the type system, you begin to realised that it's not so bad. Yes, there is complexity there, but it's not the unnecessary complexity of EJB2, it's moderate complexity that helps you be expressive, write safe code and get things done. As my knowledge of Scala has grown I've yet to find any features that I haven't been able to see the value of. It's a very well thought out language, even if it doesn't quite seems so at first.

Switching from a mindset where I have to have a full understanding or how something works before I use it to one where I'm comfortable with how to use it even if I don't know why it works was a very disconcerting change in my thinking. I'm not going to say it was an easy change to make, because it wasn't. However the effort was worth it for me because it got me using a language that I now find to be many more times more productive than Java. I think I also understand the language more deeply because I have put it to use and then come back later to understand more deeply exactly how and why it worked. I would never have got to this depth of understanding with my approach to learning other languages.

An Example

Consider the method:

  def ++ [B >: A, That] (that: TraversableOnce[B])
                        (implicit bf: CanBuildFrom[List[A], B, That]) : That

There's a lot of implementation detail in there. However, in order to use this method all I really need to understand is that it allows me to concatenate two immutable collections, returning a third immutable collection containing the elements of the other two. That's why the Scala api docs also include a use case signature for this method:

  def ++ [B](that: GenTraversableOnce[B]): List[B]

From this I understand that on a List I can concatenate anything that is GenTraversibleOnce (Scala's equivalent of a Java Iterable) and that I will get a new list returned. Initially I need understand nothing more in order to use this method. I can go away and write some pretty decent Scala code.

Some time later I learn that the >: symbol means 'same or super type of' and understand this concept. I can then refine my understanding of the method so that I now know that the collection that I want to concatenate must have elements that are the same (or a supertype) as the collection I am concatenating onto.

As I later progress and learn type classes I can see that CanBuildFrom is a type class that has a default implementation for concatenating lists. I can then take this further and create my own CanBuildFrom implementations to vary the behaviour and allow different types to be returned.

I'm first learning how to use the code, then gradually refining my understanding as my knowledge of the language grows. Trying to start out by learning exactly what the first signature meant would have been futile and frustrating.

Conclusion

If you start using Scala and you take the approach of trying to see it all, understand it all and know how and why it all works then it's a scary starting place. The size and complexity of the language (particularly its type system) can seem daunting. You could easily be confused in feeling it similar to EJB2, where reams of needless complexity just made it plain unusable.

However, by switching mindset, learning to use the language and then growing understanding of how it works over time it becomes much more manageable. As you do this you gradually realise that the perceived initial complexity is not as bad as it first seemed and that it all has great value. That initial hurdle from wanting to understand it all to being happy to understand just enough is very hard to get over, but if you do then a very powerful, concise and productive language awaits you.

The Luhny Bin Challenge

Last week Bob Lee (@crazybob), from Square, set an interesting coding challenge. All the details can be found here: http://corner.squareup.com/2011/11/luhny-bin.html. I decided this sounded like a bit of fun a set to work on a Scala implementation. I'm pleased with my final result, but also learnt something interesting along the way. My full solution is available on my github account: https://github.com/skipoleschris/luhnybin.

The challenge was an interesting one, and more difficult that initial reading would suggest. In particular, the ability to deal with overlapping card number and the need to mask them both added significantly to the difficulty of the problem.

My Algorithm

The approach I selected was fairly simple. Work through the input string until a digit character was encountered. Walk forward from there consuming digit, space of hyphen characters until either the end of the string was reached, an different character was encountered or 16 digits had been collected. Next try to mach the LUHN, first on 16 digits, then 15 and finally 14. Record an object describing the index and how many characters to mask. Repeat for the next digit character and so on. After visiting all characters in the string, apply all the masks to the input.

There were some optimisations along the way. For example, if encountering 14 or less characters then there was no chance of an overlapping card number so you could safely skip over these remaining digits after the first one had been evaluated.

My Implementation

I decided to implement in Scala as this is my language of choice at the moment. I considered both a traditional imperative approach as well as a more functional programming solution. In the end I went for a very functional implementation, based around immutable data. My main reason for this choice was because I'm working on improving my functional programming skills and I though it looked like an interesting problem to try and solve in this way.

The implementation consist of for main pieces of code. Each implements a transformation of input into output, is fairly self contained. I fell that by writing them in this way I have created code that is possible to reason about and also reuse in other scenarios. I tried for elegant code rather then optimising for the most efficient and minimal code base. I think I achieved my goals.

Conclusion

While I am very happy with my solution and had great fun building it, it was very interesting to look at other solutions that had been attempted. In particular it's quite clear that the ~900ms that I was able to active on my mid-2010 MBP was a number of times slower than the fastest solutions. The inevitable conclusion being that in some cases adopting a more mutable and imperative approach may be preferable. This is a typical example of this case, when we are in effect creating a logging filter, and speed of operation could easily be argued as more important than adhering to the principles of immutability. An interesting conclusion.

Concise, Elegant and Readable Unit Tests

Just recently I was pair programming with another developer and we were writing some unit tests for the code we were about to write (yes, we were doing real TDD!). During this exercise we came to a point where I didn't feel that the changes being made to the code met my measure of what makes a good test. As in all the best teams we had a quick discussion and decided to leave the code as it was (I save my real objections for times when it is more valuable). However, this got me thinking about what actually I was uncomfortable with, and hence the content of this post.

As a rule I like my code to have three major facets. I like it to be concise: there should be no more code than necessary and that code should be clear and understandable with minimum contextual information required to read it. It should be elegant: the code should solve the problem at hand in the cleanest and most idiomatic way. Also, it must be readable: clearly laid out, using appropriate names, following the style and conventions of the language it is written in and avoiding excessive complexity.

Now, the test in question was written in Groovy, but it could just as well be in any language as it was testing a fairly simple class that maps error information into a map that will be converted to Json. The test is:

@Test
void shouldGenerateAMapOfErrorInformation() {
    String item = "TestField"
    String message = "There was an error with the test field"
    Exception cause = new IllegalStateException()

    ErrorInfo info = new ErrorInfo()
    info.addError(item, message, cause)

    Map content = info.toJsonMap()
    assert content.errors.size() == 1
    assert content.errors[0].item == item
    assert content.errors[0].message == message
    assert content.errors[0].cause == cause.toString()
}

Now, to my mind this test is at least three lines too long - the three lines declaring variables that are then used to seed the test and in the asserts. These break my measure of conciseness by being unnecessary and adding to the amount of context needed in order to follow the test case. Also, the assertions of the map contents are less than elegant (and also not very concise), which further reduces the readability of the code. I'd much rather see this test case look more like this:

@Test
void shouldGenerateAMapOfErrorInformation() {
    ErrorInfo = new ErrorInfo()
    info.add("TestField", "There was an error with the test field", 
                 new IllegalStateException())

    Map content = info.toJsonMap()
    assert content.errors.size() == 1
    assert hasMatchingElements(contents.errors[0], 
          [ item : "TestField",
             message : "There was an error with the test field",
             cause : "class java.lang.IllegalStateException" ])
}

This test implementation is more concise. There are less lines of code and less context to keep track of. To my eye I find this much more elegant and far more readable.

"But, you've added duplication of the values used in the test", I hear you cry! That is true and it was totally intentional. Let me explain why…

Firstly, as we have already mentioned, duplicating some test values actually makes the code more concise and readable. You can look at either the test setup/execution or the assertions in isolation without needing any other context. Generally, I would agree that avoiding duplication in code is a good idea, but in cases such at this, the clarity and readability far out way the duplication of a couple of values!

Secondly, I like my tests to be very explicit about what they are expecting the result to be. For example, in the original test, we asserted that the cause string returned was the same as the result of calling toString() on the cause exception object. However, what would happen if the underlying implementation of that toString() method changed. Suddenly my code would be returning different Json and my tests would know nothing about it. This might event break my clients - not a good situation.

Thirdly, I like the intention of my code to be very clear, especially so in my tests. For example, I might create some specific, but strange, test data value to exercise a certain edge case. If I just assigned it to a variable, then it would be very easy for someone to come along, think it was an error and correct it. However, if the value was explicitly used to both configure/run the code and in the assertion then I would hope another developer might think more carefully as to my intent before making a correction in multiple places.

My final, and possibly most important reason for not using variables to both run the test and assert against is that this can mask errors. Consider the following test case:

@Test
void shouldCalculateBasketTotals() {
    Basket basket = new Basket()
    LineItem item1 = new LineItem("Some item", Price(23.88))
    LineItem item2 = new LineItem("Another item", Price(46.78))
 
    basket.add(item1)
    basket.add(item2)
 
    assert basket.vatExclusivePrice == 
                       item1.vatExclusivePrice + item2.vatExclusivePrice
    assert basket.vat == item1.vat + item2.vat
    assert basket.totalPrice == item1.price + item2.price
}

In this test, what would happen if the LineItem class had an unnoticed rounding error in its VAT calculation that was then replicated into the basket code? As we are asserting the basket values based on the fixture test data we may never notice as both sides of the assertion would report the same incorrectly rounded value. By being more explicit in our test code we not only create code that is more concise, elegant and readable but we also find more errors:

@Test
void shouldCalculateBasketTotals() {
    Basket basket = new Basket()
    LineItem item1 = new LineItem("Some item", Price(23.88))
    LineItem item2 = new LineItem("Another item", Price(46.78))
 
    basket.add(item1)
    basket.add(item2)
 
    assert basket.vatExclusivePrice == 58.88
    assert basket.vat == 11.78
    assert basket.totalPrice == 70.66
}

Finally, a couple more points that people might raise and my answers to them (just for completeness!):

Q: Can't we move the constant variables up to the class level and initialise them in a setup to keep the tests methods concise?
A: This makes an individual method contain less lines of code but fails my consiseness test as there is context outside of the test method that is required to understand what it does. You actually have to go and look elsewhere to find out what values your test is executing against. This is also less readable!

Q: Isn't is better to share test fixtures across test methods in order to keep the test class as a whole more concise?
A: This might be appropriate for some facets, such as any mocks that are used in the test or if there is a particularly large data set required for the test which is not part of the direct subject of the test. However, I'd rather see slightly longer individual test methods that are explicit in their test data and assertions, even if this means some duplication or a slightly longer overall test class.

Type Classes and Bounds in Scala

I've been doing some initial experiments with the Scalaz library (https://github.com/scalaz/). This is a great extension to the Scala standard library that adds a wide range of functional programming concepts to the language. Many of its features are based around the concept of Type Classes.

The type class concept originally comes from Haskell, and provides a way to define that a particular type supports a certain behavioural concept without the type having to explicitly know about it. For example, a type class may define a generic 'Ordered' contract and implementations can be defined for different types separate from the definition of those types. Any type that has an implementation of the 'Ordered' type class can then be used in any functions that work with ordering.

The Scala language provides a way to support type classes via its implicit mechanism. In order to get my head around this fully I created some examples to experiment with how this works. Now I fully grasp the mechanisms, the Scalaz library makes much more sense. I therefore thought I'd share my experiment in case it proves useful to anyone and as an aide-mémoire to myself for the future.

In addition, my examples also make use of the different type bounds mechanisms provided by Scala, so I will demonstrate these in my examples as well.

The full source code can be found in the following gist: https://gist.github.com/1247752.

Without Type Classes

In the first example, I built a simple solution that doesn't make any use of the type class concept. This solution is much like you would implement in Java, with an interface defining the behaviour and classes that implement this interface:

trait Publishable {  
  def asWebMarkup: String
}

case class BlogPost(title: String, text: String) extends Publishable {
  def asWebMarkup =
    """|
%s
       |
%s
""".stripMargin format(title, text)
}

Here we have a trait Publishable that indicates that something can represent itself as a web markup string. Then we have a case class that implements the trait and provides the method to return the markup. Next, we declare a class that can do the publishing:

class WebPublisher[T <: Publishable] {
  def publish(p: T) = println(p.asWebMarkup)
}

Note that we have defined this as a templates class and that we have used the <: bounds notation to require that our type T is only valid if it extends the Publishable trait. All we need to do now is use it:

val post = new BlogPost("Test", "This is a test post")
val web = new WebPublisher[BlogPost]()
web publish(post)

Ok, so that works fine. However, what happens when we either can't or don't want BlogPost to implement the Publishable trait? There can be many reasons for this: perhaps we don't have the BlogPost domain object source; perhaps the object is already quite complex and we don't want to pollute it with publishing knowledge; perhaps it's shared by multiple teams or projects and only ours needs publishing knowledge. So, what do we do?

Without type classes there are some options: we might extends the domain class to implement the trait; we might create a wrapper class or we might create a helper utility. However, all of these result in a level of indirection and complication in our code. Let me explain...

If we internalise the knowledge of the wrapper/helper/sub-class in our publishing code we end up with some horrific type matching:

class WebPublisher {
  def publish(p: AnyRef) = p match {
    case blogPost: BlogPost => BlogPostPublishHelper.publish(blogPost)
    case _ => …
  }
}

However, if we externalise the knowledge of publishing then our client code has to do the conversion:

class WebPublisher {
  def publish(p: Publishable) = ...
}

web publish(new PublishableBlogPostWrapper(blogPost))

Clearly, both solutions lead to fragile boilerplate code that pollutes our main application logic. Type classes provide a mechanism for isolating and reducing this boilerplate so that it is largely invisible to both sides of the contract.

Type Classes with Implicit Views

Fortunately, Scala provides a mechanism whereby we can declare an implicit conversion between our BlogPost and a Publishable version of our instance. So, let's start with the simple stuff:

trait Publishable {
  def asWebMarkup: String
}

case class BlogPost(title: String, text: String) 

So, we now have a blog post that doesn't implement the Publishable trait. Let's now define the conversion that can implicitly turn our blog post into something that supports publishing (we would typically add this into our publishing code rather than the domain object in order to isolate all knowledge of Publishable to just the area that needs it):

implicit def BlogPostToPublishable(blogPost: BlogPost) = new Publishable {
  def asWebMarkup =
    """|
%s
       |
%s
""".stripMargin format(blogPost title, blogPost text)
}

Our conversion just creates a new Publishable instance that wraps our blog post and implements the required methods. But, how do we make use of this? We have to change our web publisher very slightly:

class WebPublisher[T <% Publishable] {  
  def publish(p: T) = println(p.asWebMarkup)
}

All we have in fact changed is the <: bounds to a <% bounds. This new one is called a view bounds and defines that we can instantiate a WebPublisher with type T only if there is a view (in this case as implicit conversion) in scope from T to Publishable.

Our code to call this remains the same:

val post = new BlogPost("Test", "This is a test post")
val web = new WebPublisher[BlogPost]()
web publish(post)

Fantastic, our publish code gets objects that it knows are publishable, while our client code can just pass domain objects. We have all the boilerplate for conversion separated out from the main code.

However, while this all seems good, this approach does have its downsides. As you can see we are using the wrapper approach: creating a new instance of a Publishable class that wraps the original object. In a high volume system, the additional object allocations of new Publishable wrapper instances on each call to the publish method may have some less than ideal memory and garbage collection impacts.

The other problem with this approach is that is reduces our ability to compose methods in interesting ways. The reason for this is that once the publishing code has actually invoked the implicit conversion it now has the Publishable wrapper rather than the original object. If it passes this Publishable to other methods or classes than these are not aware of the original wrapped type or instance.

We can overcome this problem by modifying the Publishable trait to have a generic parameter type and support a get method to extract the wrapped value - but this then should really be called PublishableWrapper and some of the simplicity starts to break down.

I think the root of the problem here is that type classes are a very functional concept and implicit views tries to coerce these into a hybrid object/functional world. Fortunately, as of Scala 2.8 there is an additional way to implement the type class approach that leads to a more functional style of coding...

Type Classes with Implicit Contexts

An alternative to implicitly converting one class to a wrapper version of that class that adds additional behaviour is to follow more of a helper like approach. In this model we provide an implicit evidence parameter that implements the type class specific behaviour. This is a much more functional approach in that we don't alter the type we are working on. So, on with the code...

trait Publishable[T] {     
  def asWebMarkup(p: T): String
}

case class BlogPost(title: String, text: String)

This is our new Publishable trait and BlogPost class. Note that our Publishable is no longer intended to be implemented or used as a wrapper. Instead, it is now a contract definition of functional behaviour that takes a parameter of type T and transforms it into a String. A much cleaner abstraction. In fact, we can even create a single object instance that implements this behaviour for blog posts:

object BlogPostPublisher extends Publishable[BlogPost] {     
  def asWebMarkup(p: BlogPost) =
     """|
%s
        |
%s
""".stripMargin format(p title, p text)
}

We are also going to need our implicit. This time however, rather than being a conversion it becomes an evidence that we have something that implements Publishable for the type BlogPost:

implicit def Publishable[BlogPost] = BlogPostPublisher 

We also need to update our WebPublisher a bit:

class WebPublisher[T: Publishable] {  
  def publish(p: T) = println(implicitly[Publishable[T]] asWebMarkup(p))
}

There are two interesting things about this class. First, the bounds has now switched from view (<%) to context (:). The context bounds effectively modifies the class declaration to be:

class WebPublisher[T](implicit evidence$1: Publishable[T]) {  ...
}

The second change is the use of implicitly[Publishable[T]] which is a convenience for getting the implicit evidence of the correct type so that you can call methods on it.

Our code to call this remains exactly the same:

val post = new BlogPost("Test", "This is a test post")
val web = new WebPublisher[BlogPost]()
web publish(post)

One advantage that should be immediately obvious is that we are no longer creating new instances for each implicit use. Instead, we are using the implicit evident parameter (which is in this case an object) and passing our instance to it. This is more efficient in terms of allocations.

Also, we explicitly show where we make use of the implicit evidence, which is clearer. This also means that we are never creating a new wrapped type of T that we pass on to other code. We always pass on instances of type T that have implicit evidence parameters that allow T to behave as a particular type class instance.

Conclusion

We have looked at two different approaches to solving the problem of adding behaviour to an existing class without requiring it to explicitly implement a particular contract or extend a specific base class. Both of these make use of Scala implicits and type classes.

Implicit conversions with view bounds provides an approach where a type T can be converted into the type required by the type class. This works, but there are issues associated with the wrapping or transformation aspects of the conversion.

Implicit evidence parameters within context bounds overcome these problems and provide a far more functional approach to solving the same problem.

Learnings From A Scala Project

I’ve recently been working as an associate for Equal Experts. I’ve been part of a successful project to build a digital marketing back-end solution. The project started out life as a JVM project with no particular language choice. We started out with Java and some Groovy. However, the nature of the domain was one that leant itself to functional transformations of data so we started pulling in some Scala code. In the end we were fully implementing in Scala. This post describes the learning gained on this project.

The Project

The project team consisted of five experienced, full-time developers and one part-time developer/scrum master. One of the developers (me) was proficient in Scala development (18 months, part-time) while the others were all new to the language.

The initial project configuration (sprint zero) was based on a previous Java/Groovy project undertaken by Equal Experts. The initial tooling, build and library stack was taken directly from this project with some examples moved from Groovy to Scala. The initial project stack included:

• Gradle (groovy based build system) with the Scala plugin bolted in
• IntelliJ IDEA with the Scala plugin
• Jersey for RESTful web service support
• Jackson for Json/Domain Object mapping
• Spring Framework
• MongoDB with the Java driver

Tooling

The biggest hurdle faced by the project was tooling, specifically tool support for the Scala language. While Scala tooling has come on a huge amount over the last year, it is still far from perfect. The project faced two specific challenges: build tool support and IDE support.

Build Tool Support

The Gradle build tool is primarily a build tool for Java and Groovy. It supports Scala via a plugin. Similar plugins exist for other build tools like Maven and Buildr.

What we experienced on this project was that this was not the ideal scenario for building Scala projects. Build times for loading the Scala compiler were rather slow and support for incremental compilation was not always as good as we would have liked. More often than not we just had to undertake a clean and build each time. This really slowed down test-driven development.

Currently (in my opinion) the only really workable build solution for Scala is the Simple Build Tool, also known as SBT (https://github.com/harrah/xsbt/wiki). This is a Scala specific build tool and has great support for fast and incremental Scala compilation plus incremental testing.

With hindsight it would probably have been better to switch over from Gradle to SBT as soon as the decision was made to use Scala for the primary implementation language.

IDE Support

The Scala plugin for IntellJ IDEA is one of the best editors for Scala. In particular its code analysis and highlighting has come on in leaps and bounds over the last few months. However, the support for compilation and running tests within the IDE is still less than ideal. Even on core i7 laptops we were experiencing long build times due to slow compiler startup. Using the Fast Scala Compiler (FSC) made a difference, but we found it somewhat unstable.

What was more annoying was that after failed compilations in IDEA, we often found that incremental compilation would no longer work and that a full rebuild of the project was required. This was VERY slow and really hampered development.

One proven way of doing Scala development within IDEA is to use the SBT plugin and run the compilation and tests within SBT inside an IDEA window. This has proven very successful on other Scala projects I have been involved with and again with hindsight we should have investigated this further.

Some developers on the team also found the lack of refactoring options, in IDEA, when working with Scala somewhat limiting. This is a major challenge for the IDE vendors given the nature of the language and its ability to nest function and method definitions and support implicits. Personally this doesn’t bother me quite as much as my early days were spent writing C and C++ code in vi!

Libraries

Scala has excellent integration with the JVM and can make good use of the extensive range of Java libraries and frameworks. However, what we experienced in a number of cases was that integrating with Java libraries required us to implement more code than we would have liked to bridge between the Java and Scala world. Also, much of this code was less than idiomatic Scala.

What we found as the project progressed was that it became increasingly useful to switch out the Java libraries and replace them with Scala written alternatives. For example, very early we stopped using the MongoDB Java driver and introduced the Scala driver, called Casbah. Then we added the Salat library for mapping Scala case classes to and from MongoDB. This greatly simplified our persistence layer and allowed us to use much less and more idiomatic Scala code.

Aside: If you are working with MongoDB then I strongly recommend Scala. The excellent work by Brendan McAdams of 10gen and the team at Novus has created a set of Scala libraries for MongoDB that I think are unrivaled in any other language. The power of these libraries, while maintaining simplicity and ease of use, is amazing.

We never got a chance to swap out some of the other major libraries that the original project structure was built on. However with more hindsight we should perhaps have made the effort to make these swaps as they would have resulted in much less and much cleaner code. Some specific swaps that we could have done:

• Jersey for Unfiltered or Bowler
• Jackson for sjson or lift-json
• Spring for just plain Scala code

Scala projects work well with Java libraries. However when a project is being fully implemented in Scala then it’s much better to swap these Java libraries for their Scala implemented alternatives. This results in much less and more idiomatic Scala code being required.

Team Members

Having highly experienced team members makes a big difference on any project and this one was no exception. The other big advantage on this project was that all of the developers were skilled in multiple languages, even though most had not known Scala before. One thing I have found over many years is that developers who know multiple languages can learn new ones much more easily than those who only know one. I’d certainly recommend recruiting polygot developers for new Scala projects.

It also helped having at least one experienced Scala developer on the team. The knowledge of the Scala eco-system was helpful as was the ability to more quickly solve any challenging Scala type errors. However, the main benefit was knowledge transfer. All of the team members learned the Scala language, libraries and techniques much more quickly by pairing with someone experienced in the language.

Additionally, as the experienced Scala developer I found it very valuable working with good developers who were still learning the language. I found that explaining concepts really clarified my understanding of the language and how to pass this on to others. This will be of great benefit for future projects.

Any new project that is planning on using Scala should always bring on at least one developer who already has experience in the language. It creates a better product and helps the rest of the team become proficient in the language much more quickly.

Object-Oriented/Functional Mix

We found Scala’s ability to mix both object-oriented and functional approaches very valuable. All team members came from an object-oriented background and some also had experience with functional approaches. Initially development started mainly using object-oriented techniques, but leaning towards immutable data wherever possible.

Functional approaches were then introduced gradually where this was really obvious, such as implementing transformations across diverse data sets using map and filter functions. As the team got more familiar with the functional programming techniques more functionality was refactored into functional style code. We found this made the code smaller, easier to test and more reliable.

Perhaps the only real disadvantage of moving to more functional leaning Scala code is that some code became less readable for external developer with less exposure to the functional style. It became necessary to explain some of the approaches taken to external developers: something that would probably have not been necessary with more procedural style code. This is worth bearing in mind for projects were there will be a handover to a team not skilled in Scala and functional programming techniques.

Conclusions

The project was very successful. We delivered on time and more features than the customer originally expected. I don’t think we would have achieved this if the implementation was in Java, so from that point the smaller, more functional code base produced by developing in Scala was a real winner. Using Scala with MongoDB was a major benefit. Tools was the real let-down on the project. Some problems could be alleviated by moving from Java libraries and solutions to their Scala equivalents. However, there is still some way to go before Scala tools (expecially IDEs) match the speed an power of their Java equivalents.