Showing posts with label development. Show all posts
Showing posts with label development. Show all posts

Wednesday, 4 December 2013

Scala's Maturing Community

I've been involved in the Scala community and attending Scala conferences for about four years now. I've just come back from 2013 Scala eXchange, hosted by Skillsmatter in London. It was a great conference, one of the best I’ve been to, and the organisers and speakers should be thoroughly pleased with their efforts. One thing that I did think quite interesting was a significant change in emphasis from all of the previous Scala conferences that I’ve attended.

When I first started going to Scala conferences four years ago, the emphasis was definitely on an introduction to Scala. The talks focused on basic language features, how to use the collections libraries and core functional programming concepts. There were also some side talks about interesting libraries built in Scala, like Akka and Lift.

Last year the focus moved to a higher level, with more time spent on talking about where Scala was going and on more advanced functional programming concepts. There were talks focusing on things like Lenses and Monads and lots of detail about highly functional libraries developed using Scala. Presentaions about Akka and Lift were still present and people were starting to talk about Futures. In all of these talks, however, functional programming was the primary focus.

At Scala eXchange this year the emphasis was almost entirely flipped around. Most talks were about reactive programming using asynchronous approaches. Loads of stuff about Akka, actors, futures, events and the like. Some talks focused on Scala features such as macros and using type classes. However, there was very little direct talk of functional programming: it was just assumed that everyone present was using immutable data and following a functional programming approach. A huge shift in perspective from just a year ago.

I believe that this represents a massive shift in the maturity of the Scala community. We have moved from an immature group learning about how to use this exciting new language, how to work with immutable data and how to blend functional approaches into our code. We have instead become a group who are using this fantasic language and a set of fairly mature products and reactive techniques to build highly scalable systems. A huge leap in just a couple of years.

I remember a similar transition in the Java community when conferences went from talking about basic Java concepts like POJOs, collections and design patterns to talking about building complex solutions using advanced libraries like Spring and JMS. The Scala community seems to have made this leap much more quickly than the Java community did.

The one thing that worries me slightly about this change is that using immutable data and functional programming has almost become an unwritten assumption. Each presentation just seemed to assume that you would be programming in this way by default. While this is great for those of us in the community who have made this transition, I think we need to be careful not to skip this step for those people just transitioning into the Scala world.

The worst case would be developers transitioning from a language like Java straight into an asynchronous reactive programming model without first going through the transformation of thinking in terms of immutable data and functional programming. Bringing the concepts of mutable state and imperative code straight into an async world is a recipe for disastrous software projects. These in turn could tarnish Scala's representation and significantly complicate the life of those of us trying to bring Scala into traditionally conservative organisations and companies.

It's great the the Scala community has come so far and matured so fast. But, let's not forget the core concepts that underly this transformation. We must ensure that we continue to emphasise their importance as we move forward into the brave new world of highly scalable, asynchronous, reactive systems that Scala seems to the targeting so directly and successfully.

Wednesday, 6 February 2013

Fast Track to Haskell Course

I've spent the last two days on a Fast Track to Haskell course. It was a very interesting two days and I learnt a huge amount. The course was taught by Andres Löh from Well-Typed and hosted by the excellent people at Skillsmatter.

I went into the course having read the superb Learn You A Haskell book and having built a couple of experimental Haskell programs. From that starting point there was not a huge amount of content in the course that I was not aware of. However, it was great to have my learning verified and my understanding in many areas significantly improved. There were also a few things that changed my thinking on certain topics. All in all, a well spent two days.

The first day started with a whistle stop tour of the Haskell language and how to build things in it. There were plenty of excellent exercises to help cement learning and understanding. Then we looked in more detail at types and how to reason about software using types. The day concluded with looking at some more advanced type concepts including higher-order functions and type classes. The second day followed on with some more about types and then went on to look at how Haskell deals with I/O (which is very different to most other languages I have encountered). The rest of the day was then spent looking at common patterns that can be found in Haskell code and how these can be generalised into concepts such as Functors and Monads. Plenty more exercises followed.

I will now be going away and working on some private projects in Haskell, with the hope of attending the advanced course some time later this year. In the mean time, this blog post looks at some of the key things that I came away from the course with and how they relate back to the Scala and Java that I tend to do for my day job.

Focus on the Types, they are the API

When using an OO language, such as Java (or even Scala), we tend to think about classes, their interfaces and what behaviour they have, and then use this as a starting point to develop from. In Haskell you tend to think first about the types that are involved and then work forward from there. This is a very interesting approach as it makes it possible to think about a problem in a very general and quite abstract way without getting too bogged down in detail too quickly. The types become an API to your system (rather than the interfaces, methods and domain model in an OO solution).

My experience from the course was that this tends to lead to many more types and type aliases in Haskell than I would typically define in Java or Scala. However, this soon proves to be a huge advantage as it then makes it much easier to reason about what each function does/should do and makes its behaviour very clear via the type signature. I will certainly be looking at following this practice in any Haskell programs I write and also trying to introduce more types into my Scala code.

It's Pattern Matching and Recursion All The Way Down

In a language like Java there is nothing like pattern matching and recursion has to be used sparingly if you want to avoid blowing up the stack and writing poorly performing code. In Scala there is much more scope for both, although writing tail recursive functions is still very important to ensure that you get the best optimisations from the compiler. In Haskell, pattern matching and recursion are the bread and butter of the language. Almost every single function tends to be a pattern match on arguments and the language supports a naturally recursive style across the board. Given the lazy semantics of Haskell and the optimisation in the runtime for recursive algorithms this is the best approach for building programs in Haskell.

One area for improvement that I can see in my Scala code is to make more use of pattern matching, especially partial functions.

Thinking Curried Helps a Lot

In the Scala world we typically only curry functions when there are very obvious places to partially apply them. More often than not we just partially apply a function using the underscore for one of its parameters. This is needed to ensure compatibility with Java and the object-oriented model. However, in Haskell every function is completely curried. There are no multi-argument functions. Everything is a single argument function that returns either a result or another function.

Initially I found that this made sense in the model that I'm used to: partially applying functions. However, once it came to defining new types as actually being functions as well I found that this started to fry my mind slightly. The conclusion that I came to is that it's best to just think of everything from a curried mindset and throughout the course things gradually became clearer. This thinking is essential to understand things like the State monad (which I'm still not 100% confident about to be totally honest).

Parametric Polymorphism is Very Useful

One of my biggest take-aways from the course was how useful and important parametric polymorphism is. As an (overly) simple example, what does a function from Int -> Int do? It could return the same number, add something to it, multiply it, factor it, modulus it, ignore the input and return a constant - the possibilities are nearly endless. However, what does a function from a -> a do? Given no other constraints it can only do one thing - return itself (this is called the identity function - id in Haskell).

The ability to define functions in this polymorphic way makes it much easier to reason about what a function might do and also to restrict what a function actually can do. This second point greatly reduces the possibility of introducing unexpected defects or creating functions that have multiple interweaved concerns. As another example, consider the function: Ord a => [a] -> [a]. What does this do? Well, it could just return the same input list, but because it takes the Ord type class as a constraint it's quite likely to be some form of sorting or filtering by order function. As the writer of this function I'm restricted to working with lists and just using equality and ordering functionality so the possibility of doing anything unexpected is greatly reduced.

Parametric Polymorphism is possible in Scala, but not quite as powerful as there aren't type classes for may common concepts (e.g. equality, ordering, numbers, functor, applicative, monoid, monad etc.). Introducing Scalaz fixes this for quite a lot of cases, but that's not to the taste of many projects and mixed Scala/Java teams may find this a step too far. However, there's certainly more scope for using parametric polymorphism in everyday Scala code and this is something I'm going to work on ove the coming weeks (expect some more blog posts).

Don't Try to Over Generalise

Something that I've seen in every language is that once developers discover the power of generalising concepts they tend to go overboard with this. Remember the time when every piece of code was a combination of seven billion design patterns? The power of Haskell's type classes and parametric polymorphism makes it very easy to see patterns that may not actually hold. I can see that it would be very easy to waste a lot of time and effort trying to make a data type fit into various different type classes or trying to spot type class patterns in your code and pull them out into generic concepts.

As with any kind of design pattern, generalisation or similar, the best approach (in any language) is to just build what you need to solve a problem and then when it becomes apparent that it either fits an existing pattern or represents a general pattern then this change can be made as part of a refactoring process.

Try to Keep Pure and I/O Code Separate

Haskell's I/O model at first seems very different, unusual and restrictive. Once you get your head around it, it makes so much sense. Separating pure code from code that talks about doing an I/O action from code that actually does the I/O makes for software that is much more easy to reason about, test and reuse. Very powerful and something I'm looking forward to exploring more during my journey into Haskell.

In the Scala world, I'm not sure about the need to go to such lengths as using an I/O Monad or similar. However, I can really see the advantage of separating pure code from code that performs I/O. Having pure functions that transform data and then wiring them together with the I/O performing functions at a higher level seems a natural way to create software that is better structured and easier to test. I'll certainly be playing with some approaches in Scala to experiment with this concept further.

Functors, Applicatives, Monoids and Monads are Nothing Special

A final observation from the course is that many people learning and talking about functional programming get a bit obsessed with concepts like Functors, Applicatives, Monoids and Monads (especially Monads). What I learnt is that most of these are just simple patterns that can be represented by one or two basic functions and a few simple rules. Nothing more to them really. Certainly nothing to get all worked up about!

Yes, there's a great deal of skill involved in knowing when to use these patterns, when to make your own code follow a pattern and so on, but that's true of any programming language or design pattern. It's called experience.

Conclusion

If you are a Scala developer who is enjoying the functional programming aspects of the language I would certainly recommend taking a look at Haskell. I found that it clarified lots of things that I thought I understood from Scala but still had some doubts about. Haskell is, in my opinion, a more elegant language than Scala and once you start using it you realise just how many hoops the Scala team had to jump through to maintain compatibility with the JVM, Java code and object-oriented features. Over the two days I learnt a lot about Haskell, a lot about functional programming and a lot about how to get even more value from Scala.

Thursday, 24 January 2013

Type Classes Make For Better Software Structure

When people usually talk about type classes in languages like Haskell and Scala they tend to be thinking of highly generic concepts like Ordering or Numeric operations. However, in some recent experiments with Haskell I have found that type classes can be very useful for more concrete things. When used appropriately they can lead to more loosely coupled software that is more flexible and easier to change.

I Just Met You, This Is Crazy, Here's My Number, Call Me Function?

As an example, let's consider a telephone number. Fairly simple right, it's a string of digits?

def sendMessage(toNumber: String, theMessage: Message)

Or perhaps we should create a type alias to give us some better info and scope for change:

type TelephoneNumber = String

def sendMessage(toNumber: TelephoneNumber, theMessage: Message)

But wait, I'm trying to build a library that sends messages to telephones and I need to know much more about a telephone number than this 'stringly' typed data is able to tell me. Things like:

  • Is it in international format?
  • What's the country prefix code?
  • Is it a mobile number or a landline number?
  • What is the area code?
  • Is it some premium rate number?
  • and so on…

The Naive Solution

It's clear that my 'stringly' typed representation is not sufficient for this task unless I want to encode a whole load of rules about the world's telephone numbering systems into my library (believe me when I say I don't!). So, a naive approach would be to introduce a new type that holds the information in a more usable format. Something like:

case class TelephoneNumber(countryCode: String, areaCode: String, number: String, isMobile: Boolean, isLandline: Boolean, …)

Unfortunately, this approach suffers from a whole host of problems, including:

  • If I build my library implementation against this class then I can't easily change implementation details without breaking the API
  • If I copy from an instance of this type into an internal representation then I have a lot of duplication and extra code to test
  • If a client uses this type in their domain model they are tightly coupled to my library (and also a specific version of it)
  • If a client copies from their own representation into an instance of this type there is more duplication and extra code to test

A More Object-Oriented Approach

So, how do we deal with this? Well what we need to do is separate the behaviour of a telephone number from its actual data representation. In an object-oriented language we would do this by introducing an interface (or in Scala, a trait):

trait TelephoneNumber {
  def countryCode: String
  def areaCode: String
  def number: String
  def isMobile: Boolean
  def isLandline: Boolean
  // and so on...
}

Internally in my library I write all my code against this interface without any care as to how the underlying telephone number data is structured. Client applications are free to implement telephone numbers in whatever way they wish, provided they also implement the interface that exposes the behaviour that my library needs. Yay, problem solved. Or is it? There's still one problem in this object-oriented approach: somewhere in the client code there must be a class that implements the TelephoneNumber interface. This is problematical in a number of ways:

  • The class hierarchy of the client domain model is still quite tightly coupled to my library
  • It might not be possible to modify an existing domain model to implement the TelephoneNumber interface (no access to source code, coupling restrictions etc.)
  • If the client domain represents telephone numbers as Strings then this class can be extended to implement the interface

Both of the last two points would result in needing to implement the same sort of mapping layer to copy between representations that we already said lead to duplication and extra code to test.

How Haskell Does It

So, in a purely functional language that doesn't have the concept of interfaces how do we solve this problem. Enter the Type Class. These clever little beasties provide a mechanism whereby the required behaviour is captured as a series of function definitions (with possible implementations if available). For example:

class TelephoneNumber a where
    countryCode :: a -> String
    areaCode :: a -> String
    number :: a -> String
    isMobile :: a -> Bool
    isLandline :: a -> Bool
    ...

So far this looks pretty much like our interface definition above except that each method takes an instance of type 'a' as a parameter and returns the result. However, the clever bit comes in that we can implement the type class for any type in Haskell without needing to modify that type in any way. For example, we could easily implement it for Strings:

instance TelephoneNumber String where
    countryCode s = ...
    areaCode s = ...
    ...

Or our client code could define its own telephone number data type and an implementation of the type class along side it:

data UKTelephoneNumber = ...

instance TelephoneNumber UKTelephoneNumber where
    countryCode t = ...
    ...

The final change is then that we have our library function require that something implementing the type class is provided rather than some concrete data type:

sendMessage :: (TelephoneNumber t) => t -> Message -> IO ()
sendMessage to msg = do
    ...
    let cc = countryCode t   -- call function on the type class passing the instance we have
    ...

In this Haskell solution we have neatly decoupled the behaviour that our library requires of telephone numbers from the data type used to represent them. Also, any client code of our library does not need to couple its data structures/types directly to the library. They can define and modify them in any way they like. All they need to do is keep the instance of the type class up-to-date.

And Finally, A Better Scala Solution

As I have hopefully shown, the Haskell solution to the problem is both elegant and leads to cleaner, more loosely coupled code. So can we do something similar in Scala? Well, yes we can because Scala also supports type classes. They are a bit less elegant than Haskell, but they work just fine. First, we need to change the TelephoneNumber trait into a type class:

trait TelephoneNumber[T] {
  def countryCode(t: T): String
  def areaCode(t: T): String
  def number(t: T): String
  def isMobile(t: T): Boolean
  def isLandline(t: T): Boolean
  // and so on...
}

Note that all we have done is added a type parameter and modified all the functions to take an instance of that type. Notice that it's now almost identical in structure to the Haskell equivalent. Next we need an implementation of this type class. Let's define one for String:

object TelephoneNumber {

  object StringIsATelephoneNumber extends TelephoneNumber[String] {
    def countryCode(t: String) = ...
    ...
  }

  implicit def TelephoneNumber[String] = StringIsATelephoneNumber
}

I've put the implementation inside a TelephoneNumber object. If you are defining your own types, convention is usually that the type classes go in the companion object (e.g. for a case class UkTelephoneNumber the type class implementations would be in the UkTelephoneNumber companion object). This insures that the type class instances are always in scope when the type is used.

Finally, lets update our sendMessage function to work with type classes:

def sendMessage[T : TelephoneNumber](toNumber: T, theMessage: Message) = {
  ...
  val telNo = implicitly[TelephoneNumber[T]]
  val cc = telNo countryCode toNumber
  ...
}

Note the use of the context bound symbol ':' plus the implicitly function to define the need for and access to the correct instance of the type class.

So there we have it, an elegant Scala solution the overcome all of the coupling problems inherent with the object-oriented approach of implementing a required interface. When should you use this? My opinion is anywhere that two system components communicate, where that communication can be defined in terms of behavioural functions rather than actual data and where you want to minimise the coupling between them and the leaking of types from one component to another.

Monday, 26 November 2012

Encoding State Requirements in Types

In my previous post I looked at a simple way to eliminate conditional expressions by encoding them into the Scala type system. In this follow-up post I want to look at taking this further by using the Scala type system to encode state rules. The aim, as always, is to create code that fails to compile rather than code that fails at runtime. By doing this we also reduce the number of unit tests required.

The Problem

Consider a simple shopping workflow. I collect information about a Basket, the Customer and their Payment Method. Once I have all this in place I can create a processOrder function that completes the workflow process. As a naive starting point, lets encode this as a simple domain model holding optional values:

  case class CheckoutWorkflow(basket: Option[Basket], 
                              customer: Option[Customer], 
                              paymentMethod: Option[PaymentMethod])

  object CheckoutWorkflow {
    def empty = new CheckoutWorkflow(None, None, None)
  }                             

Then we need some functions that populate the workflow:

  def processBasket(workflow: CheckoutWorkflow): CheckoutWorkflow = {
    if ( workflow.basket.isDefined )
      throw new IllegalStateException("Basket workflow step already processed")

    // Do some processing...
    workflow.copy(basket = Some(basket))
  }

  def processCustomer(workflow: CheckoutWorkflow): CheckoutWorkflow = {
    if ( workflow.customer.isDefined )
      throw new IllegalStateException("Customer workflow step already processed")

    // Do some processing...
    workflow.copy(customer = Some(customer))
  }

  def processPaymentMethod(workflow: CheckoutWorkflow): CheckoutWorkflow = {
    if ( workflow.paymentMethod.isDefined )
      throw new IllegalStateException("Payment Method workflow step already processed")

    // Do some processing...
    workflow.copy(paymentMethod = Some(paymentMethod))
  }

Note that each of the above functions has a guard condition to stop them being called multiple times for the same workflow. Each of these guard conditions would require a separate unit test to ensure that it works and to avoid regressions should it be accidentally removed in the future.

Finally, we need the method to process the order. Given our domain model above, this class needs to contain some conditional check to ensure that all the workflow requirements are satisfied before processing of the order can commence. Something like:

  def processOrder(workflow: CheckoutWorkflow) = workflow match {
    case CheckoutWorkflow(Some(basket), Some(customer), Some(paymentMethod)) => {
      // Do the order processing
    }
    case _ => throw new IllegalStateException("Workflow requirements not satisfied")
  }

None of the above is obviously ideal as there are a number of places that have the potential to error at runtime. The conditionals pollute our code with non-business logic. Also, we have to write good unit tests to ensure all the conditionals are working correctly and have not been accidentally removed. Even then, any client may call our code having not met the requirements encoded in the conditionals and they will receive a runtime error. Let's hope they unit test as thoroughly as we do! Surely we can do better than this?

A Less Than Ideal Solution

Well, we could use the approach outlined in my previous post and use domain model extensions:

  case class WorkflowWithBasket(basket: Basket)
  case class WorkflowWithBasketAndCustomer(basket: Basket, customer: Customer)
  case class WorkflowWithAllRequirements(basket: Basket, 
                                         customer: Customer, 
                                         paymentMethod: PaymentMethod)

  def processOrder(workflow: WorkflowWithAllRequirements) = {
     // Do the order processing
  }                                         

While this does allow removal of all the conditionals and associated tests, it unfortunatley also reduces the flexibility of our model quite significantly in that the order that the workflow must be processed is now encoded into the domain model. Not ideal. We'd like to keep the flexibility from the first solution but in a type safe way. Is there a way that we can encode the requirements into the type system?

Levaraging The Type System

First, lets consider what we want to achieve. Our aim to encode unsatisfied and satisfied workflow requirements and only allow methods to be called when the correct combinations are set. So, let's first encode the concept of requirements:

  trait BasketRequirement
  case object UnsatisfiedBasketRequirement extends BasketRequirement
  
  trait CustomerRequirement
  case object UnsatisfiedCustomerRequirement extends CustomerRequirement
  
  trait PaymentMethodRequirement
  case object UnsatisfiedPaymentMethodRequirements extends PaymentMethodRequirement

Here we have defined requirement traits for each of the different workflow stages. We also defined case objects to represent the unsatisfied state of each requirement. Next we need to indicate the satisfied states, which are our actual domain object classes:

  case class Basket(items: List[LineItem]) extends BasketRequirement
  case class Customer(id: String) extends CustomerRequirement
  case class PaymentMethod(paymentType: PaymentType) extends PaymentMethodRequirement

Next job is to make sure that our workflow object can represent these requirements and be strongly typed on either the satisfied or unsatisfied state. We do this by adding type bounds to each of the workflow types. This also allows us to eliminate the need for the Option types. We also define an 'unsatisfied' instance as the starting point for our workflow:

  case class CheckoutWorkflow[B <: BasketRequirement,
                              C <: CustomerRequirement,
                              PM <: PaymentMethodRequirement]
                              (basket: B, customer: C, paymentMethod: PM)

  object CheckoutWorkflow {
    val unsatisfied = CheckoutWorkflow(UnsatisfiedBasketRequirement,
                                       UnsatisfiedCustomerRequirement,
                                       UnsatisfiedPaymentMethodRequirements)
  }

Now we need the functions that actually process each individual workflow stage. Note how each one defines type bounded parameters for the things it doesn't care about. However, for the stage that it actually manipulates it requires that it is called with the Unsatisfied type and returns the Satisfied type. Thus, you can no longer call any of these methods with a workflow that already has that stage satisfied: the compiler won't allow it:

  def processBasket[C <: CustomerRequirement, PM <: PaymentMethodRequirement]
    (workflow: CheckoutWorkflow[UnsatisfiedBasketRequirement.type, C, PM]): 
      CheckoutWorkflow[Basket, C, PM] = {

    // Do some processing...
    workflow.copy(basket = basket)
  }

  def processCustomer[B <: BasketRequirement, PM <: PaymentMethodRequirement]
    (workflow: CheckoutWorkflow[B, UnsatisfiedCustomerRequirement.type, PM]): 
      CheckoutWorkflow[B, Customer, PM] = {

    // Do some processing...
    workflow.copy(customer = customer)
  }

  def processPaymentMethod[B <: BasketRequirement, C <: CustomerRequirement]
    (workflow: CheckoutWorkflow[B, C, UnsatisfiedPaymentMethodRequirements.type]): 
      CheckoutWorkflow[B, C, PaymentMethod] = {

    // Do some processing...
    workflow.copy(paymentMethod = paymentMethod)
  }

Finally, our processOrder function becomes super simple. You just can't call it any more unless it has no Unsatisfied types:

  def processOrder(workflow: CheckoutWorkflow[Basket, Customer, PaymentMethod]) = {
    // Process the order
  }

One observation that was made to me by someone (a Java dev) who looked at my initial draft of the code was that the final solution looks more complex due to all the type bounds and that there's actually more classes due to the need to define the requirement traits and the Unsatisfied state objects. However, don't forget that this solution eliminates at least four conditional blocks and associated unit tests, simplifies others and also possibly reduces the number of tests that clients need to write on their code as well. Also, there's no possibility of runtime failure. If the code compiles then we have a much higher confidence that it will work. Well worth a tiny bit more type complexity in my book.

Wednesday, 21 November 2012

Eliminating Conditionals Using Types

I've been at Scala eXchange for the last two days. One of the topics that came up a couple of times was how the type system can be used to encode knowledge and rules, making it possible for the compiler to enforce these rather than needing unit tests to verify them. There were questions from some about how this actually works in practice, so I thought I'd produce some blog posts to demonstrate. This first one covers the most simple case.

Eliminating Conditionals Using Types

Consider the following (highly simplified) domain model:

  case class CreditCheckResult(statusCode: String, creditLimit: Int)
  case class Customer(id: Long, name: String, creditCheckStatus: Option[CreditCheckResult])

This model indicates that a Customer may or may not have completed a credit check. However, we only want to register the customer if they have successfully passed through a credit check. The registration function therefore looks something like:

  def register(customer: Customer): Either[String, String] = customer.creditCheckStatus match {
    case None => Left("Attempt to register non-credit checked customer")
    case Some(ccs) => {
      // Do the registration here
      Right(registrationCode)
    }
  }

This is a very common pattern in any places where an Option (or in Java a null) is used to indicate some conditional state (rather than a truly optional piece of data). The result is that we now need a unit test for both conditional cases:

  class RegistrationSpec extends Specification {

    "The registration process" should {
 
      "register a credit checked customer" in {
        // Test code here
      }
   
      "fail to register a non-credit checked customer" in {
        register(Customer(1L, "Test")) must_== Left("Attempt to register non-credit checked customer")
      }
    }
  }

However, we can use the type system to completely remove the need for this conditional check and thus remove an entire unit test case from our code base. How do we do this? By representing the different states that a customer can be in with different types:

  case class CreditCheckResult(statusCode: String, creditLimit: Int)

  trait Customer {
    def id: Long
    def name: String
}

  case class UncheckedCustomer(id: Long, name: String) extends Customer
  case class CreditCheckedCustomer(id: Long, name: String, creditCheckStatus: CreditCheckResult) extends Customer

Our registration method can now be greatly simplified:

  def register(customer: CreditCheckedCustomer) = {
    // Do the registration here
  }

And our test now needs to cover only the valid registration case:

  class RegistrationSpec extends Specification {

    "The registration process" should {
 
      "register a credit checked customer" in {
        // Test code here
      }
    }
  }

Aaaahaaa, I hear you cry, but doesn't the complexity instead move to the creation of the correct type of Customer? In some cases this might be true, but you could easily validate this by constructing customers using this code as part of the above test. Hopefully you will be using a functional programming style and you will already have a function somewhere that takes an UncheckedCustomer and transforms them into a CreditCheckedCustomer as part of the credit check process: which will already by type safe and tested!

I'll add some more examples of using types to reduce code and unit testing over the coming week or two.

Tuesday, 17 July 2012

Unit Testing with MongoDB

Back in June I presented at the London Scala User Group (video here) on our experiences on a recent Scala project that used MongoDB.

One of the questions I was asked was how we go about mocking MongoDB for testing purposes (especially unit testing)? My answer was that we don’t! This raised a couple of eyebrows in the audience, but we’ve found this works just fine for us.

In this blog post I will explore our thoughts and reasoning in a bit more detail.

How do we test with MongoDB?

I’ll be honest: for the majority of our tests (even the unit ones) we just run against a local MongoDB instance. When we are testing repository classes we write tests against a real repository wired up against a real MongoDB instance. We assert against the contents in the MongoDB collections. In some cases, we even test service classes or even controllers with a live repository backend that writes to and retrieves from a real MongoDB instance.

“But you can’t do that!”, I hear you cry. Traditional thinking says that writing tests directly against the database will be too slow. We’ve actually found MongoDB (especially when running locally) to be so blindingly fast that we don’t need to consider doing anything else. For most updates and queries we tend to get sub-millisecond responses.

It’s also a hell of a lot easier and less fragile to back tests with real repository instances than it is to create and configure a load of mocks or strange database testing frameworks.

I’m pragmatic. If using a real database backend was making our tests run too slow or causing them to be too complex then I’d happily consider alternatives. Our experience with MongoDB is that it is both fast enough and that it’s query syntax is succinct enough that we don’t need to bother with those alternatives.

Do we use mocks at all for the database then?

Actually, I rarely use mocks in any of my Scala project now days. I’ve switched much more towards a style of isolating my calls to dependencies and then using mixin traits to override specific behavior for testing. (I’ll write a blog post about this approach soon).

From time to time we do need to write tests that make a repository dependency exhibit a specific behavior which is difficult to emulate against an actual database. In these cases we just use a custom test trait mixed into the repository in order to override methods to exhibit the behavior that we require. Keeps it simple and makes the tests much more readable and much less fragile than using mocks or other frameworks.

Thursday, 12 July 2012

Making Agile Go Fast

Just recently I was speaking with a potential customer who had concerns about the velocity of their Scrum teams. They had recently (9 months ago) switched to an agile approach and had expected to get much more delivered than they had.

Now they were looking in to scaling up the number of Scrum teams on the project to try and increase velocity and deliver more quickly. In my experience of many agile projects, increasing the number of people on the project, especially one that is struggling, never has the desired effect of making things go faster. The advice to this customer was to therefore look at making their existing teams and process more effective rather than increasing the number of people on the project.

This got me thinking about what makes agile projects go slow. Across a number of projects that I have experienced, I’ve identified four common traits: two related to people and process and two related to technical concerns.

The Wrong People

Agile is all about using the right people. In fact this is true for any project or process, but for agile it is even more important. Use the right people and the agile process will work. Use the wrong ones and you will struggle.

The people who you don’t want on your agile project are those who are not disciplined enough to follow sound engineering practices and those who try to build complex frameworks/toolkits and who conduct personal science projects on your codebase. If you want to go fast then it’s vital that you have a clean, simple and well-refactored codebase that is backed up by a set of quality tests. Team members who don’t support this vision slow down the whole team.

To get back on track, remove those devs who aren’t following good engineering discipline and those who keep making things more complex. Bring in experienced agile developers to pair with the remaining team, giving them the responsibility of ensuring quality engineering practices are followed.

Putting The Process First

Another common mistake of companies starting out with agile projects is that they assume the process will make them go faster. Sorry to disappoint, but this is just not true. What makes agile teams go fast is creating open channels of communication and self organising and empowered teams.

If an organisation can’t create an excellent environment for communicating then no agile process will help. Agile helps teams go faster by eliminating the unnecessary documentation and waterfall steps. However, this information transfer must still happen and it happens through closer communication and collaboration.

Sadly, many organisations create agile teams and give them a process to follow but then insist on telling them how to organise, what tools and techniques to use, how to work and exactly what to do. This isn’t agile, it’s just another way of organising a traditional team. If you want agile to go fast then the team need total control over how they work and what they do.

To get back on track from here, relax on the process. Work on increasing communications. Give the agile teams the chance to decide how they will organise and work. Give them control and they will go faster. Agile is not a process, it’s about letting people get the job done.

Using The Wrong Technologies

So many agile projects struggle because they are using the wrong technologies. When this is the case it’s usually because the technology approach has been foisted on the team by an ‘ivory-tower’ architect or some senior manager who was wooed by the sales consultant from a large vendor. In other cases it may be that the team are forced into using a ‘corporate standard’ technology set.

In many cases the team will repeatedly identify the technology as a major factor in retrospectives. However, more often than not they will be told that this is not something that can be changed and that they have to stick with it. Why throw away hundreds of thousands or even millions on staff costs when backtracking on some poor technology choices would make so much difference.

Again, giving the teams the authority to make technology decisions is essential. Allowing technology choices to be reversed if they prove to be hindering delivery is also an important part of being agile.

Making The Architecture Too Complex

Many software systems are designed, or become, far too complex. In an agile environment this is a guaranteed way of making things go slower. Software must be kept as simple as possible so that it is easy to continually change and enhance.

If the architecture of an agile system has become so complex that it hinders development then it must be revisited and greatly simplified. Layers must be removed, interfaces reduced and code and technologies radically refactored to make everything simpler. Once that is done then the project must work all out on stopping it from getting more complex again in the future.


Only by using the right people, correctly empowered and communicating can the team have the ability to go fast and be truly agile. Only by making the correct technology choices, reversing bad technology choices and keeping everything simple can the ability to turned into real agile delivery.

Sunday, 10 June 2012

Pairing is Wearing

In this post back in August 2011 I was talking about the many benefits of Pair Programming. Having now been full-time pair programming for almost an entire year, I wanted to mention one of the downsides that I have discovered.

A Holiday

I've just finished my holiday. It wasn't the best one ever, mainly due to camping in the UK in June: which meant heavy rain and strong winds. Still, we had a good time and it was very relaxing.

As the week progressed and I had a chance to relax, unwind and get some lie-ins I started to discover that my mind was starting to become more alert. I was feeling less tired. Great ideas were starting to flow again. It was at this point I realised just how weary I had been at the start of the holiday. I had known I was feeling pretty knackered, just not quite how much.

This then got me thinking as the the possible cause of this tiredness and how it could gradually build up over time without me being fully aware of it. I looked back at what I've been doing for the past year...

  • I've been travelling a lot to get to/from work - but less than my previous job.
  • I've been learning lots of new stuff - but I'm a compulsive learner, so nothing new here.
  • We've put at least 3 major releases live - but I've put plenty of software live before.
  • We've been pairing full time - this is new!

Is Pairing More Tiring?

The conclusions that I have had to draw is that working in a paired environment is more mentally tiring that working alone or individually within a team. There's more of a requirement to stay focused when working as a pair - it's particularly noticable when one member of the pair is feeling unwell or tired. Also while pairing you are multitasking more - writing code, thinking about solutions but also communicating your thoughts and maintaining a running dialogue. All of this extra work requires extra mental energy and thus increases fatigue.

So, how can we pair over a period of time without burning out? Here's some thoughts that I hope to experiment with over the coming months:

  • Take more frequent and longer breaks than you would do while working individually. The productivity gain of working in pairs easily allows this to be supported
  • Do a lot more walking at lunchtime!
  • Try to provide team members regular tasks that they can undertake outside of a pair to give their mental energy drain a rest. Stuff that doesn't require extensive multitasking.

Anyone else got any thoughts or ideas?

Monday, 30 April 2012

Life Without Objects

Over the last few years I have been learning and experimenting with a wider range of programming languages. In particular I have started using Scala as my primary development language, adopting a more and more functional style. I have also become very interested in Haskell (a pure functional language) and Clojure (a modern lisp dialect).

I have therefore been moving away from the object-oriented development principles that have made up the bulk of my 17 year career to date. More and more I am beginning to feel that objects have been a diversion away from building concise, well structured and reusable software.

As I pondered on this topic, I realised that this isn’t a sudden switch in my thinking. The benefits of objects have been gradually declining over a long period of time. The way I use objects today is very different to how I used them when they were new and shiny. In this post I explore this change in my thinking about object-oriented development.

The Promise of Objects

Back in the 1990s, objects were new and exciting. The promise of being able to create reusable classes built around common design patterns was seductive. The ability to then combine these classes into reusable and configurable business components seemed like the Mecca of software development. New languages like C++ and then, slightly later, Java held the promise of a new way of building great software.

Business Components Aren’t Reusable
It didn’t take us long to discover that the ability to create reusable business components was just a giant fallacy. Each business is significantly different from another, even in the same industry. Each similar project has very different business rules.

The only way to build reusable business components at this level is to make them hyper-configurable by adding such things as rules engines and embedded scripting languages. Hardly a component model and more like a bloatware model. This promise gone, people either buy into the big bloatware systems (suckers) or build their custom business objects on a project by project basis.

Patterns Don’t Build Well Structured Software
The next thing we learnt was that excessive reliance on design patterns doesn’t lead to the good software structure. Instead it leads to software that is overly complex, hard to understand and difficult to maintain. Some patterns even turned out to be anti-patterns (the singleton pattern makes software almost impossible to unit test, for example).

We soon learnt to use patterns judiciously. More often than not it’s just cleaner to code the software as you understand the model rather than try to abstract it into a more generalised pattern.

Frameworks for Class and Component Reuse Give Few Benefits
Another early promise of objects was rich, tightly coupled frameworks of classes which when used together would make building applications from reusable component a breeze by hiding all the technical complexity and implementation plumbing. Think EJB and similar. Experience soon showed that these just did not work. They were just too restrictive and cumbersome for what people were trying to achieve.

These heavy-weight frameworks soon died out to be replaced with more lightweight libraries and toolkit type approaches. Collections of more loosely coupled classes that you can pull together as needed are now the preferred way to build software. Take just what you need and nothing more.

Inheritance Creates Brittle Software
The ability to support interface and implementation inheritance was one of the key tenets of object oriented software development. We could spot common code and behaviour and push this down into a shared base class so that future abstractions could benefit from having this shared code available to build on.

Sadly, this just didn’t work out well. Each sub-class turns out to be subtly different from its peers, resulting in lots of overrides of base class behaviour or making the base classes even more generic. The net result was super-fragile software, where any small changes to a common base class would break most, if not all, of the sub-class implementations.

These days we don’t use inheritance much, and especially not for creating technical base classes. Its use is pretty much restricted to interface inheritance to indicate an object supports a certain behaviour or to occasional domain models where there is a true inheritance relationship. Other than that we tend to extract commonality in to separate ‘mixin’ type classes and compose them together through an aggregation approach.

Violation of Encapsulation
Another key feature of the object-oriented model was the ability to encapsulate state and then expose behaviours (via methods) that access and update this state. Unfortunately it turns out that there are a large number of cases, where we are actually interested in the vales of the state rather than the behaviour.

For example, asking an object to render itself as HTML turns out to be a pretty poor approach. Knowledge of HTML rendering gets spread across the code base and a small change in approach causes us to change many, many classes. Instead we tend to pass the object to a dedicated HTML rendering/template component, which pulls the data values from the object.

Anti-patterns have even emerged around this to allow us to have light-weight objects that just encapsulate state without behaviour (Java Beans, Data Transfer Objects and so on). If we are doing this, then why not just work directly with first-class structured data as opposed to objects?

Mutable State Causes Pain

Another perceived benefit of encapsulation was the ability to mutate the state of an object instance without impacting on the clients that use that object. However, anyone who has built a significant sized object-oriented system can tell you stories of trawling through many files of code to find the location that mutated the state of an object to an unexpected value that happened to make your software blow up in a completely different place (usually where you output or store the state of that object).

More and more we now favour immutable state and stateless services so that these problems do not occur. There’s also the additional benefit that immutable state is a much better model for building highly concurrent systems and for getting the most out of modern multi-core hardware. It’s also far easier and less error prone than trying to work with threads, locks and concurrency safe data structures.

Behavioural Interfaces Cause Code Complexity and Bloat
One of the things we do frequently in object-oriented languages is create small marker interfaces that have just a single method. Any class wanting to support this behaviour extends the interface and implements the method. We can also declare anonymous implementations for ad-hoc use.

However, we have found that neither of these approaches are particularly good. Implementing the marker interfaces often pollutes classes with implementations that are not their direct concern (thus violating the single responsibility principle). Anonymous classes are just unnecessary bolierplate that makes our code more difficult to understand and maintain.

Life Without Objects

So, is it possible to go back on 17 years of experience and contemplate a life without objects? I’m not sure that I’m 100% there just yet, but using a multi-paradigm language like Scala is allowing me to overcome many of the limitations of the object-oriented approach.

For example, Scala’s support for mixin traits makes it almost unnecessary to ever use implementation inheritance. It’s rich collections framework plus the ability to use case classes to create data structure like concepts obviates working around encapsulation issues. A recommendation to use immutable data and collections makes code easier to debug and reason about. The ability to use functions as general abstractions and type classes to extend behaviour while maintaining single responsibilities makes it much easier to build well structured, reusable software.

In fact, what I find I am doing more and more is using simple objects in the form of case classes to represent data structures, with a few behavioural methods to simplify working with this data. Then I’m just using mixin traits as a modular approach for grouping related functions together. Then I’m combining these together to form components in which I compose together various functions that transform data from one for into another.

Perhaps I’m further away from the pure object-oriented approach than I’d thought. I’m certainly building smaller, cleaner and better structured software than I ever was before.

Tuesday, 20 March 2012

Map, Flatten and FlatMap

I've recently been working with some developers who are new to the Scala world. They've picked up the language really easily and in only a few week have become competent Scala developers. Who says it's too complex and difficult to learn?

Anyway, one of these developers is a visual thinker such as myself. We prefer communication through diagrams rather than words. While explaining Scala I've been drawing some pictures to illustrate some of the more interesting concepts. She suggested that I should publish these and we had the idea that an animation would be fun.

Here's my first animated post showing the concepts for Map, Flatten and FlatMap. It's a pretty amiturish stab at animation, but hopefully it explains the concepts while also adding a bit of humour along the way.

If you are interested here's the Scala code for the concepts covered in the animation:

case class World[+A](revolvesAround: A) {

  def map[B](f: A => B): World[B] = World(f(revolvesAround)) 

  def flatten[B](implicit ev: A <:< World[B]): World[B] = World(revolvesAround.revolvesAround)

  def flatMap[B](f: A => World[B]) = World(f(revolvesAround).revolvesAround)

  def get: A = revolvesAround
}

def lift[A](entity: A): World[A] = World(entity)

case class Person(name: String) {
  def smite = DeadBody(name)
}
case class DeadBody(usedToBe: String)
case class Insect(species: String)

def kill(p: Person): DeadBody = p.smite
def reincarnate(b: DeadBody): World[Insect] = World(Insect("ant"))

val dave: Person = Person("Dave")
val antDave: Insect = lift(dave) map kill flatMap reincarnate get 

println(antDave)

Wednesday, 14 March 2012

Done and Dusted

In a recent retrospective our team dug out our Definition of Done and gave it a good review. We ended up removing a number of unnecessary items. This got me thinking about what is Done?

In previous places that I have worked the Definition of Done was a long check-list of things that had to have been completed and verified. Is this the best that a philosophy that claims to value "individuals and interactions over processes and tools" can come up with? This got me thinking...

Isn’t Done actually just the state when the team agree that the story is complete? Why should it need to be any more than that?

Done: The state when all team members agree that the story is complete

We could expand this slightly to define the roles that the different team members play in this definition:

  • Developers agree that the code is complete, tested and refactored to an acceptable standard
  • QAs agree that the acceptance criteria for the story has been met, that the functionality works and that they have not found any defects introduced by the new feature
  • BAs and POs agree that the feature meets the business objectives for the story
  • (we could also add points for DBAs, Ops etc, as needed)

So, if we accept that the team can decide for themselves when something is Done, why do we end up with long checklists, often including a whole range of metrics? It could be because 'management' aren’t able to release their need to monitor everything and produce nice reports. However, it’s more likely a matter of trust.

Teams are just not trusted to self-organise and make good decisions. If the team say that a story is Done then for many managers there is an immediate suspicion that the team is not telling the truth and is trying to cut corners. They then require that there is concrete proof that this is not the case. This is despite the fact that a group of professionals all happen to agree that it is Done. It just doesn’t make sense.

If the team has never released a story as Done when it clearly wasn’t then why doubt they will suddenly stop doing this in the future? Show trust in the team that is delivering completed work.

If the team are marking stories as Done when they clearly aren’t then there is a much more serious problem with the team and they probably either need replacing, mentoring or a firm guiding hand. Adding a requirement to satisfy a long checklist and provide associated metrics will never improve a bad team.

As with many things agile, it’s not about the process: it’s about changing attitudes; it’s about trusting teams to deliver the best solutions possible; it’s about not bogging the teams down with unnecessary controls.

Monday, 13 February 2012

Sometimes you just have to be lazy...

...or, solving problems with lazy evaluation.

I’ve recently been working on a simple template system, which also has the potential to grow into a full-blown document generation system. The basic principle is that a document is composed of a number of fragments. Each fragment is called in turn to generate its piece of the document and these pieces are concatenated together to product the final result.

My first implementation was a super simple one. We just fold over the list of fragments, accumulating the output from each into a string builder. Each fragment may alternatively generate an error, which we collect along the way as well. At the end of the fold we return either the string document or a list of errors.

  def render(fragments: List[Fragment]): Either[List[String], String] = {
    fragments.foldLeft(RenderResult()) { (acc, fragment) =>
      fragment.generate fold (
        { err => acc.copy(errors = err :: acc.errors) },
        { text => acc.doc.append(text); acc }     
      )
    }.toEither
  }

  case class RenderResult(doc: StringBuilder = new StringBuilder(), errors: List[String] = List()) {
    def toEither: Either[List[String], String] = ...
  }

Now, this works great for documents of only a few kilobytes in size. However, as the documents grow to multi-megabyte sizes this approach becomes infeasible. The amount of memory required to hold the document being generated becomes huge. Multiple concurrent generations become impossible. We need a better solution.

The typical suggestion at this point is to stream output from each fragment straight into an output writer of some form rather than collect and build a string.

  def render(fragments: List[Fragment], writer: Writer): Option[List[String]] = {
    fragments.foldLeft(List[String]()) { (acc, fragment) =>
      fragment.generate fold (
        { err => err :: acc.errors },
        { text => writer.write(text); acc }     
      )
    }.getErrors
  }

Unfortunately this doesn’t work here because of the possibility that fragments may generate errors. We don’t want to stream out partially complete documents. We could output to a temporary file and then copy this to the ultimate output on success, but this seems like a less than ideal solution.

A common approach in an imperative style would be to first run a check phase to see if there are any errors and then run a second render phase to produce the output.

  def check(fragments: List[Fragment]): List[String] = 
    fragmens.foldLeft(List[String]()) { (acc, fragment) =>
      fragment.checkForErrors map (_ :: acc) getOrElse acc
    }

  def render(fragments: List[Fragment], writer: Writer): Unit = 
    fragments foreach { fragment => writer.write(fragment.render) }

However, in this case the building of fragments might be quite costly and we don’t want to have to process them once to see if there is an error and then again to render.

However, as we are using Scala and programming in a functional style, we have an alternative approach. Instead of returning a string or writing direct to a writer we can return a lazily evaluated function. This function can encapsulate all the side-effect generating logic of doing the actual write - which is also nice from the point of view of being able to reason about our rendering code.

  def render(fragments: List[Fragment]): Either[List[String], (Writer) => Unit] = {
    fragments.foldLeft(RenderResult()) { (acc, element) =>
      fragment.generate fold (
        { err => acc.copy(errors = err :: acc.errors) },
        { func => acc.copy(fragmentFuncs = func :: fragmentFuncs }     
      )
    }.toEither
  }

  case class RenderResult(fragmentFuncs: List[() => String] = List(), errors: List[String] = List()) {
    def toEither: Either[List[String], List[(Writer) => Unit] = {
      ...
      (writer: Writer) => fragmentFuncs foreach { f => writer.write(f()) }
      ...
    }
}

The way this works is that the render method folds over each fragment, asking the fragment to do enough work to be certain that no errors will occur (e.g. validate data, take a temporary copy of any volatile values and so on). However, it doesn’t need to do the actual string generation. Instead, each fragment returns a function that will be called at some time in the future (or an error). The render code then accumulates all these small functions into one larger function and returns this.

The returned function, when called, takes a writer and iterates over all the small string generating functions, writing the output of each straight into the writer. Job done and in a mere fraction of the memory required by the original solution.

In cases where you need to generate large amounts of data or where you want to delay side effecting execution to a well-defined location in your code then lazy evaluation is a great approach to follow.

NOTE: All above code example are significantly simplified from the real code in order to demonstrate the approach.

Wednesday, 11 January 2012

Thoughts on the UK Government's New Approach To ICT

The UK Government today announced a new approach to teaching computing in schools. The current sylabus is based on teaching how to use applications like Word, Excel and PowerPoint. This is, quite rightly, seen as highly boring and unecessary for today's computer literate pupils. The new strategy is to switch to a more 'computer science' based sylabus. This would include more on how computers actually work and would also teach programming. The Government's aim is to create a new generation of highly skilled graduates with excellent computing skills.

While this new approach sounds very interesting and laudable, I have some serious doubts about whether it will actually work. My concern can been seen if you break down the types of pupils that will be studying this new computing sylabus into three broad categories:

The Uninterested

There is likely to be a (fairly large?) category of pupils who, like with any subject at school, aren't really interested. These will be the pupils for whom a computer is about Facebook, playing games and downloading music and videos. It's pretty likely the these pupils won't really be interested in programming and will thus find it very challenging and probably quite boring. However, the sylabus won't be allowed to leave them behind, so it will have to run at a fairly slow pace in order to keep them on board.

The Swayable

This is the category of pupils who might have some interest in computing but whom have never really attempted any programming before. It's quite likely that a good portion of these might find the subject really enjoyable and will thus become the future genration of computer skilled graduates that the Government is hoping for. Unfortunately I think this category is MUCH smaller that those advocating the new sylabus believe that it is. In any given class I would think that it will be just one or two pupils - hardly a revolution.

The Hooked

Finally, there will be a group of pupils who will really love computing and programming. They will be the people likely to become the hard-core programmers of the future. Unfortunately, it's probably likely that these pupils will already have computing as a hobby at home. They will likely already be building websites and writing software. Even if they aren't at the start, then they soon will be once they get hooked. However, a sylabus created to cater for the slower pace of the uninterested will likely soon become very boring for those able and interested pupils. My fear would therefore be that they become unmotivated, unchallenged and bored by doing work that is way below their ability. This could then have a negative effect and turn those potential developers away from computing forever.

In fact, I know this will likely be the case as it happened to me. I was at school 28 years ago when computing first started being introduced. I got hooked early and my (very supportive parents) ensured that I was one of the first people in school to have a home computer. I taught myself to program at age 10 and never looked back. Unfortunately I found I was always way ahead of the curve when it came to computer studies lesson, talking to teachers about computers and so on. I got bored through not being stimulated. I had a few years of just playing games and word processing essays. I even toyed with a career in accountancy! Luckily I rediscovered my interest, changed tact, and went on to study Computer Science at University. It could have turned out so different.


My real fear of the proposed strategy is that rather than producing a new generation of highly skilled computer graduates it will instead produce a large number of moderately skilled people while turing the highly skilled pupils with the best potential away from the subject. Hey, let's build a large army of mediocracy!

So, what do I think needs to be done. I agree with the general goal of the new strategy, but I think it needs to be covered by a dual-sylabus approach. A sylabus for the uninterested and swayable that teaches general computer skills, applications, effective use of the Internet, basic programming and so on. To be taught by ordinary teachers with standard levels of IT skills. There also needs to be an Advanced Computing sylabus for the hooked and highly able that teaches programming and computing to a much greater depth. It would need to be taught by highly skilled teachers with a heavy input from universities and industry. Its aim would be to stretch the most able who could become the highly skilled people that our industry really needs.

Thursday, 1 December 2011

Increasing Agile Capacity

A scenario: you have a successful agile project. It’s made up of a number of small teams, each working on a separate component of the application suite. This has been working extremely well. You are capacity planning for the coming six months and you identify that one of these teams is likely to become a bottleneck. There are just too many stories that fit into their component area. How do you deal with this?

The above is something that looks likely on one of the projects that I am working on. I’ve been putting in some thinking on how this might pan out. This post documents my thoughts.

Traditional Reaction

There are typically two standard reactions to above problem: grow the team that is likely to be the bottleneck, or add an additional team to work on the component. In my experience neither of these works. Adding more people is rarely ever a good way to make a project go faster!

Growing the team

The best agile teams I have worked on have had one thing in common. They have all had between 4 and 6 devs, a QA, and a Scrum Master. As soon as the team gets any bigger than this I’ve found that the following occurs:

  • The performance gain of adding each additional dev is less each time.
  • It becomes much more difficult to gain consensus.
  • Team meetings become more long-winded and less productive.
  • Communication becomes more difficult.
  • It becomes difficult to maintain team values.
  • Code cruft (technical debt) increases.

Thus, adding additional devs and QAs to a team, either permanently or by borrowing from another team, just doesn’t work. Typically I’ve even found that the team goes slower as it has to spend more time cleaning up after itself.

Adding temporary devs (perhaps for a day or two) is, I’ve found, often worse than adding a permanent dev as there is less sense of ownership and shared team values. Working with good developers makes this less so, but I still believe it occurs more frequently than we like to admit.

Adding more teams

Having multiple teams working on a single component’s code base just doesn’t work well. I worked on one project where we had four teams working on the same code. We were tripping over each other all the time. Particular problems I have seen working in this way:

  • Communications between the teams becomes a big overhead.
  • More difficult to gain consensus on major technical decisions.
  • Scheduling work to avoid multiple pairs changing the same code is very difficult.
  • Very difficult to maintain shared values across multiple teams.
  • One team’s nice code is another team’s cruft.
  • Code cruft increases because one team is less willing to refactor another team’s code.
  • Different approaches to achieving the same implementation become more prevalent.
  • One team undoes another team’s work because they disagree with how it was done.
  • Important aspects like performance tend to slip between the cracks.
  • Productivity of the combined teams is far less than the sum of the productivity of each team working independently.

Multiple teams working on the same code base is a recipe for disaster. It results in more overhead, lower code quality and more cruft. Also, you get far less productivity gain that you would had you allowed each team to work independently on their own component.

Other Approaches

So, given that there is more work than the measured velocity of the team shows they can achieve in the time available. And, given that we don’t want to grow the team or add more teams. How do we solve the problem?

The aim is to keep small teams who can each work on an area of code that they have sole ownership of. This will give them the incentive to ensure that this component is clean, simple, maintainable and has a low level of cruft. It will also maximise the productivity of each individual team by minimising communication overhead.

I think a combination of four specific solutions are the best approach. These being: reducing waste, simplifying requirements, moving complexity and splitting components.

Reduce Waste

A good agile team should already be pretty good at reducing their waste. Retrospectives that generate actions which are then implemented go a long way towards this. A good Scrum Master who removes impediments is vital. The team will be provided with the best tools for the job.

Still, there is likely to be further waste that can be eliminated to allow the team to go faster. The first set comes from the category of unnecessary process waste. This includes things like:

  • Having to attend full meetings where there is very little need for members from the team to be present.
  • Long-winded design sessions that could be cut down with a bit more decisiveness.
  • Unnecessarily revisiting architecture and design decisions over and over again.
  • Re-enacting the same debate repeatedly because consensus wasn’t achieved.

The other set comes from giving the bottlenecked team stories to play that aren’t essential to what they are trying to deliver. This can be things like:

  • Helping ops with tracking down a problem
  • Adding additional logging of messages to an external system
  • Trying to reproduce an infrequent defect
  • Updating a build pipeline

In an ideal world, all of the above should be done by the team. However, if that team has a high volume of work to get through then these tasks can easily be divested out to teams that are perhaps not running at full capacity, allowing the team that is to maximise their throughput of important functional stories.

Simplify Requirements

Often performance of teams can be greatly hampered by having requirements that are either too complex or not clearly thought out. Ensure the backlog is groomed to contain only the most essential stories. Make sure these have been paired down to the absolute minimum set of requirements needed to fulfill the business needs.

The worst scenario is having a heavily loaded team working on features that are overly complex and time consuming. I’ve seen many cases where teams have worked on a fairly complex story only to later find that the clients uses just the core 80% of the feature, which could have been built in 20% of the time. The extra 80% of the time spent delivering that last 20% of non-essential requirement is just waste.

Analysts and the business need to scrutinise the backlog, squeezing every unnecessary piece of work from the stories. If the business doesn’t need some small aspect right now then create a separate story and play it later when the team has less critical path work.

Move complexity

In most large software systems it is possible to locate complexity in different components. For example table paging could be implemented in the server code or in javascript on the browser. If complexity in one place is causing capacity problems then it may be possible to move elements of this complexity to another team that has more spare capacity.

This may not always be the most technically pure solution, but it may be the best trade-off in order to deliver a product on time and budget. The complexity can always be relocated in the future.

As an example, consider a story where one component has 8 points of work do to delivery a feature and it’s two clients each have 2 points of work to consume the feature. Total 12 points. However, it might be possible to split the work differently, so that the core component implements something much simpler for 3 points, while the two clients take on some complexity and implement 5 points each. Total 13 points. So, the two approaches are roughly equivalent in size. The second might result in some duplication on behalf of two teams, but this allows the team with a large backlog to complete more stories. A pragmatic trade-off.

Split Components

If a team has multiple components, then the logical option is to relocate one or more of those components to another team. What if they only have one component?

Ideally, buy reducing waste, simplifying requirements, and moving complexity it should be possible to shrink the size of the backlog for a component. Hopefully the smaller backlog should be sufficiently sized that the team can complete it. What if they still can’t?

To me this seems to be a problem of decomposition. A component that is so large and complex that a team of six devs can’t maintain and enhance it is a component that is too big. In this case it should be split into two or more smaller components, with the current team keeping one part and a different team taking the other part(s).

Making the split is always technically challenging, but provided it is made across a clear interface boundary then my experience is that it is usually possible divide a component up into smaller constituent parts.

This is probably the only way that I would recommend going about adding more people to a project - introduce a new team (or repurpose an existing team) and give them their own code base and component(s) to work on.

Friday, 25 November 2011

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.

Monday, 21 November 2011

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.

Tuesday, 1 November 2011

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.

Wednesday, 28 September 2011

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.