Clojure, Programming, Scala

Horses for courses: choosing Scala or Clojure

So one of the questions after my recent talk trying to compare Scala and Clojure (something that I suspect is going to be an ongoing project as I hone the message and the tone) was about whether the languages had problem domains they were more suited too. That’s an interesting question because I think they do and I thought I might be interesting to go through some of the decision making process in a more considered fashion than answering questions after a talk allows you to do.

So some of the obvious applications are that if you want to leverage some Java frameworks and infrastructure then you definitely want to use Scala. Things like JPA, Spring-injection, Hibernate and bean-reflection are a lot easier with Scala; in Clojure you tend to be dancing around the expectations these frameworks have that they are working with concrete bean-like entities.

If you are going to work with concurrency or flexible data formats like CSV and JSON I think you definitely want to be using Clojure. Clojure has good multi-core concurrency that is pretty invisible to you as a programmer. The key thing is avoiding functions with side effects and making sure you update dependent state in a single function (transaction). After that you can rely on the language and its attendant frameworks to provide a lot of powerful concurrency.

Similarly LISP syntax and flexible data go hand in hand so writing powerful data transforms seems second nature because you are using fundamental concepts in the language syntax.

Algorithm and closed-domain problems are interesting. My personal view is that I find recursion easier in Clojure due to things like the explicit¬†recur¬†function and the support for variable-arity function definitions. Clojure’s default lazy sequences also make it easier to explore very large problem spaces. On the other hand if you have problems that can be expressed by state machines or transitions then you might be able to express the solution to a problem very effectively in a Scala case class hierarchy.

When it comes to exploring the capabilities of Java libraries I tend to use the Scala console but for general programming (slide code examples, exploratory programming) I do tend to find myself spending more time in LightTable‘s Instarepl.

When it comes to datastore programming both languages are actually pretty clunky because they devolve handling this down to various third-party libraries. Clojure does pretty well with document databases and key-value stores. Scala is great for interacting with the AWS Java libraries and neither deals particularly well with relational data.

For web programming neither is brilliant but Scala definitely has the edge in terms of mature and full-featured web frameworks. Clojure is definitely more in the log cabin phase of framework support currently.


What’s the problem with Scala’s “lazy” vals?

Scala has a value modifier called “lazy” and I have a bit of a problem with it. Let’s start with the name, lazy is used to indicate that the value is not defined when declared but when the value is read. Really it isn’t lazy at all it is actually a memoisation wrapper around the value.

Now that’s a pretty handy thing and there are definitely some use cases for this. In particular where the calculation of the value is truly invariant and expensive. Some people suggest that it can be used to cache the results of service or database lookups but that is clearly madness unless the query really does return an invariant.

However I think that there is a very subtle problem with the misapplication of lazy and that is that it introduces state into an otherwise pure function, namely whether the value has been calculated or not and what the value is when calculated. This state to me actually reduces the utility of a pure function in terms or reuse as the consumer has to be very aware in time of when the function is going to be invoked and what its value is going to be over time.

It’s rare that lazy over-application goes wrong because I mostly see it in short-lived instances. However that very lifespan seems to suggest that there is no real value in lazy evaluation over eager declaration. After all if the function never gets called then it doesn’t cost anything and true constants should be in the companion object. In long-lived objects though I think there is serious potential for lazy to go wrong and therefore it should be applied sparingly where a positive impact can be proved.


Using function passing to separate concerns

So I had a difficult problem today where I needed to access a service but I couldn’t inject it due to the service having slightly too many concerns and hence a circular dependency in the Dependency Injection. So there is right way of dealing with this, which is to refactor the concerns of the collaborator until the dependency goes away. Unfortunately there is also the incomplete user journey that is holding everyone up unless a quick hacky fix is found.

Enter function passing to the rescue! In my case we are talking about an MVC web application where the Model is generating the circular dependency but it is possible to inject the collaborator into the Controller. The trouble is that my code separates the concerns of the Controller and the Model such that the Controller asks the Model to perform an operation and the Model returns the result of executing the operation. This is clean to develop and read but it also means that the Controller cannot access any of the internal operations of the Model, which is what I need for the invocation of the collaborator.

For a moment I thought I had to break the encapsulation between the two and pass the internal information of the Model operation out with the operation result, however function passing is a more elegant solution that keeps my concerns separate. Essentially the Controller will ask the Model to do something and also provide a function for the Model to execute if it wishes to invoke the collaborator.

Let’s try to illustrate this in code

class Controller (stuff: StuffRepository, messages : MessageDispatcher) {

post("/handle/stuff/:stuffId") {

def messageCallback(id: StuffId, message : Message) { messages.send(Messages.StuffUpdated, message, Some(id)) }

stuff.storeChange(params("stuffId"), messageCallback) }}

class StuffRepository {

def storeChange(id : String, callback : (StuffId, Message) => Unit) = {

makeChanges(id) match {

case Success => callback(StuffId(id), Message("Changes completed")) ; Success

case Failure => Incomplete }}

Hopefully that’s all relatively obvious, you can also type the callback for clarity and make it an Option if you don’t always want the callback invoked. If you don’t like the closure you can probably re-write is as a function that partially applies MessageDispatcher and then returns the function.

Some of you are going to be thinking this is totally obvious (particularly if you a Javascript fiend) and that’s cool but I do think it is an interesting technique for keeping responsibilities separated without very complex patterns. It is also something that is only possible with proper first order functions.


Scala can be the replacement for Enterprise Java

Last week I had the chance to take a look at one of my worse fears about Scala: really bad Scala code. As I thought it might it did include a partial application, I was very grateful it didn’t abuse apply. It also managed to bring in a lot of classic bad behaviour like catching Exception and then swallowing it.

It has always been one of my fears that when someone went rogue with Scala the resulting mess would be so bad it would be a kind of code Chernobyl, its fearsome incomprehensibleness would be like the Lift code only without the brevity.

When I finally had to confront it I thought: this is actually no worse than really bad Java code.

I know that it could potentially be worse but actually it would take someone who was actually really good at Scala to construct the nightmare-ish worst case scenario I had imagined. In most cases terrible Scala code is going to look a lot like terrible Java code.

So that is really my last concern out of the way. We might as well use Scala for the good things it offers like immutability and first-order functions. The boost to productivity is going to outweigh the potential crap that might also be generated.

Programming, Scala

Mockito and Scala

Scala is a language that really cares about types and mocking is the art of passing one type off as another so it is not that surprising that the two don’t get along famously. It is also a bit off probably that we are using Java mocking frameworks with Scala code which means you sometimes need to know too much about how Scala creates its bytecode.

Here are two recent problems: the “ongoing stubbing” issue and optional parameters with defaults (which can generally be problematic anyway as they change the conventions of Scala function calling).

Ongoing stubbing is an error that appears when you want to mock untyped (i.e. Java 1.4) code. You can recognise it by the hateful “?” characters that appear in the error messages. Our example was wanting to mock the request parameters of Servlet 2.4. Now we all know that the request parameters (like everything else in a HTTP request) are Strings. But in Servlet 2.4 they are “?” or untyped. Servlet 2.5 is typed and the first thing I would say about an ongoing stubbing issue is to see if there is Java 1.5 compatible version of whatever it is you are trying to mock. If it is your own code, FFS, add generics now!

If it is a library that you have no control over (like Servlet) then I have some bad news, I don’t know of any way to get around this issue, Scala knows that the underlying code is unknown so even if you specify Strings in your mock code it won’t let it compile and if you don’t specify a type your code still won’t compile. In the end I created a Stub sub-class of HttpServletRequest that returned String types (which is exactly what happens at runtime, thank you very much Scala compiler).

Okay so optional parameters in mocked code? So I love named parameters and default values because I think they are probably 100% (no, perhaps 175%) better at communicating a valid operating state for the code than dependency injection. However when you want to mock and set an expectation on a Scala function that uses a default value you will often get an error message saying that the mock was not called or was not called with the correct parameters.

This is because when the Scala code is compiled you are effectively calling a chain of methods and your mock needs to set matchers for every possible argument not the ones that your Scala code is actually calling. The simplest solution is to use the any() matcher on any default argument you will not be explicitly setting. Remember that this means the verification must consist entirely of matchers, e.g. eq() and so on.

What to do when you want to verify that a default parameter was called with an explicit value? I think you do it based on the order of the parameters in the Scala declaration but I haven’t done it myself and I’m waiting for that requirement to become a necessary thing for me to know.