Of course, there are many people, including us Object Mentors, who consider TDD to be an essential part of professional software development. Obviously not everyone agrees. James Coplien has been another critic of this view.

We should never accept any dogma. Why is TDD considered important? What does it purport to do? TDD provides two important benefits.

• Driving the design.
• Building a suite of automated regression tests.

So, if you can satisfy both requirements without TDD, then technically you don’t need it. In Rich’s case, he said he spends a lot of time thinking about what he’s going to do before he does it. In this way, he satisfies the first requirement, driving the design. I had a spirited discussion with some Thoughtworkers afterwards and Ola Bini said what a lot of us think, “I do that thinking by writing tests.” I’ll freely admit that when I am really experimenting with ideas, I might just write code, but once I know how to proceed, I return to the beginning and test drive the “production” code.

Rich also made an off-hand comment that if he screws something up, he’s got thousands of users who will let him know! That ex post facto testing, along with the Rich’s own devotion to doing high-quality work, does a good job of handling regressions.

But Rich mentioned something else that is also very important. In a functional language, where values are immutability and mutable state is handled in specific, principled ways, regressions don’t happen nearly as often. Clojure has one of the most deeply thought out approaches for handling state, which is the genius of Clojure.

I asked Rich how long he worked on Clojure before releasing it to the world. He spent about 2 1/2 years, much of that time working exclusively on Clojure (and eating through his savings). When he finally “announced” it, his “marketing” consisted of one email to some friends in the Common Lisp community. The rest was viral, a testament to the justified excitement Clojure has generated.

For me, I’ll probably always do my design thinking through tests, especially when I’m writing code in imperative languages, like Java and Ruby. I’ll continue to encourage my clients to use TDD, because I find that TDD is the most productive way to achieve high quality. I want the safety net of a good test suite. I’m also writing more and more of my code in a functional style, with minimal side effects and mutable data. You should, too.

New Features

Here are the new features for this release.

Named and Default Arguments

Scala method parameters can be declared to with default values, so callers don’t have to specify a value and the implicit convention doesn’t have to be used. The default “values” aren’t limited to constants. Any valid expression can be used. Here is an example that I made up (not in Martin’s slides) that illustrates both specifying and using one default argument and using named arguments.

    
def joiner(strings: List[String], separator: String = " ") = strings.mkString(separator)

val strs = List("Now", "is", "the", "time", "for", "all", "good", "men", "...")
println(joiner(strs))
println(joiner(strs, "|"))
println(joiner(strings = strs, separator = "-"))
    

Named and default arguments enable an elegant enhancement to case classes. It’s great that I can declare a succinct value class like this.

    
case class Person(firstName: String, lastName: String, age: Int)
    

What if I want to make a copy that modifies one or more fields. There’s no elegant way to add such a method in 2.7 without implementing every permutation, that is every possible combination of fields I might want to change. The new copy method will make this easy.

    
case class Person(firstName: String, lastName: String, age: Int)

val youngPerson = Person("Dean", "Wampler", 29)
val oldPerson = youngPerson copy (age = 30)
    

I’m using the infix notation for method invocation on the last line (i.e., it’s equivalent to ... youngPerson.copy(...)). I can specify any combination of the fields I want to change in the list passed to copy. The generated implementation of copy will use the current values of any other fields as the default values.

The implementation looks something like this.

    
case class Person(firstName: String, lastName: String, age: Int) {
  def copy (fName: String = this.firstName, 
            lName: String = this.lastName, 
            aje: Int = this.age) = new Person(fName, lName, aje)
}
    

Quite elegant, once you have default and named arguments!!

Defaults for parameters can’t refer to previous parameters in the list, unless the function is curried. (I’m not sure I got this right, nor do I understand the reasons why this is true – if it’s true!)

By the way, Martin reminded us that method parameters are always evaluated left to right at the call site. Do you remember the rules for Java, C++, C#,...?

Nested Annotations

Annotations can now be nested, which is important for using some of the standard annotation definitions in the JDK and JEE. This feature also exploits named and default arguments.

    
@Annotation1(foo = @Annotation2)
    

Package Objects

People have complained that they want to define top-level definitions for a package, but they have to put those definitions, like types and methods, in an object or class, which doesn’t quite fit and it’s awkward for referencing through package and type qualification. The problem was especially obvious when the team started working on the major reorganization of the collections (discussed below). So, Scala 2.8 will support “package objects”.

    
package object scala {
  type List[+T] = scala.collection.immutable.List
  val List = scala.collection.immutable.List
}
    

Our friend List is now moved to scala.collection.immutable.List, but we would still like to reference it as if it were in the scala package. The definition defines a package-level type and val the effectively make List accessible in the scala scope. In Scala 2.7 you would have to do something like the following (ignoring Predef for a moment).

    
package scala {
  object toplevel {
    type List[+T] = scala.collection.immutable.List
    val List = scala.collection.immutable.List
  }
}
    

But then you would have to reference List using scala.toplevel.List.

Now, they got around this problem previously by putting a bunch of stuff like this in Predef and importing it automatically, but that has several disadvantages.

• Predef is a big, amorphous collection of stuff.
• You can’t define your own Predef with the same convenient usage semantics, i.e., no special import required and no way to reference definitions like package.type. You would have to use the alternative I just showed with toplevel in the middle.

Package objects give you a place for definitions that you want to appear at the package scope without having to define them in a singleton object or class.

Finally, besides types and fields as shown, package objects can also define methods. They can also inherit from traits and classes.

@specialized

Scala generics are fully specified at declaration time using a uniform representation, not when they are used, like C++ templates. This supports the way Java works, where there isn’t a giant link step to resolve all references, etc. However, this has a major performance disadvantage for generic types when they are actually used with AnyVal types that Scala optimizes to primitives.

For example, any closures require the use of FunctionN[T1, T2, ...], e.g.,

    
def m[T](x: T, f: T => T) = f(x)

m(2, (x:Int) => x * 2)
    

The f closure in the definition of m will require instantiation of Function2[T,T]. However, when use AnyVal classes, as in the last line , this has the effect of causing primitive boxing and unboxing several times, hurting performance when this is completely unnecessary in the special case of primitives being used. This is also bad for arrays and some other data structures.

The new @specialized annotation fixes this problem by causing scala to generate different versions of the user-specified generic type or method for each of the primitive types.

    
def m[@specialized T](x: T, f: T => T) = f(x)

m(2, (x:Int) => x * 2)
    

There is a real risk of an explosion of code. Consider what would have to be generated to support every type permutation for Function22! For this reason they only do cases with up to two type parameters in the library. You can also choose to annotate only some of the type parameters, as appropriate, and the annotation will support parameters that let you limit the primitive types that will be supported, e.g., only Ints and Longs.

This feature is not yet in the 2.8 trunk, but it will be soon.

Improved Collections

Collections are getting a major revamp. First they want to eliminate gratuitous differences in package structure and implementations. In many cases, the map method and others have to be redefined for each basic collection type, rather than shared between them.

New Collections Design

The new version of the library will support the following.

• Uniform structure.
• Every operation is implemented only once.
• Selection of building blocks in a separate package called scala.collection.generic. These are normally only used by implementers of immutable and mutable collections.

Because of the reorganization, some Scala 2.7 source code won’t be compatible with 2.8 without modifications.

Better Tools

• The REPL will have command completion, in addition to other enhancements.
• They have greatly improved the IDE and compiler interface. Miles Sabin and Iulian Dragos worked on this with Martin. There is limited and somewhat unstable support in Eclipse now.

New Control Abstractions

Several new control abstractions are being introduced.

• Continuations will be supported with a compiler plugin.
• Scala has not had the break keyword. It will now exist, but as a library method.
• Scala will optimize trampolining tail calls (e.g., foo1 tail calls foo2, which tail calls foo1, and back and forth).

More features

• The Swing wrapper library has been enhanced.
• The performance has been improved in several ways.
  • Structural type dispatch
  • Actors
  • Vectors, sets, and maps. Their long-term goal is to implement the fastest ones available for the JVM.

These changes are not yet in the trunk.

Beyond 2.8

Longer term, they plan significant improvements in support for parallelism and concurrency, including new concurrency models besides actors, such as:

• Transactions (STM)
• Data parallelism
• stream processing

Clojure is influencing this. Martin praised the competition ;) Fortunately, the original designer of the data structures and algorithms used heavily by Clojure is working on Scala versions. (Name?)

Doug Lea wants to work with the team on concurrency data structures. The lack of closures in Java makes this effort difficult in Java.

There is some exciting work in advanced type system support for guaranteeing actor isolation and effect tracking. For example, this technology wouuld allow actors to exchange references to big objects without copying them while ensuring that they aren’t modified concurrently.

On a final note, Bill Wake described a conversation he had with Joshua Bloch today who admitted that the time has arrived for him to look seriously at Scala. A possible endorsement from Joshua Bloch would be a major step for Scala.

I say this because of recent trends in our industry and my hands-on experience with many enterprise and Internet applications, mostly at client sites. You might be thinking that I’m referring to the mainstream breakout of Functional Programming (FP), which is happening right now. The killer app for FP is concurrency. We’ve all heard that more and more applications must be concurrent these days (which doesn’t necessarily mean multithreaded). When we remove side effects from functions and disallow mutable variables, our concurrency issues largely go away. The success of the Actor model of concurrency, as used to great effect in Erlang, is one example of a functional-style approach. The rise of map-reduce computations is another example of a functional technique going mainstream. A related phenomenon is the emergence of key-value store databases, like BigTable and CouchDB, is a reaction to the overhead of SQL databases, when the performance cost of the Relational Model isn’t justified. These databases are typically managed with functional techniques, like map-reduce.

But actually, I’m thinking of something else. Hybrid languages like Scala, F#, and OCaml have demonstrated that OOP and FP can complement each other. In a given context, they let you use the idioms that make the most sense for your particular needs. For example, immutable “objects” and functional-style pattern matching is a killer combination.

What’s really got me thinking that objects are losing their supremacy is a very mundane problem. It’s a problem that isn’t new, but like concurrency, it just seems to grow worse and worse.

The problem is that there is never a stable, clear object model in applications any more. What constitutes a BankAccount or Customer or whatever is fluid. It changes with each iteration. It’s different from one subsystem to another even within the same iteration! I see a lot of misfit object models that try to be all things to all people, so they are bloated and the teams that own them can’t be agile. The other extreme is “balkanization”, where each subsystem has its own model. We tend to think the latter case is bad. However, is lean and mean, but non-standard, worse than bloated, yet standardized?

The fact is, for a lot of these applications, it’s just data. The ceremony of object wrappers doesn’t carry its weight. Just put the data in a hash map (or a list if you don’t need the bits “labeled”) and then process the collection with your iterate, map, and reduce functions. This may sound heretical, but how much Java code could you delete today if you replaced it with a stored procedure?

These alternatives won’t work for all situations, of course. Sometimes polymorphism carries its weight. Unfortunately, it’s too tempting to use objects as if more is always better, like cow bell.

So what would replace objects for supremacy? Well, my point is really that there is no one true way. We’ve led ourselves down the wrong path. Or, to be more precise, we followed a single, very good path, but we didn’t know when to take a different path.

Increasingly, the best, most nimble designs I see use objects with a light touch; shallow hierarchies, small objects that try to obey the Single Responsibility Principle, composition rather than inheritance, etc. Coupled with a liberal use of functional idioms (like iterate, map, and reduce), these designs strike the right balance between the protection of data hiding vs. openness for easy processing. By the way, you can build these designs in almost any of our popular languages. Some languages make this easier than others, of course.

Despite the hype, I think Domain-Specific Languages (DSLs) are also very important and worth mentioning in this context. (Language-Oriented Programming – LOP – generalizes these ideas). It’s true that people drink the DSL Kool-Aid and create a mess. However, when used appropriately, DSLs reduce a program to its essential complexity, while hiding and modularizing the accidental complexity of the implementation. When it becomes easy to write a user story in code, we won’t obsess as much over the details of a BankAccount as they change from one story to another. We will embrace more flexible data persistence models, too.

Back to OOP and FP, I see the potential for their combination to lead to a rebirth of the old vision of software components, but that’s a topic for another blog post.

First, let’s look at a typical example that handles the arguments in an ad hoc fashion. Consider the following Person class. You can pass three arguments to the initializer, the first_name, the last_name, and the age. Or, you can pass a hash using the keys :first_name, :last_name, and :age.

require "rubygems" 
require "spec" 

class Person
  attr_reader :first_name, :last_name, :age
  def initialize *args
    arg = args[0]
    if arg.kind_of? Hash       # 1
      @first_name = arg[:first_name]
      @last_name  = arg[:last_name]
      @age        = arg[:age]
    else
      @first_name = args[0]
      @last_name  = args[1]
      @age        = args[2]
    end
  end
end

describe "Person#initialize" do 
  it "should accept a hash with key-value pairs for the attributes" do
    person = Person.new :first_name => "Dean", :last_name => "Wampler", :age => 39
    person.first_name.should == "Dean" 
    person.last_name.should  == "Wampler" 
    person.age.should        == 39
  end
  it "should accept a first name, last name, and age arguments" do
    person = Person.new "Dean", "Wampler", 39
    person.first_name.should == "Dean" 
    person.last_name.should  == "Wampler" 
    person.age.should        == 39
  end
end

The condition on the # 1 comment line checks to see if the first argument is a Hash. If so, the attribute’s values are extracted from it. Otherwise, it is assumed that three arguments were specified in a particular order. They are passed to #initialize in a three-element array. The two rspec examples exercise these behaviors. For simplicity, we ignore some more general cases, as well as error handling.

Another approach that is more flexible is to use duck typing, instead. For example, we could replace the line with the # 1 comment with this line:

if arg.respond_to? :has_key?

There aren’t many objects that respond to #has_key?, so we’re highly confident that we can use [symbol] to extract the values from the hash.

This implementation is fairly straightforward. You’ve probably written code like this yourself. However, it could get complicated for more involved cases.

Pattern Matching, a Functional Programming Approach

Most programming languages today have switch or case statements of some sort and most have support for regular expression matching. However, in functional programming languages, pattern matching is so important and pervasive that these languages offer very powerful and convenient support for pattern matching.

Fortunately, we can get powerful pattern matching, typical of functional languages, in Ruby using the Case gem that is part of the MenTaLguY’s Omnibus Concurrency library. Omnibus provides support for the hot Actor model of concurrency, which Erlang has made famous. However, it would be a shame to restrict the use of the Case gem to parsing Actor messages. It’s much more general purpose than that.

Let’s rework our example using the Case gem.

require "rubygems" 
require "spec" 
require "case" 

class Person
  attr_reader :first_name, :last_name, :age
  def initialize *args
    case args
    when Case[Hash]       # 1
      arg = args[0]
      @first_name = arg[:first_name]
      @last_name  = arg[:last_name]
      @age        = arg[:age]
    else
      @first_name = args[0]
      @last_name  = args[1]
      @age        = args[2]
    end
  end
end

describe "Person#initialize" do 
  it "should accept a first name, last name, and age arguments" do
    person = Person.new "Dean", "Wampler", 39
    person.first_name.should == "Dean" 
    person.last_name.should  == "Wampler" 
    person.age.should        == 39
  end
  it "should accept a has with :first_name => fn, :last_name => ln, and :age => age" do
    person = Person.new :first_name => "Dean", :last_name => "Wampler", :age => 39
    person.first_name.should == "Dean" 
    person.last_name.should  == "Wampler" 
    person.age.should        == 39
  end
end

We require the case gem, which puts the #=== method on steroids. In the when statement in #initialize, the expression when Case[Hash] matches on a one-element array where the element is a Hash. We extract the key-value pairs as before. The else clause assumes we have an array for the arguments.

So far, this is isn’t very impressive, but all we did was to reproduce the original behavior. Let’s extend the example to really exploit some of the neat features of the Case gem’s pattern matching. First, let’s narrow the allowed array values.

require "rubygems" 
require "spec" 
require "case" 

class Person
  attr_reader :first_name, :last_name, :age
  def initialize *args
    case args
    when Case[Hash]       # 1
      arg = args[0]
      @first_name = arg[:first_name]
      @last_name  = arg[:last_name]
      @age        = arg[:age]
    when Case[String, String, Integer]
      @first_name = args[0]
      @last_name  = args[1]
      @age        = args[2]
    else
      raise "Invalid arguments: #{args}" 
    end
  end
end

describe "Person#initialize" do 
  it "should accept a first name, last name, and age arguments" do
    person = Person.new "Dean", "Wampler", 39
    person.first_name.should == "Dean" 
    person.last_name.should  == "Wampler" 
    person.age.should        == 39
  end
  it "should accept a has with :first_name => fn, :last_name => ln, and :age => age" do
    person = Person.new :first_name => "Dean", :last_name => "Wampler", :age => 39
    person.first_name.should == "Dean" 
    person.last_name.should  == "Wampler" 
    person.age.should        == 39
  end
  it "should not accept an array unless it is a [String, String, Integer]" do
    lambda { person = Person.new "Dean", "Wampler", "39" }.should raise_error(Exception)
  end
end

The new expression when Case[String, String, Integer] only matches a three-element array where the first two arguments are strings and the third argument is an integer, which are the types we want. If you use an array with a different number of arguments or the arguments have different types, this when clause won’t match. Instead, you’ll get the default else clause, which raises an exception. We added another rspec example to test this condition, where the user’s age was specified as a string instead of as an integer. Of course, you could decide to attempt a conversion of this argument, to make your code more “forgiving” of user mistakes.

Similarly, what happens if the method supports default values some of the parameters. As written, we can’t support that option, but let’s look at a slight variation of Person#initialize, where a hash of values is not supported, to see what would happen.

require "rubygems" 
require "spec" 
require "case" 

class Person
  attr_reader :first_name, :last_name, :age
  def initialize first_name = "Bob", last_name = "Martin", age = 29
    case [first_name, last_name, age]
    when Case[String, String, Integer]
      @first_name = first_name
      @last_name  = last_name
      @age        = age
    else
      raise "Invalid arguments: #{first_name}, #{last_name}, #{age}" 
    end
  end
end

def check person, expected_fn, expected_ln, expected_age
  person.first_name.should == expected_fn
  person.last_name.should  == expected_ln
  person.age.should        == expected_age
end

describe "Person#initialize" do 
  it "should require a first name (string), last name (string), and age (integer) arguments" do
    person = Person.new "Dean", "Wampler", 39
    check person, "Dean", "Wampler", 39
  end
  it "should accept the defaults for all parameters" do
    person = Person.new
    check person, "Bob", "Martin", 29
  end
  it "should accept the defaults for the last name and age parameters" do
    person = Person.new "Dean" 
    check person, "Dean", "Martin", 29
  end
  it "should accept the defaults for the age parameter" do
    person = Person.new "Dean", "Wampler" 
    check person, "Dean", "Wampler", 29
  end
  it "should not accept the first name as a symbol" do
    lambda { person = Person.new :Dean, "Wampler", "39" }.should raise_error(Exception)
  end
  it "should not accept the last name as a symbol" do
  end
  it "should not accept the age as a string" do
    lambda { person = Person.new "Dean", "Wampler", "39" }.should raise_error(Exception)
  end
end

We match on all three arguments as an array, asserting they are of the correct type. As you might expect, #initialize always gets three parameters passed to it, including when default values are used.

Let’s return to our original example, where the object can be constructed with a hash or a list of arguments. There are two more things (at least …) that we can do. First, we’re not yet validating the types of the values in the hash. Second, we can use the Case gem to impose constraints on the values, such as requiring non-empty name strings and a positive age.

require "rubygems" 
require "spec" 
require "case" 

class Person
  attr_reader :first_name, :last_name, :age
  def initialize *args
    case args
    when Case[Hash]
      arg = args[0]
      @first_name = arg[:first_name]
      @last_name  = arg[:last_name]
      @age        = arg[:age]
    when Case[String, String, Integer]
      @first_name = args[0]
      @last_name  = args[1]
      @age        = args[2]
    else
      raise "Invalid arguments: #{args}" 
    end
    validate_name @first_name, "first_name" 
    validate_name @last_name, "last_name" 
    validate_age
  end

  protected

  def validate_name name, field_name
    case name
    when Case::All[String, Case.guard {|s| s.length > 0 }]
    else
      raise "Invalid #{field_name}: #{first_name}" 
    end
  end

  def validate_age
    case @age
    when Case::All[Integer, Case.guard {|n| n > 0 }]
    else
      raise "Invalid age: #{@age}" 
    end
  end
end

describe "Person#initialize" do 
  it "should accept a first name, last name, and age arguments" do
    person = Person.new "Dean", "Wampler", 39
    person.first_name.should == "Dean" 
    person.last_name.should  == "Wampler" 
    person.age.should        == 39
  end
  it "should accept a has with :first_name => fn, :last_name => ln, and :age => age" do
    person = Person.new :first_name => "Dean", :last_name => "Wampler", :age => 39
    person.first_name.should == "Dean" 
    person.last_name.should  == "Wampler" 
    person.age.should        == 39
  end
  it "should not accept an array unless it is a [String, String, Integer]" do
    lambda { person = Person.new "Dean", "Wampler", "39" }.should raise_error(Exception)
  end
  it "should not accept a first name that is a zero-length string" do
    lambda { person = Person.new "", "Wampler", 39 }.should raise_error(Exception)
  end    
  it "should not accept a first name that is not a string" do
    lambda { person = Person.new :Dean, "Wampler", 39 }.should raise_error(Exception)
  end    
  it "should not accept a last name that is a zero-length string" do
    lambda { person = Person.new "Dean", "", 39 }.should raise_error(Exception)
  end    
  it "should not accept a last name that is not a string" do
    lambda { person = Person.new :Dean, :Wampler, 39 }.should raise_error(Exception)
  end    
  it "should not accept an age that is less than or equal to zero" do
    lambda { person = Person.new "Dean", "Wampler", -1 }.should raise_error(Exception)
    lambda { person = Person.new "Dean", "Wampler", 0 }.should raise_error(Exception)
  end    
  it "should not accept an age that is not an integer" do
    lambda { person = Person.new :Dean, :Wampler, "39" }.should raise_error(Exception)
  end    
end

We have added validate_name and validate_age methods that are invoked at the end of #initialize. In validate_name, the one when clause requires “all” the conditions to be true, that the name is a string and that it has a non-zero length. Similarly, validate_age has a when clause that requires age to be a positive integer.

Final Thoughts

So, how valuable is this? The code is certainly longer, but it specifies and enforces expected behavior more precisely. The rspec examples verify the enforcement. It smells a little of static typing, which is good or bad, depending on your point of view. ;)

Personally, I think the conditional checks are a good way to add robustness in small ways to libraries that will grow and evolve for a long time. The checks document the required behavior for code readers, like new team members, but of course, they should really get that information from the tests. ;) (However, it would be nice to extract the information into the rdocs.)

For small, short-lived projects, I might not worry about the conditional checks as much (but how many times have those “short-lived projects” refused to die?).

You can read more about Omnibus and Case in this InfoQ interview with MenTaLguY. I didn’t discuss using the Actor model of concurrency, for which these gems were designed. For an example of Actors using Omnibus, see my Better Ruby through Functional Programming presentation or the Confreak’s video of an earlier version of the presentation I gave at last year’s RubyConf.

The Interwebs are full of technical comparisons between Java and the different languages, e.g., why language X fixes Java’s perceived issue Y. I won’t rehash those arguments here, but I will describe some language features, as needed.

A similar “polyglot” trend is happening on the .NET platform.

The New JVM Languages

I’ll limit my discussion to these representative (and best known) alternative languages for the JVM.

1. JRuby – Ruby running on the JVM.
2. Scala – A hybrid object-oriented and functional language that runs on .NET as well as the JVM. (Disclaimer: I’m co-writing a book on Scala for O’Reilly.)
3. Clojure – A Lisp dialect.

I picked these languages because they seem to be the most likely candidates for most enterprises considering a new JVM language, although some of the languages listed below could make that claim.

There are other deserving languages besides these three, but I don’t have the time to do them justice. Hopefully, you can generalize the subsequent discussion for these other languages.

1. Groovy – A dynamically-typed language designed specifically for interoperability with Java. It will appeal to teams that want a dynamically-typed language that is closer to Java than Ruby. With Grails, you have a combination that’s comparable to Ruby on Rails.
2. Jython – The first non-Java language ported to the JVM, started by Jim Hugunin in 1997. Most of my remarks about JRuby are applicable to Jython. Django is the Python analog of Rails. If your Java shop already has a lot of Python, consider Jython.
3. Fan – A hybrid object-oriented and functional language that runs on .NET, too. It has a lot of similarities to Scala, like a scripting-language feel.
4. Ioke – (pronounced “eye-oh-key”) An innovative language developed by Ola Bini and inspired by Io and Lisp. This is the newest language discussed here. Hence, it has a small following, but a lot of potential. The Io/Lisp-flavored syntax will be more challenging to average Java developers than Scala, JRuby, Jython, Fan, and JavaScript.
5. JavaScript, e.g., Rhino – Much maligned and misunderstood (e.g., due to buggy and inconsistent browser implementations), JavaScript continues to gain converts as an alternative scripting language for Java applications. It is the default scripting language supported by the JDK 6 scripting interface.
6. Fortress – A language designed as a replacement for high-performance FORTRAN for industrial and academic “number crunching”. This one will interest scientists and engineers…

Note: Like a lot of people, I use the term scripting language to refer to languages with a lightweight syntax, usually dynamically typed. The name reflects their convenience for “scripting”, but that quality is sometimes seen as pejorative; they aren’t seen as “serious” languages. I reject this view.

To learn more about what people are doing on the JVM today (with some guest .NET presentations), a good place to start is the recent JVM Language Summit.

Criteria For Evaluating New JVM Languages

I’ll frame the discussion around a few criteria you should consider when evaluating language choices. I’ll then discuss how each of the languages address those criteria. Since we’re restricting ourselves to JVM languages, I assume that each language compiles to valid byte code, so code in the new language and code written in Java can call each other, at least at some level. The “some level” part will be one criterion. Substitute X for the language you are considering.

1. Interoperability: How easily can X code invoke Java code and vice versa? Specifically:
   1. Create objects (i.e., call new Foo(...)).
   2. Call methods on an object.
   3. Call static methods on a class.
   4. Extend a class.
   5. Implement an interface.
2. Object Model: How different is the object model of X compared to Java’s object model? (This is somewhat tied to the previous point.)
3. New “Ideas”: Does X support newer programming trends:
   1. Functional Programming.
   2. Metaprogramming.
   3. Easier approaches to writing robust concurrent applications.
   4. Easier support for processing XML, SQL queries, etc.
   5. Support internal DSL creation.
   6. Easier presentation-tier development of web and thick-client UI’s.
4. Stability: How stable is the language, in terms of:
   1. Lack of Bugs.
   2. Stability of the language’s syntax, semantics, and library API’s. (All the languages can call Java API’s.)
5. Performance: How does code written in X perform?
6. Adoption: Is X easy to learn and use?
7. Tool Support: What about editors, IDE’s, code coverage, etc.
8. Deployment: How are apps and libraries written in X deployed?
   1. Do I have to modify my existing infrastructure, management, etc.?

The Interoperability point affects ease of adoption and use with a legacy Java code base. The Object Model and Adoption points address the barrier to adoption from the learning point of view. The New “Ideas” point asks what each language brings to development that is not available in Java (or poorly supported) and is seen as valuable to the developer. Finally, Stability, Performance, and Deployment address very practical issues that a candidate production language must address.

Comparing the Languages

JRuby

JRuby is the most popular alternative JVM langauge, driven largely by interest in Ruby and Ruby on Rails.

Interoperability

Ruby’s object model is a little different than Java’s, but JRuby provides straightforward coding idioms that make it easy to call Java from JRuby. Calling JRuby from Java requires the JSR 223 scripting interface or a similar approach, unless JRuby is used to compile the Ruby code to byte code first. In that case, shortcuts are possible, which are well documented.

Object Model

Ruby’s object model is a little different than Java’s. Ruby support mixin-style modules, which behave like interfaces with implementations. So, the Ruby object model needs to be learned, but it is straightforward or the Java developer.

New Ideas

JRuby brings closures to the JVM, a much desired feature that probably won’t be added in the forthcoming Java 7. Using closures, Ruby supports a number of functional-style iterative operations, like mapping, filtering, and reducing/folding. However, Ruby does not fully support functional programming.

Ruby uses dynamic-typing instead of static-typing, which it exploits to provide extensive and powerful metaprogramming facilities.

Ruby doesn’t offer any specific enhancements over Java for safe, robust concurrent programming.

Ruby API’s make XML processing and database access relatively easy. Ruby on Rails is legendary for improving the productivity of web developers and similar benefits are available for thick-client developers using other libraries.

Ruby is also one of the best languages for defining “internal” DSL’s, which are used to great affect in Rails (e.g., ActiveRecord).

Stability

JRuby and Ruby are very stable and are widely used in production. JRuby is believed to be the best performing Ruby platform.

The Ruby syntax and API are undergoing some significant changes in the current 1.9.X release, but migration is not a major challenge.

Performance

JRuby is believed to be the best performing Ruby platform. While it is a topic of hot debate, Ruby and most dynamically-typed languages have higher runtime overhead compared to statically-typed languages. Also, the JVM has some known performance issues for dynamically-typed languages, some of which will be fixed in JDK 7.

As always, enterprises should profile code written in their languages of choice to pick the best one for each particular task.

Adoption

Ruby is very easy to learn, although effective use of advanced techniques like metaprogramming require some time to master. JRuby-specific idioms are also easy to master and are well documented.

Tool Support

Ruby is experiencing tremendous growth in tool support. IDE support still lags support for Java, but IntelliJ, NetBeans, and Eclipse are working on Ruby support. JRuby users can exploit many Java tools.

Code analysis tools and testing tools (TDD and BDD styles) are now better than Java’s.

Deployment

JRuby applications, even Ruby on Rails applications, can be deployed as jars or wars, requiring no modifications to an existing java-based infrastructure. Teams use this approach to minimize the “friction” of adopting Ruby, while also getting the performance benefits of the JVM.

Because JRuby code is byte code at runtime, it can be managed with JMX, etc.

Scala

Scala is a statically-typed language that supports an improved object model (with a full mixin mechanism called traits; similar to Ruby modules) and full support for functional programming, following a design goal of the inventor of Scala, Martin Odersky, that these two paradigms can be integrated, despite some surface incompatibilities. Odersky was involved in the design of Java generics (through earlier research languages) and he wrote the original version of the current javac. The name is a contraction of “scalable language”, but the first “a” is pronounced like “ah”, not long as in the word “hay”.

The syntax looks like a cross between Ruby (method definitions start with the def keyword) and Java (e.g., curly braces). Type inferencing and other syntactic conventions significantly reduce the “cluuter”, such as the number of explicit type declarations (“annotations”) compared to Java. Scala syntax is very succinct, sometimes even more so than Ruby! For more on Scala, see also my previous blog postings, part 1, part 2, part 3, and this related post on traits vs. aspects.

Interoperability

Scala’s has the most seamless interoperability with Java of any of the languages discussed here. This is due in part to Scala’s static typing and “closed” classes (as opposed to Ruby’s “open” classes). It is trivial to import and use Java classes, implement interfaces, etc.

Direct API calls from Java to Scala are also supported. The developer needs to know how the names of Scala methods are encoding in byte code. For example, Scala methods can have “operator” names, like ”+”. In the byte code, that name will be ”$plus”.

Object Model

Scala’s object model extends Java’s model with traits, which support flexble mixin composition. Traits behave like interfaces with implementations. The Scala object model provides other sophisticated features for building “scalable applications”.

New Ideas

Scala brings full support for functional programming to the JVM, including first-class function and closures. Other aspects of functional programming, like immutable variables and side-effect free functions, are encouraged by the language, but not mandated, as Scala is not a pure functional language. (Functional programming is very effective strategy for writing tread-safe programs, etc.) Scala’s Actor library is a port of Erlang’s Actor library, a message-based concurrency approach.

In my view, the Actor model is the best general-purpose approach to concurrency. There are times when multi-threaded code is needed for performance, but not for most concurrent applications. (Note: there are Actor libraries for Java, e.g., Kilim.)

Scala has very good support for building internal DSL’s, although it is not quite as good as Ruby’s features for this purpose. It has a combinator parser library that makes external DSL creation comparatively easy. Scala also offers some innovative API’s for XML processing and Swing development.

Stability

Scala is over 5 years old and it is very stable. The API and syntax continue to evolve, but no major, disruptive changes are expected. In fact, the structure of the language is such that almost all changes occur in libraries, not the language grammar.

There are some well-known production deployments, such as back-end services at twitter.

Performance

Scala provides comparable performance to Java, since it is very close “structurally” to Java code at the byte-code level, given the static typing and “closed” classes. Hence, Scala can exploit JVM optimizations that aren’t available to dynamically-typed languages.

However, Scala will also benefit from planned improvements to support dynamically-typed languages, such as tail-call optimizations (which Scala current does in the compiler.) Hence, Scala probably has marginally better performance than JRuby, in general. If true, Scala may be more appealing than JRuby as a general-purpose, systems language, where performance is critical.

Adoption

Scala is harder to learn and master than JRuby, because it is a more comprehensive language. It not only supports a sophisticated object model, but it also supports functional programming, type inferencing, etc. In my view, the extra effort will be rewarded with higher productivity. Also, because it is closer to Java than JRuby and Clojure, new users will be able to start using it quickly as a “better object-oriented Java”, while they continue to learn the more advanced features, like functional programming, that will accelerate their productivity over the long term.

Tool Support

Scala support in IDE’s still lags support for Java, but it is improving. IntelliJ, NetBeans, and Eclipse now support Scala with plugins. Maven and ant are widely used as the build tool for Scala applications. Several excellent TDD and BDD libraries are available.

Deployment

Scala applications are packaged and deployed just like Java applications, since Scala files are compiled to class files. A Scala runtime jar is also required.

Clojure

Of the three new JVM languages discussed here, Clojure is the least like Java, due to its Lisp syntax and innovative “programming model”. Yet it is also the most innovative and exciting new JVM language for many people. Clojure interoperates with Java code, but it emphasizes functional programming. Unlike the other languages, Clojure does not support object-oriented programming. Instead, it relies on mechanisms like multi-methods and macros to address design problems for which OOP is often used.

One exciting innovation in Clojure is support for software transactional memory, which uses a database-style transactional approach to concurrent modifications of in-memory, mutable state. STM is somewhat controversial. You can google for arguments about its practicality, etc. However, Clojure’s implementation appears to be successful.

Clojure also has other innovative ways of supporting “principled” modification of mutable data, while encouraging the use of immutable data. These features with STM are the basis of Clojure’s approach to robust concurrency.

Finally, Clojure implements several optimizations in the compiler that are important for functional programming, such as optimizing tail call recursion.

Disclaimer: I know less about Clojure than JRuby and Scala. While I have endeavored to get the facts right, there may be errors in the following analysis. Feedback is welcome.

Interoperability

Despite the Lisp syntax and functional-programming emphasis, Clojure interoperates with Java. Calling java from Clojure uses direct API calls, as for JRuby and Scala. Calling Clojure from Java is a more involved. You have to create Java proxies on the Clojure side to generate the byte code needed on the Java side. The idioms for doing this are straightforward, however.

Object Model

Clojure is not an object-oriented language. However, in order to interoperate with Java code, Clojure supports implementing interfaces and instantiating Java objects. Otherwise, Clojure offers a significant departure for develops well versed in object-oriented programming, but with little functional programming experience.

New Ideas

Clojure brings to the JVM full support for functional programming and popular Lisp concepts like macros, multi-methods, and powerful metaprogramming. It has innovative approaches to safe concurrency, including “principled” mechanisms for supporting mutable state, as discussed previously.

Clojure’s succinct syntax and built-in libraries make processing XML succinct and efficient. DSL creation is also supported using Lisp mechanisms, like macros.

Stability

Clojure is the newest of the three languages profiled here. Hence, it may be the most subject to change. However, given the nature of Lisps, it is more likely that changes will occur in libraries than the language itself. Stability in terms of bugs does not appear to be an issue.

Clojure also has the fewest known production deployments of the three languages. However, industry adoption is expected to happen rapidly.

Performance

Clojure supports type “hints” to assist in optimizing performance. The preliminary discussions I have seen suggest that Clojure offers very good performance.

Adoption

Clojure is more of a departure from Java than is Scala. It will require a motivated team that likes Lisp ;) However, such a team may learn Clojure faster than Scala, since Clojure is a simpler language, e.g., because it doesn’t have its own object model. Also, Lisps are well known for being simple languages, where the real learning comes in understanding how to use it effectively!

However, in my view, as for Scala, the extra learning effort will be rewarded with higher productivity.

Tool Support

As a new language, tool support is limited. Most Clojure developers use Emacs with its excellent Lisp support. Many Java tools can be used with Clojure.

Deployment

Clojure deployment appears to be as straightforward as for the other languages. A Clojure runtime jar is required.

Comparisons

Briefly, let’s review the points and compare the three languages.

Interoperability

All three languages make calling Java code straightforward. Scala interoperates most seamlessly. Scala code is easiest to invoke from Java code, using direct API calls, as long as you know how Scala encodes method names that have “illegal” characters (according to the JVM spec.). Calling JRuby and Clojure code from Java is more involved.

Therefore, if you expect to continue writing Java code that needs to make frequent API calls to the code in the new language, Scala will be a better choice.

Object Model

Scala is closest to Java’s object model. Ruby’s object model is superficially similar to Scala’s, but the dynamic nature of Ruby brings significant differences. Both extend Java’s object model with mixin composition through traits (Scala) or modules (Ruby), that act like interfaces with implementations.

Clojure is quite different, with an emphasis on functional programming and no direct support for object-oriented programming.

New Ideas

JRuby brings the productivity and power of a dynamically-typed language to the JVM, along with the drawbacks. It also brings some functional idioms.

Scala and Clojure bring full support for functional programming. Scala provides a complete Actor model of concurrency (as a library). Clojure brings software transactional memory and other innovations for writing robust concurrent applications. JRuby and Ruby don’t add anything specific for concurrency.

JRuby, like Ruby, is exceptionally good for writing internal DSL’s. Scala is also very good and Clojure benefits from Lisp’s support for DSL creation.

Stability

All the language implementations are of high quality. Scala is the most mature, but JRuby has the widest adoption in production.

Performance

Performance should be comparable for all, but JRuby and Clojure have to deal with some inefficiencies inherent to running dynamic languages on the JVM. Your mileage may vary, so please run realistic profiling experiments on sample implementations that are representative of your needs. Avoid “prematurely optimization” when choosing a new language. Often, team productivity and “time to market” are more important than raw performance.

Adoption

JRuby is the the easiest of the three languages to learn and adopt if you already have some Ruby or Ruby on Rails code in your environment.

Scala has the lowest barrier to adoption because it is the language that most resembles Java “philosophically” (static typing, emphasis on object-oriented programming, etc.). Adopters can start with Scala as a “better Java” and gradually learn the advanced features (mixin composition with traits and functional programming). Scala will appeal the most to teams that prefer statically-typed languages, yet want some of the benefits of dynamically-typed languages, like a succinct syntax.

However, Scala is the most complex of the three languages, while Clojure requires the biggest conceptual leap from Java.

Clojure will appeal to teams willing to explore more radical departures from what they are doing now, with potentially great payoffs!

Deployment

Deployment is easy with all three languages. Scala is most like Java, since you normally compile to class files (there is a limited interpreter mode). JRuby and Clojure code can be interpreted at runtime or compiled.

Summary and Conclusions

All three choices (or comparable substitutions from the list of other languages), will provide a Java team with a more modern language, yet fully leverage the existing investment in Java. Scala is the easiest incremental change. JRuby brings the vibrant Ruby world to the JVM. Clojure offers the most innovative departures from Java.