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Java.next() - Clojure: The Return of the Lispers

Prelude

This is the third part of the series of Java.next(). Last time we’ve discussed the merits of Scala – an OO language with a strong emphasis on functional and parallel programming. Today we’ll be discussing Clojure – a language that pushes the envelop a lot further than Scala as far as functional and parallel programming are concerned. You’ll see that Clojure is radically different from Scala and Groovy in many aspects – it’s not an OO language, it doesn’t have Algol-derived syntax and it introduces a few rather radical ideas on subjects such as identity and state.

Clojure’s approach to concurrency is characterized by the concept of identities, which represent a series of immutable states over time. Since states are immutable values, any number of workers can operate on them in parallel, and concurrency becomes a question of managing changes from one state to another. For this purpose, Clojure provides several mutable reference types, each having well-defined semantics for the transition between states. This is a pretty complex topic for a language overview so I’ll discuss it here only cursory.

Clojure was initially conceived as targeting both the JVM and the CLR. For various reason currently the core development team is targeting mainly the JVM and the CLR port is receiving less attention (though it’s not abandoned and it’s regularly being updated). Like Scala it’s not technically correct to call it a JVM languages, because it’s both a JVM and a .Net language, but since it’s used a lot more often with Java it doesn’t make that much of a difference.

A brief history of Clojure

Clojure is a very young project (compared to most popular programming languages at least). It was created in 2007 by Rich Hickey (project leader and benevolent dictator for life). He developed Clojure because he wanted a modern Lisp for functional programming, symbiotic with the established Java platform, and designed for concurrency. What started as a hobby project turned into some much bigger with Rich getting so excited about the project that left his job to work full-time on the project, effectively through burning his life’s savings in the process. This was quite the gamble, but it turned out to be a lucky one, since Clojure’s popularity rapidly spiked and loyal and energetic community was quickly formed around the project.

Clojure became the new language of choice for many well respected hackers from different communities. I’ve noticed something of a trend in that department – most Clojure hackers used to be Ruby hackers. I guess some of the Ruby hackers were not satisfied by partial subset of Lisp features, available in Ruby, and wanted to gain access to all of Lisp’s power.

The current Clojure version is 1.2 with 1.3 being just around the corner.

Installing Clojure

Before installing Clojure make sure you have Java installed. Java 5.0 SE will do, but Java 6 SE is highly recommended.

Installing Clojure is just a matter of downloading and extracting a single archive – clojure.zip. Many high quality Clojure libraries come prepackaged in another archived named Clojure Contrib. Clojure Contrib is kind of a staging area for Clojure development – it’s a common practice to move some of the best parts of contrib into core.

In the directory in which you expanded clojure.zip, run:

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$ java -cp clojure.jar clojure.main

This will bring up a simple read-eval-print loop (REPL) a.k.a. an interactive console. Much of Clojure is defined in Clojure itself (in the core.clj file included in the src directory of distribution), which is automatically loaded from the .jar file when Clojure starts. The file user.clj, if found in the classpath, will be auto-loaded as well. You can leverage this to cause code to run when Clojure starts. Reading the code in core.clj is a very good way to get started with language and see how real Clojure hackers work.

When core.clj is loaded you will have the language as described herein fully available. Try:

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user=> (+ 1 2 3)
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user=> (println "Hello, Clojure!")
Hello, Clojure!
nil
user=> (javax.swing.JOptionPane/showMessageDialog nil "Hello World")

The REPL has very rudimentary editing. For a better experience, try running it via the JLine ConsoleRunner:

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java -cp jline-0_9_5.jar:clojure.jar jline.ConsoleRunner clojure.main

This will give you left/right arrow key navigation and up/down arrow command history.

Later on we’ll see how to setup a real Clojure development environment in which you’ll be able to leverage the full power of the Clojure REPL and interactive programming.

Did you say Clojure was a Lisp???

Most developers tend to have a very negative attitude towards the word Lisp in general. I guess it spawns all kinds of nasty associations in their brains – like prefix syntax notation, tons of parentheses and a lot of crap they heard about Lisp from their 70 year-old professor, teaching introduction to functional programming in college, whose notion of functional programming is that in Lisp everything is a function (believe me when I tell you this – such professors do exist).

You know, I wasn’t born coding in Lisp myself. I was initially exposed to Lisp when I tried to learn the Emacs text editor and I was baffled by many things – the strange syntax, the talk about atoms and lists, code as data, macros, continuation, tail-call optimizations, what to quote and that to evaluate, what is a s-expression and what isa form. To put it shortly – I was like Alice down the rabbit whole or like Neo when he found out what the Matrix is… The world of programming that I was familiar with was turned upside down.

The interesting thing is that I felt similarly when I learnt my first programming language Pascal in the 8th grade. It’s not that Lisp is any harder than the other programming languages – it’s just that it’s different and you’re usually approaching it from a position in which you know one or several programming languages, using the much more widespread Algol syntax. I advise you to simply keep an open mind while you read this article and don’t just dismiss Clojure because of its Lisp heritage.

You likely know at least one person who constantly keeps telling you how Lisp is the one true programming language, how everything pales next to it and how Lisp is the actual answer to that fundamental question about the life, the universe and everything else (not 42). These people might very well be telling the truth, but their zealous rants tend to create a negative attitude towards the Lisp community as well. Please, ignore them.

Now it’s time to take the red pill… So fasten your seat-belt Dorothy, ‘cause Kansas is going bye-bye!

Clojure at a glance

Mutable state is the new spaghetti code!

Rich Hickey Creator of Clojure

Time is the new memory!

Rich Hickey Creator of Clojure

Clojure feels like a general-purpose language beamed back from the
near future. Its support for functional programming and software
transactional memory is well beyond current practice and is well
suited for multicore hardware. At the same time, Clojure is well
grounded in the past and the present. It brings together Lisp and the
Java Virtual Machine. Lisp brings wisdom spanning most of the history
of programming, and Java brings the robustness, extensive libraries,
and tooling of the dominant platform available today.

Stuart Halloway Author of “Programming Clojure”

If I had only a few minutes to describe Clojure this would be the gist of it:

  • Dynamic language for the JVM
    • Clojure uses dynamic typing like languages such Ruby and Python and a very smart compiler that will generally generate very efficient bytecode. If you want to push the performance envelope even further you can add some explicit type hints in your Clojure code to ensure the generation of even faster bytecode.
  • Lisp reloaded
    • Clojure is Lisp down to its core – it has all the features that are known and loved, and in the same time it improves upon them in several ways – for instance we have a literal syntax for most common collection types. Sure – with Common Lisp’s reader macros one can do this as well, but nobody bothers to…
  • Elegant
    • Clojure code tends to be very concise, but very readable non the less. A core idea in Clojure is to strip the incidental complexity of problems solving and to be able to just solve the problems straight away without much ceremony.
  • Functional
    • Clojure puts heavy emphasis on functional programming, but it’s a practical functional programming language – not a pure one like Haskell. Clojure acknowledges that some stuff simply change and operations have to generally produce some effects, and makes those things easy to model. Clojure has a library full of rock-solid immutable data structures, relies heavily on lazy evaluation, tail-recursion and higher-order functions.
  • Designed with concurrency in mind
    • Clojure has an extensive built-in high-level facilities to deal with concurrent access to data and parallel programming in general.
  • Fast
    • Unlike many dynamic languages (even those running on top the JVM) Clojure is quite fast – it some scenarios it’s performance can rival that of a statically type language. You can browse a long list of benchmark comparisons to verify this statement. Given the fact that performance if often cited as a big drawback of dynamic languages, this is quite the win for Clojure.
  • Capable of easily leveraging existing Java code
    • Using Java code from Clojure is direct, easy and idiomatic. There are no intermediate layers or the need to write wrapper for everything.

I’ll discuss in greater detail some of those features as I moving along.

A whirlwind tour of Clojure

Clojure is elegant

  • Removes a lot of accidental complexity
  • Clojure programs are generally expressive and concise
  • Concise programs are naturally easier to understand and maintain

Consider this Java example from our Scala discussion:

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public boolean hasUpperCase(String word) {
    if (word == null) {
        return false;
    }
    int len = word.length();
    for (int i = 0; i < len; i++) {
        if (Character.isUpperCase(word.charAt(i))) {
            return true;
        }
    }
    return false;
}

As a reader pointed out using the third party Guava library the code can be reduced to:

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public boolean hasUpperCase(String word) {
    if (null != word)
        return any(charactersOf(word), new Predicate() {
                public boolean apply(Character c) {
                    return isUpperCase(c);
                }
            })
        else
            return false;
}

It still looks quite ugly to me. For the sake of comparison here’s the Clojure version:

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(defn has-uppercase? [string]
  (some #(Character/isUpperCase %) string))

The definition of the problems is “A string has an uppercase character if some of the characters in it is uppercase” (doesn’t sound good, but will do). The Clojure code reads more or less like the definition of the problem. A finer point is that it will work correctly even if you pass nil to the has-uppercase? function.

Clojure is concise

We already saw in the previous example the conciseness of Clojure code, but here we’ll add another example. This is Java:

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class Person {
    private String name;
    private int age;

    Person(String name, int age) {
        this.name = name;
        this.age = age;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public int getAge() {
        return age;
    }

    public void setAge(int age) {
        this.age = age;
    }
}

In Clojure you’d probably model this class like this:

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user> (defrecord person [name age])
user.person
user> (person. "Bozhidar" 26)
#:user.person{:name "Bozhidar", :age 26}
user> (def me (person. "Bozhidar" 26))
#'user/me
user> (:name me)
"Bozhidar"
user> (:age me)
26

Clojure’s version of the type is a one liner and remain shorter even with a few examples of its usage. It’s not equivalent to Java definition, however – the Clojure version of the structure is immutable.

Clojure is a Lisp

For better or for worse Clojure is a Lisp dialect and we should accept this. I have always viewed Lisp as weapon from a more elegant era when people were more concerned with elements of style and empowering the developers to easily translate their thoughts into programs. For one reason or another every Lisp dialect has failed to capture the attention of a critical mass of developers that may propel it into the mainstream. This, of course, doesn’t mean that existing Lisp dialects should be considered a failure. Scheme (the dialect that has probably had the greatest impact on Clojure’s design) was designed to be as simple as possible so it could be appropriate as a teaching tool – in that area Scheme certainly excelled. Common Lisp was created to bring together the various Lisp dialects under a common denominator and it also excelled in that endeavour. I guess that its creators hoped that it would capture a significant market share at some point as well, but alas – that never happened.

The syntax and programming model of Lisp so far have been too much for a typical developer to absorb. Yet, there’s something special about Lisp that’s worth revisiting, so the new dialects continue to emerge. Some of the best programming universities start teaching programming with the Lisp language to form young minds while they are still open. I have witnessed something of a cycle – every 5 or so years the interest in Lisp spikes because of something major that happened in the land of Lisp. In the beginning of the nineties the work on Common Lisp excited a lot of developers. Five years later Paul Graham took the stage – he showed the world how Lisp can empower small teams and make real money (if you haven’t heard of Viaweb fire up google now). Five years ago Peter Seibel published the “Practical Common Lisp” book that appeared on Amazon’s bestseller list and got a lot of new developers excited about Lisp. A now… well I guess you can figure that one out on your own.

Lisp just won’t let go and die. There is something magical about it. And there is this inexplicable smugness on the face of Lisp developers…

So let’s review the typical Lisp features that are present in Clojure:

  • Lisp-1 dialect
    • This means that functions and variables share the same namespace, unlike in dialects like Common Lisp. Both approaches have their merits and drawbacks as usual. In Clojure you cannot have a variable named the say way as a function, but you can pass function names around without any special syntax. In Common Lisp you can have function and variables sharing the same name, but you have to mark functions explicitly when you’re passing them around (with the #‘ reader macro or the function function).
  • Dynamic – both in term of dynamic typing and dynamic development. Lisp is the language that made popular the technique of incremental interactive development (we’ll talk about it more in a bit)
  • Code is data
    • List code is defined in term of Lisp data structures. When the reader read the source code of the application it converts it to standard Lisp objects and they represent the program. No special transformations, no AST. This is the heart of Lisp macros and a centrepiece in Lisp philosophy.
  • Reader
    • The reader can read valid Clojure forms and translate them into Clojure objects – object, function call, everything that has a readable representation
  • Small core, next to none syntax
    • In terms of compact syntax and uniformity of the syntax it has always been hard to beat Lisp. Most programming languages are full of special syntax constructs, keywords, etc. In lisp programs are just lists and the low-level plumbing comes in the form of special functions called special forms. Everything else is implemented in terms of those special forms.
  • Sequences
    • Clojure abstracts common collection traits into an abstraction called seq which allows you to use a similar API for many tasks
  • Macros
    • Forget about C macros, Word macros, editor macros. Lisp macros are the most powerful metaprogramming technique out there – they enable you generate new syntax abstractions unlike anything else. If you need a new operator in Java you’d have a hard time convincing the guys in Oracle to add it for you. With Lisp you’re in charge and you can define any syntax abstractions that you wish. As an appetiser consider the and boolean statement. In most languages it’s built into the language itself. In Clojure it’s just a short macro:
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(defmacro and
  "Evaluates exprs one at a time, from left to right. If a form
  returns logical false (nil or false), and returns that value and
  doesn't evaluate any of the other expressions, otherwise it returns
  the value of the last expr. (and) returns true."
  {:added "1.0"}
  ([] true)
  ([x] x)
  ([x & next]
   `(let [and# ~x]
      (if and# (and ~@next) and#))))

I hope this example gives you an idea about the power that Clojure offers you – you can be more than just a programmer; you can be the language designer.

Persistent data structures

Data structures in Clojure are immutable. Operations executed on them return a new data structure of the same type instead of modifying the structure in place. Understandably most people are immediately concerned about the performance implications of such a technique – this seems like quite a lot of overhead. They needn’t worry, though.

Data structures in Clojure happen to be persistent as well. A persistent data structure is a data structure which always preserves the previous version of itself when it is modified – such data structures are effectively immutable, as their operations do not (visibly) update the structure in-place, but instead always yield a new updated structure. A persistent data structure is not a data structure committed to persistent storage, such as a disk; this is a different and unrelated sense of the word “persistent”.

Persistent data structures save a lot of copying around and improve greatly the performance. Knowing this should be enough for most developers, those that are more curious can find a lot of interesting articles on the subject on-line.

The core data structures in Clojure are:

  • List
  • Set – all the items in it are unique
  • Map – also known as associative array and dictionary in other languages
  • Vector – also know as one dimensional array

Let’s see them in action:

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;;; Lists
;; list creation
user> (list 1 2 3)
(1 2 3)
;; quoted list creation
user> (def a-list '(1 2 3 4 5 6 7 8 9 10))
#'user/a-list
;; find the size of a list
user> (count a-list)
10
user> (first a-list)
1
user> (rest a-list)
(2 3 4 5 6 7 8 9 10)
user> (last a-list)
10
;; find the elements of the list matching a predicate(boolean function)
user> (filter even? a-list)
(2 4 6 8 10)
user> (filter odd? a-list)
(1 3 5 7 9)
;; map an anonymous(lambda) function to all elements of the list
user> (map #(* % 2) a-list)
(2 4 6 8 10 12 14 16 18 20)
;; add an element to the beginning of the list
user> (cons 0 a-list)
(0 1 2 3 4 5 6 7 8 9 10)
;; cons in a list specific function, conj is a general purpose one and
;; works on all collection (but in a different manner)
user> (conj a-list 0)
(0 1 2 3 4 5 6 7 8 9 10)
;; retrieve the first five items in a list
user> (take 5 a-list)
(1 2 3 4 5)
;; retrieve all but the first five items in a list
user> (drop 5 a-list)
(6 7 8 9 10)
user> (take-while #(< % 3) a-list)
(1 2)
user> (drop-while #(> % 3) a-list)
(1 2 3 4 5 6 7 8 9 10)
user> (drop-while #(< % 3) a-list)
(3 4 5 6 7 8 9 10)

;;; Sets

user> (set '(1 2 3 4 5 1 2 3 4))
#{1 2 3 4 5}
user> (def a-set #{1 2 3 4 5})
#'user/a-set
user> (contains? a-set 3)
true
user> (contains? a-set 7)
false
user> (conj a-set 5)
#{1 2 3 4 5}
user> (conj a-set 6)
#{1 2 3 4 5 6}
user> (disj a-set 1)
#{2 3 4 5}
user> (get a-set 1)
1
user> (get a-set 7)
nil
;; most set functions live in the clojure.set namespace
user> (use 'clojure.set)
nil
user> (difference #{1 2 3} #{1 3 5})
#{2}
user> (intersection #{1 2 3} #{1 3 5})
#{1 3}
user> (union #{1 2 3} #{1 3 5})
#{1 2 3 5}

;;; Maps
user> (hash-map :Bozhidar :Batsov :Bruce :Wayne :Selina :Kyle)
{:Selina :Kyle, :Bozhidar :Batsov, :Bruce :Wayne}
user> (def a-map {:Bozhidar :Batsov, :Bruce :Wayne, :Selina :Kyle})
#'user/a-map
user> a-map
{:Bozhidar :Batsov, :Bruce :Wayne, :Selina :Kyle}
user> (get a-map :Bozhidar)
:Batsov
user> (contains? a-map :Bozhidar)
true
user> (contains? a-map :Clark)
false
user> (:Bozhidar a-map)
:Batsov
user> (assoc a-map :Lois :Lane)
{:Lois :Lane, :Bozhidar :Batsov, :Bruce :Wayne, :Selina :Kyle}
user> (keys a-map)
(:Bozhidar :Bruce :Selina)
user> (vals a-map)
(:Batsov :Wayne :Kyle)
user> (dissoc a-map :Bruce)
{:Bozhidar :Batsov, :Selina :Kyle}
user> (merge a-map {:Alia :Atreides, :Arya :Stark})
{:Arya :Stark, :Alia :Atreides, :Bozhidar :Batsov, :Bruce :Wayne, :Selina :Kyle}

;;; Vectors

user> (vector 1 2 3 4)
[1 2 3 4]
user> [1 2 3 4]
[1 2 3 4]
user> (def a-vector [1 2 3 4 5])
#'user/a-vector
user> (count a-vector)
5
user> (conj a-vector 13)
[1 2 3 4 5 13]
;; random access is a constant time operation in vectors
user> (nth a-vector 3)
4
user> (pop a-vector)
[1 2 3 4]
user> (peek a-vector)
5

Most data structures in Clojure are part of a common Sequence API, that we’ll briefly discuss shortly.

The Seq API

Clojure defines many algorithms in terms of sequences (seqs). A seq is a logical list, and unlike most Lisps where the list is represented by a concrete, 2-slot structure, Clojure uses the ISeq interface to allow many data structures to provide access to their elements as sequences. The seq function yields an implementation of ISeq appropriate to the collection. Seqs differ from iterators in that they are persistent and immutable, not stateful cursors into a collection. As such, they are useful for much more than foreach – functions can consume and produce seqs, they are thread safe, they can share structure, etc.

Most of the sequence library functions are lazy, i.e. functions that return seqs do so incrementally, as they are consumed, and thus consume any seq arguments incrementally as well. Functions returning lazy seqs can be implemented using the lazy-seq macro. The laziness allows us to deal with infinite data structures easily (as long as we don’t try to act on all of their elements that is):

user> (take 10 (filter even? (iterate inc 1)))
(2 4 6 8 10 12 14 16 18 20)

iterate returns an infinite lazy sequence. filter returns a lazy sequence as well. With take we can take only the elements we need without have to process the entire infinite collection.

Functional programing with Clojure

Clojure is a functional programming language and as such it offers quite the selection of features that make it easy to leverage the functional programming techniques.

  • functions are objects
  • all built-in data structures are immutable
  • most functions in the core library are pure (they don’t produce any side results and they don’t interact with the outside world in any manner other than just receiving their parameters from it)
  • there are no iteration constructs like for and while in other languages. In place of iteration list comprehensions and recursion are commonly used.
  • everything is an expression that yields some result – even things that are traditionally statements in other languages such as if and print (although the return value of print is not particularly useful)

Parallel programming

Functional programming and parallel programming complement each other. When most of your code is contained in pure functions it’s naturally tread safe (not to mention much easier to test). Since you don’t have any mutable state there, it’s absolutely safe to fire those pure functions in as many threads as you wish and you’ll have absolutely nothing to worry about.

Programs, however, eventually produce side effects and often they really have to have some mutable state. Clojure makes it easy to model such situations in your programs in a way that doesn’t compromise the ability to parallelize the programs.

Refs and transactions

Transactional references (known as refs in Clojure) ensure safe shared use of mutable storage locations via a software transactional memory (STM) system. Refs are bound to a single storage location for their lifetime, and only allow mutation of that location to occur within a transaction.

Clojure transactions should be easy to understand if you’ve ever used database transactions – they ensure that all actions on refs are atomic, consistent, and isolated. Atomic means that every change to refs made within a transaction occurs or none do. Consistent means that each new value can be checked with a validator function before allowing the transaction to commit. Isolated means that no transaction sees the effects of any other transaction while it is running. Another feature common to STMs is that, should a transaction have a conflict while running, it is automatically retried.

There are many ways to do STMs (locking/pessimistic, lock-free/optimistic and hybrids) and it is still a research problem. The Clojure STM uses multiversion concurrency control with adaptive history queues for snapshot isolation, and provides a distinct commute operation. Here’s a short example:

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(def picked-numbers (ref #{})

(def secret-num (.nextInt (java.util.Random.) 10))

(defn guess-number [n]
        (print "Enter a guess between 1 and 10: ")
        (flush)
        (let [guess (java.lang.Integer/parseInt (read-line)) ]
             (cond
               (= guess n) (println "You guessed correctly")
               (contains? (deref picked-numbers) n) (println "Pick another number! You already tried that one.")
               :else (dosync
                      (alter picked-numbers conj guess)))))

user=> (guess-number secret-num)
Enter a guess between 1 and 10: 1
#{1}
user=> (guess-number secret-num)
Enter a guess between 1 and 10: 3
#{1 3}
user=> (guess-number secret-num)
Enter a guess between 1 and 10: 5
#{1 3 5}

I hope you got the basic idea from this simple, but mostly useless snippet. Generally when we have only one object that we’ll be changing in this manner an atom is a more appropriate choice as we’ll see shortly.

Agents

Like refs, agents provide shared access to mutable state. Where refs support coordinated, synchronous change of multiple locations, agents provide independent, asynchronous change of individual locations (to put it into simpler term you’d use refs if you had to updated several things and wait for the update to happen and agents if you need to update only one thing and don’t really care when the update will happen – only that it will happen). Agents are bound to a single storage location for their lifetime, and only allow mutation of that location (to a new state) to occur as a result of an action. Actions are functions (with, optionally, additional arguments) that are asynchronously applied to an agent’s state and whose return value becomes the agent’s new state. Let’s see an agent (Smith maybe?) in action:

user> (def some-agent (agent 0))
#'user/some-agent
user> (dotimes [i 100]
               (send some-agent inc))
nil
user> some-agent
#<Agent@15c024c: 100>
user> @some-agent
100

Atoms

Atoms provide a way to manage shared, synchronous, independent state. They are a reference type like refs and vars. You create an atom with atom, and can access its state with deref (or @). Let’s rework the refs example to use an atom:

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(def picked-numbers (atom #{})

(def secret-num (.nextInt (java.util.Random.) 10))

(defn guess-number [n]
        (print "Enter a guess between 1 and 10: ")
        (flush)
        (let [guess (java.lang.Integer/parseInt (read-line)) ]
             (cond
               (= guess n) (println "You guessed correctly")
               (contains? (deref picked-numbers) n) (println "Pick another number! You already tried that one.")
               :else (swap! picked-numbers conj guess))))

user=> (guess-number secret-num)
Enter a guess between 1 and 10: 1
#{1}
user=> (guess-number secret-num)
Enter a guess between 1 and 10: 3
#{1 3}
user=> (guess-number secret-num)
Enter a guess between 1 and 10: 5
#{1 3 5}

Vars

Bindings created with the binding macro cannot be seen by any other thread. Bindings created with binding can be assigned to, which provides a means for a nested context to communicate with code before it on the call stack.

I’ll not be discussing them further, because Vars are a vast subject deserving its own post.

OOP, Lisp style

Object oriented programming in most programming languages is based on a single dispatch message passing. The object on which we invoke a method (poor choice of words, but easier to comprehend) is the receiver, the method name and it’s arguments are the message. The method’s invoked solely on the base of the type of the receiver object.

Lisps have traditionally implemented OOP with generic methods, that don’t have a receiver and are dispatched on the basis of the types of all of their arguments. In the world of multiple dispatch the more traditional single dispatch is just a special case in which only the type of the first method argument matters. Here’s a taste of multimethods in Clojure:

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(defmulti my-add (fn [x y] (and (string? x) (string? y))))

(defmethod my-add true [x y]
    (str x y))

(defmethod my-add false [x y]
    (+ x y))

user=> (my-add 3 4) ; => 7
user=> (my-add "3" "4") ; => "34"

Here we defined a multi-method that behaves differently for string and numeric arguments – strings args are concatenated and numeric args are added together.

Interactive development

Traditional programming languages have more or less the following work flow:

  • write the unit tests (power to TDD)
  • write the source file
  • compile the source file (if needed)
  • run the source file with an interpreter or run the binary file that resulted from the compilation step
  • if you need to modify something you edit the code and repeat the other steps

Those programs naturally have some entry points. Alternative you can simple use the test suite you’ve initially developed.

A Lisp developers generally has a very different work flow:

  • start a REPL
  • write some unit tests
  • write a few functions definitions in a source file
  • compile them interactively and load them in a REPL
  • test these functions directly from the REPL
  • if you need to modify a function – just edit it and reload in the REPL

I’ve been a C/C++ and Java developer for a long time and I didn’t see anything wrong with first model. In fact – I had been practising it for so very long that it felt quite natural to me. After I’ve started hacking with Lisp, however, my old work flow started to feel very unproductive to me (especially with statically typed languages). Here are some key aspects of the interactive (and iterative) development that is so common in Lisp (not only in Common Lisp ;–) ):

  • The REPL is an integral process of the coding process. Ruby and Python developers generally tend to use it only for exploratory programming although many of the techniques common for Lisp could be applied for Ruby and Python as well.
  • Functions can be defined (and redefined) in real time
  • Loading & compilation of code at runtime
  • Powerful introspection features
  • Interactive development
  • Iterative development – you know the old saying “Lather, rinse, repeat…” (probably one of the oldest examples of recursion).

The tools of the trade

The single biggest problem with Clojure in the moment (at least in my opinion) has nothing to do with the language itself. The problem is the lack of decent tooling and infrastructure around it. Lisp hackers have traditionally favoured the SLIME development environment for Emacs. Unfortunately the maintainers are not especially interested in having Clojure support (since SLIME targets Common Lisp) and nobody in Clojure community seems to be capable of writing an adequate swank component for Clojure that can be used with an up-to-date version of SLIME. The existing one is quite rudimentary and doesn’t work with stock SLIME distributions. But this is not the worst part – the worst part is that even this crippled SLIME is still the best development tool for Clojure. IntelliJ’s plug-in is almost unusable, Eclipse’s is barely usable and NetBeans’s cannot be installed half the time…

  • IDEs
    • IntelliJ IDEA – everyone knows how much I love IntelliJ IDEA. Sadly I cannot say a good word about the La Clojure plug-in. Its mostly unmaintained (and often broken), has pretty limited features and is generally good for… nothing. It’s sad to see it’s so far back in JetBrains’s priority list – they are doing wonderful things in their Groovy and Scala plugins.
    • Eclipse – Eclipse certainly boasts the best IDE Clojure plug-in at the moment (Counter clock-wise). It has a lot of features found otherwise only in SLIME. It even features partial paredit support. Being the best in such a sorry bunch is not quite an achievement, but at least the guys there are trying really hard and I’m sure that within one or two releases they’ll have a great product.
    • NetBeans – The Enclojure plug-in seems to be abandoned currently. It has no released a new version in over an year and the old one were buggy as hell (when they the decency to get themselves installed, that is).
    • SLIME – The Ultimate Clojure programming environment. Even though it’s lacking a few features of the Common Lisp counterpart, SLIME still is the best option for Clojure development, uniquely attuned to the Lisp philosophy of interactive and incremental development.

Luckily there’s no lack of good build tools that one can use with Clojure.

  • Build tools
    • Apache Maven – Maven has a nice Clojure plugin
    • Leiningen – probably the most popular Clojure-specific build tool
    • Cake – a solid alternative to Leiningen
    • Gradle – a build system for Java-based applications, written in Groovy, that supports Clojure
    • Apache Builder – a build system for Java-based applications, written in Ruby , that supports Clojure

Resources

Epilogue

Clojure is a radical departure from both traditional Algol-derived languages and existing Lisp dialects. Its advanced support for functional programming, combined with a state of the art concurrency support make it attractive language for the development of heavy duty enterprise grade systems. Coupled with the seamless Java integration the sky seems to be the limit for Clojure…

Unfortunately Clojure has to face several problems if it’s to succeed. First of all it has to attract a critical mass of developers that are not afraid of the syntactic difference with common languages. Then there is the concept of functional thinking – a way of thinking quite foreign to most developers. To be able to properly leverage the full power of Clojure developers have to be ready to overcome a steep learning curve, but rest assured, the prize at the end of the journey is well worth it.

Another gripe with Clojure at this point is the lack of decent tooling. Sure, we have the supported SLIME, available in ELPA, but even Emacs users are not particularly happy with it. And let’s face reality – it’s quite unlikely that many developers will be willing to give up the comfort of their beloved IDEs just to be able to code in Clojure. Eclipse, IntelliJ & NetBeans are unfortunately nowhere near providing a good Clojure experience. Hopefully, this will change soon…

I’m a very big fan of Lisp in general, that is no secret. I’ve tried to be as objective as possible and abstained myself from over extolling some of Clojure’s virtues. I’d like to say, however, that I’m very excited that we finally have a Lisp dialect that is modern, simple, powerful, elegant and most of all – capable of getting the job done. I really hope that Clojure will be instrument of Lisp return on the centre stage of programming, where it deserves to be.

As usual the article is very shallow overview and doesn’t even mention some important features of the language. Be sure to check out some of the resources mentioned.

P.S. This is the last of the articles that I’d originally intended to write. I’m considering the possibility to write a couple of more chapter of the Java.next() series if people are interested to read them. JRuby will probably make an appearance, but I’d like to hear some reader input as well. So, what JVM language do you think is worthy enough to make an appearance in the Java.next() series?