This is a post I have been meaning to write for quite some time.  Despite being an officially excommunicated former-member of the Ruby community, I still like to keep up on the latest developments.  A few months ago, my Ruby Inside feed produced an interesting post introducing a new library by Peter Cooper (the author of Ruby Inside).  Unlike the most “new” Ruby libraries which are most often just bad clones of Rails snibblets, Peter’s WhatLanguage seemed to be quite unique and interesting.  For the first time in years, I was reading about an innovation in Ruby-land which actually sounded quite exciting.  A good feeling to be sure.

In a nutshell, WhatLanguage (project page at GitHub) is a statistical language identification API which analyses arbitrary input String(s), scoring them against a set of built-in languages (including English, French, Spanish, Russian, and others).  Add to that a little bit of meta-programming magic and the possibilities are eye-catching to say the least:

require 'whatlanguage'
 
'Here is some text to identify'.language       # => :english
'¿Hola, pero donde está su pelo?'.language     # => :spanish

To be honest, I’m not sure what practical application I would have for such a library, but it sure looks like fun!  :-)  Under the surface, the implementation is deceptively simple.  WhatLanguage bundles a set of words for each language it supports.  The implementation then searches these sets for each word in the string, aggregating a score for each language depending on whether or not the word was found.  If exactly one language has a higher score than the others once the end of the string is reached, it is “declared the winner” and returned as a result.  Otherwise, the analysis is undecided and nil is returned.  Simple, right?

The tricky part is somehow storing and searching those gigantic language word sets in an efficient manner.  As it turns out, this isn’t so easy.  Just stop and consider the number of words which exist in your native tongue.  Unless you speak a very strange, primarily-tonal language, the number is likely in the upper hundreds of thousands.  Just going off of Peter’s word lists, Russian is the real trouble maker, weighing in with a nearly one million word vocabulary.  Any attempt to store that much data would require megabytes of memory and several minutes to process, even on a fast machine.  Any sort of hash set is completely out of the question due to the nature of contiguous memory allocation, but can you imagine trying to perform a search traversal on a tree with almost a million nodes?  Obviously, a more effective solution is required.

Probabilistic Sets

So far we have been focusing our imaginary efforts on data structures which guarantee 100% accuracy and full reversibility.  That is to say, data structures which allow you to get out what you put in and which ensure that if something is “in” it will be found and if something is “out” it will not.  After all, is a data structure without these guarantees really all that useful?

As it turns out, the answer is “yes”.  Consider a standard hash set as an example.  Sets make absolutely no guarantee about the ordering of their elements.  If you attempt to iterate over a set, you will eventually reach everything inside, but you cannot rely on a certain element coming before the others.  Of course, the advantage we gain by making this trade-off is constant time searching by item identity.  Lists cannot achieve this performance, neither can arrays.  If you want to search either one of these structures, you must look through every element from start to finish.  So with hash sets, we have effectively traded a fundamental guarantee about our data for efficiency in some other respect.

The same is true for probabilistic sets.  We don’t actually need all of the properties of a set to implement WhatLanguage.  For example, we don’t need to actually retrieve a word from our dictionary, we only need to discover whether or not that word is in the set.  In fact, we really only need our data structure to have two important properties:

• Lightning fast boolean searches (does the set contain this element?)
• Minimal memory footprint

Hash sets can make the first guarantee, but as we have already noticed, they don’t really require any less memory than just storing everything in an array.  Since we can’t afford one million words-worth of memory per language, we need to come up with some way of storing information about the presence of an element without retaining the data it represents.  In order to do this, we have to make one important sacrifice: accuracy.

We basically need to accept the fact that our data structure may sometimes return inaccurate results.  As it turns out though, this is just fine.  Remember, we’re scoring languages one token at a time; an incorrect answer now and again isn’t going to kill us.  Given a large enough input string, a minimal percentage of inaccuracy will literally be lost in the noise.

As it turns out, there already exists a very old technique for accomplishing exactly these requirements.  Bloom filters have been around since the beginning of time (or at least the 1980s), but unfortunately very few developers are aware of them.  In a nutshell, they precisely satisfy all of the needs we just finished enumerating.  They provide true (not amortized) constant-time searches and will consistently return true or false for a given element, depending on whether or not the value is in the set.  Occasionally they will give false positives (claiming that an element is in the set when in fact it is not), but never false negatives.  This property is extremely useful in many applications, but it turns out that this is even more than we need for WhatLanguage.

Most importantly though, bloom filters are extremely compact in memory.  Rather than storing an entire value, they hash every element to one or (usually) more indexes in an array of bits (0 or 1).  In an optimal bloom filter guaranteeing no false positives, each element would correspond to precisely k bits of memory (where k is the number of hash functions, usually in the vicinity of 3 or 4).  Once this hashing is complete, the value itself may be discarded.  Thus, assuming an optimal bloom filter with 3 hash functions, the storage requirements for one million words would only be 375 KB of memory.  Compare this to the 8 MB required just to store the pointers to each of the words, much less the values themselves!  If we include the underlying character values, we start pushing closer to 20 MB.

375 KB for a million elements is impressive, but bloom filters can actually do a lot better than that.  Remember, we agreed to accept some false positives.  It’s easy to imagine how even an ideal bloom filter (such as the one we have been analyzing) could have some inaccuracy, but if we’re willing to live with a few more false positives, we can allow hash collisions and drop our memory requirements into the 200 KB range.  Not bad, considering how much data we’re storing!

Bloom Filters

So how does the magic actually work?  We’ve hinted at the implementation in our analysis of memory requirements, but we’re still missing some important details.  Consider the following diagram (courtesy of Wikipedia):

Under the surface, a bloom filter is just an array of boolean values.  The length of the array is referred to as the width of the bloom filter.  When the filter is empty, all of the array values are set to false.  To insert an element, we compute k different hash indexes in the array and set them all to true.  The real beauty of the data structure is the manner in which we handle collisions: we don’t worry about it.  Whenever one (or more) of the hash functions returns an index which is already true, we simply leave that index as-is and move on.  Thus, the procedure for inserting an element into a “full” bloom filter is precisely the same as inserting into an empty one: set all of the hash indexes to true.  In a sense, bloom filters can never be full, they just get less and less accurate.

Bloom filters define only one operation other than insertion: searching.  To determine whether or not a value is in the set, we run all of our hash functions and find the indexes to which it corresponds.  If all of these indexes are true, then we can assume that the value is indeed in the set.  Conversely, if any one of indexes is false, then the value cannot be in the set since we would have set that value to true upon insertion.

It is important to note that removal is not supported by bloom filters.  Intuitively we could just take the value to be removed, calculate its hash indexes and then set the corresponding elements to false.  However, if any other values also hash to even one of those locations, we will be effectively removing it from the bloom filter “by accident”.  Subsequent searches for that value will hash to the index which we erroneously set to false, resulting in a false negative on the search (saying that an element is not in the set when in fact it is).  Fortunately for us, WhatLanguage only requires insertion and searching for values, removal is not on the agenda.

Scala Implementation

Well, this has probably been a nostalgic (for those of you who enjoyed your university days) stroll through the land of data structure theory, but we’re still lacking one critical factor: a concrete implementation.  Bloom filters don’t sound that complicated on paper, but to do anything useful with them we’re going to need some code.

Before we dive in, we might want to define some more precise requirements for our implementation.  We’re writing this in Scala, so it would be nice to implement bloom filters as a functional data structure.  I’m not going to get into all the benefits of immutable data structures; suffice it to say, I really think that they’re the only way to go for most serious applications.  Unfortunately, in order to do this, we’re going to need some sort of immutable, array-like data structure under the surface which provides random access in constant time.  Sound familiar?

This is actually where the whole saga of persistent vectors in Scala found its roots.  The first time I tried to implement a functional bloom filter in Scala I made use of a conventional array under the surface with copy-on-write semantics.  As you can imagine, this is hideously inefficient in both space and time.  In order to solve the problem, I had to implement a data structure which was just as efficient for reading and writing, but which also shared structure with subsequent versions.  Now, two months later, I’ve borrowed just such a design from Clojure and created a first-class implementation in Scala.

With a persistent vector now at our disposal, we can easily implement a persistent bloom filter by using a vector to represent our boolean indexes.  Every time we perform an insertion, we will “modify” the relevant indexes, creating a new instance of the vector.  This vector can then be used in our “modified” bloom filter, returned as a new instance of our container class.  Since persistent vectors share unmodified structure between versions, so does our bloom filter.  All of the hard work of efficient immutability is already done for us, we just need to implement a thin facade.  We start out by defining the class skeleton:

import BloomSet._
 
class BloomSet[A] private (val size: Int, val k: Int, 
                           private val contents: Vector[Boolean]) extends ((A)=>Boolean) {
  val width = contents.length
 
  def this(width: Int, k: Int) = this(0, k, alloc(width))
}
 
object BloomSet {
  def alloc(size: Int) = {
    (0 until size).foldLeft(Vector[Boolean]()) { (c, i) => c + false }
  }
}

We’ll keep all of our utility functions in the companion object.  There’s really no need to put them in the main class, and the JVM is able to optimize dispatch to static members to a slightly higher degree than it can instance members (invokestatic).  Technically, we don’t really need the alloc function, it just hides some of the boiler-plate involved in initializing a vector to a certain length.  Note that this operation is extremely fast, even for high values of width.  Despite the fact that we are creating a huge number of Vector objects, very little memory is actually wasted.  Even at this early stage in the implementation, the advantages of persistence are evident.

A Little Math

You’ll notice that our bloom filter has three major properties: size, k, and width.  As previously mentioned, k is the number of hash functions to be used, while width is the length of the array used to store the boolean values.  These values have to be user-configurable because different applications will call for different optimizations.  For example, implementing WhatLanguage requires the storage of almost a million elements per set.  Obviously we would not want to attempt to use a bloom filter which only had a width of (say) 200.  This wouldn’t cause any errors per se (remember, bloom filters are never “full”), but the accuracy of such a filter would be almost nil.  With a million elements all hashed into a mere 200 indexes, it is likely that that every single index would have a value of true.  Such a bloom filter would return true when searching for any value, including those which were not inserted.  We said we would tolerate some false positives, but we still want the results to be meaningful.  Since the width of a bloom filter cannot be changed once data is inserted (at least, not without “losing” data), the user must be able to decide in advance how much space will be required and then instantiate BloomSet accordingly.

The value of k is an even more interesting proposition.  The intuition is that the larger our value of k (thus, the more hash functions we use), the greater the accuracy of our bloom filter and the fewer false positives we will return.  This is technically true when the bloom filter does not contain very many elements, but as more and more values are inserted, higher values of k become problematic.  Wikipedia has an excellent (and extremely readable) mathematical analysis of this and other properties of bloom filters.  As it turns out, the optimal value for k (one which will yield the fewest false positives) can be computed by the following expression (where m is the width and n is the size, or the number of elements contained in the filter):

For the sake of quick-computation, this is essentially equivalent to:

The equivalence isn’t precise (logarithms are almost always irrational), but since we’re already rounding the result to the nearest integer, we don’t need to be too concerned.

While it would be nice if we could just auto-magically set k to the optimum value and be done with it, that just isn’t the case.  Like width, k cannot be changed after data has been inserted.  Unfortunately, its optimum value is dependent on the number of items which will eventually be in the set.  Since we don’t really “know” this value within the data structure until after the insertions are complete, there is no way for us to self-optimize.  To provide maximum configurability, we need to allow this value to be set by the API consumer on a case-by-case basis.

As an interesting aside, we can actually compute the accuracy of a bloom filter on a scale from 0 to 1 given values for width, k and size.  In this case, accuracy is defined as the odds that a search result is indeed accurate (the chance of it not being a false positive).  Wikipedia gives the following expression for this accuracy:

Thanks to the marvels of approximation, this is a fairly efficient computation on modern platforms.  Translated into Scala, it looks something like this:

lazy val accuracy = {
  val exp = ((k:Double) * size) / width
  val probability = Math.pow(1 - Math.exp(-exp), k)
 
  1d - probability
}

Core Functionality

As interesting as this minor detour has been, we still haven’t arrived at a working implementation of the bloom filter data structure.  We have the basic structure in place, but we still need to implement the two core operations: insert and search, rendered in Scala as + and contains:

def +(e: A) = new BloomSet[A](size + 1, k, add(contents)(e))
 
def contains(e: A) = {
  (0 until k).foldLeft(true) { (acc, i) => 
    acc && contents(hash(e, i, width)) 
  }
}
 
protected def add(contents: Vector[Boolean])(e: Any) = {
  var back = contents
 
  for (i <- 0 until k) {
    back = back(hash(e, i, width)) = true
  }
 
  back
}

Feels sort of anti-climactic doesn’t it?  After all that analysis, we find that it is possible to implement the essence of a bloom filter in less than 17 lines of code.  Technically, I probably could trim this down to more like 8 lines, but this isn’t an exercise in Scala golf.  These three methods are just the materialization of all of the hand-wavy descriptions that we’ve spent the last five pages working through.  Each time we insert an element, we calculate k different hash values and set the appropriate vector indexes to true.  To find that element, we calculate the same k hashes and ensure that all of the indexes have an appropriate boolean value.

Hashing

Those of you who are actually reading the code snippets rather than just blindly trusting my veracity will probably notice that I’ve omitted a fairly important function from the above sample; specifically: hash.  Somehow, we need to define a function which will simulate the existence of a potentially unlimited number of hash functions.  Based on our usage of this function, we need to be able to grab a hash value for a given value and function index, restricted to a given width.  This is not a trivial problem.

As a good developer, you should already be trying to think up ways to reduce the code involved in implementing these requirements.  I mean, we could potentially just dream up a few dozen different hash functions with a fairly wide distribution of results.  However, I don’t know about you, but I really don’t have that much imagination.  I have a hard enough time developing just one solid hash function, much less dozens.  What’s more, if each hash function corresponds with a different method, then we can’t really scale the number of hash functions to any integer k.

A better approach would be to create a single hash function and then skew its value n times for every n between 0 and k.  Like most things that have to do with hashing, it’s cheating, but providing we do our job right, it should give us a fairly decent way of computing the required k hash functions.  Of course, the key phrase there is “do our job right”.  We need to make sure that we skew the initial hash value sufficiently for each successive hash, otherwise successive hash functions might get “stuck in a groove”.  For example, let’s assume that each successive hash function is equivalent to the previous hash + 2.  If we then inserted two elements into the bloom filter with k = 3, there would be the potential that the hash(0) for the first element would be precisely 2 greater than the hash(0) for the second.  If this is the case, then hash(1) for the second would equal hash(0) for the first.  Thus, the first and second elements would collide on two out of three indexes.  Not exactly an optimal distribution.

To avoid this problem, we should be sure to skew the hash value by a different amount for each hash function.  A convenient way of doing this is to use the number of iterations, or the index of the particular hash function.  To further improve the distribution, we will also use the bitwise XOR operation rather than simple addition.  It’s not really intuitively obvious, but XOR is mathematically far superior for hashing and just as cheap in terms of processor cycles.  Put it all together, and the implementation looks something like this:

def hash(e: Any, iters: Int, bounds: Int): Int = {
  Math.abs(
    if (iters == 0) e.hashCode 
    else iters ^ hash(e, iters - 1, bounds)
    ) % bounds
}

The final bit of math in this function mods our skewed hash value with the given bounds.  Whenever we call hash, we always pass the width of the bloom filter.  This is a trick that you’ll see a lot in any hash-based data structure.  Essentially, we’re just mapping our hash value into the required domain.  Hash values can be anything between -2³¹ and 2³¹ - 1, but our vector indexes can only be between 0 and width - 1.  Just a rule of thumb, but any time you have a problem where you must “map values from a large domain to a more restricted one”, mod is generally the correct approach.

Observant readers will notice that our implementation of hash is a little inefficient.  Actually, it’s worse than just a “little” inefficient: it’s downright terrible.  Remember that we have to call this method once for every integer n between 0 and k.  This isn’t so bad, since k is usually comparatively small, but the real problem is our sneaky use of recursion within hash.  The time complexity of hash is precisely O(n), where n is the number of iterations.  Once you factor this in with the number of times we call this function and it is easy to see how the total complexity of computing k hash values is actually O(k²).  Thus, for k = 4, we actually call hash 16 times!  You can imagine how this could get very problematic for higher values of k.

Technically, we could rewrite add and contains to compute the hash values incrementally from the bottom up.  This would bring the hashing complexity down to O(k).  However, this is a bit more elegant in terms of code, not to mention easier to talk about in a modular fashion.  In practice, this inefficiency is only a problem for higher values of k due to the fact that computing a single-step hash value is actually a very fast operation.  We will run into trouble in a little bit though when we attempt to optimize the bloom sets for shorter wordlists in WhatLanguage.

Serialization

One of the important requirements of WhatLanguage that we haven’t really touched on so far is the ability to store freeze-dried bloom filters on disk and then suck them back into memory quickly and efficiently.  If you think about it, this requirement only makes sense since one of our primary motivations from the start was to avoid the burdens associated with huge word lists.  After all, if our in-memory representation of a language is only a few hundred kilobytes, shouldn’t the on-disk representation be no worse?  Furthermore, if we had to parse a wordlist into a bloom filter every time we wanted to check a string’s language, the performance imposition would be utterly prohibitive.

Fortunately, there is a fairly obvious format which we could use to efficiently serialize an arbitrary bloom filter.  Recall that the real heart of a bloom filter is the array of boolean flags, an array which can also be represented as an array of bits.  We could very easily select these bits eight at a time, combine them together using bit-shifting and produce a byte value which could be sent to an OutputStream.  Thus, a bloom filter with an array of {true, false, true, true, true, false, false, true, false} would be stored as the following series of bits: 101110010, which in hexadecimal byte form becomes B9 00.

In addition to the array, we also need to store the size, k, and width of the bloom filter, otherwise it will be impossible to find anything reliably in the deserialized result.  The Scala store and load methods to accomplish this are verbose, but fairly straightforward:

class BloomSet[A] ... {
  ...
 
  def store(os: OutputStream) {
    os.write(convertToBytes(size))
    os.write(convertToBytes(k))
    os.write(convertToBytes(contents.length))
 
    var num = 0
    var card = 0
    for (b <- contents) {
      num = (num << 1) | (if (b) 1 else 0)    // construct mask
      card += 1
 
      if (card == 8) {
        os.write(num)
 
        num = 0
        card = 0
      }
    }
 
    if (card != 0) {
      os.write(num)
    }
  }
}
 
object BloomSet {
  def load[A](is: InputStream) = {
    val buf = new Array[Byte](4)
 
    is.read(buf)
    val size = convertToInt(buf)
 
    is.read(buf)
    val k = convertToInt(buf)
 
    is.read(buf)
    val width = convertToInt(buf)
 
    var contents = Vector[Boolean]()
    for (_ <- 0 until (width / 8)) {
      var num = is.read()
      var buf: List[Boolean] = Nil
 
      for (_ <- 0 until 8) {
        buf = ((num & 1) == 1) :: buf
        num >>= 1
      }
 
      contents = contents ++ buf
    }
 
    if (width % 8 != 0) {
      var buf: List[Boolean] = Nil
      var num = is.read()
 
      for (_ <- 0 until (width % 8)) {
        buf = ((num & 1) == 1) :: buf
        num >>= 1
      }
 
      contents = contents ++ buf
    }
 
    new BloomSet[A](size, k, contents)
  }
 
  ...
}

It is interesting to note that store is a good example of what David MacIver meant when he says that functional code good code.  I actually tried to write this in a functional style to start with, but I gave up after it became horribly ugly (having four nested folds is never a good sign).  The imperative rendering (in this case) is concise and elegant, one of the many advantages of a hybrid languages like Scala.

This particular format for rendering a bloom set is almost precisely identical to the format used by Peter Cooper’s Bloomin’ Simple, the library which sits underneath WhatLanguage.  However, due to hashing differences it is impossible to use his serialized bloom filters in Scala or vice versa.

WhatLanguage in Scala

So now that we have our magic bloom filter ready and waiting, it’s time to take a crack at Peter’s fancy language identifier!  As it turns out, this is a lot simpler than the bloom filter itself.  Conceptually, all we have to do is load a different bloom filter for each language and then use that bloom filter to check each input string token individually.  For the sake of uniformity, we will convert all tokens to lower-case.

Of course, before we can load the language bloom filters we need to first store them, and we cannot store a bloom filter before it is generated.  To that end, we need to create a simple Scala script (yes, a script) which reads in a file of newline-delimited words, inserts them all into an instance of BloomSet and then stores the result in a corresponding language file.  Scala’s scripting functionality is actually quite good, especially for a statically typed language, and really deserves some better recognition.  The complete language generator script is as follows:

import com.codecommit.collection.BloomSet
import java.io.{BufferedOutputStream, File, FileOutputStream}
import scala.io.Source
 
val WIDTH = 2000000
 
def computeK(lines: Int) = (((9:Double) * WIDTH) / ((13:Double) * lines)).intValue
 
for (file <- new File("wordlists").listFiles) {
  if (!file.isDirectory) {
    println(file.getName)
    println("==========================")
 
    val src = Source.fromFile(file)
    val count = src.getLines.foldLeft(0) { (i, line) => i + 1 }
    println("  Word count: " + count)
 
    val optimalK = computeK(count)
    val init = new BloomSet[String](WIDTH, Math.min(optimalK, 100))
 
    println("  Optimal K: " + optimalK)
    println("  Actual K: " + init.k)
 
    val set = src.reset.getLines.foldLeft(init) { _ + _.trim }
 
    println("  Accuracy: " + set.accuracy)
 
    val os = new BufferedOutputStream(
        new FileOutputStream("src/main/resources/lang/" + file.getName))
    set.store(os)
    os.close()
 
    println()
  }
}

I originally created this script using JRuby, thinking of course that it would be much easier to perform a simple, one-off task like this in a dynamically typed language.  Interestingly enough, the JRuby version was about twice as long and actually took dramatically longer to run.  By “dramatically” I mean on the order of seventy times longer.  The above Scala script takes just over 30 seconds to run on my machine using the wordlists from WhatLanguage.  This stands in stark contrast to the 35 minutes required for the JRuby script.  Both scripts use the same underlying data structure to perform all the work (BloomSet), so it’s really hard to claim that one implementation was fundamentally slower than the other.  In short: JRuby does not seem terribly well-suited for computationally-intensive tasks, even when most of that computation is taking place in Java-land.  Anyway…

This script is fairly straightforward.  The one interesting landmark is the fact that it actually makes two separate passes over the wordlist file.  The first pass just counts the number of lines, while the second pass reads the words individually, converts them to lower-case and stores them in the bloom filter.  This two-pass approach allows us to calculate the optimal value for k in our bloom filter.  This contrasts with the Ruby implementation of WhatLanguage, which just uses 3 as a global default (the width is the same in both libraries).  According to my math, this per-language optimization actually ekes out an average 2-3% better accuracy in the bloom filters.  This means fewer false positives and nominally better language detection.

I say “nominally” because such a miniscule improvement in bloom filter performance actually makes very little difference in the grand scheme of things.  Most strings are decidedly one language or another, meaning that results should be essentially identical between the two implementations.  Regardless, it’s the thought that counts.  (right?)

Just for the curious, the script produces the following output when run:

  dutch
  ==========================
    Word count: 222908
    Optimal K: 6
    Actual K: 6
    Accuracy: 0.986554232499401

  english
  ==========================
    Word count: 234936
    Optimal K: 5
    Actual K: 5
    Accuracy: 0.9827068384240777

  farsi
  ==========================
    Word count: 339747
    Optimal K: 4
    Actual K: 4
    Accuracy: 0.9408664843803285

  french
  ==========================
    Word count: 629569
    Optimal K: 2
    Actual K: 2
    Accuracy: 0.7817441534106517

  german
  ==========================
    Word count: 298729
    Optimal K: 4
    Actual K: 4
    Accuracy: 0.9590696917792074

  pinyin
  ==========================
    Word count: 399
    Optimal K: 3470
    Actual K: 100
    Accuracy: 1.0

  portuguese
  ==========================
    Word count: 386393
    Optimal K: 3
    Actual K: 3
    Accuracy: 0.9148904670664727

  russian
  ==========================
    Word count: 951830
    Optimal K: 1
    Actual K: 1
    Accuracy: 0.6213162918878307

  spanish
  ==========================
    Word count: 595946
    Optimal K: 2
    Actual K: 2
    Accuracy: 0.7984358472107859

  swedish
  ==========================
    Word count: 54818
    Optimal K: 25
    Actual K: 25
    Accuracy: 0.9999999755910407

I’m not sure why Pinyin only has a vocabulary of 399 words, but that seems to be the way things are.  This means of course that we’re storing barely 400 elements in a bloom filter with a width of 2,000,000.  Needless to say, I wasn’t surprised to see that the optimal k was in the mid thousands.  Unfortunately, this is where our inefficient hash implementation comes back to bite us.  I discovered that if I were to allow a k value of 3470, even when only inserting 399 elements, the time required to process just the single language was upwards of 20 minutes.  This may have something to do with the fact that 3470² is very, very large.

To get around this problem, I cheat and cap the k value at 100 in the script.  This still produces a computed accuracy of 100% in the bloom filter, and it takes much less time to process.  A fast hash is actually even more important during lookups.  Considering that we have to check each token in a string against each and every supported language, a fast lookup is crucial to ensuring performance of the final product.  If it took even 10 ms to compute the hash of a single token when checking against the Pinyin language, the implementation would be completely unusable.

A Little API Design

Now that we have our word lists encoded as bloom filters, we can turn our attention to slightly more important problems; specifically: what do we want the API to look like?  I decided to base my Scala implementation primarily on Peter’s Ruby API.  However, there is one small wrinkle in this plan: WhatLanguage uses symbols for everything.  That’s fine and in line with Ruby idioms, and technically we could use Scala symbols if we really wanted to, but Scala is a statically typed language with a lot of powerful idioms of its own.  It would be a lot more conventional if we found a type-safe way of representing the same concepts.  To that end, I decided to go with a similar (but not identical) approach as exemplified in the following code snippet:

import com.codecommit.lang._
 
"Hello, this is a test".language       // => english
 
val wl = new WhatLanguage(english, french, spanish)
val text = "Bonjour, my name is Daniel. Estoy bien. Como estas? Êtes-vous ennuyer?"
 
wl.processText(text)   // => Map(English -> 4, French -> 5, Spanish -> 6)

The neat trick of the day is our use of static values in scope as a form of type-safe symbol.  We create a new instance of WhatLanguage, passing it three instances of class Language representing (oddly enough) the languages we wish it to use during the analysis.  We can also use “all” as a shortcut for enumerating every supported language.

Another cute little API feature is our use of the lang “package” to bring everything into scope, including two implicit conversions and a small boat-load of language values.  This is made possible by the fact that lang is not actually a package but a singleton object.  Even Scala does not allow values and functions as top-level elements in a package, but it does allow them within objects.  Our import at the head of the snippet is actually equivalent to the following bit of Java:

import static com.codecommit.lang.*;

With all of this in mind, the rest of the library is fairly easy to create.  All we need to do is design an algorithm which splits an input string into tokens, loops over each one keeping track of how many tokens are matched by each language, and finally selects the highest-scoring language from the result and returns its corresponding static value.  As one would expect, this is easily accomplished:

package com.codecommit
 
import java.io.BufferedInputStream
import com.codecommit.collection.BloomSet
 
import scala.collection.mutable.ArrayBuffer
 
object lang {
  case object all extends Language("all") {
    val langs = List(dutch, english, farsi, french, german, pinyin, 
                     portuguese, spanish, swedish)
 
    override lazy val words = BloomSet[String]()
 
    override val toString = "All languages meta-variable"
  }
 
  val dutch = Language("dutch")
  val english = Language("english")
  val farsi = Language("farsi")
  val french = Language("french")
  val german = Language("german")
  val pinyin = Language("pinyin")
  val portuguese = Language("portuguese")
  val russian = Language("russian")
  val spanish = Language("spanish")
  val swedish = Language("swedish")
 
  implicit def conversion(str: String) = new {
    val language = new WhatLanguage(all).language(str).getOrElse(null)
  }
 
  implicit def languageToString(lang: Language): String = lang.toString
 
  sealed case class Language private[lang] (file: String) {
    lazy val words = {
      val is = new BufferedInputStream(getClass.getResourceAsStream("/lang/" + file))
 
      try {
        BloomSet.load[String](is)
      } finally is.close()
    }
 
    override val toString = file(0).toUpperCase + file.substring(1)
 
    override def equals(other: Any) = other match {
      case lang: Language => toString == lang.toString
      case _ => false
    }
 
    override val hashCode = file.hashCode
  }
 
  class WhatLanguage(langs: Language*) {
 
    def language(str: String) = {
      val back = new ArrayBuffer[Language]
      var max = 0
 
      for ((lang, score) <- processText(str)) {
        if (score > max) {
          back.clear
 
          back += lang
          max = score
        } else if (score == max) {
          back += lang
        }
      }
 
      if (back.length == 1) Some(back(0)) else None
    }
 
    def processText(str: String) = {
      val langs = if (this.langs.contains(all)) all.langs else this.langs
      val prime = langs.foldLeft(Map[Language, Int]()) { _(_) = 0 }
 
      str.split("""\s+""").foldLeft(prime) { (map, token) =>
        langs.foldLeft(map) { (map, lang) =>
          if (lang.words.contains(token.toLowerCase)) {
            map(lang) = map(lang) + 1 
          } else map
        }
      }
    }
  }
}

Conclusion

Well, it’s been a fun and informative journey through the fertile land of bloom sets and simple statistical language analysis.  As usual, all of the source (and binaries) are available for download.  It’s a bit of a shame, but I didn’t get a chance to discuss a number of rather interesting features also available in my bloom filter implementation (such as concatenation of bloom filters).

Bloom filters are quite interesting in and of themselves, and definitely a useful technique that every developer should keep in their back pocket.  Peter Cooper definitely merits kudos for his implementation of WhatLanguage based on bloom filters, but despite the fact that I took the time to port it to Scala, I still don’t see much practical benefit.  At any rate, the library is now available in Scala; hopefully someone else will find it more useful than I do!

• Download whatlanguage (sources)
• Download collection library (sources)
• Download BloomSet.scala
• Download Vector.scala

Oh yeah, we’re really getting into Digg-friendly titles now, aren’t we?

The topic of persistent vectors is one of those odd backwaters of functional programming that few dare to approach.  The basic idea behind it all is to try to devise an immutable data structure which has the performance characteristics of a mutable vector.  What this means for practical application is that you shouldn’t have to deal with O(n) efficiency on random access like you do with List(s).  Instead, accessing arbitrary indexes should be constant time (O(1)), as should computing its length.  Additionally, modifying an arbitrary index should be reasonably efficient - as close to constant-time as possible.

Of course, the word “modifying” is a trifle inaccurate as it implies a mutable data structure.  One of the primary qualifications for a purely functional vector is that it is completely immutable.  Any changes to the vector result in the creation of a new vector, rather than modifying the old.  Basically, it’s an immutable data structure just like any other, but one which retains the brutal efficiency of its mutable counterpart.

Unfortunately, this turns out to be a very tough nut to crack.  A number of researchers have attempted different strategies for solving the problem, none of which have been entirely successful.  Rich Hickey, the creator of Clojure, has a brilliant presentation that essentially describes the solution I have chosen.  For the impatient, the good stuff starts at about 36:00 and lasts for about ten minutes.  I’ll elaborate on the problems with functional vectors a bit more in a second, but first a bit of motivational propaganda…

Thou Shalt Not Mutate

There is a single principle which should be drilled into skulls of all programmers everywhere: mutable data structures are bad news.  Don’t get me wrong, I love ArrayList as much as the Java addict, but such structures cause serious problems, particularly where concurrency is concerned.  We can consider the trivial example where two threads are attempting to populate an array simultaneously:

private String[] names = new String[6];
private int index = 0;
 
public static void main(String[] args) {
    Thread thread1 = new Thread() {
        public void run() {
            names[index++] = "Daniel";
            names[index++] = "Chris";
            names[index++] = "Joseph";
        }
    };
 
    Thread thread2 = new Thread() {
        public void run() {
            names[index++] = "Renee";
            names[index++] = "Bethany";
            names[index++] = "Grace";
        }
    };
 
    thread1.start();
    thread2.start();
 
    thread1.join();
    thread2.join();
 
    for (String name : names) {
        System.out.println(name);
    }
}

What does this snippet print?  I don’t know.  It’s actually indeterminate.  Now we can guess that on most machines the result will be essentially interleaved between the two threads, but there is no way to guarantee this.  Part of the reason for this is the fact that arrays are mutable.  As such, they enable (and indeed, encourage) certain patterns which are highly destructive when employed asynchronously.

However, concurrency is not even the only motivation for immutable data structures.  Consider the following example:

List<String> names = ...
for (String name : names) {
    names.add(name.toUpperCase());
}

I’m sure all of us have done something like this, most likely by accident.  The result is (of course) a ConcurrentModificationException caused by the fact that we are attempting to add to a List while we are iterating over its contents.  I know that the first time I was faced with this error message I became extremely confused.  After all, no threads are being employed, so why is this a problem?

Iterators are extremely dependent on the internal state of their data structure.  Anyone who has ever implemented an iterator for a linked list or (even better) a tree will attest to this fact.  This means that generally speaking, there is no way for an iterator to guarantee correctness if that structure is changing out from underneath it (so to speak).  Things may be fine in a linear structure like a list, but as soon as you get into anything non-linear like a tree or a hash table it becomes difficult to even define what the “correct” behavior should be.  Think about it; should the iterator backtrack to hit the missing elements?  Should this backtracking include elements which have already been consumed?  What if the order changes dramatically and pre-consumed elements are now ahead of the current index?  There are a whole host of problems associated with iterating over mutable data structures, and so rather than vainly attempt to solve these issues in a sane and consistent manner, JDK collections simply throw an exception.

All of this becomes moot once you start using immutable data structures.  There is no way to modify a structure while iterating over it because there is no way to modify the structure at all!  Concurrency is not an issue because without any mutable state to require locking, every thread can operate simultaneously on the structure.  Not only is it thread safe, but it is unsynchronized and thread safe.  Immutable data structures retain all of the asynchronous throughput of non-locking implementations without any of the race conditions and non-determinacy which usually results.

A Brief Note on Idioms

At this point, the question you must be asking yourself is: “So if the data structure is immutable, what good is it?”  The answer is “for reading”.  Data structures spend most of their lives being read and traversed by other code.  Immutable data structures can be read in exactly the same fashion as mutable ones.  The trick of course is constructing that data structure in the first place.  After all, if the data structure is completely immutable, where does it come from?  A simple example from a prior article is sufficient to demonstrate both aspects:

def toSet[T](list: List[T]): Set[T] = list match {
  case hd :: tail => hd + toSet(tail)
  case Nil => Set[T]()
}

This is neither the most concise nor the most efficient way to accomplish this task.  The only purpose served by this example is to illustrate that it is very possible to build up immutable data structures without undue syntactic overhead.  You’ll notice that every time we want to “change” a data structure - either removing from the list or adding to the set - we use a function call and either pass or return the modified structure.  In essence, the state is kept entirely on the stack, with each new version of the data structure in question becoming the “changed” version from the previous operation.

This idiom is actually quite powerful and can be applied to even more esoteric (and less uniformly iterative) operations.  As long as you are willing to let execution jump from one function to the next, it becomes extremely natural to deal with such immutability.  In fact, you start to think of immutable data structures as if they were in fact mutable, simply due to the fact that you are idiomatically “modifying” them at each step.  Note that this pattern of modifying data between functions is critical to actor-based programming and any seriously concurrent application design.

Problems Abound

For the sake of argument, let’s assume that my pitiful arguments have convinced you to lay aside your heathen mutating ways and follow the path of functional enlightenment.  Let’s also assume that you’re consumed with the desire to create an application which tracks the status of an arbitrary number of buttons.  These buttons may be pressed in any order regardless of what other buttons are already pressed.  Following the path of immutability and making use of the patron saint of persistent data structures (List), you might come up with the following solution:

class ButtonStrip private (buttons: List[Boolean]) {
  def this(num: Int) = {
    this((0 until num).foldLeft(List[Boolean]()) { (list, i) =>
      false :: list
    })
  }
 
  def status(index: Int) = buttons(index)
 
  def push(index: Int) = modify(index, true)
 
  def unpush(index: Int) = modify(index, false)
 
  /**
   * Modify buttons list and return a new ButtonStrip with the results.
   */
  private def modify(index: Int, status: Boolean) = {
    val (_, back) = (buttons :\ (buttons.length - 1, List[Boolean]())) { (tuple, button) =>
      val (i, total) = tuple
      (if (i == index) status else button) :: total
    }
    new ButtonStrip(back)
  }
}

This is a horrible mess of barely-idiomatic functional code.  It’s difficult to read and nearly impossible to maintain; but it’s purely immutable!  This is not how you want your code to look.  In fact, this is an excellent example of what David MacIver would call “bad functional code“.

Perhaps even worse than the readability (or lack thereof) is the inefficiency of this code.  It’s terribly slow for just about any sort of operation.  Granted, we can imagine this only being used with a list of buttons of limited length, but it’s the principle of the thing.  The fact is that we are relying on a number of operations which are extremely inefficient with lists, most prominently length and apply() (accessing an arbitrary index).  Not only that, but we’re recreating the entire list every time we change the status of a single button, something which is bad for any number of reasons.

What we really need here is a random-access structure, something which allows us to access and “change” any index with some degree of efficiency.  Likely the most intuitive thing to do here would be to just use a good ol’ array of Boolean(s) and make a new copy of this array any time we need to change something.  Unfortunately, this is almost as bad as our copying the list every time.  Normally, when you use an immutable data structure, modifications do not require copying large amounts of data.  Our toSet example from above uses almost zero data copying under the surface, due to the way that Set and List are implemented.

Specifically, Set and List are persistent data structures.  This doesn’t mean that they live on a filesystem.  Rather, the term “persistent” refers to the fact that each instance of the collection may share significant structure with another instance.  For example, prepending an element onto an immutable list yields a new list which consists of the new element and a tail which is precisely the original list.  Thus, each list contains its predecessor (if you will) within itself.  List is an example of a fully persistent data structure; not everything can be so efficient.  Set and Map for example are usually implemented as some sort of tree structure, and so insertions require some data copying (specifically, the parent nodes).  However, this copying is minimized by the nature of the structure.  This notion of persistence in the data structure works precisely because these structures are immutable.  If you could change an element in a persistent data structure it would likely result in the modification of that same element in totally disparate instances of that structure across the entire runtime (not a pleasant outcome).

So List is persistent, arrays are not.  Even if we treat arrays as being completely immutable, the overhead of copying a potentially huge array on each write operation is rather daunting.  What we need is some sort of data structure with the properties of an array (random access, arbitrary modification) with the persistent properties of a list.  As I said before, this turns out to be a very difficult problem.

Partitioned Tries

One solution to this problem which provides a compromise between these two worlds is that of a partitioned trie (pronounced “try” by all that is sane and holy).  In essence, a partitioned trie is a tree of vectors with a very high branching factor (the number of children per node).  Each vector is itself a tree of vectors and so on.  Note that these are not not like the binary search trees that every had to create back in college, partitioned tries can potentially have dozens of branches per node.  As it turns out, it is this unusual property which makes these structures so efficient.

To get a handle on this concept, let’s imagine that we have a trie with a branching factor of 3 (much smaller than it should be, but it’s easier to draw).  Into this vector we will insert the following data:

After all the jumbling necessary to make this structure work, the result will look like the following:

It’s hard to see where the “trie” part of this comes into play, so bear with me.  The important thing to notice here is the access times for indexes 0-2: it’s O(1).  This is of course a tree, so not all nodes will be one step away from the root, but the point is that we have achieved constant-time access for at least some of the nodes.  Mathematically speaking, the worst-case access time for any index n is O( log₃(n) ).  Not too horrible, but we can do better.

First though, we have to lay some groundwork.  I said that the structures we were working with are tries rather than normal trees.  A trie implies that the key for each element is encoded in the path from the root to that node.  So far, it just appears that we have built a fancy tree with numeric keys and a higher branching factor than “normal”.  This would be true if all we were given is the above diagram, but consider the slightly modified version below:

Structurally, nothing has changed, but most of the edges have been renumbered.  It is now a bit more apparent that each node has three branches numbered from 0 to 2.  Also, with a little more thought, we can put the rest of the pieces together.

Consider the “Moya” node.  In our input table, this bit of data has an index of 5.  To find its “index” in our renumbered trie, we follow the edges from the root down to the destination node, arriving at a value of 12.  However, remember that each node has only 3 branches.  Intuitively, we should be thinking about base-3 math somewhere about now.  And indeed, converting 12 into base-3 yields a final value of 5, indicating that the index of the node is indeed encoded in the path from the root based on column.  By the way, this works on any node (try it yourself).  The path to “Karen” is 100, which converted into base-3 becomes 9, the input index of the element.

This is all fine and dandy, but we haven’t really solved our original problem yet: how to achieve constant-time access to arbitrary indexes in a persistent structure.  To really approach a solution to our problem, we must increase the branching factor substantially.  Rather than working with a branching factor of 3 (and thus, O( log₃(n) ) efficiency), let’s dial the branching factor up to 32 and see what happens.

The result is completely undiagramable; but it does actually provide constant time access for indexes between 0 and 31.  If we were to take our example data set and input it into our revised trie, the result would be a single layer of nodes, numbered at exactly their logical index value.  In the worst case, the efficiency of our more complicated trie is O( log₃₂(n) ).  Generally speaking, we can infer (correctly) that for any branching factor b and any index n, the lookup efficiency will be precisely log_b(n).  As we increase the branching factor, the read-efficiency of our trie increases exponentially.  To put a branching factor of 32 into perspective, this means that the algorithmic complexity of accessing index 1,000,000 would only be 3.986!  That’s incredibly small, especially given the sheer magnitude of the index in question.  It’s not technically constant time, but it’s so incredibly small for all conceivable indexes that we can just pretend that it is.  As Rich Hickey says:

…when it’s a small enough value, I really don’t care about it.

So that takes care of the reading end of life, but what about writing?  After all, if all we needed was constant time lookups, we could just use an array.  What we really need to take care to do is ensure that modifications are also as fast as we can make them, and that’s where the tricky part comes in.

We can think of an array as a partitioned trie with a branching factor of .  When we modify an array immutably, we must copy every element from the original array into a new one which will contain the modification.  This contrasts with List - effectively, a partitioned trie with a branching factor of 1 - which in the best case (prepending) requires none of the elements to be copied.  Our 32-trie is obviously somewhere in between.  As I said previously, the partitioned trie doesn’t really solve the problem of copying, it just compromises on efficiency somewhat (the difference between 1 and 3.986).

The truth is that to modify a partitioned trie, every node in the target sub-trie must be copied into a new subtrie, which then forces the copying of its level and so-on recursively until the root is reached.  Note that the contents of the nodes are not being copied, just the nodes themselves (a shallow copy).  Thus, if we return to our example 3-trie from above and attempt to insert a value at index 12, we will have to copy the “Larry” node along with our new node to form the children of a copy of the “Renee” node.  Once this is done, the “Grace” and “Moya” nodes must also be copied along with the new “Renee” to form the children of a new “Bethany”.  Finally, the “Daniel” and “Joseph” nodes are copied along with the new “Bethany” to form the children of a new root, which is returned as the modified trie.  That sounds like a lot of copying, but consider how much went untouched.  We never copied the “Karen” or the “Chris” nodes, they just came over with their parent’s copies.  Instead of copying 100% of the nodes (as we would have had it been an array), we have only copied 80%.  Considering that this was an example contrived to force the maximum copying possible, that’s pretty good!

Actually, we can do even better than this by storing the children of each node within an array (we would have to do this anyway for constant-time access).  Thus, only the array and the modified nodes need be copied, the other nodes can remain untouched.  Using this strategy, we further reduce the copying from 80% to 30%.  Suddenly, the advantages of this approach are becoming apparent.

Now of course, the higher the branching factor, the larger the arrays in question and so the less efficient the inserts.  However, insertion is always going to be more efficient than straight-up arrays so long as the inserted index is greater than the branching factor.  Considering that most vectors have more than 32 elements, I think that’s a pretty safe bet.

Implementation

I bet you thought I was going to get to this section first.  Foolish reader…

Once we have all this theoretical ground-work, the implementation just falls into place.  We start out with a Vector class parameterized covariantly on its element type.  Covariant type parameterization just means that a vector with type Vector[B] is a subtype of Vector[A] whenever B is a subtype of A.  List works this way as well, as do most immutable collections, but as it turns out, this sort of parameterization is unsafe (meaning it could lead to a breakdown in the type system) when used with mutable collections.  This is part of why generics in Java are strictly invariant.

Coming back down to Earth (sort of), we consider for our design that the Vector class will represent the partitioned trie.  Since each child node in the trie is a trie unto itself, it is only logical to have each of the nodes also represented by Vector.  Thus, a Vector must have three things:

• data
• length
• branches

Put into code, this looks like the following:

class Vector[+T] private (private val data: Option[T], 
      val length: Int, private val branches: Seq[Vector[T]]) extends RandomAccessSeq[T] {
  def this() = this(None, 0, new Array[Vector[T]](BRANCHING_FACTOR))
 
  // ...
}

RandomAccessSeq is a parent class in the Scala collections API which allows our vector to be treated just like any other collection in the library.  You’ll notice that we’re hiding the default constructor and providing a no-args public constructor which instantiates the default.  This only makes sense as all of those fields are implementation-specific and should not be exposed in the public API.  It’s also worth noting that the branches field is typed as Seq[Vector[T]] rather than Array[Vector[T]].  This is a bit of a type-system hack to get around the fact that Array is parameterized invariantly (as a mutable sequence) whereas Seq is not.

With this initial design decision, the rest of the implementation follows quite naturally.  The only trick is the ability to convert an index given in base-10 to the relevant base-32 (where 32 is the branching factor) values to be handled at each level.  After far more pen-and-paper experimentation than I would like to admit, I finally arrived at the following solution to this problem:

def computePath(total: Int, base: Int): List[Int] = {
  if (total < 0) {
    throw new IndexOutOfBoundsException(total.toString)
  } else if (total < base) List(total) else {
    var num = total
    var digits = 0
    while (num >= base) {
      num /= base
      digits += 1
    }
 
    val rem = total % (Math.pow(base, digits)).toInt
 
    val subPath = computePath(rem, base)
    num :: (0 until (digits - subPath.length)).foldRight(subPath) { (i, path) => 0 :: path }
  }
}

As a brief explanation, if our branching factor is 10 and the input index (total) is 20017, the result of this recursive function will be List(2, 0, 0, 1, 7).  The final step in the method (dealing with ranges and folding and such) is required to solve the problem of leading zeroes dropping off of subsequent path values and thus corrupting the final coordinates in the trie.

The final step in our implementation (assuming that we’ve got the rest correct) is to implement some of the utility methods common to all collections.  Just for demonstration, this is the implementation of the map function.  It also happens to be a nice, convenient example of good functional code. 

override def map[A](f: (T)=>A): Vector[A] = {
  val newBranches = branches map { vec =>
    if (vec == null) null else vec map f
  }
 
  new Vector(data map f, length, newBranches)
}

Properties

Before moving on from this section, it’s worth noting that that our implementation of the vector concept has some rather bizarre properties not held by conventional, mutable vectors.  For one thing, it has a logically infinite size.  What I mean by this is it is possible to address any positive integral index within the vector without being concerned about resize penalties.  In fact, the only addresses which throw an IndexOutOfBoundsException are negative.  The length of the vector is defined to be the maximum index which contains a valid element.  This actually mirrors the semantics of Ruby’s Array class, which also allows placement at any positive index.  Interestingly enough, the efficiency of addressing arbitrary indexes is actually worst-case much better in the persistent trie than it is in a conventional amortized array-based vector.

Vectored Buttons

Since we now have an immutable data structure with efficient random-access, we may as well rewrite our previous example of the button strip using this structure.  Not only is the result far more efficient, but it is also intensely cleaner and easier to read:

class ButtonStrip private (buttons: Vector[Boolean]) {
  def this(num: Int) = this(new Vector[Boolean])     // no worries about length
 
  def status(index: Int) = buttons(index)
 
  def push(index: Int) = new ButtonStrip(buttons(index) = true)
 
  def unpush(index: Int) = new ButtonStrip(buttons(index) = false)
}

You’ll notice that the update method is in fact defined for Vector, but rather than returning Unit it returns the modified Vector.  Interestingly enough, we don’t need to worry about length allocation or anything bizarre like that due to the properties of the persistent vector (infinite length).  Just like arrays, a Vector is pre-populated with the default values for its type.  In the case of most types, this is null.  However, for Int, the default value is 0; for Boolean, the default is false.  We exploit this property when we simply return the value of dereferencing the vector based on any index.  Thus, our ButtonStrip class actually manages a strip of infinite length.

Conclusion

The truth is that we didn’t even go as far as we could have in terms of optimization.  Clojure has an implementation of a bit-partitioned hash trie which is basically the same thing (conceptually) as the persistent vector implemented in this article.  However, there are some important differences. 

Rather than jumping through strange mathematical hoops and hacky iteration to develop a “path” to the final node placement, Clojure’s partitioned trie uses bit masking to simply choose the trailing five bits of the index.  If this node is taken, then the index is right-shifted and the next five bits are masked off.  This is far more efficient in path calculation, but it has a number of interesting consequences on the final shape of the trie.  Firstly, the average depth of the tree for random input is less, usually by around 1 level (on average).  This means that the array copying on insert must occur less frequently, but must copy more references at each step.  Literally, the trie is more dense.  This probably leads to superior performance.  Unfortunately, it also requires that the index for each value be stored along with the node, requiring more memory.  Also, this sort of “bucket trie” (to coin a phrase) is a little less efficient in lookups in the average case.  Not significantly so, but the difference is there.  Finally, this masking technique requires that the branching factor be a multiple of 2.  This isn’t such a bad thing, but it does impose a minor restriction on the flexibility of the trie.

Most importantly though, Clojure’s trie uses two arrays to contain the children: one for the head and one for the tail.  This is a phenomenally clever optimization which reduces the amount of array copying by 50% across the board.  Rather than having a single array of length 32, it has two arrays of length 16 maintained logically “end to end”.  The correct array is chosen based on the index and recursively from there on down the line.  Naturally, this is substantially more efficient in terms of runtime and heap cost on write.

At the end of the day though, these differences are all fairly academic.  Just having an idiomatic partitioned trie for Scala is a fairly significant step toward functional programming relevance in the modern industry.  With this structure it is possible to still maintain lightning-fast lookup times and decent insertion times without sacrificing the critical benefits of immutable data structures.  Hopefully structures like this one (hopefully, even better implementations) will eventually find their way into the standard Scala library so that all may benefit from their stream-lined efficiency.

• Vector.scala
• VectorSpecs.scala  (ScalaCheck rocks)

Update

The implementation of Vector which I present in this article is inherently much less efficient than the one Rich Hickey created for Clojure.  I finally broke down and created a line-for-line port from Clojure’s Java implementation of PersistentVector into a Scala class.  I strongly suggest that you use this (much faster) implementation, rather than my own flawed efforts.  :-)  You can download the improved Vector here: Vector.scala.