Writing JavaScript using OCaml

output

In this post we’ll explore BuckleScript, a framework that enables developers to write JavaScript applications using OCaml syntax.

Note that BuckleScript is not a way to convert general OCaml code into JavaScript, but rather write JavaScript applications using OCaml’s syntax and type system. The end runtime and libraries are still JavaScript.

For example, if you want to work with dates, you wouldn’t look for the corresponding OCaml library, but instead include the JavaScript Date module in your OCaml code.

Let’s first compare BuckleScript with similar frameworks to understand better why it exists and when to use it. Then we’ll follow with several examples to get a better grasp of BuckleScript and how it translates to JavaScript.

It’s better to have some knowledge of OCaml, and we have written extensively about it, but it’s also easy enough to get the gist of the benefits from our simple examples.

Comparisons

BuckleScript vs js_of_ocaml

js_of_ocaml is another framework that connects the OCaml and JavaScript worlds.

According to [4] both projects aim to compile OCaml code to JavaScript. The differences pointed are:

* js_of_ocaml takes low-level bytecode from OCaml compiler, BuckleScript takes the high-level rawlambda representation from OCaml compiler
* js_of_ocaml focuses more on existing OCaml ecosystem(opam) while BuckleScript’s major goal is to target npm

A simplistic way to see the differences is that BuckleScript is for JavaScript developers to start using better language features from OCaml, while js_of_ocaml is for OCaml developers to be able to run their code in the browser.

BuckleScript vs ReasonML

ReasonML is often mentioned together with BuckleScript, which makes it a bit confusing to understand their differences at first.

Compared do OCaml, ReasonML has a friendlier syntax (for people coming from JS) and better support for JSX (inline XML tags). The difference in syntax is significant that we are really talking about some dialect of OCaml.

Note that BuckleScript and ReasonML are complementary. BuckleScript can compile either OCaml or ReasonML to JavaScript. ReasonML in mostly about the syntax.

BuckleScript vs TypeScript

Typescript is a framework for adding types to a JavaScript codebase, similar to Flow.

At first glance, TypeScript and BuckleScript seem to serve different purpose, but one of the main advantages of using OCaml to write JavaScript applications is to provide type safety.

In [2], Hongbo provides a comparison between the two systems. Some of the pros and cons raised are:

TypeScript

  •  Pros:
    • Designed for JS, easy inter-operate with JS
  • Cons:
    • Compiler slow (not scalable for FB/Google scale)
    • Verbose, very limited type inference
    • Start up time slow (very hard to build traditional build tools)
    • Types are only used for tooling – soundness is not the design goal, not very reliable
    • No code optimizations

BuckleScript

  • Pros:
    • Compiles much faster, scales better
    • Sound type system, global type inference
    • Types are used in code optimization, optimizing compiler
    • Outputs to JavaScript but can also generate native backend for X86, ARM
  • Cons:
    • Learning curve is higher compared with TypeScript

The author also suggests that OCaml is a better language than JavaScript, without providing more details.

Setup

The easiest way to try out examples is through this BuckleScript playground.

To try it locally, we can follow [5]. It generates an initial bsconfig.json file and the compilation process can be done via npm. The process involve converting a OCaml file (.ml) to a JavaScript file (.bs.js). The latter should be included in your application, but both should be considered part of your codebase and hence committed.

Examples

Because most people learning BuckleScript are familiar with JavaScript and less familiar with OCaml, I’ll provide the reverse examples: how to do implement a given JavaScript snippet of code in OCaml?

console.log(‘Hello World’)

How to print to stdout using BucketScript? The most basic program is simply printing a Hello World. In OCaml we would do “Print.printf” to print to stdout, but OCaml modules are not readily available for BuckleScript. We can use the Js.log() function for this purpose:

This maps to:

Note that Js log is a library provided by BuckleScript to make the integration with JavaScript more seamless.

Looping over an Array

We can use an imperative-style code in OCaml:

Note that arrays have a different syntax than in JavaScript: [| 10; 20; 30 |]. If we generate this code we get:

The interesting thing here is that we do have access to some basic OCaml libraries, for example Array.

Since we are dealing with a functional language, we might as well use some more idiomatic OCaml code. If we are to translate the code to native JavaScript structures, we have to use functions from the Js module:

Which maps to

Looping over a List

So Array is the basic structure to represent a sequence of items in JavaScript but in OCaml it’s List. What happens if we use List instead?

We simply dropped the | to use a List instead of Array and now we can use the more standard fold_left instead of reduce. This translates to:

This is very interesting! We studied functional data structures OCaml extensively in the past and the key concept is that by default data structures are persistent. If we look closely what is being passed to sum(), we see it’s a linked-list like structure: [10, [20, [30, 0]]], the 0 being used to indicate the end.

Modules

Another common pattern in JavaScript is to group a bunch of functions inside an Object. The Object name serves as namespace or a module name that can be referenced elsewhere. A natural mapping here are the OCaml modules:

which maps to:

Note that the signature is only used for compilation/transpilation checks and is erased from the final JavaScript code. Another curious thing is that the functions are exported as arrays to MyModule. To me it would make more sense to export them as Object.

Currying Functions

A nice feature from functional languages is the concept of currying, which allow us to perform partial application of functions. For example, if we have a function that adds two numbers, we can derive an increment function that partially applies sum by binding the first value to 1:

The resulting JavaScript code is:

Note that we can already perform partial application of function in vanilla JavaScript via bind(), but the syntax is not as neat:

Chaining Functions

Another neat syntax from OCaml is the chaining operator. One can chain functions via the |> operator: the result of the lefthand function is passed as argument to the righthand function.

A common use for this is when we want to apply a series of function in sequence without assigning to a variable.

For example, if we have a function to normalize a given string by converting it to lower case, trimming the leading and trailing whitespaces and also converting intermediate spaces to underscores, we could write, in JavaScript:

An alternative would be to nest the calls so we don’t have to repeat the variable, but that would hurt legibility. In OCaml, we could chain these calls:

Note the [%bs.re] tag. It is a macro that allows us to write regexes using JavaScript syntax. We can avoid repeating the module names if they are all the same:

Using JavaScript libraries

One of the main selling points of BuckleScript is that you can adopt it gradually, module by module. This is possible because we can require JavaScript modules inside our OCaml code. For example, if we were to convert the following code that reads a file asynchronously in JavaScript:

We could do:

Here, the OCaml code is more verbose but we provided a stronger contract by typing the function readFile(). The syntax for importing modules is

Note: if is the same as , the latter can be omitted.

Objects as Maps

In JavaScript Objects are often used either as maps (key-value) or records (entries of distinct fields). In OCaml we can rely on types to enforce the specific use we want via types. In the example below, we declare a map with type string to int. If we try to set a value with a different type we get a compilation error:

Objects as Records

To represent an Object as a record, we can use a OCaml record type syntax:

We added the [@bs.optional] to indicate that a field is optional. We also added the [@@bs.deriving abstract] attribute to indicate it should not be directly instantiated like

Instead, it generates a “constructor” function. In the same way, the properties of a record are not directly available. They also need to go through intermediate auto-generated accessors:

The generated JavaScript code translates to an Object:

The interesting thing is that the generated JavaScript Object is mutable, but within the OCaml code, the record cannot be modified. It’s possible to mark it mutable, but the default immutability makes it easier to reason about code.

Downsides

The benefits being stated, there are two main potential drawbacks of using BuckleScript.

Mixed languages. Adopting BuckleScript will cause the codebase to have a mix of different languages, which makes it harder for new developers to ramp up. Of course this can be mitigated by converting the entire codebase to use OCaml.

Debugging. We’ll be writing code in on language but it’s another language that will end up being executed. If a problem happens in the underlying JavaScript code, how to figure out which OCaml code is generating the faulty code?

BuckleScript tries to solve this issue by preserving the structure of the code as much as possible so that it’s easier to understand what parts maps to what. This works well if we are using the Js wrappers that resembles the JavaScript code patterns, but it’s unclear how easy the structure is preserved if we use more of OCaml persistent data structures or functional programming patterns like currying.

One possible improvement would be to add some traceability to the generated JavaScript code such that you won’t need to look at the JavaScript code most of the time, in the same way that one doesn’t usually need to inspect Assembly code when their C++ application crashes.

Conclusion

In this post we did a comparison of BuckleScript with different frameworks and libraries to understand why and when to use it. Following that, we studied a few basic examples which one might encounter in a day-to-day JavaScript programming and how to express that in OCaml.

Through these examples, we saw the OCaml type system in use, as well as some neat syntax and immutable data structures, which can lead to more readable, succinct and reliable code.

References

[1] BuckleScript Examples
[2] BuckleScript: An OCaml to JavaScript compiler (Hacker News discussion)
[3] BuckleScript playground
[4] BuckleScript/bucklescript – Comparisons
[5] BuckleScript – New Project

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Generalized Tries in OCaml

In Chapter 10 of Purely Functional Data Structures, Okasaki describes a generalized trie. In this post we’ll see how tries can be seen as maps over a specific type of keys and how we can construct maps over any type of keys.

We discussed tries in OCaml in a previous post. At first glance, it looks like a special data structure but we’ll now see it’s basically a map.

The basic form of a map is a pair of (key, value). In most programming languages the key has to be a scalar (e.g. int, string) or some complex that can be converted to one (think of hashCode() in Java).

For simplicity, let’s assume our scalar is an int and our value is of generic type T. Also, note that the syntax we present below is not valid OCaml (I find OCaml’s type syntax a bit hard to read). A map with int keys can be represented as:

map<int, T>

If we want a two dimensional key, for example (int, int), we can have a map of maps:

map<int, map<int, T>>

and if the dimensions are not fixed, for example, a list of integers, then we can define a recursive structure for our map

map<list<int>, T> = 
  Node of (option<T> * map<int, mapOverList>)

If we think of strings as lists of characters, a trie is then a map where the key is a list of characters. From the type above we can simply change the key of our map above to a list of characters and for a basic trie T is a boolean

map<string, bool> = 
  Node of (bool * map<char, mapOverList>)

Note that option<bool> is redundant, so we can use bool only.

Maps over trees

tree2

We can now generalize the key to more complex recursive types such as trees. Suppose we fave the following type for tree:

tree<int> = (int * tree<int> * tree<int>)

The outermost map is indexed by the root of the tree. The inner maps are indexed by the left and right subtrees respectively:

map<tree<int>, T> =
  map<int, map<tree<int>, map<tree<int>, T>>>

If we expand the key of the second map we get the following:

map<tree<int>, map<tree<int>, T>> =
  map<int, map<tree<int>, map<tree<int>, map<tree<int>, T>>>

It gets pretty involved very quickly but because we traverse these types recursively, the implementation is still simple. Let’s see how to implement these in OCaml. The type of a map over an int tree can be defined as follows:

Note that the outermost map is a regular IntMap, which uses the root element of the map, a scalar, as key.

The search function takes a tree representing the key, and the map. The base case is when the tree is empty, when we’re just past a leaf. The recursive case consists in obtaining the maps in order, first using the root element, then using the left tree and finally using the right tree:

Note that the recursive call is non-uniform, so we need explicit annotations, as we discussed previously.

The insertion is similar, expect that when we fail to find a key in any of the maps, we need to first insert it with an empty element before recursing.

Because the Map.find() implementation throws exceptions when a key doesn’t exist, we can wrap the call with a try and if an exception occurs, we can insert the missing key (alternatively we could use Map.mem()).

Maps over products and sums

There are two ways a structure can branch out recursively: through combination or through alternative. For a combination, refer to our definition of Node:

Node of 'a * 'a tree * 'a tree

In theory, any combination of values for 'a, 'a tree, 'a tree are possible values for a Node. The * in between the components in fact represent the cartesian product operator. This is also known as product.

For alternative, the type of the tree itself can be either Empty or Node:

type 'a tree = Empty | Node of (...)

In this case, the valid values for a tree is the sum of values of each alternative. Hence, this is also known as sum.

We can generalize the map with keys of any types by looking at their definition. If it’s a product, we end up with nested maps. For example, if

Tk = (Tk1 * Tk2)

then the map over Tk can be defined as

map<Tk, T> = map<Tk1, map<Tk2, T>>

In our example, this came the nested maps 'a trie trie IntMap.t.

For sums, if the type is

Tk = Tk1 | Tk2

the map over Tk would end up as a product of maps:

map<Tk, T> = (map<Tk1, T> * map<Tk2, T>)

In our example, this came the product ('a option) and ('a trie trie IntMap.t). option can be thought as a one-dimensional map.

Conclusion

In this post we saw how a trie can be modeled as a map using the same principles as the ones we use to construct matrices, that is, two-dimensional maps (nested maps). We then generalized the string keys to trees, and implemented the insert/find functions in OCaml. I found it pretty hard to reason about these structures.

We then went a step further and saw how to construct maps based on the key structure. And we learned about product and sum of types when discussing recursive types.

Mutually Recursive Modules in OCaml

In Chapter 10 of Purely Functional Data Structures, Okasaki describes a technique called data structure bootstrapping. It’s a way to reuse existing implementation of data structures to construct (bootstrap) new ones.

In one of the examples he creates a new heap implementation with an efficient merge operation using another heap as basis, but it turns out that to implement this, we need to rely on mutually recursive modules, that is, two modules A and B, where A depends on B, and B depends on A.

In this post we’ll study the bootstrapped heap and learn how to implement mutually recursive modules in OCaml.

Heap with efficient merging

Assume we have a heap implementation with O(1) insert, and O(log n) merge, findMin and deleteMin operations. We’ve seen such an implementation with Skewed Binomial Heaps

We’ll see how to construct a new heap implementation which will improve the merge complexity to O(1).

Let’s call the base heap PrimaryHeap and define our heap type as

this type can be either empty or a node with an element (root) and a primary heap whose element is the bootstrapped heap itself, that is, heap and PrimaryHeap.heap form a mutually recursive types. Note that the above is not a valid OCaml code. We’re using it to explain the theoretical concepts.

We can think of this as a k-ary tree where the element is the root and the children of that node are the subtrees, but these subtrees are stored in a heap instead of an array.

The root element at each node is the smallest among all of the subtrees. Hence, to obtain the minimum element for a heap, findMin, is trivial: we can simply return that element:

Merging two bootstrapped heaps is analogous to linking two trees. The only invariant we need to maintain is that the smallest root continues being the root.

Since the primary heap has O(1) insert, the bootstrapped heap has O(1) merge, which was our goal. Note that we can implement insert using merge by creating a singleton node and merging it with an existing heap.

We need to handle the deletion of the minimum element, which is the more involved operation. It consists in discarding the root of the present node, and finding a new root from the primary heap.

Since each element in the primary heap is a bootstrapped heap, we first obtain the bootstrapped heap containing the smallest element:

then we remove this node from the primaryHeap, and we merge the minPrimaryHeap back into primaryHeap.

finally we make newMinElem the new root element of our top level bootstrapped heap. The complete code is

The only missing part is defining the correct type of the bootstrapped heap.

Mutually Recursive Modules

escher

Drawing Hands by M. C. Escher

Okasaki mentions that recursive structures are not supported in Standard ML (at least at the time my copy of the book was printed), but they are supported in OCaml.

To make modules mutually depend on another, we need to mark it as recursive via the rec keyword, and declaring both modules at the same time by using the and connector. Let’s work with a toy example: two modules Even and Odd, where each depend on the other.

This will lead to a compilation error:

Error: Recursive modules require an explicit module type.

We need to write the signatures explicitly:

This blog post from Jane Street describes a way to define mutually recursive modules by only listing its signatures:

The OCaml compiler can infer the implementation part from the type definitions, but unfortunately this won’t work if the module has function definitions, which is the case for our heap implementation.

Things get more complicated in our case because the primary heap implementation uses a functor to generate a heap taking the element’s module as parameter. In this case the element’s module is our bootstrapped heap. A valid module definition is given below:

Let’s understand what is going on.

type t = Empty | Heap of Element.t * PrimaryHeap.heap

is the definition we presented above. We also implement the methods from the Set.OrderedType interface, namely compare, since this is the interface the heap maker expects. The comparison is based solely on the root element.

Then we declare the PrimaryHeap type at the same time, with type IHeapWithMerge, and because tv is unbound in that interface, we need to bind it to BootstrappedElement.t:

PrimaryHeap: IHeapWithMerge with type tv := BootstrappedElement.t

Finally we provide the implementation, using the result of the SkewBinomialHeap() functor having the BootstrappedElement module as element type:

PrimaryHeap (...) = SkewBinomialHeap(BootstrappedElement)

The syntax is pretty involved, but it accomplishes what we wanted. We can further refine this definition by adding

include Set.OrderedType with type t := t

to the BootstrappedElement signature. This includes all the interface of Set.OrderedType.

These newly defined modules are defined within a functor, the BootstrappedHeap, together with the methods we defined above. Like other heap generators, the functor takes a module representing the element type as parameter. In this case we can also allow the primary heap type to be passed as parameter so we don’t have to use SkewBinomialHeap as implementation. Any heap with merge will do.

The constructors define within BootstrappedElement are visible within BootstrappedHeap but they need qualification, such as BootstrappedElement.Heap. To avoid repeating this qualifier, we can use:

include BootstrappedElement

The complete implementation for BootstrappedHeap can be found on github.

Conclusion

The idea of using implementations of a given data structure to yield improve implementations is amazing! The mutual recursion nature of the bootstrap heap got me at first, but making analogies with a k-ary tree made it easier to understand.

I was struggling a lot to get the syntax right for the recursive modules required for this implementation until I stumbled upon this github repository, from which I learned many new things about OCaml.

References

[1] Purely Function Data Structures, Chapter 10 – Chris Okasaki
[2] Jane Street Tech Blog – A trick: recursive modules from recursive signatures
[3] Github yuga/readpfds: bootStrappedHeap.ml

Polymorphic Recursion in OCaml

In Chapter 10 of Purely Functional Data Structures, Okasaki describes recursive types that are non-uniform. In this post we’ll learn more about these types, how to implement them in OCaml and see an example by studying the implementation of Random Access Binary Lists using such a construct.

Uniform recursive type

As an example of uniform recursive data structure we have a simple list

Cons(1, Cons(2, Cons(3, Nil)))

Which has a recursive type, for example

type 'a myList = Nil | Cons of 'a * 'a myList

Each element of the list is either Nil (terminal) or it has a value of a polymorphic type 'a, followed a recursive list also of type 'a.

Non-uniform recursive type

Now, say that the type of the recursive list is not the same as the current list? Then we have a non-uniform polymorphic recursive type, for example:

type 'a seq = Nil | Cons of 'a * ('a * 'a) seq

We’ll name this a sequence. A int seq would have the value in the first node would have type int, but the element from the second node would have type (int, int), the third type ((int, int), (int, int)) and so on. This structure is equivalent to a complete binary tree, where the i-th element of seq represents the i-th level of the tree.

An example of value with this type is:

Cons (1, Cons ((2, 3), Cons (((4, 5), (6, 7)), Nil)))

We need a special syntax to type recursive functions that take recursive non-uniform types, because the type of the function called recursively might be a different polymorphic type than the caller. OCaml by default tries to infer the generic types of the function and bind them to specific instances [2]. For example, in

let f: 'a list -> 'a list = fun x -> 13 :: x

OCaml will assume 'a is int and will compile fine. We can see this by pasting that code in the command line, utop.

utop # let f: 'a list -> 'a list = fun x -> 13 :: x;;
val f : int list -> int list =

The function will then not be polymorphic anymore. To prevent OCaml from auto-binding specific type instances, we can use a special syntax introduced in OCaml 3.12 [3]

utop # let f3: 'a. 'a list -> 'a list = fun x -> 13 :: x;;

This time we’ll get a compilation error:

Error: This definition has type int list -> int list which is less general than ‘a. ‘a list -> ‘a list

The important thing is that this allow us binding the recursive calls with different types. According to the Jane Street Tech Blog [3]:

Note that a polymorphic type annotation holds inside the body of a recursive definition as well as outside, allowing what is known as polymorphic recursion, where a recursive call is made at some non-trivial instantiation of the polymorphic type.

So for example, we can write this function to calculate the size of a sequence:

The problem with this structure is that it can only represent lists of size in the form of 2^k - 1. To work around that, we allow some items to not hold any elements at all, so that each item corresponds to a digit in the binary representation of the size of the list.

type 'a seq = Nil | Zero of ('a * 'a) seq | One of 'a * ('a * 'a) seq

For example, we can now represent a list of 10 elements, as

Zero(One((1, 2), Zero(One(((3, 4), (5, 6)), ((7, 8),(9, 10))), Nil))))

mandelbrot

Sequence binary random access list

We can use a sequence to implement a random access binary access list in a concise way.

Insertion

Inserting an element at the beginning is analogous to incrementing the binary number, that is, starting from the least significant digit, if it’s a zero, we make it one, if it’s one we make it a 0, and carry over a 1, by adding it to the next digit.

The carry over process is simple in this structure. Because the type of an item following an item of type 'a is ('a, 'a), to merge the element to be added with the existing element, we simply make a tuple and pass it to the recursive call.

Head and Tail

Removing or retrieving the first element is analogous to decrementing a binary number. If the digit is one, we make it zero and return the element and the resulting list. If it’s a zero, we make a recursive call to get the next available element. However since the returned element is of type ('a, 'a), and our return type is 'a, we only use the first value of the pair.

Implementing head and tail using popAux is now trivial

Lookup

Finding an element can be done by transforming the problem into smaller instances.

It helps to look at some simple examples. Let’s consider 3 cases.

Case 1. If we had a single element, we either return it if the index is 0, or throw if it’s larger than that.

0: (0)

Case 2. If we have 2 elements,

0: ()
1: (0, 1)

Notice that when we go from the first level to the second, all items “doubled” in size, so we can “transform” this to the single element case by treating pairs as a single element, but since the index has individual elements granularity, we need to transform it by halving it. We reduced it to Case 1.

If our initial index was either 0 or 1, it’s now 0, and we found our element in level 1.


1: (0)

The problem is that we need to return a single element at level 0, not a pair, so we need to undo the transformation. We can use the parity of the original index will to decide which side of the pair to return. If it’s even, we return the first element, otherwise the second one.

Case 3. If we have 3 elements,
0: (0)
1: (1, 2)

and our index is larger than 0, we move to the next level but we need to account for the level we’re skipping, so the transformation of index would look like:
0: ()
1: (0)

which is reduced to Case 2.

These 3 cases can be used to find elements larger than 3. For example, say we have 10 elements and want to find the element at position 6:

0: ()
1: (0, 1)
2: ()
3: (((2, 3), (4, 5)), ((6, 7), (8, 9)))

Step 1. This is Case 2. We need to transform this by treating pairs as elements and halving the index:

1': (0)
2': ()
3': ((1, 2), (3, 4))

Note how this reduced the problem of finding the element at position 3 of a list with size 5. Step 2. We now are in case 3, where we need to skip the current element:

1': ()
2': ()
3': (((0), (1)), ((2), (3)))

Our index is now 2. Step 3. we go one level down

2: ()
3: (0, 1)

With an index of 1. Step 4. Finally, we halve it once again and we finally found the right level that contains our index.

3: (0)

We now need to recurse back to find exactly which element on that level to pick. On Step 4, we can see our index 1 was on the right side of the pair in level 3, so we pick the right side, that is, ((6, 7), (8, 9)).

On Step 3, our index 2 was on the left size of the innermost pair, that is (6, 7). On Step 2, we skipped the current element but didn’t change levels, so there’s no need to choose an element from the pair. Finally, on Step 1, the index 6 was on the left side of the innermost pair, which should return the element with a corresponding index 6.

In general, we can tell which side of the innermost pair to pick by observing that the indexes are ordered from left to right in a given level. And because every level has an even number of elements, we can assume that the first index in the level – or the first element in the first pair – is even. Hence the parity of the index is sufficient to determine which side of the pair to pick.

With this algorithm in mind, the lookup function is quite compact:

The update follows a similar idea as the lookup, but the problem is that we need to return the updated level when returning from the recursion. That is, we need to update the level before returning.

To accomplish that, we can pass a callback, the updater, that encodes which pair we would pick at each level. We start with a function that simply returns the element to be updated

(fun _ -> element)

Then, at each level we create a new updater, which applies the previous updater on the left or right side of the pair, depending on the parity of the index:

When we finally find the level that has our index, we can apply the function, which has the effect of “narrowing” down the elements from a given level to a single element, replacing the value at the target index and then returning the updated elements when returning from the recursion.

After applying the updater, we return the updated level recursively.

Structural Decomposition

Okasaki introduces this implementation in the context of Structural Decomposition, which is a technique for creating data structures from incomplete ones. In this example, the raw implementation of the sequence can only represent lists of size 2^k, but modeling each node in the sequence to be zero or one, zero not having any elements, we can work around the size restriction.

Conclusion

The implementation of random access binary access list using sequences is very neat, but very hard to understand.

One of the major selling points of shorter code is that it tends to contain less bugs and also less corner cases. On the other hand, if the code is also harder to read and understand, it might be harder to spot bugs.

This post helped me understand a bit more about OCaml’s type system. Digging a little also led me to interesting topics such as Parametric Polymorphism [4] and Existential vs. Universally quantified types [5].

References

[1] Purely Function Data Structures – Chris Okasaki
[2] Jane Street – Ensuring that a function is polymorphic
[3] Ensuring that a function is polymorphic in Ocaml 3.12
[4] Wikipedia – Parametric polymorphism
[5] Existential vs. Universally quantified types in Haskell

Numerical Representations as inspiration for Data Structures

Book

In this chapter Okasaki describes a technique for developing efficient data structures through analogies with numerical representations, in particular the binary and its variations.

We’ve seen this pattern arise with Binomial Heaps in the past. Here the author presents the technique in its general form and applies it to another data structure, binary random access lists.

Binary Random Access Lists

These lists allows efficient insertion at/removal from the beginning, and also access and update at a particular index.

The simple version of this structure consists in distributing the elements in complete binary leaf trees. A complete binary leaf tree (CBLF) is one that only stores elements only at the leaves, so a tree with height i, has 2^(i+1)-1 nodes, but only 2^i elements.

Consider an array of size n, and let Bn be the binary representation of n. If the i-th digit of Bn is 1, then we have a tree containing 2^i leaves. We then distribute the elements into these trees, starting with the least significant digit (i.e. the smallest tree) and traversing the tree in
pre-order.

For example, an array of elements (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11) has 11 elements, which is 1011 in binary. So we have one tree with a single leave (1), a tree with 2 leaves (2, 3) and another containing 8 leaves (4, 5, 6, 7, 8, 9, 10, 11).

We use a dense representation of the binary number, by having explicit elements for the 0 digit. Here’s a possible signature for this implementation:

Inserting a new element consists in adding a new tree with a single leave. If a tree already exists for a given size, they have to be merged into a tree of the next index. Merging two CBLFs of the same size is straightforward. We just need to make them children of a new root. Since elements are stored in pre-order, the tree being inserted or coming from carry over should be the left child.

Looping back to our example, if we want to insert the element 100, we first insert a tree with a single leaf (100). Since the least significant digit already has an element, we need to merge them into a new tree containing (100, 1) and try to insert at the next position. A conflict will arise with (2, 3), so we again merge them into (100, 1, 2, 3) and try the next position. We finally succeed in inserting at position 2, for a new list containing trees like (100, 1, 2, 3) and (4, 5, 6, 7, 8, 9, 10, 11).

The complexity of inserting an element is O(log n) in the worst case which requires merging tree for all digits (e.g. if Bn = 111...111). Merging two trees is O(1).

Removing the first element is analogous to decrementing the number, borrowing from the next digit if the current digit is 0.

Searching for an index consists in first finding the tree containing the index and then searching within the tree. More specifically, because the elements are sorted beginning from the smallest tree to the largest, we can find the right tree just by inspecting the number of elements in each tree until we find the one whose range includes the desired index. Within a tree, elements are stored in pre-order, so we can find the right index in O(height) of the tree.

After finding the right index, returning the element at that index is trivial. Updating the element at a given index requires rebuilding the tree when returning from the recursive calls.

Okasaki then proposes a few different numbering systems that allow to perform insertion/removal in O(1) time. Here we’ll only discuss the less obvious but more elegant one, using skew binary numbers.

characters

Skew Binary Random Access Lists

A skew binary number representation supports the digits 0, 1 and 2.

The weight of the i-th digit is 2^(i+1) - 1. In its canonical form, it only allows the least significant non-zero digit to be 2.

Examples:

table-v2

Decimal and Skewed Binary

It’s possible to show this number system offers a unique representation for decimal numbers. See the Appendix for a sketch of the proof and an algorithm for converting decimals to skewed binary numbers.

Incrementing a number follows these rules:

  • If there’s a digit 2 in the number, turn it into 0 and increment the next digit. By definition that is either 0 or 1, so we can safely increment it without having to continue carrying over.
  • Otherwise the least significant digit is either 0 or 1, and it can be incremented without carry overs.

The advantage of this number system is that increments (similarly, decrements) never carry over more than once so the complexity O(1), as opposed to the worst-case O(log n) for regular binary numbers.

A skew binary random access list can be implemented using this idea. We use a sparse representation (that is, not including 0s). Each digit one with position i corresponds to a tree with (2^(i+1) - 1) elements, in this case a complete binary tree with height i+1. A digit 2 is represented by two consecutive trees
with same weight.

Adding a new element to the beginning of the list is analogous to incrementing the number, which we saw can be done in O(1). Converting a digit 0 to 1 or 1 to 2, is a matter of prepending a tree to a list. To convert a 2 to 0 and increment the next position, we need to merge two trees representing it with the element to be inserted. Because each tree is traversed in pre-order, we make the element the root of the tree.

Elements are inserted in pre-order in each tree, so when searching for an
index, we can first find the right tree by looking at the tree sizes and within a tree we can do a “binary search” in O(height) of the tree.

Binomial Heaps

In this chapter, this technique is also applied to improve the worst case runtime of insertion of binomial heaps. The implementation, named Skewed Binomial Heap, is on github.

Conclusion

This chapter demonstrated that binary representations are a useful analogy to come up with data structures and algorithms, because they’re simple. This simplicity can lead to inefficient running times, though. Representations such as skewed binary numbers can improve the worst case of some operations with the trade-off of less intuitive and more complex implementations.

Appendix A – Proof

Sketch of the proof. First, it’s obvious that two different decimals cannot map to the same binary representation. Otherwise the same equation with the same weights would result in different values. We need to show that two binary representations do not map to the same decimal.

Suppose it does, and let them be B1 and B2. Let k be the largest position where these number have a different digit. Without loss of generality, suppose that B1[k] > B2[k].

First case. suppose that B1[k] = 1, and B2[k] = 0 and B2 doesn’t have any digit 2. B1 is then at least M + 2^{k+1} - 1, while B2 is at most M + \sum_{i = 1}^{k} (2^{i} - 1) which is M + 2^{k + 1} - k (M corresponds to the total weight of digits in positions > k). This implies that B2 < B1, a contradiction.

Second case. suppose that B1[k] = 1, but now B2 does have a digit 2 at position j. It has to be that j < k. Since only zeros follow it, we can write B2‘s upper bound as

M + \sum_{i = j + 1}^{k} (2^{i} - 1) + 2^{j + 1} - 1

Since 2(2^{j + 1} - 1) < 2^{j + 2} - 1, we have

\sum_{i = j + 1}^{k} (2^{i} - 1) + 2^{j + 1} - 1 < \sum_{i = j + 2}^{k} (2^{i} - 1) + 2^{j + 2} - 1

We can continue this argument until we get that B2 is less than M + 2(2^{k} - 1) which is less than M + 2^{k + 1} - 1, B1.

Third case. Finally, suppose we have B1' such that B1'[k] = 2, and B2'[k] = 1. We can subtract 2^{k+1} - 1 from both and reduce to the previous case. ▢

Appendix B – Conversion algorithm

Converting from a decimal representation to a binary one is straightforward, but it’s more involved to do so for skewed binary numbers.

Suppose we allow trailing zeros and have all the numbers with k-digits. For example, if k=2, we have 00, 01, 02, 10, 11, 12 and 20. We can construct the numbers with k+1-digits by either prefixing 0 or 1, and the additional 2 followed by k zeros. For k=3, we have 000, 001, 002, 010, 011, 012, 020, 100, 101, 102, 110, 111, 112, 120 and finally 200.

More generally, we can see there are 2^(k+1) - 1 numbers with k digits. We can construct the k+1 digits by appending 0 or 1 and then adding an extra number which is starts 2 and is followed by k zeros, for a total of 2^(k+1) - 1 + 2^(k+1) - 1 + 1 = 2^(k + 2) - 1, so we can see this invariant holds by induction on k, and we verify that is true for the base, since for k = 1 we enumerated 3 numbers.

This gives us a method to construct the skewed number representation if we know the number of its digits say, k. If the number is the first 2^(k) - 1 numbers, that is, between 0 and 2^k - 2, we know it starts with a 0. If it’s the next 2^(k) - 1, that is, between 2^k - 1 and 2^(k+1) - 3, we know it starts with a 1. If it’s the next one, exactly 2^(k+1) - 2, we know it starts with a 2.

We can continue recursively by subtracting the corresponding weight for this digit until k = 0. We can find out how many digits a number n has (if we’re to exclude leading zeros) by find the smallest k such that 2^(k+1)-1 is greater than n. For example, for 8, the smallest k is 3, since 2^(k+1)-1 = 15, and 2^(k)-1 = 7.

The Python code below uses these ideas to find the skewed binary number representation in O(log n):

One might ask: why not OCaml? My excuse is that I already have a number theory repository in Python, so it seemed like a nice addition. Converting this to functional code, in particular OCaml is easy.

This algorithm requires an additional O(log n) memory, while the conversion to a binary number can be done with constant extra memory. My intuition is that this is possible because the weights for the binary numbers are powers of the same number, 2^k, unlike the skewed numbers’ weights. Is it possible to work around this?

Lazy Rebuilding

Book

In this chapter Okasaki starts with a common technique data structures use to achieve efficient complexity times. A classic example are balanced binary search trees, which, on one hand, need to be balanced to avoid degenerated cases (think of a binary tree which can degenerate to a path), but on the other hand, balancing is usually too expensive to perform at every operation.

The tradeoff is to allow a certain degree of “imbalance”, such that the big-O complexity of operations does not degenerate, and to let us perform re-balances only every so often, in a way that leads to an efficient amortized complexity. Structures such as AVL trees and Red-black trees make use of this technique.

The general technique is named batched rebuilding. The main issue with it though is that we saw that amortized analysis does not play well persistent data structures.

To address that problem, a technique was developed by Mark Overmars, called global rebuilding.

Global Rebuilding

The basic idea is to keep two copies of a structure in parallel, one of which we perform incremental updates (write) and the other is used for read operations. After a few operations, we copy the write version into the read-only version. The idea is that the incremental updates are cheap, but might put the structure in a state that is not suitable for reading. This allows for distributing the costs across operations such that each operation has worst case efficient bounds.

The major downside of this technique is the complexity and corner cases, because we also need to account for changes to the structure that renders the write copy of the structure invalid.

We’ll now describe yet another implementation of queues that use this technique, developed by Robert Hood and Robert Melville. Similar to other queue implementations, this structure maintains a front and rear lists, the latter in reverse order. It also has the invariant that the rear list cannot be larger than the front.

When that happens we must perform a rotation, which consists in reversing the rear queue and appending it to the end of the front queue. We cannot perform this operation piecemeal, because we only have efficient access to the first element of a list. The operation we can do partially is concatenating the reverse of a list to the beginning of another list, that is, given xs and ys, we can do ~xs + ys, where ~ represents the reverse of a list. Note we can also reverse a list, that is xs to ~xs piecemeal by having ys = [].

Now, to achieve our goal which is xs + ~ys, we reverse both xs and ys, then concatenate the reverse of ~xs to ~ys:

1) Start with xs and ys
2) Reverse both: ~xs and ~ys
3) Then (~(~xs)) + ~ys which is xs + ~ys

We can perform these operations one step at a time by using a “state machine”, we first start with a state Reversing(xs, ys) which will reverse xs and ys at the same time into ~xs and ~ys, at which point we switch to the state Appending(~xs, ~yx) which will concatenate the xs into ~ys, so then we get to Done(xs + ~ys).

The problem of keeping a separate state is that it might not be accurate by the time it’s done. In particular, if we remove an element from the front, the Appending step can be shortcut. We can keep a count of how many of the elements in ~xs of Appending are still present. If, by the time we’re appending the number of present elements goes to 0, the last elements of ~xs (that is, the first elements of xs) have been removed, so they do not need to be transferred to ~ys.

A possible implementation of the states is:

The Idle is just a placeholder to make sure the state machine can keep going to the next state even when we’re not currently performing a rotation. The state transition definition is given by:

One important detail here is that Appending ({validCount = 0; front; rear}) must appear before the other matching rule for Appending, because the other one includes this.

When we remove an element from the front of the queue we need to update the number of valid elements in the front of the rotation state:

Similar to the Real-time queues, we can guarantee constant time worst case for all the operations if we execute the state machine twice for each operation. The check() function verifies if we need a rotation and also executes the nextStep() function twice.

The other operations are then straightforward. The only thing is that pop() must call the invalidate() function because it’s decreasing the size of front:

The full, up-to-date implementation in on github.

Lazy Rebuilding

As we can see, the Hood-Melville implementation is quite involved. Okasaki argues that making use of lazy evaluation and memoization simplifies the implementation. We can indeed see that the implementation of Real-time queues is much cleaner.

One simplification is that we don’t have to sync the write copy with the read copy. We just need to make sure to execute items from the schedule by the time they are needed. Memoization will take care of the rest.

Another advantage in this case is that reversing and concatenating lazily evaluated lists does not require an intermediate reversal of the front, which means that we can remove the front element without the need to invalidate any items in the schedule.

The author provided more examples of lazy rebuilding for deques (double-ended queues) by first presenting an amortized version using the Banker’s method and then a lazy rebuilding version. The Banker’s deque and Real-time deque are also on my github.

Conclusion

In this chapter the technique behind Real-time queues was formalized. I really enjoy the organization of the book in which the author presents a data structure and from there he extracts a generic pattern of technique that can be applicable to other cases.

When I was studying data structures I don’t recall learning about general techniques underlying existing data structures, such as batched rebuilding for AVL trees and Red-black trees. That would have been interesting.

Eliminating Amortization

Book

In this chapter Okasaki presents techniques to convert persistent data structures with amortized bounds to worst-case bounds. A few reasons to want worst-case guarantees include:

  • Real-time systems, where an expensive operation can cause the system to miss a hard deadline, even if it’s efficient when amortized over time.
  • Parallel systems, in which we want avoid one processor to take more time than the other if it happens to execute the expensive operation.

The intuition is to make the theoretical amortized analysis part of the actual implementation. In the amortized analysis, we saw either through the Banker’s method or the Physicist method the idea of paying debt ahead of time so by the time an expensive operation takes place, we could show it’s cost had already been distributed throughout previous operations.

To eliminate the amortization, we can literally pay off these costs when running the algorithm. We do it through a structure called schedule, which contains the unevaluated operations on the data structure. Whenever we perform an operation, we evaluate one or more items of the schedule. Due to memoization, by the time we actually need the evaluated structure, it will be evaluated.

Often the amortized analysis can dictate how the execution of the schedule is performed. For example, if the analysis tells us to pay off 2 units of debt on every action, that can be translated to executing 2 items from the schedule on every action.

The main challenge in this conversion is to modify the data structure in such a way it can be partially executed.

We’ll discuss an example using queues and then binomial heaps.

Real-time Queues

For the queue structure, the costly operation is the rotation that takes place when we need to combine the rear with the front. It’s a monolithic operation, so we need to change that.

Let’s start by defining the structure. It’s similar to the stream queue, except that we don’t need the rear to be a stream and we have a schedule field, which is also a stream. The idea is that whenever a rotation happens, it will be “scheduled” to be executed little by little.

The rotation() function is the most complicated part of the structure. The invariant is that we only call this function when |rear| = |front| + 1.

We construct a stream with the elements of rear reversed, newSchedule and on the return step of the recursion we append the elements of front to that stream.

Note that this is performed lazily, so a call to rotate only executes one step.

Now we have the execution of the schedule. It serves two purposes. One is to execute an item from the schedule and the other is to trigger a rotation when the is schedule empty.

The schedule being empty is a proxy to when |rear| = |front| + 1. Why? Because when the schedule is populated, it has the same size of front (they’re both assigned the same stream), and rear is empty. Whenever we insert an element, increasing the size of rear by one, or remove an element, reducing the size of front by one, we decrease the difference between |front| - |rear| by 1, and so the size of the schedule is decreased by 1.

Implementation-wise, maybe it would be more clear if we checked for the empty schedule outside and only called exec() is it was not empty.

With these in place, the usual operations for queue are straightforward. The ones that mutate the queue, push() and pop(), need to make sure to call exec().

Scheduled Binomial Heaps

We discussed Binomial Heaps in a previous post. The idea is that a binomial heap is a list of binomial trees, an a binomial tree is defined recursively based on it’s rank.

The heap is represented by a binary number (as a list of digits). If the k-th digit of the number is 1, it indicates the existence of a binomial tree of rank k (containing 2^(k-1)). A heap with n elements, has a unique representation, which is the binary representation of n.

For example, if n = 10, then the binary representation of the heap is 1010, meaning it contains a binomial tree of rank 2 (2 elements), and one with rank 4 (8 elements), holding 10 elements in total.

Inserting an element into the heap is analogous to incrementing a binary number by 1. We first create a binomial tree with rank 0 containing that element, and try to insert it into the beginning of the digits list. If the position we want to insert is occupied, we need to merge the trees, to generate a tree with a higher rank, which is analogous to the carry over operation when adding two binary numbers.

The schedule is a list of all the numbers generated when any operation is performed.

The structure for the heap is then the following:

As we discussed above, the insertion is analogous to incrementing the binary number. But because the digits are a stream, we need to deal with lazy evaluation:

Executing the schedule consists in evaluating one digit from the first number on the schedule. The key is to avoid evaluating the part of the number that already has been evaluated. It’s possible to show this happens when we find the first digit one. The intuition is that the trailing zeros from the current number were 1’s before the increment, so there was a mutation (linking) while the remaining digits were not modified by that operation.

For example, if we have the number 101011, an increment causes it to become 101100. The two trailing zeros in 101100 correspond to a linking of the binomial tree.

Finally, inserting a new element consists in creating a binomial tree of ranking 0, insert it, and then execute the schedule.

The full code is available on github.

Lessons learned

Because we now have several different implementations of queues, I wanted to implement tests that were applicable to any queue implementing an “interface”.

One way to do that is to put the interface in a separate module, IQueue.ml and have each implementation require it as their module type:

To make the test work with any module implementing the IQueue interface, we can use a functor (module transformation) and

Other things I’ve learned were matching lazy patterns. Matching a lazily evaluated variable with the keyword lazy forces the evaluation, so we can use a cleaner syntax, for example:

Another syntactic construct that helps with legibility is the record. The examples in Okazaki’s book use tuples for most of composed structs, but I prefer the more verbose and explicit records.

Finally, another lesson learned is that adding an insertion operation to the Stream API is likely wrong. The idea of the stream is that is avoids evaluation of its tail, so the whole block has to lazily evaluated.

For example, in the queue implementation, in the first block, we will evaluate all the rotation and then make the result lazy, which is not what we want.

Conclusion

Eliminating evaluation is a very interesting technique, but it has limited application is practice. It complicates the implementation and, as I learned, it can be tricky to spot bugs (for example, the one in which we’re evaluating the rotate() function) that would only show up if we profile the data structure.