Posted on October 3, 2019 and last updated on October 8, 2019

In which my job search leads me to verify the leftist and heap properties of a leftist heap using Haskell's type-level features and to test various implementations by way of simulation using QuickCheck. We also cover much of Haskell's type-level computation features from scratch.

The story

I’ve made it to the final stage of my computer science PhD, you know the one where you start looking for jobs, get yourself a copy of Cracking the Coding Interview (affiliate link), and realise that you haven’t done much of the kind of programming that about half of the companies expect you to do at their job interviews (and at their job interviews only).

At some point in the book, it says “know how to implement these data structures by heart: dynamically sized arrays, hash tables, […], binary heaps, […]”. It dawned on me that I remember the heap property and the heap interface, but not how to implement it. I was horrified when I remembered despite conceptually being a tree, binary heaps are implemented using arrays. Despite having used Haskell as my primary language, decided to implement it in Ruby—my prior primary language. Some time and indexing errors later, I got it working. Then ported it to Haskell’s using the ST monad. After writing STRef one too many times, I got that working too, but it left much to be desired. “Save the trees” yelled my terminal!

Finally, I consulted Dr Chris Okasaki’s Purely Functional Data Structures (affiliate link). A leftist heap is one of the first data structures discussed in the book. It has better worst-case asymptotic complexity than the binary heap, is represented as a tree, and doesn’t need mutation. Great! I was pleased to have a heap under my belt that was much easier to remember and much more difficult to get its implementation wrong.

Staring at it for a while (and being bored while trying to find various substrings with various properties in linear time and constant space under an hour over the phone), I got a burning desire to encode the titular properties of a leftist heap using fancy types. Having listened to hundreds of people complain about the state of type-level programming in Haskell, I found the process to be rough around the edges, but functional (see what I did there).

The spiel

This brings us to the post at hand. This post is a bit long, but the upside is there is something for everybody. Hopefully, some of the following piques your interest:

leftist heaps as a purely functional alternative to array-based binary heaps,

complexity analysis of operations on leftist heaps,

a case study on the internalist approach to verifying data structures,

a tutorial on most major features of type-level programming in Haskell,

a commentary on the ergonomics of verification using fancy types in Haskell,

and practical advice on avoiding pitfalls when using fancy types.

Beginners beware! Type-level programming can be daunting. It certainly was for me for a long time. I’ll attempt to explain fancy types from scratch. If you find yourself getting confused, it’s almost certainly my fault. Just let me know (contact details on my homepage) and I’ll clarify the post.

The itinerary

Here are the sections and what to expect from them.

The exposition of the code is fragmented and out of order, but a well-organised version of the SOURCE CODE exists. We won’t use any libraries except QuickCheck.

A simple heap

A heap is a (conceptually) tree-based data structure used to quickly access and maintain access to the maximum or the minimum of a collection of values. It satisfies the heap property, that is (for a maximum heap) the label of a node is bigger than or equal to that of its children. The following typeclass summarises its common operations.

This is a bit mouthful because many operations are inter-definable as reflected by the MINIMAL pragma.

The Elem type family (enabled by TypeFamilies extension) associated with Heap gives the type of elements for a particular instance. This is nothing but a function from types of containers to types of their elements. We could have equally used MultiParamTypeClasses and FunctionalDependencies extensions to establish the same container-element relationship. I chose a type family here because we will use type families in a moment anyway and because I think Elem heap has less cognitive overhead than remembering functional dependencies between type variables.

Although insert , findMax and deleteMax are the most commonly used operations of Heap , merge is the one that we care the most about. For all data structures we’ll use as heaps today, implementing isEmpty , findMax , singleton , and empty are trivial. Then with merge , we can implement insert , fromList , decompose , and deleteMax . As we see in the next section, implementing merge and deriving the rest is not only optimal in terms of productivity but also in terms of performance for leftist heaps.

Before implementing this interface for a leftist heap, let’s look at a much simpler instance.

This may be the easiest heap implementation. Insertion is \(O(1)\), merging is \(O(n)\), conversion from a list is \(O(1)\), and decomposing (and subsequently finding and deleting the maximum) is \(O(n)\). If it wasn’t for that last \(O(n)\), this would have been a perfectly fine heap implementation, alas here we are.

This implementation is obviously correct, thus any other correct heap implementation should be functionally equivalent to it. This means performing the same operations on two empty heaps of different implementations should result in two heaps with the same maximum. Hence, this simple heap implementation is perfect for testing other implementations’ correctness.

A leftist heap

Since we’ll go through the trouble of implementing leftist heaps multiple times, let’s spend a second on comparing it to array-based binary heaps.

Why bother with the leftist heap? It is persistent (hence better suited for multi-threaded computation), both conceptually and implementation-wise a tree, and more resilient against off-by-one errors. Why bother with the array-based binary heap? Better average case complexity of insertions; its operations are in place; and it probably performs better in practice because of good locality of reference (this is a hunch and I’d like to be proven wrong).

We can also look at their complexities more concretely. Leftist heaps have \(O(\lg{n})\) worst-case complexity for insertion and deleting the maximum, while maintaining \(O(1)\) complexity for finding the maximum. Building a heap out of a collection is \(O(n)\). So far we’re on par with binary heaps. But we can do one better. While merging binary heaps is \(O(n)\), it’s only \(O(\lg{n})\) for leftist heaps. In fact, this is why insertion and deletion are \(O(\lg{n})\).

The data structure and its properties

A leftist heap is as a tree and we implement it as such.

The tree is standard except for the Int parameter. This is the rank of the Node , which is the least distance to a Leaf . The rank of a Leaf is 0 and the rank of a Node is one more than the minimum of its children’s ranks.

Let’s briefly look at the relationship between the size of a tree and its rank.

A first question is how many elements there needs to be in the tree if its rank is \(R\)? If the rank of a tree is \(R\), then it must be the case that each path from the root has \(R\) Node s, otherwise the rank of the tree would be fewer. This means the tree has at least \(2^{R} - 1\) elements.

Then the followup question is, if a tree has \(N\) elements, what is its maximum rank? Well, we know that the rank imposes a lower bound on the tree size, so conversely, the tree size should impose a maximum on the rank. If \(R\) is the maximum rank, we have \(2^{R} - 1 \leq N \lt 2^{R + 1} - 1\), so \(R \leq \lg{(N + 1)} < R + 1\). Hence, \(\left\lfloor{\lg{(N + 1)}} \right\rfloor\) is the desired maximum.

The leftist heap has the leftist property. In short, the shortest path from any node to a Leaf must be the right-most one. Since each subtree in a leftist heap is also a leftist heap, the rank of any left child is at least as big as that of the right, hence the name.

How can we refine the earlier calculation about the maximum rank for leftist heaps? The distance between the root and the right-most Leaf is at most \(\left\lfloor{\lg{(N + 1)}} \right\rfloor\) if the leftist heap has \(N\) elements in it. This is the critical information we’ll use to derive the complexity of the merge operation.

Accessing the rank is handy, so let’s create a typeclass for it.

Here is the Heap instance for the LeftistHeap . The Ord constraint is for the heap property. The element of a LeftistHeap a is a . Its operations are implemented over the next two sections.

Merging two heaps

Let’s tackle the most important operation head-on.

The base cases are simple as Leaf acts as the identity element for merge .

In the inductive case, we walk over the right-most paths of the input heaps. You can see this in the recursive calls; they never touch the left children.

To preserve the heap property, we recurse on the right child of the argument heap with the bigger label.

To build a new node, we use mkNode helper rather than Node constructor directly. The helper does two things. First, it makes the child with the lowest rank the right child. Since the arguments to mkNode are leftist heaps themselves, this flip ensures the right-most path to Leaf is still the shortest. Second, it calculates the new rank which is one more than the rank of the right child.

Now what is the complexity of this? At each recursive call we potentially do a flip, increase the rank, and construct a tree node, these are all constant time operations. So the question is the number of recursive calls. If the leftist heaps being merged have \(L\) and \(R\) elements inside, we know their right-most paths are at most length \(\left\lfloor lg{(L + 1)}\right\rfloor\) and \(\left\lfloor lg{(R + 1)}\right\rfloor\) respectively. Hence, we at most do \(\left\lfloor\lg{(L + 1)} + \lg{(R + 1)}\right\rfloor\) calls. So the overall complexity is \(O(\lg{(L \times R)})\) which is a subset of \(O(\lg{(L + R)})\) (can you see why?). In short, the merge operation is logarithmic in the size of the output.

This is not where the beauty of merge ends. Recall that most leftist heap elements live outside the right-most path. Then since we only recurse over the right-most path, we never touch the trees where most elements live. We just move them around. In a purely functional language, this means the output tree does not have to allocate new memory for those trees, it can just share them with the input heaps.

Every other operation

The remaining operations needed to satisfy the typeclass are as follows.

From merge follows everything else. Maximum is maintained at the root, so accessing it is easy. The decompose operation returns the maximum along with the rest of the heap with the maximum removed by merging the two children of the root. Insertion (the default implementation) creates a singleton heap out of the inserted label and merges it into the heap.

Since merge has logarithmic complexity, so does insert and deleteMax . Since we store the maximum at the root, findMax runs in constant time.

Conversion from a list is more interesting. The obvious implementation is to fold over the list of elements and insert them into the heap, this turns out not to be the most efficient way. If we instead turn each element into a singleton heap and repeatedly merge two heaps at a time (with multiple passes) until one heap is left, conversion happens in linear time. The following default implementation does exactly that.

Why does this run in linear time? Assume for simplicity that there are \(2^R\) elements. Then in the first pass, we do \(2^{R-1}\) \(O(\lg{2})\) operations. In the next pass, we do \(2^{R-2}\) \(O(\lg{4})\), then \(2^{R-3}\) \(O(\lg{8})\) operations and so on. So the overall complexity is \(O(\sum^{R}_{i = 1}{(\lg{2^i}) 2^{R-i}}\,)\) which is \(O(\sum^{R}_{i = 1}{i \; 2^{R-i}}\,)\) and that is \(O(2^{R})\). That is the number of elements we started with, so conversion from a list is done in linear time.

Terms, types, and kinds

Before doing verification with fancy types, we need to understand terms, types, and kinds. Here’s the gist: all terms have types, all types have kinds, and there is no distinction between types and kinds since GHC 8.0, but terms and types (for now) occupy different realms.

For example, just as you can say 42 :: Int , you can also say Int :: Type and Type :: Type ( * is a deprecated synonym of Type ; import Data.Kind for Type ). We can read these as “ 42 is an Int ”, “ Int is a Type ”, and “ Type is a Type ” (yup, not a typo).

Just as you can use :type or :t learn the type of a term in ghci , you can use :k or :kind to learn the kind of a type.

We now look at types and kinds in more detail. It may be too much information to soak in at once, but the broad-strokes should be enough for this post. For a broader overview of the subject, see Diogo Castro’s amazing blog post. More generally, one can get away without an in-depth understanding of these and still be able to verify data structures. But then we’d be relying on GHC to yell at us when certain extensions are missing and not understand why we’re being yelled at.

Proofs and contradictions

Famously, Ludwig Wittgenstein wasn’t terribly concerned about inconsistencies in mathematics as most were, including Alan Turing. They even have a direct exchange on this subject. Surprisingly, Haskell’s type system seems to agree more with Wittgenstein than with Turing.

If Type :: Type makes you uncomfortable, you’re right, it is problematic and it leads to Russel’s paradox. This is one reason people don’t like type-level programming in Haskell. It means as a proof system, Haskell’s type system is inconsistent. What that means is that we don’t have the ability to tell the truth. The expectation, due to the Curry-Howard correspondance, is that if we have a type corresponding to some logical statement, a term for that type (if it exists) is a proof. Inconsistency means, we can have terms that are not valid proofs of the statement, but satisfy the type checker. In particular, Type :: Type leads to such menace.

That said, since Haskell already has let x = x in x , undefined , and error "QED" satisfying types of propositions, we didn’t have the ability to tell the truth to start with. Hence, we are not worse off. At least, this is the argument in Prof. Stephanie Weirich’s paper as well as the GHC documentation.

One might think existing flaws don’t justify adding new ways to break a system. Ordinarily, that’s right, but contradictions are infectious. As soon as there is a little crack, it is difficult to contain. So the marginal harm done by Type :: Type is less than expected.

There are paraconsistent logics limiting the harm done by inconsistencies, but they are not employed in type systems as far as I know.

To sum up, Haskell proofs are partial. If a term (proof) corresponding to a type (proposition) compiles, one of two things happened. The term is a valid proof or its evaluation will diverge. By contrast, Agda and Idris proofs are always terminating and are thus valid proofs as long as the type checker says so (up to compiler bugs). Hence, despite the syntactic similarity, you should have more faith in the latter.

Why is Type a misnomer?

The kind Type has a very confusing name. It should really be named LiftedType . Let’s understand why.

It has two important features. The term undefined (or \(\bot\) in academic papers) is a valid term for any type with kind Type . This makes Type the kind of lifted types. Consequently, all of these types are inhabited.

The GHC manual (until recently) called Type “the kind of types with values”. This is not true. If we enable the MagicHash extension and import GHC.Prim , we get access to unlifted types such as Int# . Int# definitely has values as witnessed by 42# :: Int# , but when we query :k Int# :: Type , we get an error saying “Expecting a lifted type, but Int# is unlifted”. So there are inhabited types without kind Type .

It is also wrong to say that Type is the kind of types that definitely has inhabitants. Once again the kind of Int# is TYPE 'IntRep and Int# is the only type of that kind. We already know it has inhabitants. In fact, in a sense TYPE 'IntRep is superior because data Void creates a type Void :: Type where the only inhabitants are degenerate such as error "Oops!" and undefined . Neither of these are proper values. TYPE 'IntRep can claim to be a kind of types that has non-degenerate inhabitants.

As a final piece of evidence about why Type is a bad name, you can consult GHC.Types which defines the kind Type as TYPE 'LiftedRep . Even GHC admits that Type is more specific than what the name implies.

So Type is a bad name because of non- Type types! We’ve already seen Int# , let’s find some more.

Type constructors

Maybe takes a Type and returns a Type . How about Either ? It takes two Type s and returns a Type . You can say they are type-level functions and you wouldn’t be wrong, but we can be more specific. We can say that Maybe and Either construct Type s just like (:) and [] at the term level.

Are Maybe and Either types themselves? They are types but not Type s. Asking ghci reveals that Maybe has kind Type -> Type and Either has kind Type -> Type -> Type .

Type -> Type is not the same thing as Type , but (here is the confusing part) Type -> Type has the kind Type . Get your head around that! If you can’t, that’s fine. The intuition is that types and kinds are one and the same, then so are the function arrow (->) and kind arrow (->) . A more concrete explanation will follow once we cover levity polymorphism. One implication of Type -> Type :: Type is that, it is inhabited. For example, id :: Type -> Type type checks.

We have Type s; we have things that construct Type s; and we have unlifted types such as Int# . What else?

Data type promotion

So far, we’ve only seen inhabited types. Int and Int# are obviously examples, but Type -> Type is also inhabited since that too has kind Type .

Emphasising inhabitation as a property implies that there must be some uninhabited kinds. In fact, these are the pillars of theorem proving and property encoding in Haskell.

Consider the following List declaration.

In vanilla Haskell, this generates a type List and two data constructors Nil and Cons .

With the DataKinds extension, you also get the following.

Despite looking pretty similar, these are different beasts. Since there is no distinction between types and kinds, the type constructor List is also a kind constructor. Then, 'Nil and 'Cons are type constructors, but they are not Type constructors, they are List a constructors! All promoted types are automatically uninhabited. So there is no term t with t :: 'Cons Int 'Nil .

This promotion feature alone spawns multiple reasons why people do not like fancy types in Haskell:

The ' prefix of promoted type-constructors is optional, but terms and types are completely separate. So when I type Nil , GHC figures out whether it is a term or a type constructor depending on the context. In the absence of ' , we need to disambiguate ourselves. The built-in list type [a] is automatically promoted. This means there is [] , the equivalent of Nil . There is [] , the type and kind constructor equivalent to List . Then there is '[] , the type constructor equivalent to 'Nil . Remember that ' is optional. So when I use [] , we don’t know, if it is the type constructor List or the type constructor Nil . A similar situation occurs with tuples, where the term and the type share similar syntax.

Note that this is the improved state of affairs. Kinds and types used to be separated and there was also a separate kind [] with sort (the classification of kinds) BOX .

Nevertheless, promoted types are a blessing. They act as indices to other data types and help encoding various properties at type level. We come back to this while introducing GADTs.

Kind polymorphism

Just as there are polymorphic types such as [a] -> [a] , there are also polymorphic kinds. In fact, 'Cons has kind a -> List a -> List a where a is a kind variable. We can see this in ghci .

The kind variable a can be Type ,

Or it can be the kind of a type constructor such as Type -> Type

We can also use a promoted kind such as List a , which results in another kind polymorphic type.

Levity polymorphism

The distinction between types with and without inhabitants stand on solid ground in GHC and leads to beautiful generalisations over types that have inhabitants. We now explore that.

This section consolidates the previous discussions of type habitation. If you’re solely interested in verification, you can skip it.

What is the kind of the Type constructor List ?

The return kind makes sense, it’s a Type constructor after all, but why the input kind Type ? Since Cons ’s first parameter has type a , constructing a term Cons x xs necessitates a term x :: a , hence a must be a type with kind Type .

Hopefully, my rant about Type being a misnomer made you doubt the last statement. What about Int# ? Since Int# has inhabitants, by the reasoning above a can also be Int# . More generally, we want a to be a type that has a runtime representation.

You remember TYPE ? The kind that spawns Type and TYPE 'IntRep . Let see what kind it has.

Aha! TYPE constructs things that have runtime representations. So we want the type variable of List to have kind TYPE rep , so that it ranges over everything that has a runtime representation. This idea of abstracting over runtime representations is called levity polymorphism.

But why doesn’t GHC infer that as the kind of a ? Let’s try declaring a levity polymorphic List explicitly.

A levity-polymorphic type is not allowed here: Type: a Kind: TYPE rep

The reason this doesn’t work and why GHC defaults a to Type is because if we want to create a data type, we need to know its runtime representation in advance to lay down the data while generating code. For example, Int# requires 32 bits but Int requires a pointer to a thunk, hence 64 bits. Unless you know how big the data is you can’t generate the code (at least not without introducing runtime code generation or indirection which defeats the purpose of unlifted types).

More generally, the paper introducing levity polymorphism has the following maxim for its usage:

Never move or store a levity-polymorphic value.

This rules out making a function as simple as id levity polymorphic because it moves values.

This raises the question, what can be levity polymorphic? The classic example is error . It has type String -> a , so a needs to be runtime representable. It neither stores nor moves whatever a is. Hence, it can be and is levity polymorphic in GHC:

You need -fprint-explicit-runtime-reps flag and the +v option to :t to get the signature.

> :set -fprint-explicit-runtime-reps > :t +v error

Let’s look at something more interesting. What is the kind of the function type constructor (->) ?

This shows why Type -> Type has inhabitants. It is because (->) is a Type constructor. More importantly, it shows that when we write a function between lifted and unlifted data types, we are in fact using the same arrow rather than syntactic magic.

Inhabitable out of uninhabitable

What is the kind of the following data type?

If we ask ghci , we get Type -> Type again. However, this time a does not appear as a type parameter to the sole constructor of MyProxy , so there is no reason for it to have a runtime representation. In principle, the type argument to MyProxy can be anything. This sounds kind polymorphic.

GHC, by default, assumes that the type variables of a type constructor have the kind Type even if they can be more generic. If you turn on the PolyKinds extension, GHC correctly infers the kind k -> Type to Proxy , where k is a kind variable.

This is nice because it is general, but also unmotivated at the moment because we haven’t yet made any use of poly-kindedness. Later, we define a poly-kinded equality type illustrating the utility of kind polymorphism.

Summary

Haskell is slowly evolving into a practical language that unifies terms and types. We are not quite there yet and the gradual transition creates some interesting and tough-to-wrap-your-head-around language concepts. This section gives a bird’s-eye view of these concepts that we shall use as building blocks of useful type-level programming.

Verifying the leftist property

Let’s encode the leftist property in a data type. That is, we will ensure that each the rank of each right child of a node is less than or equal to the rank of its left child. This necessitates access to ranks at the type-level. Previously, we used Int for ranks, but ranks are really just natural numbers.

We have two (main) options for type-level naturals:

importing GHC.TypeLits , which uses compiler magic to define a Nat kind or defining a Nat kind inductively from scratch.

The advantage of (1) is it is efficient and we get to use numeric literals such as 42 . The advantage of (2) is that it is not compiler magic and we get to see how theorem proving works in action. Hence, we’ll do (2).

If you reproduce this implementation using (1), you should probably use the singletons library to fake dependent types and the fantastic GHC type-checker plugin ghc-typelits-natnormalise to use arithmetic properties of natural numbers. Because type-level naturals are not inductively defined, we can’t do the kind of proofs that dependently-typed languages are good at. Thus, we rely on external solvers.

Here’s the plan: reinvent natural numbers and \(\leq\); use those to define a data type that makes non-leftist heaps illegal; attempt and fail to define merge ; go prove some properties about natural numbers; and finally succeed at implementing merge . Ready? It will be fun; I promise.

Natural numbers

We need the type-level natural numbers and \(\leq\) relation between them. Let’s start with naturals.

This gives us a type and a kind Nat , data constructors Z :: Nat and S :: Nat -> Nat , and type constructors 'Z :: Nat -> Nat and 'S :: Nat -> Nat .

Generalised algebraic data types

Type-level naturals were pretty easy. Next, we need to define the \(\leq\) relation using generalised algebraic data types (GADTs) enabled via GADTs extension. However, since \(\leq\) is a mean first example for GADTs, we start with natural numbers that remember whether they are zero or not at the type level.

GADTs provide an alternative syntax for declaring data types and the ability to discriminate types based on constructors. The AnotherNat data type in GADT syntax below is exactly the same as Nat .

The (boring) return types of constructors are now explicit. GADTs shine when the constructor choice affects the return type. Consider a data type for natural numbers encoding zeroness of a natural.

This says if a term is constructed using TZ , we have a 'Zero natural. But if it is constructed with TS instead, regardless the natural number being succeeded, we have a 'NonZero natural number.

With this we can write a total function a div :: TaggedNat a -> TaggedNat 'NonZero -> TaggedNat b and the compiler will complain every time we try to divide by zero.

Now if we implement div , it is tempting to be a good developer and write the following case.

Doing so prompts GHC to kindly inform us that this case cannot occur and thus is not needed. In fact, GHC wouldn’t even bother generating the code to raise the infamous “non-exhaustive patterns” exception because it knows the type checker wouldn’t let such an offence reach code generation. So not only do GADTs improve safety, but also, other things equal, lead to less code and consequently improve efficiency.

Less than or equal to relation

Let’s finally define \(\leq\).The TypeOperators extension is needed to use infix operators at the type level.

This defines a relation between two Nat s. It records a series of steps that gets us to the desired \(n \leq m\) starting from an indisputable fact. The record of these steps is a proof of membership to this relation.

The indisputable fact is \(\vdash_{\mathit{PA}} 0 \leq 0\) (\(\vdash_{\mathit{PA}}\) means the statement is provable with the Peano axioms which constitutes the everyday theory of arithmetic) encoded by the Base constructor. Then by applying a series of Single s and Double s, we try to produce the desired inequality \(n \leq m\). These constructors encode the following statements: \(\vdash_{\mathit{PA}} n \leq m \implies n \leq m + 1\), and \(\vdash_{\mathit{PA}} n \leq m \implies n + 1 \leq m + 1\). Hopefully, neither are controversial as our verification claims hinge on these being sound.

For example, to establish \(1 \leq 2\) we need to give a term of type 'S 'Z <= 'S ('S 'Z) .

This proof can be read as “from \(0 \leq 0\), we can get to \(1 \leq 1\) by incrementing both sides; and from there, we can get to \(1 \leq 2\) by incrementing the right side.”.

Besides soundness, we also care about completeness. Is it the case that by applying Single s and Double s to Base , we can get to all valid \(n \leq m\)? Yes, but we won’t prove it in this post. In fact there are multitude of ways of proving a valid \(n \leq m\). As an example, let’s look at another proof of oneLEQtwo .

You might have noticed that, the order of Single s and Double s doesn’t matter. Then, a proof of \(n \leq m\) always starts with Base , and is followed by \(m - n\) Single s and \(n\) Double s in any order.

Singletons: faking dependent types

We can use the insight for reaching a valid \(n \leq m\) to recover \(n\) and \(m\) given an inequality. What would be the type of a function doing that? We could try the following.

This doesn’t work because \(n\) and \(m\) have kind Nat but the (,) type constructor has the kind signature Type -> Type -> Type . So is it just the case that (,) is not the right kind of container? The problem runs deeper. Eventually we want access to n and m at runtime, this implies there should be some terms t :: n and r :: m . We know this won’t work because n and m have kind Nat and types of that kind don’t have inhabitants.

In a dependently-typed language this wouldn’t be an issue because there is no distinction between terms and types, so every entity, let it be term or type, can survive compilation.

Sadly, Haskell is not there yet, so we need to fake it using singletons. The idea is to create an indexed data type, so that there is exactly one term for each indexing type. That is all a bit vague, let’s just do it for naturals.

You see while the type 'Z has kind Nat , the type SNat 'Z has kind Type and it has exactly one inhabitant: SZ . This correspondence is true for all naturals. Hence, we can use the singleton type SNat n :: Type as the term-level representative for n :: Nat .

Now, we can write the recovery function.

This function uses the inductive structure of n <= m . We know from their types that, Single increments the right side of <= and Double increments both sides. We just turn them to explicit increments to recover the singletons for n and m .

In fact, we can be sure that our implementation is correct. The type is so specific that unless we use a degenerate term like undefined , there is simply no way of getting a buggy implementation to type check.

Rank encoded leftist heaps

We have everything needed to encode the leftist property at the type level. Since the leftist property involves comparing ranks of subheaps, we need access to rank at type level. Rank does that using a an SNat . We also define a helper to increment the rank for later use.

Since heaps need to be indexed by their rank, we use a GADT.

This data type hopefully doesn’t look scary anymore. The Leaf' constructor creates a SafeHeap of rank 0. The Node' constructor grows the heap only when we can show that the rank of the right subheap is less than or equal to that of the left subheap. Further, the resulting heap has rank one more than that of the right subheap.

What did we just do? We created a data type whose inhabitants are either vacuous or that it is a tree satisfying the leftist property. Let’s try some examples.

This fails because the Leaf' forces the rank to be 'Z instead of 'S 'Z as required by the signature.

This type checks because Leaf' s has rank 'Z and Base proves 'Z <= 'Z .

This also type checks because the right child has a lower rank ( 'Z ) than the left child ( 'S 'Z ) and Single Base proves 'Z <= 'S 'Z .

Unless we replace ??? with a degenerate term, we won’t be able to find a proof for 'S 'Z <= 'Z on the account of its being false. This is precisely how the data type prevents us from violating the leftist property.

We just made terms that violate the leftist property illegal. Pretty cool, huh?

Heap instance for SafeHeap

Making property violating terms illegal is one thing, defining operations on legal ones another.

The Heap instance for LeftistHeap was directly on that data type. Consequently, the signatures of heap operations all involved LeftistHeap . This direct approach tends to fail with property-encoding fancy types.

Say we tried to make SafeHeap rank a an instance of Heap .

We’re already in a bad place. This forces the type of merge to be SafeHeap rank a -> SafeHeap rank a -> SafeHeap rank a . This type is too restrictive! We want to be able to merge heaps of different ranks and to produce a heap of rank not identical to the input heaps.

Let’s say that we gave up on the typeclass and decided to define all the operations at the top level. Then we could give merge the type SafeHeap rank1 a -> SafeHeap rank2 a -> SafeHeap (Fx rank1 rank2) a . Here Fx is some type-level function. This allows inputting heaps of different ranks. We still need to figure out the rank of the output, but that depends on the input heaps in their entirety not just their ranks. The word “depend” is a red flag. Do we need to create singletons for SafeHeap s as well? This line of thinking will lead to too much work.

Time to take a deep breath and think. What was our original goal? It was to preserve the leftist property. Does that require knowing the effects of operations on the rank of the heap at type level? No, not really. For merge , we want to input two SafeHeap s of some rank and produce a SafeHeap of some rank. The fact that the output is a SafeHeap is enough to ensure the leftist property is preserved, which is our goal.

So we want to perform the operations a data type that is indifferent to the rank at the type-level just as the untyped version was. Existential types enabled via ExistentialQuantification can achieve this.

Despite writing forall , what we mean is “within SomeSafeHeap a , there exists a rank such that SafeHeap rank a ”. Hence, the name existential types. Let’s try the Heap instance again.

Now, merge has type SomeSafeHeap a -> SomeSafeHeap a -> SomeSafeHeap a which makes no assertions about ranks. Yet its inputs and output contain SafeHeap s and thus satisfy the leftist property.

The key take away: if you use fancy types, reach for the existential as soon as possible.

This is not to say that you should never implement operations relating fancy types of inputs and outputs. If you succeed, it will give you more static guarantees! The question is whether they justify the effort.

The singleton function for SomeSafeHeap a is a trivial example of this. We know that the singleton heap should have rank 1. Since rank information is hidden behind an existential, there is nothing preventing us from defining singleton x to be empty .

This type checks just fine. One way to improve the situation is to extract the SafeHeap ('S 'Z) a into its own definition and reduce the likelihood of such a mistake.

This is less formal verification and more trying to squeeze every bit of safety through self-discipline, but it did help me catch bugs more than once.

Merging SafeHeap s

Here’s the partial code for the merge operation on safe heaps.

This bit is almost identical to the unverified version except for some unwrapping and wrapping with SSH . This makes sense because as we pointed in the unverified version, mkNode is where the leftist property is preserved by placing the heap with the smaller rank to the right.

Comparing without forgetting

In the unverified leftist heap’s mkNode , the term-level (<=) operator decided on which child is placed to the right. It seems all we need is to provide an analogous operator for SNat s. However, we now also need a proof that the rank of the right child is less than the rank of the left child. Sadly, (<=) determines the desired property and forgets it immediately by returning a Bool .

What we want here is connexity, that is given any \(n\) and \(m\), either \(n \leq m\) or \(m \leq n\). This holds for total orders including \(\leq\) on natural numbers. We can express this in Haskell easily.

The base cases are simple, we need to show \(0 \leq m\) and \(0 \leq n\). A lemma for \(0 \leq x\) for an arbitrary \(x\) would handle both cases. Let’s suppose lemZLEQAll :: SNat n -> Z' <= n for the moment.

This make-believe with lemmas is how top-down proofs work. To focus on the proof at hand, we postulate reasonable looking statements.

The inductive case is also simple and is a good opportunity to demonstrate doing proofs with typed holes.

The _ symbol is a typed hole. It causes an error stating what type of term needs to go in _ and the types of terms within the scope. Incremental development with typed holes is helpful with fancy types because when we pattern match on a GADT, the goal and the argument types automatically get refined (especially if type-level functions are involved, more on that later). Without the compiler telling you, it’s difficult to keep track of the refined types in different branches.

Since SNat is a GADT, pattern matching on SS refines the type of _ . The goal becomes Either ('S n1 <= 'S n2) ('S n2 <= 'S n1) and the arguments become x :: SNat n1 and y :: SNat n2 . If we had Either (n1 <= n2) (n2 <= n1) , we could probably make progress. Recursively calling lemConnexity gives us that.

Now we have two typed holes. We need to build terms of Either ('S n1 <= 'S n2) ('S n2 <= 'S n1) from xLEQy :: n1 <= n2 and from yLEQX :: n2 <= n1 independently. The Double constructor produces a new inequality from an existing one by incrementing both sides. Combined with Left or Right depending on the case, we reach the desired terms.

We are now almost done with lemConnexity . Earlier we postulated lemZLEQAll . That still needs proving.

That’s it. We just proved some order theoretic properties.

The ergonomics of this process are lacking. Errors are not a productive way to interact with typed holes. Inspecting terms and their types available at a given hole without clutter would be useful, but GHC spits 50 lines per hole and very little of that is helpful. There isn’t much help with crafting the proof either.

By contrast, Idris and Agda provide editor integration to quickly inspect the context of holes, to search and fill in terms that satisfy the hole, and to refine the whole when given a partial expression.

Haskell has valid hole fits which are term suggestions for holes. They are also verbose (but can be made less so with -funclutter-valid-hole-fits flag) and, in my experience, not very helpful because imported libraries often include degenerate terms with polymorphic return types (like error ). Hence valid hole fits always include them but are not what we want. For example, in the source code of this project I’ve imported QuickCheck from early on. It exports discard :: forall a. a which gets suggested as a valid hole fit to every single hole.

That said, with some discipline about making the lemmas small and properties simple, we can go a long distance as long as we are not trying to mechanise an entire field of mathematics.

If you are interested in this style of type-driven development, there is a (w)hole book (affiliate link) on it by the Idris language creator Dr Edwin Brady.

Making nodes with proofs

By harnessing lemConnexity , implementing mkNode is a breeze.

The lemma tells us which heap has the lower rank (hence needs to be the right child) as well as giving us a proof for it which is all that is needed to construct a Node' .

Verifying the heap property

Let’s do it one last time with both the heap and the leftist properties.

So what is the heap property mathematically? For a node \(n\) and its children \(l\) and \(r\), it is the case that \(\mathit{label}(l) \leq \mathit{label}(n)\) and \(\mathit{label}(r) \leq \mathit{label}(n)\).

We now need labels in types as well as ranks. So far we used arbitrary Type s for labels. As a simplification, we decree that they must effectively be SNat s, so that we can build on our existing theory of naturals.

Rank and label encoded leftist heaps

To avoid confusion with ranks which also use SNat s, we wrap the label SNat s.

We don’t need anything else to declare the data type that makes non-heaps illegal.

SaferHeap looks a lot like SafeHeap , except for Node'' ’s fancy-typed label and two inequalities to maintain the heap property.

Leaf'' carries a label 'Z because every SaferHeap needs a type-level label. Using 'Z is a hack. It is the least natural number, hence it can’t inhibit a Node'' construction whatever its label may be.

There are two immediate alternatives to this:

Using Maybe Nat rather than Nat for the label. This requires modifying the heap property so that rather than b <= a , we’d need “given b is 'Just b' , b' <= a ” and similarly for c <= a . Using Maybe Nat again, but instead of changing the heap property, we create three Node'' like constructors: one with both children having 'Just labels, one with only the left child having a 'Just label, and one with neither having 'Just labels (why don’t we need the fourth case?). This way, the heap property remains simple.

These may be cleaner than exploiting 'Z being the least element in a total order. Both, however, complicate every function that needs to scrutinise a SaferHeap .

The next step is to wrap the data type in an existential just like last time.

At this point, if we construct some SaferHeap s, we’d get bored quickly due to constructing tedious explicit proofs. Luckily we use existentials and Heap to interact with the data type.

Heap instance for SaferHeap

Just as before the instance is for the existentially wrapped type.

Elem for SomeSaferHeap is interesting because we don’t actually store Nat s anywhere. So insert requires a conversion from SNat to Nat and findMax from Nat to SNat .

The SNat to Nat direction is easy.

But the opposite direction would have a signature Nat -> SNat n . Let’s try to write that.

Well, this doesn’t type check because one branch returns SNat 'Z and the other SNat ('S m) for some m . Neither 'Z nor 'S unifies with n in the signature.

This might initially sound like a bad reason because when recovering SNat s out of a (<=) , the function signature is n <= m -> (SNat n, SNat m) , so it seems the same unification problem should occur there as well. The difference is that because the argument also contains n and m and that (<=) is a GADT, pattern matching locally refines what n and m are. However, pattern matching on Nat in promoteAttempt does not refine the return type.

Since heap operations are performed on existentially wrapped types, we only need the type-level n in the function body and not in the type signature for the operation. So we do not really care what n is going to be so long as there is some n that we can embed in the heap. This sounds like an existential. So the promote returns an existentially wrapped SNat .

A good exercise is to implement singleton for SomeSaferHeap . It is similar to that for SomeSafeHeap , except for the use promote and the evidence for the heap property. See the source code for the answer.

Merging SaferHeap s

The merge operation is, once again, all we care about. The overall structure is going to be the same, but we’ll need more lemmas and plumbing.

Making nodes

This time let’s start from mkNode . Here’s an attempt.

This attempt fails due to a lack of evidence for the heap property.

We have the parent and the children labels, so we could decide on the evidence. But this requires us to handle the case of vc being smaller than one of the children labels. This should be established in the body of merge if it is anything like the previous version. Let’s assume merge indeed passes down the evidence.

This doesn’t work either because we have an a <= c for the first SomeSaferHeap , but that type hides a . As far as the type checker is concerned the rank of hA has nothing to do with Rank a .

It seems we’re hiding too much. Since mkNode is not part of the Heap typeclass, perhaps we can use SaferHeap instead of SomeSaferHeap in the signature of mkNode .

This type signature relates the input heaps to the evidence, but it also requires handling ranks. There are three viable choices for ??? : r3 , 'S r1 , and 'S r2 . The first one runs into the same problem as promote , the calculated node rank won’t unify with r3 . The last two can be made the work but it presupposes that the call site already knows which heap is going to be the right child, hence mkNode would be pointless.

So SomeSaferHeap hides too much and SaferHeap hides too little. What we need is something in the middle to hide the rank, but expose the label. Once again existential types come to the rescue.

With this, the mkNode we need is not too different from that for SomeSafeHeap .

Merging nodes

We’d like to work with AlmostSomeSaferHeap for the merge implementation as well. In short, unless we do that the implementation doesn’t go through, but it is going to take some time to explain exactly why. For now, bear with me.

Let’s observe the use of the intermediary function merge' and its type signature.

The type of merge' will be very precise. The label of a merge result is a function of the labels of the inputs. Particularly, it is the maximum of the input labels.

This brings me to type-level functions. We already use type families within the Heap and HasRank typeclasses, but those are both simple maps of types. Max is more sophisticated and uses recursion.

Type families

The type-level Max function is defined using a closed type family enabled by the TypeFamilies extension.

This is analogous to the following term level max function on Nat s.

You might be wondering why not just write that and get a promoted version of max just as we did with data types and kinds? It’s an excellent question and this syntactic dichotomy is another reason why people don’t like type-level programming in Haskell. In Idris and Agda, you can write one function and use it for both terms and types.

The problem is multifaceted. For one thing, type families existed in GHC since 2007, whereas data-type promotion was added in 2012, and the mandate for moving term and type levels closer is fairly recent. Further, adding type-level computation into Haskell is an after-thought, so you need to retrofit the syntax. On top of that, the behaviour of type families is different than functions, the patterns of a type family can do unification whereas pattern matches of a function can’t.

For example, the following returns the type Int if its arguments unify and Char otherwise.

So although we can promote term-level functions to the type families ( singletons library does this via Template Haskell), they are not exactly equivalent because term-level variables act differently than their type-level counterparts.

I don’t know what the current plan is, but since changing the behaviour of type families would break backwards compatibility, the way forward seems to be allowing unification at the term level or adding another extension that makes it illegal at the type-level. Since types heavily rely on unification, the former seems more likely to me, but I’m not an expert!

Type families vs GADTs

We could have encoded Max as a GADT as well and similarly we could have encoded (<=) as a type family.

These would have also worked. The proofs would have looked different, but they’d have worked. However, there are reasons to choose one over the other.

If you intend to do induction over your relation, then constructors are helpful, so GADTs get a point. If what you have is a function, then the GADT encoding forces you to add another type variable for the result and the fact that the arguments determine the result get lost. Conversely, if you have a relation that is not a function and you choose to use a type family, since there is no clear result variable, you need to return a type of kind Bool or its equivalent. With type families, when you learn more information about the type, the reduction happens automatically, whereas with GADTs you need to pattern match and pass the relation around. More pragmatically, you might already have some type-level relations in your codebase and you might want to stay consistent with the related relations. Beyond consistency, this might allow you to reuse some lemmas by exploiting duality and/or generalising the lemmas with minor effort.

My choices in this post embody the first three principles.

Getting back to the merge

We are ready to look at the base cases for merge' .

Pattern matches reveal the Leaf'' labels as 'Z to the type checker. We need to show that Max 'Z b and Max a 'Z are b and a respectively. These are proved definitionally because 'Z hits the base cases of Max .

In the previous version, term-level (<=) decided on the top label and the subheap to recurse on. We’ve already seen that lemConnexity is the replacement we need for comparing SNat s. By mimicking the structure of the previous implementation, we can provide a partial implementation.

I said at the beginning of merge implementation that I’d explain why we used AlmostSomeSaferHeap as opposed to SomeSaferHeap for the merge' implementation. We can now see why. We need to pass child2 to mkNode and also produce it as a result of a recursive call to merge' , if merge' used SomeSaferHeap , even if we extract a SaferHeap out of child2 , its label wouldn’t relate to the label used in c2LEQp . This is the same problem we ran into in mkNode with too much hiding.

Let’s focus on the Left branch first. The type checker complains about the holes, but more importantly it complains about the mkNode application. GHC says that it could not deduce Max a b ~ b , where ~ means types are equal.

But of course! We have aLEQb of type a <= b , but the type checker is too stupid to know that this implies Max a b is just b . So we need to prove this. That brings us to propositional equality.

Propositional equality

Let me introduce you to (:~:) , the data type that tells the type checker that two types are equal. It took me ages to get my head around it. So you have trouble with it, keep using it until it clicks.

It is a poly-kinded Type constructor. Good that it is poly-kinded, if it was restricted to Type or Nat that would restrict which types we can show to be equal. This definition makes no assumptions.

If we pattern match on a term with type a :~: b , the only case is the Refl constructor. Just like the previous pattern matches on GADTs, this refines the typing context. In this case, it reveals a and b to be the same (the signature for Refl says so). Hence, the type checker learns a ~ b .

We need Max n m ~ m given n <= m , so we show Max n m :~: m . Proceed by induction on n <= m .

In the base case, the pattern match on Base reveals both n and m to be 'Z . So we need to show 'Z :~: 'Z . We can use Refl by instantiating a to 'Z in a :~: a behind the scenes.

Then comes the first inductive case with Double constructor.

Here, xLEQy has type n <= m and we need to show Max ('S n) ('S m) :~: 'S m . By the inductive case of Max ’s definition, the goal reduces to 'S (Max n m) :~: 'S m . Since xLEQy is smaller than the original argument, we can recursively call lemMaxOfLEQ to get a term of type Max n m :~: m . Pattern matching on that tells the compiler Max n m ~ m , so the goal reduces to 'S m ~ 'S m . Once again, Refl trivially proves this.

The last case was fairly mechanical and this is often the case. The final inductive case is more interesting.

The term xLEQy has type n <= m and we need to prove Max n ('S m) :~: 'S m . Since we don’t know if n is built with 'S constructor (it could be 'Z ), we don’t get an automatic reduction of our goal like last time. We still have xLEQy , so we could apply lemMaxOfLEQ recursively. That would get us Max n m :~: m , but pattern matching on that doesn’t reduce the goal any further.

The mechanical process got stuck. It’s time to take a step back and think. Taking inspiration from the previous case, if we knew that n was of the form 'S k , our goal would reduce to 'S (Max k m) :~: 'S m . Then we could show Mak k m :~: m and that would reduce the overall goal to a measly 'S m ~ 'S m . To obtain Max k m :~: m , we need a recursive call to lemMaxOfLEQ with a term of type k <= m , but we only have 'S k <= m . Luckily we now from grade school that \(\vdash_{\mathit{PA}} k + 1 \leq m \implies k \leq m\). So all we need is a lemma with type 'S n <= m -> n <= m .

But we forgot something! This all hinges on n being of the form 'S k , what if it isn’t? Well, then it must be 'Z , and Max 'Z m :~: m reduces to m :~: m , so we are good.

Alright, we can prove our final lemma.

There is nothing particularly difficult about this lemma apart from the number of arguments the induction involves, but it has some lessons.

Haskell doesn’t have a termination checker. This is a feature, but when we do proofs, it feels like walking barefoot right after breaking a glass. We can easily create an infinite loop that type checks but does not constitute a valid proof (the circular argument fallacy).

This proof is particularly vulnerable to accidental loops because we pattern match on three variables which makes it difficult to see that we are recursing on something smaller in every case.

We remedy this in lemDecLEQ by making the recursive calls in the body of go to lemDecLEQ rather than go itself. This is less efficient, but makes it easier to confirm that each recursive call is to something strictly smaller than what we started with.

The other interesting bit is the base case of go .

We use error due to a deficiency of Haskell. When the second argument is SZ , there is no constructor of (<=) that can make 'S n <= m , but we have a proof of this, namely the third argument. We can see this by pattern matching on the third argument.

If you compile this, GHC will give you pattern match inaccessible warnings combined with type errors about why these arguments can’t coexist together.

In Agda and Idris, you can syntactically make it an inaccessible case which the compiler can confirm or deny. In Idris, it looks like this:

We don’t have this in Haskell, but there is a stale proposal. What if we omit this case altogether? This leads to a non-exhaustive pattern match warning because only when we pattern match on the second argument GHC learns that the pattern is inaccessible.

Using error as a way around is dangerous because we might prove a lemma, tweak it slightly, and not realise that the inaccessible case is now perfectly accessible. It would type check because we have error "Inaccessible case" . This makes the proof incomplete at best.

Getting back to the merge again

Focusing on the Left branch only, we make it known to the compiler that a <= b implies Max a b ~ b using lemMaxOfLEQ .

This gets rid of the type error due to the application of mkNode .

Inspecting the errors for the holes should inform us about the terms we need. Well, according to GHC, both holes demand type t (distinct rigid t s). This is incredibly unhelpful and I don’t know why we get t .

Inlining the let binding improves the situation.

The first hole needs a term of type l <= b and that’s exactly the type of lLEQb . The second hole needs Max r a <= b , but we do not yet have a term corresponding to this type. However, we have rLEQb :: r <= b and aLEQb :: a <= b . Since Max is a selective function (one that returns one of its arguments), the required result holds we just need to turn it into a lemma.

The proofs are left as exercises and the solutions are in the source code.

We can now give the full definition of the Left branch.

The right branch is analogous to the left one, so you should be able to fill it yourself. There is going to be a technicality requiring a simple lemma that is not required in the Left branch because of the way we set things up. If you write the Right as we did Left , the type error should give you a clue about what is needed. If you can’t figure it out, that too is in the source.

Other operations?

We could implement the other methods, but they are all too simple. We are done! No more verification.

Simulating heap operations

So after going through all this trouble to prove properties of our code, why bother testing?

We didn’t verify everything. Earlier we gave the example of implementing singleton with the wrong rank by ignoring the input. A common theme is to accidentally discard a subpart of a data structure or an input and use another part twice. Haskell’s type system is unsound. So we might have a fallacious proof somewhere that makes it look like the property holds while being buggy. It gives me a chance to talk about another type-level feature: visible type applications. I really want to use QuickCheck to run simulations on the heap.

It is worth noting that if Haskell had linear types (it soon will!), we could have addressed (1) by forcing inputs to be used.

Generating actions

We’re only going to simulate insertion and deleting the min. Here’s the initial encoding of the actions.

Let’s give Arbitrary instances for Nat and Action to randomly generate sequences of Action s.

Somewhat arbitrarily we choose between a deletion and an insertion with \(50\%\) probability. This may or may not be a realistic simulation, but it is something easy to adjust. We can have multiple wrappers over Action a such as DeleteHeavy a and InsertionHeavy a to simulate different scenarios.

We haven’t seen the @ symbols in the Arbitrary instances before. They are visible type applications enabled by TypeApplications extension. In addition to drastically improving handling ambiguous types, they allow us to learn more about type variables.

There is \(\Lambda\) then there is \(\lambda\)

If someone asks for the explicitly-typed polymorphic lambda term for the identity function (as one does), we’d probably write \(\lambda x : \alpha. x\), where \(\alpha\) is a polymorphic type variable. We’d expect this function to be closed, that is to say it shouldn’t depend on the context. Indeed, our identity function looks unaffected by the term-level context because the only variable \(x\) is \(\lambda\)-bound. However, \(\alpha\) doesn’t seem to be bound by anything, hence it looks dependent on the context.

Appearances can be deceiving. If this term should be interpreted as Haskell interprets terms and their signatures, it just abbreviates \(\Lambda \alpha : \mathit{Type}. \lambda x : \alpha. x\). All free type variables in signatures are implicitly \(\Lambda\)-bound. The Haskell syntax is forall and is only allowed in signatures (with ExplicitForAll enabled).

The following is the same identity function in Haskell but with explicit quantification.

Just as the binders are hidden behind the scenes, so are the applications. When we apply id to 42 , the Int gets passed to the type-level function first. TypeApplications enables syntax to do this explicitly. We just pass the type with a @ prefix. For example in ghci , :t id @Int yields Int -> Int .

This works as an alternative to using :: when a type is ambiguous. Often it lets us get away with fewer parentheses and looks cleaner in general.

What happens if there are multiple type variables? The applied type is unified with the first type variable. This is how term-level application works as well. This raises the question of type-variable ordering.

In the absence of an explicit forall , the ordering of type variables is up to GHC. We can query the order of type variables used by GHC via :type +v and -fprint-explicit-foralls flag. However, I think placing explicit forall s is good practice.

We look at type applications more interesting than those in the Arbitrary instances momentarily.

Executing actions

We can now interpret the initial representation of our actions. Nothing exciting here.

QuickChecking functional equivalence

It’s time to use QuickCheck. The desired property: given two data types implementing Heap and a series of actions, executing these actions on the empty of each should yield the same maximum.

The computation is not very interesting, but the way we direct it is. We use an explicit forall . This is not to fix the type variable ordering. Since both heap and heap' are passed to the same function, the ordering is irrelevant. ScopedTypeVariables extension allows forall quantified type variables to be referenced in the function body which we need to pick the implementation maxOfActions should use.

The type applications @heap and @heap' determine the computation. This is not just disambiguation. If both maxOfActions were applied to heap instead, we’d create a property that is trivially satisfied.

We use another type application in the body of maxOfActions . This is for disambiguation. Without it, all carryOutActions sees is Elem h and Elem isn’t injective. This means given Elem h , we can’t know what h is. For example, if I told you Elem h was Int , h could be LeftistHeap or SomeSafeHeap as both can have Int labels. Hence, we use a type application to tell carryOutActions what h is.

All there remains is to actually check the property between different implementations.

Remember that the list-based heap implementation was our reference implementation. Using type applications, we test functional equivalence between the reference implementation and the untyped leftist heap, the leftist property verified leftist heap, and the leftist and heap property verified leftist heap.

Then I just sample some actions and see the result of carrying them out on the terminal because I’m paranoid like that (the Arbitrary instance could also be buggy 🙃).

At this point, we can be reasonably sure that these implementations work (for insertion and deletion).

Conclusion

After such a long post, I’ll keep the conclusion short and sweet. Here’s what we did in a gist:

learnt about leftist heaps a purely functional replacement to array-based binary heaps;

looked at major parts of Haskell’s type-level computation features;

ran simulations to test functional equivalence of various implementations;

did a commentary on Haskell as an interactive theorem prover.

What is the overall verdict on that last point? It’s not ideal at all, but it works for simple data structures and properties. If we had used the singletons library and type-checker plugins, we could have gone further quicker.

The often overlooked point is this: if we want to do verification natively in a language that is designed to build programs with an optimising compiler and a massive ecosystem, Haskell is the singular choice. I for one am looking forward to Haskell’s typed and bright future.

Acknowledgements

I’d like to thank Dr Dominic Orchard and Lex van der Stoep for their comments on the drafts of this post and I appreciate Vilem Liepelt’s post-publication corrections and suggestions.

This post wouldn’t be possible without the heroic work of a vibrant research community and GHC implementers. They are too many to name exhaustively, but the following deserves a special round of applause: Dr Richard Eisenberg and Prof. Stephanie Weirich (closed type families, singletons), Dr James Cheney (GADTs), and Dr Hongwei Xi (also GADTs).

The code wouldn’t be as slick if it wasn’t for Prof. Weirich’s presentations on verifying red-black trees (alternative).