Go 1.18 added the biggest and probably one of the most requested features of all time to the language: Generics. If you want a comprehensive introduction to the topic, there are many out there and I would personally recommend this talk I gave at the Frankfurt Gopher Meetup.

This blog post is not an introduction to generics, though. It is about this sentence from the spec:

Implementation restriction: A compiler need not report an error if an operand’s type is a type parameter with an empty type set.

As an example, consider this interface:

type C interface {
int
M()
}


This constraint can never be satisfied. It says that a type has to be both the predeclared type int and have a method M(). But predeclared types in Go do not have any methods. So there is no type satisfying C and its type set is empty. The compiler accepts it just fine, though. That is what this clause from the spec is about.

This decision might seem strange to you. After all, if a type set is empty, it would be very helpful to report that to the user. They obviously made a mistake - an empty type set can never be used as a constraint. A function using it could never be instantiated.

I want to explain why that sentence is there and also go into a couple of related design decisions of the generics design. I’m trying to be expansive in my explanation, which means that you should not need any special knowledge to understand it. It also means, some of the information might be boring to you - feel free to skip the corresponding sections.

That sentence is in the Go spec because it turns out to be hard to determine if a type set is empty. Hard enough, that the Go team did not want to require an implementation to solve that. Let’s see why.

## P vs. NP#

When we talk about whether or not a problem is hard, we often group problems into two big classes:

1. Problems which can be solved reasonably efficiently. This class is called P.
2. Problems which can be verified reasonably efficiently. This class is called NP.

The first obvious follow up question is “what does ‘reasonably efficient’ mean?”. The answer to that is “there is an algorithm with a running time polynomial in its input size”1.

The second obvious follow up question is “what’s the difference between ‘solving’ and ‘verifying’?”.

Solving a problem means what you think it means: Finding a solution. If I give you a number and ask you to solve the factorization problem, I’m asking you to find a (non-trivial) factor of that number.

Verifying a problem means that I give you a solution and I’m asking you if the solution is correct. For the factorization problem, I’d give you two numbers and ask you to verify that the second is a factor of the first.

These two things are often very different in difficulty. If I ask you to give me a factor of 297863737, you probably know no better way than to sit down and try to divide it by a lot of numbers and see if it comes out evenly. But if I ask you to verify that 9883 is a factor of that number, you just have to do a bit of long division and it either divides it, or it does not.

It turns out, that every problem which is efficiently solvable is also efficiently verifiable. You can just calculate the solution and compare it to the given one. So every problem in P is also in NP2. But it is a famously open question whether the opposite is true - that is, we don’t really know, if there are problems which are hard to solve but easy to verify.

This is hard to know in general. Because us not having found an efficient algorithm to solve a problem does not mean there is none. But in practice we usually assume that there are some problems like that.

One fact that helps us talk about hard problems, is that there are some problems which are as hard as possible in NP. That means we were able to prove that if you can solve one of these problems you can use that to solve any other problem in NP. These problems are called “NP-complete”.

That is, to be frank, plain magic and explaining it is far beyond my capabilities. But it helps us to tell if a given problem is hard, by doing it the other way around. If solving problem X would enable us to solve one of these NP-complete problems then solving problem X is obviously itself NP-complete and therefore probably very hard. This is called a “proof by reduction”.

One example of such problem is boolean satisfiability. And it is used very often to prove a problem is hard.

## SAT#

Imagine I give you a boolean function. The function has a bunch of bool arguments and returns bool, by joining its arguments with logical operators into a single expression. For example:

func F(x, y, z bool) bool {
return ((!x && y) || z) && (x || !y)
}


If I give you values for these arguments, you can efficiently tell me if the formula evaluates to true or false. You just substitute them in and evaluate every operator. For example

f(true, true, false)
→ ((!true && true) || false) && (true || !true)
→ ((false && true) || false) && (true || !true)
→ ((false && true) || false) && (true || false)
→ ((false && true) || false) && true
→  (false && true) || false
→   false && true
→   false


This takes at most one step per operator in the expression. So it takes a linear number of steps in the length of the input, which is very efficient.

But if I only give you the function and ask you to find arguments which make it return true - or even to find out whether such arguments exist - you probably have to try out all possible input combinations to see if any of them does. That’s easy for three arguments. But for $$n$$ arguments there are $$2^n$$ possible assignments, so it takes exponential time in the number of arguments.

The problem of finding arguments that makes such a function return true (or proving that no such arguments exists) is called “boolean satisfiability” and it is NP-complete.

It is extremely important in what form the expression is given, though. Some forms make it pretty easy to solve, while others make it hard.

For example, every expression can be rewritten into what is called a “Disjunctive Normal Form” (DNF). It is called that because it consists of a series of conjunction (&&) terms, joined together by disjunction (||) operators3:

func F_DNF(x, y, z bool) bool {
return (x && z) || (!y && z)
}


(You can verify that this is the same function as above, by trying out all 8 input combinations)

Each term has a subset of the arguments, possibly negated, joined by &&. The terms are then joined together using ||.

Solving the satisfiability problem for an expression in DNF is easy:

1. Go through the individual terms. || is true if and only if either of its operands is true. So for each term:
• If it contains both an argument and its negation (x && !x) it can never be true. Continue to the next term.
• Otherwise, you can infer valid arguments from the term:
• If it contains x, then we must pass true for x
• If it contains !x, then we must pass false for x
• If it contains neither, then what we pass for x does not matter and either value works.
• The term then evaluates to true with these arguments, so the entire expression does.
2. If none of the terms can be made true, the function can never return true and there is no valid set of arguments.

On the other hand, there is also a “Conjunctive Normal Form” (CNF). Here, the expression is a series of disjunction (||) terms, joined together with conjunction (&&) operators:

func F_CNF(x, y, z bool) bool {
return (!x || z) && (y || z) && (x || !y)
}


(Again, you can verify that this is the same function)

For this, the idea of our algorithm does not work. To find a solution, you have to take all terms into account simultaneously. You can’t just tackle them one by one. In fact, solving satisfiability on CNF (often abbreviated as “CNFSAT”) is NP-complete4.

It turns out that every boolean function can be written as a single expression using only ||, && and !. In particular, every boolean function has a DNF and a CNF.

Very often, when we want to prove a problem is hard, we do so by reducing CNFSAT to it. That’s what we will do for the problem of calculating type sets. But there is one more preamble we need.

## Sets and Satisfiability#

There is an important relationship between sets and boolean functions.

Say we have a type T and a Universe which contains all possible values of T. If we have a func(T) bool, we can create a set from that, by looking at all objects for which the function returns true:

var Universe Set[T]

func MakeSet(f func(T) bool) Set[T] {
s := make(Set[T])
for v := range Universe {
if f(v) {
}
}
return s
}


This set contains exactly all elements for which f is true. So calculating f(v) is equivalent to checking s.Contains(v). And checking if s is empty is equivalent to checking if f can ever return true.

We can also go the other way around:

func MakeFunc(s Set[T]) func(T) bool {
return func(v T) bool {
return s.Contains(v)
}
}


So in a sense func(T) bool and Set[T] are “the same thing”. We can transform a question about one into a question about the other and back.

As we observed above it is important how a boolean function is given. To take that into account we have to also convert boolean operators into set operations:

// Union(s, t) contains all elements which are in s *or* in t.
func Union(s, t Set[T]) Set[T] {
return MakeSet(func(v T) bool {
return s.Contains(v) || t.Contains(v)
})
}

// Intersect(s, t) contains all elements which are in s *and* in t.
func Intersect(s, t Set[T]) Set[T] {
return MakeSet(func(v T) bool {
return s.Contains(v) && t.Contains(v)
})
}

// Complement(s) contains all elements which are *not* in s.
func Complement(s Set[T]) Set[T] {
return MakeSet(func(v T) bool {
return !s.Contains(v)
})
}


And back:

// Or creates a function which returns if f or g is true.
func Or(f, g func(T) bool) func(T) bool {
return MakeFunc(Union(MakeSet(f), MakeSet(g)))
}

// And creates a function which returns if f and g are true.
func And(f, g func(T) bool) func(T) bool {
return MakeFunc(Intersect(MakeSet(f), MakeSet(g)))
}

// Not creates a function which returns if f is false
func Not(f func(T) bool) func(T) bool {
return MakeFunc(Complement(MakeSet(f)))
}


The takeaway from all of this is that constructing a set using Union, Intersect and Complement is really the same as writing a boolean function using ||, && and !.

And proving that a set constructed in this way is empty is the same as proving that a corresponding boolean function is never true.

And because checking that a boolean function is never true is NP-complete, so is checking if one of the sets constructed like this.

With this, let us look at the specific sets we are interested in.

## Basic interfaces as type sets#

Interfaces in Go are used to describe sets of types. For example, the interface

type S interface {
X()
Y()
Z()
}


is “the set of all types which have a method X() and a method Y() and a method Z()”.

We can also express set intersection, using interface embedding:

type S interface { X() }
type T interface { Y() }
type U interface {
S
T
}


This expresses the intersection of S and T as an interface. Or we can view the property “has a method X()” as a boolean variable and think of this as the formula x && y.

Surprisingly, there is also a limited form of negation. It happens implicitly, because a type can not have two different methods with the same name. Implicitly, if a type has a method X() it does not have a method X() int for example:

type X interface { X() }
type NotX interface{ X() int }


There is a small snag: A type can have neither a method X() nor have a method X() int. That’s why our negation operator is limited. Real boolean variables are always either true or false, whereas our negation also allows them to be neither. In mathematics we say that this logic language lacks the law of the excluded middle (also called “Tertium Non Datur” - “there is no third”). For this section, that does not matter. But we have to worry about it later.

Because we have intersection and negation, we can express interfaces which could never be satisfied by any type (i.e. which describe an empty type set):

interface{ X; NotX }


The compiler rejects such interfaces. But how can it do that? Did we not say above that checking if a set is empty is NP-complete?

The reason this works is that we only have negation and conjunction (&&). So all the boolean expressions we can build with this language have the form

x && y && !z


These expressions are in DNF! We have a term, which contains a couple of variables - possibly negated - and joins them together using &&. We don’t have ||, so there is only a single term.

Solving satisfiability in DNF is easy, as we said. So with the language as we have described it so far, we can only express type sets which are easy to check for emptiness.

Go 1.18 extends the interface syntax. For our purposes, the important addition is the | operator:

type S interface{
A | B
}


This represents the set of all types which are in the union of the type sets A and B - that is, it is the set of all types which are in A or in B (or both).

This means our language of expressible formulas now also includes a ||-operator - we have added set unions and set unions are equivalent to || in the language of formulas. What’s more, the form of our formula is now a conjunctive normal form - every line is a term of || and the lines are connected by &&:

type X interface { X() }
type NotX interface{ X() int }
type Y interface { Y() }
type NotY interface{ Y() int }
type Z interface { Z() }
type NotZ interface{ Z() int }

// (!x || z) && (y || z) && (x || !y)
type S interface {
NotX | Z
Y | Z
X | NotY
}


This is not quite enough to prove NP-completeness though, because of the snag above. If we want to prove that it is easy, it does not matter that a type can have neither method. But if we want to prove that it is hard, we really need an exact equivalence between boolean functions and type sets. So we need to guarantee that a type has one of our two contradictory methods.

“Luckily”, the | operator gives us a way to fix that:

type TertiumNonDatur interface {
X | NotX
Y | NotY
Z | NotZ
}

// (!x || z) && (y || z) && (x || !y)
type S interface {
TertiumNonDatur

NotX | Z
Y | Z
X | NotY
}


Now any type which could possibly implement S must have either an X() or an X() int method, because it must implement TertiumNonDatur as well. So this extra interface helps us to get the law of the excluded middle into our language of type sets.

With this, checking if a type set is empty is in general as hard as checking if an arbitrary boolean formula in CNF has no solution. As described above, that is NP-complete.

Even worse, we want to define which operations are allowed on a type parameter by saying that it is allowed if every type in a type set supports it. However, that check is also NP-complete.

The easy way to prove that is to observe that if a type set is empty, every operator should be allowed on a type parameter constrained by it. Because any statement about “every element of the empty set“ is true5.

But this would mean that type-checking a generic function would be NP-complete. If an operator is used, we have to at least check if the type set of its constraint is empty. Which is NP-complete.

## Why do we care?#

A fair question is “why do we even care? Surely these cases are super exotic. In any real program, checking this is trivial”.

That’s true, but there are still reasons to care:

• Go has the goal of having a fast compiler. And importantly, one which is guaranteed to be fast for any program. If I give you a Go program, you can be reasonably sure that it compiles quickly, in a time frame predictable by the size of the input.

If I can craft a program which compiles slowly - and may take longer than the lifetime of the universe - this is no longer true.

This is especially important for environments like the Go playground, which regularly compiles untrusted code.

• NP complete problems are notoriously hard to debug if they fail.

If you use Linux, you might have occasionally run into a problem where you accidentally tried installing conflicting versions of some package. And if so, you might have noticed that your computer first chugged along for a while and then gave you an unhelpful error message about the conflict. And maybe you had trouble figuring out which packages declared the conflicting dependencies.

This is typical for NP complete problems. As an exact solution is often too hard to compute, they rely on heuristics and randomization and it’s hard to work backwards from a failure.

• We generally don’t want the correctness of a Go program to depend on the compiler used. That is, a program should not suddenly stop compiling because you used a different compiler or the compiler was updated to a new Go version.

But NP-complete problems don’t allow us to calculate an exact solution. They always need some heuristic (even if it is just “give up after a bit”). If we don’t want the correctness of a program to be implementation defined, that heuristic must become part of the Go language specification. But these heuristics are very complex to describe. So we would have to spend a lot of room in the spec for something which does not give us a very large benefit.

Note that Go also decided to restrict the version constraints a go.mod file can express, for exactly the same reasons. Go has a clear priority, not to require too complicated algorithms in its compilers and tooling. Not because they are hard to implement, but because the behavior of complicated algorithms also tends to be hard to understand for humans.

So requiring to solve an NP-complete problem is out of the question.

## The fix#

Given that there must not be an NP-complete problem in the language specification and given that Go 1.18 was released, this problem must have somehow been solved.

What changed is that the language for describing interfaces was limited from what I described above. Specifically

Implementation restriction: A union (with more than one term) cannot contain the predeclared identifier comparable or interfaces that specify methods, or embed comparable or interfaces that specify methods.

This disallows the main mechanism we used to map formulas to interfaces above. We can no longer express our TertiumNonDatur type, or the individual | terms of the formula, as the respective terms specify methods. Without specifying methods, we can’t get our “implicit negation” to work either.

The hope is that this change (among a couple of others) is sufficient to ensure that we can always calculate type sets accurately. Which means I pulled a bit of a bait-and-switch: I said that calculating type sets is hard. But as they were actually released, they might not be.

The reason I wrote this blog post anyways is to explain the kind of problems that exist in this area. It is easy to say we have solved this problem once and for all.

But to be certain, someone should prove this - either by writing a proof that the problem is still hard or by writing an algorithm which solves it efficiently.

There are also still discussions about changing the generics design. As one example, the limitations we introduced to fix all of this made one of the use cases from the design doc impossible to express. We might want to tweak the design to allow this use case. We have to look out in these discussions, so we don’t re-introduce NP-completeness. It took us some time to even detect it when the union operator was proposed.

And there are other kinds of “implicit negations” in the Go language. For example, a struct can not have both a field and a method with the same name. Or being one type implies not being another type (so interface{int} implicitly negates interface{string}).

All of which is to say that even if the problem might no longer be NP-complete - I hope that I convinced you it is still more complicated than you might have thought.

If you want to discuss this further, you can find links to my social media on the bottom of this site.

They took a frankly unreasonable chunk of time out of their day. And their suggestions were invaluable.

1. It should be pointed out, though, that “polynomial” can still be extremely inefficient. $$n^{1000}$$ still grows extremely fast, but is polynomial. And for many practical problems, even $$n^3$$ is intolerably slow. But for complicated reasons, there is a qualitatively important difference between “polynomial” and “exponential”6 run time. So you just have to trust me that the distinction makes sense. ↩︎

2. These names might seem strange, by the way. P is easy to explain: It stands for “polynomial”.

NP doesn’t mean “not polynomial” though. It means “non-deterministic polynomial”. A non-deterministic computer, in this context, is a hypothetical machine which can run arbitrarily many computations simultaneously. A program which can be verified efficiently by any computer can be solved efficiently by a non-deterministic one. It just tries out all possible solutions at the same time and returns a correct one.

Thus, being able to verify a problem on a normal computer means being able to solve it on a non-deterministic one. That is why the two definitions of NP “verifiable by a classical computer” and “solvable by a non-deterministic computer” mean the same thing. ↩︎

3. You might complain that it is hard to remember if the “disjunctive normal form” is a disjunction of conjunctions, or a conjunction of disjunctions - and that no one can remember which of these means && and which means || anyways.

You would be correct. ↩︎

4. You might wonder why we can’t just solve CNFSAT by transforming the formula into DNF and solving that.

The answer is that the transformation can make the formula exponentially larger. So even though solving the problem on DNF is linear in the size the DNF formula, that size is exponential in the size of the CNF formula. So we still use exponential time in the size of the CNF formula. ↩︎

5. This is called the principle of explosion or “ex falso quodlibet” (“from falsehoold follows anything”).

Many people - including many first year math students - have anxieties and confusion around this principle and feel that it makes no sense. So I have little hope that I can make it palatable to you. But it is extremely important for mathematics to “work” and it really is the most reasonable way to set things up.

Sorry. ↩︎

6. Yes, I know that there are complexity classes between polynomial and exponential. Allow me the simplification. ↩︎