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Andrew Werner
Andrew Werner

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Composing generic data structures in go

Update 12/1/2021: @rogpeppe gave great feedback on the code and post, it has been updated accordingly.

tl;dr provides go1.18 generic implementations of CoW btree data structures including regular a regular map/set, order-statistic trees, and interval trees all backed by a shared, generic augmented btree implementation. Given go 1.18 hasn't been released yet and it hasn't been heavily tested, don't use it yet. Benchmarks and a deeper discussion on the patterns and experience to follow.

Potentially motivating context (feel free to skip)

Recently a colleague, Nathan, reflecting on CockroachDB, remarked (paraphrased from memory) that the key data structure is the interval btree. The story of Nathan’s addition of the first interval btree to cockroach and the power of copy-on-write data structures
is worthy of its own blog post for another day. It’s Nathan’s hand-specialization of that data structure that provided the basis (and tests) for the generalization I’ll be presenting here. The reason for this specialization was as much for the performance wins of avoiding excessive allocations, pointer chasing, and cost of type assertions when using interface boxing.

I won't quarrel with the idea that interval btrees are critical, but there's other problems out there which benefit from different types of ordered collection data structures, like regular ol' sorted sets and maps. I'm not pleased with what's currently on offer for go in that space. Anyway, an interval tree is just one of a family of augmented tree search data structures. Another useful augmented tree data structure is the order-statistic tree. Wouldn't it be great if we could have a solid implementation of all the various search trees?

As we all know, generics are coming in go1.18. So what better time for an experience report building some real things with it? This post will explore, a library leveraging go1.18's parametric polymorphism to build an abstract augmented btree which offers rich core functionality and easy extensibility.

The post will go through a high-level overview of the library. Some follow-up posts will cover benchmarks, iterator patterns, and some gripes.

The library

The root of the module is a vanilla sorted set and map library. Here's an example:

m := btree.MakeMap[string, int](strings.Compare)
m.Upsert("foo", 1)
m.Upsert("bar", 2)
it := m.Iterator()
for it.First(); it.Valid(); it.Next() {
    fmt.Println(it.Cur(), it.Value())
// Output:
// 1 true
// 0 false
// foo 1
// bar 2
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Let's have a look at this Map:

// Map is a ordered map from K to V.
type Map[K, V any] struct {
    abstract.Map[K, V, struct{}]
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Here we see that we embed something called abstract.Map. This abstract.Map is a generalization of a sorted map that also allows for extra state as specified by its final type parameter. In this case, for a regular btree, it's set to struct{} is the auxiliary configuration state for the tree.

This augmentation parameter allows for implementers to provide some extra state which will get plumbed down into both the augmentation and the iterator. This is useful for, say, comparison functions or other functions to manipulate the key data. The last two type parameters refer to the augmentation which lives in each key node. Let's have a look at the implementation:

Package orderstat

Now, how do we use this augmentation? Armed with this generalization, we can build fancy things like an order-statistic tree with very little effort. This ordered.Compare thing is just a little generic helper to make a comparison function for any type.

s := orderstat.MakeSet(ordered.Compare[int])
for _, i := range rand.Perm(100) {
it := m.Iterator()
// Output:
// 100
// 90
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So, what does this augmentation look like? This:

// Set is an ordered set with items of type T which additionally offers the
// methods of an order-statistic tree on its iterator.
type Set[T any] Map[T, struct{}]

// Map is a ordered map from K to V which additionally offers the methods
// of a order-statistic tree on its iterator.
type Map[K, V any] struct {
    abstract.Map[K, V, aug]

type aug struct {
    // children is the number of items rooted at the current subtree.
    children int
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Now, in order to make this useful, we implement the Updater[K, A] interface which can be used to update the aug method on the aug and we add some special behavior to the Iterator. In order to empower the orderstat library to do something useful with its iterator, we need to expose low-level iterator operations to it. This is done through a constructor in the internal/abstract library which can be used to trade a regular Iterator for a LowLevelIterator. You can see this in action here.

The Updater interface is used to update the augmented data when the node is modified. The UpdateMeta has extra information which can be used to optimize the update.

// Updater is used to update the augmentation of the node when the subtree
// changes.
type Updater[K, V, A any] interface {

    // Update should update the augmentation of the passed node, optionally
    // using the data in the UpdataInfo to optimize the update. If the
    // augmentation changed, and thus, changes should occur in the ancestors
    // of the subtree rooted at this node, return true.
    Update(*Node[K, V, A], UpdateInfo[K, A]) (changed bool)
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Another cool augmented search tree is the interval tree which lets you search for overlaps.

type pair[T constraints.Ordered] [2]T
func (p pair[T]) first() T
func (p pair[T]) second() T
func (p pair[T]) compare(o pair[T]) int {
    if c := ordered.Compare(p[0], o[0]); c != 0 {
        return c
    return ordered.Compare(p[1], o[1])
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m := interval.MakeSet(
    nil, // used to determine whether an interval is a point
for _, p := range []pair{
    {1, 2}, {2, 3}, {1, 5}, {0, 6}, {2, 7},
} {
it := m.Iterator()
for it.FirstOverlap(pair{4, 5}); it.Valid(); it.NextOverlap() {
// Output:
// [0 6]
// [1 5]
// [2 7]
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We've now got a set of search tree structure which are generic, zero-allocation and share a common core. Each of them offer an O(1) Clone() operation, which is very handy. The business logic related to the augmented search doesn't get mixed up with the business logic of the tree itself. The performance is on par with the hand-specialized interval variant (benchmarks coming later). Expect a follow-up post at some point about rough edges and patterns in the implementation. All in all, I'm excited about the prospect of using this in 2022 in real code.

Special Thanks

A big thank you to Nathan VanBenschoten (@natevanben) who wrote the wonderful code that inspired everything here.

Also, shout out to Roger Peppe (@rogpeppe) who helped me grapple
with the type sets change in the go2go days and helped make this library actually good after I published the first draft of it.

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