Hierarchies aren’t a nightmare anymore. No matter if all you want is to go from the leaves to the root of a tree, to visit a fixed number of children or to support an unconstrained model where all the entities can have an unlimited amount of children. There exists a solution for all the cases.
The bad news are that these solutions don’t give guarantees on how the children of an entity are laid out and therefore to visit them we can incur in a few extra jumps we didn’t expect.

With this post I’ll try to explore a few alternatives to face somehow this problem and to reduce or even eliminate the jumps around in memory.

If you haven’t done it yet, read the previous part about hierarchies before to continue. It will help to fully understand this one.


In the previous post, we’ve seen that there exists a component type that can be used to fully support both trees and graphs and therefore hierarchies in general.

One of the most interesting aspects of this type is that it allows to construct an implicit list in the set of components. Because of this, we don’t need to set up and to maintain a secondary data structure to keep track of our hierarchies nor to make use of dynamic allocations to host more and more children as the time passes. This is particularly useful when you want to use per-type pools because it’s transparent from the point of view of the data structure.
On the other hand, there is also no guarantee that all the children are tightly packed in memory, unless actions are taken in this regard. It can happen in fact that nodes with different parents are interleaved.

First and foremost, note that this may not even be a problem. So before you go crazy trying to order things just to save a few nanoseconds, think carefully about whether a sorted pool is really something you need. Otherwise, move on.
Too often too many people focus too much on non-existent problems. If you are not among them and ordering a pool is really what you need, read on in the hope of finding some interesting ideas.

To be able to discuss a few points with some code that is as close as possible to a real world case, I’ll use a couple of libraries available from GitHub: EnTT and Flecs.
The snippets in the following sections will use the terminology of these libraries. It shouldn’t be complicated to bring the examples back to any other library that is at least designed around the same principles.

Go further, go faster

So far, so good. We have our hierarchy and it’s constructed directly within the pool of a given component. It means that there is nothing faster than this when we decide to iterate all the instances of that component, because they are all tightly packed in a single array (at least with EnTT).
Furthermore, we have a solution that adds nothing in terms of complexity to our container, which remains completely transparent and unaware of our work.

What’s the problem then? Intuitively, the jumps aren’t reduced to a minimum when we decide to visit all the children of a given entity. This is due to the fact that these entities (and their components) aren’t necessarily close to each other by construction.
Even worse, when we visit the entities and their components there is no certainty that the parents are returned before their children and that a tree is therefore visited starting from the root and down to the leaves, one level at a time in a breadth first approach (that is what we usually want).

Fortunately, if your ECS allows sorting (and EnTT does it because of how sparse sets work), we can go a bit further on this aspect and sort things in such a way that our entities and their components are laid out in a manner that is at least convenient.
There is even more though. In many cases, we can take advantage of the features of the software we’re working on and the library in use for the ECS part (if any) to save cpu cycles and get the same result in an easier way.

Sorting, what else?

The most obvious thing to do is to sort our components according to the parent data member. Of course, you got it right, sort as in sort a pool as a whole.
Something along this line is already an improvement:

registry.sort<relationship>([&registry](const entt::entity lhs, const entt::entity rhs) {
    const auto &clhs = registry.get<relationship>(lhs);
    const auto &crhs = registry.get<relationship>(rhs);
    return crhs.parent == lhs || clhs.next == rhs
        || (!(clhs.parent == rhs || crhs.next == lhs) && (clhs.parent < crhs.parent || (clhs.parent == crhs.parent && &clhs < &crhs)));

Discalimer: to be honest, I haven’t tested this code and I wrote it straight away during my holidays, so I don’t guarantee that it works! :)

The idea is that we are sorting things in such a way that parents and children are both grouped and have the same order. Moreover, parents come always before their children and therefore we don’t incur in the risk of updating for example the transform of an object the parent of which isn’t updated yet.
It can seems complicated at a first glance, but it is not and gets the job done at least.

A question arises anyway: is it really necessary to sort a whole pool?
The answer is: it depends, fortunately most of the times it is not.

Consider the case of the transform above mentioned, that is nothing more than a way to express a scene graph in terms of components.
What we really need isn’t to sort everything in order to keep the transform components up-to-date. In fact, all we want is to update the global transforms of the entities for which we updated the local transform. All the other entities can remain untouched for what is worth. This can drastically reduce the number of instances to update and the amount of work to do. Even more: this can reduce by far the cost of sorting our elements.
In fact, this time we can also ignore the way in which things are ordered in the pool of transforms and even be willing to pay the price of some jumps if needed.

Let’s introduce another component, an empty one called dirty. It’s a kind of boolean value in the ECS terminology.
Every time we update a transform, we can add this component to the entity that owns it. With EnTT it’s a matter of attaching a couple of listeners to the signals emitted by the registry, then use registry::replace to update the transform instead of editing the components directly:


Once done and in the best case, the amount of entities we touch per frame is only a small part of the ones still alive. What we obtain is a packed array that contains all the entities to update. However we cannot compute it as-is, because parent and children could have both entered this array and be positioned in such a way that there is the risk to update a child before its parent.
So, what? So, sort. This time with an N (as in NlogN) that is by far smaller than in the previous case. In EnTT terminology, we can both sort the pool of dirty, then iterate it and rely on the indirection from the entity identifiers to get the transforms:

registry.sort<dirty>([](auto &&...) { /* ... */ });
registry.view<dirty>().each([&registry](const auto entity) {
    const auto &instance = registry.get<transform>(entity);
    /* ... */

Or we can create a group with dirty and the transform component, so that we can directly iterate the latter once the group is sorted:

auto group = registry.group<dirty, transform>();
group.sort([](auto &&...) { /* ... */ });
group.each([](auto &&...) { /* ... */ });

In both cases, when we are done we can invoke registry.reset<dirty>() to clear the pool of the component we used to track dirty entities and we are ready for the next frame.

Hierarchies and archetypes

In an archetype-based implementation components can be stored in multiple arrays, which makes sorting much harder to do efficiently.
Fortunately this model offers other tools which allow for efficient creation of hierarchies. One such tool is normally considered a bad thing, but comes in handy here, which is fragmentation.
I suggest you to read this post if it’s not clear what I’m talking about.

Long story short, fragmentation is the measure that indicates the number of arrays a particular component is stored in. The higher the fragmentation, the more arrays to iterate and the higher the potential for cache misses.
Fragmentation should therefore be kept as low as possible. Archetype-based models have to deal with fragmentation, as each combination of components results in a separate set of arrays for the components in that type.

So how does fragmentation help hierarchies?

The trick is that we can intelligently fragment to create archetypes that exactly match the sets of entities we want to iterate as we descend the hierarchy breadth-first. This allows us to create easily a hierarchy between archetypes and not entities, that is something generic enough to solve many of the most common problems anyway. We can then walk these sets in the right order and get the job done.
This is also the approach that is taken by Flecs. Once again, this should demonstrate how sometimes the problems of a model aren’t necessarily such, but can be treated as features if kept under control.

Consider this snippet, which constructs a simple hierarchy:

ecs_entity_t parent_1 = ecs_new(world, Position);
ecs_entity_t parent_2 = ecs_new(world, Position);

ecs_entity_t child_1 = ecs_new_child(world, parent_1, Position);
ecs_entity_t child_2 = ecs_new_child(world, parent_2, Position);
ecs_entity_t grandchild = ecs_new_child(world, child_2, Position);

This code creates four archetypes:

[Position, CHILDOF | parent_1]
[Position, CHILDOF | parent_2]
[Position, CHILDOF | child_2]

Note how the parents are encoded into their types. We can now create a system that iterates all entities with Position like this:

ECS_SYSTEM(world, Transform, EcsOnUpdate, Position);

This system matches all the archetypes the example created, but it has one problem: it doesn’t iterate the entities in the right order. In other words, parents and children could be interleaved and this is usually to be avoided.
To fix that, we can change our system definition into this:

ECS_SYSTEM(world, Transform, EcsOnUpdate, Position, CASCADE.Position);

The additional CASCADE column will determine its depth by looking only at parents that have a Position component for each matched archetype. This results in the following depth annotations:

[Position] => 0
[Position, CHILDOF | parent_1] => 1
[Position, CHILDOF | parent_2] => 1
[Position, CHILDOF | child_2] => 2

This depth is then used to order the archetypes from low to high so entities will be evaluated in the right order.
A nice property of this approach is that as long as no new archetypes are created (or destroyed, if you ever decide to get rid of empty archetypes), adding or removing entities doesn’t require resorting, as the arrays from which they are added or removed are already matched with the system.
In addition, the application still iterates a packed array for each archetype, which is great.

This works quite well but there is one problem: in the example, we have two archetypes for depth 1, where one would have been sufficient.
In an example with lots of parents the number of archetypes would go through the roof, resulting in a very fragmented data space. To address this, Flecs is implementing a new kind of storage where entities with the same components and depth are combined in the same archetype.
It would take another blog post to explain exactly how it works but, seen from above, we can summarize by saying that it reduces the fragmentation and has therefore better performance during iterations at the price of a slightly higher cost on adding and removing components to entities with a parent.

As we can see, archetype-based implementations need to approach hierarchies in a very different way when compared to sparse sets, with different characteristics and trade-offs.
Even between archetype-implementations approaches may vary a lot. It’s therefore important to understand the features and limitations of the ECS framework to implement hierarchies in an efficient way.
Finally, note that in this case the tool is aware of the existence of hierarchies, which are therefore no longer transparent but, on the other hand, can be shaped on the specific problem by exploiting the underlying model.

Almost-always almost-sorted pools

Not bad. We managed to arrange our objects in a way that can apparently favor the subsequent iterations. We also found that sometimes we don’t even need to sort a whole pool and therefore we can highly reduce the number of operations to get the job done.

However, when sorting a pool or an archetype as a whole is required, the cost of this operation is all concentrated in a single frame and could give rise to peaks.
We can use a different algorithm if we have more information about our data, as an example the insertion sort, so as to exploit its features where it makes sense. However, we still have the full cost of a sorting step and this could be a problem in some cases.

Is it really necessary or can we divide the cost more cleverly over multiple frames?
Once again: it depends. Fortunately, most of the times we don’t care much if things are sorted exactly or just almost sorted.
As an example, if all we want is to arrange things so that we can further reduce jumps, it doesn’t matter if at any point in time there is an element that is only close to its final position but not quite there yet.

Consider the most trivial of the sorting algorithms: the bubble sort.
It’s defined by an iterative approach that goes on and on until no more swaps take place in a cycle. However, all the cycles affect to an extent the order of the array and move things closer to their final positions. In other terms, elements are more sorted (if it made sense at all as a concept) then the cycle before.
If you take a closer look at your favorite algorithm, it’s likely that you can spot the same pattern. This is how many sorting algorithms work after all.

Long story short, instead of a full sorting pass per frame, we could just do a single step (or a few steps) and keep our arrays almost sorted if not fully sorted for most of the time.
In this regard, some algorithms are better than others and the overall complexity isn’t much important because we are never going to sort the pool as a whole. Ironically, the faster it is the single step, the better it is for our purposes. Whatever faster means in this case, of course.

Render once and use it while it lasts

Another trick for when hierarchies are used to sort things before they are rendered (for example in case of 2D games that work in painter mode) is that of using layers.

Think for a moment to how many games are structured: they have a background and we can easily spot different layers where things are logically laid out that are drawn one on top of the other. These layers form a hierarchy between them and the entities within the layers form separate hierarchies in turn.
In this case, it’s likely that the number of logical layers is fixed and we can define as many components as there are layers. If an entity belongs to a layer, it has the component that describes it as a parent.

First of all, this time we don’t need to induce a global order. Two entities belonging to different levels are already implicitly ordered with respect to each other. Overall we reduce by far the cost of sorting elements and we can split the work on multiple threads.
It could also be the case where you don’t need to sort the entities that are part of the same layer, but only between different layers. In this way, a few additional components completely relieves us from the problem of sorting.
That’s not all, though. Often, in this type of simulation, some levels are very dynamic while others are almost static, although they may be detailed. To further reduce the computation, we can keep track of the fact that a level has changed, or that something inside it has moved. When this happens, we can draw the entire layer on a backing memory and reuse the latter, with its contents, for all subsequent frames, until it changes again. Taking this approach to the extreme, you can even split layers into zones and apply the same technique to different area.

In doing so and depending on the application we are working on, we may find ourselves in the situation where, for most of the time, rendering is reduced to drawing static images in transparency one above the other, then refining them with what remains in the higher layer.
In any case, excluding the frames where all the layers have to be redesigned, the workload is fairly low and we can use those cycles for something else.


To sum up, there doesn’t exist a one-fits-all solution when it comes to working with hierarchies. Of course, we can try to put our problem in a solution that we already have (because of laziness, because someone sold it to us as the solution to all problems, or just because nothing better came to mind), but not everything is a nail just because we have a hammer, right?
Depending on the application we are developing and the type of hierarchy we are dealing with, we can apply one or the other optimization, reducing the workload or moving it here and there as appropriate.
Obviously, there are many other cases and many other solutions out there, but it wasn’t the purpose of this post to mention them all and provide a user manual for any eventuality.

Each hierarchy must be taken for what it is, separately from the others. It should therefore be studied well and understood in detail to find its holds and exploit its weaknesses.
Sometimes we’ll need a global ordering, other times sorting a subset will be more than enough. Some hierarchies can also be exploited in such a way that we can find cycles not in a smaller number of jumps but in a better approach to the problem.
And so on. Every problem has its solution.

Thanks to

This time I want to say thanks to Sander Mertens for contributing to this post with the section Hierarchies and archetypes.
This series is in a sense the home of anyone who is trying to work on these topics by offering new ideas and tools. I am therefore very happy and grateful for this collaboration which I hope will not end here!


Part of this and the previous post have been inspired by this one from the Bitsquid blog.
The aim was to bring some of the concepts to EnTT, then go further and try to add some more information and ideas, so as to make it clear why the problem of hierarchies cannot be reduced to a ready-to-eat solution in any case.

Let me know that it helped

I hope you enjoyed what you’ve read so far.

If you liked this post and want to say thanks, consider to star the GitHub project that hosts this blog. It’s the only way you have to let me know that you appreciate my work.