This content originally appeared on Level Up Coding - Medium and was authored by Alexander Korostin
Fast Entity Component System for Game Engines
Motivation
When building games or simulations with hundreds or thousands of objects — characters, projectiles, particles, NPCs — it quickly becomes clear that the traditional object-oriented approach tends to get messy. Inheritance hierarchies grow deep and brittle, shared behaviors become entangled, and it gets harder to reason about performance. That’s where Entity Component System (ECS) architecture comes in.
ECS flips the usual model on its head. Instead of baking behavior into a complex class structure, it encourages you to decompose your world into three simple concepts: entities as identifiers, components as plain data, and systems as the logic that operates on combinations of components. It’s not just a pattern for decoupling logic — it’s a design philosophy that enables cache-friendly memory layouts and clean, data-driven programming. No surprise it’s widely used in game engines like Unity, Bevy, and even internal systems at AAA studios.
Rust is a particularly interesting fit for ECS. Its zero-cost abstractions, powerful type system, and strict ownership model offer the kind of control that ECS thrives on — while still catching bugs at compile time. Combine that with excellent performance and predictable memory behavior, and you have a language that seems almost tailor-made for building your own ECS from scratch.
In this article, we’re going to do exactly that. We’ll walk through a minimal ECS implementation in Rust, designed to be understandable, extensible, and free of dependencies. If you’ve ever wondered how ECS works under the hood — or wanted to write one yourself just for the fun of it — this is for you.
What is ECS?
At its core, the Entity Component System architecture is a way of organizing data and behavior that promotes composition over inheritance. If you’re used to object-oriented design, it may feel a bit alien at first — but it solves some real problems that tend to show up in large, dynamic systems like games.
The three pillars of ECS are exactly what the name suggests.
Entities are nothing more than unique identifiers. They don’t have any behavior or structure on their own — they’re just IDs that represent “things” in your world. A player, a tree, a bullet, or even an empty placeholder could all be entities.
Components are the data associated with those entities. They’re plain, self-contained pieces of state — like Position, Velocity, Health, or Sprite. Each component holds a single aspect of data, and by attaching different combinations of components to different entities, you define what those entities are. There’s no inheritance tree—just a flat, flexible way to describe behavior by presence or absence of data.
Systems are where the logic lives. A system might operate on every entity that has a Position and a Velocity, updating positions each frame. Another system might look for entities with Health and Damage, applying damage effects and removing entities when their health hits zero. Systems are decoupled from each other and from the entities themselves, making it easy to reason about behavior and optimize performance.
A useful way to think about ECS is like building with Lego. Each component is a block — you snap them together to form a structure. An entity with Position, Velocity, and Sprite might be a visible, moving object. One with just Position might be a static obstacle. You don’t subclass anything—you just change which blocks you attach.
This is where ECS departs significantly from classical OOP. Instead of defining a hierarchy of classes like GameObject → Enemy → Orc → Boss, you define a flat universe of entities and use data composition to express behavior. That shift brings more flexibility, better separation of concerns, and—critically for performance-minded developers—tighter control over memory layout, which can lead to massive performance wins thanks to better cache locality.
In short, ECS is not just a pattern — it’s a data-oriented way of thinking that pairs beautifully with systems-level languages like Rust. Now that we’ve got a sense of what ECS is and why it matters, let’s roll up our sleeves and start building one.
Defining Entities and Components
Before we can do anything interesting, we need a way to represent entities in our ECS. In most implementations, an entity is just a unique identifier. It doesn’t hold any data — it’s a handle used to look up associated components.
Let’s start simple. We’ll define an entity as a u32:
type Entity = u32;
Why u32? It’s compact, cheap to copy, and gives us over 4 billion possible entities, which is more than enough for most games and simulations. You could also use usize if you're working with indices in memory-heavy data structures, but u32 strikes a good balance for our purposes.
Next, we’ll need a way to generate new entities. A basic approach is to just increment a counter every time a new one is created:
pub struct EntityManager {
next_id: Entity,
}
impl EntityManager {
pub fn new() -> Self {
EntityManager { next_id: 0 }
}
pub fn create(&mut self) -> Entity {
let id = self.next_id;
self.next_id += 1;
id
}
}This gets the job done. Each call to create() returns a new, unique entity ID. There’s no fancy bookkeeping yet—no recycling of IDs when entities are deleted—but it’s perfectly fine for the first version of our ECS.
If you wanted to support reuse later, you could maintain a Vec<Entity> of free IDs and pull from it before incrementing next_id. But that adds complexity, especially when you consider invalidation and versioning (i.e. making sure old references to deleted entities don’t cause undefined behavior). For now, let’s keep it lean.
Entities by themselves don’t do much. To give them behavior and state, we need to associate data — components — with them. The simplest way to do this is to maintain a separate storage for each component type, mapping entity IDs to component values.
Let’s say we have a Position component:
#[derive(Debug, Clone, Copy)]
struct Position {
x: f32,
y: f32,
}
To keep everything simple for now, we can store these in a HashMap keyed by entity:
use std::collections::HashMap;
struct PositionStorage {
data: HashMap<Entity, Position>,
}
To make our ECS extensible and type-safe, we can wrap our component storage in a generic structure:
struct ComponentStorage<T> {
data: HashMap<Entity, T>,
}
impl<T> ComponentStorage<T> {
fn new() -> Self {
Self {
data: HashMap::new(),
}
}
fn insert(&mut self, entity: Entity, component: T) {
self.data.insert(entity, component);
}
fn remove(&mut self, entity: &Entity) {
self.data.remove(entity);
}
fn get(&self, entity: &Entity) -> Option<&T> {
self.data.get(entity)
}
fn get_mut(&mut self, entity: &Entity) -> Option<&mut T> {
self.data.get_mut(entity)
}
fn iter(&self) -> impl Iterator<Item = (&Entity, &T)> {
self.data.iter()
}
}This generic ComponentStorage<T> allows us to reuse the same structure for any component type— Position, Velocity, Health, you name it. It keeps the codebase clean and lets the type system do the work of keeping everything consistent.
Registering and Adding Components
Now that we have a ComponentStorage<T> abstraction, we need a place to actually manage all of those component storages. Remember, each component type—Position, Velocity, Health, etc.—has its own dedicated storage. We’ll keep them separate for type safety and clarity.
Let’s define a simple World struct to hold our ECS state:
struct World {
positions: ComponentStorage<Position>,
velocities: ComponentStorage<Velocity>,
}This is very explicit, which is nice when starting out. If you want something dynamic and type-erased down the road, you can explore using Any and TypeId, but it adds a lot of complexity. For now, this gives us a clear view of what’s going on.
Here’s how you’d add a component to an entity:
impl World {
fn new() -> Self {
Self {
positions: ComponentStorage::new(),
velocities: ComponentStorage::new(),
}
}
fn add_position(&mut self, entity: Entity, position: Position) {
self.positions.insert(entity, position);
}
fn add_velocity(&mut self, entity: Entity, velocity: Velocity) {
self.velocities.insert(entity, velocity);
}
}You can imagine generating entities using an EntityManager and then selectively attaching the components they need. This is the heart of composition—what makes ECS so flexible. You’re not forced into a hierarchy or a predefined set of behaviors.
Writing Systems
With entities and components in place, it’s time to define systems — the logic that updates the world. A system is typically just a function that operates on entities with specific components.
Let’s say we want to move entities by applying their Velocity to their Position. Here’s how a basic movement_system might look:
fn movement_system(world: &mut World) {
for (entity, position) in world.positions.iter() {
if let Some(velocity) = world.velocities.get(entity) {
let pos = world.positions.get_mut(entity).unwrap();
pos.x += velocity.dx;
pos.y += velocity.dy;
}
}
}This system iterates over all entities with a Position, and if they also have a Velocity, it applies the movement. You might wonder: why not iterate over velocities first? Or both together? In a real ECS engine, this would be optimized using archetypes or sparse sets to avoid iterating over mismatches—but in our version, we’re going for readability over raw speed.
A key concern here is safe mutable access. Rust won’t allow you to have two mutable borrows to the same storage at the same time. In this example, we borrow positions first immutably, then mutably. Because the first borrow is dropped before we do the mutation (thanks to lexical scoping in the loop), the borrow checker is happy. But if you try to get both at the same time outside the loop, Rust will complain—and for good reason.
This pattern scales surprisingly well for small systems and demos. As things get more complex, you might want to introduce a query abstraction or a borrowing scheduler — but again, we’re keeping it lean for now.
Bringing it Together
Let’s wire up a tiny working example. First, define your components:
#[derive(Debug, Clone, Copy)]
struct Position {
x: f32,
y: f32,
}
#[derive(Debug, Clone, Copy)]
struct Velocity {
dx: f32,
dy: f32,
}
Now build the world:
fn main() {
let mut entity_manager = EntityManager::new();
let mut world = World::new();
// Create a few entities
let e1 = entity_manager.create();
let e2 = entity_manager.create();
let e3 = entity_manager.create();
// Add components
world.add_position(e1, Position { x: 0.0, y: 0.0 });
world.add_velocity(e1, Velocity { dx: 1.0, dy: 1.0 });
world.add_position(e2, Position { x: 10.0, y: -5.0 });
world.add_velocity(e2, Velocity { dx: -2.0, dy: 0.5 });
world.add_position(e3, Position { x: 3.0, y: 3.0 });
// e3 has no velocity—won’t move
// Run the movement system a few times
for frame in 0..3 {
println!("--- Frame {} ---", frame);
movement_system(&mut world);
for (entity, pos) in world.positions.iter() {
println!("Entity {}: Position = ({:.1}, {:.1})", entity, pos.x, pos.y);
}
}
}The output should look something like this:
--- Frame 0 ---
Entity 0: Position = (1.0, 1.0)
Entity 1: Position = (8.0, -4.5)
Entity 2: Position = (3.0, 3.0)
--- Frame 1 ---
Entity 0: Position = (2.0, 2.0)
Entity 1: Position = (6.0, -4.0)
Entity 2: Position = (3.0, 3.0)
--- Frame 2 ---
Entity 0: Position = (3.0, 3.0)
Entity 1: Position = (4.0, -3.5)
Entity 2: Position = (3.0, 3.0)
And just like that, you have a working ECS: entities with composable data, systems that operate on them, and a simple loop that updates the world over time.
At this point, you’ve got a functional, bare-bones ECS that reflects how real game engines manage and update game state. But as with most things in software, there’s always room to evolve. Once you’ve internalized the basics, there are several directions you can take this implementation to make it faster, more flexible, or production-ready.
What’s Wrong With HashMaps?
When we first built our ECS, we used HashMap<Entity, Component> as the backing storage for each component type. It made sense: it’s easy to use, offers constant-time lookup and removal, and lets us work with a flexible set of entity IDs. But once your world starts filling up with thousands—or tens of thousands—of entities, the cracks start to show.
The first and most important problem is cache locality. A HashMap doesn’t store items contiguously in memory. Instead, data is scattered across buckets, often in unpredictable locations. That means when a system iterates over a bunch of components—say, all entities with Position and Velocity—the CPU has to jump around in memory, chasing pointers and blowing out the cache line every few iterations. This completely kills the performance benefits you’d expect from tight loops and SIMD-friendly code.
Then there’s the overhead of iteration itself. Iterating over a HashMap is not only slower than over a Vec, it also doesn’t let you easily coordinate multiple component types. For example, our movement_system has to loop over all entities with a Position, then check if each one also has a Velocity. That’s a lot of redundant work, especially when you know only a subset of entities will have both. In a real game with dozens of systems and millions of entities, this adds up fast.
To illustrate just how limiting this is, try profiling a system like this with 10,000 entities, each with Position and Velocity. Even in debug mode, you'll start to see significant time spent in hash map iteration, lookups, and memory allocation. In optimized builds, the bottleneck becomes even clearer: most of your time is spent jumping around in memory rather than actually doing computation.
And worst of all? This structure makes it harder to take advantage of parallelism. Since you can’t easily batch contiguous chunks of memory, systems can’t operate in SIMD-style or thread-friendly ways unless you rework the entire structure.
So while HashMap is a good educational tool, it’s not how performant ECS systems work in practice. If we want our ECS to scale, we need a better layout—something that supports fast lookup and fast iteration. That’s where sparse sets come in. Let’s take a look.
Sparse Set Storage
When we first introduced our ECS, each component type was stored in a HashMap<Entity, Component>. That was a reasonable starting point—simple, safe, and easy to understand. But as soon as we start caring about performance, especially in systems that iterate over thousands of entities every frame, that structure becomes a bottleneck. Hash maps simply aren't optimized for this kind of work. They scatter memory, fragment iteration, and add overhead we don't need.
If we want to make our ECS fast — truly fast — we need to rethink how we store components. That’s where the sparse set comes in.
A sparse set is a clever layout that keeps components tightly packed for iteration while still allowing constant-time lookups by entity. The idea is to separate storage into three pieces. First, there’s a dense vector of components — this is where the actual data lives, and it’s laid out contiguously in memory, which is perfect for cache efficiency. Then there’s a second dense vector of entities, which lines up with the component array: components[i] belongs to entities[i]. And finally, there's the sparse index, which lets you look up the position of a given entity in those dense arrays.
Here’s how that looks in code. We’ll define a SparseSet<T> struct like this:
struct SparseSet<T> {
dense_components: Vec<T>,
dense_entities: Vec<Entity>,
sparse: Vec<Option<usize>>,
}To illustrate, imagine you have three entities with Position components: Entity 4, Entity 2, and Entity 9. They might be stored like this:
dense_components = vec![
Position { x: 1.0, y: 2.0 }, // belongs to Entity 4
Position { x: 0.0, y: 0.0 }, // belongs to Entity 2
Position { x: 3.0, y: 5.0 }, // belongs to Entity 9
];
dense_entities = vec![4, 2, 9];
sparse = vec![
/* index = entity ID */
None, // 0
None, // 1
Some(1), // 2 → Entity 2 is at index 1
...
Some(0), // 4 → Entity 4 is at index 0
...
Some(2), // 9 → Entity 9 is at index 2
...
];
This structure gives us everything we want. Iteration is as fast as it gets — just walk the dense arrays. Lookups are instant: you fetch the index from sparse[entity_id], then access the dense vector. And deletion stays clean and fast with the classic swap-remove trick. When you remove a component, you just overwrite it with the last item in the dense array, update the sparse index of the moved entity, and shrink the vector. There are no holes, no shifting, no wasted cycles.
It’s also easy to see how this helps systems that operate on multiple component types. If two systems each use sparse sets, you can quickly determine whether a given entity has both components by comparing their sparse indices or intersecting their dense entity lists. There’s no need to hash, branch, or iterate over irrelevant data.
Of course, there’s a trade-off. The sparse index uses a vector indexed by entity ID, which means that if your entity IDs are large and scattered, you might end up allocating a lot of unused space. But in practice, this can be managed — by reusing entity IDs or capping the maximum number of entities — and the performance payoff is worth it.
This is the kind of layout used in real engines. Libraries like hecs, legion, and bevy_ecs rely on sparse sets internally, because they’re fast, predictable, and scale well.
Now that we understand how sparse sets improve both iteration and lookup performance, let’s implement one in Rust. Our goal is to create a generic SparseSet<T> that we can use for any component type. It will store data densely for performance, and track which entity owns which data using a sparse index. The dense part will consist of two parallel Vec’s: one for the components, and one for the entities. The sparse part will be a Vec<Option<usize>>, which maps an entity ID to an index in the dense arrays.
Here’s the basic structure:
pub struct SparseSet<T> {
dense_components: Vec<T>,
dense_entities: Vec<Entity>,
sparse: Vec<Option<usize>>,
}To support efficient access, we assume that entity IDs can be cast to usize and used as indices. In a more advanced version, you might include versioning or handle gaps more gracefully, but we’ll keep things clean and educational here.
Let’s start with the constructor:
impl<T> SparseSet<T> {
pub fn new() -> Self {
Self {
dense_components: Vec::new(),
dense_entities: Vec::new(),
sparse: Vec::new(),
}
}
}The insert logic is where the sparse set really shines. When we insert a component for an entity, we push it to the end of the dense arrays and record the index in the sparse map. If the sparse vector isn’t large enough to hold the entity ID, we resize it first.
impl<T> SparseSet<T> {
pub fn insert(&mut self, entity: Entity, component: T) {
let index = self.dense_components.len();
let id = entity.0 as usize;
if id >= self.sparse.len() {
self.sparse.resize(id + 1, None);
}
if self.sparse[id].is_some() {
panic!("Entity {:?} already has component", entity);
}
self.dense_components.push(component);
self.dense_entities.push(entity);
self.sparse[id] = Some(index);
}
}Lookups are straightforward. To get a reference, we fetch the index from the sparse array and use it to access the dense one:
impl<T> SparseSet<T> {
pub fn get(&self, entity: Entity) -> Option<&T> {
let id = entity.0 as usize;
self.sparse.get(id)?.map(|&index| &self.dense_components[index])
}
pub fn get_mut(&mut self, entity: Entity) -> Option<&mut T> {
let id = entity.0 as usize;
self.sparse.get(id)?.map(|&index| &mut self.dense_components[index])
}
}Now comes the slightly trickier part: removal. When removing an entity’s component, we swap the last element into its place and update the sparse index accordingly. This keeps the dense arrays compact and avoids leaving gaps.
impl<T> SparseSet<T> {
pub fn remove(&mut self, entity: Entity) -> Option<T> {
let id = entity.0 as usize;
let index = self.sparse.get(id)?.take()?;
let last_index = self.dense_components.len() - 1;
self.dense_components.swap(index, last_index);
self.dense_entities.swap(index, last_index);
let moved_entity = self.dense_entities[index];
self.sparse[moved_entity.0 as usize] = Some(index);
self.dense_entities.pop();
let removed = self.dense_components.pop();
removed
}
}Finally, let’s expose a way to iterate over the set, which systems will use to process components. Since the dense array is always tightly packed, iteration is simple and fast.
impl<T> SparseSet<T> {
pub fn iter(&self) -> impl Iterator<Item = (Entity, &T)> {
self.dense_entities
.iter()
.cloned()
.zip(self.dense_components.iter())
}
pub fn iter_mut(&mut self) -> impl Iterator<Item = (Entity, &mut T)> {
self.dense_entities
.iter()
.cloned()
.zip(self.dense_components.iter_mut())
}
}And with that, we’ve got a fully functioning sparse set. It gives us constant-time insert, remove, and lookup operations, and it iterates as fast as a Vec. The layout is simple, but it supports the kinds of workloads real games and simulations demand. You can now store components in a format that scales—whether you’re dealing with 100 entities or 100,000.
In the next step, we’ll plug this SparseSet<T> into our ECS world and see how much faster it runs compared to our original HashMap-based version. Spoiler: it’s not even close.
Updating the ECS World
Now that we’ve implemented SparseSet<T>, it’s time to put it to use. The first step is to update our ECS world so that each component type is stored using a SparseSet instead of a HashMap. This change is almost entirely internal—our API for inserting and querying components remains the same—but under the hood, iteration will be drastically faster and lookups will be truly constant-time.
We’ll start by modifying our ComponentStorage<T>. Previously, it might have looked something like this:
pub struct ComponentStorage<T> {
map: HashMap<Entity, T>,
}We can now replace that with:
pub struct ComponentStorage<T> {
set: SparseSet<T>,
}All the methods — insert, remove, get, and get_mut—can simply delegate to the corresponding methods on SparseSet<T>. For example:
impl<T> ComponentStorage<T> {
pub fn new() -> Self {
Self { set: SparseSet::new() }
}
pub fn insert(&mut self, entity: Entity, component: T) {
self.set.insert(entity, component);
}
pub fn get(&self, entity: Entity) -> Option<&T> {
self.set.get(entity)
}
pub fn get_mut(&mut self, entity: Entity) -> Option<&mut T> {
self.set.get_mut(entity)
}
pub fn remove(&mut self, entity: Entity) -> Option<T> {
self.set.remove(entity)
}
pub fn iter(&self) -> impl Iterator<Item = (Entity, &T)> {
self.set.iter()
}
pub fn iter_mut(&mut self) -> impl Iterator<Item = (Entity, &mut T)> {
self.set.iter_mut()
}
}With that in place, we can now update our World type. The structure doesn’t change much, but the behavior improves substantially. Here’s an example with Position and Velocity:
pub struct World {
positions: ComponentStorage<Position>,
velocities: ComponentStorage<Velocity>,
}Component insertion works just as before:
world.positions.insert(entity, Position { x: 1.0, y: 2.0 });
world.velocities.insert(entity, Velocity { dx: 0.1, dy: 0.0 });But now comes the fun part: systems. Let’s say we want to update each entity’s Position using its Velocity. With our original design, we would iterate over all positions and check whether each entity also had a velocity. But now, since our components are stored densely, we can improve that significantly.
We can iterate over the smaller of the two sets — let’s say positions—and look up Velocity directly using the sparse index. Here’s how the movement_system might look:
fn movement_system(world: &mut World) {
for (entity, pos) in world.positions.iter_mut() {
if let Some(vel) = world.velocities.get(entity) {
pos.x += vel.dx;
pos.y += vel.dy;
}
}
}This approach avoids unnecessary lookups and branch mispredictions. But we can go even further. Since SparseSet gives us access to the list of entities in dense form, we could implement an optimized join that iterates only over entities that have both Position and Velocity. There are a few ways to do this, but a simple and effective approach is to pick the smaller of the two sets and check for presence in the other:
fn movement_system(world: &mut World) {
let (positions, velocities) = (&mut world.positions, &world.velocities);
// Choose the smaller set to iterate over
let iter = if positions.set.dense_entities.len() <= velocities.set.dense_entities.len() {
positions.iter_mut().filter_map(|(entity, pos)| {
velocities.get(entity).map(|vel| (pos, vel))
})
} else {
velocities.iter().filter_map(|(entity, vel)| {
positions.get_mut(entity).map(|pos| (pos, vel))
})
};
for (pos, vel) in iter {
pos.x += vel.dx;
pos.y += vel.dy;
}
}This form of dynamic join is efficient and doesn’t require any additional data structures. In more advanced ECS implementations, this logic is typically abstracted behind a query system, but even in this manual form, it gives you real performance improvements for almost no extra complexity.
Now our ECS world is not only clean and ergonomic — it’s also fast. We’ve eliminated hash map overhead, enabled cache-friendly iteration, and created the foundation for even more advanced optimizations down the line.
Further Improvements
At this point, we have a solid, fast, and minimal ECS built entirely from scratch in Rust. It stores components efficiently, iterates quickly, and already gives us the kind of performance and flexibility that simpler engines need. But there’s still a long road ahead if you want to build a full-featured ECS suitable for complex games or simulations. Let’s briefly look at some of the more advanced ideas you might want to explore as you grow this architecture.
One of the first low-hanging optimizations is using bitsets to track component membership. Right now, to check whether an entity has a component, we do a lookup in the sparse index. That’s fine, but if we want to join multiple component types — say, entities with both Position and Velocity—bitsets give us a fast way to intersect sets. Each component type maintains a bitmask where bit i is set if entity i has that component. Joining just becomes a bitwise AND operation. This is extremely cache-friendly, especially when you have large numbers of entities and want to filter them rapidly.
Another extension — much more complex, but common in large ECS engines — is archetype-based storage. Instead of storing components in isolation, archetype ECS groups entities by the exact combination of components they have. So if three entities have Position and Velocity, they live in the same contiguous chunk of memory. Entities with a different set of components—say, Position and Health—live in another. This model allows entire systems to iterate over chunks of memory without needing to check which components are present. It's extremely fast, but it comes with real complexity: dynamic component grouping, chunk lifetimes, and versioning all become necessary. If you're familiar with Bevy or Unity's DOTS, you're already seeing this pattern in action.
Parallelism is another direction worth pursuing. Our ECS currently runs systems one at a time, but there’s no reason we couldn’t run independent systems in parallel — especially on modern multicore CPUs. Once you ensure that no system mutably accesses the same component type as another in the same frame, you can split the workload and run them across threads. One way to do this is with chunked iteration, where each thread processes a range of entities or components. With a little help from rayon or crossbeam, this becomes surprisingly manageable, and it can offer dramatic speedups in simulation-heavy scenarios.
If you’re building a game engine that supports mods, dynamic loading, or scripting, you’ll eventually want to register components at runtime. That leads naturally to automatic component registration and type-erased component containers. Instead of having your World hardcode positions, velocities, and so on, you store all component sets in a dynamic registry, keyed by TypeId. Components become just data—registered, inserted, and queried via trait objects or reflection. This turns your ECS into a true runtime system, capable of loading new types without recompiling. Of course, this also means giving up some static guarantees, and careful design is required to preserve safety.
Not all of these features are necessary for every use case. You can build a highly capable game with just the sparse set model we’ve implemented. But if you’re curious to push further — and want your ECS to feel more like a small operating system and less like a data container — these are the directions you’ll likely go.
Whether you choose to keep things minimal or keep building, the foundation is now there. You understand the core ideas of ECS, how to implement them idiomatically in Rust, and how to evolve the architecture without compromising performance. That’s a strong place to be, and you’re more than ready to build real things on top of it.
Fast ECS from Scratch in Rust for Your Game Engine was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Level Up Coding - Medium and was authored by Alexander Korostin
Alexander Korostin | Sciencx (2025-07-11T02:01:27+00:00) Fast ECS from Scratch in Rust for Your Game Engine. Retrieved from https://www.scien.cx/2025/07/11/fast-ecs-from-scratch-in-rust-for-your-game-engine/
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