Ecs Architecture

ECS Architecture (Entity Component System) — Deep Guide

ECS is a data-driven design pattern for game engines that separates entities (IDs), components (pure data), and systems (logic operating on component sets). Its core win: cache-friendly iteration over contiguous component arrays plus implicit parallelism.

1. Core Concepts

• Entity: a unique ID (uint32 index + generation). No data, just identity.
• Component: a plain struct; data only, no behavior.
• System: logic that reads/writes specific component sets, executed each frame (or at defined rate).

struct Transform { Vec3 pos; Quat rot; Vec3 scale; };
struct Velocity  { Vec3 linear; Vec3 angular; };

void moveSystem(Slice<Transform> t, Slice<Velocity> v, float dt) {
    for (i) t[i].pos += v[i].linear * dt;      // two contiguous arrays
}

2. Why ECS: Performance Reasoning

Traditional OOP (GameObject with Actor::Update() virtual call per object) has:

• Vtable indirection → breaks ILP, no inlining.
• Objects scattered in heap → cache misses (each 64-byte cache line may hold wasted data).
• Member layout interleaves hot/cold data.

ECS wins:

• Components stored in contiguous arrays (SoA) → cache streaming.
• Systems are for loops over slices → auto-vectorizable (SIMD).
• Systems with disjoint write sets run in parallel.

3. Storage Strategies

3.1 Sparse-Set (Sparse Array)

dense[]:  component data in entity order, tightly packed
sparse[]: entity id -> index into dense

• Add/remove O(1) with swap-remove.
• Iteration touches only dense[] (contiguous, cache-friendly) + entity ids.
• Used by EnTT default.

3.2 Archetype (Structure of Arrays per group)

Entities grouped by exact component signature (archetype). Each archetype owns a column array per component:

Archetype "Transform+Velocity":
  entityIDs: [12, 17, 9]
  Transform: [t12, t17, t9]   contiguous
  Velocity:  [v12, v17, v9]   contiguous

• Adding/removing a component moves the entity to another archetype (type change).
• Iterating a system = iterate matching archetypes only. Perfect locality.
• Used by Unity DOTS, Flecs, Bevy (design).

3.3 Hybrid / ECS-storage-as-optional

Many engines allow per-entity "prefab-like" dynamic storage. Choose based on:

4. Querying & Matching

A system declares required components: Query<Transform, Velocity>(All).

• Archetype-based: match on bitmask of signature → iterate matching archetypes.
• Sparse-set: iterate dense[], check presence via storage.contains(entity).

Entity IDs filtering: any/maybe-style optional components need a "present bitset" check; optional components can iterate a second storage.

5. System Scheduling

5.1 Ordering & Dependencies

Systems schedule in a DAG respecting read/write on component storages:

TransformSystem <-> PlayerMovement <-> CameraSystem
(Velocity RW)     (Transform RW)     (Transform R)

• Build a graph of per-storage access; parallelize systems whose storages don't conflict.
• After(system), Before(system) ordering; or "this system writes X so those that read X run next".
• Job-based: each system dispatched to worker threads (Bevy ParForEach, Unity ScheduleParallel).

5.2 Per-Frame vs Fixed-Rate

• Simulation systems tied to fixed timestep (30/60/120Hz).
• Rendering interpolation reads sim state with lerp factor.

5.3 Command Buffers / Deferred Structural Changes

Adding/removing components mid-iteration invalidates iterators. Rule: queue structural changes in a command buffer / deferred list applied after iteration. (Unity EntityCommandBuffer, Bevy Commands.)

6. Cross-Cutting Concerns

6.1 Change Detection (Dirty Flags)

• Each storage keeps a per-frame "changed" bitset or version per archetype row.
• Systems like "network send", "recompute bounds", or "update render node" only run on entities whose components changed.
• Bevy Added<T>, Changed<T> filters; Unity SystemAPI.GetComponentLookup.

6.2 Relationships & Query Hierarchies

Parent/child: store optional Parent + Children components. World-space recompute only on dirty leaf.

6.3 Events & Messages

ECS events (collision events, input) as transient entity/component pools — event systems read then delete. Avoid per-frame entity spam; use component-versioned event queues for perf.

7. Serialization / Networking / Replays

• Entities serialized by id + comp list; archetype moves restored on load.
• Network: replicate only changed components using change detection; hide float noise by bit-snapping.
• Replays: log system inputs; deterministic ECS reproduces.

8. Real Engines Comparison

8.1 When NOT to use ECS

• Tiny simple games: GameObject/MonoBehaviour faster to prototype.
• UI, narrative systems, inventory logic with heavy dynamic mixing.
• Deep polymorphic AI with per-entity context → still can be ECS but readability drops.
• If the team lacks data-oriented mindset, ECS can hurt velocity.

9. ECS Anti-Patterns

10. ECS Decision Tree

flowchart TD
    A{Per-frame hot? Many entities?} -->|Yes| B{Change component set often?}
    A -->|No| C[Classic OOP ok]
    B -->|Yes| D[Sparse-set storage]
    B -->|No| E[Archetype storage]
    E --> F{SIMD + safety via language?}
    F -->|Rust| G[Bevy]
    F -->|C#| H[Unity DOTS]
    F -->|C++| I[EnTT / Flecs]
    D --> J{Simplicity? Language?}
    J -->|C++| I

11. Deep Dive References

Deep storage internals, scheduling, and engine-specific patterns live in:

• skills/game/game-engine/ecs-pattern/SKILL.md (full deep dive: archetypes, scheduling, SIMD, benchmarks)
• skills/game/game-engine/ecs-pattern/references/archetype-storage-deep-dive.md
• skills/game/game-engine/ecs-pattern/references/system-scheduling-pipelines.md
• skills/game/game-engine/ecs-pattern/references/unity-dots-in-practice.md
• skills/game/game-engine/ecs-pattern/references/bevy-ecs-patterns.md
• skills/game/game-engine/ecs-pattern/references/entt-and-flecs.md

12. Getting Started Checklist

1. Model components as structs; split hot vs cold data.
2. Choose storage (sparse-set for churn, archetype for bulk).
3. Define systems per frame; build dependency graph.
4. Implement command buffer for structural changes.
5. Add change-detection for dirty-driven systems.
6. Measure: iteration throughput, cache misses, parallel speedup.