#ecs #bevy-ecs #entity #game-engine #entities #hecs #world

no-std bevy_hecs

Bevy fork of hecs: a fast, minimal, and ergonomic entity-component-system

5 unstable releases

0.3.0 Nov 3, 2020
0.2.1 Sep 20, 2020
0.2.0 Sep 19, 2020
0.1.3 Aug 22, 2020
0.1.0 Aug 10, 2020

#2516 in Game dev

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550 downloads per month

Apache-2.0

140KB
3K SLoC

hecs

Documentation Crates.io License: Apache 2.0

hecs provides a high-performance, minimalist entity-component-system (ECS) world. It is a library, not a framework. In place of an explicit "System" abstraction, a World's entities are easily queried from regular code. Organize your application however you like!

Bevy Fork Information

This is the Bevy project's fork of hecs with changes that accommodate the needs of the Bevy game engine. Some notable changes:

  • Entity indices are now queryable and are not returned in queries by default. This both improves ergonomics and significantly boosts performance in some cases.
  • Expose more interfaces as public so that we can build higher-level apis on top of the core hecs codebase (multithreading, functions-as-systems, world builders, schedules, etc)
  • Change Tracking

Why ECS?

Entity-component-system architecture makes it easy to compose loosely-coupled state and behavior. An ECS world consists of:

  • any number of entities, which represent distinct objects
  • a collection of component data associated with each entity, where each entity has at most one component of any type, and two entities may have different components

That world is then manipulated by systems, each of which accesses all entities having a particular set of component types. Systems implement self-contained behavior like physics (e.g. by accessing "position", "velocity", and "collision" components) or rendering (e.g. by accessing "position" and "sprite" components).

New components and systems can be added to a complex application without interfering with existing logic, making the ECS paradigm well suited to applications where many layers of overlapping behavior will be defined on the same set of objects, particularly if new behaviors will be added in the future. This flexibility sets it apart from traditional approaches based on heterogeneous collections of explicitly defined object types, where implementing new combinations of behaviors (e.g. a vehicle which is also a questgiver) can require far-reaching changes.

Performance

In addition to having excellent composability, the ECS paradigm can also provide exceptional speed and cache locality. hecs internally tracks groups of entities which all have the same components. Each group has a dense, contiguous array for each type of component. When a system accesses all entities with a certain set of components, a fast linear traversal can be made through each group having a superset of those components. This is effectively a columnar database, and has the same benefits: the CPU can accurately predict memory accesses, bypassing unneeded data, maximizing cache use and minimizing latency.

Why Not ECS?

An ECS world is not a be-all end-all data structure. Most games will store significant amounts of state in other structures. For example, many games maintain a spatial index structure (e.g. a tile map or bounding volume hierarchy) used to find entities and obstacles near a certain location for efficient collision detection without searching the entire world.

If you need to search for specific entities using criteria other than the types of their components, consider maintaining a specialized index beside your world, storing Entity handles and whatever other data is necessary. Insert into the index when spawning relevant entities, and include a component with that allows efficiently removing them from the index when despawning.

Other Libraries

hecs would not exist if not for the great work done by others to introduce and develop the ECS paradigm in the Rust ecosystem. In particular:

  • specs played a key role in popularizing ECS in Rust
  • legion reduced boilerplate and improved cache locality with sparse components

hecs builds on these successes by focusing on further simplification, boiling the paradigm down to a minimal, light-weight and ergonomic core, without compromising on performance or flexibility.

Disclaimer

This is not an official Google product (experimental or otherwise), it is just code that happens to be owned by Google.

Dependencies

~0.7–1.4MB
~20K SLoC