How do you design a cache-efficient ECS for web games?

Long Answer

A production-grade ECS for a browser game must keep iteration fast, memory local, and logic flexible. My approach combines archetype chunks (for cache locality) with data-oriented APIs (for hot loops), plus rigorous profiling to target real bottlenecks.

1) Data model: entities, components, archetypes

• Entities are integer IDs from a free-list.
  
• Components are plain data, no methods. Each component type owns SoA columns (e.g., position.x[], position.y[]).
  
• Archetypes group entities by exact component set; each archetype stores chunks (e.g., 128 entities) with columns per component. Adding/removing components moves an entity between archetypes by mem-copy of its row index; a sparse-set maps entity → (archetype, row) in O(1).
  

2) Queries and scheduling

Systems declare bitset signatures (required/optional/excluded components). Matching archetypes are discovered once, then cached. Each frame, a system iterates archetype chunks with contiguous column access (SoA), avoiding pointer chasing. A scheduler orders systems by data hazards (read/write sets) and optionally runs independent systems in parallel (Web Workers) when the graph permits.

3) Memory layout & mutation

• SoA columns maximize SIMD/CPU/GPU cache hits during tight numeric loops (movement, physics integration).
  
• Chunk size tuned to CPU cache (e.g., 32–128 rows); columns are typed arrays (Float32Array, Int16Array) to avoid GC churn.
  
• Add/remove uses swap-remove within a chunk; update the sparse map. Structural changes are buffered and applied at frame fences to keep iterators stable.
  

4) Time management and determinism

Use a fixed update (e.g., 60 Hz) for simulation and physics; render uses variable refresh with interpolation between last/next simulation snapshots. This stabilizes collisions and AI. Where determinism matters (replays, multiplayer), quantize floats (e.g., fixed-point or Float32 with clamped ops) and seed PRNG per tick.

5) Physics integration

Split physics into broad-phase → narrow-phase → resolution.

• Broad-phase: uniform grids or sweep-and-prune (axis-aligned intervals), updated incrementally. Partition per chunk to keep positions/ AABBs local.
  
• Narrow-phase: vectorized SAT/GJK for select pairs; reuse contact manifolds; cap iterations per island.
  
• Resolution: accumulate impulses; warm-start using cached lambdas; sleep islands below thresholds.
  

6) Rendering boundaries

ECS feeds render data through a read-only view (transform, sprite, material) to OffscreenCanvas or main thread. Avoid sharing mutable ECS references with rendering; publish a compact snapshot (indices into texture atlases, matrix floats) per frame. This reduces locks and data races if render runs in a Worker.

7) Profiling methodology

• Macro: performance.measure() around update, physics, AI, render; log to a ring-buffer with frame index.
  
• Micro: per-system timers; count entities processed and bytes touched.
  
• Flamegraphs (Performance panel) reveal deopts and monomorphism issues; check INP/jank and long tasks >50ms.
  
• Memory: sample typed-array sizes, GC pauses, growth of archetype chunks.
  
• Physics: counters for pair counts per stage, island sizes, iterations, early-out rates.
  

8) Fixing hotspots

• Data fixes: align SoA columns, remove sparse branches in inner loops, hoist invariants, precompute lookup tables.
  
• Algorithmic: better broad-phase (bin sizes tuned to velocity), frame-to-frame coherence to avoid rebuilding pairs, event culling (e.g., skip stationary actors).
  
• Concurrency: run broad-phase or AI in Web Workers; use Atomics.wait/notify for barriers; deep math in WASM (e.g., narrow-phase kernels).
  
• Memory: object pools for transient contacts, command buffers for structural changes, arena allocators for per-frame temporaries.
  
• JS engine: keep hot arrays monomorphic; avoid mixing number/object types; don’t capture megascopes in closures in hot paths.
  

9) Flexibility without cost

• Use tag components (empty) for fast filtering.
  
• Provide commands (addComponent, removeComponent, spawn) that enqueue structural changes to apply at frame end; expose views for read-only iteration to discourage random writes.
  
• Allow scripted systems (game logic) to run atop data-oriented primitives: batch event streams (collisions, inputs) rather than per-entity callbacks.
  

10) Testing and tooling

• Unit tests on sparse-set integrity, move semantics, and archetype transitions.
  
• Golden benchmarks (N entities) run in CI to prevent regressions.
  
• Debug visualizers (grid cells, contact pairs, sleeping islands) toggled via dev overlay.
  

This ECS favors cache locality and predictable loops while remaining flexible. With disciplined profiling and targeted fixes, you ship smooth, scalable browser games.

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Table

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Common Mistakes

• Storing components as objects of arrays (AoS) with scattered memory; inner loops thrash caches.
  
• Frequent structural edits mid-iteration; invalidates iterators and causes hidden O(n) moves.
  
• Rebuilding broad-phase each frame instead of exploiting coherence.
  
• Branchy per-entity callbacks; no batched data paths.
  
• Variable timestep physics coupled to render; tunneling and non-determinism explode.
  
• Mixing types in hot arrays (numbers/objects), triggering deopts.
  
• Over-allocating temporary objects (contacts, events) causing GC spikes.
  
• High-cardinality logs in hot loops; instrumentation itself becomes the bottleneck.
  
• Parallelizing without considering write hazards; data races and heisenbugs.
  
• Ignoring counters; “optimize” the wrong code because profiling wasn’t systematic.
  
  

Sample Answers

Junior:
“I’d use an ECS with SoA typed arrays so systems iterate quickly. A fixed timestep runs updates; render interpolates. I’d profile with the Performance panel, wrap systems with timers, and look for long tasks. If physics is slow, I’d add a grid to reduce collision checks.”

Mid:
“I group entities by archetype and store components in chunked SoA. Systems declare component signatures; queries iterate contiguous columns. Structural changes are queued and applied at frame end. I run physics on a fixed tick and profile pair counts and iterations; broad-phase uses sweep-and-prune. Object pools and typed arrays reduce GC.”

Senior:
“Data-oriented ECS with sparse-set indexing, archetype chunks sized to L1/L2, and SoA columns. Scheduler orders systems by read/write sets; some stages offload to Workers or WASM. Physics uses coherent SAP + island solvers with warm-starts. Profiling combines macro timers, flamegraphs, and per-stage counters (pairs, bytes touched). Hot loops are branch-free, monomorphic, and SIMD-friendly.”

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Evaluation Criteria

• Architecture: Uses SoA + archetypes/chunks and sparse-set indexing; understands signatures/queries.
  
• Simulation: Fixed timestep with render interpolation; clear separation of update/physics/render.
  
• Physics: Sound broad-phase choice (grid/SAP), coherent updates, capped iterations, sleeping islands.
  
• Profiling: Concrete plan (Performance API, flamegraphs, counters, memory) and interprets results.
  
• Optimization: Data-oriented loops, pooling, reduced branches, typed arrays, WASM/Workers where justified.
  
• Correctness: Buffered structural changes; hazard-aware scheduling; determinism considerations.
  
• Tooling: Dev overlays, CI benchmarks, invariants on indices.
  Red flags: Opaque OOP objects everywhere, AoS layouts, variable-timestep physics, premature micro-opts without profiling, parallelism sans data hazards.
  
  

Preparation Tips

• Implement a tiny ECS twice: AoS vs SoA; benchmark 100k entities moving & colliding.
  
• Build a sparse-set + archetype prototype; practice swap-remove and buffered structural edits.
  
• Add fixed-timestep loop with interpolation; visualize jitter differences.
  
• Prototype two broad-phases (grid vs SAP); log pair counts and choose by workload.
  
• Write per-system timers and counters (entities processed, bytes touched); export to a HUD.
  
• Hunt deopts: keep arrays monomorphic, avoid hidden classes in hot code.
  
• Pool contacts/events; use typed arrays and per-frame arenas.
  
• Try Web Worker offload of broad-phase and WASM narrow-phase; measure win.
  
• Create CI perf tests (golden fps/cpu) to catch regressions after refactors.
  
• Rehearse a 60-sec summary: “SoA chunks + fixed tick + profiled physics pipeline → stable FPS.”
  
  

Real-world Context

Top-down shooter: Switched from AoS objects to chunked SoA; movement system time fell 55%. Added uniform grid; collision pairs dropped 8×, eliminating 20ms spikes.
Racer: Variable-timestep physics caused drift. Fixed tick + interpolation stabilized steering; sleeping islands halved CPU on straightaways.
City sim: Broad-phase in a Worker reduced main-thread jank; narrow-phase in WASM saved 30% CPU. Counters showed pairs flat despite population growth due to hierarchical grids.
Platformer: GC frames traced to contact allocations. A small pool + arena stopped pauses; FPS stabilized at 60.
Tooling: A dev HUD charting entities/s, pairs, and ms/system made regressions visible within a day; a PR that broke archetype swap-remove was caught by CI benches.

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Key Takeaways

• Favor SoA + archetype chunks with sparse-set indexing for cache locality.
  
• Run a fixed-timestep simulation; render interpolates for smoothness.
  
• Tame physics with broad-phase coherence and bounded solvers.
  
• Profile with timers, flamegraphs, and counters; optimize the hot data path.
  
• Use Workers/WASM, pools, and typed arrays to crush jank and GC.
  
  

Practice Exercise

Scenario:
You’re building a browser arena-brawler with 2k entities (players, bots, projectiles, pickups). CPU spikes appear during swarms and explosions. Design an ECS + profiling plan and ship fixes that hold 60fps on mid-range laptops.

Tasks:

1. ECS core: Implement entities as integers with a sparse-set and archetype chunks (128 rows). Components (Position, Velocity, AABB, Sprite, Health) are typed-array SoA columns. Provide a command buffer for add/remove to apply at frame end.
   
2. Systems: Movement (Position+=Velocity*dt), Decay (lifetimes), Health/Death, Rendering snapshot, and Physics split into Broad-phase (uniform grid or SAP) and Narrow-phase (AABB checks).
   
3. Loop: Fixed 60 Hz update; render interpolates Position_prev→Position_next. Cap physics iterations; sleep entities with low velocity.
   
4. Profiling: Add performance.mark() around each system; HUD shows per-system ms, entity counts, collision pairs, GC pauses. Record flamegraphs on spikes.
   
5. Optimizations:
   
   • Tune chunk size; ensure tight column access.
     
   • Switch from AoS to SoA if not already; remove branches in inner loops.
     
   • Introduce grid cell sizes ~ average AABB; keep moving entities coherent between frames.
     
   • Pool contact/event objects; allocate per-frame arenas.
     
   • Optional: move broad-phase to a Web Worker; compile narrow-phase to WASM; render via OffscreenCanvas.
     
6. Validation: CI perf test spawns 2k entities and 5 explosions; assert p95 frame <16.6ms and no long tasks >50ms. Provide before/after charts.
   
   

Deliverable:
A repo demonstrating a cache-efficient ECS, a profiled physics pipeline, and concrete optimizations that remove spikes—documented with metrics, flamegraphs, and a dev HUD proving stable 60fps.