SwiftCollections.FixedMathSharp 4.0.0

SwiftCollections

SwiftCollections is a high-performance collection library for performance-sensitive .NET workloads, including game systems, simulations, and spatial queries.

🛠️ Key Features

• Optimized for Performance: Designed for low time complexity and minimal memory allocations.
• Framework Agnostic : Works with .NET, Unity, and other game engines.
• Full Serialization Support: Out-of-the-box round-trip serialization via MemoryPack across most core collections, with System.Text.Json constructor support on .NET 8+.
• Fast core collections: SwiftDictionary, SwiftHashSet, SwiftList, SwiftQueue, SwiftStack, SwiftSortedList
• Specialized containers: SwiftBucket, SwiftGenerationalBucket, SwiftPackedSet, SwiftSparseMap, SwiftBiDictionary
• Flat 2D/3D storage: SwiftArray2D, SwiftArray3D, SwiftBoolArray2D, SwiftShortArray2D
• Pools: SwiftObjectPool, SwiftArrayPool, SwiftCollectionPool, and typed pool helpers
• Observable collections for change-tracking scenarios
• Spatial queries via typed SwiftBVH<TKey, TVolume>, SwiftSpatialHash<TKey, TVolume>, and SwiftOctree<TKey, TVolume> plus default numerics wrappers
• Lightweight diagnostics via SwiftCollections.Diagnostics for opt-in low-level log/event routing

🚀 Installation

NuGet

dotnet add package SwiftCollections

NuGet (Fixed-Point Companion)

dotnet add package SwiftCollections.FixedMathSharp

Source

git clone https://github.com/mrdav30/SwiftCollections.git

Then reference src/SwiftCollections/SwiftCollections.csproj or build the package locally.

Unity

Unity support is maintained separately:

SwiftCollections-Unity

🧩 Dependencies

• Core package dependency: MemoryPack
• Optional fixed-point companion: FixedMathSharp via SwiftCollections.FixedMathSharp

📦 Library Overview

Core Data Structures

• SwiftDictionary: A high-performance dictionary optimized for O(1) operations and minimal memory usage.
• SwiftBiDictionary: A bidirectional dictionary for efficient forward and reverse lookups in O(1).
• SwiftHashSet: An optimized set for unique values with fast operations.
• SwiftBucket: High-performance collection for O(1) addition and removal with stable indexing.
• SwiftGenerationalBucket: A bucket variant that tracks generations to prevent stale references.
• SwiftPackedSet: A compact set implementation for dense integer keys.
• SwiftSparseMap: A memory-efficient map for sparse key distributions.
• SwiftQueue: Circular-buffer-based queue for ultra-low-latency operations.
• SwiftList: A dynamic list optimized for speed-critical applications.
• SwiftSortedList: Dynamically sorted collection with O(log n) operations.
• SwiftStack: Fast array-based stack with O(1) operations.
• SwiftArray2D / SwiftArray3D: Efficient, flat-mapped arrays for 2D and 3D data.
• SwiftBVH: Bounding Volume Hierarchy for broad-phase spatial queries.
• SwiftSpatialHash: Spatial hash for high-churn, uniform-size, and sparse huge-world scenes.
• SwiftOctree: Hierarchical octree for dynamic scenes with uneven density.

SwiftDictionary<TKey, TValue> and SwiftHashSet<T> use deterministic default comparers for string keys when no comparer is supplied. object keys also get a SwiftCollections default comparer that hashes strings deterministically, but non-string object-key determinism still depends on the underlying key type. Custom comparers are still supported.

Pools

• SwiftObjectPool: Thread-safe generic object pooling for improved memory usage and performance.
• SwiftArrayPool: Array-specific pool for efficient reuse of arrays.
• SwiftCollectionPool: Pool for reusable collection instances (e.g., List, HashSet).
• Default Collection Pools: Ready-to-use pools are available for SwiftList, SwiftQueue, SwiftHashSet, SwiftDictionary, SwiftStack, SwiftPackedSet, and SwiftSparseMap.

Spatial Data Structures

• SwiftBVH: Bounding Volume Hierarchy for broad-phase queries with mixed or extreme object-size variance.
• SwiftSpatialHash: Spatial hash for sparse huge-world needle queries and uniform-size high-churn workloads.
• SwiftOctree: Hierarchical octree for dynamic scenes, uneven density, and repeated region queries.

Use them by workload:

• SwiftBVH is the best fit for scenes with mixed or extreme object-size variance (e.g. tiny units alongside large terrain pieces), large churning objects, and general broad-phase intersection queries over heterogeneous populations. It is not thread-safe; synchronize access externally if needed. Avoid it for dense same-size clustered scenes and for sparse huge-world needle (tiny query window) lookups.
• SwiftSpatialHash is the best fit for sparse, huge-world scenes where small query windows rarely overlap many cells (O(1) bucket lookup dominates), and for high-frequency updates with mostly uniform-size objects. Performance degrades when object sizes vary widely, since a fixed cell size becomes either too coarse or too fine.
• SwiftOctree is the strongest all-around performer for dynamic scenes with uniform or small objects, mixed broad-phase, and repeated regional queries over uneven distributions. Prefer it when most objects are similar in size or when queries target specific spatial sub-regions repeatedly.

Observable Collections

• SwiftObservableArray / SwiftObservableList / SwiftObservableDictionary: Reactive, observable collections with property and collection change notifications.

Diagnostics

• DiagnosticChannel / DiagnosticEvent / DiagnosticLevel: Lightweight diagnostics primitives for routing informational, warning, or error events without coupling the library to a higher-level logging framework.
• SwiftCollectionDiagnostics.Shared: Ready-to-use shared channel for library-wide diagnostics.

Diagnostics are opt-in and disabled by default until you configure a minimum level and sink.

📖 Usage Examples

SwiftBVH for Spatial Queries

var bvh = new SwiftBVH<int>(100);
var volume = new BoundVolume(new Vector3(0, 0, 0), new Vector3(1, 1, 1));
bvh.Insert(1, volume);

var results = new SwiftList<int>();
bvh.Query(new BoundVolume(new Vector3(0, 0, 0), new Vector3(2, 2, 2)), results);
Console.WriteLine(results.Count); // Output: 1

SwiftBVH with Custom Typed Volumes

var typedBvh = new SwiftBVH<int, BoundVolume>(100);
typedBvh.Insert(1, new BoundVolume(new Vector3(0, 0, 0), new Vector3(1, 1, 1)));

SwiftSpatialHash for Broad-Phase Cell Queries

var spatialHash = new SwiftSpatialHash<int>(64, 2f);
spatialHash.Insert(1, new BoundVolume(new Vector3(0, 0, 0), new Vector3(1, 1, 1)));

var nearby = new List<int>();
spatialHash.QueryNeighborhood(
    new BoundVolume(new Vector3(0, 0, 0), new Vector3(1, 1, 1)),
    nearby);

SwiftOctree for Hierarchical Region Queries

var worldBounds = new BoundVolume(new Vector3(0, 0, 0), new Vector3(64, 64, 64));
var octree = new SwiftOctree<int>(
    worldBounds,
    new SwiftOctreeOptions(maxDepth: 6, nodeCapacity: 8),
    minNodeSize: 1f);

octree.Insert(1, new BoundVolume(new Vector3(2, 2, 2), new Vector3(4, 4, 4)));

var visible = new List<int>();
octree.Query(new BoundVolume(new Vector3(0, 0, 0), new Vector3(8, 8, 8)), visible);

Fixed-Point SwiftBVH (Companion Package)

var fixedBvh = new SwiftFixedBVH<int>(100);
fixedBvh.Insert(1, new FixedBoundVolume(new Vector3d(0, 0, 0), new Vector3d(1, 1, 1)));

SwiftArray2D

var array2D = new SwiftArray2D<int>(10, 10);
array2D[3, 4] = 42;
Console.WriteLine(array2D[3, 4]); // Output: 42

SwiftQueue

var queue = new SwiftQueue<int>(10);
queue.Enqueue(5);
Console.WriteLine(queue.Dequeue()); // Output: 5

Populating Arrays

var array = new int[10].Populate(() => new Random().Next(1, 100));

Diagnostic Example

using System;
using SwiftCollections.Diagnostics;

DiagnosticChannel diagnostics = SwiftCollectionDiagnostics.Shared;
diagnostics.MinimumLevel = DiagnosticLevel.Warning;
diagnostics.Sink = static (in DiagnosticEvent diagnostic) =>
{
    Console.WriteLine($"[{diagnostic.Channel}] {diagnostic.Level}: {diagnostic.Message} ({diagnostic.Source})");
};

diagnostics.Write(DiagnosticLevel.Info, "Skipped because the minimum level is Warning.", "Bootstrap");
diagnostics.Write(DiagnosticLevel.Error, "Pool allocation failed.", "Bootstrap");

🧪 Development

Build the solution:

dotnet build SwiftCollections.sln -c Debug

Run the unit tests:

dotnet test tests/SwiftCollections.Tests/SwiftCollections.Tests.csproj -c Debug --no-build

Run benchmarks:

dotnet run --project tests/SwiftCollections.Benchmarks/SwiftCollections.Benchmarks.csproj -c Release -f net8

Useful benchmark runner commands:

dotnet run --project tests/SwiftCollections.Benchmarks/SwiftCollections.Benchmarks.csproj -c Release -f net8 -- list
dotnet run --project tests/SwiftCollections.Benchmarks/SwiftCollections.Benchmarks.csproj -c Release -f net8 -- dictionary
dotnet run --project tests/SwiftCollections.Benchmarks/SwiftCollections.Benchmarks.csproj -c Release -f net8 -- query --list flat
dotnet run --project tests/SwiftCollections.Benchmarks/SwiftCollections.Benchmarks.csproj -c Release -f net8 -- hashset --filter "*Contains*"
dotnet run --project tests/SwiftCollections.Benchmarks/SwiftCollections.Benchmarks.csproj -c Release -f net8 -- all --list flat

With no extra arguments, BenchmarkDotNet's default switcher behavior is used. Leading non-option arguments are treated as benchmark selection aliases, and any remaining arguments are forwarded to BenchmarkDotNet.

🛠️ Compatibility

• netstandard2.1
• net8.0
• Windows, Linux, and macOS

Fixed-point BVH support is provided by the separate SwiftCollections.FixedMathSharp companion package.

🤝 Contributing

We welcome contributions! Please see our CONTRIBUTING guide for details on how to propose changes, report issues, and interact with the community.

👥 Contributors

• mrdav30 - Lead Developer
• Contributions are welcome! Feel free to submit pull requests or report issues.

💬 Community & Support

For questions, discussions, or general support, join the official Discord community:

👉 Join the Discord Server

For bug reports or feature requests, please open an issue in this repository.

We welcome feedback, contributors, and community discussion across all projects.

📄 License

This project is licensed under the MIT License.

See the following files for details:

• LICENSE – standard MIT license
• NOTICE – additional terms regarding project branding and redistribution
• COPYRIGHT – authorship information