HLL.NET – HyperLogLog for .NET

A fast and memory-efficient implementation of the HyperLogLog algorithm in .NET for approximate cardinality estimation of large data sets.

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🚀 Features

• ⚡ Efficient cardinality estimation with HyperLogLog algorithm
• 🧠 Built-in support for multiple types: string, int, Guid, and more via custom hashers
• 🔧 Easy to extend with your own IHasher implementations for any data type
• 🧪 Optional multiple-run estimation for improved accuracy and reduced variance
• 🧱 Configurable precision (4–16) with built-in validation
• 💼 Fully compatible with .NET Standard for broad platform support

✅ Accuracy Highlights (based on benchmark tests)

• Precision 10: < 3% average error up to 100,000 unique items
• Precision 12: < 1.5% average error up to 100,000 items
• Precision 14: < 0.7% average error at 100,000 cardinality

🔁 Duplicate Safety

Handles duplicate values correctly, estimating only unique cardinality. Example:

• Input: 10,000 identical values
• Estimated: ~10,000 (error: <1%)

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📦 Installation

dotnet add package HLL.NET

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🧑‍💻 Usage

using HLL.NET;

var items = new List<string>();
for (int i = 0; i < 10_000; i++)
    items.Add($"user_{i}");

double estimate = HyperLogLog.EstimateWithMultipleRuns(items, runs: 5, precision: 14);

Console.WriteLine($"Estimated unique count: {estimate:F2}");

For more fine-grained control:

var hll = new HyperLogLog<string>(precision: 14);

hll.Add("apple");
hll.Add("banana");
hll.Add("apple");

double estimate = hll.Estimate();
Console.WriteLine($"Estimated: {estimate:F2}");

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✅ Running Tests

dotnet test

Tests are located in the tests/HLL.NET.Tests/ project and cover various edge cases and expected behaviors.

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🤝 Contributing

PRs and suggestions welcome! Please:

1. Fork the repo
2. Create a feature branch
3. Add tests if needed
4. Submit a PR 🚀

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📄 License

MIT © 2025 — MCUnderground