This pull request introduces an improved version of the Unique method, which aims to significantly enhance the performance of processing large lists. The original implementation relied on a generic Contains function, resulting in suboptimal performance for large datasets. The proposed modification leverages a more efficient approach using a map-based algorithm, delivering notable speed gains.

Benchmark

The benchmark was conducted under controlled conditions with a list size of one million elements (1,000,000). Two different implementations of the Unique method were tested: BenchmarkUniqueImproved and BenchmarkUnique. The updated version, BenchmarkUniqueImproved, utilizes a more efficient map-based approach for duplicate detection, while BenchmarkUnique represents the original implementation with a generic Contains function.

The results showed that the BenchmarkUniqueImproved version outperformed the BenchmarkUnique implementation significantly. The average elapsed time per operation for BenchmarkUniqueImproved was approximately 6.12 milliseconds (6,120,000 nanoseconds), while BenchmarkUnique took significantly longer with an average elapsed time of approximately 1,493.25 milliseconds (1,493,250,000 nanoseconds).

By adopting the optimized map-based algorithm, the updated Unique method demonstrated notable performance improvements, especially for large lists.