⚡ Bolt: Optimize unique random number generation#60
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Implemented a hybrid sampling strategy in random.html to solve the "Coupon Collector's Problem" when generating a high density of unique random numbers. For requests where the count is >50% of the range, the algorithm now uses exclusion-based sampling followed by a Fisher-Yates shuffle, avoiding the exponential collision penalty of rejection sampling. Impact: ~12.4x speedup for 999,990 numbers in a 1,000,000 range (~3.3s down to ~0.26s). Co-authored-by: babelman97 <186798789+babelman97@users.noreply.github.com>
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💡 What: Optimized the unique random number generator in
random.htmlby replacing the simple rejection sampling with a hybrid strategy. It now uses exclusion-based sampling (picking numbers NOT to include) for dense requests (>50% of range).🎯 Why: The original implementation suffered from the "Coupon Collector's Problem," where finding the last few unique numbers in a nearly full set would take an exponentially increasing number of trials, causing significant UI hangs for large, dense requests.
📊 Impact:
🔬 Measurement: Benchmarked using Playwright by measuring
performance.now()around thegenerateRandomNumbers()call for both sparse (1,000/100,000) and dense (999,990/1,000,000) scenarios. Correctness was verified by ensuring the output contained the exact count of unique numbers within the requested range.PR created automatically by Jules for task 2180869317366178159 started by @babelman97