Beyond the XOR Trick: Finding Thousands of Missing IDs with Invertible Bloom Filters
2025-07-18
This article introduces Invertible Bloom Filters (IBFs), a data structure that efficiently solves the problem of finding thousands of missing IDs in a massive dataset. Starting with the simple XOR trick, the article progressively explains the workings of IBFs, overcoming the limitations of the traditional XOR trick through partitioning and iterative recovery. IBFs use hashing to partition sets, then iteratively recover the symmetric difference using a 'peeling' algorithm to efficiently find missing elements. A Python implementation is provided for learning and experimentation.
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Invertible Bloom Filters