Entropy-Learned Hashing: Constant Time Hashing with Controllable Uniformity

Hashing is a widely used technique for creating uniformly random numbers from arbitrary data. This is required in a large range of core data-driven operations including indexing, partitioning, filters, and sketches. As such, hashing is a core component in numerous systems including relational data systems, key-value stores, compilers, and networks. Due to both the computational and data heavy nature of hashing, it is a core systems bottleneck. For example, a typical database query in the standard TPC-H benchmark may spend 50% of its total cost in hash tables. Similarly, Google spends at least 2% of its total computational cost on C++ hash tables, resulting in a massive yearly cost footprint just from one hashing operation.