PGM-index
A learned index structure enabling fast lookups, range searches, and updates on billions of items with minimal space usage.
What is PGM-index?
PGM-index is a learned data structure that provides fast lookup, predecessor, range search, and update operations on sorted arrays containing billions of items. It solves the problem of traditional indexes consuming excessive memory by using orders of magnitude less space while maintaining the same worst-case query time guarantees. The index uses a piecewise geometric model to approximate data distribution and enable efficient queries.
Developers and researchers working with large-scale datasets who need memory-efficient indexing for databases, search systems, or data-intensive applications. Particularly valuable for those implementing custom database engines or optimizing query performance on sorted data.
Developers choose PGM-index because it dramatically reduces memory usage compared to traditional indexes like B-trees while providing provable worst-case performance guarantees. Its header-only C++ implementation makes it easy to integrate, and it offers specialized variants for different use cases including dynamic updates, multidimensional data, and compressed storage.
Overview
🏅State-of-the-art learned data structure that enables fast lookup, predecessor, range searches and updates in arrays of billions of items using orders of magnitude less space than traditional indexes
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