Hyperion: Building the largest in-memory search tree

Indexes are essential in data management systems to increase the speed of data retrievals. Widespread data structures to provide fast and memory-efficient indexes are prefix tries. Implementations like Judy, ART, or HOT optimize their internal alignments for cache and vector unit efficiency. While these measures usually improve the performance substantially, they can have a negative impact on memory efficiency. In this paper we present Hyperion, a trie-based main-memory key-value store achieving extreme space efficiency. In contrast to other data structures, Hyperion does not depend on CPU vector units, but scans the data structure linearly. Combined with a custom memory allocator, Hyperion accomplishes a remarkable data density while achieving a competitive point query and an exceptional range query performance. Hyperion can significantly reduce the index memory footprint, while being at least two times better concerning the performance to memory ratio compared to the best implemented alternative strategies for randomized string data sets.