Learned indexes use machine learning techniques to improve index construction. However, they often face a fundamental trade-off between performance and memory consumption, especially in dynamic environments with frequent insert and delete operations. This trade-off stems from the construction approaches used in learned indexes: The top-down approach increases performance at the cost of significant memory overhead, while the bottom-up approach focuses on memory efficiency but introduces performance issues due to prediction errors. % A unified solution that simultaneously optimizes performance and memory consumption in dynamic data management scenarios is therefore highly desirable. We propose Hyper, a highly efficient learned index with a novel two-phase hybrid construction approach. Our approach combines bottom-up construction for leaf nodes with top-down construction for inner nodes to achieve an optimal balance between performance and memory consumption. Hyper effectively handles concurrent writes and structure adjustments without sacrificing query performance. We evaluated Hyper on both simple and complex real-world datasets and compared it to seven state-of-the-art learned indexes and several traditional data structures for dynamic workloads. The evaluation results show that Hyper achieves a remarkable performance boost of up to 3.75× with significantly reduced index memory consumption of up to 1610× in the single-thread evaluation. In high concurrency scenarios, Hyper even achieves improvements up to 5.73×, 3.72×, and 3.99× in read-only, read-write, and write-only workloads.

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 and its performance-to-memory ratio is more than two times better than the best implemented alternative strategy for randomized string data sets.