On Self-Designing Learned Indexes

Learned indexes show promising performance compared with traditional indexes. However, most existing learned index structures rely on fixed heuristics, which limit their robustness under dynamic workloads. In this paper, we present SELIX, a self-designing learned index that automatically generates and optimizes index structures tailored to different workloads. We introduce a unified index template that allows different nodes to adopt structural configurations, node layouts, conflict resolution policies, and search strategies for flexible and fine-grained structural composition. On top of this template, we formulate structural optimization as a learnable function that maps workload states to index configurations, enabling automated search for optimal structures in the vast design space. We train this function using a deep reinforcement learning paradigm enhanced with meta-learning, and propose a dedicated online update strategy, which together enable fast and stable structural adaptation under workload drift. Experimental results show that SELIX consistently outperforms state-of-the-art learned indexes (ALEX, LIPP, FITing-Tree, PGM, XIndex, and FINEdex) in both in-memory and on-disk settings, achieving significantly higher throughput across diverse and dynamic workloads.