# APEX: A High-Performance Learned Index on Persistent Memory

arXiv: https://arxiv.org/abs/2105.00683 · HTML: https://mlsanity.com/paper/2105.00683/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2021.

## Built on by (most cited first, 6 of 6)

- [A Survey of Learned Indexes for the Multi-dimensional Space](https://mlsanity.com/paper/2403.06456/) (2024)
- [Accelerating String-Key Learned Index Structures via Memoization-based Incremental Training](https://mlsanity.com/paper/2403.11472/) (2024)
- [NFL: Robust Learned Index via Distribution Transformation](https://mlsanity.com/paper/2205.11807/) (2022)
- [A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach](https://mlsanity.com/paper/2502.05001/) (2025)
- [DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees](https://mlsanity.com/paper/2502.05369/) (2025)
- [Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions](https://mlsanity.com/paper/2603.00537/) (2026)

## Abstract (arXiv)

The recently released persistent memory (PM) offers high performance, persistence, and is cheaper than DRAM. This opens up new possibilities for indexes that operate and persist data directly on the memory bus. Recent learned indexes exploit data distribution and have shown great potential for some workloads. However, none support persistence or instant recovery, and existing PM-based indexes typically evolve B+-trees without considering learned indexes. This paper proposes APEX, a new PM-optimized learned index that offers high performance, persistence, concurrency, and instant recovery. APEX is based on ALEX, a state-of-the-art updatable learned index, to combine and adapt the best of past PM optimizations and learned indexes, allowing it to reduce PM accesses while still exploiting machine learning. Our evaluation on Intel DCPMM shows that APEX can perform up to ~15x better than existing PM indexes and can recover from failures in ~42ms.