# UpLIF: An Updatable Self-Tuning Learned Index Framework

arXiv: https://arxiv.org/abs/2408.04113 · HTML: https://mlsanity.com/paper/2408.04113/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [RadixSpline: A Single-Pass Learned Index](https://mlsanity.com/paper/2004.14541/) (2020)
- [SOSD: A Benchmark for Learned Indexes](https://mlsanity.com/paper/1911.13014/) (2019)
- [CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm](https://mlsanity.com/paper/2103.00858/) (2021)
- [Approximate Denial Constraints](https://mlsanity.com/paper/2005.08540/) (2020)
- [On sampling from data with duplicate records](https://mlsanity.com/paper/2008.10549/) (2020)
- [MetaHive: A Cache-Optimized Metadata Management for Heterogeneous Key-Value Stores](https://mlsanity.com/paper/2407.19090/) (2024)

## Built on by (most cited first, 1 of 1)

- [Sig2Model: A Boosting-Driven Model for Updatable Learned Indexes](https://mlsanity.com/paper/2509.20781/) (2025)

## Abstract (arXiv)

The emergence of learned indexes has caused a paradigm shift in our perception of indexing by considering indexes as predictive models that estimate keys' positions within a data set, resulting in notable improvements in key search efficiency and index size reduction; however, a significant challenge inherent in learned index modeling is its constrained support for update operations, necessitated by the requirement for a fixed distribution of records. Previous studies have proposed various approaches to address this issue with the drawback of high overhead due to multiple model retraining. In this paper, we present UpLIF, an adaptive self-tuning learned index that adjusts the model to accommodate incoming updates, predicts the distribution of updates for performance improvement, and optimizes its index structure using reinforcement learning. We also introduce the concept of balanced model adjustment, which determines the model's inherent properties (i.e. bias and variance), enabling the integration of these factors into the existing index model without the need for retraining with new data. Our comprehensive experiments show that the system surpasses state-of-the-art indexing solutions (both traditional and ML-based), achieving an increase in throughput of up to 3.12 times with 1000 times less memory usage.