# LEA: A Learned Encoding Advisor for Column Stores

arXiv: https://arxiv.org/abs/2105.08830 · HTML: https://mlsanity.com/paper/2105.08830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2021.

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

- [Morphing-based Compression for Data-centric ML Pipelines](https://mlsanity.com/paper/2504.11067/) (2025)

## Abstract (arXiv)

Data warehouses organize data in a columnar format to enable faster scans and better compression. Modern systems offer a variety of column encodings that can reduce storage footprint and improve query performance. Selecting a good encoding scheme for a particular column is an optimization problem that depends on the data, the query workload, and the underlying hardware. We introduce Learned Encoding Advisor (LEA), a learned approach to column encoding selection. LEA is trained on synthetic datasets with various distributions on the target system. Once trained, LEA uses sample data and statistics (such as cardinality) from the user's database to predict the optimal column encodings. LEA can optimize for encoded size, query performance, or a combination of the two. Compared to the heuristic-based encoding advisor of a commercial column store on TPC-H, LEA achieves 19% lower query latency while using 26% less space.