# Baihe: SysML Framework for AI-driven Databases

arXiv: https://arxiv.org/abs/2112.14460 · HTML: https://mlsanity.com/paper/2112.14460/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 29 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Deep Unsupervised Cardinality Estimation](https://mlsanity.com/paper/1905.04278/) (2019)
- [Learning Multi-dimensional Indexes](https://mlsanity.com/paper/1912.01668/) (2019)
- [Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation](https://mlsanity.com/paper/2109.05877/) (2021)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation](https://mlsanity.com/paper/2011.09022/) (2020)
- [DeepDB: Learn from Data, not from Queries!](https://mlsanity.com/paper/1909.00607/) (2019)
- [Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings](https://mlsanity.com/paper/2002.12393/) (2020)
- [BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation](https://mlsanity.com/paper/2012.14743/) (2020)
- [A Unified Transferable Model for ML-Enhanced DBMS](https://mlsanity.com/paper/2105.02418/) (2021)
- [A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning](https://mlsanity.com/paper/1901.10183/) (2019)
- [Multi-Attribute Selectivity Estimation Using Deep Learning](https://mlsanity.com/paper/1903.09999/) (2019)
- [No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees](https://mlsanity.com/paper/2108.10130/) (2021)
- [The Case for Learned In-Memory Joins](https://mlsanity.com/paper/2111.08824/) (2021)

## Abstract (arXiv)

We present Baihe, a SysML Framework for AI-driven Databases. Using Baihe, an existing relational database system may be retrofitted to use learned components for query optimization or other common tasks, such as e.g. learned structure for indexing. To ensure the practicality and real world applicability of Baihe, its high level architecture is based on the following requirements: separation from the core system, minimal third party dependencies, Robustness, stability and fault tolerance, as well as stability and configurability. Based on the high level architecture, we then describe a concrete implementation of Baihe for PostgreSQL and present example use cases for learned query optimizers. To serve both practitioners, as well as researchers in the DB and AI4DB community Baihe for PostgreSQL will be released under open source license.