# Database Workload Characterization with Query Plan Encoders

arXiv: https://arxiv.org/abs/2105.12287 · HTML: https://mlsanity.com/paper/2105.12287/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 26 May 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [An End-to-End Learning-based Cost Estimator](https://mlsanity.com/paper/1906.02560/) (2019)
- [Automatic Source Code Summarization with Extended Tree-LSTM](https://mlsanity.com/paper/1906.08094/) (2019)
- [BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning](https://mlsanity.com/paper/1710.03439/) (2017)
- [Plan-Structured Deep Neural Network Models for Query Performance Prediction](https://mlsanity.com/paper/1902.00132/) (2019)
- [Improving Tree-LSTM with Tree Attention](https://mlsanity.com/paper/1901.00066/) (2019)

## Built on by (most cited first, 4 of 4)

- [Real-time Workload Pattern Analysis for Large-scale Cloud Databases](https://mlsanity.com/paper/2307.02626/) (2023)
- [ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges](https://mlsanity.com/paper/2308.13641/) (2023)
- [LearnedWMP: Workload Memory Prediction Using Distribution of Query Templates](https://mlsanity.com/paper/2401.12103/) (2024)
- [DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning](https://mlsanity.com/paper/2603.15540/) (2026)

## Abstract (arXiv)

Smart databases are adopting artificial intelligence (AI) technologies to achieve em instance optimality, and in the future, databases will come with prepackaged AI models within their core components. The reason is that every database runs on different workloads, demands specific resources, and settings to achieve optimal performance. It prompts the necessity to understand workloads running in the system along with their features comprehensively, which we dub as workload characterization. To address this workload characterization problem, we propose our query plan encoders that learn essential features and their correlations from query plans. Our pretrained encoders capture the em structural and the em computational performance of queries independently. We show that our pretrained encoders are adaptable to workloads that expedite the transfer learning process. We performed independent assessments of structural encoder and performance encoders with multiple downstream tasks. For the overall evaluation of our query plan encoders, we architect two downstream tasks (i) query latency prediction and (ii) query classification. These tasks show the importance of feature-based workload characterization. We also performed extensive experiments on individual encoders to verify the effectiveness of representation learning and domain adaptability.