EncoderForge: Generating Efficient SQL for Encoders in Machine Learning Inference Pipelines
Summary: EncoderForge translates ML encoders into optimizer-friendly joins rather than poorly optimized CASE expressions. Its plan selector explores mixed join/CASE alternatives via dynamic and priority-based search, delivering up to 10× faster SQL across databases. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Qingfeng Pan (East China Normal University)
- 2. Qingyuan Jing (East China Normal University)
- 3. Chenyang Zhang (East China Normal University)
- 4. Jiahe Zhi (East China Normal University)
- 5. Chen Xu (East China Normal University)
- 6. Heng Long (PingCAP)
BibTeX Citation
@inproceedings{pan_sigmod26,
title = {{EncoderForge: Generating Efficient SQL for Encoders in Machine Learning Inference Pipelines}},
author = {Pan, Qingfeng and Jing, Qingyuan and Zhang, Chenyang and Zhi, Jiahe and Xu, Chen and Long, Heng},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802050},
url = {https://dl.acm.org/doi/10.1145/3802050},
year = {2026}
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