End-to-end Optimization of Machine Learning Prediction Queries

Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, cleaning, featurizing the datasets) and a machine learning (ML) part invoking one or more trained models to perform predictions. These parts have so far been optimized in isolation, leaving significant opportunities for optimization unexplored. We present Raven, a production-ready system for optimizing prediction queries. Raven follows the enterprise architectural trend of collocating data and ML runtimes. It relies on a unified intermediate representation that captures both data and ML operators in a single graph structure to unlock two families of optimizations. First, it employs logical optimizations that pass information between the data part (and the properties of the underlying data) and the ML part to optimize each other. Second, it introduces logical-to-physical transformations that allow operators to be executed on different run-times (relational, ML, and DNN) and hardware (CPU, GPU). Novel data-driven optimizations determine the runtime to be used for each part of the query to achieve optimal performance. Our evaluation shows that Raven is able to improve performance of prediction queries on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. Finally, for complex models where GPU acceleration is beneficial, Raven provides up to 8× speedup compared to state-of-the-art systems.

Original languageEnglish
Title of host publicationSIGMOD 2022 - Proceedings of the 2022 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Number of pages15
ISBN (Electronic)9781450392495
Publication statusPublished - 2022 Jun 10
Event2022 ACM SIGMOD International Conference on the Management of Data, SIGMOD 2022 - Virtual, Online, United States
Duration: 2022 Jun 122022 Jun 17

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Conference2022 ACM SIGMOD International Conference on the Management of Data, SIGMOD 2022
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2022 ACM.

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems


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