In the last two decades, both researchers and vendors have built advisory tools to assist database administrators in various aspects of system tuning and physical design. Most of this previous work, however, is incomplete because they still require humans to make the final decisions about any changes to the database and are reactionary measures that fix problems after they occur.

What is needed for a truly “self-driving” database management system (DBMS) is a new architecture that is designed for autonomous operation. This is different than earlier attempts because all aspects of the system are controlled by an integrated planning component that not only optimizes the system for the current workload, but also predicts future workload trends so that the system can prepare itself accordingly. With this, the DBMS can support all of the previous tuning techniques without requiring a human to determine the right way and proper time to deploy them. It also enables new optimizations that are important for modern high-performance DBMSs, but which are not possible today because the complexity of managing these systems has surpassed the abilities of human experts.

Peloton is a relational database management system designed for fully autonomous optimization of hybrid workloads.



  • A. Pavlo, G. Angulo, J. Arulraj, H. Lin, J. Lin, L. Ma, P. Menon, T. Mowry, M. Perron, I. Quah, S. Santurkar, A. Tomasic, S. Toor, D. V. Aken, Z. Wang, Y. Wu, R. Xian, and T. Zhang, "Self-Driving Database Management Systems," in CIDR 2017, Conference on Innovative Data Systems Research, 2017. [PDF] [BIBTEX]
      author = {Andrew Pavlo and Gustavo Angulo and Joy Arulraj and Haibin Lin and Jiexi Lin and Lin Ma and Prashanth Menon and Todd Mowry and Matthew Perron and Ian Quah and Siddharth Santurkar and Anthony Tomasic and Skye Toor and Dana Van Aken and Ziqi Wang and Yingjun Wu and Ran Xian and Tieying Zhang},
      title = {Self-Driving Database Management Systems},
      booktitle = {{CIDR} 2017, Conference on Innovative Data Systems Research},
      year = {2017},
      url = {},
  • J. Arulraj, A. Pavlo, and P. Menon, "Bridging the Archipelago Between Row-Stores and Column-Stores for Hybrid Workloads," in Proceedings of the 2016 International Conference on Management of Data, 2016, pp. 583-598. [PDF] [BIBTEX]
      author = {Arulraj, Joy and Pavlo, Andrew and Menon, Prashanth},
      title = {Bridging the Archipelago Between Row-Stores and Column-Stores for Hybrid Workloads},
      booktitle = {Proceedings of the 2016 International Conference on Management of Data},
      series = {SIGMOD '16},
      year = {2016},
      pages = {583--598},
      numpages = {16},
      doi = {10.1145/2882903.2915231},
      url = {},
  • J. Arulraj, M. Perron, and A. Pavlo, "Write-Behind Logging," Proc. VLDB Endow., vol. 10, pp. 337-348, 2016. [PDF] [BIBTEX]
      author = {Arulraj, Joy and Perron, Matthew and Pavlo, Andrew},
      title = {Write-Behind Logging},
      journal = {Proc. VLDB Endow.},
      volume = {10},
      issue = {4},
      month = {December},
      year = {2016},
      pages = {337--348},
      publisher = {VLDB Endowment},
      url = {},
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