Contact: Andy Pavlo, Joy Arulraj

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. It is built by students and researchers at the Carnegie Mellon Database Research Group. See the people page for the full listing of contributors.

Key Features:



Andy Pavlo
Anthony Tomasic
Todd Mowry


Leon Ang
Gustavo Angulo
Joy Arulraj
Patrick Huang
Hao Jin
Haibin Lin
Jiexi Lin
Lin Ma
Prashanth Menon
Matt Perron
Ian Quah
Siddharth Santurkar
Bili Sun
Skye Toor
Dana Van Aken
Allison Wang
Ziqi Wang
Yingjun Wu
Ran Xian




We thank the members and companies of the PDL Consortium: Alibaba Group, Amazon, Datrium, Facebook, Google, Hewlett Packard Enterprise, Hitachi Ltd., Intel Corporation, IBM, Micron, Microsoft Research, NetApp, Inc., Oracle Corporation, Salesforce, Samsung Semiconductor Inc., Seagate Technology, and Two Sigma for their interest, insights, feedback, and support.




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Last updated 20 April, 2017