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About

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:

  • Postgres wire-protocol and JDBC compatible.
  • Native support for byte-addressable non-volatile memory (NVM) storage technology.
  • Lock-free multi-version concurrency control.
  • Just-in-time query compilation using the LLVM.
  • Integrated artificial intelligence components that enable autonomous optimizations.
  • High-performance, lock-free Bw-Tree for indexing.
  • 100% Open-Source (Apache Software License v2.0)

Additional information about the DBMS architecture is covered in Carnegie Mellon University’s 15-721 – Advanced Database Systems course.

Acknowledgements:

This project supported (in part) by Intel Labs, Google, Amazon, Samsung Research, Alfred P. Sloan Research Fellowship, and the U.S. National Science Foundation (CCF-1438955, IIS-1718582, SPX-1822933, IIS-1846158).