Non-Volatile Memory Database Management Systems

Erscheinungsjahr 2019
ISBN: 978-1-68173-484-2
Verlag: Morgan & Claypool Publishers

Buch, Englisch, 191 Seiten, Paperback, Format (B × H): 190 mm x 235 mm

Reihe: Synthesis Lectures on Data Management

This book explores the implications of non-volatile memory (NVM) for database management systems (DBMSs). The advent of NVM will fundamentally change the dichotomy between volatile memory and durable storage in DBMSs. These new NVM devices are almost as fast as volatile memory, but all writes to them are persistent even after power loss. Existing DBMSs are unable to take full advantage of this technology because their internal architectures are predicated on the assumption that memory is volatile. With NVM, many of the components of legacy DBMSs are unnecessary and will degrade the performance of data-intensive applications.

We present the design and implementation of DBMS architectures that are explicitly tailored for NVM. The book focuses on three aspects of a DBMS: (1) logging and recovery, (2) storage and buffer management, and (3) indexing. First, we present a logging and recovery protocol that enables the DBMS to support near-instantaneous recovery. Second, we propose a storage engine architecture and buffer management policy that leverages the durability and byte-addressability properties of NVM to reduce data duplication and data migration. Third, the book presents the design of a range index tailored for NVM that is latch-free yet simple to implement. All together, the work described in this book illustrates that rethinking the fundamental algorithms and data structures employed in a DBMS for NVM improves performance and availability, reduces operational cost, and simplifies software development.

Autoren/Hrsg.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval

Inhaltsverzeichnis

- Acknowledgments
- Introduction
- The Case for a NVM-Oriented DBMS
- Storage Management
- Logging and Recovery
- Buffer Management
- Indexing
- Related Work
- Future Work
- Conclusion
- Bibliography
- Authors' Biographies

Über Autor(innen)

Joy Arulraj is an Assistant Professor of Database Systems in the School of Computer Science at Georgia Institute of Technology. His doctoral research focused on the design and implementation of non-volatile memory database management systems. He is a member of the Database Group and the Center for Experimental Research in Computer Systems at Georgia Tech. His work is also in collaboration with the Intel Science and Technology Center for Big Data, Microsoft Research, and Samsung Research.

Andrew Pavlo is an Assistant Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. At CMU, he is a member of the Database Group and the Parallel Data Laboratory. His work is also in collaboration with the Intel Science and Technology Center for Big Data.

H. V. Jagadish is Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science, and Distinguished Scientist at the Institute for Data Science, at the University of Michigan in Ann Arbor. Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ. Professor Jagadish is well known for his broad-ranging research on information management, and has approximately 200 major papers and 37 patents. He is a fellow of the ACM, ""The First Society in Computing,"" (since 2003) and serves on the board of the Computing Research Association (since 2009). He has been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014). Among his many awards, he won the ACM SIGMOD Contributions Award in 2013 and the David E Liddle Research Excellence Award (at the University of Michigan) in 2008.