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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 astorage 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.
Table des matières
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.
A propos de l'auteur
Joy Arulraj is an Assistant Professor of Database Systems in the School of Computer Scienceat 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 DatabaseGroup 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.
