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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 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.
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Data Management on New Hardware - 7th International Workshop on Accelerating Data Analysis and Data Management Systems Using Modern Processor and Storage Architectures, ADMS 2016 and 4th International Workshop on In-Memory Data Management and Analytics, IMDM 2016, New Delhi, India, September 1, 2016, Revised Selected Papers (Paperback, 1st ed. 2017)
Spyros Blanas, Rajesh Bordawekar, Tirthankar Lahiri, Justin Levandoski, Andrew Pavlo
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R2,046
Discovery Miles 20 460
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Ships in 10 - 15 working days
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This book contains selected papers from the 7th International
Workshop on Accelerating Analytics and Data Management Systems
Using Modern Processor and Storage Architectures, ADMS 2016, and
the 4th International Workshop on In-Memory Data Management and
Analytics, IMDM 2016, held in New Dehli, India, in September 2016.
The joint Workshops were co-located with VLDB 2016. The 9 papers
presented were carefully reviewed and selected from 18 submissions.
They investigate opportunities in accelerating analytics/data
management systems and workloads (including traditional OLTP, data
warehousing/OLAP, ETL streaming/real-time, business analytics, and
XML/RDF processing) running memory-only environments, using
processors (e.g. commodity and specialized multi-core, GPUs and
FPGAs, storage systems (e.g. storage-class memories like SSDs and
phase-change memory), and hybrid programming models like CUDA,
OpenCL, and Open ACC. The papers also explore the interplay between
overall system design, core algorithms, query optimization
strategies, programming approaches, performance modeling and
evaluation, from the perspective of data management applications.
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In Memory Data Management and Analysis - First and Second International Workshops, IMDM 2013, Riva del Garda, Italy, August 26, 2013, IMDM 2014, Hongzhou, China, September 1, 2014, Revised Selected Papers (Paperback, 2015 ed.)
Arun Jagatheesan, Justin Levandoski, Thomas Neumann, Andrew Pavlo
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R1,742
Discovery Miles 17 420
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed post conference
proceedings of the First and Second International Workshops on In
Memory Data Management and Analysis held in Riva del Garda, Italy,
August 2013 and Hangzhou, China, in September 2014. The 11 revised
full papers were carefully reviewed and selected from 18
submissions and cover topics from main-memory graph analytics
platforms to main-memory OLTP applications.
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