NDLI: Self-managing online partitioner for databases (SMOPD): a vertical database partitioning system with a fully automatic online approach

Content Provider	ACM Digital Library
Author	Li, Liangzhe Gruenwald, Le
Abstract	A key factor of measuring database performance is query response time, which is dominated by I/O time. Database partitioning is among techniques that can help users reduce the I/O time significantly. However, how to efficiently partition tables in a database is not an easy problem, especially when we want to have this partitioning task done automatically by the system itself. This paper introduces an algorithm called Self-Managing Online Partitioner for Databases (SMOPD) in vertical partitioning based on closed item sets mining from a query set and system statistic information mined from system statistic views. This algorithm can dynamically monitor the database performance using user-configured parameters and automatically detect the performance trend so that it can decide when to perform a re-partitioning action without feedback from DBAs. This algorithm can free DBAs from the heavy tasks of keeping monitoring the system and struggling against the large statistic tables. The paper also presents the experimental results evaluating the performance of the algorithm using the TPC-H benchmark.
Starting Page	168
Ending Page	173
Page Count	6
File Format	PDF
ISBN	9781450320252
DOI	10.1145/2513591.2513649
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-09
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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Self-managing online partitioner for databases (SMOPD): a vertical database partitioning system with a fully automatic online approach