NDLI: Progressive optimization in a shared-nothing parallel database

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Progressive optimization in a shared-nothing parallel database

Content Provider	ACM Digital Library
Author	Ng, Jack Han, Wook-Shin Kache, Holger Markl, Volker Kandil, Mokhtar
Abstract	Commercial enterprise data warehouses are typically implemented on parallel databases due to the inherent scalability and performance limitation of a serial architecture. Queries used in such large data warehouses can contain complex predicates as well as multiple joins, and the resulting query execution plans generated by the optimizer may be sub-optimal due to mis-estimates of row cardinalities. Progressive optimization (POP) is an approach to detect cardinality estimation errors by monitoring actual cardinalities at run-time and to recover by triggering re-optimization with the actual cardinalities measured. However, the original serial POP solution is based on a serial processing architecture, and the core ideas cannot be readily applied to a parallel shared-nothing environment. Extending the serial POP to a parallel environment is a challenging problem since we need to determine when and how we can trigger re-optimization based on cardinalities collected from multiple independent nodes. In this paper, we present a comprehensive and practical solution to this problem, including several novel voting schemes whether to trigger re-optimization, a mechanism to reuse local intermediate results across nodes as a partitioned materialized view, several flavors of parallel checkpoint operators, and parallel checkpoint processing methods using efficient communication protocols. This solution has been prototyped in a leading commercial parallel DBMS. We have performed extensive experiments using the TPC-H benchmark and a real-world database. Experimental results show that our solution has negligible runtime overhead and accelerates the performance of complex OLAP queries by up to a factor of 22.
Starting Page	809
Ending Page	820
Page Count	12
File Format	PDF
ISBN	9781595936868
DOI	10.1145/1247480.1247569
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-06-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Parallel databases Olap Autonomous computing Query optimization
Content Type	Text
Resource Type	Article

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