Counting, enumerating, and sampling of execution plans in a cost-based query optimizer
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Toward a progress indicator for database queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Estimating progress of execution for SQL queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Reducing energy consumption of queries in memory-resident database systems
Proceedings of the 2004 international conference on Compilers, architecture, and synthesis for embedded systems
Analyzing plan diagrams of database query optimizers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A performance-conserving approach for reducing peak power consumption in server systems
Proceedings of the 19th annual international conference on Supercomputing
JouleSort: a balanced energy-efficiency benchmark
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Parametric query optimization for linear and piecewise linear cost functions
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Why you should run TPC-DS: a workload analysis
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Database servers tailored to improve energy efficiency
SETMDM '08 Proceedings of the 2008 EDBT workshop on Software engineering for tailor-made data management
Proceedings of the VLDB Endowment
The Claremont report on database research
ACM SIGMOD Record
Energy-efficient server clusters
PACS'02 Proceedings of the 2nd international conference on Power-aware computer systems
Analyzing the energy efficiency of a database server
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
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Database engines often consume significant power during query processing activities, motivating researchers to investigate the redesign of their internals to minimize these overheads. While the prior literature has dealt exclusively with average power considerations, our focus here is on peak power consumption. We begin by profiling the peak power behavior of a representative suite of popular commercial database engines in benchmark query processing environments, and demonstrate that their consumption can often be substantial. Then, we develop a pipeline-based model of query execution plans that lends itself to accurately estimating peak power consumption, suggesting its gainful employment in server design and capacity planning. More potently, given a space of competing plan choices, it could help identify plans with attractive tradeoffs between peak-power and time-efficiency considerations, and we present sample instances of such tradeoffs. Finally, we discuss extensions of our modeling approach to inductive pipelines and multi-query workloads.