Simultaneous Pipelining in QPipe: Exploiting Work Sharing Opportunities Across Queries

Authors:
Kun Gao;Stavros Harizopoulos;Ippokratis Pandis;Vladislav Shkapenyuk;Anastassia Ailamaki
Affiliations:
Carnegie Mellon University;MIT CSAIL;Carnegie Mellon University;Rutgers University;Carnegie Mellon University
Venue:
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Year:
2006

Citing 0
Cited 5

Adaptive query processing

Foundations and Trends in Databases
Context-aware prefetching at the storage server

ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
Cost-Based Vectorization of Instance-Based Integration Processes

ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
Cost-based vectorization of instance-based integration processes

Information Systems
A data-oriented transaction execution engine and supporting tools

Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

Quantified Score

Hi-index	0.01

Visualization

Abstract

Data warehousing and scientific database applications operate on massive datasets and are characterized by complex queries accessing large portions of the database. Concurrent queries often exhibit high data and computation overlap, e.g., they access the same relations on disk, compute similar aggregates, or share intermediate results. Unfortunately, run-time sharing in modern database engines is limited by the paradigm of invoking an independent set of operator instances per query, potentially missing sharing opportunities if the buffer pool evicts data early.