Abstract
Real life databases exhibit highly skewed access patterns. These skewed access patterns can be exploited to partition the data considering the query workload. The presented work proposes Workload Aware Hybrid Partitioning (WAHP). WAHP identifies clusters of attributes which are queried together. It identifies workload aware clusters for the actual query workload using a hybrid combination of horizontal and vertical partitioning. The paper demonstrates WAHP experiment using TPC-C benchmark, where 9% of the actual TPC-C data in workload aware clusters, is able to answer 73% of hottest query-workload with an average execution time gain of 37% against original database.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.