Trupti Padiya
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.
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- Workload Aware Hybrid Partitioning
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