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Real-time analytical systems need to handle workloads comprised of expensive decision support queries with diverse quality of service requirements known as contracts. Contract driven multi-query processing, being an NP-hard problem, remains unaddressed to date. The traditional approach of blindly pipelining the entire input through a shared execution plan is not viable due to the diversity in query contracts. To tackle this challenge, we now develop a flexible model to express contracts and accompany it with an effective means to measure the run-time contract satisfaction. We propose our Contract-Aware Query Execution framework CAQE. In this work, we exploit the principle that “different portions of the input contribute to disparate subsets of queries with varying degrees of progressiveness.” Therefore, CAQE’s processing of the input chunks is driven by how the different query contracts are being met at run-time. To maximize the contract satisfaction of the workload, CAQE leverages the dependencies of input chunks across the queries. This enables us to determine the impact of processing particular input chunks on improving the run-time contract satisfaction. Our experiments demonstrate the effectiveness of CAQE in increasing the overall contract satisfaction of the workload, specifically 2 fold better than existing multi-query processing techniques.

CAQE: A Contract Driven Approach to Processing Concurrent Decision Support Queries