Resource-efficient Shared Query Execution via Exploiting Time Slackness

Abstract

Shared query execution can reduce resource consumption by sharing common sub-expressions across concurrent queries. We show that this is not always the case when regularly querying a dataset under change. Depending on latency goals, how eagerly to incrementally process the new data differs. Naively sharing the execution of queries with different latency goals will push the whole shared plan to meet the lowest latency goal and execute more eagerly than each participating query. The overhead introduced by the eager execution can even offset the benefit of shared query execution. We propose an optimization framework iShare to exploit the benefit of shared execution and avoid the overhead of eager execution. iShare judiciously shares queries with different latency goals and selectively executes parts of the share plan lazily. iShare can significantly reduce resource consumption compared to eagerly executing share plans from the state-of-the-art multi-query optimizer or approaches that execute queries separately.