Resource allocation for serverless query processing is a challenge. Unfortunately, prior approaches have treated queries as black boxes, thereby missing significant resource optimization opportunities. In this paper, we propose a plan-aware resource allocation approach where the resources are adaptively allocated based on the runtime characteristics of the query plan. We show the savings opportunity from such an allocation scheme over production SCOPE workloads at Microsoft. We present our current implementation of a greedy version that periodically estimates the peak resource for the remaining of the query as the query execution progresses. Our experimental evaluation shows that such an implementation could already save more than 8% resource usage over one of our production virtual clusters. We conclude by opening the discussion on various strategies for plan-aware resource allocation and their implications on the cloud computing stack.<\/p>\n","protected":false},"excerpt":{"rendered":"
Resource allocation for serverless query processing is a challenge. Unfortunately, prior approaches have treated queries as black boxes, thereby missing significant resource optimization opportunities. In this paper, we propose a plan-aware resource allocation approach where the resources are adaptively allocated based on the runtime characteristics of the query plan. We show the savings opportunity from 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