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arXiv · 1708.08435

Analyzing Query Performance and Attributing Blame for Contentions in a Cluster Computing Framework

Prajakta Kalmegh·Shivnath Babu·Sudeepa Roy
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Abstract

There are many approaches is use today to either prevent or minimize the impact of inter-query interactions on a shared cluster. Despite these measures, performance issues due to concurrent executions of mixed workloads still prevail causing undue waiting times for queries. Analyzing these resource interferences is thus critical in order to answer time sensitive questions like 'who is causing my query to slowdown' in a multi-tenant environment. More importantly, dignosing whether the slowdown of a query is a result of resource contentions caused by other queries or some other external factor can help an admin narrow down the many possibilities of performance degradation. This process of investigating the symptoms of resource contentions and attributing blame to concurrent queries is non-trivial and tedious, and involves hours of manually debugging through a cycle of query interactions. In this paper, we present ProtoXplore - a Proto or first system to eXplore contentions, that helps administrators determine whether the blame for resource bottlenecks can be attributed to concurrent queries, and uses a methodology called Resource Acquire Time Penalty (RATP) to quantify this blame towards contentious sources accurately. Further, ProtoXplore builds on the theory of explanations and enables a step-wise deep exploration of various levels of performance bottlenecks faced by a query during its execution using a multi-level directed acyclic graph called ProtoGraph. Our experimental evaluation uses ProtoXplore to analyze the interactions between TPC-DS queries on Apache Spark to show how ProtoXplore provides explanations that help in diagnosing contention related issues and better managing a changing mixed workload in a shared cluster.

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Prajakta Kalmegh, Shivnath Babu, Sudeepa Roy. 2017-08-28. Analyzing Query Performance and Attributing Blame for Contentions in a Cluster Computing Framework. https://arxiv.org/abs/1708.08435

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