Companies providing cloud-scale data services have increasing needs to store and analyze massive data sets. For cost and performance reasons, processing is typically done on large clusters of tens of thousands of commodity machines. Developers use high-level scripting languages that simplify understanding various system trade-offs, but introduce new challenges for query optimization. One key optimization challenge is missing accurate data statistics, typically due to massive data volumes and their distributed nature, complex computation logic, and frequent usage of user-defined functions. In this paper we describe a technique to optimize a class of jobs that are recurring over time in a cloud-scale computation environment. By leveraging information gathered during previous executions we are able to obtain accurate statistics for new instances of recurring jobs, resulting in better execution plans. Experiments on a large-scale production system show that our techniques significantly improve cluster utilization.<\/p>\n","protected":false},"excerpt":{"rendered":"
Companies providing cloud-scale data services have increasing needs to store and analyze massive data sets. For cost and performance reasons, processing is typically done on large clusters of tens of thousands of commodity machines. Developers use high-level scripting languages that simplify understanding various system trade-offs, but introduce new challenges for query optimization. One key optimization 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