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A data analytics application submitted to a Spark cluster often has to finish executing by a specified time target. To use cluster resources effectively, the key challenge is having the ability to gain quick insights on how the execution time of any given application is likely to be impacted by the resources allocated to the application, e.g., the number of Spark executor cores assigned, and the size of the data to be processed. Such insights can be used to quickly estimate the required resources and configure a Spark application for a desired execution time using the least amount of resources. Our paper proposes an automated execution time estimation approach called PERIDOT that involves executing a given application under a fixed resource setting with two different-sized, small subsets of its input data to offer fast, lightweight execution time predictions. It analyzes logs from these two executions to estimate the dependencies between internal stages of the application. Information on these dependencies combined with knowledge of Spark's data partitioning mechanisms is used to derive an analytic model that can estimate execution times for other resource settings and input data sizes. Our results from a wide range of applications and multiple Spark clusters show that PERIDOT can accurately estimate the execution time of an application from limited historical data, and suggest the minimum amount of resources required to closely meet an execution time target.

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