With the rapidly growing volume of data, it is more attractive than ever to leverage approximations to answer analytic queries. Sampling is a powerful technique which has been studied extensively from the point of view of facilitating approximation. Yet, there has been no large-scale study of effectiveness of sampling techniques in big data systems. In this paper, we describe an in-depth study of the sampling-based approximation techniques that we have deployed in Microsoft’s big data clusters. We explain the choices we made to implement approximation, identify the usage cases, and study detailed data that sheds insight on the usefulness of doing sampling based approximation.<\/p>\n","protected":false},"excerpt":{"rendered":"
With the rapidly growing volume of data, it is more attractive than ever to leverage approximations to answer analytic queries. Sampling is a powerful technique which has been studied extensively from the point of view of facilitating approximation. Yet, there has been no large-scale study of effectiveness of sampling techniques in big data systems. 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