Processing online aggregation on skewed data in mapreduce
Proceedings of the fifth international workshop on Cloud data management
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Online aggregation is a promising solution to achieving fast early responses for interactive ad-hoc queries that compute aggregates on massive data. To process large datasets on large-scale computing clusters, MapReduce has been introduced as a popular paradigm into many data analysis applications. However, typical MapReduce implementations are not well-suited to analytic tasks, since they are geared towards batch processing. With the increasing popularity of ad-hoc analytic query processing over enormous datasets, processing aggregate queries using MapReduce in an online fashion is therefore an emerging important application need. We present a MapReduce-based online aggregation system called COLA, which provides progressive approximate aggregate answers for both single table and multiple joined tables. COLA provides an online aggregation execution engine with novel sampling techniques to support incremental and continuous computing of aggregation, and minimize the waiting time before an acceptably precise estimate is available. In addition, user-friendly SQL queries are supported in COLA. Furthermore, COLA can implicitly convert non-OLA jobs into online version so that users don't have to write any special-purpose code to make estimates.