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Aggregation has been an important operation since the early days ofrelational databases. Today's Big Data applications bring further challengeswhen processing aggregation queries, demanding adaptive aggregation algorithmsthat can process large volumes of data relative to a potentially limited memorybudget (especially in multiuser settings). Despite its importance, the designand evaluation of aggregation algorithms has not received the same attentionthat other basic operators, such as joins, have received in the literature. Asa result, when considering which aggregation algorithm(s) to implement in a newparallel Big Data processing platform (AsterixDB), we faced a lack of "off theshelf" answers that we could simply read about and then implement based onprior performance studies.  In this paper we revisit the engineering of efficient local aggregationalgorithms for use in Big Data platforms. We discuss the salient implementationdetails of several candidate algorithms and present an in-depth experimentalperformance study to guide future Big Data engine developers. We show that theefficient implementation of the aggregation operator for a Big Data platform isnon-trivial and that many factors, including memory usage, spilling strategy,and I/O and CPU cost, should be considered. Further, we introduce precise costmodels that can help in choosing an appropriate algorithm based on inputparameters including memory budget, grouping key cardinality, and data skew.

Revisiting Aggregation for Data Intensive Applications: A Performance Study