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Aggregation is widely used to extract useful information from large volumes of data. In-memory databases are rising in popularity due to the demands of big data analytics applications. Many diferent algorithms and data structures can be used for in-memory aggregation, but their relative performance characteristics are inadequately studied. Prior studies in aggregation primarily focused on small selections of query workloads and data structures, or I/O performance. We present a comprehensive analysis of in-memory aggregation that covers baseline and state-of-the-art algorithms and data structures. We describe 6 analysis dimensions which represent the independent variables in our experiments: (1) algorithm and data structure, (2) query and aggregate function, (3) key distribution and skew, (4) group by cardinality, (5) dataset size and memory usage, and (6) concurrency and multithread scaling. We conduct extensive evaluations with the goal of identifying the trade-ofs of each algorithm and ofering insights to practitioners. We also provide a glimpse on how the CPU cache and TLB are afected by these dimensions.We show that some persisting notions about aggregation, such as the relative performance of hashing and sorting, do not necessarily apply to modern platforms and state-of-the-art implementations. Our results show that the ideal approach in a given situation depends on the input and the workload. For instance, sorting algorithms are faster in holistic aggregate queries, whereas hash tables perform better in distributive queries.

A Six-dimensional Analysis of In-memory Aggregation.