Processing Aggregates in Parallel Database Systems

Aggregates are rife in real life SQL queries. However, in the parallel query processing literature aggregate processing has received surprisingly little attention; furthermore, the way current parallel database systems do aggregate processing is far from optimal in many scenarios. We describe two hashing based algorithms for parallel evaluation of aggregates. A performance analysis via an analytical model and an implementation on the Intel Paragon multi-computer shows that each works well for some aggregation selectivities but poorly for the remaining. Fortunately, where one does poorly the other does well and vice-versa. Thus, the two together cover all possible selectivities. We show how, using sampling, an optimizer can decide which of the two algorithms to use for a particular query. Finally, we investigate the impact of data skew on the performance of these algorithms. 1 Introduction SQL queries in the real world are replete with aggregate operations. One measure of the perceived..