Sharing data and work across concurrent analytical queries

Today’s data deluge enables organizations to collect massive data, and analyze it with an ever-increasing number of concurrent queries. Traditional data warehouses (DW) face a challenging problem in executing this task, due to their query-centric model: each query is optimized and executed independently. This model results in high contention for resources. Thus, modern DW depart from the query-centric model to execution models involving sharing of common data and work. Our goal is to show when and how a DW should employ sharing. We evaluate experimentally two sharing method-ologies, based on their original prototype systems, that exploit work sharing opportunities among concurrent queries at run-time: Si-multaneous Pipelining (SP), which shares intermediate results of common sub-plans, and Global Query Plans (GQP), which build and evaluate a single query plan with shared operators. First, after a short review of sharing methodologies, we show that SP and GQP are orthogonal techniques. SP can be applied to shared operators of a GQP, reducing response times by 20%-48 % in workloads with numerous common sub-plans. Second, we corrob-orate previous results on the negative impact of SP on performance for cases of low concurrency. We attribute this behavior to a bot-tleneck caused by the push-based communication model of SP. We show that pull-based communication for SP eliminates the over-head of sharing altogether for low concurrency, and scales better on multi-core machines than push-based SP, further reducing response times by 82%-86 % for high concurrency. Third, we perform an ex-perimental analysis of SP, GQP and their combination, and show when each one is beneficial. We identify a trade-off between low and high concurrency. In the former case, traditional query-centric operators with SP perform better, while in the latter case, GQP with shared operators enhanced by SP give the best results. 1