Exploiting parallelism is a key to building high-performance database systems. Several approaches to building database systems that support both inter- and intra-query parallelism have been proposed. These approaches can be broadly classified as either Shared Nothing (SN) or Shared Everything (SE). Although the SN approach is highly scalable, it requires complex data partitioning and tuning to achieve good performance whereas the SE approach suffers from non-scalability. We propose a scalable sharing approach which combines the advantages of both SN and SE. We propose a comprehensive database architecture that includes the underlying hardware, and data partitioning and scheduling strategies, to promote scalable sharing. We analyze the performance and scalability of our approach and compare with that of a SN system. We find that for a variety of workloads and data skew our approach performs and scales at least as well as a SN system that uses the best possible data partitioning strategy.
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