Storage architecture includes more and more processing power for increasing requirement of reliability, managibility and scalability. For example, an IBM storage server is equipped with 4 or 8 state-of-the-art processors and gigabytes of memories. This trend enables analyzing data locally inside a storage server. Processing data locally is appealing under the following circumstances: (1) huge reduction of data flowing to the host, (2) reduction of CPU consumption on host. Accordingly, the benefits are (1) less data traffic through IO channel to the host, (2) better utilization of host bufferpool, and (3) enabling more workload on the host. One crucial task is to understand how DBMS can benefit from such hardware. That is to identify which database operations are beneficial to be offloaded given a query workload in a particular setting. For certain operations, we establish value proposition via various approaches and show the analytical and experimental results. In particular, starjoin queries are commonly used in business warehouses. We propose to offload a portion of a starjoin query from host to the POWER5 P processors on a storage server, which dramatically reduces the amount of channel IO and host CPU consumption. Moreover, the query elapsed time is improved via the exploitation of the state-of-the-art P processors on a storage server.

RID-List (row id list) intersection is a common strategy in query processing, used in star joins, column stores, and even search engines. To apply a conjunction of predicates on a table, a query process ordoes index lookups to form sorted RID-lists (or bitmap) of the rows matching each predicate, then intersects the RID-lists via an AND-tree, and finally fetches the corresponding rows to apply any residual predicates and aggregates. This process can be expensive when the RID-lists are large. Furthermore, the performance is sensitive to the order in which RID lists are intersected together, and to treating the right predicates as residuals. If the optimizer chooses a wrong order or a wrong residual, due to a poor cardinality estimate, the resulting plan can run orders of magnitude slower than expected. We present a new algorithm for RID-list intersection that is both more efficient and more robust than this standard algorithm. First, we avoid forming the RID-lists up front, and instead form this lazily as part of the intersection. This reduces the associated IO and sort cost significantly, especially when the data distribution is skewed. It also ameliorates the problem of wrong residual table selection. Second, we do not intersect the RID-lists via an AND-tree, because this is vulnerable to cardinality mis-estimations. Instead, we use an adaptive set intersection algorithm that performs well even when the cardinality estimates are wrong. We present detailed experiments of this algorithm on data with varying distributions to validate its efficiency and predictability.