Abstract. We introduce AdaptPD, an automated physical design tool that improves database performance by continuously monitoring changes in the workload and adapting the physical design to suit the incoming workload. Current physical design tools are offline and require specification of a representative workload. AdaptPD is “always on ” and incorporates online algorithms which profile the incoming workload to calculate the relative benefit of transitioning to an alternative design. Efficient query and transition cost estimation modules allow AdaptPD to quickly decide between various design configurations. We evaluate AdaptPD with the SkyServer Astronomy database using queries submitted by SkyServer’s users. Experiments show that AdaptPD adapts to changes in the workload, improves query performance substantially over offline tools, and introduces minor computational overhead. 1

Users of distributed database systems often observe performance problems such as unexpectedly low throughput or high latency. Determining the cause of the performance problems can be very hard task. Bottlenecks can occur in any of the components through which the data flows: the applications, the operating systems, the network interfaces and hardware. Horizontal and vertical partitioning are important aspects of physical design in relational database system that has a significant impact on performance. The distribution design involves making decisions on the fragmentation and the allocation of data across the sites of a computer network. In this paper we address the fragmentation phase of distributed database systems. In this paper, vertical partitioning problem during the design of distributed databases is discussed by conducting a comparative study for different vertical partitioning algorithms to reach the most efficient vertical fragmentation scheme that leads to a proper data allocation and replication.

In this paper, we describe a main memory hybrid database system called HYRISE, which automatically partitions tables into vertical partitions of varying widths depending on how the columns of the table are accessed. For columns accessed as a part of analytical queries (e.g., via sequential scans), narrow partitions perform better, because, when scanning a single column, cache locality is improved if the values of that column are stored contiguously. In contrast, for columns accessed as a part of OLTP-style queries, wider partitions perform better, because such transactions frequently insert, delete, update, or access many of the fields of a row, and co-locating those fields leads to better cache locality. Using a highly accurate model of cache misses, HYRISE is able to predict the performance of different partitionings, and to automatically select the best partitioning using an automated database design algorithm. We show that, on a realistic workload derived from customer applications, HYRISE can achieve a 20 % to 400 % performance improvement over pure all-column or all-row designs, and that it is both more scalable and produces better designs than previous vertical partitioning approaches for main memory systems. 1.