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.

heterogeneous storage hierarchies

HYRISE is a new relational storage engine for main memory database systems. It is built on the premise that enterprise application workloads can benefit from a dedicated main-memory storage engine. The key idea behind HYRISE is that it provides dynamic vertical partitioning of the tables it stores. Since enterprise applications typically use a large number of very wide tables, we designed a novel layout algorithm specifically tailored for enterprise data. Our algorithm uses a main memory cost model that is able to precisely estimate the physical access cost of database operators and to determine a vertical partitioning that yields the best execution performance. 1