Extended collective I/O for efficient retrieval of large objects

Object-relational database management systems (OR-DBMS) extend the capabilities of the relational databases by allowing definition of new data types and methods to operate on these data types while retaining most of the relational model semantics. In this paper we examine issues related to parallel processing of queries in the object-relational model with respect to efficient storage and retrieval of large objects. We extend the concept of collective I/O and other related techniques such as request merging and data sieving in the database domain to achieve high performance in the retrieval of large objects. We deal with the I/O optimization problem in the query executor, access methods and the low level runtime system. We also propose a new technique called pooled striping for efficient storage of large objects on multiple disks. The results presented in this paper clearly show the effectiveness of the proposed I/O optimization techniques in handling large amounts of data in a parallel object-relational database system