Approximating Block Accesses in Random Files: The Case of Blocking Factors Lower than One

Expressions available in the current literature to estimate the number of blocks accessed in a random file fail to work when the blocking factor is lower than one. A new expression is developed in this article to estimate the number of blocks accessed; this expression is valid for blocking factors that are higher as well as lower than one. It is shown using simulation experiments that this expression is quite accurate in most situations

Expressions for Batched Searching of Sequential and Hierarchical Files

Batching yields significant savings in access costs in sequential, tree-structured, and random files. A direct and simple expression is developed for computing the average number of records/pages accessed to satisfy a batched query of a sequential file. The advantages of batching for sequential and random files are discussed. A direct equation is provided for the number of nodes accessed in unbatched queries of hierarchical files. An exact recursive expression is developed for node accesses in batched queries of hierarchical files. In addition to the recursive relationship, good, closed-form upper- and lower-bound approximations are provided for the case of batched queries of hierarchical files

The Effect of Buffer Size on Pages Accessed in Random Files

Prior works, for estimating the number of pages (blocks) accessed from secondary memory to retrieve a certain number of records for a query, have ignored the effect of main memory buffer size. While this may not cause any adverse impact for special cases, in most cases the impact of buffer sizes will be to increase the number of page accesses. This paper describes the reasons for the impact due to a limited buffer size and develops new expressions for the number of pages accessed. The accuracy of the expressions is evaluated by simulation modeling; and the effects of limited buffer size are discussed. Analytical works in database analysis and design should use the new expressions: especially when the effect of the buffer size is significant

On the Selection of Optimal Index Configuration in OO Databases

An operation in object-oriented databases gives rise to the processing of a path. Several database operations may result into the same path. The authors address the problem of optimal index configuration for a single path. As it is shown an optimal index configuration for a path can be achieved by splitting the path into subpaths and by indexing each subpath with the optimal index organization. The authors present an algorithm which is able to select an optimal index configuration for a given path. The authors consider a limited number of existing indexing techniques (simple index, inherited index, nested inherited index, multi-index, and multi-inherited index) but the principles of the algorithm remain the same adding more indexing technique

On the selection of secondary indices in relational databases

An important problem in the physical design of databases is the selection of secondary indices. In general, this problem cannot be solved in an optimal way due to the complexity of the selection process. Often use is made of heuristics such as the well-known ADD and DROP algorithms. In this paper it will be shown that frequently used cost functions can be classified as super- or submodular functions. For these functions several mathematical properties have been derived which reduce the complexity of the index selection problem. These properties will be used to develop a tool for physical database design and also give a mathematical foundation for the success of the before-mentioned ADD and DROP algorithms

DReAM: Dynamic Re-arrangement of Address Mapping to Improve the Performance of DRAMs

The initial location of data in DRAMs is determined and controlled by the 'address-mapping' and even modern memory controllers use a fixed and run-time-agnostic address mapping. On the other hand, the memory access pattern seen at the memory interface level will dynamically change at run-time. This dynamic nature of memory access pattern and the fixed behavior of address mapping process in DRAM controllers, implied by using a fixed address mapping scheme, means that DRAM performance cannot be exploited efficiently. DReAM is a novel hardware technique that can detect a workload-specific address mapping at run-time based on the application access pattern which improves the performance of DRAMs. The experimental results show that DReAM outperforms the best evaluated address mapping on average by 9%, for mapping-sensitive workloads, by 2% for mapping-insensitive workloads, and up to 28% across all the workloads. DReAM can be seen as an insurance policy capable of detecting which scenarios are not well served by the predefined address mapping

Batched Searching in Database Organizations

Savings in the number of page accesses due to batching on sequential, tree-structured, and random files are well known and have been reported in the literature. This paper asserts that substantial savings can also be obtained in database organizations by batching the requests for records (in queries), and also by batching intermediate processing requests while traversing the database. A simple database having two interrelated files is used to demonstrate such savings. For the simple database, three variations on batching are reported and compared with the case of unbatched requests. New mathematical expressions have been developed for the batched cases as well as for the unbatched case, and the savings are demonstrated with some example problems. As an extension, larger databases will enjoy even greater savings due to batching. The paper also discusses several strategies for applying the batching approach to current databases, and the advantages of emerging very large main memories for the batching approach

Heuristic Optimization of Physical Data Bases: Using a Generic and Abstract Design Model

Designing efficient physical data bases is a complex activity, involving the consideration of a large number of factors. Mathematical programming-based optimization models for physical design make many simplifying assumptions; thus, their applicability is limited. In this article, we show that heuristic algorithms can be successfully used in the development of very good, physical data base designs. Two heuristic optimization algorithms are proposed in the contest of a genetic and abstract model for physical design. One algorithm is based on generic principles of heuristic optimization. The other is based on capturing and using problem-specific information in the heuristics. The goodness of the algorithms is demonstrated over a wide range of problems and factor values