Query evaluation techniques for large databases

Abstract

Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate it: In order to manipulate large sets of complex objects as efficiently as today's database systems manipulate simple records, query processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software.This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and post-relational database systems, including iterative execution of complex query evaluation plans, the duality of sort-and hash-based set matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

A Foundation for the Replacement of Pipelined Physical Join Operators in Adaptive Query Processing

“…Assume that R is the left, and S the right, input of a join. Let H , N L , M , IN and SH , respectively, denote the above physical join operators, the algorithms for which are described briefly below: -Hash Join [7]: All R-tuples are read and stored in a hash table, indexed on the join attribute(s). Then, each S-tuple is read in turn and used to probe the hash table.…”

“…In this paper, pipelined evaluation is assumed to be implemented using the the iterator model [7], which has three principal functions: Open, Next and Close. The Open function prepares the operator for result production.…”

liquid: Context-Aware Distributed Queries

“…liquid operators use the standard iterator model of query processing. Interested readers are referred to [7] for an introduction to these concepts.…”

Adding a Performance-Oriented Perspective to Data Warehouse Design

“…However, the answers obtained from a reduced data set are only approximate and in most cases the error is large [11], which greatly limits the applicability of data reduction in the data warehouse context. Many parallel databases systems have appeared both as research prototypes [12], [13], [14], [15] and as commercial products such as NonStop SQL fromTandem [16] or Oracle. However, even these "brute force" approaches have several difficulties when used in data warehouses such as the well-known problems of finding effective solutions for parallel data placement and parallel joins.…”