Imagine yourself standing in front of an exquisite buffet filled with numerous delicacies. Your goal is to try them all out, but you need to decide in what order. What exchange of tastes will maximize the overall pleasure of your palate? Although much less pleasurable and subjective, that is the type of problem that query optimizers are called to solve. Given a query, there are many plans that a database management system (DBMS) can follow to process it and produce its answer. All plans are equivalent in terms of their final output but vary in their cost, i.e., the amount of time that they need to run. What is the plan that needs the least amount of time? Such query optimization is absolutely necessary in a DBMS. The cost difference between two alternatives can be enormous. For example, consider the following database schema, which will be...

One of the basic functionalities of database management systems (DBMSs) is to be able to process declarative user queries. The first generation of object-oriented DBMSs did not provide declarative query capabilities. However, the last decade has seen significant research in defining query models (including calculi, algebra and user languages) and in techniques for processing and optimizing them. Many of the current commercial systems provide at least rudimentary query capabilities. In this chapter we discuss the techniques that have been developed for processing object-oriented queries. Our particular emphasis is on extensible query processing architectures and techniques. The other chapters in this book on query languages and optimization techniques complement this chapter. 1

Recent developments in database technology, such as deductive database systems, have given rise to the demand for new, cost-effective optimization techniques for join expressions. In this paper many different algorithms that compute approximate solutions for optimizing join orders are studied since traditional dynamic programming techniques are not appropriate for complex problems. First, two possible solution spaces, the space of left-deep and bushy processing trees, respectively, are evaluated from a statistical point of view. The result is that the common limitation to leftdeep processing trees is only advisable for certain join graph types. Basically, optimizers from three classes are analysed: heuristic, randomized and genetic algorithms. Each one is extensively scrutinized with respect to its working principle and its fitness for the desired application. It turns out that randomized and genetic algorithms are well suited for optimizing join expressions. They generate solutions of...

With relations growing larger and queries becoming more complex, parallel query processing is an increasingly attractive option for improving the performance of database systems. The objective of this paper is to examine the various issues encountered in parallel query processing and the techniques available for addressing these issues. The focus of the paper is on the join operation with both sort-merge join and hash joins being considered. Three types of parallelism can be exploited, namely intra-operator, inter-operator, and inter-query parallelism. In intra-operator parallelism the major issue is task creation, and the objective is to split a join operation into tasks in a manner such that the load can be spread evenly across a given number of processors. This is a challenge when the values on the join attribute are not uniformly distributed. Inter-operator parallelism can be achieved either through parallel execution of independent operations or through pipelining. In either case,...

In this paper, we explore the execution of pipelined hash joins in a multiprocessor-based database system. To improve the query execution, an innovative ap-proach on query execution tree selection is proposed to exploit segmented right-deep trees, which are bushy trees of right-deep subtrees. We first derive an analyt-ical model for the execution of a pipeline segment, and then, in light of the model, develop heuristic schemes to determine the query execution plan based on a seg-mented right-deep tree so that the query can be ef-ficiently executed. As shown by our simulation, the proposed approach, without incurring additional over-head on plan execution, possesses more flexibility in query plan generation, and leads to query plans of sig-nificantly better performance than those achievable by the previous schemes using right-deep trees. 1

. In data integration systems, queries posed to a mediator need to be translated into a sequence of queries to the underlying data sources. In a heterogeneous environment, with sources of diverse and limited query capabilities, not all the translations are feasible. In this paper, we study the problem of finding feasible and efficient query plans for mediator systems. We consider conjunctive queries on mediators and model the source capabilities through attribute-binding adornments. We use a simple cost model that focuses on the major costs in mediation systems, those involved with sending queries to sources and getting answers back. Under this metric, we develop two algorithms for source query sequencing -- one based on a simple greedy strategy and another based on a partitioning scheme. The first algorithm produces optimal plans in some scenarios, and we show a linear bound on its worst case performance when it misses optimal plans. The second algorithm generates optimal plans in mor...

The query optimizer is one of the most important components of a database system. Most commercial query optimizers today are based on a dynamic-programming algorithm, as proposed in [SAC+79]. While this algorithm produces good optimization results (i.e., good plans), its high complexity can be prohibitive if complex queries need to be processed, new query execution techniques need to be integrated, or in certain programming environments (e.g., distributed database systems). In this paper, we present and thoroughly evaluate a new class of query optimization algorithms that are based on a principle that we call iterative dynamic programming, or IDP for short. IDP has several important advantages: First, IDP-algorithms produce the best plans of all known algorithms in situations in which dynamic programming is not viable because of its high complexity. Second, some IDP variants are adaptive and produce as good plans as dynamic programming if dynamic programming is viable an...

We identify two unreasonable, though standard, assumptions made by database query optimizers that can adversely affect the quality of the chosen evaluation plans. One assumption is that it is enough to optimize for the expected case---that is, the case where various parameters (like available memory) take on their expected value. The other assumption is that the parameters are constant throughout the execution of the query. We present an algorithm based on the "System R"-style query optimization algorithm that does not rely on these assumptions. The algorithm we present chooses the plan of the least expected cost instead of the plan of least cost given some fixed value of the parameters. In execution environments that exhibit a high degree of variability, our techniques should result in better performance. 1 Introduction A database query is specified declaratively, not procedurally. Given a query, it is the job of the DBMS to choose an appropriate evaluation plan for it. This task i...

We present the application of the Planning by Rewriting (PbR) framework to query planning in distributed and heterogeneous environments. PbR is a new paradigm for efficient high-quality planning that exploits plan rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. The resulting planner is scalable, flexible, has anytime behavior, and, applied to query planning, yields a novel combination of traditional query optimization with heterogeneous information source selection. Query planners are the core component of mediator systems, which are becoming increasingly important in a world of interconnected information, and constitute excellent testbeds for planning technology.

Recent developments in database technology, such as deductive database systems, have given rise to the demand for new, cost-effective optimization techniques for join expressions. In this paper many different algorithms that compute approximative solutions for optimizing join orders are studied since traditional dynamic programming techniques are not appropriate for complex problems. First, two possible solution spaces, the space of left-deep and bushy processing trees, respectively, are evaluated from a statistical point of view. The result is that the common limitation to left-deep processing trees is, from a purely statistical point of view, only advisable for certain cost models. Basically, optimizers from three classes are analysed: heuristic, randomized and genetic algorithms. Each one is extensively scrutinized with respect to its working principle and its fitness for the desired application. It turns out that randomized and genetic algorithms are well suited for op...