Query Processing and Optimization, and Database Tuning
Algorithms for Query Processing and Optimization
In this chapter we discuss the techniques used internally by a DBMS to process, optimize, and execute high-level queries. A query expressed in a high-level query language such as SQL must first be scanned, parsed, and validated. The scanner identifies the query tokens—such as SQL keywords, attribute names, and relation names—that appear in the text of the query, whereas the parser checks the query syntax to determine whether it is formulated according to the syntax rules (rules of grammar) of the query language. The query must also be validated by checking that all attribute and relation names are valid and semantically meaningful names in the schema of the particular database being queried. An internal representation of the query is then created, usually as a tree data structure called a query tree. It is also possible to rep-resent the query using a graph data structure called a query graph. The DBMS must then devise an execution strategy or query plan for retrieving the results of the query from the database files. A query typically has many possible execution strategies, and the process of choosing a suitable one for processing a query is known as query optimization.
Figure 19.1 shows the different steps of processing a high-level query. The query optimizer module has the task of producing a good execution plan, and the code generator generates the code to execute that plan. The runtime database processor has the task of running (executing) the query code, whether in compiled or interpreted mode, to produce the query result. If a runtime error results, an error mes-sage is generated by the runtime database processor.
The term optimization is actually a misnomer because in some cases the chosen execution plan is not the optimal (or absolute best) strategy—it is just a reasonably efficient strategy for executing the query. Finding the optimal strategy is usually too time-consuming—except for the simplest of queries. In addition, trying to find the optimal query execution strategy may require detailed information on how the files are implemented and even on the contents of the files—information that may not be fully available in the DBMS catalog. Hence, planning of a good execution strategy may be a more accurate description than query optimization.
For lower-level navigational database languages in legacy systems—such as the net-work DML or the hierarchical DL/1 (see Section 2.6)—the programmer must choose the query execution strategy while writing a database program. If a DBMS provides only a navigational language, there is limited need or opportunity for extensive query optimization by the DBMS; instead, the programmer is given the capability to choose the query execution strategy. On the other hand, a high-level query language—such as SQL for relational DBMSs (RDBMSs) or OQL (see Chapter 11) for object DBMSs (ODBMSs)—is more declarative in nature because it specifies what the intended results of the query are, rather than identifying the details of how the result should be obtained. Query optimization is thus necessary for queries that are specified in a high-level query language.
We will concentrate on describing query optimization in the context of an RDBMS because many of the techniques we describe have also been adapted for other types of database management systems, such as ODBMSs. A relational DBMS must systematically evaluate alternative query execution strategies and choose a reasonably efficient or near-optimal strategy. Each DBMS typically has a number of general database access algorithms that implement relational algebra operations such as SELECT or JOIN (see Chapter 6) or combinations of these operations. Only execution strategies that can be implemented by the DBMS access algorithms and that apply to the particular query, as well as to the particular physical database design, can be considered by the query optimization module.
This chapter starts with a general discussion of how SQL queries are typically translated into relational algebra queries and then optimized in Section 19.1. Then we discuss algorithms for implementing relational algebra operations in Sections 19.2 through 19.6. Following this, we give an overview of query optimization strategies. There are two main techniques that are employed during query optimization. The first technique is based on heuristic rules for ordering the operations in a query execution strategy. A heuristic is a rule that works well in most cases but is not guaranteed to work well in every case. The rules typically reorder the operations in a query tree. The second technique involves systematically estimating the cost of different execution strategies and choosing the execution plan with the lowest cost estimate. These techniques are usually combined in a query optimizer. We discuss heuristic optimization in Section 19.7 and cost estimation in Section 19.8. Then we provide a brief overview of the factors considered during query optimization in the Oracle commercial RDBMS in Section 19.9. Section 19.10 introduces the topic of semantic query optimization, in which known constraints are used as an aid to devising efficient query execution strategies.
The topics covered in this chapter require that the reader be familiar with the mate-rial presented in several earlier chapters. In particular, the chapters on SQL (Chapters 4 and 5), relational algebra (Chapter 6), and file structures and indexing (Chapters 17 and 18) are a prerequisite to this chapter. Also, it is important to note that the topic of query processing and optimization is vast, and we can only give an introduction to the basic principles and techniques in this chapter.
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