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A Close Look at the SQL Query

If you want to get a better understanding of the SQL query, you've come to the right place. This article is excerpted from chapter four of The Art of SQL, written by Stephane Faroult and Peter Robson (O'Reilly; ISBN: 0596008945). Copyright © 2006 O'Reilly Media, Inc. All rights reserved. Used with permission from the publisher. Available from booksellers or direct from O'Reilly Media.

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January 03, 2008
  1. · A Close Look at the SQL Query
  2. · SQL and the Optimizer
  3. · Limits of the Optimizer
  4. · Five Factors Governing the Art of SQL

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A Close Look at the SQL Query
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In this chapter, we are going to take a close look at the SQL query and examine how its construct can vary according to the tactical demands of particular situations. This will involve examining complex queries and reviewing how they can be decomposed into a succession of smaller components, all interdependent, and all contributing to a final, complete query.

The Nature of SQL

Before we begin examining query constructs in detail, we need to review some of the general characteristics of SQL itself: how it relates to the database engine and the associated optimizer, and what may limit the efficiency of the optimizer.

SQL and Databases

Relational databases owe their existence to pioneering work by E.F. Codd on the relational theory. From the outset, Codd’s work provided a very strong mathematical basis to what had so far been a mostly empirical discipline. To make an analogy, for thousands of years mankind has built bridges to span rivers, but frequently these structures were grossly overengineered simply because the master builders of the time didn’t fully understand the true relationships between the materials they used to build their bridges, and the consequent strengths of these bridges. Once the science of civil engineering developed a solid theoretical knowledge of material strengths, bridges of a far greater sophistication and safety began to emerge, demonstrating the full exploitation of the various construction materials being used. Indeed, the extraordinary dimensions of some modern bridges reflect the similarly huge increase in the data volumes that modern DBMS software is able to address. Relational theory has done for databases what civil engineering has done for bridges.

It is very common to find confusion between the SQL language, databases, and the relational model. The function of a database is primarily to store data according to a model of the part of the real world from which that data has been obtained. Accordingly, a database must provide a solid infrastructure that will allow multiple users to make use of that same data, without, at any time, prejudicing the integrity of that data when they change it. This will require the database to handle contention between users and, in the extreme case, to keep the data consistent if the machine were to fail in mid-transaction. The database must also perform many other functions outside the scope of this book.

As its name says, Structured Query Language, or SQL for short, is nothing other than a language, though admittedly with a very tight coupling to databases. Equating the SQL language with relational databases—or even worse with the relational theory—is as misguided as assuming that familiarity with a spreadsheet program or a word processor is indicative of having mastered “information technology.” In fact, some products that are not databases support SQL,* and before becoming a standard SQL had to compete against other languages such as RDO or QUEL, which were considered by many theorists to be superior to SQL.

Whenever you have to solve what I shall generically call an SQL problem, you must realize that there are two components in action: the SQL expression of the query and the database optimizer. These two components interact within three distinct zones, as shown in Figure 4-1. At the center lies the relational theory, where mathematicians freely roam. If we simplify excessively, we can say that (amongst other useful things) the theory informs us that we can retrieve data that satisfies some criteria by using a handful of relational operators, and that these operators will allow us to answer basically any question. Most importantly, because the relational theory is so firmly grounded in mathematics, we can be totally confident that relational expressions can be written in different ways and yet return the same result. In exactly the same way, arithmetic teaches us that 246/369 is exactly the same as 2/3.

Figure 4-1.  DBMS Protagonists

However, despite the crucial theoretical importance of relational theory, there are aspects of great practical relevance that the relational theory has nothing to say about. These fall into an area I call “reporting requirements.” The most obvious example in this area is the ordering of result sets. Relational theory is concerned only with the retrieval of a correct data set, as defined by a query. As we are practitioners and not theorists, for us the relational phase consists in correctly identifying the rows that will belong to our final result set. The matter of how some attributes (columns) of one row relate to similar attributes in another row doesn’t belong to this phase, and yet this is what ordering is all about. Further, relational theory has nothing to say about the numerous statistical functions (such as percentiles and the like) that often appear in various dialects of the SQL language. The relational theory operates on set, and knows nothing of the imposition of ordering on these sets. Despite the fact that there are many mathematical theories built around ordering, none have any relevance to the relational theory.

At this stage I must point out that what distinguishes relational operations from what I have called reporting requirements is that relational operations apply to mathematical sets of theoretically infinite extent. Irrespective of whether we are operating on tables of 10, one million, or one billion rows, we can apply any filtering criterion in an identical fashion. Once again, we are concerned only with identifying and returning the data that matches our criteria. Here, we are in the environment where the relational theory is fully applicable. Now, when we want to order rows (or perform an operation such asgroup bythat most people would consider a relational operation) we are no longer working on a potentially infinite data set, but on a necessarily finite set. The consequent data set thus ceases to be a relation in the mathematical sense of the word. We are outside the bounds of the relational theory. Of course, this doesn’t mean that we cannot still do clever and useful things against this data using SQL.

So we may, as a first approximation, represent an SQL query as a double-layered operation as shown in Figure 4-2; first, a relational core identifying the set of data we are going to operate on, second, a non-relational layer which works on this now finite set to give the polishing touch and produce the final result that the user expects.

Figure 4-2.  The various layers of an SQL query

Despite Figure 4-2’s appealingly simple representation of the place of SQL within the data environment, an SQL query will in most cases be considerably more complex than Figure 4-2 may suggest; Figure 4-2 only represents the overall pattern. The relational filter may be a generic name for several independent filters combined, for instance, through aunionconstruct or by the means of subqueries, and the complexity of some SQL constructs can be considerable. I shall come back to the topic of SQL code a little later. But first I must talk about the relationship between the physical implementation of data and the database optimizer.

Do not confuse the true relational functionality of the SQL query execution with the additional presentation layer.

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