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Data Redundancy and Database Design
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What I tell you three times is true. Lewis Carroll
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edundant: de trop, diffuse, excessive, extra, inessential, inordinate, padded, periphrastic, pleonastical, prolix, repetitious, supererogatory, superfluous, supernumerary, surplus, tautological, unemployed, unnecessary, unneeded, unwanted, verbose, wordy I found the foregoing splendid list of synonyms in Chambers Twentieth Century Thesaurus (which also gives the following nice list of antonyms: concise, essential, necessary). I quoted it in an earlier two-part paper, Grievous Bodily Harm (DBP&D 11, Nos. 5 and 6, May/June 1998, reprinted in my self-published book Relational Database Writings 1997 2001). In that paper, however, I was concerned with redundancy in the SQL language; here, by contrast, I m concerned with data redundancy, and hence, by extension, with the question of (logical) database design. Throughout this chapter, therefore, I take the term redundancy to mean data redundancy specifically, and the term database design, or just design for short, to mean logical database design specifically (barring explicit statements to the contrary in both cases, of course). Now, I ve said many times that database design is not my favorite subject; it s simply too, well, subjective for my taste. Of course, there is some solid design theory that can be brought to bear on the problem (and I want to examine that theory in this chapter), but there are many issues the theory just doesn t address at all. In other words, database design in general is still mostly an artistic endeavor, not a scientific one a state of affairs that, to repeat, I at any rate find less than fully satisfactory. Be that as it may, design theory in general can be regarded as a set of principles and techniques for reducing redundancy (thereby reducing the potential for certain inconsistencies and update anomalies that might otherwise occur). But what exactly is redundancy We don t seem to have a very precise definition of the term; we just have a somewhat vague idea that it can lead to problems, at least if it isn t managed properly. Note: I remark as an aside that similar remarks could be made in connection with the associated term update anomaly, which has become sanctified by use but also suffers from the lack of a universally agreed and precise definition. In his paper A Normal Form for Relational Databases that Is Based on Domains and Keys (ACM TODS 6, No. 3, September 1981),
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CHAPTER 12 DATA REDUNDANCY AND DATABASE DESIGN
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Ronald Fagin gives precise definitions for the related terms insertion anomaly and deletion anomaly, and those definitions might be used as a basis for a good definition of the more general term update anomaly. However, it s not my aim in this chapter to attempt such a thing. Back to the main thread of the discussion. In order to get a slightly better handle on what redundancy really means, the first thing we need to do is distinguish clearly between the logical and physical levels of the system. Obviously the design goals are different at the two levels. At the physical level, redundancy will almost certainly exist in some shape or form. Here are a couple of reasons why: Indexes and other such access path structures necessarily entail some redundancy, because certain data values are stored both in those auxiliary structures and in the structures to which they provide that fast access. Derived relations that are physically stored in some way what are known variously as snapshots or summary tables or materialized queries or materialized views also obviously involve some redundancy. The reason for redundancy at the physical level is performance, of course. But such redundancy has (or should have!) no effect on the logical level; it s managed by the DBMS, in the sense that the DBMS assumes responsibility for keeping redundant copies of the same data consistent, and it s never directly seen by the user. In this chapter, however, I m more interested in the question of redundancy at the logical level; from this point forward, therefore, I ll take the term database to refer to the logical level, meaning the database as it s perceived by the user (barring explicit statements to the contrary). At the logical level, then, it s tempting just to say that redundancy is always bad. But of course this statement is much too simplistic, thanks to the availability of the view mechanism if nothing else. Let me digress for a moment to elaborate on this latter point. It s well known, but worth stating explicitly nevertheless, that views serve two rather different purposes: 1. The user who actually defines view V is, obviously, aware of the expression X in terms of which V is defined. That user can use the name V wherever the expression X is intended, but such uses are basically just shorthand (like the use of macros in a programming language). 2. By contrast, a user who s merely informed that V exists and is available for use is supposed1 not to be aware of the expression X; to that user, in fact, V is supposed to look and feel just like a base relvar. Note: I hope you re familiar with the term relvar (see the section Some Prerequisites, later). It s short for relation variable. A base relvar also known as a real relvar is the relational model s counterpart to what SQL would call a base table. As an example of Case 1, suppose the user perceives the database as containing two relvars A and B and goes on to define their join A JOIN B as a view V; clearly, then, V is redundant so far as that user is concerned, and it could be dropped without any loss of information. For definiteness, therefore, I m going to assume from this point forward (barring explicit statements to the contrary) that no relvar in the database is defined in terms of any others, so that at least this
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1. Emphasis on supposed I m describing an ideal situation here; the reality is a little messier. The true state of affairs is analyzed in detail by Hugh Darwen and myself in our book Databases, Types, and the Relational Model: The Third Manifesto, 3rd edition (Addison-Wesley, 2006).
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