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Reduce redundancy: Normalization is a good start on this problem too, but it's only a start. For one thing,
it's a process of taking projections, and we've seen that not all redundancies can be removed by taking
projections; indeed, there are many kinds of redundancy that normalization simply doesn't address at all.
(Chapter 15 discusses this issue in detail.) For another thing, taking projections, even when the
decomposition is nonloss, can cause dependencies to be lost, as we saw in Chapter 6 and elsewhere.
Avoid update anomalies: This point is, at least in part, just the previous one by another name. It's well
known that designs that aren't properly normalized can be subject to certain update anomalies, precisely
because of the redundancies they entail. In relvar STP, for example (see Fig. 1.2 in Chapter 1), supplier S1
might be shown as having status 20 in one tuple and status 25 in another. Of course, this particular anomaly
can arise only if a less than perfect job is being done on integrity constraint enforcement ... Perhaps a better
way to think about the update anomaly issue is this: The constraints needed to prevent such anomalies will
be easier to state, and might be easier to enforce, if the design is properly normalized than they would be if it
isn't (see the next paragraph). Yet another way to think about it is: More single tuple updates 1 will be
logically acceptable if the design is properly normalized than would be the case if it isn't (because
unnormalized designs imply redundancy─i.e., several tuples saying the same thing─and redundancy implies
that sometimes we have to update several things at the same time).
Simplify statement and enforcement of constraints: As we know from earlier chapters, some dependencies
imply others. (More generally, in fact, constraints of any kind can imply others. As a trivial example, if
shipment quantities must be less than or equal to 5000, they must certainly be less than or equal to 6000.)
Now, if constraint A implies constraint B , then stating and enforcing A will effectively state and enforce B
“automatically” (indeed, B won't need to be separately stated at all, except perhaps by way of
documentation). And normalization to 5NF gives a very simple way of stating and enforcing certain
important constraints; basically, all we have to do is define keys and enforce their uniqueness─which we're
going to do anyway─and then all applicable JDs (and therefore all MVDs and FDs as well) will effectively
be stated and enforced automatically, because they'll all be implied by those keys. So normalization does a
pretty good job in this area too. (Of course, I'm ignoring here the various multirelvar constraints that the
normalization process is likely to give rise to.)
Here on the other hand are several more reasons, over and above those already given, why normalization is
no panacea:
First, JDs and MVDs and FDs aren't the only kind of constraint, and normalization doesn't help with any
Second, given a particular set of relvars, there'll often be several distinct nonloss decompositions into 5NF
projections, and there's little or no formal guidance available to tell us which one to choose in such cases.
(To be honest, though, I doubt whether this lack is likely to cause major problems in practice.)
Third, there are many design issues that normalization simply doesn't address. For example, what is it that
tells us there should be just one suppliers relvar, instead of one for London suppliers, one for Paris suppliers,
and so on? It certainly isn't normalization as classically understood.
1 Perhaps better, more singleton set updates.
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