In this blog I have done at many articles on Normalization and Denormalization, but I have never put all of the arguments together in one place, so that is what I would like to do today.
There are links to related essays on normalization and denormalization at the bottom of this post.
The What and Why of Normalization
Normalization is the process of designing tables so that each fact is stored in exactly one place. A "fact" in this case is any detail that we have to keep track of, such as a product's description, a product's price, an employee's social security number, and so forth.
The process is all about figuring out what tables you need and what columns each table will have. If we are talking about an employee's social security number, then we can guess right from the start that will have a table of EMPLOYEES, and that one of the columns will be SSN. As we get more details, we add more tables and columns.
The advantage of normalization comes when your application writes data to the database. In the simplest terms, when the application needs to store some fact, it only has to go to one place to do it. Writing this kind of code is very easy. Easy to write, easy to debug, easy to maintain and improve.
When the database is not normalized, you end up spending more time writing more complicated application code that is harder to debug. The chances of bad data in your production database go way up. When a shop first experiences bad data in production, it starts to become tempting to "lock down" access to the database, either by forcing updates to go through stored procedures or by trying to enforce access to certain tables through certain codepaths. Both of these strategies: stored procedures and code paths, are the actually the same strategy implemented in different tiers, they both try to prevent bugs by routing access through some bit of code that "knows what to do." But if the database is normalized, you do not need any magic code that "knows what to do."
So that, in brief, is what normalization is and why we do it. Let's move on now to denormalization.
Denormalization is Harder to Talk About
Normalization is easy to explain because there is a clearly stated end-goal: correct data. Moreover, there are well-defined methods for reaching the goal, which we call the normal forms, First Normal Form, Second Normal Form, and higher forms. By contrast, denormalization is much harder to talk about because there is no agreed-upon end goal. To make matters worse, denormalization violates the original theory of Relational Databases, so you still have plenty of people screaming not to do it all, making things even more confusing. What we have now in our industry is different shops denormalizing in different ways for different reasons.
The arguments that I have heard in my career boil down to two basic groups. The first set of arguments centers around calculated or derived values, and the second set centers around programmer convenience.
Arguments for Derived Values
My own experience comes down heavily in favor of denormalizing by storing derived values directly into the tables, with the extremely signficant caveat that you must have a way to ensure that they are always correct. In this paradigm you maintain strict normalization for facts supplied from the outside, and then layer on additional facts that are calculated during write operations and saved permanently.
Here is a very simple example. A strictly normalized database happens to be missing data that many programmers would automatically assume should be stored. Believe it or not, a simple value in a shopping cart like EXTENDED_PRICE is forbidden by 3rd normal form because it is a non-key dependency, or, in plain English, since it can be derived from other values (QTY * PRICE), then it is redundant, and we no longer have each fact stored in exactly one place. The value of EXTENDED_PRICE is only correct if it always equals QTY * PRICE, and so there is now a "fact" that is spread across three locations. If you store EXTENDED_PRICE, but do not have a way to ensure that it will always 100% of the time equal QTY * PRICE, then you will get bad data.
So, given the risk of bad data, what is to be gained by putting EXTENDED_PRICE into the cart? The answer is that it adds value to the database and actually simplifies application code. To see why, imagine a simple eCommerce shopping cart that does not store any derived values. Every single display of the cart to the user must go all over the place to gather lots of details and recalculate everything. This means re-calculating not just the EXTENDED_PRICE, but adding in item level discounts, taking account of possible tax exemptions for different items, rolling the totals to the cart, adding in tax, shipping, perhaps a customer discount, a coupon, and who knows what else. All of this just to display the cart, every time, no matter what the purpose.
This situation leads to three problems. A pitifully slow application (too many disk reads and lots of cycles calculating the values), maddening bugs when an application update has subtle changes to the calculations so the customer's order no longer displays the same numbers as it did yesterday, and the frustrating requirement that the simplest of reports must route through application code to calculate these values instead of simply reading them off the disk, which leads to reporting systems that are orders of magnitude slower than they could be and horribly more complicated than they need to be because they can't just read straight from the tables.
Now let's look at how that same shopping cart would be used if all of those calculated values were generated and saved when the order is written. Building on your foundation of normalized values (price, qty), you need only one body of code that has to perform calculations. This magic body of code takes the user-supplied values, adds in the calculations, and commits the changes. All other subsequent operations need only to read and display the data, making them faster, simpler, and more robust.
So the obvious question is how to make sure the derived values are correct. If they are correct, we gain the benefits with no down side. If there is the smallest chance of bad data, we will quickly pay back any benefit we gained by chasing down the mistakes.
From a technical standpoint, what we really need is some technology that will make sure the calculations cannot be subverted, it cannot be possible for a stray bit of program code or SQL Statement to put the wrong value in for EXTENDED_PRICE. There are a few generally accepted ways to do this:
- Require all writes to go through a certain codepath. The only PRO here is that you keep the logic in the application code, and since most shops have more programmers than database people, this makes sense. The only CON is that it never works. One programmer working alone can maintain discipline, but a team cannot. All it takes is one programmer who did not know about the required codepath to screw it all up. Also, it makes your system inflexible, as it is no longer safe to write to the database except through a single application.
- Require all writes to go through stored procedures. This is nominally better than the codepath solution because it is not subvertible, and you can allow different side apps and utilities to safely write to the database. But it makes a lot of work and tends to be very inflexible.
- Putting triggers onto tables that perform the calculations and throw errors if a SQL statement attempts to explicitly write to a derived column. This makes the values completely non-subvertible, ensures they will always be correct, and allows access from any application or utility. The downside is that the triggers cannot be coded by hand except at extreme cost, and so must be generated from a data dictionary, which is fairly easy to do but tends to involve extreme psychological barriers. In these days of ORM many programmers mistakenly believe their class files define reality, but this is not true. Reality is defined by the users who one way or another create the paychecks, and by the database, which is the permanent record of facts. But a programmer who thinks his classes define reality simply cannot see this and will reject the trigger solution for any number of invalid reasons.
So denormalizing by putting in derived values can make a database much more valuable, but it does require a clear systematic approach to generating the derived values. There is no technical problem associated with ensuring the values are correct because of course the application has to do that somehow somewhere anyway, the real barriers tend to be the psychological and political.
Arguments For Programmer Convenience
The second set of arguments for denormalization tend to be rather weak, and come down to something like this (you have to picture the programmer whining like a child when he says this), "I don't like my data scattered around so many tables, can't we play some other game instead?"
Many programmers, when they first learn about normalization and build a normalized database, discover that the data they need to build a screen is "scattered" about in many tables, and that it is tedious and troublesome to get it all together for presentation to the user. A simple example might be a contacts list. The main table is CONTACTS, and it contains not much more than first and last name. A second table is a list of PHONES for each contact, and a third table is a list of various mailing addresses. A fourth table of EMAILS stores their email addresses. This makes four tables just to store a simple contact! We programmers look at this and something inside of us says, "That's just way too complicated, can't I do something else instead?"
This is a case of programmer convenience clashing with correctness of data. Nobody argues (at least not that I've heard) that they do not want the data to be correct, they just wonder if it is possible to simplify the tables so that they do not have to go out to so many places to get what they need.
In this case, programmers argue that denormalization will make for simpler code if they deliberately skip one or more steps in the normalizing process. (Technically I like to call the result a "non-normalized" database instead of denormalized, but most people call it denormalized, so we will go with that.)
The argument goes something like this: I know for a fact that nobody in the contacts list will have more than 3 emails, so I'm going to skip the EMAILS table and just put columns EMAIL1, EMAIL2, and EMAIL3 into the main CONTACTS table. In this case, the programmer has decided to skip 1st Normal Form and put a repeating group into the CONTACTS table. This he argues makes for simpler database retrieval and easier coding.
The result is painfully predictable. The simplification the programmer sought at one stage becomes a raft of complications later on. Here is an example that will appear trivial but really gets to the heart of the matter. How do you count how many emails a user has? A simple SELECT COUNT(*)...GROUP BY CONTACT that would have worked before now requires more complicated SQL. But isn't this trivial? Is it really that bad? Well, if all you are coding is a CONTACTS list probably not, but if you are doing a real application with hundreds of tables and this "convenience" has been put out there in dozens of cases, than it becomes a detail that programmers need to know on a table-by-table basis, it is an exception to how things ought to be that has to be accounted for by anybody who touches the table. In any shop with more than 5 programmers, whatever convenience the original programmer gained is lost quickly in the need to document and communicate these exceptions. And this is only a single trivial example.
Other examples come when it turns out you need more than three slots for phone. In the normalized case this never comes up. Any user can have any number of phones, and the code to display the phones is running through a loop, so it does not need to be modified for the case of 1 phone, 2 phones, etc. But in the "convenient" denormalized case you now must modify the table structure and the code that displays the contacts, making it quite inconvenient.
Then you have the case of how to define unused slots. If the user has only one email, do we make EMAIL2 and EMAIL3 empty or NULL? This may also seem like a silly point until you've sat through a flamewar at the whiteboard and discovered just how passionate some people are about NULL values. Avoiding that argument can save your shop a lot of wasted time.
In short, programmer convenience should never lead to a shortcut in skipping normalization steps because it introduces far more complications than it can ever pay for.
The normalization essays on this blog are:
- Revisiting Normalization and Denormalization (this essay).
- Pay Me Now Or Pay Me Later
- The Argument for Normalization
- First Normal Form
- Second Normal Form
- Third Normal Form and Calculated Values
- The Argument for Denormalization
- Denormalization Patterns
- Keeping Denormalized Values Correct
- Triggers, Encapsulation and Composition
- The Data Dictionary and Calculations, Part 1
- The Data Dictionary and Calculations, Part 2