The Top Ten DB2 Development Best Practices

If you have been reading my blogs lately you know that I have been posting Top Ten lists of various types, and today's post offers up yet another one. This time, the list provides guidance for DB2 developers to keep in mind as they build DB2 applications...

1. Minimize network calls

The fewer number of times you need to make calls across the network the better your program will perform.

2. Minimize passes through the data

Try to read each data item only once. The more times you access the same data the worse performance will be.

3. Put the work into the SQL, not the program

Let SQL do the work... DB2 can optimize SQL better than you can optimize your programming language of choice.

4. Unlearn the “flat file” mentality

SQL is a set-based language. Each SQL statement can operate on multiple rows of data at once. And joins are more efficient than opening multiple cursors and performing "master file" processing logic on them.

5. Be sure data type and length match in predicates

Even though DB2 no longer automatically degrades non-matching predicates to Stage 2, it is still a best practice to match the data type and length for columns and host variables that participate in predicates.

6. Know your Stage 1, Stage 2, and Indexable predicates

...and avoid Stages 3 and 4!

7. Document your code

Without proper documentation application maintenance becomes difficult... especially if you used any SQL tuning tricks/techniques.

8. Always check the SQLCODE or SQLSTATE

If you don't check for an error you may be processing with bad, incorrect, or missing data.

9. Analyze your access paths (and tune your SQL in test)

If you do not examine the access paths formulated by the DB2 optimizer for your SQL then how do you know how efficient (or not) your code is (or will be).

10. Avoid Bachelor Programming Syndrome

Be sure to code COMMITs in your application programs

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Which is better? "BETWEEN" vs "<=" and >"="

This was a recent topic on the DB2-L mailing list so I thought I'd weigh in with my two cents worth on the topic.

As with most DB2 (and, indeed, IT) issues, the correct answer is "it depends!" Let's dig a bit deeper to explain what I mean.

From a maintainability perspective, BETWEEN is probably better. The BETWEEN predicate is easier to understand and code than the equivalent combination of the less than or equal to predicate (<=) and the greater than or equal to predicate (>=). In past releases, in many cases it was more efficient, too. But today the Optimizer recognizes the two formulations as equivalent and there usually is no performance benefit one way or the other. Performance reasons aside, one BETWEEN predicate is easier to understand and maintain than multiple <= and >= predicates. For this reason, I tend to favor using BETWEEN.

But not always. Consider the scenario of comparing a host variable to two columns. Usually BETWEEN is used to compare one column to two values, here shown using host variables:

WHERE COLUMN1 BETWEEN :HOST-VAR1 AND :HOST-VAR2

However, it is possible to use BETWEEN to compare one value to two columns, as shown:

WHERE :HOST-VAR BETWEEN COLUMN1 AND COLUMN2

This statement should be changed to

WHERE :HOST_VAR >= COLUMN1 and :HOST-VAR <= COLUMN2

The reason for this exception is that a BETWEEN formulation comparing a host variable to two columns is a Stage 2 predicate, whereas the preferred formulation is Stage 1. And we all know that Stage 1 outperforms Stage 2, right?

Remember too, that SQL is flexible and often the same results can be achieved using different SQL formulations. Sometimes one SQL statement will dramatically outperform a functionally equivalent SQL statement just because it is indexable and the other is not. For example, consider this SQL statement

SELECT EMPNO, FIRSTNME, MIDINIT, LASTNAME
FROM EMP
WHERE MIDINIT NOT BETWEEN 'A' AND 'G';

It is not indexable because it uses the NOT BETWEEN predicate. However, if we understand the data in the table and the desired results, perhaps we can reformulate the SQL to use indexable predicates, such as

SELECT EMPNO, FIRSTNME, MIDINIT, LASTNAME
FROM EMP
WHERE MIDINIT >= 'H';

Or we could code MIDINIT BETWEEN 'H' AND 'Z' in place of MIDINIT >= 'H'. Of course, for either of these solutions to work correctly we would need to know that MIDINIT never contained values that collate lower than the value 'A'.

So, as usual, there is no one size fits all answer to the question!

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Stages 3 and 4

All good DB2 developers and DBAs know about Stage 1 and Stage 2 predicates, right? But have you ever heard of Stage 3 and Stage 4 predicates? Well, you’re about to!

First of all, let’s do a quick review to catch those readers who don’t know what Stage 1 and 2 are. You may have heard about sargable and nonsargable, and if so, Stage 1 is sargable and Stage 2 is nonsargable. If not, don’t worry about those terms, they are obsolete.

A predicate that can be evaluated in the Data Manager (DM) component of DB2, that is at the earliest stage of query execution, is called a Stage 1 predcicate. Stage 2 predicates need to be passed up to the Relational Data System (RDS) to process. So Stage 1 predicates are more efficient than Stage 2 predicates because the Data Manager component of DB2 is at a level closer to the data than the Relational Data System. Stage 1 predicates, being evaluated earlier in the data retrieval process, avoid the overhead of passing data from component to component of DB2. For this reason, developers are encourage to use Stage 1 predicates rather than Stage 2 predicates to optimize performance.

What makes a predicate Stage 2 instead of Stage 1? Well, it is all in the type of predicate you code and how you write your SQL. There is a list of Stage 1 and Stage 2 predicates in Chapter 12 of the DB2 Performance and Tuning manual. (The same chart also tells you whether a predicates is indexable or not.) Whenever you move from one release of DB2 to another one of the first things you should do is consult this manual to see if any predicates have changed from Stage 2 to Stage 1… and you should make sure all of your developers have a copy of that chart taped to their cubicle wall!

OK, so what is all of this about Stage 3 and Stage 4, then? Well, it is a way of thinking about some bad SQL practices. Instead of coding a SQL predicate some programmers choose to bring all (or most) of the data into their program and then filter it using IF-THEN or CASE statements. You can think of these as Stage 3 predicates because it is one more place that the data must be passed to before it can be determined whether the data is needed.

Stage 4? That is when you use a black box (see the link for an explanation if you don't know what a black box is)... Instead of filtering the data in the DM or the RDS or even in your program, you have to work with another program altogether – the black box – to return the right data.

So just remember 1… 2… 3… 4… and that is the order of efficiency for those types of predicates. 1 is better than 2 is better than 3 is better than 4…

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