Guideline 2 - Understand COBOL Operations and Database Operations

The more you understand about your RDBMS and how it operates, the more you can help your COBOL applications to work efficiently with it.

File Input and Output

Consider these standard COBOL I/O operations:

• READ
• REWRITE
• WRITE
• DELETE
• OPEN

Each has an associated cost in terms of database performance. Each asks the database to do something that takes time. So if there are I/O operations that are optional in your COBOL application, you may want to remove them.

For example, developers sometimes OPEN and CLOSE files unnecessarily, using the OPEN–CLOSE pairing as a way to demarcate each new procedure:

OPEN file-A
procedural code
CLOSE file-A

But it's important to know that file OPENs are expensive in terms of performance. If you can eliminate non-essential OPENs from portions of your application, you may improve performance.

READ operations can also affect performance. All COBOL I/O is based on key indexes. Examining the output of your query optimizer allows you to determine if the most efficient execution path is being used for READs. The execution path for a given query can change as your data changes and as the size of the tables changes. It is also affected by database initialization parameters and any statistical information that you may have gathered on your tables. It might be helpful to know that, typically, the index path is the most efficient for Database Connectors applications.

Tables with Multiple Indexes

If using tables with multiple indexes, keep in mind that when a record is written or updated, locks are put onto all of the indexes and they are all basically rewritten during the course of the write/update. This is a costly process. There may be multiple columns per index, and multiple indexes per table. Each rewrite implies a certain amount of wait time. Tables with a large number of indexes can be slow on writes/updates, possibly leading other operations in the database query optimizer to become confused.

There are two things you can do in this circumstance:

• Restructure your data. The benefits of data restructuring may be significant. For example, if you have any situations in which two indexes start out with the same column or set of columns, you may be able to improve performance appreciably by changing your data definition.

  Suppose two indexes both start with MONTH, DAY, YEAR. These identical first three columns can cause the RDBMS's query optimizer to choose the wrong index, in which case you generate a significant amount of unnecessary and unproductive overhead. Restructuring one of the indexes can make a difference.

• Use the file system for some of your data. If you cannot restructure your data but are finding certain operations to be too expensive, you might want to consider moving some data into the indexed file system.

Guiding the Data Searches

You can guide the data searches that result from database queries, and thus improve performance, by making use of an external variable called A4GL_WHERE_CONSTRAINT. This process is explained in The WHERE Constraint.