When working with databases, one of the most critical and costly tasks developers face is data model maintenance. The ease with which data structures can be managed can make a significant difference in the amount of time and effort developers spend. In this article, we will examine the DBF/ADT/NTX/CDX/ADI ISAM data models and the PostgreSQL DBMS with the Xbase++ PostgreSQL client database engine with ISAM emulation and their different approaches to data model maintenance.

Data Model Maintenance in ISAM​

In Indexed Sequential Access Method (ISAM), structure changes such as adding a field or extending a field's character size require a well-thought process. Developers have to unload the data from the table, recreate the table structure, and reload the data back into the table. Imagine a scenario where a simple field extension from 30 to 40 characters would require developers to write specific code for each of these steps. This makes data maintenance in ISAM databases demanding and time-consuming, especially for large schemas or frequent changes.

PostgreSQL Approach to Data Model Maintenance​

On the other hand, PostgreSQL, an object-relational database management system (DBMS), uses SQL Data Definition Language (DDL) commands for data model maintenance. Structure changes, including adding a column, changing a char column size, or changing numeric column values, require a simple SQL script. The DBMS automatically manages the rest, eliminating the need for unloading, recreating, and reloading data.

PostgreSQL, as a DBMS, provides more advanced features over ISAM-like flat files such as DBF/ADT/NTX/CDX/ADI. It offers transactional integrity, the ability to recover from crashes, complex querying power, and most importantly, simple and straightforward data model maintenance.

Practical Benefits for Developers​

The implications of this approach to data model maintenance are significant for ISAM developers. It allows them to focus more on their core tasks rather than chore-like data model maintenance issues. They do not have to write additional code for structure changes, making the entire process more efficient.

Moreover, with SQL scripts, developers can review and understand changes easily, ensuring maintainability and traceability of changes across the data models. Furthermore, automatic data maintenance reduces the chances of human errors in handling data, thus improving data integrity.

PostgreSQL Examples​

Let's look at some simple examples, demonstrating the convenience of using PostgreSQL for data model maintenance:

1. Adding a Column​

SQL: 
ALTER TABLE employees ADD COLUMN department VARCHAR(40);

2. Changing a Char Column Size​

SQL: 
ALTER TABLE employees ALTER COLUMN name TYPE VARCHAR(50);

3. Changing Numeric Column Length or Decimals​

SQL: 
ALTER TABLE product ALTER COLUMN price TYPE NUMERIC(10,4);

4. Deleting a Column​

SQL: 
ALTER TABLE employees DROP COLUMN department;

Conclusion​

In conclusion, PostgreSQL brings a massive improvement in data model maintenance over ISAM-like systems. It simplifies the maintenance process and allows developers to focus on implementing the actual business logic. This increased efficiency and reduced complexity can be added to any existing DBF/ADT/NTX/CDX/ADI based application by simply migrating to the PostgreSQL DBMS using the Xbase++ ISAM emulation, making the move from ISAM to SQL a snap.