The Use of SELECT DISTINCT in SQL

In the field of SQL (Structured Query Language), when you're dealing with a dataset with identical or similar entries, there's a method to filter out these redundancies. The SELECT DISTINCT statement is used for this purpose, to return only distinct or unique values from a dataset.

The SELECT DISTINCT command is most commonly used in SQL to eliminate duplicate records and deliver a list of unique records. This becomes particularly important when dealing with large datasets, where repeated values are not unusual, but may not be the most efficient or desired for the data analysis or transactions at hand.

Practical Examples of SELECT DISTINCT in SQL

Consider a situation where you have a database of customers, containing the customers' names, their cities, and the products they've purchased. If you simply want a list of the different cities in which your customers reside, a query using SELECT DISTINCT would be an effective solution. Below is a realistic SQL query illustrating this usage:

SELECT DISTINCT City FROM Customers;

This SQL statement will return all unique city names from the Customers table, ignoring any duplicate entries. As a result, you'll have an unduplicated list of cities where your clients are located.

SQL Best Practices and Additional Insights

In general, while the SELECT DISTINCT command can be a handy tool for controlling data redundancy, it should be used judiciously. As with other SQL operations, the computational cost of using SELECT DISTINCT rises with the size of the dataset being handled. For large data sets, it can be more efficient to handle duplicates within the application logic, or by other means.

It's worth noting that although some may think SELECT UNIQUE could serve the same function, there is actually no such command in SQL. The correct SQL keyword is SELECT DISTINCT. Similarly, SELECT DIFFERENT and SELECT INDENTITY are not legitimate SQL statements.

In conclusion, SELECT DISTINCT plays a vital role in the SQL toolkit to help data analysts and programmers manage and interpret large datasets, returning unique and meaningful data that can be analyzed further or used to make strategic decisions.