Eliminating Duplicate Rows with DISTINCT

The DISTINCT keyword in Oracle 19c SQL is used to eliminate duplicate rows from the result set of a SELECT statement. When you specify DISTINCT, Oracle processes the selected columns and returns only one instance of each unique combination of values. This is incredibly useful for reporting, data analysis, and ensuring data integrity. It's a fundamental concept for anyone learning SQL and aiming for efficient data retrieval in Oracle databases.

Example 1: Basic SELECT DISTINCT on a Single Column

SELECT DISTINCT job_id -- Selects only unique job IDs from the employees table
FROM employees;

Explanation This Oracle 19c SQL query demonstrates the simplest use of DISTINCT. It queries the employees table and retrieves a list of all unique job_id values. If there are multiple employees with the same job_id, DISTINCT ensures that each job_id appears only once in the result set, making it perfect for understanding your workforce's job diversity.

Example 2: SELECT DISTINCT on Multiple Columns

SELECT DISTINCT department_id, job_id -- Selects unique combinations of department ID and job ID
FROM employees
ORDER BY department_id, job_id; -- Orders the results for better readability

Explanation This SQL example showcases how DISTINCT works across multiple columns. Oracle considers the combination of department_id and job_id as a single unit. The query returns only unique pairings of these two columns. For instance, if department '10' has multiple employees with job_id 'IT_PROG', this query will only show '10, IT_PROG' once. This is vital for complex analytical queries in Oracle 19c.

Example 3: Using DISTINCT with Aggregate Functions

SELECT COUNT(DISTINCT department_id) AS unique_departments -- Counts the number of unique department IDs
FROM employees;

Explanation Here, DISTINCT is used within an aggregate function, COUNT(). This Oracle 19c SQL query counts the number of unique department_id values in the employees table. It's a common practice for data analysts who need to quickly ascertain the number of distinct categories within a dataset, providing a clear count of your operational departments.

Example 4: SELECT DISTINCT and NULL Values

SELECT DISTINCT manager_id -- Selects unique manager IDs, treating NULL as a distinct value
FROM employees;

Explanation This example illustrates how DISTINCT handles NULL values. In Oracle 19c, NULL is treated as a distinct value for the purpose of DISTINCT. If there are multiple NULL entries in the manager_id column, DISTINCT will return only one NULL in the result set. This behavior is important to remember when dealing with potentially incomplete datasets in your Oracle 19c database.

Example 5: SELECT DISTINCT and ORDER BY Clause

SELECT DISTINCT country_id -- Selects unique country IDs
FROM locations
ORDER BY country_id DESC; -- Orders the unique country IDs in descending order

Explanation This Oracle 19c SQL query combines DISTINCT with the ORDER BY clause. It first identifies all unique country_id values from the locations table and then sorts these unique values in descending order. This is a common pattern for presenting distinct data in a structured and easily digestible format, optimizing data presentation in your Oracle 19c applications.

Considerations for Performance with DISTINCT

While DISTINCT is incredibly useful, it's important to be aware of its potential impact on query performance, especially with large datasets in Oracle 19c. When you use DISTINCT, the Oracle database needs to sort the data to identify and remove duplicates. This sorting operation can be resource-intensive, consuming CPU and memory. For optimal Oracle 19c SQL performance, consider the following:

Index Usage: Ensure that the columns used with DISTINCT are indexed. Indexes can significantly speed up the sorting process required to identify unique values. This is a key Oracle 19c performance tuning strategy.

Column Selection: Only select the columns truly necessary with DISTINCT. Selecting many columns increases the data volume that needs to be sorted, impacting performance.

Alternative Approaches: In some complex scenarios, especially when dealing with very large tables, alternative methods like GROUP BY or analytic functions (ROW_NUMBER()) might offer better performance, particularly if you need to perform aggregations or more complex filtering in your Oracle 19c queries.

Data Volume: Be mindful of the size of your tables. DISTINCT on a table with millions of rows will take longer than on a smaller table. Always test your queries on representative datasets to gauge their performance in Oracle 19c.

Statistics: Keep your database statistics up-to-date. The Oracle optimizer relies on accurate statistics to determine the most efficient execution plan for your queries, including those with DISTINCT. Regular statistics gathering is crucial for optimal Oracle 19c database performance.