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SQL-Union vs Union All
Difference between the UNION vs UNION ALL
| Feature | UNION | UNION ALL |
|---|---|---|
| Purpose | Combines the result sets of two or more SELECT statements and removes duplicate rows from the final result. | Combines the result sets of two or more SELECT statements and includes all rows, including duplicates, in the final result. |
| Duplicate Handling | Automatically eliminates duplicate rows by comparing all columns in the result sets. | Retains duplicate rows; does not perform any duplicate elimination. |
| Performance | Slower than UNION ALL because of the additional step to check and remove duplicates. | Faster than UNION because no duplicate checking is performed. |
| Use Case | Used when you need a distinct set of rows in the final result. | Used when duplicates are acceptable or desired in the final result. |
| Sorting | Performs an implicit sorting of the combined result set to facilitate duplicate elimination. | Does not perform any implicit sorting, making it more efficient for larger datasets. |
| Error Handling | Requires the same number and type of columns in all SELECT statements to avoid syntax errors. | Same as UNION; the number and type of columns in all SELECT statements must match. |
| Memory Usage | Consumes more memory as it needs to store intermediate results and sort them to identify duplicates. | Consumes less memory since no duplicate checking or sorting is required. |
| Example Syntax | SELECT FirstName, LastName FROM EmployeesA UNION SELECT FirstName, LastName FROM EmployeesB; |
SELECT FirstName, LastName FROM EmployeesA UNION ALL SELECT FirstName, LastName FROM EmployeesB; |
| Output Example | Combines rows from both tables but excludes duplicate rows: John Doe, Jane Smith, Sam Wilson (if duplicates existed, only one is shown). |
Combines rows from both tables, including all duplicates: John Doe, Jane Smith, Sam Wilson, John Doe (duplicates are retained). |
| Performance Tip | Use only when duplicates need to be removed, as duplicate elimination can be resource-intensive for large datasets. | Use for better performance when duplicates are irrelevant or desirable in the output. |
| Primary Use Case | - When you need distinct values from multiple tables or queries. | - When combining datasets where duplicates are meaningful, such as log data or full lists of records. |
| Example Scenario | - Retrieving a list of unique employees from two departments. | - Combining sales records from multiple branches, where each record must be preserved for further analysis. |
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