SQL UNION is a powerful clause in Structured Query Language that combines the results of two or more SELECT statements into a single result set, eliminating duplicate rows. It is essential for data manipulation and retrieval in databases, allowing users to merge data efficiently from different tables or queries. Remember, when using SQL UNION, ensure that all involved SELECT statements have the same number of columns and compatible data types for seamless integration.
SQL UNION is a SQL operation that combines the result sets of two or more SELECT queries into a single result set. It eliminates duplicate rows between the result sets, returning only unique rows in the final output.
The SQL UNION operator is essential when you want to merge data from different tables or queries that have the same number of columns and compatible data types. For successful implementation, ensure that the number of columns and their respective data types match in each SELECT statement. Here is the general syntax for using SQL UNION:
SELECT column1, column2, ... FROM table1UNIONSELECT column1, column2, ... FROM table2;
This operation can be extremely useful in various scenarios, such as:
Combining records from different geographical locations
Merging results from similar data entries storing in separate tables
Aggregating user interactions from different platforms
Consider two sample tables:
Customers_2022
Customers_2023
John Doe
Jane Smith
Emily Davis
Michael Johnson
To retrieve a combined list of customers from both years without duplicates, the SQL statement would be:
SELECT CustomerName FROM Customers_2022UNIONSELECT CustomerName FROM Customers_2023;
This operation results in a unique list of customer names.
Remember, if you need to include duplicates in your results, consider using SQL UNION ALL instead.
SQL UNION vs UNION ALL: While UNION filters out duplicate records, UNION ALL retains all duplicate records in the combined result set. Choosing between these two often depends on the specific requirements of the analysis being performed. Using UNION can optimize query performance when duplicates are not needed, while UNION ALL can be preferred when handling larger datasets where duplicates are relevant. Here’s an illustration of the difference:
SELECT column1 FROM table1UNIONSELECT column1 FROM table2; # Only unique resultsSELECT column1 FROM table1UNION ALLSELECT column1 FROM table2; # Includes duplicates
When working with SQL UNION, it's essential to remember the following factors:
Both queries must have the same number of columns.
Column names are taken from the first SELECT statement.
Order of result sets can be managed by including an ORDER BY clause at the end of the final query.
SQL UNION Syntax Explained
SQL UNION is an operator that combines the results from two or more SELECT statements into a single result set, removing any duplicate rows.
To effectively use the SQL UNION operator, it is crucial that all SELECT statements involved return the same number of columns and that those columns are of compatible data types. The general syntax for using SQL UNION is as follows:
SELECT column1, column2, ... FROM table1UNIONSELECT column1, column2, ... FROM table2;
It can also be expanded to include several SELECT statements, which allows for comprehensive data retrieval from multiple sources in a single query.
Suppose you have two tables that record customer feedback for different years:
Feedback_2022
'Great service!'
'Satisfactory experience.'
Feedback_2023
'Excellent staff!'
'Will return again.'
To retrieve a unique list of feedback from both years, you can run the following query:
SELECT Feedback FROM Feedback_2022UNIONSELECT Feedback FROM Feedback_2023;
The result will combine feedback from both tables while ensuring that duplicates are removed.
For situations where you want to include all records, including duplicates, you can use SQL UNION ALL instead.
Understanding the Differences Between SQL UNION and UNION ALL: When working with large datasets, performance considerations become vital. The UNION operator can introduce overhead because it processes only unique results, while UNION ALL simply combines all results without filtering duplicates, making it faster. Consider the following points when choosing between UNION and UNION ALL:
Uniqueness: Use UNION if unique results are required; use UNION ALL for performance with duplicates.
Sorting: Both can be paired with an ORDER BY clause, but UNION ALL may require more attention to sorting methods due to the presence of duplicates.
Usage Context: Choose UNION in analytics requiring unique entries, and UNION ALL in data merging tasks for speed.
An example of UNION ALL syntax looks like this:
SELECT column1 FROM table1UNION ALLSELECT column1 FROM table2;
Thus, understanding these nuances is key in optimizing SQL queries and achieving desired results.
SQL UNION Example - Step by Step
When working with multiple tables in a database, you may find that you need to combine data retrieved from different sources. The SQL UNION operator serves this purpose well by merging two or more SELECT statements. To illustrate how to use it effectively, let’s dive into a practical example.Consider two separate tables storing customer orders from different years. The first table, Orders_2022, lists orders from 2022, while the second table, Orders_2023, lists orders from 2023.Here’s the structure of the two tables:
Orders_2022
OrderID
CustomerName
1
John Doe
2
Jane Smith
Orders_2023
OrderID
CustomerName
1
Emily Davis
2
John Doe
To combine the customer names from both years without duplicates, the SQL command would look like this:
SELECT CustomerName FROM Orders_2022UNIONSELECT CustomerName FROM Orders_2023;
The result of this SQL UNION operation will generate a unique list of customer names who have placed orders in either year.
When using SQL UNION, ensure that each SELECT statement returns the same number of columns with similar data types.
Understanding the Flow of SQL UNION:When executing the SQL statement mentioned above, the SQL database engine carries out the following steps:
Executes the first SELECT statement.
Retrieves all unique customer names from the Orders_2022 table.
Executes the second SELECT statement.
Retrieves unique customer names from the Orders_2023 table.
Merges the results while eliminating any duplicates.
This process ensures that the output contains each customer's name listed only once, regardless of how many times they ordered across the defined years.
SQL UNION vs UNION ALL - Key Differences
When working with SQL, it is crucial to understand the differences between SQL UNION and UNION ALL as they serve different purposes when combining result sets from multiple SELECT statements. The primary distinction lies in how they handle duplicate records. SQL UNION removes duplicates from the final result set, while UNION ALL includes all records, regardless of whether they are duplicates.
Example: Consider two tables, Employees_Q1 and Employees_Q2, which contain employee names and IDs for the first and second quarters of the year:
Employees_Q1
EmployeeID
EmployeeName
101
John Doe
102
Jane Smith
Employees_Q2
EmployeeID
EmployeeName
101
John Doe
103
Emma Brown
Using SQL UNION:
SELECT EmployeeName FROM Employees_Q1UNIONSELECT EmployeeName FROM Employees_Q2;
The result will return:
John Doe
Jane Smith
Emma Brown
Conversely, using UNION ALL:
SELECT EmployeeName FROM Employees_Q1UNION ALLSELECT EmployeeName FROM Employees_Q2;
The result will return:
John Doe
Jane Smith
John Doe
Emma Brown
This illustrates that UNION ALL includes all records, showing duplicates.
Use SQL UNION when you want a unique set of results, and UNION ALL when you need to include all records, including duplicates.
Performance Considerations: When choosing between SQL UNION and UNION ALL, understanding performance implications is crucial. Since SQL UNION removes duplicates, it requires additional processing time, especially for large datasets. On the other hand, UNION ALL is faster because it simply concatenates the results of the SELECT statements without performing any duplication checks. Consider the following factors when deciding which to use:
Dataset Size: For large datasets, prefer UNION ALL if duplicates are acceptable.
Memory Usage:UNION may consume more memory due to duplicate elimination.
Analysis Needs: Use SQL UNION in scenarios requiring distinct results.
Understanding these differences and their impacts on query performance can significantly affect the efficiency of database operations.
SQL UNION - Key takeaways
SQL UNION combines the result sets of multiple SELECT queries into a single result set, ensuring only unique rows are returned, effectively eliminating duplicates from the final output.
An essential requirement for using SQL UNION is that all SELECT statements must have the same number of columns with compatible data types.
The general SQL UNION syntax consists of executing SELECT statements followed by the UNION keyword, combining results from different sources efficiently.
When comparing SQL UNION vs UNION ALL, note that UNION filters out duplicates while UNION ALL retains all records, making UNION ALL faster for large datasets.
Both SQL UNION and UNION ALL can be used with an ORDER BY clause to manage the order of the final results, but care must be taken to ensure proper sorting when using UNION ALL due to duplicates.
Choosing between SQL UNION and UNION ALL depends on the requirement for uniqueness versus performance; use UNION for unique results and UNION ALL for a complete dataset.
Learn faster with the 27 flashcards about SQL UNION
Sign up for free to gain access to all our flashcards.
Frequently Asked Questions about SQL UNION
What is the difference between SQL UNION and SQL UNION ALL?
SQL UNION combines the result sets of two or more SELECT queries and removes duplicate rows. In contrast, SQL UNION ALL also combines the result sets but retains all duplicates. Thus, UNION provides a distinct set while UNION ALL is faster and includes duplicates.
How do I use SQL UNION in a query?
To use SQL UNION, combine the result sets of two or more SELECT queries. Each query must have the same number of columns with compatible data types. Use the syntax: `SELECT column1, column2 FROM table1 UNION SELECT column1, column2 FROM table2;`. UNION eliminates duplicate rows; use UNION ALL to include duplicates.
What are the performance implications of using SQL UNION?
Using SQL UNION can impact performance due to the need for the database to eliminate duplicate records from the combined result sets. This process requires additional resources, which can slow down query execution. To enhance performance, consider using UNION ALL if duplicates are not a concern, as it skips the deduplication step.
Can SQL UNION be used with different data types?
Yes, SQL UNION can be used with different data types as long as the corresponding columns in each SELECT statement can be implicitly converted to a common data type. The resulting column types in the final output will be based on the highest precedence data type among the combined columns.
What are the rules for combining columns with SQL UNION?
To combine columns using SQL UNION, the number of columns in each SELECT statement must be the same, and the data types of corresponding columns must be compatible. The column names in the result set will be taken from the first SELECT statement. Duplicate rows are removed by default unless UNION ALL is used.
How we ensure our content is accurate and trustworthy?
At StudySmarter, we have created a learning platform that serves millions of students. Meet
the people who work hard to deliver fact based content as well as making sure it is verified.
Content Creation Process:
Lily Hulatt
Digital Content Specialist
Lily Hulatt is a Digital Content Specialist with over three years of experience in content strategy and curriculum design. She gained her PhD in English Literature from Durham University in 2022, taught in Durham University’s English Studies Department, and has contributed to a number of publications. Lily specialises in English Literature, English Language, History, and Philosophy.
Gabriel Freitas is an AI Engineer with a solid experience in software development, machine learning algorithms, and generative AI, including large language models’ (LLMs) applications. Graduated in Electrical Engineering at the University of São Paulo, he is currently pursuing an MSc in Computer Engineering at the University of Campinas, specializing in machine learning topics. Gabriel has a strong background in software engineering and has worked on projects involving computer vision, embedded AI, and LLM applications.