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SQL Exercises : SQL Practice with Solution for Beginners and Experienced

SQL ( Structured Query Language ) is a powerful tool used for managing and manipulating relational databases. Whether we are beginners or experienced professionals, practicing SQL exercises is important for improving your skills. Regular practice helps you get better at using SQL and boosts your confidence in handling different database tasks.

So, in this free SQL exercises page, we'll cover a series of SQL practice exercises covering a wide range of topics suitable for beginners , intermediate , and advanced SQL learners. These exercises are designed to provide hands-on experience with common SQL tasks, from basic retrieval and filtering to more advanced concepts like joins window functions , and stored procedures.

Table of Content

SQL Exercises for Practice

Sql practice exercises for beginners, sql practice exercises for intermediate, sql practice exercises for advanced, more questions for practice.

Practice SQL questions to enhance our skills in database querying and manipulation. Each question covers a different aspect of SQL , providing a comprehensive learning experience.

We have covered a wide range of topics in the sections beginner , intermediate and advanced .

• Basic Retrieval
• Arithmetic Operations and Comparisons:
• Aggregation Functions
• Group By and Having
• Window Functions
• Conditional Statements
• DateTime Operations
• Creating and Aliasing
• Constraints
• Stored Procedures:
• Transactions

let's create the table schemas and insert some sample data into them.

Create Sales table

Create products table.

This hands-on approach provides a practical environment for beginners to experiment with various SQL commands, gaining confidence through real-world scenarios. By working through these exercises, newcomers can solidify their understanding of fundamental concepts like data retrieval, filtering, and manipulation, laying a strong foundation for their SQL journey.

1. Retrieve all columns from the Sales table.

Explanation:

This SQL query selects all columns from the Sales table, denoted by the asterisk (*) wildcard. It retrieves every row and all associated columns from the Sales table.

2. Retrieve the product_name and unit_price from the Products table.

This SQL query selects the product_name and unit_price columns from the Products table. It retrieves every row but only the specified columns, which are product_name and unit_price.

3. Retrieve the sale_id and sale_date from the Sales table.

This SQL query selects the sale_id and sale_date columns from the Sales table. It retrieves every row but only the specified columns, which are sale_id and sale_date.

4. Filter the Sales table to show only sales with a total_price greater than $100.

This SQL query selects all columns from the Sales table but only returns rows where the total_price column is greater than 100. It filters out sales with a total_price less than or equal to $100.

5. Filter the Products table to show only products in the 'Electronics' category.

This SQL query selects all columns from the Products table but only returns rows where the category column equals 'Electronics'. It filters out products that do not belong to the 'Electronics' category.

6. Retrieve the sale_id and total_price from the Sales table for sales made on January 3, 2024.

This SQL query selects the sale_id and total_price columns from the Sales table but only returns rows where the sale_date is equal to '2024-01-03'. It filters out sales made on any other date.

7. Retrieve the product_id and product_name from the Products table for products with a unit_price greater than $100.

This SQL query selects the product_id and product_name columns from the Products table but only returns rows where the unit_price is greater than $100. It filters out products with a unit_price less than or equal to $100.

8. Calculate the total revenue generated from all sales in the Sales table.

This SQL query calculates the total revenue generated from all sales by summing up the total_price column in the Sales table using the SUM() function.

9. Calculate the average unit_price of products in the Products table.

This SQL query calculates the average unit_price of products by averaging the values in the unit_price column in the Products table using the AVG() function.

10. Calculate the total quantity_sold from the Sales table.

This SQL query calculates the total quantity_sold by summing up the quantity_sold column in the Sales table using the SUM() function.

11. Retrieve the sale_id, product_id, and total_price from the Sales table for sales with a quantity_sold greater than 4.

This SQL query selects the sale_id, product_id, and total_price columns from the Sales table but only returns rows where the quantity_sold is greater than 4.

12. Retrieve the product_name and unit_price from the Products table, ordering the results by unit_price in descending order.

This SQL query selects the product_name and unit_price columns from the Products table and orders the results by unit_price in descending order using the ORDER BY clause with the DESC keyword.

13. Retrieve the total_price of all sales, rounding the values to two decimal places.

This SQL query calculates the total sales revenu by summing up the total_price column in the Sales table and rounds the result to two decimal places using the ROUND() function.

14. Calculate the average total_price of sales in the Sales table.

This SQL query calculates the average total_price of sales by averaging the values in the total_price column in the Sales table using the AVG() function.

15. Retrieve the sale_id and sale_date from the Sales table, formatting the sale_date as 'YYYY-MM-DD'.

This SQL query selects the sale_id and sale_date columns from the Sales table and formats the sale_date using the DATE_FORMAT() function to display it in 'YYYY-MM-DD' format.

16. Calculate the total revenue generated from sales of products in the 'Electronics' category.

This SQL query calculates the total revenue generated from sales of products in the 'Electronics' category by joining the Sales table with the Products table on the product_id column and filtering sales for products in the 'Electronics' category.

17. Retrieve the product_name and unit_price from the Products table, filtering the unit_price to show only values between $20 and $600.

This SQL query selects the product_name and unit_price columns from the Products table but only returns rows where the unit_price falls within the range of $20 and $600 using the BETWEEN operator.

18. Retrieve the product_name and category from the Products table, ordering the results by category in ascending order.

This SQL query selects the product_name and category columns from the Products table and orders the results by category in ascending order using the ORDER BY clause with the ASC keyword.

19. Calculate the total quantity_sold of products in the 'Electronics' category.

This SQL query calculates the total quantity_sold of products in the 'Electronics' category by joining the Sales table with the Products table on the product_id column and filtering sales for products in the 'Electronics' category.

20. Retrieve the product_name and total_price from the Sales table, calculating the total_price as quantity_sold multiplied by unit_price.

This SQL query retrieves the product_name from the Sales table and calculates the total_price by multiplying quantity_sold by unit_price, joining the Sales table with the Products table on the product_id column.

These exercises are designed to challenge you beyond basic queries, delving into more complex data manipulation and analysis. By tackling these problems, you'll solidify your understanding of advanced SQL concepts like joins, subqueries, functions, and window functions, ultimately boosting your ability to work with real-world data scenarios effectively.

1. Calculate the total revenue generated from sales for each product category.

This query joins the Sales and Products tables on the product_id column, groups the results by product category, and calculates the total revenue for each category by summing up the total_price.

2. Find the product category with the highest average unit price.

This query groups products by category, calculates the average unit price for each category, orders the results by the average unit price in descending order, and selects the top category with the highest average unit price using the LIMIT clause.

3. Identify products with total sales exceeding 30.

This query joins the Sales and Products tables on the product_id column, groups the results by product name, calculates the total sales revenue for each product, and selects products with total sales exceeding 30 using the HAVING clause.

4. Count the number of sales made in each month.

This query formats the sale_date column to extract the month and year, groups the results by month, and counts the number of sales made in each month.

5. Determine the average quantity sold for products with a unit price greater than $100.

This query joins the Sales and Products tables on the product_id column, filters products with a unit price greater than $100, and calculates the average quantity sold for those products.

6. Retrieve the product name and total sales revenue for each product.

This query joins the Sales and Products tables on the product_id column, groups the results by product name, and calculates the total sales revenue for each product.

7. List all sales along with the corresponding product names.

This query joins the Sales and Products tables on the product_id column and retrieves the sale_id and product_name for each sale.

8. Retrieve the product name and total sales revenue for each product.

This query will give you the top three product categories contributing to the highest percentage of total revenue generated from sales. However, if you only have one category (Electronics) as in the provided sample data, it will be the only result.

9. Rank products based on total sales revenue.

This query joins the Sales and Products tables on the product_id column, groups the results by product name, calculates the total sales revenue for each product, and ranks products based on total sales revenue using the RANK () window function.

10. Calculate the running total revenue for each product category.

This query joins the Sales and Products tables on the product_id column, partitions the results by product category, orders the results by sale date, and calculates the running total revenue for each product category using the SUM() window function.

11. Categorize sales as "High", "Medium", or "Low" based on total price (e.g., > $200 is High, $100-$200 is Medium, < $100 is Low).

This query categorizes sales based on total price using a CASE statement. Sales with a total price greater than $200 are categorized as "High", sales with a total price between $100 and $200 are categorized as "Medium", and sales with a total price less than $100 are categorized as "Low".

12. Identify sales where the quantity sold is greater than the average quantity sold.

This query selects all sales where the quantity sold is greater than the average quantity sold across all sales in the Sales table.

13. Extract the month and year from the sale date and count the number of sales for each month.

This query extracts the year and month from the sale_date and counts the number of sales for each month. CONCAT(YEAR(sale_date), '-', LPAD(MONTH(sale_date), 2, '0')): Formats the year and month as YYYY-MM. COUNT(*): Counts the total sales for each month. GROUP BY YEAR(sale_date), MONTH(sale_date): Groups the sales by year and month. This results in the sales count for each month in the format YYYY-MM.

14. Calculate the number of days between the current date and the sale date for each sale.

This query calculates the number of days between the current date and the sale date for each sale using the DATEDIFF function.

15. Identify sales made during weekdays versus weekends.

This query categorizes sales based on the day of the week using the DAYOFWEEK function. Sales made on Sunday (1) or Saturday (7) are categorized as "Weekend", while sales made on other days are categorized as "Weekday".

This section likely dives deeper into complex queries, delving into advanced features like window functions, self-joins, and intricate data manipulation techniques. By tackling these challenging exercises, users can refine their SQL skills and tackle real-world data analysis scenarios with greater confidence and efficiency.

1. Write a query to create a view named Total_Sales that displays the total sales amount for each product along with their names and categories.

This query creates a view named Total_Sales that displays the total sales amount for each product along with their names and categories.

2. Retrieve the product details (name, category, unit price) for products that have a quantity sold greater than the average quantity sold across all products.

This query retrieves the product details (name, category, unit price) for products that have a quantity sold greater than the average quantity sold across all products.

3. Explain the significance of indexing in SQL databases and provide an example scenario where indexing could significantly improve query performance in the given schema.

With an index on the sale_date column, the database can quickly locate the rows that match the specified date without scanning the entire table. The index allows for efficient lookup of rows based on the sale_date value, resulting in improved query performance.

4. Add a foreign key constraint to the Sales table that references the product_id column in the Products table.

This query adds a foreign key constraint to the Sales table that references the product_id column in the Products table, ensuring referential integrity between the two tables.

5. Create a view named Top_Products that lists the top 3 products based on the total quantity sold.

This query creates a view named Top_Products that lists the top 3 products based on the total quantity sold.

6. Implement a transaction that deducts the quantity sold from the Products table when a sale is made in the Sales table, ensuring that both operations are either committed or rolled back together.

The quantity in stock for product with product_id 101 should be updated to 5.The transaction should be committed successfully.

7. Create a query that lists the product names along with their corresponding sales count.

This query selects the product names from the Products table and counts the number of sales (using the COUNT() function) for each product by joining the Sales table on the product_id. The results are grouped by product name using the GROUP BY clause.

8. Write a query to find all sales where the total price is greater than the average total price of all sales.

The subquery (SELECT AVG(total_price) FROM Sales) calculates the average total price of all sales. The main query selects all columns from the Sales table where the total price is greater than the average total price obtained from the subquery.

9. Analyze the performance implications of indexing the sale_date column in the Sales table, considering the types of queries commonly executed against this column.

Query without indexing:, query with indexing:.

This format clearly displays the operations and details of the query execution plan before and after indexing.

Without indexing, the query performs a full table scan, filtering rows based on the sale date, which is less efficient. With indexing, the query uses the index to quickly locate the relevant rows, significantly improving query performance.

10. Add a check constraint to the quantity_sold column in the Sales table to ensure that the quantity sold is always greater than zero.

All rows in the Sales table meet the condition of the check constraint, as each quantity_sold value is greater than zero.

11. Create a view named Product_Sales_Info that displays product details along with the total number of sales made for each product.

This view provides a concise and organized way to view product details alongside their respective sales information, facilitating analysis and reporting tasks.

12. Develop a stored procedure named Update_Unit_Price that updates the unit price of a product in the Products table based on the provided product_id.

The above SQL code creates a stored procedure named Update_Unit_Price. This stored procedure takes two parameters: p_product_id (the product ID for which the unit price needs to be updated) and p_new_price (the new unit price to set).

13. Implement a transaction that inserts a new product into the Products table and then adds a corresponding sale record into the Sales table, ensuring that both operations are either fully completed or fully rolled back.

This will update the unit price of the product with product_id 101 to 550.00 in the Products table.

14. Write a query that calculates the total revenue generated from each category of products for the year 2024.

When you execute this query, you will get the total revenue generated from each category of products for the year 2024.

If you're looking to sharpen your SQL skills and gain more confidence in querying database s, consider delving into these articles. They're packed with query-based SQL questions designed to enhance your understanding and proficiency in SQL .

By practicing with these exercises, you'll not only improve your SQL abilities but also boost your confidence in tackling various database-related tasks. The Questions are as follows:

• How to Insert a Value that Contains an Apostrophe in SQL?
• How to Select Row With Max Value in SQL?
• How to Efficiently Convert Rows to Columns in SQL?
• How To Use Nested Select Queries in SQL
• How to Select Row With Max Value on a Column in SQL?
• How to Specify Condition in Count() in SQL?
• How to Find the Maximum of Multiple Columns in SQL?
• How to Update Top 100 Records in SQL?
• How to Select the Last Records in a One-To-Many Relationship Using SQL Join
• How to Join First Row in SQL?
• How to Insert Row If Not Exists in SQL?
• How to Use GROUP BY to Concatenate Strings in SQL?
• How Inner Join works in LINQ to SQL
• How to Get the Identity of an Inserted Row in SQL
• How to Declare a Variable in SQL?

Mastering SQL requires consistent practice and hands-on experience. By working through these SQL practice exercises , you'll strengthen your skills and gain confidence in querying relational databases.

Whether you're just starting or looking to refine your expertise, these exercises provide valuable opportunities to hone your SQL abilities. Keep practicing , and you'll be well-equipped to tackle real-world data challenges with SQL.

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Assignment Operator in SQL Server

Back to: SQL Server Tutorial For Beginners and Professionals

Assignment Operator in SQL Server with Examples

In this article, I am going to discuss Assignment Operator in SQL Server with Examples. Please read our previous article, where we discussed Clauses in SQL Server . Before understanding Assignment Operator in SQL Server, let us first understand what are operators and why we need operators, and what are the different types of operators available in SQL Server.

What is an Operator in SQL Server?

A n operator is a symbol that performs some specific operation on operands or expressions. These operators are classified as follows in SQL Server.

• Assignment operator
• Arithmetic operator
• Comparison operator
• Logical operator
• Set operator

Note: In this article, I am going to discuss Assignment Operator,  rest of all other operators will discuss one by one from our upcoming articles.

Understanding the Assignment Operator in SQL Server:

Let us understand how to use the Assignment Operator in SQL Server with an example. We are going to use the following Employee table to understand the Assignment Operator.

Please use the below script to create and populate the Employee table with the required data.

Assignment operator:.

The assignment operator (=) in SQL Server is used to assign the values to a variable. The equal sign (=) is the only Transact-SQL assignment operator. In the following example, we create the @MyCounter variable, and then the assignment operator sets the @MyCounter variable to a value i.e. 1 .

DECLARE @MyCounter INT; SET @MyCounter = 1;

The assignment operator can also be used to establish the relationship between a column heading and the expression that defines the values for that column. The following example displays the column headings as FirstColumn and SecondColumn. The string ‘ abcd ‘ is displayed for all the rows in the FirstColumn column heading. Then, each Employee ID from the Employee table is listed in the SecondColumn column heading.

SELECT FirstColumn = ‘abcd’,  SecondColumn = ID  FROM Employee;

Compound Assignment Operators in SQL Server:

SQL SERVER 2008 has introduced a new concept of Compound Assignment Operators. The Compound Assignment Operators are available in many other programming languages for quite some time. Compound Assignment Operators are operated where variables are operated upon and assigned in the same line. Compound-assignment operators provide a shorter syntax for assigning the result of an arithmetic or bitwise operator. They perform the operation on the two operands before assigning the result to the first operand.

Example without using Compound Assignment Operators

The following example is without using Compound Assignment Operators.

Example using Compound Assignment Operators

The above example can be rewritten using Compound Assignment Operators as follows.

Following are the list of available compound operators in SQL Server

+= Adds some amount to the original value and sets the original value to the result. -= Subtracts some amount from the original value and sets the original value to the result. *= Multiplies by an amount and sets the original value to the result. /= Divides by an amount and sets the original value to the result. %= Divides by an amount and sets the original value to the modulo.

In the next article, I am going to discuss Arithmetic Operators in SQL Server. Here, in this article, I try to explain the Assignment Operator in SQL Server with Examples. I hope this article will help you with your needs. I would like to have your feedback. Please post your feedback, question, or comments about this article.

About the Author: Pranaya Rout

Pranaya Rout has published more than 3,000 articles in his 11-year career. Pranaya Rout has very good experience with Microsoft Technologies, Including C#, VB, ASP.NET MVC, ASP.NET Web API, EF, EF Core, ADO.NET, LINQ, SQL Server, MYSQL, Oracle, ASP.NET Core, Cloud Computing, Microservices, Design Patterns and still learning new technologies.

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Operators in SQL Server covers almost all the important areas of SQL. This tutorial is very good.

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When to use SET vs SELECT when assigning values to variables in SQL Server

By: Atif Shehzad   |   Updated: 2009-11-25   |   Comments (13)   |   Related: > TSQL

SET and SELECT may be used to assign values to variables through T-SQL. Both fulfill the task, but in some scenarios unexpected results may be produced. In this tip I elaborate on the considerations for choosing between the SET and SELECT methods for assigning a value to variable.

In most cases SET and SELECT may be used alternatively without any effect.

Following are some scenarios when consideration is required in choosing between SET or SELECT. Scripts using the AdventureWorks database are provided for further clarification.

Part 1 and 2 are mentioned in the scripts below. It would be better if you run each part of the script separately so you can see the results for each method.

Returning values through a query

Whenever you are assigning a query returned value to a variable, SET will accept and assign a scalar (single) value from a query. While SELECT could accept multiple returned values. But after accepting multiple values through a SELECT command you have no way to track which value is present in the variable. The last value returned in the list will populate the variable. Because of this situation it may lead to un-expected results as there would be no error or warning generated if multiple values were returned when using SELECT. So, if multiple values could be expected use the SET option with proper implementation of error handling mechanisms.

To further clarify the concept please run script # 1 in two separate parts to see the results

Part 1 of the script should be successful. The variable is populated with a single value through SET. But in part 2 of the script the following error message will be produced and the SET statement will fail to populate the variable when more than one value is returned.

Error message generated for SET

Hence SET prevented assignment of an ambiguous value.

In case of SELECT, even if multiple values are returned by the query, no error will be generated and there will be no way to track that multiple values were returned and which value is present in the variable. This is demonstrated in the following script.

Both part 1 and 2 were executed successfully. In part 2, multiple values have been assigned and accepted, without knowing which value would actually populate the variable. So when retrieval of multiple values is expected then consider the behavioral differences between SET and SELECT and implement proper error handling for these circumstances.

Assigning multiple values to multiple variables

If you have to populate multiple variables, instead of using separate SET statements each time consider using SELECT for populating all variables in a single statement. This can be used for populating variables directly or by selecting values from database.

Consider the following script comparing the use of SELECT and SET.

If you are using SET then each variable would have to be assigned values individually through multiple statements as shown below.

Obviously SELECT is more efficient than SET while assigning values to multiple variables in terms of statements executed, code and network bytes.

What if variable is not populated successfully

If a variable is not successfully populated then behavior for SET and SELECT would be different. Failed assignment may be due to no result returned or any non-compatible value assigned to the variable. In this case, SELECT would preserve the previous value if any, where SET would assign NULL. Because of the difference functionality, both may lead to unexpected results and should be considered carefully.

This is shown in following script

We can see that part 1 generates NULL when no value is returned for populating variable. Where as part 2 produces the previous value that is preserved after failed assignment of the variable. This situation may lead to unexpected results and requires consideration.

Following the standards

Using SELECT may look like a better choice in specific scenarios, but be aware that using SELECT for assigning values to variables is not included in the ANSI standards. If you are following standards for code migration purposes, then avoid using SELECT and use SET instead.

Best practice suggests not to stick to one method. Depending on the scenario you may want to use both SET or SELECT.

Following are few scenarios for using SET

• If you are required to assign a single value directly to variable and no query is involved to fetch value
• NULL assignments are expected (NULL returned in result set)
• Standards are meant to be follow for any planned migration
• Non scalar results are expected and are required to be handled

Using SELECT is efficient and flexible in the following few cases.

• Multiple variables are being populated by assigning values directly
• Multiple variables are being populated by single source (table , view)
• Less coding for assigning multiple variables
• Use this if you need to get @@ROWCOUNT and @ERROR for last statement executed
• Click here to look at assigning and declaring variables through single statement along with other new T-SQL enhancements in SQL Server 2008.
• Click here to read more about @@ROWCOUNT
• Click here to read more about @@ERROR

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How to use Assignment Operator in SQL Server - SQL Server / TSQL Tutorial Part 128

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DECLARE @local_variable (Transact-SQL)

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Variables are declared in the body of a batch or procedure with the DECLARE statement and are assigned values by using either a SET or SELECT statement. Cursor variables can be declared with this statement and used with other cursor-related statements. After declaration, all variables are initialized as NULL, unless a value is provided as part of the declaration.

The following syntax is for SQL Server and Azure SQL Database:

The following syntax is for Azure Synapse Analytics and Parallel Data Warehouse and Microsoft Fabric:

@ local_variable

The name of a variable. Variable names must begin with an at (@) sign. Local variable names must comply with the rules for identifiers .

data_type Any system-supplied, common language runtime (CLR) user-defined table type, or alias data type. A variable can't be of text , ntext , or image data type.

For more information about system data types, see Data Types (Transact-SQL) . For more information about CLR user-defined types or alias data types, see CREATE TYPE (Transact-SQL) .

= value Assigns a value to the variable in-line. The value can be a constant or an expression, but it must either match the variable declaration type or be implicitly convertible to that type. For more information, see Expressions (Transact-SQL) .

@ cursor_variable_name

The name of a cursor variable. Cursor variable names must begin with an at (@) sign and conform to the rules for identifiers.

CURSOR Specifies that the variable is a local cursor variable.

@ table_variable_name The name of a variable of type table . Variable names must begin with an at (@) sign and conform to the rules for identifiers.

<table_type_definition> Defines the table data type. The table declaration includes column definitions, names, data types, and constraints. The only constraint types allowed are PRIMARY KEY, UNIQUE, NULL, and CHECK. An alias data type can't be used as a column scalar data type if a rule or default definition is bound to the type.

<table_type_definition>

A subset of information used to define a table in CREATE TABLE. Elements and essential definitions are included here. For more information, see CREATE TABLE (Transact-SQL) .

n A placeholder indicating that multiple variables can be specified and assigned values. When declaring table variables, the table variable must be the only variable being declared in the DECLARE statement.

column_name

The name of the column in the table.

scalar_data_type Specifies that the column is a scalar data type.

computed_column_expression An expression defining the value of a computed column. It is computed from an expression using other columns in the same table. For example, a computed column can have the definition cost AS price * qty . The expression can be a noncomputed column name, constant, built-in function, variable, or any combination of these connected by one or more operators. The expression can't be a subquery or a user-defined function. The expression can't reference a CLR user-defined type.

[ COLLATE collation_name ]

Specifies the collation for the column. collation_name can be either a Windows collation name or a SQL collation name, and is applicable only for columns of the char , varchar , text , nchar , nvarchar , and ntext data types. If not specified, the column is assigned either the collation of the user-defined data type (if the column is of a user-defined data type) or the collation of the current database.

For more information about the Windows and SQL collation names, see COLLATE (Transact-SQL) .

Specifies the value provided for the column when a value isn't explicitly supplied during an insert. DEFAULT definitions can be applied to any columns except those defined as timestamp or those with the IDENTITY property. DEFAULT definitions are removed when the table is dropped. Only a constant value, such as a character string; a system function, such as a SYSTEM_USER(); or NULL can be used as a default. To maintain compatibility with earlier versions of SQL Server, a constraint name can be assigned to a DEFAULT.

constant_expression A constant, NULL, or a system function used as the default value for the column.

Indicates that the new column is an identity column. When a new row is added to the table, SQL Server provides a unique incremental value for the column. Identity columns are commonly used with PRIMARY KEY constraints to serve as the unique row identifier for the table. The IDENTITY property can be assigned to tinyint , smallint , int , decimal(p,0) , or numeric(p,0) columns. Only one identity column can be created per table. Bound defaults and DEFAULT constraints can't be used with an identity column. You must specify both the seed and increment, or neither. If neither is specified, the default is (1,1).

seed The value used for the first row loaded into the table.

increment The incremental value added to the identity value of the previous row that was loaded.

Indicates that the new column is a row global unique identifier column. Only one uniqueidentifier column per table can be designated as the ROWGUIDCOL column. The ROWGUIDCOL property can be assigned only to a uniqueidentifier column.

NULL | NOT NULL

Indicates if null is allowed in the variable. The default is NULL .

PRIMARY KEY

A constraint that enforces entity integrity for a given column or columns through a unique index. Only one PRIMARY KEY constraint can be created per table.

A constraint that provides entity integrity for a given column or columns through a unique index. A table can have multiple UNIQUE constraints.

CLUSTERED | NONCLUSTERED

Indicate that a clustered or a nonclustered index is created for the PRIMARY KEY or UNIQUE constraint. PRIMARY KEY constraints use CLUSTERED, and UNIQUE constraints use NONCLUSTERED.

CLUSTERED can be specified for only one constraint. If CLUSTERED is specified for a UNIQUE constraint and a PRIMARY KEY constraint is also specified, the PRIMARY KEY uses NONCLUSTERED.

A constraint that enforces domain integrity by limiting the possible values that can be entered into a column or columns.

logical_expression A logical expression that returns TRUE or FALSE.

<index_option>

Specifies one or more index options. Indexes can't be created explicitly on table variables, and no statistics are kept on table variables. Starting with SQL SQL Server 2014 (12.x), new syntax was introduced which allows you to create certain index types inline with the table definition. Using this new syntax, you can create indexes on table variables as part of the table definition. In some cases, performance may improve by using temporary tables instead, which provide full index support and statistics.

For a complete description of these options, see CREATE TABLE .

Table variables and row estimates

Table variables don't have distribution statistics. In many cases, the optimizer will build a query plan on the assumption that the table variable has zero rows or one row. For more information, review table data type - Limitations and restrictions .

For this reason, you should be cautious about using a table variable if you expect a larger number of rows (greater than 100). Consider the following alternatives:

• Temp tables may be a better solution than table variables when it is possible for the rowcount to be larger (greater than 100).
• For queries that join the table variable with other tables, use the RECOMPILE hint, which will cause the optimizer to use the correct cardinality for the table variable.
• In Azure SQL Database and starting with SQL Server 2019 (15.x), the table variable deferred compilation feature will propagate cardinality estimates that are based on actual table variable row counts, providing a more accurate row count for optimizing the execution plan. For more information, see Intelligent query processing in SQL databases .

Variables are often used in a batch or procedure as counters for WHILE, LOOP, or for an IF...ELSE block.

Variables can be used only in expressions, not in place of object names or keywords. To construct dynamic SQL statements, use EXECUTE.

The scope of a local variable is the batch in which it's declared.

A table variable isn't necessarily memory resident. Under memory pressure, the pages belonging to a table variable can be pushed out to tempdb .

You can define an inline index in a table variable.

A cursor variable that currently has a cursor assigned to it can be referenced as a source in a:

• CLOSE statement
• DEALLOCATE statement
• FETCH statement
• OPEN statement
• Positioned DELETE or UPDATE statement
• SET CURSOR variable statement (on the right side)

In all of these statements, SQL Server raises an error if a referenced cursor variable exists but doesn't have a cursor currently allocated to it. If a referenced cursor variable doesn't exist, SQL Server raises the same error raised for an undeclared variable of another type.

A cursor variable:

Can be the target of either a cursor type or another cursor variable. For more information, see SET @local_variable (Transact-SQL) .

Can be referenced as the target of an output cursor parameter in an EXECUTE statement if the cursor variable doesn't have a cursor currently assigned to it.

Should be regarded as a pointer to the cursor.

A. Using DECLARE

The following example uses a local variable named @find to retrieve contact information for all last names beginning with Man .

Here's the result set.

B. Using DECLARE with two variables

The following example retrieves the names of Adventure Works Cycles sales representatives who are located in the North American sales territory and have at least $2,000,000 in sales for the year.

C. Declaring a variable of type table

The following example creates a table variable that stores the values specified in the OUTPUT clause of the UPDATE statement. Two SELECT statements follow that return the values in @MyTableVar and the results of the update operation in the Employee table. The results in the INSERTED.ModifiedDate column differ from the values in the ModifiedDate column in the Employee table. This is because the AFTER UPDATE trigger, which updates the value of ModifiedDate to the current date, is defined on the Employee table. However, the columns returned from OUTPUT reflect the data before triggers are fired. For more information, see OUTPUT Clause (Transact-SQL) .

D. Declaring a variable of type table, with inline indexes

The following example creates a table variable with a clustered inline index and two nonclustered inline indexes.

The following query returns information about the indexes created in the previous query.

E. Declaring a variable of user-defined table type

The following example creates a table-valued parameter or table variable called @LocationTVP . This requires a corresponding user-defined table type called LocationTableType . For more information about how to create a user-defined table type, see CREATE TYPE (Transact-SQL) . For more information about table-valued parameters, see Use Table-Valued Parameters (Database Engine) .

Examples: Azure Synapse Analytics and Analytics Platform System (PDW)

F. using declare.

The following example uses a local variable named @find to retrieve contact information for all last names beginning with Walt .

G. Using DECLARE with two variables

The following example retrieves uses variables to specify the first and last names of employees in the DimEmployee table.

• EXECUTE (Transact-SQL)
• Built-in Functions (Transact-SQL)
• SELECT (Transact-SQL)
• table (Transact-SQL)
• Compare Typed XML to Untyped XML

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