MSDN Documentation

SQL Data Manipulation Language (DML)

Data Manipulation Language (DML) is a subset of SQL commands used to retrieve, insert, update, and delete data from a database. DML commands are essential for managing the actual content within your database tables.

Core DML Statements

INSERT

The INSERT statement is used to add new records to a database table.

Syntax

INSERT INTO table_name (column1, column2, column3, ...) VALUES (value1, value2, value3, ...);

Example

INSERT INTO Customers (FirstName, LastName, Email) VALUES ('John', 'Doe', 'john.doe@example.com');

UPDATE

The UPDATE statement is used to modify existing records in a table.

Syntax

UPDATE table_name SET column1 = value1, column2 = value2, ... WHERE condition;

Example

UPDATE Customers SET Email = 'john.doe.updated@example.com' WHERE CustomerID = 101;

Note: Be cautious with UPDATE statements. Omitting the WHERE clause will update all rows in the table.

DELETE

The DELETE statement is used to remove records from a table.

Syntax

DELETE FROM table_name WHERE condition;

Example

DELETE FROM Customers WHERE CustomerID = 102;

Note: Similar to UPDATE, omitting the WHERE clause will delete all records from the table.

SELECT (for data retrieval)

While primarily a Data Query Language (DQL) statement, SELECT is fundamental for understanding and verifying data manipulation operations. It's used to retrieve data from one or more tables.

Syntax

SELECT column1, column2, ... FROM table_name WHERE condition;

Example

SELECT FirstName, LastName FROM Customers WHERE Country = 'USA';

Common Scenarios and Best Practices

  • Data Integrity: Always use the WHERE clause with UPDATE and DELETE to ensure you only affect the intended records.
  • Transactions: For complex operations involving multiple DML statements, use transactions to ensure atomicity. If any part of the transaction fails, the entire operation can be rolled back.
  • Error Handling: Implement appropriate error handling in your applications to catch and manage potential issues during data manipulation.
  • Performance: Optimize your queries and consider indexing relevant columns to improve the performance of DML operations, especially on large tables.

Understanding and effectively using DML statements is crucial for any database developer or administrator.