What is the difference between DDL and DML in SQL?

DDL (Data Definition Language) and DML (Data Manipulation Language) are two core subsets of SQL with distinct roles in database operations. DDL focuses on defining and modifying the structure of a database and its objects, such as tables, indexes, and schemas. Common DDL commands include CREATE, ALTER, DROP, and TRUNCATE. For example, CREATE TABLE defines a new table’s columns and data types, while ALTER TABLE adds or removes columns. DDL changes are typically auto-committed, meaning they take effect immediately and cannot be rolled back. This makes DDL operations critical for setting up or restructuring a database but requires caution, as mistakes can lead to permanent data loss or schema issues.

DML, on the other hand, deals with manipulating the data stored within the database structures defined by DDL. Key DML commands include SELECT, INSERT, UPDATE, and DELETE. For instance, INSERT INTO users VALUES (...) adds a new row to a table, while UPDATE users SET name='John' modifies existing data. Unlike DDL, DML operations are transactional, meaning they can be grouped and controlled within transactions using COMMIT or ROLLBACK. This allows developers to ensure data consistency—for example, undoing a mistaken DELETE command before finalizing changes. DML is used daily for routine tasks like querying data, updating records, or deleting obsolete entries, making it central to application functionality.

The distinction between DDL and DML impacts how developers interact with databases. DDL is used during initial setup, migrations, or schema changes, often requiring administrative privileges. For example, altering a table to add a column might require downtime or careful planning. DML, however, is used in application code for CRUD (Create, Read, Update, Delete) operations. A practical difference lies in commands like TRUNCATE (DDL) versus DELETE (DML): TRUNCATE removes all rows by deallocating data pages, which is faster but irreversible, while DELETE removes rows one by one and can be rolled back. Understanding these differences ensures developers choose the right tools for structural changes versus data handling, avoiding unintended consequences.