The difference between DROP and DELETE in SQL lies in their scope and impact on database objects. DELETE is a Data Manipulation Language (DML) command used to remove specific rows from a table, while DROP is a Data Definition Language (DDL) command that removes an entire database object (e.g., a table, index, or view) and its structure. For example, DELETE FROM employees WHERE id = 5; removes one row, leaving the table intact. In contrast, DROP TABLE employees; deletes the entire table, including its schema, data, and dependencies like indexes or triggers.
Key differences include transactional behavior and reversibility. DELETE operations can be rolled back if executed within a transaction (using ROLLBACK in databases like PostgreSQL or MySQL with transactional support). This makes DELETE safer for incremental data removal. DROP, however, is typically auto-committed in many database systems, meaning it cannot be undone once executed. For instance, if you accidentally drop a table, you’ll need to restore it from backups or recreate it manually. Additionally, DELETE logs individual row deletions, which can slow performance on large datasets, whereas DROP removes data and metadata in one action, making it faster but irreversible.
Use DELETE when you need to remove specific data without affecting the table’s structure. For example, deleting outdated user accounts while retaining the table for active users. Use DROP when you want to eliminate an entire object and its definition, such as removing a temporary table after a batch process. Developers should also consider dependencies: DROP cascades to dependent objects (if using CASCADE), while DELETE only affects rows. Always double-check DROP commands, as they permanently delete database objects, unlike DELETE, which focuses on data removal within an existing structure.