TRUNCATE TABLE is a SQL command used to remove all rows from a table. It's faster than DELETE but doesn't allow for filtering or using a WHERE clause.
The TRUNCATE TABLE command is a powerful tool for quickly emptying a table in a SQL database. Unlike the DELETE command, which can be used to remove rows based on specific criteria, TRUNCATE TABLE removes *all* rows from the table. This makes it significantly faster than DELETE, especially for large tables, because it doesn't require processing individual row deletions. It's crucial to understand that TRUNCATE TABLE is a DML (Data Manipulation Language) statement, meaning it directly modifies the data within a table. It's important to note that TRUNCATE TABLE is a DML statement that permanently removes all rows from a table. This operation cannot be undone unless you have a backup or use a transaction that can be rolled back. It's also important to remember that TRUNCATE TABLE does not return any rows or information about the rows that were deleted. This is a key difference from DELETE, which often returns a count of the rows deleted.
TRUNCATE TABLE is essential for quickly clearing a table's contents, especially when preparing for large-scale data loads or maintenance tasks. Its speed advantage over DELETE makes it a valuable tool for database administrators and developers.
TRUNCATE TABLE de-allocates the data pages that store a table’s rows instead of logging and processing each individual row deletion like DELETE. Because it bypasses row-by-row logging and constraint checking, the operation finishes dramatically faster on large datasets.
Not by default. TRUNCATE TABLE is a DML command that permanently removes all rows. The only ways to undo it are (1) executing the statement inside an explicit transaction you later roll back, or (2) restoring from a backup taken before the truncate.
Modern SQL editors like Galaxy help prevent mishaps. Galaxy’s AI copilot can flag destructive commands, require confirmation for TRUNCATE statements, and let teams review or “endorse” critical queries before they run—reducing the risk of accidental data loss.