


What is the difference between deleting table data and deleting table structure in SQL?
The difference between deleting table data and deleting table structure in SQL: 1. When deleting table data, the table structure is still there, while deleting table structure means deleting the entire table, including structure and data; 2. Command Different, delete table data [delete from table name], while delete table structure is [drop table table name].
The difference between deleting table data and deleting table structure in SQL:
1. Different definitions.
Deleting the contents of the table refers to deleting the data of the table. The structure of the table is still there.
Deleting the structure of a table means deleting the entire table, including structure and data.
2. The commands are different.
Delete table data:
truncate table table name;
or
delete from table name;
Delete table structure:
drop table table name;
Extended information:
truncate
: Delete content, release space but do not delete definitions (retain the data structure of the table). Truncate cannot delete row data. To delete it, the table must be cleared.
delete
: The delete statement is used to delete rows in the table. The deletion process of the delete statement is to delete one row from the table at a time, and at the same time, the deletion operation of the row is saved in the log as a transaction record for rollback operation.
truncate and delete without where: only delete the data, but not the structure (definition) of the table.
If you want to retain the identity count value, please use delete instead. If you want to drop a table definition and its data, use the drop table statement.
drop
: Delete content and definitions to free up space. To put it simply, remove the entire table. No new data can be added in the future unless a new table is added.
The drop statement will delete the constraints, triggers, and indexes that the table structure depends on; the stored procedures/functions that depend on the table will be retained, but their status will change For: invalid.
Related learning recommendations: SQL video tutorial
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