What happens to a view if the underlying table structure is changed?

If the underlying table structure of a view is changed, the view itself may be affected, and you might need to take certain actions to ensure the view remains functional and accurate. The impact of changing the table structure depends on the type of changes made and the nature of the view.

Here's what can happen to a view when the underlying table structure changes:

1. **Column Addition or Removal:** If columns are added to or removed from the underlying table, the view may still work as long as the view's query doesn't explicitly reference the added or removed columns. The view's query will continue to present data based on the columns it references.
2. **Column Renaming or Data Type Change:** If columns are renamed or their data types are changed in the underlying table, and the view's query references those columns, the view may become invalid. You might need to modify the view's query to reflect the changes in the underlying table.
3. **Primary Key or Unique Key Changes:** If the primary key or unique key of the underlying table changes, this could potentially affect queries involving joins or other data relationships in the view's query.
4. **Data Truncation:** If data types of columns in the underlying table are changed to narrower data types, data truncation could occur for values that no longer fit within the new data types.
5. **Table Renaming or Deletion:** If the underlying table is renamed or deleted, the view will become invalid. You might need to recreate the view using the new table name or restore the table.

It's important to note that Snowflake does not automatically update views when the underlying table structure changes. You will need to manually review and modify the view's query if necessary to accommodate any changes in the underlying table.

In general, if you plan to make changes to the structure of an underlying table that is used by one or more views, it's a good practice to review and update the views accordingly to ensure their continued accuracy and functionality. Additionally, it's wise to test the impact of changes in a non-production environment before applying them to a live system.