This page says DDL statements reset the clock (switch from long term to active) so I think the answer is yes, this would switch you to active on the whole table.
I’m not clear enough on the underlying storage to know if this is a real cost they pass on for updating/shuffling all partitions, or it’s purely a metadata change for the partitions that already exist.
TL;DR my answer is based on the docs and the docs may be misleading. So best to wait for someone who can speak with more confidence…
After further reading, I thing it won’t change the storage of existing data for few reason. First BigQuery store data in columnar format so if I change data in a partition for a single column it should only move that particular set of byte to active not the entire partition
If the table is edited, the price reverts back to the regular storage pricing, and the 90-day timer starts counting from zero. Anything that modifies the data in a table resets the timer, including:
The interesting bits are
Using data manipulation language (DML)
and
Using data definition language (DDL)
DML are: INSERT, DELETE, UPDATE, ... So it does not apply here. Worth considering that I think only the data that actually change because of the DML will be moved to Active.
All other actions do not reset the timer, including the following:
• Querying a table
• Creating a view that queries a table
• Exporting data from a table
• Copying a table (to another destination table)
• Patching or updating a <https://cloud.google.com/bigquery/docs/reference/rest/v2/tables|table resource>
Particularly relevant is Patching or updating a table resource. It seems to me that adding a column is a patch operation so it will not reset the clock
we regularly add columns to schemas, and while it hadn’t crossed my mind that it could affect storage, I just went back and looked, and it does not change the storage tier
by default, it’ll be cheaper storage than BQ, plus we’ve seen better compression than what BQ provides with Snappy, and with AutoClass, there’s an opportunity for it to get way cheaper for rarely accessed data