As described here , the initial try of creating a slice of production data was made by using the Django ORM, and it did not work, because even deleting in batches the job was still running after more than 2 hours and the pipeline timed out before finishing it. After taking a closer look at it, deleting is the expensive direction in both Django, where the cascade is resolved in Python by the Collector (as described in the first post), and Postgres (everything goes through the WAL , and the deleted rows are still there until a VACUUM ), and also, its cost grows with the rows that we throw away, which are the majority of the table.…