Greenplum DB is open source, based on Postgres, and is a proper sharded OLAP database in the vein of Teradata. Its answer to these types of large analytic queries is that it stores the answers and as long as the underlying table data doesn't change, the query can run at near 0 cost. I believe the same approach is probably in use here.

Shame generate_series() isn't a valid function here - joining to an artificial table is a classic way to break the pre-aggregated values, and force a re-calc.

Redshift does something similar IIRC.