Reading 3 columns instead of 7

movie-ratings has 7 columns and 9,000 rows across 901 days. A query that needs three of those columns for one day is doing a lot less work than one that does not say so.

Naming 3 of 7 columns cuts the scan to roughly 42.9%. Filtering to one of 901 days cuts it to 0.1%. Together — and they multiply — the query reads about 0.05% of what select * would.

-- reads every column, every day
select * from movie_ratings;

-- reads 3 columns, one day
select user_id, movie_id, title
from movie_ratings
where rated_at >= date '2023-06-01'
  and rated_at <  date '2023-06-02';

The second version is not a micro-optimisation. On a real warehouse those two queries differ by three orders of magnitude in cost, and the expensive one is the one that is easier to type.

The trap worth knowing: wrapping the partition column in a function — date(rated_at) = '2023-06-01' — usually defeats pruning, because the engine can no longer reason about the raw column. Same result, full scan, no warning. Compare against a range on the bare column instead.

Read the bytes-scanned line in the plan before optimising anything else. It is usually the entire answer. Why your query costs what it costs has the rest.