Reading 3 columns instead of 8

ab-test-checkout has 8 columns and 6,000 rows across 28 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 8 columns cuts the scan to roughly 37.5%. Filtering to one of 28 days cuts it to 3.6%. Together — and they multiply — the query reads about 1.3% of what select * would.

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

-- reads 3 columns, one day
select user_id, variant, exposed_on
from ab_test_checkout
where exposed_on >= date '2023-06-01'
  and exposed_on <  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(exposed_on) = '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.