Reading 3 columns instead of 11

data-job-postings has 11 columns and 3,500 rows across 966 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 11 columns cuts the scan to roughly 27.3%. Filtering to one of 966 days cuts it to 0.1%. Together — and they multiply — the query reads about 0.03% of what select * would.

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

-- reads 3 columns, one day
select posting_id, posted_on, title
from data_job_postings
where posted_on >= date '2023-06-01'
  and posted_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(posted_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.