Here is an analytical query that you (and I) will often need to do if you work in e-commerce, marketing or similar domain. It answers the question, within each group of items (e.g. partitioned by territory, age groups or something else) what are the top-k items for some utility function over the items (e.g. the number of units sold)?
Assume we have a table,
aggregated_sales(id INT, item TEXT, country TEXT, total_sold INT). Here is a sample:
ID ITEM COUNTRY TOTAL_SOLD 1 Basketball DK 125 2 Basketball US 432943 3 Leather football FO 64773 4 Leather football DK 56230 5 Boxing gloves CU 9812
In SQL, here is how to get the top-3 selling items by country:
SELECT id, item, country, total_sold FROM ( SELECT *, row_number() OVER (PARTITION BY country ORDER BY total_sold DESC) as rn FROM aggregated_sales ) t WHERE rn = 1;
Bonus info: the table sample was generated from a CSV file with the command
column -s , -t filename.csv.