The task involved querying a table named user_events to retrieve the most recent event for every user. A conventional SQL query was used, which performed poorly in production despite returning correct results. The inner query groups the entire table by user to find the latest event time, requiring Postgres to scan 100 million rows and perform 5 million aggregations, resulting in a temporary table with 5 million rows. The outer query then checks each row against this large list, significantly degrading performance due to the repetitive evaluations across the vast dataset.