## Problem (SQL) You work on a music app and want to recommend new friend connections based on listening similarity. ### Tables Assume the following schemas: **listens** - `user_id` (BIGINT) - `song_id` (BIGINT) - `listened_at` (TIMESTAMP) **friendships** (undirected friendship; may be stored as one row per pair) - `user_id_1` (BIGINT) - `user_id_2` (BIGINT) - `created_at` (TIMESTAMP) ### Definitions / assumptions - Two users have an *overlapping song* on a given day if **both** listened to the **same `song_id`** on that **same calendar date**. - Count **distinct** overlapping songs per pair per day (ignore repeat listens of the same song). - Treat pairs as **unordered** (e.g., output `(min_user, max_user)`). - A pair should be recommended only if they are **not already friends** (no matching pair in `friendships`, regardless of ordering). - Unless stated otherwise, treat `listened_at` as UTC when deriving the calendar `date`. ### Task Write a SQL query to return all recommended pairs of users `(user_id_a, user_id_b)` such that there exists at least one day where they listened to **more than 3** of the **same songs** on that day. ### Output Return: - `user_id_a` - `user_id_b` - `listen_date` - `overlap_song_count` Only include rows where `overlap_song_count > 3` and the pair is not already in `friendships`.