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IshaSQL

Interview Core Track · Medium · 25 min

Movie Rating

Table: Movies +---------------+---------+ | Column Name | Type | +---------------+---------+ | movie_id | int | | title | varchar | +---------------+--------...

Interview Core Track
Medium
25 min
aggregation
joins
filtering

Company labels are directional practice context, not official interview guidance.

Timer 00:00
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Objective

Practice aggregation through a IshaSQL-tagged business scenario.

Approach

Use this track to improve speed, edge-case handling, and accuracy under timed conditions.

Company context

Company labels are directional practice context, not official interview guidance.

Prereq: query basics
Prereq: filtering

Table: Movies +---------------+---------+ | Column Name | Type | +---------------+---------+ | movie_id | int | | title | varchar | +---------------+---------+ movie_id is the primary key (column with unique values) for this table. title is the name of the movie. Each movie has a unique title. Table: Users +---------------+---------+ | Column Name | Type | +---------------+---------+ | user_id | int | | name | varchar | +---------------+---------+ user_id is the primary key (column with unique values) for this table. The column 'name' has unique values. Table: MovieRating +---------------+---------+ | Column Name | Type | +---------------+---------+ | movie_id | int | | user_id | int | | rating | int | | created_at | date | +---------------+---------+ (movie_id, user_id) is the primary key (column with unique values) for this table. This table contains the rating of a movie by a user in their review. created_at is the user's review date. Write a solution to: Find the name of the user who has rated the greatest number of movies. In case of a tie, return the lexicographically smaller user name. Find the movie name with the highest average rating in February 2020 . In case of a tie, return the lexicographically smaller movie name. The result format is in the following example. Example 1: Input: Movies table: +-------------+--------------+ | movie_id | title | +-------------+--------------+ | 1 | Avengers | | 2 | Frozen 2 | | 3 | Joker | +-------------+--------------+ Users table: +-------------+--------------+ | user_id | name | +-------------+--------------+ | 1 | Daniel | | 2 | Monica | | 3 | Maria | | 4 | James | +-------------+--------------+ MovieRating table: +-------------+--------------+--------------+-------------+ | movie_id | user_id | rating | created_at | +-------------+--------------+--------------+-------------+ | 1 | 1 | 3 | 2020-01-12 | | 1 | 2 | 4 | 2020-02-11 | | 1 | 3 | 2 | 2020-02-12 | | 1 | 4 | 1 | 2020-01-01 | | 2 | 1 | 5 | 2020-02-17 | | 2 | 2 | 2 | 2020-02-01 | | 2 | 3 | 2 | 2020-03-01 | | 3 | 1 | 3 | 2020-02-22 | | 3 | 2 | 4 | 2020-02-25 | +-------------+--------------+--------------+-------------+ Output: +--------------+ | results | +--------------+ | Daniel | | Frozen 2 | +--------------+ Explanation: Daniel and Monica have rated 3 movies ("Avengers", "Frozen 2" and "Joker") but Daniel is smaller lexicographically. Frozen 2 and Joker have a rating average of 3.5 in February but Frozen 2 is smaller lexicographically.

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