Is Find Books with Polarized Opinions easy or hard?

It is rated Medium with an acceptance rate of about 46 percent. The difficulty comes from translating the polarization rule into conditional aggregation and handling the join correctly, not from complex logic. If you are comfortable with GROUP BY and CASE expressions, you can solve it in one pass.

Find Books with Polarized Opinions Python/Java solution?

In Python with Pandas, merge the books and sessions DataFrames on book_id, create boolean columns for high and low ratings, group by the book, sum those columns, and filter with a boolean mask. For Java or other languages, the same logic is expressed as a SQL query with a JOIN, GROUP BY, and HAVING clause. The FleetCode solution page provides both forms.

How to solve Find Books with Polarized Opinions in O(n)?

Join the books table to the sessions table, group by book_id, and compute high and low rating counts with conditional aggregation in one pass. Each session contributes to exactly one group, so the total work is linear in the number of sessions. Apply the polarization condition as a filter on the aggregated counts.

What is the best approach for Find Books with Polarized Opinions?

The best approach is a join between the books and reading sessions tables, followed by a single group aggregation. Use conditional sums to count high and low ratings per book, then filter with a HAVING clause or a Pandas mask. This runs in O(S) time, where S is the number of sessions.

What data structure is used in Find Books with Polarized Opinions?

The solution relies on a hash-based grouping structure, which the database engine or a Pandas groupby builds internally, keyed by book. Each group stores running high and low counts. No explicit tree or heap is needed.

What is the time complexity of Find Books with Polarized Opinions?

The optimal solution runs in O(S) time, where S is the number of reading sessions, because each session row is processed once during grouping. The space complexity is O(B), where B is the number of books, for the per-book aggregation results. A naive per-book scan takes O(B * S) time.

Is Find Books with Polarized Opinions asked at Google, Amazon, or Meta?

This problem is a Medium database question on LeetCode, and it is most relevant if you are preparing for SQL-heavy data roles or screens that include aggregation. Companies that test SQL in interviews, including large tech firms, often use joins and group-by questions of this type. Check recent company tag lists on LeetCode for confirmed frequency.

Description Solution Video 12:22

3642. Find Books with Polarized Opinions

Medium45.9% acceptance

Table: books

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| book_id     | int     |
| title       | varchar |
| author      | varchar |
| genre       | varchar |
| pages       | int     |
+-------------+---------+
book_id is the unique ID for this table.
Each row contains information about a book including its genre and page count.

Table: reading_sessions

+----------------+---------+
| Column Name    | Type    |
+----------------+---------+
| session_id     | int     |
| book_id        | int     |
| reader_name    | varchar |
| pages_read     | int     |
| session_rating | int     |
+----------------+---------+
session_id is the unique ID for this table.
Each row represents a reading session where someone read a portion of a book. session_rating is on a scale of 1-5.

Write a solution to find books that have polarized opinions - books that receive both very high ratings and very low ratings from different readers.

A book has polarized opinions if it has at least one rating ≥ 4 and at least one rating ≤ 2
Only consider books that have at least 5 reading sessions
Calculate the rating spread as (highest_rating - lowest_rating)
Calculate the polarization score as the number of extreme ratings (ratings ≤ 2 or ≥ 4) divided by total sessions
Only include books where polarization score ≥ 0.6 (at least 60% extreme ratings)

Return the result table ordered by polarization score in descending order, then by title in descending order.
The polarization score should be rounded to 2 decimal places.

The result format is in the following example.

Example:

Input:

books table:

+---------+------------------------+---------------+----------+-------+
| book_id | title                  | author        | genre    | pages |
+---------+------------------------+---------------+----------+-------+
| 1       | The Great Gatsby       | F. Scott      | Fiction  | 180   |
| 2       | To Kill a Mockingbird  | Harper Lee    | Fiction  | 281   |
| 3       | 1984                   | George Orwell | Dystopian| 328   |
| 4       | Pride and Prejudice    | Jane Austen   | Romance  | 432   |
| 5       | The Catcher in the Rye | J.D. Salinger | Fiction  | 277   |
+---------+------------------------+---------------+----------+-------+

reading_sessions table:

+------------+---------+-------------+------------+----------------+
| session_id | book_id | reader_name | pages_read | session_rating |
+------------+---------+-------------+------------+----------------+
| 1          | 1       | Alice       | 50         | 5              |
| 2          | 1       | Bob         | 60         | 1              |
| 3          | 1       | Carol       | 40         | 4              |
| 4          | 1       | David       | 30         | 2              |
| 5          | 1       | Emma        | 45         | 5              |
| 6          | 2       | Frank       | 80         | 4              |
| 7          | 2       | Grace       | 70         | 4              |
| 8          | 2       | Henry       | 90         | 5              |
| 9          | 2       | Ivy         | 60         | 4              |
| 10         | 2       | Jack        | 75         | 4              |
| 11         | 3       | Kate        | 100        | 2              |
| 12         | 3       | Liam        | 120        | 1              |
| 13         | 3       | Mia         | 80         | 2              |
| 14         | 3       | Noah        | 90         | 1              |
| 15         | 3       | Olivia      | 110        | 4              |
| 16         | 3       | Paul        | 95         | 5              |
| 17         | 4       | Quinn       | 150        | 3              |
| 18         | 4       | Ruby        | 140        | 3              |
| 19         | 5       | Sam         | 80         | 1              |
| 20         | 5       | Tara        | 70         | 2              |
+------------+---------+-------------+------------+----------------+

Output:

+---------+------------------+---------------+-----------+-------+---------------+--------------------+
| book_id | title            | author        | genre     | pages | rating_spread | polarization_score |
+---------+------------------+---------------+-----------+-------+---------------+--------------------+
| 1       | The Great Gatsby | F. Scott      | Fiction   | 180   | 4             | 1.00               |
| 3       | 1984             | George Orwell | Dystopian | 328   | 4             | 1.00               |
+---------+------------------+---------------+-----------+-------+---------------+--------------------+

Explanation:

The Great Gatsby (book_id = 1):
- Has 5 reading sessions (meets minimum requirement)
- Ratings: 5, 1, 4, 2, 5
- Has ratings ≥ 4: 5, 4, 5 (3 sessions)
- Has ratings ≤ 2: 1, 2 (2 sessions)
- Rating spread: 5 - 1 = 4
- Extreme ratings (≤2 or ≥4): All 5 sessions (5, 1, 4, 2, 5)
- Polarization score: 5/5 = 1.00 (≥ 0.6, qualifies)
1984 (book_id = 3):
- Has 6 reading sessions (meets minimum requirement)
- Ratings: 2, 1, 2, 1, 4, 5
- Has ratings ≥ 4: 4, 5 (2 sessions)
- Has ratings ≤ 2: 2, 1, 2, 1 (4 sessions)
- Rating spread: 5 - 1 = 4
- Extreme ratings (≤2 or ≥4): All 6 sessions (2, 1, 2, 1, 4, 5)
- Polarization score: 6/6 = 1.00 (≥ 0.6, qualifies)
Books not included:
- To Kill a Mockingbird (book_id = 2): All ratings are 4-5, no low ratings (≤2)
- Pride and Prejudice (book_id = 4): Only 2 sessions (< 5 minimum)
- The Catcher in the Rye (book_id = 5): Only 2 sessions (< 5 minimum)

The result table is ordered by polarization score in descending order, then by book title in descending order.

12:22

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1# Write your MySQL query statement below

Starter codeMySQLSpaces: 4

Examples

Case 1

Input

{"headers":{"books":["book_id","title","author","genre","pages"],"reading_sessions":["session_id","book_id","reader_name","pages_read","session_rating"]},"rows":{"books":[[1,"The Great Gatsby","F. Scott","Fiction",180],[2,"To Kill a Mockingbird","Harper Lee","Fiction",281],[3,"1984","George Orwell","Dystopian",328],[4,"Pride and Prejudice","Jane Austen","Romance",432],[5,"The Catcher in the Rye","J.D. Salinger","Fiction",277]],"reading_sessions":[[1,1,"Alice",50,5],[2,1,"Bob",60,1],[3,1,"Carol",40,4],[4,1,"David",30,2],[5,1,"Emma",45,5],[6,2,"Frank",80,4],[7,2,"Grace",70,4],[8,2,"Henry",90,5],[9,2,"Ivy",60,4],[10,2,"Jack",75,4],[11,3,"Kate",100,2],[12,3,"Liam",120,1],[13,3,"Mia",80,2],[14,3,"Noah",90,1],[15,3,"Olivia",110,4],[16,3,"Paul",95,5],[17,4,"Quinn",150,3],[18,4,"Ruby",140,3],[19,5,"Sam",80,1],[20,5,"Tara",70,2]]}}

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