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Big Countries

Table: World

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| name        | varchar |
| continent   | varchar |
| area        | int     |
| population  | int     |
| gdp         | bigint  |
+-------------+---------+
name is the primary key (column with unique values) for this table.
Each row of this table gives information about the name of a country, the continent to which it belongs, its area, the population, and its GDP value.

 

A country is big if:

  • it has an area of at least three million (i.e., 3000000 km2), or
  • it has a population of at least twenty-five million (i.e., 25000000).

Write a solution to find the name, population, and area of the big countries.

Return the result table in any order.

The result format is in the following example.

 

Example 1:

Input: 
World table:
+-------------+-----------+---------+------------+--------------+
| name        | continent | area    | population | gdp          |
+-------------+-----------+---------+------------+--------------+
| Afghanistan | Asia      | 652230  | 25500100   | 20343000000  |
| Albania     | Europe    | 28748   | 2831741    | 12960000000  |
| Algeria     | Africa    | 2381741 | 37100000   | 188681000000 |
| Andorra     | Europe    | 468     | 78115      | 3712000000   |
| Angola      | Africa    | 1246700 | 20609294   | 100990000000 |
+-------------+-----------+---------+------------+--------------+
Output: 
+-------------+------------+---------+
| name        | population | area    |
+-------------+------------+---------+
| Afghanistan | 25500100   | 652230  |
| Algeria     | 37100000   | 2381741 |
+-------------+------------+---------+

Solution Explanation for LeetCode Problem 595: Big Countries

This problem requires retrieving information about "big" countries from a World table. A country is considered big if its area is at least 3,000,000 km² or its population is at least 25,000,000. The solution needs to return the name, population, and area of these big countries.

Approach 1: Using a Single WHERE Clause

This approach uses a single WHERE clause with a logical OR condition to efficiently filter the countries.

MySQL Code:

SELECT name, population, area
FROM World
WHERE area >= 3000000 OR population >= 25000000;

Explanation:

  1. SELECT name, population, area: This selects the required columns: name, population, and area.
  2. FROM World: This specifies the table to retrieve data from.
  3. WHERE area >= 3000000 OR population >= 25000000: This is the core of the query. It filters the rows based on the condition that either the area is greater than or equal to 3,000,000 OR the population is greater than or equal to 25,000,000. The OR operator ensures that a country is included if it satisfies at least one of these conditions.

Time Complexity: O(N), where N is the number of rows in the World table. This is because the database needs to scan the entire table to filter the rows based on the WHERE clause. However, modern database systems optimize this process, and the actual time taken will depend on indexing and other database-specific factors.

Space Complexity: O(M), where M is the number of rows that satisfy the condition in the WHERE clause. This represents the space used to store the result set.

Approach 2: Using UNION

This approach uses two SELECT statements combined with a UNION operation. Each SELECT statement retrieves big countries based on a single condition (area or population), and UNION combines the results without duplicates.

MySQL Code:

SELECT name, population, area
FROM World
WHERE area >= 3000000
UNION
SELECT name, population, area
FROM World
WHERE population >= 25000000;

Explanation:

  1. SELECT name, population, area FROM World WHERE area >= 3000000: This selects countries with an area greater than or equal to 3,000,000.
  2. UNION: This combines the results of the two SELECT statements. UNION automatically removes duplicate rows.
  3. SELECT name, population, area FROM World WHERE population >= 25000000: This selects countries with a population greater than or equal to 25,000,000.

Time Complexity: O(N log N) in the worst case. This is because, although each SELECT statement has O(N) time complexity, UNION typically involves sorting the results to remove duplicates, resulting in a logarithmic factor. Again, database optimizations may affect this significantly.

Space Complexity: O(M), similar to Approach 1, where M is the number of rows satisfying either condition.

Which Approach is Better?

Approach 1 (using a single WHERE clause with OR) is generally more efficient than Approach 2 (using UNION). The UNION operation can be more computationally expensive, especially with larger datasets. Therefore, Approach 1 is the preferred solution unless there's a specific reason to use UNION. Modern database optimizers may make the difference less significant in practice, but the single WHERE clause remains conceptually cleaner and potentially faster.