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The task requires selecting names, populations, and areas of countries based on certain criteria: whether the area is at least 3 million or the population is at least 25 million. We can achieve this using a SQL query that utilizes the WHERE clause to filter the specified conditions.
Time Complexity: O(n) where n is the number of rows in the World table, as all rows potentially need to be scanned once.
Space Complexity: O(n) due to storing the result set that meets the criteria.
1SELECT name, population, area FROM World WHERE area >= 3000000 OR population >= 25000000;This SQL query selects the name, population, and area columns from the World table. It uses a WHERE clause to filter the records where either the area is greater than or equal to 3 million or the population is greater than or equal to 25 million. The OR operator is used to check if any of the conditions are met. The result consists of records that satisfy at least one of these conditions.
In the iterative filtering approach, we simulate the results of the SQL query using an iterative process in various programming languages. We loop through each record, check if a record satisfies either of the conditions, and collect the results accordingly. This is done using simple loops and condition checks.
Time Complexity: O(n), where n is the number of countries.
Space Complexity: O(n), to store the resulting list of countries that meet the criteria.
1def big_countriesThis Python function takes a list of dictionaries where each dictionary represents a country's information. It iterates over the list and checks if the 'area' is at least 3 million or the 'population' is at least 25 million. If a condition is satisfied, the country is added to the result list.