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To solve this problem, we can utilize SQL's LEFT JOIN operation. The LEFT JOIN operation will allow us to join the Person table with the Address table on the personId. It returns all records from the left table (Person), and the matched records from the right table (Address). If there is no match, NULL values are returned for columns from the right table. This operation is suitable because we need to retrieve each person's details regardless of whether they have an address recorded. Thus, using LEFT JOIN will fulfill the requirement of including persons with NULL city and state when there is no matching entry in the Address table.
Time Complexity: O(n + m), where n is the number of rows in the Person table and m is the number of rows in the Address table.
Space Complexity: O(n), where n is the number of rows in the result set.
1SELECT p.firstName, p.lastName, A.city, A.state FROM Person p LEFT JOIN Address A ON p.personId = A.personId;This SQL query selects the first and last names from the Person table and combines them with the city and state fields from the Address table. We use a LEFT JOIN to ensure that even if a person's address doesn't exist, they still appear in the results, with NULL values for the city and state fields.
This approach involves using subqueries to fetch data from the Address table if it exists. This is done by selecting fields from the Person table and then using subqueries to attempt to find the corresponding city and state for each personId from the Address table. If there's no match, the subquery will result in NULL, which aligns with the problem's requirement of reporting NULL when no address is available.
Time Complexity: O(n * m), where n is the number of rows in the Person table and m is the number of rows in the Address table because each subquery runs a separate lookup operation for each person.
Space Complexity: O(n), where n is the number of rows in the result set.
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3 p.firstName,
4This approach involves sorting the input data, which allows us to leverage properties of sorted sequences to efficiently solve the problem.
Time Complexity: O(n log n), Space Complexity: O(1) for in-place sorting
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This approach uses a hash map (or dict) to store elements for quick access. This is particularly useful if you need to quickly check for the existence of an element or store counts.
Time Complexity: O(1) average for insert/search, Space Complexity: O(n) where n is the number of elements.
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This solution fetches the first and last names directly from the Person table and utilizes subqueries to search for the corresponding city and state. Each subquery runs a lookup in the Address table for the personId. When no corresponding address is found, NULL is returned for both city and state.
Python lists have a built-in sort method which alters the list in place. The sorted list can then be easily manipulated as per problem's requirement.
Python dictionaries are hash maps, perfect for O(1) average time operations for insertion and lookup, making it ideal for tracking element presence and counts.