Solution Explanation: Apples & Oranges

The problem requires calculating the daily difference between the number of apples and oranges sold. The solution leverages SQL's grouping and aggregation capabilities.

Approach

The core idea is to:

1. Group the sales data by sale_date: This ensures we calculate the difference for each day independently.
2. Calculate the difference: For each day, we need to sum the sold_num for apples and subtract the sold_num for oranges. This is efficiently done using conditional aggregation (e.g., MySQL's IF function or similar functions in other SQL dialects).
3. Order the results: The final result set should be ordered by sale_date in ascending order.

SQL Code (MySQL)

SELECT
    sale_date,
    SUM(IF(fruit = 'apples', sold_num, -sold_num)) AS diff
FROM Sales
GROUP BY sale_date
ORDER BY sale_date;

Explanation:

• SELECT sale_date, SUM(...) AS diff: This selects the sale_date and calculates the sum, aliasing it as diff.
• SUM(IF(fruit = 'apples', sold_num, -sold_num)): This is the crucial part. The IF function checks the fruit column.
  • If fruit is 'apples', it adds sold_num to the sum (positive contribution).
  • If fruit is 'oranges', it subtracts sold_num from the sum (negative contribution).
• FROM Sales: This specifies the table to query.
• GROUP BY sale_date: This groups the rows by sale_date, so the SUM function operates on each day's sales separately.
• ORDER BY sale_date: This sorts the final result by sale_date in ascending order.

Time and Space Complexity

• Time Complexity: The time complexity is dominated by the GROUP BY operation. In general, the time complexity of GROUP BY is O(N log N) or O(N) depending on the specific database implementation and the presence of indexes. N is the number of rows in the Sales table.
• Space Complexity: The space complexity is determined by the size of the intermediate result set after the GROUP BY operation. In the worst case, if there are unique dates for each row, the space complexity would be O(N). However, in practice, this is often much smaller because there will likely be multiple sales entries for the same date. The space used for the final result set is proportional to the number of unique dates, which is typically much less than N.

Other SQL Dialects

The core logic can be adapted to other SQL dialects. Here's an example using PostgreSQL:

SELECT
    sale_date,
    SUM(CASE WHEN fruit = 'apples' THEN sold_num ELSE -sold_num END) AS diff
FROM Sales
GROUP BY sale_date
ORDER BY sale_date;

The CASE statement in PostgreSQL serves the same purpose as the IF function in MySQL. Similar conditional expressions exist in other database systems like SQL Server, Oracle, etc. The overall approach remains consistent across different SQL dialects.