Solution Explanation

This problem requires finding sessions from the Playback table that did not have any ads shown during their duration, as recorded in the Ads table. The solution uses a subquery to identify sessions with ads and then excludes those sessions from the result.

Approach

1. Identify Sessions with Ads: A subquery joins the Playback and Ads tables. The join condition checks if a customer in a session (Playback.customer_id) also had an ad shown (Ads.customer_id) during the session's time range (Ads.timestamp between Playback.start_time and Playback.end_time). This subquery returns the session_id of all sessions that had at least one ad shown during them.

2. Exclude Sessions with Ads: The main query selects all session_id from the Playback table. It uses the NOT IN clause to exclude the session_id values obtained from the subquery. This effectively filters out all sessions that had ads, leaving only the sessions without ads.

MySQL Code Explanation

SELECT session_id
FROM Playback
WHERE
    session_id NOT IN (
        SELECT session_id
        FROM
            Playback AS p
            JOIN Ads AS a
                ON p.customer_id = a.customer_id AND a.timestamp BETWEEN p.start_time AND p.end_time
    );

• SELECT session_id FROM Playback: This selects all session IDs from the Playback table.
• WHERE session_id NOT IN (...): This filters the results, keeping only session IDs that are not present in the subquery's result set.
• Subquery:
  • SELECT session_id FROM Playback AS p JOIN Ads AS a ...: This joins Playback (aliased as p) and Ads (aliased as a) tables. The AS p and AS a are aliases to make the query more readable.
  • ON p.customer_id = a.customer_id AND a.timestamp BETWEEN p.start_time AND p.end_time: This is the join condition. It ensures that only sessions where the customer ID matches and the ad timestamp falls within the session's start and end times are considered.

Time Complexity Analysis

The time complexity of this SQL query depends on the size of the Playback and Ads tables. The join operation in the subquery has a time complexity that's generally proportional to the product of the sizes of the two tables in the worst case (nested loop join). However, database systems usually optimize joins using indexes and other techniques, leading to a complexity that can be significantly better in practice (e.g., close to linear if appropriate indexes exist). The NOT IN operation then needs to check each session_id against the result of the subquery, adding another potentially linear step.

Therefore, while a worst-case analysis might suggest a complexity close to O(M*N), where M and N are the number of rows in Playback and Ads respectively, the actual performance will heavily depend on the database's query optimizer and the presence of suitable indexes on the relevant columns (customer_id, start_time, end_time, timestamp). With proper indexing, the effective complexity could be closer to O(M + N) or even better.