Codestin Search App

Reported Posts II - Problem

Database Medium

You're working as a content moderator for a social media platform, analyzing the effectiveness of your spam detection system. Your goal is to calculate the daily removal rate of posts that were reported as spam.

You have access to two tables:

Actions: Records all user interactions with posts, including reports
Removals: Records when posts were actually removed by moderators

Your task is to find the average daily percentage of spam-reported posts that got removed, rounded to 2 decimal places.

Key Challenge: You need to match posts that were both reported as spam AND subsequently removed, then calculate the daily removal rate across all days.

Think of it like measuring how effective your content moderation team is at acting on spam reports!

Input & Output

basic_example.sql — Basic Case

$ Input: Actions: | user_id | post_id | action_date | action | extra | |---------|---------|-------------|--------|---------| | 1 | 1 | 2019-07-01 | view | null | | 1 | 1 | 2019-07-01 | like | null | | 1 | 1 | 2019-07-01 | share | null | | 2 | 2 | 2019-07-01 | report | spam | | 2 | 3 | 2019-07-02 | report | spam | Removals: | post_id | remove_date | |---------|-------------| | 2 | 2019-07-02 |

› Output: | average_daily_percent | |-----------------------| | 50.00 |

💡 Note: Day 1: 1 spam report (post_id=2), 1 removed → 100%. Day 2: 1 spam report (post_id=3), 0 removed → 0%. Average = (100 + 0) / 2 = 50.00%

no_removals.sql — No Spam Removals

$ Input: Actions: | user_id | post_id | action_date | action | extra | |---------|---------|-------------|--------|---------| | 1 | 1 | 2019-07-01 | report | spam | | 2 | 2 | 2019-07-01 | report | spam | Removals: | post_id | remove_date | |---------|-------------|

› Output: | average_daily_percent | |-----------------------| | 0.00 |

💡 Note: 2 posts reported as spam on 2019-07-01, but none were removed. Daily percentage = 0/2 * 100 = 0.00%

all_removed.sql — All Spam Removed

$ Input: Actions: | user_id | post_id | action_date | action | extra | |---------|---------|-------------|--------|---------| | 1 | 1 | 2019-07-01 | report | spam | | 2 | 2 | 2019-07-01 | report | spam | | 3 | 3 | 2019-07-02 | report | spam | Removals: | post_id | remove_date | |---------|-------------| | 1 | 2019-07-02 | | 2 | 2019-07-03 | | 3 | 2019-07-02 |

› Output: | average_daily_percent | |-----------------------| | 100.00 |

💡 Note: Day 1: 2 reports, both removed → 100%. Day 2: 1 report, removed → 100%. Average = (100 + 100) / 2 = 100.00%

Constraints

1 ≤ user_id, post_id ≤ 10⁵
action_date and remove_date are valid dates
action ∈ {'view', 'like', 'reaction', 'comment', 'report', 'share'}
extra field is 'spam' only for report actions we care about
A post can be reported multiple times but should be counted once per day

Visualization

Tap to expand

Understanding the Visualization

Daily Spam Reports

Collect all unique posts reported as spam each day

Match with Removals

Check which reported posts were actually removed

Calculate Daily Rate

For each day: (removed posts / reported posts) × 100

Average Performance

Average all daily percentages to get overall effectiveness

Key Takeaway

🎯 Key Insight: Group by date first to calculate daily percentages correctly, then average those percentages. This approach handles varying daily volumes and provides meaningful effectiveness metrics.

Asked in

f Meta 45 G Google 38 t Twitter 32 in LinkedIn 28

The optimal solution uses a Common Table Expression (CTE) to first identify distinct spam reports by date, then performs an efficient LEFT JOIN with the Removals table. Key insight: group by action_date first to calculate daily percentages, then take the average across all days. This approach handles edge cases like multiple reports of the same post and ensures accurate percentage calculation.

Common Approaches

Approach	Time	Space	Notes
✓ Optimized JOIN with CTE (Optimal)	O(n log n)	O(n)	Use Common Table Expression with efficient JOIN to process data in one pass
Nested Subqueries Approach	O(n²)	O(1)	Use nested subqueries to check each reported post individually

Optimized JOIN with CTE (Optimal) — Algorithm Steps

Create CTE to get distinct spam reports by date
LEFT JOIN with Removals table to identify which were removed
GROUP BY action_date to calculate daily removal percentages
Use AVG() to get average daily percentage and ROUND to 2 decimals

Visualization

Tap to expand

Step-by-Step Walkthrough

CTE: Spam Reports

Create temporary result set of distinct spam reports

JOIN with Removals

Efficiently match reports with actual removals

Group by Date

Calculate daily removal percentages

Final Average

Compute average across all days

Code -

solution.c — C

// Optimal SQL solution using CTE
#include <stdio.h>
#include <string.h>

char* solution() {
    static char query[] = """
    WITH spam_reports AS (
        SELECT DISTINCT post_id, action_date
        FROM Actions
        WHERE action = 'report' AND extra = 'spam'
    ),
    daily_removal_rates AS (
        SELECT 
            s.action_date,
            ROUND(
                100.0 * COUNT(r.post_id) / COUNT(s.post_id), 2
            ) as daily_percent
        FROM spam_reports s
        LEFT JOIN Removals r ON s.post_id = r.post_id
        GROUP BY s.action_date
    )
    SELECT ROUND(AVG(daily_percent), 2) as average_daily_percent
    FROM daily_removal_rates;
    """;
    return query;
}

Time & Space Complexity

Time Complexity

⏱️

O(n log n)

Single scan of Actions table plus efficient JOIN operation with Removals table

⚡ Linearithmic

Space Complexity

O(n)

Space needed for intermediate CTE results and JOIN operation

⚡ Linearithmic Space

28.4K Views

High Frequency

~25 min Avg. Time

892 Likes

Ln 1, Col 1

Smart Actions

💡 Explanation

AI Ready

💡 Suggestion Tab to accept Esc to dismiss

// Output will appear here after running code

Code Editor Closed

Click the red button to reopen

Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Optimized JOIN with CTE (Optimal) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Select Compiler