Write SQL for character safety and engagement metrics

You are working with a conversational product that has users, AI characters, and conversations. Write SQL queries for each of the tasks below.

Assume UTC timestamps and that day means DATE(created_at).

Tables

users

• user_id BIGINT PRIMARY KEY
• created_at TIMESTAMP

characters

• character_id BIGINT PRIMARY KEY
• created_by_user_id BIGINT REFERENCES users(user_id)
• created_at TIMESTAMP
• safety_flag BOOLEAN
• TRUE = safe, FALSE = unsafe

conversations

Each row represents a user engaging in a conversation associated with a character.

• conversation_id BIGINT
• character_id BIGINT REFERENCES characters(character_id)
• user_id BIGINT REFERENCES users(user_id)
• created_at TIMESTAMP
• safety_flag BOOLEAN
• TRUE = safe, FALSE = unsafe

Notes:

• A single conversation_id may appear multiple times if multiple users engaged.

Task 1: Find the “top 100 characters”

Define “top” as the characters with the largest number of conversation engagements (rows in conversations) across all time.

Return:

• character_id
• engagement_count

Order by engagement_count DESC and return only the top 100.

Task 2: Compute the unsafe character ratio

Compute the overall ratio of unsafe characters among all characters.

Return:

• unsafe_character_ratio (a decimal between 0 and 1)

Where:

• unsafe character = characters.safety_flag = FALSE

Task 3: 7-day moving average of daily unsafe-character percentage

For each day (based on characters.created_at), compute:

1. daily_unsafe_pct = (# characters created that day that are unsafe) / (total # characters created that day)
2. A 7-day trailing moving average of daily_unsafe_pct (including the current day).

Return:

• day
• daily_unsafe_pct
• daily_unsafe_pct_ma7

Order by day ascending.

Task 4 (open-ended, but answer with SQL outputs)

4a) Relationship between unsafe characters and unsafe conversations

Create a 2×2 breakdown of conversation engagements by:

• character safety (characters.safety_flag)
• conversation safety (conversations.safety_flag)

Return:

• character_safety_flag
• conversation_safety_flag
• engagement_count

4b) By day, unsafe-user engagement in unsafe conversations

For each day (based on conversations.created_at), compute metrics related to users who engaged in unsafe conversations (conversations.safety_flag = FALSE).

Return:

• day
• unsafe_conversation_engagements (count of rows where conversations.safety_flag = FALSE)
• unsafe_users (distinct users engaging in unsafe conversations)
• total_engagements (all conversation rows that day)
• total_users (distinct users that day)
• unsafe_engagement_ratio = unsafe_conversation_engagements / total_engagements
• unsafe_user_ratio = unsafe_users / total_users

Order by day ascending.