Write SQL for content-view analytics

Context

You work with page-view event logs and need to compute several engagement/usage summaries.

Assume a single table:

page_views

Assumptions:

• Treat mobile as device_type IN ('phone','tab') , and desktop as device_type = 'desktop' .
• Unless otherwise specified, interpret timestamps in UTC .

Tasks (write SQL)

1) Unique contents viewed by device group

Compute, for each device group (mobile vs desktop):

• unique_contents_viewed : number of distinct content_id values viewed (ignore NULLs)
• total_views : total number of page-view events

Return columns: device_group, unique_contents_viewed, total_views.

Additionally (in 1–2 sentences), explain whether unique_contents_viewed and total_views will generally be equal, and why.

2) Peak usage hours by device type

For each device type (phone, tab, desktop), find the top 3 hours of day (0–23) with the highest number of views.

Return columns: device_type, hour_of_day, views.

Notes:

• Use an hour-extraction function on viewed_at .
• Break ties by choosing the smaller hour_of_day first.

3) Unique content count and NULL content count

Compute:

• unique_contents_viewed : number of distinct non-NULL content_id values that were viewed
• null_content_events : number of events where content_id IS NULL

Return columns: unique_contents_viewed, null_content_events.

4) Distribution of first-seen content type per agent per day

For each agent_id and each calendar date (derived from viewed_at), identify the first page view of that day (earliest viewed_at; if ties, use smallest view_id). Take that event’s content_type.

Then compute the distribution of these first-seen content_type values across all agent-days.

Return columns:

• event_date
• content_type
• agent_days (count of agent-days whose first view had that content_type )
• share (agent_days divided by total agent-days for that date)