Trips and Users

Daily cancellation rate among unbanned users

Trips(id, client_id, driver_id, status, request_at), Users(users_id, banned, role). For each day in 2013-10-01 .. 2013-10-03, compute the cancellation rate of requests where neither the client nor the driver is banned, rounded to 2 decimals. The robust way to exclude banned users is to join to Users twice (client and driver) filtering banned = 'No' — not NOT IN, which misbehaves with NULLs.

SELECT t.request_at AS Day,
       ROUND(SUM(t.status <> 'completed') / COUNT(*), 2) AS "Cancellation Rate"
FROM Trips t
JOIN Users c ON t.client_id = c.users_id AND c.banned = 'No'
JOIN Users d ON t.driver_id = d.users_id AND d.banned = 'No'
WHERE t.request_at BETWEEN '2013-10-01' AND '2013-10-03'
GROUP BY t.request_at;

The two ideas that make it correct

• Inner-joining to Users on banned = 'No' drops any trip whose client or driver is banned — both must survive both joins. (WHERE client_id NOT IN (SELECT users_id … banned='Yes') returns nothing if that subquery ever yields a NULL.)
• SUM(t.status <> 'completed') counts cancellations (the boolean is 1/0 in MySQL); dividing by COUNT(*) — all non-banned requests that day — gives the rate. "Cancelled by driver" and "cancelled by client" both count as not-completed.

Pitfalls

• BETWEEN on dates is inclusive of both endpoints — correct here since the window is three specific days.
• In PostgreSQL, status <> 'completed' is boolean; wrap with (… )::int or use COUNT(*) FILTER (WHERE status <> 'completed') and cast for the division.

Takeaways

• Exclude banned users by inner-joining Users twice on banned='No', not NOT IN.
• Cancellation rate = SUM(status <> 'completed') / COUNT(*) over the surviving rows, per day.

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Re-authored for correctness for this guide (the prior version's NOT IN filter is NULL-fragile). Pattern: LeetCode 262. See also: INNER JOIN, Handle NULLs, Aggregate Functions.

🎯 STRICT STANDOUT: Why / mental model / when-not / worked / failure / hostile panel — Trips and Users

Why this concept exists (judgment layer)

Cancellation rate with dual ban filters teaches semi-join correctness: join Users twice on banned='No' beats NOT IN (NULL trap). Boolean sum is the rate numerator pattern.

Mental model (install this intuition)

Keep trip only if client AND driver pass banned='No' (two INNER JOINs). Per day: cancelled_count / all_kept_trips. Cancelled = status <> 'completed' (both cancel types).

Worked example with numbers or traced steps

JOIN Users c ON client_id AND c.banned='No'
JOIN Users d ON driver_id AND d.banned='No'
Trip with banned driver disappears (fails second join)
SUM(status<>'completed')/COUNT(*) → rate; ROUND 2 decimals
NOT IN (SELECT id WHERE banned='Yes') dies if subquery has NULL

When NOT to use / named alternative

Do not use NOT IN for ban lists. Do not filter only clients. On Postgres use FILTER or ::int for boolean aggregates — not MySQL 1/0 silent cast alone.

Failure mode & ops fingerprint

Fingerprint: rate includes banned parties; NOT IN empties whole day; division integer truncation without ROUND/float; BETWEEN date off-by-one on timestamps.

Hostile-panel drills (defend the decision)

Q1. Why two joins not one?
Model answer: Client and driver are different Users rows; both must be unbanned independently.

Q2. Why avoid NOT IN for banned ids?
Model answer: If subquery returns NULL, NOT IN becomes UNKNOWN for all candidates → empty result.

Q3. Numerator for cancellation rate?
Model answer: Count of non-completed statuses among trips that survived both unbanned joins — not only 'cancelled by client'.

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