Table Schema
Inspect
Table
| Column | Type |
|---|---|
| trip_id | integer |
| city | varchar |
| fare | decimal |
| request_time | datetime |
Surge
Pricing
Detection
Find time periods where average fare increased by more than 50% compared to the previous hour.
Inspect
Sample Data
Input
Output
Sample Input: trips
| trip_id | city | fare | request_time |
|---|---|---|---|
| 1 | NYC | 10 | 09:00 |
| 2 | NYC | 12 | 09:30 |
| 3 | NYC | 25 | 10:00 |
| 4 | NYC | 30 | 10:30 |
| 5 | NYC | 20 | 11:00 |
| 6 | NYC | 22 | 11:30 |
Expected Output
| hour | avg_fare |
|---|---|
| 10:00 | 27.50 |
SQL Editor
Run
Query
Waiting for query
| hour | avg_fare |
|---|---|
| 10:00 | 27.50 |
Hints
Unlock
Clues
Hint 01: Identify the grouping level required by the output.
Hint 02: Aggregate with COUNT, SUM, AVG, or a window function as needed.
Hint 03: Filter after aggregation with HAVING or after ranking with an outer query.
Solution
Locked
Answer
Solution is locked until you decide to reveal it. Try the editor first, then open this when you want the reference answer.
WITH hourly AS (
SELECT DATE_TRUNC('hour', request_time) AS hour, AVG(fare) AS avg_fare
FROM trips
GROUP BY DATE_TRUNC('hour', request_time)
)
SELECT hour, avg_fare
FROM (
SELECT hour, avg_fare, LAG(avg_fare) OVER (ORDER BY hour) AS prev_avg_fare
FROM hourly
) h
WHERE avg_fare > prev_avg_fare * 1.5;Explanation
Step By
Step
01
Read the expected output columns to determine the final grain.
02
Aggregate or rank the input rows to calculate the requested metric.
03
Filter, sort, and alias the final columns to match the output.
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