Hopper Company Queries III - Solution & Explanation

HardPremiumFree on FleetCodeDatabase7 min readAsked at: Uber

Problem Statement

Table: Drivers

+-------------+---------+
| Column Name | Type    |
+-------------+---------+
| driver_id   | int     |
| join_date   | date    |
+-------------+---------+
driver_id is the column with unique values for this table.
Each row of this table contains the driver's ID and the date they joined the Hopper company.

Table: Rides

+--------------+---------+
| Column Name  | Type    |
+--------------+---------+
| ride_id      | int     |
| user_id      | int     |
| requested_at | date    |
+--------------+---------+
ride_id is the column with unique values for this table.
Each row of this table contains the ID of a ride, the user's ID that requested it, and the day they requested it.
There may be some ride requests in this table that were not accepted.

Table: AcceptedRides

+---------------+---------+
| Column Name   | Type    |
+---------------+---------+
| ride_id       | int     |
| driver_id     | int     |
| ride_distance | int     |
| ride_duration | int     |
+---------------+---------+
ride_id is the column with unique values for this table.
Each row of this table contains some information about an accepted ride.
It is guaranteed that each accepted ride exists in the Rides table.

Write a solution to compute the average_ride_distance and average_ride_duration of every 3-month window starting from January - March 2020 to October - December 2020. Round average_ride_distance and average_ride_duration to the nearest two decimal places.

The average_ride_distance is calculated by summing up the total ride_distance values from the three months and dividing it by 3. The average_ride_duration is calculated in a similar way.

Return the result table ordered by month in ascending order, where month is the starting month's number (January is 1, February is 2, etc.).

The result format is in the following example.

Example 1:

Input: 
Drivers table:
+-----------+------------+
| driver_id | join_date  |
+-----------+------------+
| 10        | 2019-12-10 |
| 8         | 2020-1-13  |
| 5         | 2020-2-16  |
| 7         | 2020-3-8   |
| 4         | 2020-5-17  |
| 1         | 2020-10-24 |
| 6         | 2021-1-5   |
+-----------+------------+
Rides table:
+---------+---------+--------------+
| ride_id | user_id | requested_at |
+---------+---------+--------------+
| 6       | 75      | 2019-12-9    |
| 1       | 54      | 2020-2-9     |
| 10      | 63      | 2020-3-4     |
| 19      | 39      | 2020-4-6     |
| 3       | 41      | 2020-6-3     |
| 13      | 52      | 2020-6-22    |
| 7       | 69      | 2020-7-16    |
| 17      | 70      | 2020-8-25    |
| 20      | 81      | 2020-11-2    |
| 5       | 57      | 2020-11-9    |
| 2       | 42      | 2020-12-9    |
| 11      | 68      | 2021-1-11    |
| 15      | 32      | 2021-1-17    |
| 12      | 11      | 2021-1-19    |
| 14      | 18      | 2021-1-27    |
+---------+---------+--------------+
AcceptedRides table:
+---------+-----------+---------------+---------------+
| ride_id | driver_id | ride_distance | ride_duration |
+---------+-----------+---------------+---------------+
| 10      | 10        | 63            | 38            |
| 13      | 10        | 73            | 96            |
| 7       | 8         | 100           | 28            |
| 17      | 7         | 119           | 68            |
| 20      | 1         | 121           | 92            |
| 5       | 7         | 42            | 101           |
| 2       | 4         | 6             | 38            |
| 11      | 8         | 37            | 43            |
| 15      | 8         | 108           | 82            |
| 12      | 8         | 38            | 34            |
| 14      | 1         | 90            | 74            |
+---------+-----------+---------------+---------------+
Output: 
+-------+-----------------------+-----------------------+
| month | average_ride_distance | average_ride_duration |
+-------+-----------------------+-----------------------+
| 1     | 21.00                 | 12.67                 |
| 2     | 21.00                 | 12.67                 |
| 3     | 21.00                 | 12.67                 |
| 4     | 24.33                 | 32.00                 |
| 5     | 57.67                 | 41.33                 |
| 6     | 97.33                 | 64.00                 |
| 7     | 73.00                 | 32.00                 |
| 8     | 39.67                 | 22.67                 |
| 9     | 54.33                 | 64.33                 |
| 10    | 56.33                 | 77.00                 |
+-------+-----------------------+-----------------------+
Explanation: 
By the end of January --> average_ride_distance = (0+0+63)/3=21, average_ride_duration = (0+0+38)/3=12.67
By the end of February --> average_ride_distance = (0+63+0)/3=21, average_ride_duration = (0+38+0)/3=12.67
By the end of March --> average_ride_distance = (63+0+0)/3=21, average_ride_duration = (38+0+0)/3=12.67
By the end of April --> average_ride_distance = (0+0+73)/3=24.33, average_ride_duration = (0+0+96)/3=32.00
By the end of May --> average_ride_distance = (0+73+100)/3=57.67, average_ride_duration = (0+96+28)/3=41.33
By the end of June --> average_ride_distance = (73+100+119)/3=97.33, average_ride_duration = (96+28+68)/3=64.00
By the end of July --> average_ride_distance = (100+119+0)/3=73.00, average_ride_duration = (28+68+0)/3=32.00
By the end of August --> average_ride_distance = (119+0+0)/3=39.67, average_ride_duration = (68+0+0)/3=22.67
By the end of Septemeber --> average_ride_distance = (0+0+163)/3=54.33, average_ride_duration = (0+0+193)/3=64.33
By the end of October --> average_ride_distance = (0+163+6)/3=56.33, average_ride_duration = (0+193+38)/3=77.00

Approach Overview

Problem Overview: You need to report the 3‑month rolling average of ride distance and ride duration for each month of 2020. The result must include all months from January to December, even if no rides happened. Data comes from the rides table joined with accepted ride details, then aggregated by month.

Approach 1: Monthly Aggregation + Window Function (O(n) time, O(n) space)

Start by joining rides with accepted ride information and filtering rows where requested_at falls in 2020. Aggregate rides by month using MONTH(requested_at), computing total distance and duration per month. Since the result must include months with zero rides, generate a fixed month list (1–12) and left join the aggregated data.

Once monthly totals are available, compute the rolling averages using a SQL window function. Use AVG(... ) OVER (ORDER BY month ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) so each row considers the current month and the previous two months. This produces the required 3‑month moving average directly in SQL. Window functions make this clean and efficient because they avoid repeated scans or correlated subqueries. Time complexity is O(n) for scanning and aggregating rides, with O(n) intermediate storage for monthly results.

This pattern appears frequently in analytics queries where you need moving averages or running totals. Window functions are part of advanced SQL and are heavily used in database reporting workloads. Understanding the window functions syntax makes problems like this straightforward.

Approach 2: Self Join for Rolling Window (O(n^2) time, O(n) space)

Another way to compute the 3‑month average is by first generating monthly aggregates and then joining each month with its previous two months. For every month m, join rows where month BETWEEN m-2 AND m and compute the average distance and duration across those rows. This works even without window function support.

The drawback is performance and complexity. Each month potentially scans multiple rows, producing roughly O(n^2) work in the worst case. The SQL also becomes harder to read compared with the concise window frame syntax.

Recommended for interviews: The window function approach is the expected solution. It shows you understand SQL analytics features and can compute rolling metrics efficiently. The self‑join version demonstrates the concept but lacks the elegance and performance of the window function solution.

Solution

Code

MySQL

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Detailed Complexity Analysis

Approach	Time	Space	When to Use
Monthly Aggregation + Window Function	O(n)	O(n)	Best approach when the database supports SQL window functions. Clean and efficient for rolling averages.
Self Join Rolling Window	O(n^2)	O(n)	Useful in older SQL engines without window functions, but less efficient and harder to maintain.

Video Solution

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Frequently Asked Questions

Is Hopper Company Queries III easy or hard?

Hopper Company Queries III is classified as Hard because it requires understanding rolling window calculations, SQL joins, and ensuring all months appear even when there is no data. Familiarity with SQL window functions makes the solution significantly easier.

Hopper Company Queries III Python/Java solution

This problem is a SQL database problem rather than a typical algorithm question, so the official solution is written in SQL (MySQL). Python or Java implementations would normally query the database and rely on SQL to perform the aggregation and rolling average logic.

How to solve Hopper Company Queries III in O(n)?

Filter rides for 2020, join with accepted ride data, and group by month to compute total distance and duration. Create rows for months 1 through 12 and left join the aggregates. Then apply AVG(distance) and AVG(duration) using a window frame covering the current month and the previous two months.

What is the best approach for Hopper Company Queries III?

The best approach uses SQL window functions. First aggregate rides by month for 2020, then apply a 3‑month rolling average using AVG() OVER with a frame like ROWS BETWEEN 2 PRECEDING AND CURRENT ROW. This computes moving averages in O(n) time and keeps the query concise.

Is Hopper Company Queries III asked at Google/Amazon/Meta?

Database analytics problems involving rolling averages and window functions frequently appear in interviews at companies like Google, Amazon, and Meta. Variations of this problem test SQL aggregation, joins, and window function usage.

What data structure is used in Hopper Company Queries III?

The problem is solved using relational database operations rather than traditional data structures. Key concepts include SQL joins, monthly aggregation, and window functions that operate over ordered partitions of rows.

What is the time complexity of Hopper Company Queries III?

The optimal SQL solution runs in O(n) time where n is the number of ride records processed for 2020. The database scans the rides, aggregates by month, and applies a window function over the monthly results. Space complexity is O(n) for intermediate aggregation results.

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Hopper Company Queries I

Hopper Company Queries II

Problem Info

DifficultyHard

Acceptance65.6%

Approaches1

Reading time7 min

Asked at

Uber

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