Many-to-Many Relationship Joining: Tracking Sales Based on Device While a User Has Many Transactions on Multiple Devices
Introduction
In this article, we will explore the challenge of joining two tables with a many-to-many relationship to track sales based on device while a user has many transactions on multiple devices. We’ll dive into the technical details of how to solve this problem using SQL and provide an example solution.
Background
A many-to-many relationship occurs when one entity can have multiple instances of another entity, and vice versa. In the context of our problem, we have two tables: user_dev and user_trans. The user_dev table represents the devices used by each user, while the user_trans table represents the transactions made by each user.
The user_id column is not unique in both tables, which makes it challenging to join them using a traditional INNER JOIN. However, we can use subqueries and clever SQL techniques to solve this problem.
Problem Statement
Given two tables:
| user_id | dev_id | dev_created_date |
|---|---|---|
| 1 | 1 | 2018-07-05 07:38:23 |
| 1 | 2 | 2018-08-05 09:39:44 |
| 2 | 2 | 2018-07-15 02:28:35 |
| 2 | 3 | 2018-09-11 14:17:32 |
And the corresponding user_trans table:
| user_id | trans_id | trans_time |
|---|---|---|
| 1 | 1 | 2018-07-05 07:48:23 |
| 1 | 2 | 2018-07-08 05:13:12 |
| 1 | 3 | 2018-08-15 11:18:15 |
| 2 | 4 | 2018-08-29 13:21:22 |
| 2 | 5 | 2018-09-12 02:37:17 |
We need to find the expected return table, which is:
| user_id | trans_id | dev_id |
|---|---|---|
| 1 | 1 | 1 |
| 1 | 2 | 1 |
| 1 | 3 | 2 |
| 2 | 4 | 2 |
| 2 | 5 | 3 |
Solution
To solve this problem, we’ll use a combination of subqueries and clever SQL techniques.
First, let’s calculate the minimum difference between trans_time and dev_created_date for each trans_id. We can do this using a subquery:
SELECT t.trans_id, MIN(t.trans_time - u.dev_created_date) AS min_diff
FROM user_dev u
JOIN user_trans t ON u.user_id = t.user_id AND t.trans_time >= u.dev_created_date
GROUP BY t.trans_id;
This will give us the minimum difference for each trans_id.
Next, we’ll join the original tables with this subquery using an INNER JOIN:
SELECT u.user_id, ut.trans_id, u.dev_id
FROM user_dev u
JOIN user_trans ut ON u.user_id = ut.user_id AND (ut.trans_time - u.dev_created_date) = (
SELECT MIN(t.trans_time - u.dev_created_date)
FROM user_dev u
JOIN user_trans t ON u.user_id = t.user_id AND t.trans_time >= u.dev_created_date
WHERE u.user_id = ut.user_id AND t.trans_id = ut.trans_id
);
This will give us the expected return table, which is:
| user_id | trans_id | dev_id |
|---|---|---|
| 1 | 1 | 1 |
| 1 | 2 | 1 |
| 1 | 3 | 2 |
| 2 | 4 | 2 |
| 2 | 5 | 3 |
Conclusion
In this article, we explored the challenge of joining two tables with a many-to-many relationship to track sales based on device while a user has many transactions on multiple devices. We used subqueries and clever SQL techniques to solve this problem.
The key takeaway is that when working with many-to-many relationships, you need to use creative and clever SQL techniques to join the tables correctly. In this case, we used a combination of subqueries and INNER JOINs to achieve the expected result.
Last modified on 2023-10-17