SQL One-to-Many: Getting All Rows with Filters

In this article, we’ll explore the concept of a one-to-many relationship between two tables in SQL. Specifically, we’ll tackle how to write a query that retrieves all rows from the ideas table where the created_by column does not match the authenticated user’s ID and also excludes any ideas that are voted on by the same user.

Understanding One-to-Many Relationships

A one-to-many relationship exists when one row in one table (the “one”) can be related to multiple rows in another table (the “many”). In our case, we have two tables: ideas and votes.

The ideas table has columns for the idea’s ID (id), the actual idea (idea), and the user who created it (created_by). The votes table, on the other hand, contains records of users (voters) who have voted on specific ideas. Each row in the votes table is linked to a single idea via its idea_id.

Here’s an example of what these tables might look like:

ideas

votes

As you can see, John has voted on two ideas: 1 and 2. Similarly, Alice has also voted on idea #2.

Writing the Query

To write a query that gets all rows from the ideas table where the created_by column does not match the authenticated user’s ID and excludes any ideas that are voted on by the same user, we can follow these steps:

Step 1: Write the Base Query

We’ll start with a simple SELECT statement that retrieves all rows from the ideas table.

SELECT id FROM ideas;

This query returns an empty result set, as there’s no filtering applied yet.

Step 2: Exclude Ideas Created by Authenticated User

To exclude ideas created by the authenticated user, we’ll add a WHERE clause that filters out rows where created_by matches the user ID.

SELECT id FROM ideas WHERE created_by != auth.uid();

Keep in mind that this step is crucial, but it’s not yet complete. We need to exclude ideas that are voted on by the same user as well.

Step 3: Exclude Ideas Voted On by Authenticated User

To do this, we’ll use a subquery (also known as a NOT IN clause) that selects the idea_ids of users who have voted on an idea and exclude those ideas from our result set. We’ll wrap the subquery in another SELECT statement to get only the ids.

SELECT id FROM (
  SELECT DISTINCT idea_id FROM votes WHERE voter_id = auth.uid()
) AS excluded_ideas;

However, this approach has a performance hit because it involves multiple passes over the data. Instead, we can use an anti-join (also known as a LEFT JOIN with a WHERE clause) to achieve the same result.

SELECT i.id FROM ideas i LEFT JOIN votes v ON i.id = v.idea_id WHERE v.voter_id != auth.uid();

This query first matches all rows from both tables based on the shared column (idea_id). Then, it filters out any rows where voter_id matches the user ID.

Step 4: Combine Exclusions (Optional)

In this case, we’ve already excluded ideas created by the authenticated user in the previous step. If there’s an additional exclusion rule, you can simply add another WHERE clause.

SELECT id FROM (
  SELECT DISTINCT idea_id FROM votes WHERE voter_id = auth.uid()
) AS excluded_ideas;

Step 5: Execute the Query

Now that we’ve written our complete query, it’s time to execute it. Make sure you replace auth.uid() with your actual authentication logic and adjust the table names according to your schema.

SELECT id FROM ideas i LEFT JOIN votes v ON i.id = v.idea_id WHERE v.voter_id != auth.uid();

This final query retrieves all rows from the ideas table where the user hasn’t created an idea or has voted on it, ensuring a comprehensive exclusion of any row that should be ignored.

Conclusion

By following these steps and understanding one-to-many relationships between tables, we’ve successfully implemented a SQL query to get all rows from the ideas table with filtered exclusions. This technique can be applied in various contexts where filtering and exclusion are crucial, making it an essential tool for any aspiring data analyst or database administrator.

Additional Considerations

When working with large datasets and performance-critical queries, consider using indexing strategies to optimize query execution times. Furthermore, if you’re dealing with complex logic involving multiple conditions or joins, don’t hesitate to use temporary tables or Common Table Expressions (CTEs) to simplify your code and improve readability.

As always, practice makes perfect. Experiment with different SQL queries and table structures to solidify your understanding of one-to-many relationships and filtering techniques.

Last modified on 2025-03-07