Understanding Different SQL Joining Methods
Introduction
In my previous article, I gave out an SQL question that was asked in almost all my recent interviews. This question tests your knowledge of SQL joining techniques and how each joining is performed. These methods are essential for combining data from different tables and are a fundamental part of working with relational databases. In this article, I will explain what SQL joining methods we can have and refresh your memory if you find it difficult to answer the question.
Whether you’re preparing for interviews or working as a data analyst, this article will serve as a valuable reference.
If you found this article insightful, don’t forget to clap and leave a comment to let me know your thoughts. Stay connected by following me for future articles on various topics in Data Science. Thank you for your support!
The Five SQL Joining Methods
There are five primary SQL joining methods that we often encounter in our work. These methods allow us to combine data intelligently. We’ll explore each method and provide real-world examples of when they are used.
Understanding Joining
Before diving into the details, let’s clarify what joining means in SQL. When we join two tables, we are essentially connecting related data based on common key(s). These keys should be unique in each table and hold the same meaning across tables.
1. Inner join
Purpose: An inner join is perhaps the most commonly used joining method. It returns records that exist in both the left and right tables. Rows without a match in either table are excluded from the final result. I usually use this joining method when I want to filter out some rows based on the criteria from other tables.
Example: Suppose you have a user dimension table, and you only want information about users who joined a specific marketing campaign. You can perform an inner join between the user dimension table and the campaign-related table, using unique user keys to filter the relevant data.
Answer to the question: Minimum Rows: 0, if there are no matching keys between the two tables Maximum Rows: 1000, if every row in Table A matches a row in Table B and there are no duplicate values( unique keys)
2. Left join
Purpose: A left join returns all rows from the left table and includes matching rows from the right table. When there’s no match in the right table, the corresponding values are represented as Null in the final result. Usually, left join will be used when we want to add some extra information/ feature on top of the main table (left table here)
Example: If you wish to add aggregated transaction history to a user table, you can use a left join with the user table as the left table and the transaction history as the right table. This retains all users while incorporating their transaction data.
Answer to the question: Minimum Rows: 1000, all rows from Table A will be included Maximum Rows: 1000 ( The keys are unique in table B).
3. Right join
Purpose: A right join is essentially the opposite of a left join. It preserves all records from the right table and includes matching rows from the left table.
Answer to the question: Minimum Rows: 1000, all rows from Table B will be included Maximum Rows: 1000 ( The keys are unique in table A).
4. Outer join
Purpose: An outer join combines the features of both left and right joins. It retains all records from both the left and right tables and combines them as one record when their keys match. While it’s used less frequently, it’s valuable for comparing two tables and identifying differences while retaining all data.
Answer to the question: Minimum Rows: 1000, all 1000 unique keys can be matched from each table Maximum Rows: 2000 rows, The maximum number of rows will be the sum of the rows from both tables (keys are unique in the tables)
5. Cross join
Purpose: A cross join can join all rows from one table with all rows from another table. This results in a large number of rows, which can be challenging to use effectively.
Cross join is probably not that famous because it is hard to find a good use case for it, and the resulting table will have a larger number of rows (# of rows in the left table multiplied by # of rows in the right table)
Example: Cross joins are less common, but they can be handy when calculating aggregated data like rolling sums or averages over time periods. For instance, to calculate the 7-day rolling average of customer data from a transaction table, you can use a cross join to ensure each user has all dates recorded, even if they didn’t have transactions on specific dates.
Answer to the question: Minimum Rows: 1,000,000 (1000 rows from table A times 1000 rows from table B) Maximum Rows: 1,000,000 (1000 rows from table A times 1000 rows from table B)
Summary
In this article, we’ve provided straightforward explanations of different SQL joining methods that you’ll frequently encounter in your data work. Each method serves a specific purpose and offers a unique way to combine data from multiple tables. If you’ve been daunted by these concepts before, we encourage you to try them out for yourself and witness the powerful results they can deliver. Understanding and mastering SQL joining methods is a key skill for any data professional.
If you found this article insightful, don’t forget to clap and leave a comment to let me know your thoughts. Stay connected by following me for future articles on various topics in Data Science. Thank you for your support!
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