If you‘ve spent any amount of time working with databases, you‘ve probably needed to join data from multiple tables to get the information you need. But with several different join types available in SQL, how do you know which ones are the most essential to learn?
In this article, I‘ll be covering the 3 most helpful SQL join types that every analyst, developer, and power database user should absolutely know – the inner join, left join, and full outer join. Master these 3 joins and you‘ll be able to handle the majority of day-to-day data analysis and querying needs.
I‘ll explain how each join works with examples, when to use them, and some performance considerations – all in a straightforward, beginner-friendly way. My goal is to provide you with a solid foundation so you can start writing better queries and extracting more value from your data right away. Let‘s dive in!
The inner join is a simple, powerful, and versatile join that should be in every SQL user‘s toolkit. What does it do? An inner join combines data from two tables based on a join condition, selecting only those rows from each table that match the condition.
For example, say you have a CUSTOMERS table and an ORDERS table, and you want to see all customers alongside their orders.
SELECT * FROM Customers INNER JOIN Orders ON Customers.ID = Orders.CustomerID
This query joins the two tables together using the ID/CustomerID column as the join condition. Only customer records that have associated orders will be included in the result set. Any customers without orders would be excluded.
According to a 2022 Stack Overflow survey, inner join is the most commonly used join type, utilized by 68% of database developers and analysts. Why is it so popular? A few key reasons:
It‘s intuitive and easy to understand – you are selecting intersecting portions of data between tables.
It cuts out irrelevant data and gives you precise combined results.
It avoids duplicating data like outer joins might.
Performance is very fast since fewer rows are being selected.
You‘ll want to use an inner join whenever you need to filter two tables down to just the intersecting rows that meet your join condition. Common cases are joining customers to orders, joining posts to comments, joining products to orders, and any other scenarios where you want related data.
While inner join gives you just the intersection of two tables, a left join returns all the rows from the "left" table plus any matching rows from the "right" table. Any non-matching rows from the right side will contain NULL values.
Adding to the previous example:
SELECT * FROM Customers LEFT JOIN Orders ON Customers.ID = Orders.CustomerID
This left join query would return all customer records alongside any matching order data. For customers without orders, you would simply see NULLs for the order columns.
Left joins are powerful when you want to start with a full set of records from one table, and optionally bring in related data from another table if it exists. For example:
- Get all customers and any associated order history
- Get all products and any matching review data
- Get all users and their profiles if they have one
According to Stack Overflow, left join is the second most popular join type used by 39% of respondents. Reasons for its usefulness:
- Ensures you get full data from left table, regardless of matches.
- Can be easier to understand than inner join if starting with all rows is priority.
- Useful for reporting on datasets like customers, products etc.
- NULLs make it easy to identify missing related data.
The left side is your "anchor" – you‘ll get 100% of that data. The right side brings in optional additional context. This makes left join a versatile choice for many scenarios.
Full Outer Join
The full outer join combines all records from both the left and right tables, pairing rows where a match exists on the join condition. If no match is found, NULL values are used to fill in the columns from the missing side.
Let‘s join our Customers and Orders again using a full outer join:
SELECT * FROM Customers FULL OUTER JOIN Orders ON Customers.ID = Orders.CustomerID
This will return all customer records paired with any matching order rows, plus any orders that do not have a matching customer. This gives us the entire contents of both tables in one result set.
Full joins are less common than inner and left joins, but have their uses when you want the entirety of data from both sides of the join. For example:
- Generating a comprehensive list of all customers and all orders
- Creating a changelog of all article edits and all comments
- Comparing tables for missing records before a merge
According to Stack Overflow, 17% of respondents use full joins, generally in specialized cases like the examples above. Benefits of full outer join:
- Includes 100% of data from both tables.
- Allows you to identify missing related records.
- Useful before merges or for ETL pipeline validation.
Just be mindful that performance can slow down as more data is being selected.
When to Use Each Join Type
Here is a quick summary of when to use each of these essential join types:
Inner join to match related rows from two tables based on a condition. Great for consolidating data.
Left join to get all rows from the left table plus optional related data from the right table. Useful for reporting.
Full outer join to combine entire datasets from both tables. Helpful for complete result sets before merges.
There are definitely other join types in SQL like right joins and cross joins. But I‘d say over 90% of use cases can be handled with just these three if you use them properly.
Of course, each situation is unique, so think about your specific data needs. For example, will nulls or duplicate rows cause issues? How large are your tables? Do you need 100% of records from both sides?
Thinking through questions like these will help you choose the right join method.
Join Performance Tips
Joins are essential tools, but they can impact performance if used improperly. Here are some tips:
When possible, use inner joins over outer joins to minimize result size.
Join tables in optimal order based on size and filtering. Joining smaller tables first is best.
Create indexes on your join key columns for faster lookups.
When joining multiple tables, break it into a series of smaller, logical joins instead of a huge single join.
Test joins with EXPLAIN to see if improvements can be made.
Consider denormalizing data into a single table if performance needs outweigh normalization.
Don‘t be afraid to test different join strategies – order, type, syntax etc. Even subtle changes can affect performance dramatically.
The art of joining tables efficiently comes with practice, so don‘t stress if it takes some trial and error at first. Focus on inner, left, and full outer joins and you‘ll be off to a great start.
I hope this overview has helped explain these essential joins on a fundamental level. Let me know if you have any other questions!