4 Best Practices for Writing Joins in SQL

Joins are one of the most important SQL clauses for data science teams to use when manipulating data. That being said, it’s critical to understand the dos and don’ts of writing SQL joins.

This article touches on 4 of our top tips for optimizing your SQL join queries. So whether you’re diving into the world of joins or looking to ensure your team is following best practices, this article is for you.

What are SQL Joins?

Join clauses combine rows of data from two or more tables into a single view. To connect two tables, you must join them using a key field, which is a column that is similar in both tables.

In most cases, this column will be some sort of ID or primary key field. For example, suppose one table contains customer purchase data, and another contains their website activity data. In that case, you can combine the two datasets on the related ID field to get a holistic picture of how customers are engaging with your business.

Why are Joins Important?

Relational databases are designed to disperse data into various tables that contain data about an individual subject, such as customer or product data. Databases are designed to reduce redundancy while ensuring the accuracy and integrity of the information.

Because databases are designed in this way, it can be tricky for data analysts to collect this data for analysis. That is where join statements come in. Joins help stitch the data back together, like a needle and thread.

Top 4 Best Practices for Join Queries

1. Understanding different joins

It’s essential to understand exactly what JOINS can do for you and which type of join will best suit your use case. There are 4 fundamental types of SQL joins that we will go over here.

Inner Join:

Inner joins combine related data from a left and a right table together and will only connect when the key fields exactly match.

Full Join:

Full joins combine all data from the left and the right table into a single view regardless of whether they have a matching row. If one table doesn’t have a value, NULL is used to fill in the spot.

Left Join:

A left join takes every row from the left table and combines with matching rows from the right table.

Right Join:

Right joins are the opposite of left joins, where the data in the right table stays, while only matching rows from the left table are added.

2. Avoid using the WHERE clause to join tables

Although you can get the same result when joining tables using the WHERE clause, we don’t recommend doing so. Not because of performance reasons or anything, but simply because of the readability.

You want others interacting with the same code to comprehend what’s going on quickly. Furthermore, if the two tables don’t contain the same column, the query will return an error. We recommend just steering clear of this method to avoid any unnecessary confusion.

3. Use Aliases

You can use aliases to change the name of a column or table temporarily. The purpose of using an alias is to keep your code more readable. Using an alias is especially useful when a database is big and not very readable and should be used when there is more than one table in a query.

It’s important to note that aliases only stick around for the query duration.

Below is an example of using an alias:

4. Only SELECT what you need

SELECT * is a great way to view all of the columns in a table quickly. However, although it’s a convenient way to query your data fast, it comes with some constraints, especially when used with join queries.

Often, tables might have the same column name, and if you try to select all of the columns, it can cause complications and return an error. In some cases, when your database contains a lot of records, it can be a very expensive query and take a long time to load.

Wrapping it up

Understanding the best practices for joins will ensure comprehensive and optimal performing queries on the job. Share these tips with your team, and ensure everyone is on the same page.