Feature image PostgreSQL Array Functions 101_ The Practical Guide

PostgreSQL Array Functions 101: The Practical Guide

By Brian Laleye · October 20, 2022 · 7 min read

If you’re a database administrator or developer, it’s important to be familiar with PostgreSQL, the world’s most advanced open source database.

PostgreSQL has a rich set of features, including an impressive array of functions. In this article, we’ll give you a complete guide to PostgreSQL Array Functions.

Let’s dive into what PostgreSQL Array Functions are and how to use them properly!

An Introduction to PostgreSQL Array Functions

PostgreSQL Array Functions are functions that allow you to manipulate array data types.

There are a number of different types of array functions, including aggregate functions, element functions, and set-returning functions.

In addition, there are a number of utility functions that are useful for working with arrays.

Array functions are a powerful tool for working with data in PostgreSQL, and can be used to solve a variety of different problems.

In this article, we’ll take a look at some of the most common array functions, and how they can be used to make working with array data types easier.

Benefits of PostgreSQL Array Functions

Array functions are very powerful and can be used to solve a number of different problems.

For example, you can use an array function to calculate the average of all the values in an array, or to concatenate two arrays together.

Array functions can also be used to find the maximum or minimum value in an array, or to search an array for a specific value.

Array functions are a great way to manipulate data in PostgreSQL, and can be used to solve a variety of different problems.

If you’re looking for a way to perform calculations on an array of data, or to concatenate two arrays together, array functions are the way to go. You can also use array functions to find the maximum or minimum value in an array, or to search an array for a specific value.

The Different Types of PostgreSQL Array Functions

As we mentioned above, there are a number of different types of array functions. In this section, we’ll take a closer look at each of these function types.

Aggregate Functions

Aggregate functions are used to calculate a single value from an array of values.

For example, the avg() function can be used to calculate the average value of all the values in an array. Other aggregate functions include max(), min(), and sum().

Element Functions

Element functions are used to manipulate individual array elements.

For example, the array_append() function can be used to add a new element to the end of an array. Other element functions include array_prepend(), array_remove(), and array_replace().

Set-Returning Functions

Set-returning functions are used to return a subset of an array.

For example, the array_slice() function can be used to return a portion of an array. Other set-returning functions include array_length(), array_position(), and array_distinct().

Conditional Functions

Conditional functions are used to return a value based on a condition.

For example, the array_if() function can be used to return one value if a condition is true, and another value if the condition is false.

Other conditional functions include array_case() and array_coalesce().

How to Use PostgreSQL Array Functions

Now that we’ve taken a look at the different types of array functions, let’s take a closer look at how to actually use them.

In this section, we’ll walk through a few examples of how to use PostgreSQL Array Functions.

Example 1 - Calculating the Average Value of an Array

In this example, we’ll use the avg() function to calculate the average value of an array

The avg() function takes an array as an argument and returns the average value of all the elements in the array.

				
					SELECT avg(array[1,2,3,4,5]) AS "Average Value"

				
			

In the example above, we’re using the array[] constructor to create an array with the values 1, 2, 3, 4, and 5. We then pass this array into the avg() function, which calculates the average value and returns it as a floating point number.

Example 2 - Concatenating Two Arrays Together

In this example, we’ll use the array_cat() function to concatenate two arrays together

The array_cat() function takes two arrays as arguments and returns a new array that is the concatenation of the two input arrays.

In the example above, we’re using the array[] constructor to create two arrays with the values 1, 2, 3, and 4, 5, 6. We then pass these arrays into the array_cat() function, which concatenates them together and returns a new array with the values 1, 2, 3, 4, 5, and 6.

				
					SELECT array_cat(array[1,2,3], array[4,5,6]) AS "Concatenated Array"
				
			

In the example above, we’re using the array[] constructor to create two arrays with the values 1, 2, 3, and 4, 5, 6. We then pass these arrays into the array_cat() function, which concatenates them together and returns a new array with the values 1, 2, 3, 4, 5, and 6.

Example 3 - Finding the Maximum Value in an Array

In this example, we’ll use the max() function to find the maximum value in an array.

The max() function takes an array as an argument and returns the maximum value in the array. Here are some examples:

				
					SELECT max(array[1,2,3,4,5]) AS "Maximum Value";

SELECT max(array[-5,-4,-3,-2,-1]) AS "Maximum Value";

SELECT max(array[5,4,3,2,1]) AS "Maximum Value";

SELECT max(array['a','b','c',NULL,'d']) AS "Maximum Value";

SELECT max(array['a',NULL,'b','c','d']) AS "Maximum Value";

SELECT max(array[NULL,'a',NULL,'b',NULL]) AS "Maximum Value";
				
			

Conclusion

At RestApp, we’re building a Data Activation Platform for modern data teams with a set of natve connectors to your favorite databases, including PostgreSQL, data warehouses and bsuiness apps.

We have designed our next-gen data modeling editor to be intuitive and easy to use.

If you’re interested in starting with connecting all your favorite tools, check out the RestApp website or try it for free with a sample dataset.

Discover the next-gen end-to-end data pipeline platform with our built-in No Code SQL, Python and NoSQL functions. Data modeling has never been easier and safer thanks to the No Code revolution, so you can simply create your data pipelines with drag-and-drop functions and stop wasting your time by coding what can now be done in minutes! 

Play Video about Analytics Engineers - Data Pipeline Feature - #1

Discover Data modeling without code with our 14-day free trial!

Category

Share

Subscribe to our newsletter

Brian Laleye
Brian Laleye
Brian is the co-founder of RestApp. He is a technology evangelist and passionate about innovation. He has an extensive experience focusing on modern data stack.

Related articles

Build better data pipelines

With RestApp, be your team’s data hero by activating insights from raw data sources.