# Introduction to Spark 3.0 - Part 6 : Min and Max By Functions

Spark 3.0 is the next major release of Apache Spark. This release brings major changes to abstractions, API’s and libraries of the platform. This release sets the tone for next year’s direction of the framework. So understanding these few features is critical to understand for the ones who want to make use all the advances in this new release. So in this series of blog posts, I will be discussing about different improvements landing in Spark 3.0.

This is the sixth post in the series where I am going to talk about min and max by SQL functions. You can access all posts in this series here.

TL;DR All code examples are available on github.

## Finding Minimum and Maximum ID using It’s Value

Let’s say we have data as below with a id and value columns

Let’s say we want to find the id with least value. We can easily find minimum value with **min** method but it’s not easy to find it’s associated id. We need to use complicated window functions in Spark 2.x

## MinBy in Spark 2.x

The below code calculates the minimum id by it’s value using window API.

In above code, we first create a window where the values are ordered and then create a rank by **dense_rank** function. Then we filter the values by **rank==1**. This gives us the minimum id.

This is all way complicated for a simple operation. So there is a new easy way introduced in 3.0

## MinBy and MaxBy SQL Function in 3.0

Spark 3.0 add two function **min_by** and **max_by** to compute the min and max by a column. They are simple to use and doesn’t need all the complexity of window operations.

Let’s calculate the same with these new functions

These functions take two parameters. The first parameter is minimum/maximum we want to find and second parameter the value on which we want to find. It’s that simple.

## Code

You can access complete code on github.