---
title: "How to configure Spark to maximize resource usage while using AWS EMR"
description: "How to configure EMR to use all available resources when running a Spark cluster"
author: "Bartosz Mikulski"
author_bio: "Principal AI Engineer & MLOps Architect. I bridge the gap between \"it works in a notebook\" and \"it works for 200 million users.\""
author_url: https://mikulskibartosz.name
author_linkedin: https://www.linkedin.com/in/mikulskibartosz/
author_github: https://github.com/mikulskibartosz
canonical_url: https://mikulskibartosz.name/configure-spark-to-maximize-resource-usage-on-emr
---

When we run a Spark cluster on EMR, we often create a new cluster for every Spark job. In that case, we want to use all available resources, but changing the configuration is annoying and error-prone. How many times have you forgotten to change the Spark settings after changing the EMR instances to less powerful ones? Of course, the Spark job failed because it could not allocate the resources you wanted.

What is even worse, when you forget to change the settings after changing the instance to a bigger one, you pay for a better cluster, but you are not using it entirely.

Fortunately, Spark's EMR version has a special configuration parameter that replaces all of the cumbersome parameters, such as the executor memory, the executor cores, or parallelism.

Instead of them, we should enable the maximizeResourceAllocation feature:

```
--conf maximizeResourceAllocation=true
```

when we call the `spark-submit` script.