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According to [1] EMR serverless uses something that is known as ‘workers’ to execute workloads. These workers vary based on the application type and Amazon’s EMR release version .
Essentially, EMR serverless will prepare and configure the resources as needed. It will automatically calculate and allocate the compute and memory resources needed to process job requests and scale them at the time of running the job.
In addition, these ‘workers’ are utilizing Spark’s Dynamic Resource Allocation so that each job gets the resources it needs. Because it automatically scales depending on the need, you will not be over/under provisioning resources. There is no need to worry about resizing a cluster, cluster size, or type of instance.
Hope this answers your question
References : [1] Workers https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/emr-serverless.html#concepts-workers
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