EMR On EKS: Request For Minimal Spark Image

Hey guys! Today, we're diving into a feature request that could significantly improve the efficiency of your EMR on EKS deployments. Specifically, we're talking about the need for a minimal or base Spark image. Let's break down why this is important and how it can make your life easier.

The Problem: Bloated Spark Images

Currently, the standard Spark image for Amazon EMR on EKS is quite hefty. We're looking at images around 2.2 GB in size, like public.ecr.aws/emr-on-eks/spark/emr-7.10.0:20250801-x86_64. Now, when you start building your custom application image on top of this, the total size can balloon to ~3.2 GB or even more. This increase in size has some serious implications for your Kubernetes environment. The main keyword here is bloated Spark images.

First off, pod spin-up times take a hit. Imagine waiting several minutes for your pods to become ready, especially during those critical cluster autoscaling events. It's not just annoying; it can impact your application's performance and responsiveness. Then there is also high network bandwidth usage. Every time a new pod needs to be created, that massive image has to be pulled from the registry, which eats up your network resources and can lead to increased costs. All in all, the large image size reduces the elasticity and responsiveness of your cluster. You want your cluster to scale quickly and efficiently, but these oversized images are holding you back. For data engineers and data scientists, this translates to longer waiting times, less efficient resource utilization, and potentially higher infrastructure costs. Ultimately, optimizing the size of these Spark images contributes directly to streamlining workflows and improving overall operational efficiency.

Why a Minimal Image Matters

So, why is a minimal or base image the solution? Think of it this way: you want a lean, mean starting point. A minimal image would contain only the core Spark runtime components, stripping away any unnecessary fluff. This approach offers several key benefits. Firstly, smaller images translate directly to faster pod startup times. Your applications become more responsive, and your cluster can scale more quickly to meet demand. Secondly, smaller images mean less network bandwidth usage. This is especially crucial in environments where network costs are a concern. By reducing the amount of data that needs to be transferred, you can save money and improve overall network performance. Thirdly, it allows for greater flexibility and customization. By starting with a minimal base, you have more control over what goes into your custom application image. You can add only the dependencies you need, further reducing the final image size and optimizing it for your specific workload. It also promotes better security practices. By minimizing the number of components in the image, you reduce the attack surface and make it easier to manage vulnerabilities. This aligns with the principle of least privilege, ensuring that your containers only have access to the resources they absolutely need. Basically, the benefit of using minimal images is a win-win for performance, cost, and security.

The Current Pain Points

Right now, the lack of a smaller base image is a real headache for many of us using EMR on EKS. We're forced to work around the issue, but the available solutions are less than ideal. For example, we might host our custom image in Amazon ECR within the same region to minimize image pull latency. It helps a bit, but it doesn't address the fundamental problem of the large base image size. AWS Support has also suggested general Kubernetes optimizations like image pre-pulling and using the IfNotPresent image pull policy. Again, these are helpful tweaks, but they only offer marginal improvements. The core issue remains: we're starting with a base image that's far larger than it needs to be. So far, there is not a direct workaround for reducing the image size itself. This forces us to spend time and resources on mitigating the symptoms rather than addressing the root cause. This ultimately slows down development cycles and increases operational overhead.

Real-World Impact

Let's talk about the real-world impact of these large images. Imagine you're running a data processing pipeline that needs to scale up quickly to handle a surge in incoming data. With the current image size, each new pod takes several minutes to start, which means you're losing valuable time and potentially missing critical SLAs. Or consider a scenario where you're running multiple EMR on EKS clusters across different regions. The network costs associated with pulling these large images can quickly add up, impacting your overall budget. These are just a few examples of how the large image size can affect your bottom line and your ability to deliver timely and reliable data processing services. So what can we do? We need to address this issue to unlock the full potential of EMR on EKS.

Proposed Solution: A Minimal or Modular Image

The solution is simple: provide a minimal or modular image option for EMR on EKS. This could be a