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The traditional answer is that hash tables are designed to be efficient when storing data in memory, while B-Trees are designed for slower storage that is accessed in blocks. However, this is not a fundamental property of these data structures. There are hash tables designed to be used on disk (e.g. MySQL’s hash index), many in-memory trees (e.g. Java’s TreeMap, C++’s map), and even in-memory B-Trees. I think the most important answer is that B-Trees are more ‘general purpose,’ which results in lower ‘total cost’ for very large persistent data.

Kubernetes is well-known for running scalable workloads. It scales your workloads based on their resource usage. When a workload is scaled up, more instances of the application get created. When the application is critical for your product, you want to make sure that these new instances are scheduled even when your cluster is under resource pressure. One obvious solution to this problem is to over-provision your cluster resources to have some amount of slack resources available for scale-up situations.