『VPA vs HPA for Stateful Workloads: Autoscaler Deep Dive』のカバーアート

VPA vs HPA for Stateful Workloads: Autoscaler Deep Dive

VPA vs HPA for Stateful Workloads: Autoscaler Deep Dive

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概要
 Your Cassandra node is getting evicted at 2 a.m. or your PostgreSQL replica is sitting on 4× the memory it needs — this episode breaks down exactly which autoscaler to reach for and why VPA vs HPA is a staple senior SRE interview question. You'll learn: Why HPA's scale-out model breaks for StatefulSets (token rings, replication slots, unacknowledged messages) and when vertical scaling is the only safe lever VPA's three components — recommender, admission controller, updater — and why update mode auto is the one that pages you at 2 a.m. for stateful workloads The safe progression: start in Off mode for two weeks, apply recommendations manually, then consider Initial mode — and why Auto is rarely worth it for anything with persistent state The HPA + VPA feedback loop failure: both watching CPU on the same workload, pod count oscillating, resource allocation in chaos The clean split that actually works: HPA on queue depth (custom metric), VPA managing memory requests — distinct signals, no interference Keywords: VPA vs HPA Kubernetes, vertical pod autoscaler stateful workloads, autoscaler SRE interview, HPA VPA conflict, Kubernetes StatefulSet autoscaling 🎧 Listen, then go deeper — DevOps & Cloud interview-prep ebooks at DevOpsInterview.Cloud ▶ Daily 30-second interview drills: DevOps Interview Cloud on YouTube Transcript Your stateful app is getting evicted every few hours, or it's sitting on four times the memory it actually needs, and you're not sure which autoscaler to reach for. That's the exact scenario interviewers love, because most candidates only know one half of the answer. Let's set the stage. You have a Cassandra node, a RabbitMQ broker, or a PostgreSQL read replica running in Kubernetes. It's a StatefulSet. Traffic is not uniform. Some days it's busy, some days it's quiet. Someone on your team says "just add HPA" and someone else says "we need VPA." Who's right? That question shows up in senior SRE and platform engineer interviews constantly, because the answer is not obvious and the wrong choice causes real incidents. Interviewers ask this because autoscaling is one of those topics where surface-level knowledge falls apart fast. Saying "HPA scales pods out, VPA scales pods up" is correct but incomplete. The follow-up is always: okay, so which one do you use for a stateful workload, and why? And then: what happens if you use both at the same time? If you can't answer those, you signal that you've only worked with stateless services. Here's the mental model you need. The HPA, the Horizontal Pod Autoscaler, works by changing the number of pod replicas. It watches a metric, CPU or memory or a custom one, and when that metric crosses a threshold it adds or removes pods. That works beautifully for stateless services. Each replica is identical, sessions don't matter, and spinning up a new pod behind a load balancer is invisible to users. Stateful workloads break that assumption. A Cassandra node owns a subset of the token ring. A RabbitMQ broker may hold unacknowledged messages. A PostgreSQL replica has an open replication slot. Adding a new pod doesn't instantly help because the new pod has to join the cluster, sync data, or acquire state before it can carry load. Scaling out fast is often dangerous. Scaling in is even worse because you might be removing a pod that holds data not yet replicated elsewhere. So for stateful workloads, the more useful lever is usually vertical. Give the existing pod more CPU or more memory so it can handle the load without needing a new replica. That's where the VPA, the Vertical Pod Autoscaler, comes in. VPA has three components. The recommender watches historical resource usage and calculates what your requests and limits should be. The admission controller patches those values into new pods at scheduling time. And the updater, this is the dangerous one, can evict running pods so they restart with the new resource values. The key knob is the update mode, and this is a common interview question on its own. Update mode off means the VPA only calculates recommendations. It never touches your pods. You query the VPA object and apply changes yourself. This is the right starting point for any stateful workload. You get the data without the risk. Update mode initial means the admission controller sets resource values when a pod is first created, but the VPA never evicts a running pod. The pod keeps whatever resources it started with until it's naturally replaced, for example during a rollout. This is a reasonable middle ground for apps that tolerate restarts as part of deployments but not random evictions. Update mode auto, and this is the one that bites teams, allows the VPA updater to evict pods at any time to apply new recommendations. For a stateless deployment this is often fine. For a stateful pod it can be catastrophic. Imagine your primary Redis instance getting evicted at two in the morning because the VPA decided it needed fifty percent more ...
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