Kubernetes 1.32 In-Place Dynamic Pod Resizing and Cgroupv2 Controller Invariant

--- description: Kubernetes 1.32 In-Place Dynamic Pod Resizing and Cgroupv2 Controller Invariant - Enforces zero-downtime resource resizing via Kubernetes 1.32 InPlacePodVerticalScaling by mutating pod resource allocations directly against the kubelet and cgroupv2 controllers. Prevents pod recreation churn, restart loops, and OOM kills by establishing strict resize policies, validating Node resize allocations, and handling Deferred resizing states deterministically. globs: deployments/**/*.{yaml,yml,json}, infrastructure/**/*.{ts,js,py}, src/k8s/**/*.{ts,tsx,go}, helm/**/*.{yaml,yml} alwaysApply: true verified_by: "agentrules.org" license: "CC-BY-4.0" source: "https://agentrules.org/rules/k8s-132-inplace-pod-dynamic-resizing-invariant" --- # Kubernetes 1.32 In-Place Dynamic Pod Resizing and Cgroupv2 Controller Invariant > **Severity**: CRITICAL | **Category**: devops > **Anti-Pattern Warning**: Patching pod specs with immutable recreation triggers or omitting resizePolicy, resulting in SIGTERM evictions and container restarts during vertical scaling. ## System Constraints You are a Principal Cloud Architect enforcing Kubernetes 1.32 In-Place Pod Dynamic Resizing invariants. You must mandate zero-downtime resource mutation via InPlacePodVerticalScaling on cgroupv2 runtimes without triggering Pod or Container churn. ## Strict Guidelines - Engineers MUST explicitly define 'resizePolicy' for both 'cpu' and 'memory' resources under each container specification. - Engineers MUST set 'resizePolicy[resource=cpu].restartPolicy' to 'NotRequired' to guarantee live runtime adjustment via cgroupv2 'cpu.max' and 'cpu.weight'. - Engineers MUST verify the underlying runtime supports non-restart memory expansion before setting 'resizePolicy[resource=memory].restartPolicy' to 'NotRequired', and handle kernel memory reclamation boundaries safely. - Engineers MUST patch resources using the Pod subresource '/resize' or dynamic status-aware patch APIs rather than recreating Pods or altering immutable Deployment Pod templates for real-time spikes. - Code interacting with Kubernetes dynamic resizing MUST inspect 'containerStatuses[].allocatedResources' and 'resize' status conditions ('Proposed', 'InProgress', 'Deferred', 'Infeasible') to guard against capacity deadlocks. - Engineers MUST NOT reduce memory limits below active working set usage without verifying that the cgroup 'memory.current' is safely beneath the target boundary to avoid immediate OOMKills. - Automated controllers MUST NEVER treat 'Deferred' resize states as permanent failures; reconciliation loops must implement exponential backoff while waiting for Node-level capacity release. ## Bad vs Good Implementations ### TypeScript Kubernetes In-Place Pod Resize Patching and Status Invariant #### Bad Antipattern (AI Common Hallucination) > **Why it fails**: Omitting explicit 'resizePolicy' configurations causes the kubelet to fall back to container restarts upon spec mutation. Furthermore, failing to query current Pod status conditions ('AllocatedResources' vs 'Resources') risks thrashing and ignores 'Deferred' states caused by node capacity constraints. ```typescript import * as k8s from '@kubernetes/client-node'; // Anti-pattern: Modifying pod spec directly without resizePolicy and status inspection triggers Pod replacement export async function updatePodResources(client: k8s.CoreV1Api, name: string, ns: string, cpu: string, memory: string) { const patch = { spec: { containers: [{ name: 'app-engine', resources: { limits: { cpu, memory }, requests: { cpu, memory } } }] } }; // Blindly applies spec patch; if resizePolicy is absent or defaulted to RestartContainer, container restarts return await client.patchNamespacedPod( name, ns, patch, undefined, undefined, undefined, undefined, undefined, { headers: { 'Content-Type': 'application/strategic-merge-patch+json' } } ); } ``` #### Production Standard (Correct) > **Standard**: Explicitly declares 'resizePolicy' with 'restartPolicy: NotRequired' for CPU and memory, ensuring the cgroupv2 controllers dynamically expand allocations without restarting the container or evicting the pod. Checks PodResizeStatus to prevent concurrent conflicting mutations. ```typescript import * as k8s from '@kubernetes/client-node'; interface DynamicResizePatch { spec: { containers: Array<{ name: string; resources: { requests?: { cpu?: string; memory?: string }; limits?: { cpu?: string; memory?: string }; }; resizePolicy?: Array<{ resourceName: 'cpu' | 'memory'; restartPolicy: 'NotRequired' | 'RestartContainer'; }>; }>; }; } export async function applyInPlaceDynamicResize( client: k8s.CoreV1Api, podName: string, namespace: string, containerName: string, targetCpu: string, targetMemory: string ): Promise<void> { // 1. Fetch current runtime state to evaluate in-flight resizing const { body: pod } = await client.readNamespacedPod(podName, namespace); const containerStatus = pod.status?.containerStatuses?.find(c => c.name === containerName); if (!containerStatus) { throw new Error(`Container ${containerName} not found in pod ${podName}`); } // 2. Validate against existing active resize conditions const resizeCondition = pod.status?.conditions?.find(c => c.type === 'PodResizeStatus'); if (resizeCondition?.status === 'InProgress') { throw new Error(`In-place resize already in progress for Pod ${podName}`); } // 3. Construct In-Place resize patch targeting cgroupv2 non-restart controllers const patch: DynamicResizePatch = { spec: { containers: [{ name: containerName, resources: { requests: { cpu: targetCpu, memory: targetMemory }, limits: { cpu: targetCpu, memory: targetMemory } }, resizePolicy: [ { resourceName: 'cpu', restartPolicy: 'NotRequired' }, { resourceName: 'memory', restartPolicy: 'NotRequired' } ] }] } }; await client.patchNamespacedPod( podName, namespace, patch, undefined, undefined, undefined, undefined, undefined, { headers: { 'Content-Type': 'application/strategic-merge-patch+json' } } ); } ```

Architectural Specification & Invariant Rationale

Architectural Invariant Design: Kubernetes 1.32 In-Place Dynamic Pod Resizing and Cgroupv2 Controller Invariant

Kubernetes 1.32 In-Place Dynamic Pod Resizing and Cgroupv2 Controller Invariant defines an industrial-grade behavioral standard for devops engineering. Enforces zero-downtime resource resizing via Kubernetes 1.32 InPlacePodVerticalScaling by mutating pod resource allocations directly against the kubelet and cgroupv2 controllers. Prevents pod recreation churn, restart loops, and OOM kills by establishing strict resize policies, validating Node resize allocations, and handling Deferred resizing states deterministically. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.

Framework Paradigm Shift & Drift MechanicsLLMs are trained on millions of open-source repositories containing outdated code, legacy idioms, and unpatched antipatterns. When coding assistants generate implementations for this stack, they default to common statistical tokens: Patching pod specs with immutable recreation triggers or omitting resizePolicy, resulting in SIGTERM evictions and container restarts during vertical scaling. Without an explicit system instruction file, developers spend hours debugging silent failures.

Failure Modes & Production Blast Radius

Hazard Analysis: What Breaks Without This Rule

The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:

Failure Mode	AI Symptom (The Anti-Pattern)	Severity	Production Impact & Remedy
AI Hallucination & Deprecation Drift	Patching pod specs with immutable recreation triggers or omitting resizePolicy, resulting in SIGTERM evictions and container restarts during vertical scaling.	Critical	Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production. Enforce explicit behavioral system directives across all AI coding assistants in your repository.
Silent Architecture Inconsistency	AI assistants produce mixed paradigms across files, creating fragmented codebase conventions.	Medium	Increases cognitive load during code reviews and leads to maintenance debt across engineering teams. Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails.

Architectural Directives in Detail

Step-by-Step Production Directives

Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:

Engineers MUST explicitly define 'resizePolicy' for both 'cpu' and 'memory' resources under each container specification.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.