{"id":1655,"date":"2026-02-15T11:34:48","date_gmt":"2026-02-15T11:34:48","guid":{"rendered":"https:\/\/noopsschool.com\/blog\/vpa\/"},"modified":"2026-02-15T11:34:48","modified_gmt":"2026-02-15T11:34:48","slug":"vpa","status":"publish","type":"post","link":"https:\/\/noopsschool.com\/blog\/vpa\/","title":{"rendered":"What is VPA? Meaning, Architecture, Examples, Use Cases, and How to Measure It (2026 Guide)"},"content":{"rendered":"\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Quick Definition (30\u201360 words)<\/h2>\n\n\n\n<p>Vertical Pod Autoscaler (VPA) automatically recommends or adjusts container resource requests and limits to match observed usage. Analogy: VPA is like a smart thermostat for container CPU and memory. Formal: VPA continuously observes pod resource metrics and computes target resource configurations to reduce underprovisioning and overprovisioning.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">What is VPA?<\/h2>\n\n\n\n<p>What it is \/ what it is NOT<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>VPA is an autoscaling mechanism focused on changing resource requests and limits for running workloads, primarily in Kubernetes environments.<\/li>\n<li>It is NOT horizontal scaling; it does not change pod replica counts to handle concurrency.<\/li>\n<li>It is NOT a replacement for application tuning or proper capacity planning.<\/li>\n<\/ul>\n\n\n\n<p>Key properties and constraints<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Operates on resource requests and optionally updates pod specs.<\/li>\n<li>Works best for stateful or single-replica workloads where vertical scaling is feasible.<\/li>\n<li>Requires resource metrics (CPU, memory) and historical data to make decisions.<\/li>\n<li>Can be configured in recommendation-only, update, or evict mode depending on risk tolerance.<\/li>\n<li>Changes can cause pod restarts; may be disruptive for some workloads.<\/li>\n<li>Interacts with cluster scheduler and may require coordination with HPA, PodDisruptionBudget, and cluster autoscaler.<\/li>\n<\/ul>\n\n\n\n<p>Where it fits in modern cloud\/SRE workflows<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Complements Horizontal Pod Autoscaler (HPA) by improving per-pod resource accuracy.<\/li>\n<li>Reduces manual resource engineering toil by automating request\/limit tuning.<\/li>\n<li>Supports cost optimization by shrinking unnecessary headroom and reducing OOMs by raising requests when needed.<\/li>\n<li>Fits into CI\/CD pipelines for continuous tuning, into observability pipelines for telemetry, and into incident response playbooks for resource-related incidents.<\/li>\n<\/ul>\n\n\n\n<p>A text-only \u201cdiagram description\u201d readers can visualize<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Metrics collector ingests CPU and memory usage from node-level and cAdvisor streams.<\/li>\n<li>VPA recommender analyzes time series and calculates target resource requests.<\/li>\n<li>VPA updater optionally evicts pods to apply new resource requests.<\/li>\n<li>Scheduler attempts to place updated pods; cluster autoscaler may trigger if nodes lack capacity.<\/li>\n<li>Observability and alerting report recommendations, applied changes, and failures.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">VPA in one sentence<\/h3>\n\n\n\n<p>VPA automatically recommends or applies per-pod resource request and limit adjustments based on observed usage to improve reliability and reduce cost.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">VPA vs related terms (TABLE REQUIRED)<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Term<\/th>\n<th>How it differs from VPA<\/th>\n<th>Common confusion<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>T1<\/td>\n<td>HPA<\/td>\n<td>Scales pod count based on load metrics<\/td>\n<td>Confused as same autoscaling direction<\/td>\n<\/tr>\n<tr>\n<td>T2<\/td>\n<td>Cluster Autoscaler<\/td>\n<td>Scales nodes based on unschedulable pods<\/td>\n<td>People think VPA adds nodes<\/td>\n<\/tr>\n<tr>\n<td>T3<\/td>\n<td>Vertical Scaling VM<\/td>\n<td>Resizes VMs not pods<\/td>\n<td>Assumed to change infra rather than pods<\/td>\n<\/tr>\n<tr>\n<td>T4<\/td>\n<td>Resource Quotas<\/td>\n<td>Limit resource consumption per namespace<\/td>\n<td>Mistaken as automatic tuning<\/td>\n<\/tr>\n<tr>\n<td>T5<\/td>\n<td>PodDisruptionBudget<\/td>\n<td>Controls allowable pod evictions<\/td>\n<td>People think it prevents VPA updates<\/td>\n<\/tr>\n<tr>\n<td>T6<\/td>\n<td>LimitRange<\/td>\n<td>Sets default request and limit bounds<\/td>\n<td>Mistaken as dynamic tuning mechanism<\/td>\n<\/tr>\n<tr>\n<td>T7<\/td>\n<td>OOM Killer<\/td>\n<td>Kernel action on OOM events<\/td>\n<td>Mistaken for prevention instead of reaction<\/td>\n<\/tr>\n<tr>\n<td>T8<\/td>\n<td>cAdvisor<\/td>\n<td>Collects container metrics<\/td>\n<td>Assumed to adjust resources<\/td>\n<\/tr>\n<tr>\n<td>T9<\/td>\n<td>KEDA<\/td>\n<td>Event-driven autoscaling HPA style<\/td>\n<td>Confused with VPA being event-driven<\/td>\n<\/tr>\n<tr>\n<td>T10<\/td>\n<td>Vertical Pod Resizer<\/td>\n<td>Nonstandard term<\/td>\n<td>Confused as official Kubernetes component<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if any cell says \u201cSee details below\u201d)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Why does VPA matter?<\/h2>\n\n\n\n<p>Business impact (revenue, trust, risk)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Reduces downtime caused by out-of-memory (OOM) kills and CPU starvation, protecting revenue-critical services.<\/li>\n<li>Lowers cloud spend by shrinking idle overprovisioned resources, improving margin.<\/li>\n<li>Increases customer trust via consistent performance and fewer capacity-related incidents.<\/li>\n<li>Reduces regulatory and contractual risk by maintaining SLAs through automated resource correction.<\/li>\n<\/ul>\n\n\n\n<p>Engineering impact (incident reduction, velocity)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Reduces mean time to resolution (MTTR) for resource-related incidents.<\/li>\n<li>Lowers toil by automating routine resource tuning, freeing engineers to work on features.<\/li>\n<li>Enables faster onboarding of new services via automated baseline provisioning.<\/li>\n<li>Improves deployment velocity by reducing back-and-forth about request sizing.<\/li>\n<\/ul>\n\n\n\n<p>SRE framing (SLIs\/SLOs\/error budgets\/toil\/on-call)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>SLIs impacted: request latency, error rate, successful deployments, pod availability.<\/li>\n<li>SLOs: resource stability SLOs could be defined for percentage of pods within recommended resource ranges.<\/li>\n<li>Error budget consumption spikes when VPA changes cause unexpected restarts; track this in incidents.<\/li>\n<li>Toil reduced by automated recommendations; however, operational toil may increase temporarily during tuning.<\/li>\n<li>On-call responsibilities: ensure VPA recommendations are safe and do not cause cascading failures.<\/li>\n<\/ul>\n\n\n\n<p>3\u20135 realistic \u201cwhat breaks in production\u201d examples<\/p>\n\n\n\n<p>1) OOM after workload change: A new feature increases memory use per request and pods are OOM killed until VPA raises requests and restarts pods.\n2) Eviction storms: VPA triggers many pod evictions simultaneously, causing traffic disruption when pods restart on busy nodes.\n3) Scheduler fails to place updated pods: VPA increases requests but cluster lacks node capacity; pods stay pending.\n4) Conflicting autoscalers: HPA reduces replicas while VPA raises requests, causing resource churn and poor utilization.\n5) Cost drift: VPA overestimates steady-state requests and keeps expensive pods sized larger than necessary.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Where is VPA used? (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Layer\/Area<\/th>\n<th>How VPA appears<\/th>\n<th>Typical telemetry<\/th>\n<th>Common tools<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>L1<\/td>\n<td>Application<\/td>\n<td>Per-pod request recommendations<\/td>\n<td>CPU mem time series and percentiles<\/td>\n<td>VPA recommender metrics<\/td>\n<\/tr>\n<tr>\n<td>L2<\/td>\n<td>Service<\/td>\n<td>Steady-state services single replica<\/td>\n<td>Latency error rate and resource usage<\/td>\n<td>Prometheus Grafana<\/td>\n<\/tr>\n<tr>\n<td>L3<\/td>\n<td>Stateful<\/td>\n<td>Databases and caches with single pods<\/td>\n<td>Memory RSS and pagefaults<\/td>\n<td>Custom exporters<\/td>\n<\/tr>\n<tr>\n<td>L4<\/td>\n<td>Kubernetes infra<\/td>\n<td>Control plane addons tuning<\/td>\n<td>Component CPU mem and restart counts<\/td>\n<td>Metrics server<\/td>\n<\/tr>\n<tr>\n<td>L5<\/td>\n<td>CI\/CD<\/td>\n<td>Pre-deploy recommendations in pipelines<\/td>\n<td>Historical usage per branch<\/td>\n<td>CI runners<\/td>\n<\/tr>\n<tr>\n<td>L6<\/td>\n<td>Cost mgmt<\/td>\n<td>Rightsizing reports for pods<\/td>\n<td>Cost per pod time and resource<\/td>\n<td>Cost tools<\/td>\n<\/tr>\n<tr>\n<td>L7<\/td>\n<td>Serverless\/managed-PaaS<\/td>\n<td>Not typical; sometimes integrated<\/td>\n<td>Invocation durations and memory<\/td>\n<td>Platform metrics<\/td>\n<\/tr>\n<tr>\n<td>L8<\/td>\n<td>Observability<\/td>\n<td>Alerts for recommendation drift<\/td>\n<td>Recommendation delta and events<\/td>\n<td>Alertmanager<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">When should you use VPA?<\/h2>\n\n\n\n<p>When it\u2019s necessary<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Stateful single-replica applications that cannot be horizontally scaled.<\/li>\n<li>Workloads with variable but predictable per-pod resource needs that change over time.<\/li>\n<li>Teams with frequent OOM incidents or frequent underprovisioned CPU causing latency spikes.<\/li>\n<\/ul>\n\n\n\n<p>When it\u2019s optional<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Stable services with good manual sizing and low variance.<\/li>\n<li>Batch jobs where resources can be set via job tooling.<\/li>\n<li>Environments with strong horizontal scaling patterns and stateless services, where HPA handles load.<\/li>\n<\/ul>\n\n\n\n<p>When NOT to use \/ overuse it<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Highly replicated microservices where HPA and service autoscaling is sufficient.<\/li>\n<li>Latency-sensitive low-latency services if VPA evictions cause jitter.<\/li>\n<li>Systems without reliable metrics pipelines or with intermittent metric gaps.<\/li>\n<\/ul>\n\n\n\n<p>Decision checklist<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>If single-replica OR slow-to-scale stateful workload AND frequent OOMs -&gt; enable VPA in recommend or update mode.<\/li>\n<li>If service is stateless with autoscaling replicas AND predictable horizontal scaling works -&gt; prefer HPA.<\/li>\n<li>If cluster capacity is constrained AND you lack cluster autoscaler coordination -&gt; use recommendations only, not automated updates.<\/li>\n<\/ul>\n\n\n\n<p>Maturity ladder: Beginner -&gt; Intermediate -&gt; Advanced<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Beginner: Recommendation-only mode; surface suggestions in dashboards and pipelines.<\/li>\n<li>Intermediate: Automated updates in maintenance windows; PDBs and staged rollouts to limit disruption.<\/li>\n<li>Advanced: Feedback loop with CI and cost systems, automated patching with safety constraints and ML-driven prediction.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">How does VPA work?<\/h2>\n\n\n\n<p>Step-by-step: Components and workflow<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Metrics collection: Resource usage sampled from kubelet, cAdvisor, and metrics server or Prometheus.<\/li>\n<li>Recommender analyzes usage patterns over time, computes target requests using statistical models.<\/li>\n<li>Advisor stores recommendations and exposes them via CRDs for review.<\/li>\n<li>Updater optionally evicts pods to apply new requests; controller coordinates to avoid mass evictions.<\/li>\n<li>Scheduler places rescheduled pods considering new requests; cluster autoscaler may add nodes if needed.<\/li>\n<li>Observability captures recommendations, evictions, and outcomes for auditing and iteration.<\/li>\n<\/ol>\n\n\n\n<p>Data flow and lifecycle<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ingest -&gt; Store -&gt; Analyze -&gt; Recommend -&gt; Apply -&gt; Observe.<\/li>\n<li>Loop: applied resources change usage, which feeds back to recommender.<\/li>\n<\/ul>\n\n\n\n<p>Edge cases and failure modes<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Metric staleness leading to poor recommendations.<\/li>\n<li>Burst behavior misinterpreted as steady-state needs.<\/li>\n<li>Conflicts with HPA causing resource oscillation.<\/li>\n<li>Eviction cascades when many pods updated at once.<\/li>\n<li>Scheduler inability to place resized pods due to cluster capacity.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Typical architecture patterns for VPA<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Recommendation-Only Pattern: Use VPA in readonly mode to surface suggestions in a CI pipeline before deployment. Use when risk-averse.<\/li>\n<li>Scheduled Update Pattern: Apply VPA updates during maintenance windows to minimize impact. Use for production stateful apps.<\/li>\n<li>Live Update with Rate-Limit Pattern: Allow VPA to update but limit concurrent evictions and rate. Use for medium-risk services.<\/li>\n<li>Combined VPA+HPA Pattern: Use VPA for baseline requests and HPA for replica scaling based on concurrency. Use for throughput-oriented services.<\/li>\n<li>CI Feedback Loop Pattern: Integrate VPA recommendations into PR checks to set initial requests for new services. Use for developer experience scaling.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Failure modes &amp; mitigation (TABLE REQUIRED)<\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Failure mode<\/th>\n<th>Symptom<\/th>\n<th>Likely cause<\/th>\n<th>Mitigation<\/th>\n<th>Observability signal<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>F1<\/td>\n<td>Eviction storm<\/td>\n<td>Many pods restart together<\/td>\n<td>VPA applied many updates at once<\/td>\n<td>Rate-limit updates and honor PDBs<\/td>\n<td>Pod restart rate spike<\/td>\n<\/tr>\n<tr>\n<td>F2<\/td>\n<td>Pending pods<\/td>\n<td>Pods pending scheduling after update<\/td>\n<td>No node capacity for new requests<\/td>\n<td>Trigger cluster autoscaler or reduce requests<\/td>\n<td>Pending pod count up<\/td>\n<\/tr>\n<tr>\n<td>F3<\/td>\n<td>Overprovisioning<\/td>\n<td>Increased cost after updates<\/td>\n<td>Recommender overestimates peak as steady<\/td>\n<td>Use percentile windows and manual review<\/td>\n<td>Cost per pod increases<\/td>\n<\/tr>\n<tr>\n<td>F4<\/td>\n<td>Underprovisioning<\/td>\n<td>OOMs continue<\/td>\n<td>Metrics sampling missed spikes<\/td>\n<td>Increase sampling resolution and history<\/td>\n<td>OOM kill events<\/td>\n<\/tr>\n<tr>\n<td>F5<\/td>\n<td>Metric gaps<\/td>\n<td>No recommendations<\/td>\n<td>Metrics source failure<\/td>\n<td>Failover metrics and alert on gaps<\/td>\n<td>Missing metric series<\/td>\n<\/tr>\n<tr>\n<td>F6<\/td>\n<td>HPA conflict<\/td>\n<td>Oscillating resource and replica counts<\/td>\n<td>Uncoordinated HPA and VPA<\/td>\n<td>Define clear responsibilities and use cross-controller rules<\/td>\n<td>Replica churn and resource oscillation<\/td>\n<\/tr>\n<tr>\n<td>F7<\/td>\n<td>Stateful restart issues<\/td>\n<td>Data corruption risk on restart<\/td>\n<td>Pod eviction on stateful service<\/td>\n<td>Use maintenance windows and safe restart procedures<\/td>\n<td>Application error rates<\/td>\n<\/tr>\n<tr>\n<td>F8<\/td>\n<td>Recommendation flapping<\/td>\n<td>Recommendations jump frequently<\/td>\n<td>Highly variable workload or too-short windows<\/td>\n<td>Smooth recommendations and use longer windows<\/td>\n<td>Recommendation delta frequency<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Key Concepts, Keywords &amp; Terminology for VPA<\/h2>\n\n\n\n<p>Below are 40+ terms with concise definitions, why they matter, and common pitfall.<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>VPA \u2014 Vertical Pod Autoscaler component in Kubernetes \u2014 tunes pod resource requests \u2014 Pitfall: assumed to scale replicas.<\/li>\n<li>Recommender \u2014 VPA subcomponent that computes targets \u2014 provides suggested values \u2014 Pitfall: overfitting to spikes.<\/li>\n<li>Updater \u2014 VPA component that evicts pods to apply changes \u2014 applies updates \u2014 Pitfall: causing mass restarts.<\/li>\n<li>Admission Controller \u2014 validates changes to pods \u2014 may block updates \u2014 Pitfall: misconfigured webhook can prevent updates.<\/li>\n<li>Resource Request \u2014 declared CPU and memory a pod requests \u2014 affects scheduling \u2014 Pitfall: too low causes throttling.<\/li>\n<li>Resource Limit \u2014 cap on resource usage \u2014 prevents runaway \u2014 Pitfall: too low leads to OOMs.<\/li>\n<li>cAdvisor \u2014 node agent collecting container metrics \u2014 data source for VPA \u2014 Pitfall: sampling resolution affects accuracy.<\/li>\n<li>Metrics Server \u2014 lightweight metrics API \u2014 provides CPU and memory metrics \u2014 Pitfall: not sufficient history for VPA.<\/li>\n<li>Prometheus \u2014 time series DB commonly used for metrics \u2014 stores granularity and history \u2014 Pitfall: retention policies may drop needed history.<\/li>\n<li>Percentile \u2014 statistical measure used for recommendations \u2014 balances typical vs peak \u2014 Pitfall: picking wrong percentile.<\/li>\n<li>Eviction \u2014 removal of a pod to allow rescheduling \u2014 applies new spec \u2014 Pitfall: causes transient downtime.<\/li>\n<li>PodDisruptionBudget (PDB) \u2014 limits concurrent voluntary disruptions \u2014 protects availability \u2014 Pitfall: too strict PDB blocks updates.<\/li>\n<li>HPA \u2014 Horizontal Pod Autoscaler \u2014 scales by replicas \u2014 Pitfall: mixed signals with VPA.<\/li>\n<li>Cluster Autoscaler \u2014 adds\/removes nodes based on scheduling \u2014 supports VPA-induced needs \u2014 Pitfall: slow scale-up can cause pending pods.<\/li>\n<li>Scheduler \u2014 places pods on nodes \u2014 must account for new requests \u2014 Pitfall: scheduling failures after resize.<\/li>\n<li>OOMKill \u2014 kernel action when process exceeds memory \u2014 signals underprovisioning \u2014 Pitfall: reactive instead of preventive.<\/li>\n<li>Throttling \u2014 CPU limitation causing latency \u2014 symptom of low CPU requests \u2014 Pitfall: unnoticed without proper SLIs.<\/li>\n<li>Stability Window \u2014 timeframe for recommendation smoothing \u2014 prevents reacting to short spikes \u2014 Pitfall: too long window delays fixes.<\/li>\n<li>Headroom \u2014 extra resources provisioned for spikes \u2014 balances safety and cost \u2014 Pitfall: excessive headroom wastes money.<\/li>\n<li>Right-sizing \u2014 matching request to usage \u2014 primary goal of VPA \u2014 Pitfall: chasing micro-optimizations.<\/li>\n<li>Recommendation History \u2014 recorded past suggestions \u2014 useful for audits \u2014 Pitfall: not stored long enough.<\/li>\n<li>Controller Loop \u2014 reconciliation loop for VPA \u2014 ensures actual state matches desired \u2014 Pitfall: loop thrashing with conflicting controllers.<\/li>\n<li>StatefulSet \u2014 Kubernetes object for stateful apps \u2014 VPA may require safe update strategies \u2014 Pitfall: restart risks for stateful pods.<\/li>\n<li>Deployment \u2014 common Kubernetes workload \u2014 VPA can adjust resources for pods \u2014 Pitfall: restarts may affect rolling updates.<\/li>\n<li>DaemonSet \u2014 node-local pods \u2014 VPA less relevant for DaemonSets \u2014 Pitfall: expectations mismatch.<\/li>\n<li>Admission Review \u2014 Webhook flow for mutating requests \u2014 may interact with VPA \u2014 Pitfall: cycle or blocking.<\/li>\n<li>Resource Quota \u2014 namespace-level cap \u2014 VPA may request more and hit quota \u2014 Pitfall: unbounded recommendations fail.<\/li>\n<li>LimitRange \u2014 default and max\/min bounds for resources \u2014 restricts VPA targets \u2014 Pitfall: prevents expected scaling.<\/li>\n<li>Observability \u2014 telemetry, logs, traces \u2014 required to validate VPA \u2014 Pitfall: incomplete observability breeds blindspots.<\/li>\n<li>Canary \u2014 staged rollout pattern \u2014 use with VPA updates to reduce risk \u2014 Pitfall: inconsistent environments.<\/li>\n<li>Autoscaling Policy \u2014 rules governing behavior \u2014 must include safety limits \u2014 Pitfall: overly permissive policies.<\/li>\n<li>Compaction \u2014 reducing recommendations to simpler configs \u2014 eases review \u2014 Pitfall: losing nuance.<\/li>\n<li>Regression Testing \u2014 ensures app behavior with new resources \u2014 part of CI \u2014 Pitfall: absent tests lead to surprises.<\/li>\n<li>Burstiness \u2014 workload variability \u2014 affects recommendation accuracy \u2014 Pitfall: treating bursts as steady-state.<\/li>\n<li>Telemetry Drift \u2014 change in metric semantics over time \u2014 can mislead recommender \u2014 Pitfall: silent changes in instrumentation.<\/li>\n<li>Feedback Loop \u2014 automated adjustment cycle \u2014 improves over time \u2014 Pitfall: lacking human oversight early.<\/li>\n<li>Cost Allocation \u2014 mapping resource consumption to cost centers \u2014 helps measure VPA ROI \u2014 Pitfall: missing tagging causes skewed reports.<\/li>\n<li>SLA \u2014 service level agreement \u2014 VPA changes should respect SLAs \u2014 Pitfall: changes not evaluated against SLOs.<\/li>\n<li>SLI \u2014 service level indicator \u2014 latency\/error\/availability metrics to monitor \u2014 Pitfall: choosing wrong SLIs for resource issues.<\/li>\n<li>SLO \u2014 service level objective \u2014 target for SLI \u2014 helps align VPA safety \u2014 Pitfall: overly strict SLOs cause alert noise.<\/li>\n<li>Recommendation Delta \u2014 change magnitude between current and recommended \u2014 used for gating \u2014 Pitfall: big deltas causing surprise restarts.<\/li>\n<li>Auto-tuning \u2014 applying recommendations automatically \u2014 increases automation \u2014 Pitfall: insufficient guardrails produce instability.<\/li>\n<li>TTL \u2014 time-to-live for recommendations \u2014 limits stale suggestions \u2014 Pitfall: too short TTL causes flapping.<\/li>\n<li>Sampling Interval \u2014 metric collection frequency \u2014 affects accuracy \u2014 Pitfall: coarse intervals mask short spikes.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">How to Measure VPA (Metrics, SLIs, SLOs) (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Metric\/SLI<\/th>\n<th>What it tells you<\/th>\n<th>How to measure<\/th>\n<th>Starting target<\/th>\n<th>Gotchas<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>M1<\/td>\n<td>Recommendation Acceptance Rate<\/td>\n<td>Percent of recommendations applied<\/td>\n<td>Applied recommendations divided by total<\/td>\n<td>60 80 percent<\/td>\n<td>Skewed by manual rejections<\/td>\n<\/tr>\n<tr>\n<td>M2<\/td>\n<td>Pod OOM Rate<\/td>\n<td>Frequency of OOM kills per service<\/td>\n<td>OOM events per pod hour<\/td>\n<td>Near zero<\/td>\n<td>Some apps intentionally close large heaps<\/td>\n<\/tr>\n<tr>\n<td>M3<\/td>\n<td>Pod Restart Rate<\/td>\n<td>Pod restarts per hour<\/td>\n<td>Restart events per pod<\/td>\n<td>Low single digits<\/td>\n<td>Restarts include other causes<\/td>\n<\/tr>\n<tr>\n<td>M4<\/td>\n<td>Pending Pod Time<\/td>\n<td>Time pods stay pending after updates<\/td>\n<td>Avg pending seconds<\/td>\n<td>&lt; 60s for steady apps<\/td>\n<td>Depends on autoscaler speed<\/td>\n<\/tr>\n<tr>\n<td>M5<\/td>\n<td>CPU Throttle Ratio<\/td>\n<td>Fraction of CPU time throttled<\/td>\n<td>Throttled time over total time<\/td>\n<td>&lt; 1 percent<\/td>\n<td>Requires node-level metrics<\/td>\n<\/tr>\n<tr>\n<td>M6<\/td>\n<td>Recommendation Drift<\/td>\n<td>Difference between recommended and current<\/td>\n<td>Percent delta<\/td>\n<td>Small single digits<\/td>\n<td>Big outliers on first run<\/td>\n<\/tr>\n<tr>\n<td>M7<\/td>\n<td>Cost per Pod<\/td>\n<td>Cost allocated per pod per day<\/td>\n<td>Cost from billing maps to pod runtime<\/td>\n<td>Decrease over time<\/td>\n<td>Attribution errors<\/td>\n<\/tr>\n<tr>\n<td>M8<\/td>\n<td>Update Success Rate<\/td>\n<td>Proportion of VPA-triggered updates that succeed<\/td>\n<td>Successful restarts \/ attempts<\/td>\n<td>95 percent<\/td>\n<td>Success definition varies<\/td>\n<\/tr>\n<tr>\n<td>M9<\/td>\n<td>Eviction Count<\/td>\n<td>Count of voluntary evictions by VPA<\/td>\n<td>Evictions per day<\/td>\n<td>Minimal by design<\/td>\n<td>Evictions could be manual too<\/td>\n<\/tr>\n<tr>\n<td>M10<\/td>\n<td>SLA Impact Window<\/td>\n<td>Time SLA impacted around VPA change<\/td>\n<td>Minutes of degraded SLI per change<\/td>\n<td>Zero ideally<\/td>\n<td>Hard to attribute<\/td>\n<\/tr>\n<tr>\n<td>M11<\/td>\n<td>Recommendation Latency<\/td>\n<td>Time from data to recommendation<\/td>\n<td>Seconds\/minutes<\/td>\n<td>&lt; 5 mins for near real time<\/td>\n<td>Depends on metrics pipeline<\/td>\n<\/tr>\n<tr>\n<td>M12<\/td>\n<td>Metric Coverage<\/td>\n<td>Percent of pods with usable metrics<\/td>\n<td>Count with metrics divided by total<\/td>\n<td>100 percent<\/td>\n<td>Some control plane pods lack metrics<\/td>\n<\/tr>\n<tr>\n<td>M13<\/td>\n<td>Recommendation Stability<\/td>\n<td>Frequency of recommendation changes<\/td>\n<td>Number changes per week<\/td>\n<td>Low<\/td>\n<td>High in volatile workloads<\/td>\n<\/tr>\n<tr>\n<td>M14<\/td>\n<td>Resource Utilization Gap<\/td>\n<td>Utilization vs requested<\/td>\n<td>Avg usage\/requested<\/td>\n<td>60\u201390 percent<\/td>\n<td>Varies by SLA<\/td>\n<\/tr>\n<tr>\n<td>M15<\/td>\n<td>Manual Override Rate<\/td>\n<td>How often humans override VPA<\/td>\n<td>Overrides per week<\/td>\n<td>Low<\/td>\n<td>High for conservative teams<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Best tools to measure VPA<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Prometheus<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for VPA: Time series of CPU, memory, reco metrics, container restarts.<\/li>\n<li>Best-fit environment: Kubernetes clusters with observability stack.<\/li>\n<li>Setup outline:<\/li>\n<li>Deploy exporters and scrape configs for kubelet metrics.<\/li>\n<li>Ensure retention covers recommendation windows.<\/li>\n<li>Record VPA-specific metrics and labels.<\/li>\n<li>Create PromQL queries for SLIs.<\/li>\n<li>Integrate with Alertmanager.<\/li>\n<li>Strengths:<\/li>\n<li>Flexible queries and alerting.<\/li>\n<li>Wide ecosystem and integrations.<\/li>\n<li>Limitations:<\/li>\n<li>Storage\/retention costs at scale.<\/li>\n<li>Requires maintenance and scaling.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Grafana<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for VPA: Visualization of recommendations and resource usage.<\/li>\n<li>Best-fit environment: Teams needing dashboards and alerts.<\/li>\n<li>Setup outline:<\/li>\n<li>Build dashboards for VPA recommendations and pod metrics.<\/li>\n<li>Configure panels for recommendation delta and restart rates.<\/li>\n<li>Create role-based access controls for viewers.<\/li>\n<li>Strengths:<\/li>\n<li>Good dashboards and templating.<\/li>\n<li>Diverse panel types.<\/li>\n<li>Limitations:<\/li>\n<li>Query performance depends on data source.<\/li>\n<li>Alerting capabilities vary by version.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Kubernetes Metrics Server<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for VPA: Basic CPU and memory metrics.<\/li>\n<li>Best-fit environment: Small clusters and lightweight needs.<\/li>\n<li>Setup outline:<\/li>\n<li>Deploy metrics-server with correct flags.<\/li>\n<li>Ensure kubelet config exposes metrics.<\/li>\n<li>Use for baseline VPA recommendations.<\/li>\n<li>Strengths:<\/li>\n<li>Lightweight and built-in style.<\/li>\n<li>Limitations:<\/li>\n<li>No long-term storage; not ideal for historical analysis.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Cluster Autoscaler<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for VPA: Node pressure and unschedulable pods.<\/li>\n<li>Best-fit environment: Cloud or autoscaling node pools.<\/li>\n<li>Setup outline:<\/li>\n<li>Configure cluster autoscaler with node group settings.<\/li>\n<li>Ensure interaction policies with VPA are clear.<\/li>\n<li>Monitor pending pod count.<\/li>\n<li>Strengths:<\/li>\n<li>Scales nodes automatically to accommodate VPA requests.<\/li>\n<li>Limitations:<\/li>\n<li>Scale-up latency can be minutes; may impact pending pods.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Tool \u2014 Cloud Cost Manager (generic)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it measures for VPA: Cost per pod and rightsizing impact.<\/li>\n<li>Best-fit environment: Cloud billing integrated clusters.<\/li>\n<li>Setup outline:<\/li>\n<li>Map pod labels to billing cost centers.<\/li>\n<li>Calculate cost per pod per time unit.<\/li>\n<li>Compare pre and post VPA tuning costs.<\/li>\n<li>Strengths:<\/li>\n<li>Direct cost visibility.<\/li>\n<li>Limitations:<\/li>\n<li>Attribution complexity and delay.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Recommended dashboards &amp; alerts for VPA<\/h3>\n\n\n\n<p>Executive dashboard<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels:<\/li>\n<li>High-level recommendation acceptance rate.<\/li>\n<li>Monthly cost impact from VPA actions.<\/li>\n<li>SLA impact summary across services.<\/li>\n<li>Number of services using VPA.<\/li>\n<li>Why: Provides decision makers visibility into ROI and risk.<\/li>\n<\/ul>\n\n\n\n<p>On-call dashboard<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels:<\/li>\n<li>Current VPA recommendations and deltas per service.<\/li>\n<li>Pod restart rates and OOM events last 1h and 24h.<\/li>\n<li>Pending pod counts and scheduling failures.<\/li>\n<li>Recent VPA-triggered evictions and their status.<\/li>\n<li>Why: Enables quick incident triage and correlates VPA actions with symptoms.<\/li>\n<\/ul>\n\n\n\n<p>Debug dashboard<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Panels:<\/li>\n<li>Time series of raw CPU and memory usage per pod.<\/li>\n<li>Recommendation history per pod.<\/li>\n<li>Scheduler events and node capacity.<\/li>\n<li>Cluster autoscaler events and node provisioning.<\/li>\n<li>Why: Deep troubleshooting and root cause analysis.<\/li>\n<\/ul>\n\n\n\n<p>Alerting guidance<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What should page vs ticket:<\/li>\n<li>Page: High OOM rate spikes, mass evictions causing service degradation, pending pods &gt; defined SLA window.<\/li>\n<li>Ticket: Recommendation drift that increases cost but not immediately impacting SLA.<\/li>\n<li>Burn-rate guidance:<\/li>\n<li>If SLO burn rate exceeds 2x baseline during VPA updates, page on-call.<\/li>\n<li>Noise reduction tactics:<\/li>\n<li>Group related alerts by service.<\/li>\n<li>Deduplicate alerts from multiple sources.<\/li>\n<li>Suppress transient alerts with short cooldown windows.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Implementation Guide (Step-by-step)<\/h2>\n\n\n\n<p>1) Prerequisites\n&#8211; Kubernetes cluster with metrics pipeline (Prometheus or metrics-server).\n&#8211; RBAC and permissions for VPA components.\n&#8211; Clear policies for namespaces and resource quotas.\n&#8211; Observability and cost tooling integrated.<\/p>\n\n\n\n<p>2) Instrumentation plan\n&#8211; Ensure application exposes resource-relevant metrics (memory RSS, CPU usage).\n&#8211; Tag pods with service and team labels for attribution.\n&#8211; Collect scheduler events and node metrics.<\/p>\n\n\n\n<p>3) Data collection\n&#8211; Configure Prometheus scrapes for kubelet and cAdvisor.\n&#8211; Set retention to cover recommendation windows.\n&#8211; Export VPA recommender metrics.<\/p>\n\n\n\n<p>4) SLO design\n&#8211; Define SLIs: latency p95, availability, and error rate.\n&#8211; Set SLOs and error budgets factoring in expected disruptive changes.\n&#8211; Map SLOs to services and tiers.<\/p>\n\n\n\n<p>5) Dashboards\n&#8211; Create executive, on-call, and debug dashboards as above.\n&#8211; Add panels for recommendation deltas and cost impact.<\/p>\n\n\n\n<p>6) Alerts &amp; routing\n&#8211; Define alert thresholds for OOMs, pending pods, and eviction storms.\n&#8211; Route critical alerts to paging groups and lower-priority to ticketing.<\/p>\n\n\n\n<p>7) Runbooks &amp; automation\n&#8211; Author runbooks for common VPA issues (OOMs, pending pods after update).\n&#8211; Automate safe rollouts: rate limiting, canary pods, and maintenance windows.<\/p>\n\n\n\n<p>8) Validation (load\/chaos\/game days)\n&#8211; Run load tests to validate recommendations under expected peak.\n&#8211; Conduct chaos experiments with evictions to ensure resilience.\n&#8211; Validate recovery windows and autoscaler interactions.<\/p>\n\n\n\n<p>9) Continuous improvement\n&#8211; Review recommendations weekly and tune percentiles and windows.\n&#8211; Track cost and SLOs and iterate.<\/p>\n\n\n\n<p>Checklists<\/p>\n\n\n\n<p>Pre-production checklist<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Metrics pipeline collecting required metrics.<\/li>\n<li>Namespace resource quotas and LimitRanges defined.<\/li>\n<li>Test VPA in recommendation-only mode.<\/li>\n<li>CI pipeline includes recommendation step for new services.<\/li>\n<li>Run sanity load tests.<\/li>\n<\/ul>\n\n\n\n<p>Production readiness checklist<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Alerting and dashboards configured.<\/li>\n<li>PDBs aligned with VPA update behavior.<\/li>\n<li>Cluster autoscaler tested with VPA effects.<\/li>\n<li>Team trained with runbooks for VPA incidents.<\/li>\n<li>Backout plan for quick disable of automated updates.<\/li>\n<\/ul>\n\n\n\n<p>Incident checklist specific to VPA<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Identify whether recent VPA changes preceded incident.<\/li>\n<li>Check recommendation history and recent evictions.<\/li>\n<li>Confirm cluster capacity and pending pod count.<\/li>\n<li>Rollback VPA updates or switch to recommendation-only if needed.<\/li>\n<li>Postmortem capturing root cause and mitigation.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Use Cases of VPA<\/h2>\n\n\n\n<p>Provide 8\u201312 use cases:<\/p>\n\n\n\n<p>1) Stateful Database Pod\n&#8211; Context: Single primary DB pod with fluctuating memory usage.\n&#8211; Problem: Frequent OOMs during complex queries.\n&#8211; Why VPA helps: Raises memory requests to prevent OOM and reduces manual tuning.\n&#8211; What to measure: OOM rate, query latency, memory headroom.\n&#8211; Typical tools: Prometheus, VPA recommender, PDBs.<\/p>\n\n\n\n<p>2) Legacy Monolithic Service\n&#8211; Context: Large monolith not horizontally scalable easily.\n&#8211; Problem: Manual resource tuning is error-prone.\n&#8211; Why VPA helps: Automated recommendations reduce toil.\n&#8211; What to measure: Pod restarts, CPU throttling, latency percentiles.\n&#8211; Typical tools: VPA, Grafana, CI integration.<\/p>\n\n\n\n<p>3) Batch Job Runner\n&#8211; Context: Periodic heavy ETL job with variable memory use.\n&#8211; Problem: Fixed limits cause failures or waste cost.\n&#8211; Why VPA helps: Recommend higher resources during runs and shrink otherwise.\n&#8211; What to measure: Job success rate, runtime, memory peak.\n&#8211; Typical tools: Job scheduler, Prometheus, VPA.<\/p>\n\n\n\n<p>4) Pre-production Environments\n&#8211; Context: Many dev\/test services with unknown request sizing.\n&#8211; Problem: Teams misconfigure requests creating noisy neighbors.\n&#8211; Why VPA helps: Recommendations applied in CI improve baseline.\n&#8211; What to measure: Recommendation acceptance, pod stability.\n&#8211; Typical tools: CI pipeline, VPA in recommendation-only.<\/p>\n\n\n\n<p>5) Control Plane Addons\n&#8211; Context: Monitoring and logging addons need correct sizing.\n&#8211; Problem: Underprovisioning harms observability.\n&#8211; Why VPA helps: Keep critical infra healthy.\n&#8211; What to measure: Component restarts, ingestion latency.\n&#8211; Typical tools: VPA, Prometheus.<\/p>\n\n\n\n<p>6) Cost Optimization Project\n&#8211; Context: Cloud cost pressure.\n&#8211; Problem: Overprovisioned pods inflate bills.\n&#8211; Why VPA helps: Rightsize requests to reduce idle allocation.\n&#8211; What to measure: Cost per pod and aggregate savings.\n&#8211; Typical tools: Cost manager, VPA recommender.<\/p>\n\n\n\n<p>7) Stateful Cache Node\n&#8211; Context: Single cache instance with variable working set.\n&#8211; Problem: Memory leaks and spikes cause restarts.\n&#8211; Why VPA helps: Increase memory when pattern changes and alert on growth.\n&#8211; What to measure: Memory RSS, eviction events, usage growth trend.\n&#8211; Typical tools: VPA, Prometheus, tracing.<\/p>\n\n\n\n<p>8) New Microservice Onboarding\n&#8211; Context: Developer deploys new service to cluster.\n&#8211; Problem: No historical sizing data.\n&#8211; Why VPA helps: Provide initial requests automatically via CI checks.\n&#8211; What to measure: Initial recommendation delta and acceptance.\n&#8211; Typical tools: CI, VPA, dashboards.<\/p>\n\n\n\n<p>9) Single-tenant PaaS Runtime\n&#8211; Context: Managed PaaS with diverse tenant workloads.\n&#8211; Problem: Per-tenant variability makes static sizing hard.\n&#8211; Why VPA helps: Per-tenant pod tuning reduces failure and waste.\n&#8211; What to measure: Tenant-level cost, OOMs, request latency.\n&#8211; Typical tools: VPA, tenant tagging, cost allocation.<\/p>\n\n\n\n<p>10) Long-running ML Inference Pod\n&#8211; Context: Model server with changing input sizes.\n&#8211; Problem: Memory spikes on large inference batches.\n&#8211; Why VPA helps: Increase memory budgets when patterns change.\n&#8211; What to measure: Inference latency, OOMs, resource utilization.\n&#8211; Typical tools: VPA, Prometheus, model metrics.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Scenario Examples (Realistic, End-to-End)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #1 \u2014 Kubernetes Stateful DB tuning<\/h3>\n\n\n\n<p><strong>Context:<\/strong> A single primary PostgreSQL pod handles core transactions and occasionally runs heavy analytical queries.<br\/>\n<strong>Goal:<\/strong> Prevent OOM kills while minimizing long-term memory overprovisioning.<br\/>\n<strong>Why VPA matters here:<\/strong> VPA can recommend safe memory increases during heavy periods and reduce baseline during quiet windows.<br\/>\n<strong>Architecture \/ workflow:<\/strong> Metrics server and Prometheus collect memory RSS; VPA recommender uses history; updates applied during maintenance windows with PDBs.<br\/>\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Enable VPA in recommendation-only mode for DB namespace.<\/li>\n<li>Instrument DB exporter with memory RSS and pagefaults.<\/li>\n<li>Run 2 weeks of collection under typical and heavy loads.<\/li>\n<li>Review recommendations; tune percentile and stability window.<\/li>\n<li>Switch to scheduled update mode during low-traffic window.<\/li>\n<li>Monitor OOMs and query latency.<br\/>\n<strong>What to measure:<\/strong> OOM rate, query latency p95, recommendation delta history.<br\/>\n<strong>Tools to use and why:<\/strong> Prometheus for metrics, Grafana dashboards, VPA recommender\/updater, PDB configuration.<br\/>\n<strong>Common pitfalls:<\/strong> Evicting primary unexpectedly; PDB too strict blocking updates.<br\/>\n<strong>Validation:<\/strong> Load test heavy queries and confirm no OOMs and acceptable restart windows.<br\/>\n<strong>Outcome:<\/strong> Fewer OOMs, lower manual tuning overhead, moderate cost improvement.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #2 \u2014 Serverless managed-PaaS memory tuning<\/h3>\n\n\n\n<p><strong>Context:<\/strong> Managed PaaS offering containers for customer workloads with predictable invocation patterns.<br\/>\n<strong>Goal:<\/strong> Improve per-container memory efficiency while maintaining tenant SLAs.<br\/>\n<strong>Why VPA matters here:<\/strong> For long-running containers in the platform, automated tuning reduces cost and incidents.<br\/>\n<strong>Architecture \/ workflow:<\/strong> Platform aggregates usage per workload; recommendations surfaced to tenant or applied per platform policy.<br\/>\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Start VPA in recommendation-only mode per tenant namespace.<\/li>\n<li>Surface recommendations in tenant dashboard.<\/li>\n<li>Offer opt-in automated updates for premium tenants.<\/li>\n<li>Rate-limit updates and use canaries per tenant group.<br\/>\n<strong>What to measure:<\/strong> Recommendation acceptance, tenant SLA impact, cost per tenant.<br\/>\n<strong>Tools to use and why:<\/strong> Platform metrics, VPA, tenant dashboards.<br\/>\n<strong>Common pitfalls:<\/strong> Multi-tenant resource quotas blocking changes.<br\/>\n<strong>Validation:<\/strong> Pilot with small tenant group, observe costs and SLA impact.<br\/>\n<strong>Outcome:<\/strong> Improved resource efficiency for long-running tenant workloads, opt-in automation reduced toil.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #3 \u2014 Incident response postmortem for eviction storm<\/h3>\n\n\n\n<p><strong>Context:<\/strong> Production incident: large number of pods restarted within 10 minutes causing 10% traffic drop.<br\/>\n<strong>Goal:<\/strong> Determine root cause and prevent recurrence.<br\/>\n<strong>Why VPA matters here:<\/strong> VPA-triggered mass evictions were suspected.<br\/>\n<strong>Architecture \/ workflow:<\/strong> Reconstruct timeline from recommender events, eviction logs, scheduler events, and autoscaler activity.<br\/>\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Collect VPA recommendation history and updater eviction events.<\/li>\n<li>Check PDBs and number of concurrent evictions.<\/li>\n<li>Correlate with cluster autoscaler and node provisioning logs.<\/li>\n<li>Restore service by reverting VPA updates and scaling replicas if needed.<\/li>\n<li>Postmortem identifies misconfiguration in update rate limits.<br\/>\n<strong>What to measure:<\/strong> Eviction counts, pod restart rate, pending pods.<br\/>\n<strong>Tools to use and why:<\/strong> Prometheus, logging, VPA controller metrics.<br\/>\n<strong>Common pitfalls:<\/strong> Attribution confusion between autoscaler and VPA.<br\/>\n<strong>Validation:<\/strong> Reproduce in staging with rate-limited updates.<br\/>\n<strong>Outcome:<\/strong> Change applied to rate-limit updates and improve runbook.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario #4 \u2014 Cost vs performance trade-off for microservice<\/h3>\n\n\n\n<p><strong>Context:<\/strong> Mid-tier service running 10 replicas with historically conservative requests.<br\/>\n<strong>Goal:<\/strong> Reduce cost while preserving tail latency SLOs.<br\/>\n<strong>Why VPA matters here:<\/strong> VPA can tighten requests to reduce unused headroom while HPA maintains replica scaling on load.<br\/>\n<strong>Architecture \/ workflow:<\/strong> VPA recommendations feed into CI to update base requests; HPA handles bursts.<br\/>\n<strong>Step-by-step implementation:<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Run VPA recommendations for 30 days to collect steady-state patterns.<\/li>\n<li>Analyze recommendation percentiles and choose conservative percentile for baseline.<\/li>\n<li>Update Deployment request values via CI and roll out progressively with canary.<\/li>\n<li>Monitor tail latency and SLO consumption.<br\/>\n<strong>What to measure:<\/strong> Resource Utilization Gap, tail latency p99, cost per request.<br\/>\n<strong>Tools to use and why:<\/strong> VPA, Prometheus, Grafana, CI pipeline.<br\/>\n<strong>Common pitfalls:<\/strong> Setting baseline too low causing latency spikes.<br\/>\n<strong>Validation:<\/strong> Load test with burst patterns and measure SLO impact.<br\/>\n<strong>Outcome:<\/strong> Reduced cost with maintained SLOs using conservative percentiles and canary rollouts.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Common Mistakes, Anti-patterns, and Troubleshooting<\/h2>\n\n\n\n<p>Symptom -&gt; Root cause -&gt; Fix (15\u201325 items, including 5 observability pitfalls)<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Symptom: Frequent OOM kills after VPA enabled -&gt; Root cause: Recommendations underestimating memory peaks -&gt; Fix: Increase percentile and history window; add burst handling.<\/li>\n<li>Symptom: Mass pod restarts -&gt; Root cause: VPA applied many updates at once -&gt; Fix: Rate-limit updater and honor PDBs.<\/li>\n<li>Symptom: Pods pending after update -&gt; Root cause: No node capacity for resized pods -&gt; Fix: Coordinate with cluster autoscaler or reduce target requests.<\/li>\n<li>Symptom: Recommendation flapping -&gt; Root cause: Short sampling intervals and noisy metrics -&gt; Fix: Smooth recommendations with longer stability window.<\/li>\n<li>Symptom: Higher than expected cost -&gt; Root cause: Overprovisioning by recommender using peak values -&gt; Fix: Adjust percentile and include cost checks in pipeline.<\/li>\n<li>Symptom: HPA and VPA conflicting -&gt; Root cause: Uncoordinated autoscale responsibilities -&gt; Fix: Define clear roles; use VPA only for requests, HPA for replicas.<\/li>\n<li>Symptom: No recommendations -&gt; Root cause: Metrics pipeline misconfigured -&gt; Fix: Validate scrape configs and metric labels.<\/li>\n<li>Symptom: VPA blocked by LimitRange -&gt; Root cause: Namespace limits prevent changes -&gt; Fix: Update LimitRange bounds or configure VPA to respect limits.<\/li>\n<li>Symptom: App errors after restart -&gt; Root cause: Stateful app not handling eviction gracefully -&gt; Fix: Implement graceful shutdown and preStop hooks.<\/li>\n<li>Symptom: Alerts noisy after VPA change -&gt; Root cause: Alert thresholds not adjusted for new resources -&gt; Fix: Tune alerts and use suppression windows.<\/li>\n<li>Symptom: Slow recommendation delivery -&gt; Root cause: Recommender uses long batch windows -&gt; Fix: Reduce latency threshold if safe.<\/li>\n<li>Symptom: Missing metric coverage -&gt; Root cause: Some pods not instrumented -&gt; Fix: Ensure exporters and scraping for all pods.<\/li>\n<li>Symptom: Wrong cost attribution -&gt; Root cause: Missing labels for cost mapping -&gt; Fix: Enforce labeling policies in deployments.<\/li>\n<li>Symptom: VPA updates blocked by admission webhook -&gt; Root cause: Mutating webhook conflicts -&gt; Fix: Coordinate webhook ordering and timeouts.<\/li>\n<li>Symptom: Difficulty auditing changes -&gt; Root cause: No recommendation history stored -&gt; Fix: Persist recommendations and changes in logs or DB.<\/li>\n<li>Symptom: Observability blindspot for memory -&gt; Root cause: Relying solely on metrics-server -&gt; Fix: Add Prometheus cAdvisor metrics for historical data.<\/li>\n<li>Symptom: Throttling unnoticed -&gt; Root cause: No CPU throttle metrics in dashboards -&gt; Fix: Add CPU throttle ratio panels and alerts.<\/li>\n<li>Symptom: Misinterpreting averages -&gt; Root cause: Using mean instead of percentile -&gt; Fix: Adopt p95 or p99 where appropriate.<\/li>\n<li>Symptom: Ineffective PDBs -&gt; Root cause: PDBs too permissive or too strict -&gt; Fix: Rebalance PDB concurrency limits for deployments.<\/li>\n<li>Symptom: Recommendation ignored by teams -&gt; Root cause: Lack of trust and visibility -&gt; Fix: Surface recommendations in CI and dashboards with explanations.<\/li>\n<li>Symptom: Large recommendation deltas on first run -&gt; Root cause: No baseline history for new service -&gt; Fix: Use staged rollouts and conservative initial percentile.<\/li>\n<li>Symptom: Cluster autoscaler thrash -&gt; Root cause: VPA increases requests causing frequent scale operations -&gt; Fix: Batch VPA updates and coordinate autoscaler cooldowns.<\/li>\n<li>Symptom: Test environment differs from prod -&gt; Root cause: Different LimitRanges and quotas -&gt; Fix: Mirror prod constraints in staging.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Best Practices &amp; Operating Model<\/h2>\n\n\n\n<p>Ownership and on-call<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Assign VPA ownership to platform or SRE team.<\/li>\n<li>Define on-call rotation for VPA-related incidents.<\/li>\n<li>Document escalation paths for resource-related outages.<\/li>\n<\/ul>\n\n\n\n<p>Runbooks vs playbooks<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Runbooks: step-by-step for common incidents (OOM, pending pods).<\/li>\n<li>Playbooks: higher-level decisions and postmortem actions.<\/li>\n<\/ul>\n\n\n\n<p>Safe deployments (canary\/rollback)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use canary deployments for large recommendation deltas.<\/li>\n<li>Define rollback criteria (SLO breach threshold).<\/li>\n<li>Employ progressive rollout with rate-limited evictions.<\/li>\n<\/ul>\n\n\n\n<p>Toil reduction and automation<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Automate recommendation review in CI for new services.<\/li>\n<li>Auto-apply updates with guardrails for mature services.<\/li>\n<li>Use automation to label pods and ensure cost attribution.<\/li>\n<\/ul>\n\n\n\n<p>Security basics<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ensure VPA components run with least privilege RBAC.<\/li>\n<li>Audit VPA events and recommender access.<\/li>\n<li>Protect metrics pipelines from tampering.<\/li>\n<\/ul>\n\n\n\n<p>Weekly\/monthly routines<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Weekly: Review recommendation acceptance and any recent evictions.<\/li>\n<li>Monthly: Audit cost impact and update percentile policies.<\/li>\n<li>Quarterly: Run chaos experiments covering VPA update scenarios.<\/li>\n<\/ul>\n\n\n\n<p>What to review in postmortems related to VPA<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Timeline of recommendations and updates vs incidents.<\/li>\n<li>Eviction counts and PDB interactions.<\/li>\n<li>Scheduler and autoscaler response times.<\/li>\n<li>Changes to metrics pipelines and stability windows.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Tooling &amp; Integration Map for VPA (TABLE REQUIRED)<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr>\n<th>ID<\/th>\n<th>Category<\/th>\n<th>What it does<\/th>\n<th>Key integrations<\/th>\n<th>Notes<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>I1<\/td>\n<td>Metrics<\/td>\n<td>Collects CPU and memory metrics<\/td>\n<td>kubelet Prometheus cAdvisor<\/td>\n<td>Needed for recommendations<\/td>\n<\/tr>\n<tr>\n<td>I2<\/td>\n<td>Recommender<\/td>\n<td>Computes resource targets<\/td>\n<td>VPA CRDs and metrics<\/td>\n<td>Core VPA logic<\/td>\n<\/tr>\n<tr>\n<td>I3<\/td>\n<td>Updater<\/td>\n<td>Applies updates by evicting pods<\/td>\n<td>K8s API and PDBs<\/td>\n<td>Rate limiting required<\/td>\n<\/tr>\n<tr>\n<td>I4<\/td>\n<td>Dashboard<\/td>\n<td>Visualizes recommendations and impacts<\/td>\n<td>Prometheus Grafana<\/td>\n<td>Must include deltas<\/td>\n<\/tr>\n<tr>\n<td>I5<\/td>\n<td>Autoscaler<\/td>\n<td>Scales nodes on demand<\/td>\n<td>Cloud provider APIs<\/td>\n<td>Coordinates with VPA<\/td>\n<\/tr>\n<tr>\n<td>I6<\/td>\n<td>CI\/CD<\/td>\n<td>Applies recommended values in pipelines<\/td>\n<td>GitOps pipelines<\/td>\n<td>Improves onboarding<\/td>\n<\/tr>\n<tr>\n<td>I7<\/td>\n<td>Costing<\/td>\n<td>Maps resources to spend<\/td>\n<td>Billing and labels<\/td>\n<td>Tracks ROI<\/td>\n<\/tr>\n<tr>\n<td>I8<\/td>\n<td>Alerting<\/td>\n<td>Routes critical alerts<\/td>\n<td>Alertmanager or SaaS<\/td>\n<td>Pages on OOMs and evictions<\/td>\n<\/tr>\n<tr>\n<td>I9<\/td>\n<td>Audit<\/td>\n<td>Stores recommendation history<\/td>\n<td>Logging or DB<\/td>\n<td>Useful for postmortems<\/td>\n<\/tr>\n<tr>\n<td>I10<\/td>\n<td>Security<\/td>\n<td>RBAC and policy enforcement<\/td>\n<td>Kubernetes admission controls<\/td>\n<td>Ensures safe operation<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Row Details (only if needed)<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>None<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What exactly does VPA change on a pod?<\/h3>\n\n\n\n<p>VPA modifies resource requests and optionally limits, usually by evicting pods so the scheduler can recreate them with new values.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does VPA scale replicas like HPA?<\/h3>\n\n\n\n<p>No. VPA adjusts per-pod resource sizing. For replica scaling, use HPA or other horizontal autoscalers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Will VPA prevent OOMs completely?<\/h3>\n\n\n\n<p>No. VPA reduces frequency of OOMs but cannot guarantee prevention, especially for sudden bursts not captured in metrics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can I run VPA with HPA at the same time?<\/h3>\n\n\n\n<p>Yes, but coordinate responsibilities; commonly VPA sets requests and HPA scales replicas. Misconfiguration can cause conflicts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is VPA suitable for stateless services?<\/h3>\n\n\n\n<p>Often not necessary for highly replicated stateless services; use HPA instead unless per-pod sizing matters.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How invasive are VPA updates?<\/h3>\n\n\n\n<p>They may evict pods causing restarts. Risk depends on application tolerance and PDB configuration.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What metrics are required for VPA?<\/h3>\n\n\n\n<p>CPU and memory usage over time; more granular metrics give better recommendations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How long before recommendations stabilize?<\/h3>\n\n\n\n<p>Varies depending on traffic patterns; typically days to weeks for stable recommendations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can VPA cause cost increases?<\/h3>\n\n\n\n<p>Yes if recommender overestimates steady-state needs; guardrails and percentiles help avoid that.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Should VPA be automated from day one?<\/h3>\n\n\n\n<p>Start in recommendation-only mode; automate updates gradually with safety checks.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to avoid eviction storms?<\/h3>\n\n\n\n<p>Rate-limit updater, use PDBs, and schedule updates during maintenance windows.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does VPA work with serverless platforms?<\/h3>\n\n\n\n<p>Varies \/ depends on platform; many serverless platforms handle resource allocation internally and do not expose VPA-style tuning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How to audit VPA changes?<\/h3>\n\n\n\n<p>Persist recommendations and updater events in logs or DB and link them to incidents and deployments.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What percentiles should I use for recommendations?<\/h3>\n\n\n\n<p>No universal answer; a common strategy is to use p95 for memory and p50-p95 for CPU depending on SLOs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can VPA help in cost allocation?<\/h3>\n\n\n\n<p>Indirectly; by right-sizing pods you reduce wasted costs and can map savings to cost centers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Is VPA safe for databases?<\/h3>\n\n\n\n<p>Yes with careful testing, maintenance windows, and safe restart procedures.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What are common observability pitfalls with VPA?<\/h3>\n\n\n\n<p>Missing historical metrics, lack of CPU throttle metrics, coarse sampling intervals, and absent recommendation history.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How do I roll back VPA if problems occur?<\/h3>\n\n\n\n<p>Switch to recommendation-only mode or revert applied resource changes via CI\/GitOps and monitor.<\/p>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>VPA is a valuable tool for automating per-pod resource sizing, reducing incidents, and optimizing cost when used with appropriate telemetry, guardrails, and operational practices. Start conservatively, build observability, and integrate VPA into CI and runbooks.<\/p>\n\n\n\n<p>Next 7 days plan (5 bullets)<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Day 1: Inventory candidate services and ensure metrics collection for CPU and memory.<\/li>\n<li>Day 2: Deploy VPA in recommendation-only mode for 5 low-risk services.<\/li>\n<li>Day 3: Create dashboards showing recommendations and deltas.<\/li>\n<li>Day 4: Run load tests and compare recommendations to observed peaks.<\/li>\n<li>Day 5\u20137: Review results with teams, tune percentiles, and plan staged automated updates.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Appendix \u2014 VPA Keyword Cluster (SEO)<\/h2>\n\n\n\n<p>Primary keywords<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Vertical Pod Autoscaler<\/li>\n<li>VPA Kubernetes<\/li>\n<li>VPA 2026<\/li>\n<li>Vertical scaling pods<\/li>\n<li>VPA recommender<\/li>\n<\/ul>\n\n\n\n<p>Secondary keywords<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>VPA updater<\/li>\n<li>VPA recommendations<\/li>\n<li>Kubernetes resource autoscaling<\/li>\n<li>pod resource recommendations<\/li>\n<li>vertical autoscaling<\/li>\n<\/ul>\n\n\n\n<p>Long-tail questions<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>how does vertical pod autoscaler work in kubernetes<\/li>\n<li>when to use vpa versus hpa in 2026<\/li>\n<li>how to prevent eviction storms with vpa<\/li>\n<li>best practices for vpa in production<\/li>\n<li>vpa recommendation-only mode explained<\/li>\n<\/ul>\n\n\n\n<p>Related terminology<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>resource requests and limits<\/li>\n<li>pod eviction and restart<\/li>\n<li>cAdvisor metrics for vpa<\/li>\n<li>Prometheus VPA metrics<\/li>\n<li>cluster autoscaler coordination<\/li>\n<li>poddisruptionbudget and vpa<\/li>\n<li>limitrange interactions with vpa<\/li>\n<li>recommendation percentile tuning<\/li>\n<li>resource utilization gap<\/li>\n<li>recommendation acceptance rate<\/li>\n<li>vpa and cost optimization<\/li>\n<li>vpa vs horizontal pod autoscaler<\/li>\n<li>vpa failure modes<\/li>\n<li>vpa runbooks and playbooks<\/li>\n<li>vpa implementation guide<\/li>\n<li>vpa observability dashboards<\/li>\n<li>vpa metric coverage<\/li>\n<li>vpa lifecycle and data flow<\/li>\n<li>rate limiting vpa updates<\/li>\n<li>vpa in CI\/CD pipelines<\/li>\n<li>vpa for statefulsets<\/li>\n<li>vpa and node scheduling<\/li>\n<li>vpa update success rate<\/li>\n<li>vpa recommendation stability<\/li>\n<li>vpa sampling interval importance<\/li>\n<li>vpa and pod disruption budgets<\/li>\n<li>vpa for legacy monoliths<\/li>\n<li>vpa for serverless managed-paas<\/li>\n<li>vpa for batch jobs<\/li>\n<li>vpa for ml inference pods<\/li>\n<li>vpa vs vm vertical scaling<\/li>\n<li>vpa admission controller impacts<\/li>\n<li>vpa security and rbac<\/li>\n<li>vpa cost per pod measurement<\/li>\n<li>vpa troubleshooting checklist<\/li>\n<li>vpa best practices 2026<\/li>\n<li>vpa automation and guardrails<\/li>\n<li>vpa maturity ladder<\/li>\n<li>vpa monitoring and alerts<\/li>\n<li>vpa and SLI SLO alignment<\/li>\n<li>vpa recommendation delta handling<\/li>\n<li>vpa audit and history<\/li>\n<li>vpa continuous improvement<\/li>\n<li>vpa chaos testing<\/li>\n<li>vpa canary deployments<\/li>\n<li>vpa telemetry drift<\/li>\n<li>vpa resource quota handling<\/li>\n<li>vpa limitrange considerations<\/li>\n<li>vpa for control plane addons<\/li>\n<li>vpa upgrade strategies<\/li>\n<li>vpa and horizontal scaling cooperation<\/li>\n<li>vpa implementation checklist<\/li>\n<li>vpa incident response<\/li>\n<li>vpa postmortem items<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>&#8212;<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[430],"tags":[],"class_list":["post-1655","post","type-post","status-publish","format-standard","hentry","category-what-is-series"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What is VPA? 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