gke-workload-scaling
Масштабирование приложений в GKE
Что получите
Настроенное масштабирование приложения или обновлённые запросы CPU и памяти на основе рекомендаций.
Что передать
Кластер GKE, приложение, пространство имён, метрики нагрузки и желаемые пределы масштабирования.
Проверен состав файлов. Запуск не тестировался. Что проверено
Оценки пользователей
Как использовать
Помогает администраторам GKE настроить число копий приложения и выделяемые им ресурсы. Подходит для ручного изменения количества копий, автоматического масштабирования по нагрузке через HPA и подбора CPU и памяти через VPA.
- Выберите задачу: задать фиксированное число копий, настроить HPA или подобрать ресурсы через VPA.
- Определите границы масштабирования и метрики; настройки HPA рекомендуется хранить в YAML.
- Для подбора ресурсов начните с VPA в режиме рекомендаций на 24 часа или дольше, затем сравните рекомендации с текущими значениями.
Условия работы
- Скилл охватывает приложения в GKE; масштабирование кластера и настройка машин узлов относятся к другим задачам.
- Для HPA нужен работающий Metrics Server и заданные запросы и лимиты ресурсов контейнеров; для VPA он должен быть включён в кластере.
- HPA и VPA нельзя настраивать по одной и той же метрике.
- Режим VPA Auto перезапускает поды, поэтому приложение должно выдерживать перезапуски. InPlaceOrRecreate требует GKE 1.34+ и тоже может перейти к перезапуску.
Пример запроса после установки
Настрой HPA для приложения в GKE по загрузке CPU, а VPA используй для рекомендаций по памяти; начни с наблюдения без автоматических изменений ресурсов.
Пример составлен по инструкции автора; результат запуска не проверялся.
Основание: инструкция закреплённой версииДля установки
Node.js, npm и Git для команды выше. Подключения и инструменты для работы скилла настраиваются отдельно.
Совместимые агенты
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Файлы скилла
GitHubЧитать инструкцию SKILL.md
Исходный текст закреплённой версии.
---
name: gke-workload-scaling
description: >-
Manages scaling for GKE workloads using HPA and VPA. Use when configuring
Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA),
or applying best practices for GKE workload autoscaling. Do not use for
cluster-level autoscaling (Cluster Autoscaler), static cluster sizing,
or configuring node-level machine styles directly.
metadata:
category: Containers
---
# GKE Workload Scaling
This skill provides workflows and best practices for scaling applications on
Google Kubernetes Engine (GKE). It covers manual scaling, Horizontal Pod
Autoscaling (HPA), and Vertical Pod Autoscaling (VPA).
## Workflows
### 1. Manual Scaling
Scale a deployment to a fixed number of replicas. Useful for immediate manual
intervention or testing.
**Command:**
```bash
kubectl scale deployment {deployment_name} --replicas={number} -n {namespace}
# Verify the scale event
kubectl get deployment {deployment_name} -n {namespace}
```
### 2. Horizontal Pod Autoscaling (HPA)
Automatically scale the number of pods based on observed CPU utilization, memory
utilization, or custom metrics.
**Prerequisites:**
- Metrics Server must be running (enabled by default on GKE).
- Containers clearly define resource requests/limits.
**Quick Command:**
```bash
kubectl autoscale deployment {deployment_name} --cpu-percent=50 --min=1 --max=10
```
**Manifest Approach (Recommended):** Use a YAML manifest for version-controlled
configuration. See [assets/hpa-example.yaml](assets/hpa-example.yaml) for a
template.
```bash
kubectl apply -f assets/hpa-example.yaml
# Verify HPA is created and fetching metrics
kubectl get hpa
```
**Custom Metrics & External Metrics:** For GKE, the modern and recommended
approach for scaling based on Cloud Monitoring metrics (e.g., Pub/Sub queue
length) is to use the **External** metric type, which is natively supported by
the GKE control plane without requiring the Custom Metrics Adapter. For
application-specific metrics exposed via Prometheus, you can use **Google Cloud
Managed Service for Prometheus** or the Prometheus Adapter.
### 3. Vertical Pod Autoscaling (VPA)
Automatically adjust the CPU and memory reservations for your pods to match
actual usage. This is critical for right-sizing workloads.
**Prerequisites:**
- VPA must be enabled on the cluster.
- **Autopilot:** Enabled by default.
- **Standard:** Must be enabled manually.
**Enable VPA on Standard Cluster:**
```bash
gcloud container clusters update {cluster_name} --enable-vertical-pod-autoscaling --zone {zone}
```
**Update Modes:**
- `Off`: Calculates recommendations but does not apply them. Good for "dry
run" analysis.
- `Initial`: Assigns resources only at pod creation time.
- `Auto`: Updates running pods by restarting them if recommendations differ
significantly from requests.
- `InPlaceOrRecreate`: Attempts to update Pod resources without recreating the
Pod. If in-place update is not possible, it reverts to `Auto` mode (requires
GKE 1.34+).
**Example:** See [assets/vpa-example.yaml](assets/vpa-example.yaml) for a
configuration template.
## Best Practices
1. **Define Resource Requests:** HPA and VPA rely on accurate resource
requests. Always define them in your container specs.
2. **Avoid Metric Conflicts:** Do not configure HPA and VPA to use the same
metric (e.g., both CPU). This causes thrashing.
- *Typical Pattern:* HPA on CPU, VPA on Memory.
3. **Pod Disruption Budgets (PDBs):** Define PDBs to ensure application
availability during scaling events or node upgrades.
4. **HPA Lag:** HPA has a stabilization window (default 5 mins) to prevent
rapid fluctuation.
5. **VPA "Auto" Mode Risks:** In "Auto" mode, VPA restarts pods to change
resources. Ensure your application handles restarts gracefully (e.g.,
handles SIGTERM).
- *Note:* By default, VPA requires at least 2 replicas to perform
evictions (to prevent a situation where the only running replica is
evicted, causing downtime). In GKE 1.22+, you can override this by
setting `minReplicas` in `PodUpdatePolicy`.
## Rightsizing Workflow
1. Deploy VPA in `Off` mode for 24+ hours
2. Read recommendations: `kubectl describe vpa {deployment_name}-vpa -n
{namespace}`
3. Compare `target` values against current `requests`
4. Apply with 20% buffer: `new_request = target * 1.2`
5. Use patch format or update deployment manifest to apply new resource
requests
Condition | Recommendation | Risk
----------------------------- | ------------------------------------ | ------
CPU request >5x P95 actual | Reduce to `P95 * 1.2` | Medium
Memory request >3x P95 actual | Reduce to `P95 * 1.2` | Medium
CPU request >2x P95 actual | Rightsizing with 20% buffer | Low
No resource limits set | Add limits to prevent noisy-neighbor | Low
Проверка и особенности
Автоматическая проверка источника и файлов
Автоматически сверены публичный репозиторий, лицензия, YAML-заголовок SKILL.md, состав папки и Git-хеш каждого файла. Исполняемые файлы и срабатывания статических правил исключены из этой партии. Ручной аудит всех инструкций и проверка работы не проводились; эта проверка не гарантирует безопасность. Команда закреплена на проверенном коммите.
Интерес к репозиторию оцениваем по звёздам и форкам GitHub. Это показатели всего репозитория, а не числа установок отдельного скилла.
- Проверка состава
- 16.09.2026
- Проверенная версия
569cb1d55ac3- Файлы с кодом
- Не указаны в данных проверки
- Изменения на GitHub
- Нужна повторная проверкаПоследняя проверка: 11.10.2026
Репозиторий и лицензия
Это показатели всего репозитория, а не установки скилла. Снимок требует обновления.
- Последняя активность GitHub
- 09.10.2026
- Лицензия
- Apache-2.0
- Добавлен в каталог
- 16.09.2026
Полный текст лицензии
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