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Maîtriser Kubernetes : 15 modèles avancés pour faire évoluer les microservices en production
Foundations of Kubernetes Scaling Before diving into advanced patterns, it's crucial to understand the core scaling primitives: Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscal…
belhachemi_admin
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Foundations of Kubernetes Scaling
Before diving into advanced patterns, it's crucial to understand the core scaling primitives: Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler. These form the basis upon which all scaling strategies are built.
Pattern 1: HPA Based on Custom Metrics
CPU and memory metrics are a great start, but for real production systems, you'll want to scale based on custom metrics like queue length, request latency, or business-specific KPIs.
Implementation Steps
First, install the Prometheus adapter to expose custom metrics. Then, define your HPA configuration to scale based on these metrics. Use PromQL queries to define the scaling thresholds.
Pattern 2: Pod Disruption Budgets
Ensure high availability during node maintenance or cluster upgrades by defining Pod Disruption Budgets (PDBs). A PDB specifies the minimum number of replicas that must remain available at all times.
Pattern 3: Canary Deployments
Gradually roll out new versions to a subset of users, monitor performance and error rates, and then expand to the entire user base. Tools like Argo Rollouts or Flagger can automate this process.
Patterns 4-15 Overview
Other critical patterns include Circuit Breakers, Bulkheads, CQRS, Event Sourcing, Retry with Exponential Backoff, Sidecar Containers, Init Containers, StatefulSets for Databases, Operator Framework, and more.
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