# Autoscaling — Scalability, Availability & Reliability

Source: https://www.skillbyai.com/en/scalability/s-autoscale

> Configure autoscaling that reacts in time without flapping.

## Matching capacity to demand automatically

**Autoscaling** adds instances when load rises and removes them when it falls. **Target tracking** keeps a metric near a target, such as 60% CPU or 100 requests per second per instance; **step scaling** adds fixed amounts at thresholds; **scheduled scaling** prepares for known peaks (an exam result day, a festival sale); **predictive scaling** uses historical patterns. Choose a metric that tracks load: CPU for compute-bound services, request rate or concurrency for I/O-bound services, **queue depth** for workers. Autoscaling is never instant: new instances need to boot, pull images, warm caches and pass health checks, so keep headroom and scale out early. Use **cooldowns** or stabilisation windows to prevent flapping, scale in slowly, and drain connections before terminating instances. Remember that downstream systems such as databases do not scale automatically with your web tier.

## A Kubernetes HorizontalPodAutoscaler

Scale between 3 and 30 pods at 60% CPU, scaling up quickly and down cautiously.

```yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: checkout
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: checkout
  minReplicas: 3
  maxReplicas: 30
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 60
  behavior:
    scaleUp:
      stabilizationWindowSeconds: 0
      policies: [{ type: Percent, value: 100, periodSeconds: 60 }]
    scaleDown:
      stabilizationWindowSeconds: 300
      policies: [{ type: Percent, value: 20, periodSeconds: 60 }]
```

## Pre-scale for known events

If a marketing push email goes out at 10:00, traffic arrives at 10:01, faster than any reactive autoscaler. Schedule extra capacity before the event and let autoscaling handle the rest.

**Quiz:** Which metric is usually best for autoscaling background workers that read from a queue?

- [x] Queue depth or age of the oldest message
- [ ] Disk size
- [ ] Number of deployments per day
- [ ] Memory of the load balancer

*Answer:* Queue depth or age of the oldest message. Queue backlog directly reflects how far workers are behind demand.