Quick Answer
Autoscaling in microservices: Kubernetes HPA scales pods based on CPU, memory, or custom metrics. Custom metrics via Prometheus Adapter or KEDA - scale based on Kafka consumer lag, request queue depth, or any business metric. Set min/max replicas. Ensure services are stateless so new instances can serve traffic immediately. Cluster Autoscaler adds nodes when pods can't be scheduled.
Answer
Autoscaling adjusts service instances based on metrics such as CPU or custom signals. Horizontal scaling is preferred for cloud-native systems. Managed using Kubernetes HPA and similar tools.
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Verified Answer
This answer has been peer-reviewed by industry experts holding senior engineering roles to ensure technical accuracy and relevance for modern interview standards.
