An e-commerce company is planning its Google Kubernetes Engine (GKE) cluster architecture to support two distinct workloads: a core inventory management microservice and a background batch analytics pipeline. The team needs to minimize operational overhead for cluster and node infrastructure administration for the microservices, while optimizing compute costs for the fault-tolerant batch pipeline which can tolerate node preemptions. Which TWO architectural decisions should the team select to meet these requirements?
- Deploy the inventory microservice on a GKE Autopilot cluster to eliminate manual node provisioning and infrastructure management overhead.Cevap
- Provision a dedicated Spot VM node pool for the batch analytics workload to significantly reduce compute expenses for fault-tolerant jobs.Cevap
- CDeploy the inventory microservice on a GKE Standard cluster with manually configured node pools to remove node maintenance responsibilities.
- DProvision a dedicated Spot VM node pool to host stateful transactional database instances for persistent storage needs.
- EConfigure the Horizontal Pod Autoscaler (HPA) to increase the underlying Compute Engine worker node count during batch workload spikes.
Cevap
Deploy the inventory microservice on a GKE Autopilot cluster to minimize node management overhead, and provision a dedicated Spot VM node pool to cost-optimize the fault-tolerant batch analytics pipeline.
GKE Autopilot abstracts away all worker node administration, providing fully managed cluster infrastructure ideal for minimizing operational overhead. Concurrently, utilizing Spot VM node pools for batch analytics provides maximum cost reduction while matching the workload's tolerance for preemption.
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Anahtar Kavram
Selecting between GKE Autopilot vs. Standard operational models and integrating Spot VM node pools for batch workloads.