Topic 1 Question 198
You have an application deployed in Google Kubernetes Engine (GKE) that reads and processes Pub/Sub messages. Each Pod handles a fixed number of messages per minute. The rate at which messages are published to the Pub/Sub topic varies considerably throughout the day and week, including occasional large batches of messages published at a single moment.
You want to scale your GKE Deployment to be able to process messages in a timely manner. What GKE feature should you use to automatically adapt your workload?
Vertical Pod Autoscaler in Auto mode
Vertical Pod Autoscaler in Recommendation mode
Horizontal Pod Autoscaler based on an external metric
Horizontal Pod Autoscaler based on resources utilization
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- 正解だと思う選択肢: C
Custom and external metrics allow workloads to adapt to conditions besides the workload itself. Consider an application that pulls tasks from a queue and completes them. An external metric is reported from an application or service not running on your cluster, but whose performance impacts your Kubernetes application. For information, the metric could be reported from Cloud Monitoring or Pub/Sub. D isn't the answer, before selecting an answer , please do a thorough research and understand concepts and the key words in a question, D cant be the answer in this case. https://cloud.google.com/kubernetes-engine/docs/concepts/custom-and-external-metrics
👍 3TNT872023/01/04 The answer is C. Each pod will handle a fixed number of messages, fixed being the key word here. Now let's say this fixed number is "1000" messages per minute. Do you think a 1000 messages in a minute will cause the pod autoscaler to kick in based on resource utilisation?
We need to scale using external metrics here. When Pod 1 is handling the maximum of "fixed number amount" messages, we need to spin up pod 2 etc...
👍 2[Removed]2022/12/22- 正解だと思う選択肢: D👍 1zellck2022/12/13
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