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When managing a service with thousands of pods, how do EndpointSlices enhance performance compared to traditional Endpoints?

hard📝 Workflow Q8 of 15
Kubernetes - Services
When managing a service with thousands of pods, how do EndpointSlices enhance performance compared to traditional Endpoints?
ABy partitioning endpoints into smaller slices, reducing watch and update overhead.
BBy caching all pod IPs on the client side to avoid API calls.
CBy replacing kube-proxy with a new load balancer implementation.
DBy storing endpoints in etcd with higher replication factor.
Step-by-Step Solution
Solution:

  1. Step 1: Understand EndpointSlices design

    EndpointSlices split large endpoint lists into smaller chunks to improve scalability.

  2. Step 2: Impact on performance

    This reduces the size of watch events and update payloads, lowering load on the API server and clients.

  3. Final Answer:

    By partitioning endpoints into smaller slices, reducing watch and update overhead. -> Option A

  4. Quick Check:

    EndpointSlices improve scalability by chunking endpoints [OK]
Quick Trick: EndpointSlices split endpoints to reduce API load [OK]
Common Mistakes:
  • Thinking EndpointSlices cache data client-side
  • Assuming EndpointSlices replace kube-proxy
  • Believing EndpointSlices change etcd replication

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