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Enhancing Kubernetes API Server Efficiency with API Streaming
- Managing Kubernetes clusters efficiently is critical, especially as their size is growing.
- A significant challenge with large clusters is the memory overhead caused by list requests.
- To better visualize the memory overhead challenge, a synthetic test was conducted using the kube-apiserver which crashed at approximately 16:40, when serving only 16 informers.
- The issue of overflowing memory, leading to resource exhaustion, raises a genuine risk and the need to optimize the API server for better efficiency.
- Kubernetes 1.32 has recently announced a major improvement in API streaming technology to address efficiency and stability concerns. With the graduation of the watch list feature to beta, client-go users can opt-in to streaming lists via a special category of watch requests, which reduces the temporary memory footprint significantly.
- By streaming each item individually instead of returning the entire collection, the new method maintains a constant memory overhead.
- The results showed significant improvements with the watch list feature enabled, as Kubernetes' increased memory consumption stabilized at approximately 2 GB, while memory usage with the feature disabled increased to 20 GB.
- API Priority and Fairness has proven to reasonably protect kube-apiserver from CPU overload, however, this has a smaller impact on memory protection. As an increasing number of Kubernetes API requests with large response bodies flood in simultaneously, OOM crashes can be expected without optimization.
- To enable API streaming for your component, you need to upgrade to Kubernetes 1.32 and ensure etcd is running on version 3.4.31+ or 3.5.13+, as well as changing your client software to use watch lists.
- While the feature is in beta, only core components like kube-controller-manager have it enabled by default. A watch list feature is expected to become available in other core components like kube-scheduler or kubelet, once it becomes generally available.
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