Enhancing Kubernetes API Server Efficiency with API Streaming

Enhancing Kubernetes API Server Efficiency with API Streaming Why does kube-apiserver allocate so much memory for list requests? Streaming list requests Enabling API Streaming for your component What's next? The synthetic test Kubernetes 1.33 update 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. In the existing implementation, the kube-apiserver processes list requests by assembling the entire response in-memory before transmitting any data to the client. But what if the response body is substantial, say hundreds of megabytes? Additionally, imagine a scenario where multiple list requests flood in simultaneously, perhaps after a brief network outage. While API Priority and Fairness has proven to reasonably protect kube-apiserver from CPU overload, its impact is visibly smaller for memory protection. This can be explained by the differing nature of resource consumption by a single API request - the CPU usage at any given time is capped by a constant, whereas memory, being uncompressible, can grow proportionally with the number of processed objects and is unbounded. This situation poses a genuine risk, potentially overwhelming and crashing any kube-apiserver within seconds due to out-of-memory (OOM) conditions. To better visualize the issue, let's consider the below graph. The graph shows the memory usage of a kube-apiserver during a synthetic test. (see the synthetic test section for more details). The results clearly show that increasing the number of informers significantly boosts the server's memory consumption. Notably, at approximately 16:40, the server crashed when serving only 16 informers.