Kubernetes v1.33: Efficient Data Transfer with Streaming List Responses

Managing Kubernetes cluster stability becomes increasingly critical as your infrastructure grows. One of the most challenging aspects of operating large clusters has been handling list requests that retrieve extensive datasets—a common operation that can unexpectedly impact your cluster’s stability.

Today, the Kubernetes community is excited to announce a significant architectural improvement: streaming encoding for list responses.

The Problem: Unnecessary Memory Consumption with Large Resources

Current API response encoders serialize an entire response into a single contiguous memory area and perform a single ResponseWriter.Write call to transmit the data to the client. Despite HTTP/2’s ability to split responses into smaller frames for transmission, the underlying HTTP server still holds the complete response data as a single buffer. Even when individual frames are transmitted to the client, the memory associated with these frames cannot be incrementally released.

As the cluster size grows, the single response body can be substantial—hundreds of megabytes large. At scale, the current approach becomes particularly inefficient as it prevents incremental memory release during transmission. Imagine that during network congestion, the large response body remains active for dozens of seconds or even minutes. This limitation leads to unnecessarily high and prolonged memory consumption in the kube-apiserver process. When multiple large list requests occur simultaneously, the cumulative memory consumption can quickly escalate, potentially leading to an Out-of-Memory (OOM) situation that jeopardizes cluster stability.

The encoding/json package uses sync.Pool to reuse memory allocations during serialization. While efficient for consistent workloads, this mechanism poses challenges for sporadically large list responses. When processing these large responses, the memory pools expand significantly. Due to the design of sync.Pool, these oversized buffers remain reserved after use. Subsequent small list requests continue to utilize these large memory allocations, preventing garbage collection and maintaining a persistently high memory footprint in the kube-apiserver, even after the original large responses are completed.

Moreover, Protocol Buffers are not designed for processing large datasets. However, they are excellent for handling individual messages within a large dataset. This underscores the need for streaming-based approaches that can incrementally process and transmit large collections rather than treating them as monolithic blocks.

As a general rule of thumb: If you’re dealing with messages larger than a megabyte, it might be time to consider an alternative strategy. From https://protobuf.dev/programming-guides/techniques/

Source: Kubernetes Blog