Optimizing Kubernetes Memory Usage: How We Saved 7 TiB by Disabling Namespace Listwatching in Vector
By
anurag
Crisp on the outside, thoughtful on the inside. A keeper.
Summary
The article details how a team discovered and resolved a significant memory inefficiency in their Kubernetes infrastructure. By analyzing their large-scale Kubernetes clusters with numerous namespaces, they found that Vector's namespace listwatching feature was consuming 7 TiB of memory across their clusters. The solution involved disabling this feature, which not only saved massive amounts of memory but also reduced daemonset overhead and API server load. The article provides technical insights into Kubernetes namespace management at scale and practical optimization strategies for large deployments.
Key quotes
· 5 pulledThere's one dimension where I suspect we might be near the very top: namespaces. I say that because we keep running into odd behavior in any process that has to keep track of them.
In particular, anything that listwatches them ends up using a surprising amount of memory.
How we saved 7 TiB of memory across our Kubernetes clusters by disabling namespace listwatching in Vector, reducing daemonset overhead and API server load at scale.
Getting ready to dissect what I like to call: the Kubernetes hypercube of bad vibes.
Plenty of teams run Kubernetes clusters bigger than ours. More nodes, more pods, more ingresses, you name it. In most dimensions, someone out there has us beat.
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