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airflow-helm / charts

The User-Community Airflow Helm Chart is the standard way to deploy Apache Airflow on Kubernetes with Helm. Originally created in 2017, it has since helped thousands of companies create production-ready deployments of Airflow on Kubernetes.
https://github.com/airflow-helm/charts/tree/main/charts/airflow
Apache License 2.0
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Pods accumulate lots of RAM #758

Closed robkuehl closed 11 months ago

robkuehl commented 1 year ago

Checks

Chart Version

8.7.1

Kubernetes Version

Client Version: version.Info{Major:"1", Minor:"26", GitVersion:"v1.26.1", GitCommit:"8f94681cd294aa8cfd3407b8191f6c70214973a4", GitTreeState:"clean", BuildDate:"2023-01-18T15:51:24Z", GoVersion:"go1.19.5", Compiler:"gc", Platform:"darwin/arm64"}

Server Version: version.Info{Major:"1", Minor:"23", GitVersion:"v1.23.12+7566c4d", GitCommit:"22147459669562208c3082141eece03824c0a417", GitTreeState:"clean", BuildDate:"2022-10-24T10:40:54Z", GoVersion:"go1.17.12", Compiler:"gc", Platform:"linux/amd64"}

Helm Version

version.BuildInfo{Version:"v3.11.1", GitCommit:"293b50c65d4d56187cd4e2f390f0ada46b4c4737", GitTreeState:"clean", GoVersion:"go1.19.5"}

Description

The Pods accumulate a lot of RAM quickly. Is this behavior normal? Screenshot 2023-07-06 101415

Relevant Logs

No response

Custom Helm Values

No response

thesuperzapper commented 1 year ago

@robkuehl this is not typical, so you might want to check if any of your dag definitions have a memory leak (note, I am talking about the python definitions, not the dags themselves).

Or if you have a custom airflow image, anything you have changed in it could be the cause.

Its also possible that you have found a bug in airflow itself, which version are you using?

stale[bot] commented 1 year ago

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