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Posted to issues@flink.apache.org by "xiaolong3817 (Jira)" <ji...@apache.org> on 2025/09/18 05:42:00 UTC
[jira] [Updated] (FLINK-38375) Flink Resouce leak on Flink k8s
[ https://issues.apache.org/jira/browse/FLINK-38375?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
xiaolong3817 updated FLINK-38375:
---------------------------------
Affects Version/s: 1.20.2
Description:
Hello, when using Flink on Kubernetes (K8s), we encountered an issue where *TaskManager (TM) resources failed to be released* following an abnormal shutdown of the JobManager (JM). The abnormal JM shutdown scenarios include {*}Out-of-Memory (OOM) errors, forced termination via the {{kill -9}} command, or physical machine failures{*}.
Currently, our Flink cluster operates in Application Mode on K8s. This resource leakage issue (failure to release TM resources) can be Hello, when using Flink on Kubernetes (K8s), we encountered an issue where *TaskManager (TM) resources failed to be released* following an abnormal shutdown of the JobManager (JM). The abnormal JM shutdown scenarios include {*}Out-of-Memory (OOM) errors, forced termination via the {{kill -9}} command, or physical machine failures{*}.
Currently, our Flink cluster operates in *Application Mode* on K8s. This resource leakage issue (failure to release TM resources) can be *100% reproduced* whenever the JM shuts down abnormally.
* Flink version in use: *1.20.2*
Our current relevant configurations are as follows:
* {{jobmanager.scheduler: Adaptive}} (Adaptive Scheduler is enabled for the JobManager)
* {{resourcemanager.previous-worker.recovery.timeout: 0}} (The timeout for recovering previously running workers (TMs) by the ResourceManager is set to 0, meaning immediate timeout/no recovery) whenever the JM shuts down abnormally. * Flink version in use: *1.20.2*
Our current relevant configurations are as follows: * {{jobmanager.scheduler: Adaptive}} (Adaptive Scheduler is enabled for the JobManager)
* {{resourcemanager.previous-worker.recovery.timeout: 0}} (The timeout for recovering previously running workers (TMs) by the ResourceManager is set to 0, meaning immediate timeout/no recovery)
> Flink Resouce leak on Flink k8s
> -------------------------------
>
> Key: FLINK-38375
> URL: https://issues.apache.org/jira/browse/FLINK-38375
> Project: Flink
> Issue Type: Bug
> Components: Runtime / Task
> Affects Versions: 1.20.2
> Reporter: xiaolong3817
> Priority: Major
>
> Hello, when using Flink on Kubernetes (K8s), we encountered an issue where *TaskManager (TM) resources failed to be released* following an abnormal shutdown of the JobManager (JM). The abnormal JM shutdown scenarios include {*}Out-of-Memory (OOM) errors, forced termination via the {{kill -9}} command, or physical machine failures{*}.
>
> Currently, our Flink cluster operates in Application Mode on K8s. This resource leakage issue (failure to release TM resources) can be Hello, when using Flink on Kubernetes (K8s), we encountered an issue where *TaskManager (TM) resources failed to be released* following an abnormal shutdown of the JobManager (JM). The abnormal JM shutdown scenarios include {*}Out-of-Memory (OOM) errors, forced termination via the {{kill -9}} command, or physical machine failures{*}.
> Currently, our Flink cluster operates in *Application Mode* on K8s. This resource leakage issue (failure to release TM resources) can be *100% reproduced* whenever the JM shuts down abnormally.
> * Flink version in use: *1.20.2*
> Our current relevant configurations are as follows:
> * {{jobmanager.scheduler: Adaptive}} (Adaptive Scheduler is enabled for the JobManager)
> * {{resourcemanager.previous-worker.recovery.timeout: 0}} (The timeout for recovering previously running workers (TMs) by the ResourceManager is set to 0, meaning immediate timeout/no recovery) whenever the JM shuts down abnormally. * Flink version in use: *1.20.2*
> Our current relevant configurations are as follows: * {{jobmanager.scheduler: Adaptive}} (Adaptive Scheduler is enabled for the JobManager)
> * {{resourcemanager.previous-worker.recovery.timeout: 0}} (The timeout for recovering previously running workers (TMs) by the ResourceManager is set to 0, meaning immediate timeout/no recovery)
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