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https://issues.apache.org/jira/browse/BEAM-9474?focusedWorklogId=401736&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-401736
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ASF GitHub Bot logged work on BEAM-9474:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 11/Mar/20 19:55
            Start Date: 11/Mar/20 19:55
    Worklog Time Spent: 10m 
      Work Description: mxm commented on pull request #11084: [BEAM-9474] 
Improve robustness of BundleFactory and ProcessEnvironment
URL: https://github.com/apache/beam/pull/11084#discussion_r391225776
 
 

 ##########
 File path: 
runners/java-fn-execution/src/main/java/org/apache/beam/runners/fnexecution/control/DefaultJobBundleFactory.java
 ##########
 @@ -352,20 +407,18 @@ public RemoteBundle getBundle(
         // The blocking queue of caches for serving multiple bundles 
concurrently.
         currentCache = availableCaches.take();
         client = currentCache.getUnchecked(executableStage.getEnvironment());
-        client.ref();
 
 Review comment:
   It's not true that a later `ref()` introduces a bug for 
`preparedClients.keySet().removeIf(c -> c.bundleRefCount.get() <= 0);` because 
the refcount will be >0, otherwise we wouldn't be able to retrieve the client 
from the cache.
   
   In any case, I will revert this change. 
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 401736)
    Time Spent: 5h 50m  (was: 5h 40m)

> Environment cleanup is not robust enough and may leak resources
> ---------------------------------------------------------------
>
>                 Key: BEAM-9474
>                 URL: https://issues.apache.org/jira/browse/BEAM-9474
>             Project: Beam
>          Issue Type: Bug
>          Components: java-fn-execution
>            Reporter: Maximilian Michels
>            Assignee: Maximilian Michels
>            Priority: Major
>          Time Spent: 5h 50m
>  Remaining Estimate: 0h
>
> The cleanup code in {{DefaultJobBundleFactory}} and its {{RemoteEnvironment}} 
> s may leak resources. This is especially a concern when the execution engines 
> reuses the same JVM or underlying machines for multiple runs of a pipeline.



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