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https://issues.apache.org/jira/browse/BEAM-9474?focusedWorklogId=401711&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-401711
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ASF GitHub Bot logged work on BEAM-9474:
----------------------------------------
Author: ASF GitHub Bot
Created on: 11/Mar/20 19:27
Start Date: 11/Mar/20 19:27
Worklog Time Spent: 10m
Work Description: tweise commented on pull request #11084: [BEAM-9474]
Improve robustness of BundleFactory and ProcessEnvironment
URL: https://github.com/apache/beam/pull/11084#discussion_r391209772
##########
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();
currentClient = preparedClients.get(client);
if (currentClient == null) {
// we are using this client for the first time
preparedClients.put(client, currentClient = prepare(client,
executableStage));
// cleanup any expired clients
- preparedClients.keySet().removeIf(c -> c.bundleRefCount.get() == 0);
+ preparedClients.keySet().removeIf(c -> c.bundleRefCount.get() <= 0);
Review comment:
On the flip side, this could hide a bug in the reference counting. That
could be covered with tests though. Not important for this PR.
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Issue Time Tracking
-------------------
Worklog Id: (was: 401711)
Time Spent: 5.5h (was: 5h 20m)
> 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: 5.5h
> 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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