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Posted to mapreduce-issues@hadoop.apache.org by "Karthik Kambatla (JIRA)" <ji...@apache.org> on 2012/06/07 22:52:23 UTC
[jira] [Updated] (MAPREDUCE-4311) Capacity scheduler.xml does not
accept decimal values for capacity and maximum-capacity settings
[ https://issues.apache.org/jira/browse/MAPREDUCE-4311?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Karthik Kambatla updated MAPREDUCE-4311:
----------------------------------------
Status: Patch Available (was: Open)
Uploading the patch with the following changes:
-Capacities changed to float
-Modified relevant tests to use floating point capacities (10.5)
-Ran the tests - TestCapacityScheduler, TestParentQueue, TestLeafQueue, TestRMWebServicesSched
> Capacity scheduler.xml does not accept decimal values for capacity and maximum-capacity settings
> ------------------------------------------------------------------------------------------------
>
> Key: MAPREDUCE-4311
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-4311
> Project: Hadoop Map/Reduce
> Issue Type: Bug
> Components: contrib/capacity-sched, mrv2
> Affects Versions: 0.23.3
> Reporter: Thomas Graves
> Assignee: Karthik Kambatla
>
> if capacity scheduler capacity or max capacity set with decimal it errors:
> - Error starting ResourceManager
> java.lang.NumberFormatException: For input string: "10.5"
> at
> java.lang.NumberFormatException.forInputString(NumberFormatException.java:48)
> at java.lang.Integer.parseInt(Integer.java:458)
> at java.lang.Integer.parseInt(Integer.java:499)
> at org.apache.hadoop.conf.Configuration.getInt(Configuration.java:713)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacitySchedulerConfiguration.getCapacity(CapacitySchedulerConfiguration.java:147)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.LeafQueue.<init>(LeafQueue.java:147)
> at
> org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler.parseQueue(CapacityScheduler.java:297)
> at
> 0.20 used to take decimal and this could be an issue on large clusters that would have queues with small allocations.
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