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https://issues.apache.org/jira/browse/MAHOUT-524?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13109295#comment-13109295
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Lance Norskog commented on MAHOUT-524:
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Yes, it was MAVEN_OPTS; that one helps.
With today's patch (Sep. 20, 2011 setting the job jars), I get (eventually)
this error:
{code}
11/09/20 23:10:35 INFO mapred.JobClient: Running job: job_201109191821_0016
11/09/20 23:10:36 INFO mapred.JobClient: map 0% reduce 0%
11/09/20 23:11:01 INFO mapred.JobClient: map 100% reduce 0%
11/09/20 23:11:15 INFO mapred.JobClient: Task Id :
attempt_201109191821_0016_r_000000_0, Status : FAILED
Shuffle Error: Exceeded MAX_FAILED_UNIQUE_FETCHES; bailing-out.
11/09/20 23:11:15 WARN mapred.JobClient: Error reading task outputHost is down
11/09/20 23:11:15 WARN mapred.JobClient: Error reading task outputHost is down
11/09/20 23:12:00 INFO mapred.JobClient: Task Id :
attempt_201109191821_0016_r_000000_1, Status : FAILED
Shuffle Error: Exceeded MAX_FAILED_UNIQUE_FETCHES; bailing-out.
{code}
I stripped aff.txt down to a file with 20 nodes, and get the above error. This
is on a single-node cluster on my laptop. Is it possible to run this job on
such a small device? (If not, then DisplaySpectralKMeans as a Swing app might
not be realistic :).
> DisplaySpectralKMeans example fails
> -----------------------------------
>
> Key: MAHOUT-524
> URL: https://issues.apache.org/jira/browse/MAHOUT-524
> Project: Mahout
> Issue Type: Bug
> Components: Clustering
> Affects Versions: 0.4, 0.5
> Reporter: Jeff Eastman
> Assignee: Shannon Quinn
> Labels: clustering, k-means, visualization
> Fix For: 0.6
>
> Attachments: EclipseLog_20110918.txt,
> SpectralKMeans_fail_20110919.txt, aff.txt, raw.txt, spectralkmeans.png
>
>
> I've committed a new display example that attempts to push the standard
> mixture of models data set through spectral k-means. After some tweaking of
> configuration arguments and a bug fix in EigenCleanupJob it runs spectral
> k-means to completion. The display example is expecting 2-d clustered points
> and the example is producing 5-d points. Additional I/O work is needed before
> this will play with the rest of the clustering algorithms.
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