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https://issues.apache.org/jira/browse/MAHOUT-1615?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14146924#comment-14146924
]
ASF GitHub Bot commented on MAHOUT-1615:
----------------------------------------
Github user andrewpalumbo commented on a diff in the pull request:
https://github.com/apache/mahout/pull/52#discussion_r18002168
--- Diff: pom.xml ---
@@ -701,7 +701,7 @@
<module>math-scala</module>
<module>spark</module>
<module>spark-shell</module>
- <module>h2o</module>
+ <!-- module>h2o</module -->
--- End diff --
Yeah- i left it commented for now because there's some work to be done in
h2o also. Introducing the field to the `CheckpointedDrm` trait at math-scala
required an implementation in h2o. So after adding
```scala
/** Explicit extraction of key class Tag */
def keyClassTag: ClassTag[K] = implicitly[ClassTag[K]]
```
to `CheckpointedDrmH20.scala`
The tests are failing. Unfortunately I've not had a lot of uninterupted
time to work on this over the last week so I havent really looked at the h2o
side yet- not sure yet but i thik we need to do some similar class matching in
h2o.
> SparkEngine drmFromHDFS returning the same Key for all Key,Vec Pairs for
> Text-Keyed SequenceFiles
> -------------------------------------------------------------------------------------------------
>
> Key: MAHOUT-1615
> URL: https://issues.apache.org/jira/browse/MAHOUT-1615
> Project: Mahout
> Issue Type: Bug
> Reporter: Andrew Palumbo
> Fix For: 1.0
>
>
> When reading in seq2sparse output from HDFS in the spark-shell of form
> <Text,VectorWriteable> SparkEngine's drmFromHDFS method is creating rdds
> with the same Key for all Pairs:
> {code}
> mahout> val drmTFIDF= drmFromHDFS( path =
> "/tmp/mahout-work-andy/20news-test-vectors/part-r-00000")
> {code}
> Has keys:
> {...}
> key: /talk.religion.misc/84570
> key: /talk.religion.misc/84570
> key: /talk.religion.misc/84570
> {...}
> for the entire set. This is the last Key in the set.
> The problem can be traced to the first line of drmFromHDFS(...) in
> SparkEngine.scala:
> {code}
> val rdd = sc.sequenceFile(path, classOf[Writable], classOf[VectorWritable],
> minPartitions = parMin)
> // Get rid of VectorWritable
> .map(t => (t._1, t._2.get()))
> {code}
> which gives the same key for all t._1.
>
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