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https://issues.apache.org/jira/browse/MAHOUT-1615?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14148377#comment-14148377
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ASF GitHub Bot commented on MAHOUT-1615:
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Github user andrewpalumbo commented on the pull request:
https://github.com/apache/mahout/pull/52#issuecomment-56890678
>There's another piece of information to consider. Spark itself definex
implicit conversions from some well-known Writables to their payload types.
Perhaps we should support everything that's there; and maybe even figure a way
to automatically apply everything that Spark exports, without even doing cases.
Looking at SparkContext.scala there are implicit converters for converting
to: ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable,
FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable
It should be simple to do the conversion by matching since its all set up.
is there a reason that you wouldn't want to use `match`?
> 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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