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https://issues.apache.org/jira/browse/SPARK-17629?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved SPARK-17629.
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Resolution: Not A Problem
This is probably best as a question on dev@, not a JIRA. You can easily collect
the DataFrame locally. I suppose this gives you the option.
> Should ml Word2Vec findSynonyms match the mllib implementation?
> ---------------------------------------------------------------
>
> Key: SPARK-17629
> URL: https://issues.apache.org/jira/browse/SPARK-17629
> Project: Spark
> Issue Type: Question
> Reporter: Asher Krim
> Priority: Minor
>
> ml Word2Vec's findSynonyms methods depart from mllib in that they return
> distributed results, rather than the results directly:
> {code}
> def findSynonyms(word: String, num: Int): DataFrame = {
> val spark = SparkSession.builder().getOrCreate()
> spark.createDataFrame(wordVectors.findSynonyms(word, num)).toDF("word",
> "similarity")
> }
> {code}
> What was the reason for this decision? I would think that most users would
> request a reasonably small number of results back, and want to use them
> directly on the driver, similar to the _take_ method on dataframes. Returning
> parallelized results creates a costly round trip for the data that doesn't
> seem necessary.
> The original PR: https://github.com/apache/spark/pull/7263
> [~MechCoder] - do you perhaps recall the reason?
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