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https://issues.apache.org/jira/browse/SPARK-11258?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14968814#comment-14968814
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Apache Spark commented on SPARK-11258:
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User 'FRosner' has created a pull request for this issue:
https://github.com/apache/spark/pull/9222
> Remove quadratic runtime complexity for converting a Spark DataFrame into an
> R data.frame
> -----------------------------------------------------------------------------------------
>
> Key: SPARK-11258
> URL: https://issues.apache.org/jira/browse/SPARK-11258
> Project: Spark
> Issue Type: Improvement
> Components: SparkR
> Affects Versions: 1.5.1
> Reporter: Frank Rosner
>
> h4. Introduction
> We tried to collect a DataFrame with > 1 million rows and a few hundred
> columns in SparkR. This took a huge amount of time (much more than in the
> Spark REPL). When looking into the code, I found that the
> {{org.apache.spark.sql.api.r.SQLUtils.dfToCols}} method has quadratic run
> time complexity (it goes through the complete data set _m_ times, where _m_
> is the number of columns.
> h4. Problem
> The {{dfToCols}} method is transposing the row-wise representation of the
> Spark DataFrame (array of rows) into a column wise representation (array of
> columns) to then be put into a data frame. This is done in a very inefficient
> way, yielding to huge performance (and possibly also memory) problems when
> collecting bigger data frames.
> h4. Solution
> Directly transpose the row wise representation to the column wise
> representation with one pass through the data. I will create a pull request
> for this.
> h4. Runtime comparison
> On a test data frame with 1 million rows and 22 columns, the old `dfToCols`
> method takes average 2267 ms to complete. My implementation takes only 554 ms
> on average. This effect gets even bigger, the more columns you have.
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