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https://issues.apache.org/jira/browse/SPARK-11258?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14971155#comment-14971155
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Frank Rosner commented on SPARK-11258:
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I adjusted the description to be more general. I will see if I can get some
memory profiling or something. Maybe I can also provide a reproducible example.
> Converting a Spark DataFrame into an R data.frame is slow / requires a lot of
> memory
> ------------------------------------------------------------------------------------
>
> 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. Problem
> 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 does some map and
> then {{.toArray}} which might cause the problem.
> 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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