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https://issues.apache.org/jira/browse/SPARK-11258?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14971025#comment-14971025
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Frank Rosner commented on SPARK-11258:
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Actually I am pretty confused now. Thinking about it, having a for loop and a
map should not be accessing every element more then one time. However, it still
seems to be more complex than required to me. Let me try to reproduce the fact
that we could not load it with the old function but with the new one. Maybe to
.toArray method is a problem with memory as it is first recreating the whole
shabang and then copying it to another array?
> 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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