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Posted to issues@spark.apache.org by "Frank Rosner (JIRA)" <ji...@apache.org> on 2015/10/23 17:24:27 UTC
[jira] [Updated] (SPARK-11258) Converting a Spark DataFrame into an
R data.frame is slow / requires a lot of memory
[ https://issues.apache.org/jira/browse/SPARK-11258?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Frank Rosner updated SPARK-11258:
---------------------------------
Summary: Converting a Spark DataFrame into an R data.frame is slow / requires a lot of memory (was: Remove quadratic runtime complexity for converting a Spark DataFrame into an R data.frame)
> 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. 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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