[ https://issues.apache.org/jira/browse/SPARK-11258?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-11258: ------------------------------------ Assignee: (was: Apache Spark) > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org