[jira] [Assigned] (SPARK-11086) createDataFrame should dropFactor column-wise not cell-wise

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-11086?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-11086:
------------------------------------

    Assignee: Apache Spark

> createDataFrame should dropFactor column-wise not cell-wise 
> ------------------------------------------------------------
>
>                 Key: SPARK-11086
>                 URL: https://issues.apache.org/jira/browse/SPARK-11086
>             Project: Spark
>          Issue Type: Improvement
>          Components: SparkR
>            Reporter: Maciej Szymkiewicz
>            Assignee: Apache Spark
>
> At this moment SparkR {{createDataFrame}} [is using nested loop|https://github.com/apache/spark/blob/896edb51ab7a88bbb31259e565311a9be6f2ca6d/R/pkg/R/SQLContext.R#L99] to convert {{factors}} to {{character}} when called on a local {{data.frame}}.
> {code}
> data <- lapply(1:n, function(i) {
>     lapply(1:m, function(j) { dropFactor(data[i,j]) })
> })
> {code}
> It works but is incredibly slow especially with {{data.table}} (~ 2 orders of magnitude compared to  PySpark / Pandas version on a DateFrame of size 1M rows x 2 columns).
> A simple improvement is to apply {{dropFactor}} column-wise and then reshape output list:
> {code}
> args <- list(FUN=list, SIMPLIFY=FALSE, USE.NAMES=FALSE)  
> data <- do.call(mapply, append(args, setNames(lapply(data, dropFactor), NULL)))
> {code}
> It should at least partially address [SPARK-8277|https://issues.apache.org/jira/browse/SPARK-8277].



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