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https://issues.apache.org/jira/browse/SPARK-11086?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15006292#comment-15006292
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Maciej Szymkiewicz commented on SPARK-11086:
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[~shivaram] Does it resolve [SPARK-8277] as well?
> 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: Maciej Szymkiewicz
> Fix For: 1.6.0
>
>
> 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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