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Apache Spark commented on SPARK-11086: -------------------------------------- User 'zero323' has created a pull request for this issue: https://github.com/apache/spark/pull/9099 > 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 > > 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]. -- 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