Maciej Szymkiewicz created SPARK-11086: ------------------------------------------
Summary: 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