Github user rxin commented on a diff in the pull request: https://github.com/apache/spark/pull/5842#discussion_r29567027 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala --- @@ -77,4 +79,27 @@ private[sql] object StatFunctions { }) counts.cov } + + /** Generate a table of frequencies for the elements of two columns. */ + private[sql] def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame = { + val tableName = s"${col1}_$col2" + val counts = df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).collect() --- End diff -- It doesn't matter. you can set a max number (maybe 1 million). If the dataset has less than that, it will just return the entire dataset (at a slightly higher cost to run multiple jobs).
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