Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5842#discussion_r29549052
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
---
@@ -77,4 +78,42 @@ 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 distinctCol2 = df.select(col2).distinct.orderBy(col2).collect()
--- End diff --
The first implementation uses multiple passes. We need two passes any way,
either on the original columns or on the pair counts. The latter may be better.
1. `val counts = select(col1, col2).rdd.countByValue().cache()`.
2. Get distinct values from col2:
`counts.map(_._1._2).distinct().collect()`. I'm not sure whether ordering by
counts is useful here.
3. GroupBy `col1` in `counts` and create an RDD of Row. And then apply the
schema.
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