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[SPARK-40877][SQL] Reimplement crosstab with dataframe operations #38340

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[SPARK-40877][SQL] Reimplement crosstab with dataframe operations #38340

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50 changes: 8 additions & 42 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,6 @@ import org.apache.spark.sql.errors.QueryExecutionErrors
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.unsafe.types.UTF8String
import org.apache.spark.util.collection.Utils

object StatFunctions extends Logging {

Expand Down Expand Up @@ -188,47 +187,14 @@ object StatFunctions extends Logging {

/** Generate a table of frequencies for the elements of two columns. */
def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame = {
val tableName = s"${col1}_$col2"
val counts = df.groupBy(col1, col2).agg(count("*")).take(1e6.toInt)
if (counts.length == 1e6.toInt) {
logWarning("The maximum limit of 1e6 pairs have been collected, which may not be all of " +
"the pairs. Please try reducing the amount of distinct items in your columns.")
}
def cleanElement(element: Any): String = {
if (element == null) "null" else element.toString
}
// get the distinct sorted values of column 2, so that we can make them the column names
val distinctCol2: Map[Any, Int] =
Utils.toMapWithIndex(counts.map(e => cleanElement(e.get(1))).distinct.sorted)
val columnSize = distinctCol2.size
require(columnSize < 1e4, s"The number of distinct values for $col2, can't " +
s"exceed 1e4. Currently $columnSize")
val table = counts.groupBy(_.get(0)).map { case (col1Item, rows) =>
val countsRow = new GenericInternalRow(columnSize + 1)
rows.foreach { (row: Row) =>
// row.get(0) is column 1
// row.get(1) is column 2
// row.get(2) is the frequency
val columnIndex = distinctCol2(cleanElement(row.get(1)))
countsRow.setLong(columnIndex + 1, row.getLong(2))
}
// the value of col1 is the first value, the rest are the counts
countsRow.update(0, UTF8String.fromString(cleanElement(col1Item)))
countsRow
}.toSeq
// Back ticks can't exist in DataFrame column names, therefore drop them. To be able to accept
// special keywords and `.`, wrap the column names in ``.
def cleanColumnName(name: String): String = {
name.replace("`", "")
}
// In the map, the column names (._1) are not ordered by the index (._2). This was the bug in
// SPARK-8681. We need to explicitly sort by the column index and assign the column names.
val headerNames = distinctCol2.toSeq.sortBy(_._2).map { r =>
StructField(cleanColumnName(r._1.toString), LongType)
}
val schema = StructType(StructField(tableName, StringType) +: headerNames)

Dataset.ofRows(df.sparkSession, LocalRelation(schema.toAttributes, table)).na.fill(0.0)
df.groupBy(
when(isnull(col(col1)), "null")
.otherwise(col(col1).cast("string"))
.as(s"${col1}_$col2")
).pivot(
when(isnull(col(col2)), "null")
.otherwise(regexp_replace(col(col2).cast("string"), "`", ""))
).count().na.fill(0L)
}

/** Calculate selected summary statistics for a dataset */
Expand Down