Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5842#discussion_r29528265
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/stat/ContingencyTable.scala
---
@@ -0,0 +1,38 @@
+package org.apache.spark.sql.execution.stat
+
+import org.apache.spark.sql.{Row, DataFrame}
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.functions._
+
+
+private[sql] object ContingencyTable {
+
+ /** 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 distinctVals = df.select(countDistinct(col1),
countDistinct(col2)).collect().head
--- End diff --
This implementation triggers multiple jobs. I'm thinking about the
following approach:
1. get distinct values from col2 and create a value-to-index map
2. aggregate by col1. for each value in col1, generate a Row object and
fill in counts
3. assign table schema
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
