viirya commented on a change in pull request #29587:
URL: https://github.com/apache/spark/pull/29587#discussion_r487645676
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveUnion.scala
##########
@@ -17,29 +17,107 @@
package org.apache.spark.sql.catalyst.analysis
+import scala.collection.mutable
+
import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.expressions.{Alias, Literal}
+import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute,
Expression, Literal, NamedExpression, WithFields}
import org.apache.spark.sql.catalyst.optimizer.CombineUnions
import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project,
Union}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types._
import org.apache.spark.sql.util.SchemaUtils
/**
* Resolves different children of Union to a common set of columns.
*/
object ResolveUnion extends Rule[LogicalPlan] {
- private def unionTwoSides(
+ /**
+ * Adds missing fields recursively into given `col` expression, based on the
target `StructType`.
+ * For example, given `col` as "a struct<a:int, b:int>, b int" and `target`
as
+ * "a struct<a:int, b:int, c:long>, b int, c string", this method should add
`a.c` and `c` to
+ * `col` expression.
+ */
+ private def addFields(col: NamedExpression, target: StructType):
Option[Expression] = {
+ assert(col.dataType.isInstanceOf[StructType], "Only support StructType.")
+
+ val resolver = SQLConf.get.resolver
+ val missingFields =
+ StructType.findMissingFields(col.dataType.asInstanceOf[StructType],
target, resolver)
+ if (missingFields.isEmpty) {
+ None
+ } else {
+ missingFields.map(s => addFieldsInto(col, "", s.fields))
+ }
+ }
+
+ /**
+ * Adds missing fields recursively into given `col` expression. The missing
fields are given
+ * in `fields`. For example, given `col` as "a struct<a:int, b:int>, b int",
and `fields` is
+ * "a struct<c:long>, c string". This method will add a nested `a.c` field
and a top-level
+ * `c` field to `col` and fill null values for them.
+ */
+ private def addFieldsInto(col: Expression, base: String, fields:
Seq[StructField]): Expression = {
+ fields.foldLeft(col) { case (currCol, field) =>
+ field.dataType match {
+ case st: StructType =>
+ val resolver = SQLConf.get.resolver
+ val colField = currCol.dataType.asInstanceOf[StructType]
+ .find(f => resolver(f.name, field.name))
+ if (colField.isEmpty) {
+ // The whole struct is missing. Add a null.
+ WithFields(currCol, s"$base${field.name}", Literal(null, st),
+ sortOutputColumns = true)
+ } else {
+ addFieldsInto(currCol, s"$base${field.name}.", st.fields)
+ }
+ case dt =>
+ // We need to sort columns in result, because we might add another
column in other side.
+ // E.g., we want to union two structs "a int, b long" and "a int, c
string".
+ // If we don't sort, we will have "a int, b long, c string" and "a
int, c string, b long",
+ // which are not compatible.
+ WithFields(currCol, s"$base${field.name}", Literal(null, dt),
Review comment:
Oh, I think it is bad for performance using `WithFields` like that.
`WithFields` basically extracts fields from a struct and re-construct the
struct with new field. So you can image that too many missing columns cannot
get good performance.
However, I think we currently have no better approach to fill missing
(nested) fields in structs. We might add a note to the `unionByName` API for
performance notice.
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