maropu commented on a change in pull request #29587:
URL: https://github.com/apache/spark/pull/29587#discussion_r499321425
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveUnion.scala
##########
@@ -17,29 +17,202 @@
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._
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
+import org.apache.spark.unsafe.types.UTF8String
/**
* Resolves different children of Union to a common set of columns.
*/
object ResolveUnion extends Rule[LogicalPlan] {
- private def unionTwoSides(
+ /**
+ * This method sorts recursively columns in a struct expression based on
column names.
+ */
+ private def sortStructFields(expr: Expression): Expression = {
+ val existingExprs =
expr.dataType.asInstanceOf[StructType].fieldNames.zipWithIndex.map {
+ case (name, i) =>
+ val fieldExpr = GetStructField(KnownNotNull(expr), i)
+ if (fieldExpr.dataType.isInstanceOf[StructType]) {
+ (name, sortStructFields(fieldExpr))
+ } else {
+ (name, fieldExpr)
+ }
+ }.sortBy(_._1).flatMap(pair => Seq(Literal(pair._1), pair._2))
+
+ val newExpr = CreateNamedStruct(existingExprs)
+ if (expr.nullable) {
+ If(IsNull(expr), Literal(null, newExpr.dataType), newExpr)
+ } else {
+ newExpr
+ }
+ }
+
+ /**
+ * Assumes input expressions are field expression of `CreateNamedStruct`.
This method
+ * sorts the expressions based on field names.
+ */
+ private def sortFieldExprs(fieldExprs: Seq[Expression]): Seq[Expression] = {
+ fieldExprs.grouped(2).map { e =>
+ Seq(e.head, e.last)
+ }.toSeq.sortBy { pair =>
+ assert(pair.head.isInstanceOf[Literal])
+ pair.head.eval().asInstanceOf[UTF8String].toString
+ }.flatten
+ }
+
+ /**
+ * This helper method sorts fields in a `WithFields` expression by field
name.
+ */
+ private def sortStructFieldsInWithFields(expr: Expression): Expression =
expr transformUp {
+ case w: WithFields if w.resolved =>
+ w.evalExpr match {
+ case i @ If(IsNull(_), _, CreateNamedStruct(fieldExprs)) =>
+ val sorted = sortFieldExprs(fieldExprs)
+ val newStruct = CreateNamedStruct(sorted)
+ i.copy(trueValue = Literal(null, newStruct.dataType), falseValue =
newStruct)
+ case CreateNamedStruct(fieldExprs) =>
+ val sorted = sortFieldExprs(fieldExprs)
+ val newStruct = CreateNamedStruct(sorted)
+ newStruct
+ case other =>
Review comment:
If this case means a program bug, the message should include `Please
file a bug report ...` like the others?
https://github.com/apache/spark/blob/fab53212cb110a81696cee8546c35095332f6e09/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala#L2747-L2748
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveUnion.scala
##########
@@ -17,29 +17,202 @@
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._
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
+import org.apache.spark.unsafe.types.UTF8String
/**
* Resolves different children of Union to a common set of columns.
*/
object ResolveUnion extends Rule[LogicalPlan] {
- private def unionTwoSides(
+ /**
+ * This method sorts recursively columns in a struct expression based on
column names.
+ */
+ private def sortStructFields(expr: Expression): Expression = {
+ val existingExprs =
expr.dataType.asInstanceOf[StructType].fieldNames.zipWithIndex.map {
+ case (name, i) =>
+ val fieldExpr = GetStructField(KnownNotNull(expr), i)
+ if (fieldExpr.dataType.isInstanceOf[StructType]) {
+ (name, sortStructFields(fieldExpr))
+ } else {
+ (name, fieldExpr)
+ }
+ }.sortBy(_._1).flatMap(pair => Seq(Literal(pair._1), pair._2))
+
+ val newExpr = CreateNamedStruct(existingExprs)
+ if (expr.nullable) {
+ If(IsNull(expr), Literal(null, newExpr.dataType), newExpr)
+ } else {
+ newExpr
+ }
+ }
+
+ /**
+ * Assumes input expressions are field expression of `CreateNamedStruct`.
This method
+ * sorts the expressions based on field names.
+ */
+ private def sortFieldExprs(fieldExprs: Seq[Expression]): Seq[Expression] = {
+ fieldExprs.grouped(2).map { e =>
+ Seq(e.head, e.last)
+ }.toSeq.sortBy { pair =>
+ assert(pair.head.isInstanceOf[Literal])
+ pair.head.eval().asInstanceOf[UTF8String].toString
+ }.flatten
+ }
+
+ /**
+ * This helper method sorts fields in a `WithFields` expression by field
name.
+ */
+ private def sortStructFieldsInWithFields(expr: Expression): Expression =
expr transformUp {
+ case w: WithFields if w.resolved =>
+ w.evalExpr match {
+ case i @ If(IsNull(_), _, CreateNamedStruct(fieldExprs)) =>
+ val sorted = sortFieldExprs(fieldExprs)
+ val newStruct = CreateNamedStruct(sorted)
+ i.copy(trueValue = Literal(null, newStruct.dataType), falseValue =
newStruct)
+ case CreateNamedStruct(fieldExprs) =>
+ val sorted = sortFieldExprs(fieldExprs)
+ val newStruct = CreateNamedStruct(sorted)
+ newStruct
+ case other =>
+ throw new AnalysisException(s"`WithFields` has incorrect eval
expression: $other")
+ }
+ }
+
+ def simplifyWithFields(expr: Expression): Expression = {
Review comment:
nit: `private`. Btw, all the transformations in this method will be
moved into an optimizer rule in followup? We normally add tests when adding a
new rule, but this PR does not have any test for them.
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveUnion.scala
##########
@@ -17,29 +17,202 @@
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._
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
+import org.apache.spark.unsafe.types.UTF8String
/**
* Resolves different children of Union to a common set of columns.
*/
object ResolveUnion extends Rule[LogicalPlan] {
- private def unionTwoSides(
+ /**
+ * This method sorts recursively columns in a struct expression based on
column names.
+ */
+ private def sortStructFields(expr: Expression): Expression = {
+ val existingExprs =
expr.dataType.asInstanceOf[StructType].fieldNames.zipWithIndex.map {
+ case (name, i) =>
+ val fieldExpr = GetStructField(KnownNotNull(expr), i)
+ if (fieldExpr.dataType.isInstanceOf[StructType]) {
+ (name, sortStructFields(fieldExpr))
+ } else {
+ (name, fieldExpr)
+ }
+ }.sortBy(_._1).flatMap(pair => Seq(Literal(pair._1), pair._2))
+
+ val newExpr = CreateNamedStruct(existingExprs)
+ if (expr.nullable) {
+ If(IsNull(expr), Literal(null, newExpr.dataType), newExpr)
+ } else {
+ newExpr
+ }
+ }
+
+ /**
+ * Assumes input expressions are field expression of `CreateNamedStruct`.
This method
+ * sorts the expressions based on field names.
+ */
+ private def sortFieldExprs(fieldExprs: Seq[Expression]): Seq[Expression] = {
+ fieldExprs.grouped(2).map { e =>
+ Seq(e.head, e.last)
+ }.toSeq.sortBy { pair =>
+ assert(pair.head.isInstanceOf[Literal])
+ pair.head.eval().asInstanceOf[UTF8String].toString
+ }.flatten
+ }
+
+ /**
+ * This helper method sorts fields in a `WithFields` expression by field
name.
+ */
+ private def sortStructFieldsInWithFields(expr: Expression): Expression =
expr transformUp {
+ case w: WithFields if w.resolved =>
+ w.evalExpr match {
+ case i @ If(IsNull(_), _, CreateNamedStruct(fieldExprs)) =>
+ val sorted = sortFieldExprs(fieldExprs)
+ val newStruct = CreateNamedStruct(sorted)
+ i.copy(trueValue = Literal(null, newStruct.dataType), falseValue =
newStruct)
+ case CreateNamedStruct(fieldExprs) =>
+ val sorted = sortFieldExprs(fieldExprs)
+ val newStruct = CreateNamedStruct(sorted)
+ newStruct
+ case other =>
+ throw new AnalysisException(s"`WithFields` has incorrect eval
expression: $other")
+ }
+ }
+
+ def simplifyWithFields(expr: Expression): Expression = {
+ expr.transformUp {
+ case WithFields(structExpr, names, values) if names.distinct.length !=
names.length =>
+ val newNames = mutable.ArrayBuffer.empty[String]
+ val newValues = mutable.ArrayBuffer.empty[Expression]
+ names.zip(values).reverse.foreach { case (name, value) =>
+ if (!newNames.contains(name)) {
+ newNames += name
+ newValues += value
+ }
+ }
+ WithFields(structExpr, names = newNames.reverse, valExprs =
newValues.reverse)
+ case WithFields(WithFields(struct, names1, valExprs1), names2,
valExprs2) =>
+ WithFields(struct, names1 ++ names2, valExprs1 ++ valExprs2)
Review comment:
duplicated?
https://github.com/apache/spark/blob/fab53212cb110a81696cee8546c35095332f6e09/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/WithFields.scala#L30-L31
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##########
@@ -2721,6 +2721,16 @@ object SQLConf {
.booleanConf
.createWithDefault(false)
+ val UNION_BYNAME_STRUCT_SUPPORT_ENABLED =
+ buildConf("spark.sql.unionByName.structSupport.enabled")
+ .doc("When true, the `allowMissingColumns` feature of
`Dataset.unionByName` supports " +
+ "nested column in struct types. Missing nested columns of struct
columns with same " +
+ "name will also be filled with null values. This currently does not
support nested " +
+ "columns in array and map types.")
Review comment:
How about explaining the behavior of the fields being sorted when
merging them? I'm a bit worried that users might be surprised about the
behavior.
```
scala> val df1 = spark.range(1).selectExpr("id c0", "named_struct('c', id +
1, 'b', id + 2, 'a', id + 3) c1")
scala> val df2 = spark.range(1).selectExpr("id c0", "named_struct('c', id +
1, 'b', id + 2) c1")
scala> df1.unionByName(df1, true).printSchema()
root
|-- c0: long (nullable = false)
|-- c1: struct (nullable = false)
| |-- c: long (nullable = false)
| |-- b: long (nullable = false)
| |-- a: long (nullable = false)
scala> df1.unionByName(df2, true).printSchema()
root
|-- c0: long (nullable = false)
|-- c1: struct (nullable = false)
| |-- a: long (nullable = true)
| |-- b: long (nullable = false)
| |-- c: long (nullable = false)
```
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