[GitHub] [spark] cloud-fan commented on a change in pull request #28996: [SPARK-29358][SQL] Make unionByName optionally fill missing columns with nulls
cloud-fan commented on a change in pull request #28996:
URL: https://github.com/apache/spark/pull/28996#discussion_r453777434
##
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
##
@@ -2048,19 +2088,34 @@ class Dataset[T] private[sql](
// Builds a project list for `other` based on `logicalPlan` output names
val rightProjectList = leftOutputAttrs.map { lattr =>
rightOutputAttrs.find { rattr => resolver(lattr.name, rattr.name)
}.getOrElse {
-throw new AnalysisException(
- s"""Cannot resolve column name "${lattr.name}" among """ +
-s"""(${rightOutputAttrs.map(_.name).mkString(", ")})""")
+if (allowMissingColumns) {
Review comment:
Yea it's better to have a new JIRA.
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[GitHub] [spark] cloud-fan commented on a change in pull request #28996: [SPARK-29358][SQL] Make unionByName optionally fill missing columns with nulls
cloud-fan commented on a change in pull request #28996:
URL: https://github.com/apache/spark/pull/28996#discussion_r453460284
##
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
##
@@ -2048,19 +2088,34 @@ class Dataset[T] private[sql](
// Builds a project list for `other` based on `logicalPlan` output names
val rightProjectList = leftOutputAttrs.map { lattr =>
rightOutputAttrs.find { rattr => resolver(lattr.name, rattr.name)
}.getOrElse {
-throw new AnalysisException(
- s"""Cannot resolve column name "${lattr.name}" among """ +
-s"""(${rightOutputAttrs.map(_.name).mkString(", ")})""")
+if (allowMissingColumns) {
Review comment:
I think the major problem here is we put the by-name logic in the API
method, not in the `Analyzer`. Shall we add 2 boolean parameters(byName and
allowMissingCol) to `Union`, and move the by-name logic to the type coercion
rules?
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[GitHub] [spark] cloud-fan commented on a change in pull request #28996: [SPARK-29358][SQL] Make unionByName optionally fill missing columns with nulls
cloud-fan commented on a change in pull request #28996:
URL: https://github.com/apache/spark/pull/28996#discussion_r453460284
##
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
##
@@ -2048,19 +2088,34 @@ class Dataset[T] private[sql](
// Builds a project list for `other` based on `logicalPlan` output names
val rightProjectList = leftOutputAttrs.map { lattr =>
rightOutputAttrs.find { rattr => resolver(lattr.name, rattr.name)
}.getOrElse {
-throw new AnalysisException(
- s"""Cannot resolve column name "${lattr.name}" among """ +
-s"""(${rightOutputAttrs.map(_.name).mkString(", ")})""")
+if (allowMissingColumns) {
Review comment:
I think the major problem here is we put the by-name logic in the API
method, not in the `Analyzer`. Shall we add a boolean parameter to `Union`, and
move the by-name logic to the type coercion rules?
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[GitHub] [spark] cloud-fan commented on a change in pull request #28996: [SPARK-29358][SQL] Make unionByName optionally fill missing columns with nulls
cloud-fan commented on a change in pull request #28996:
URL: https://github.com/apache/spark/pull/28996#discussion_r453459502
##
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
##
@@ -2030,7 +2030,25 @@ class Dataset[T] private[sql](
* @group typedrel
* @since 2.3.0
*/
- def unionByName(other: Dataset[T]): Dataset[T] = withSetOperator {
+ def unionByName(other: Dataset[T]): Dataset[T] = unionByName(other, false)
+
+ /**
+ * Returns a new Dataset containing union of rows in this Dataset and
another Dataset.
+ *
+ * This is different from both `UNION ALL` and `UNION DISTINCT` in SQL. To
do a SQL-style set
+ * union (that does deduplication of elements), use this function followed
by a [[distinct]].
Review comment:
Seems like we mistakenly copied the doc from `union` to `unionByName`.
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[GitHub] [spark] cloud-fan commented on a change in pull request #28996: [SPARK-29358][SQL] Make unionByName optionally fill missing columns with nulls
cloud-fan commented on a change in pull request #28996:
URL: https://github.com/apache/spark/pull/28996#discussion_r453434564
##
File path: sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
##
@@ -2048,19 +2088,34 @@ class Dataset[T] private[sql](
// Builds a project list for `other` based on `logicalPlan` output names
val rightProjectList = leftOutputAttrs.map { lattr =>
rightOutputAttrs.find { rattr => resolver(lattr.name, rattr.name)
}.getOrElse {
-throw new AnalysisException(
- s"""Cannot resolve column name "${lattr.name}" among """ +
-s"""(${rightOutputAttrs.map(_.name).mkString(", ")})""")
+if (allowMissingColumns) {
Review comment:
Does it work with nested columns?
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[GitHub] [spark] cloud-fan commented on a change in pull request #28996: [SPARK-29358][SQL] Make unionByName optionally fill missing columns with nulls
cloud-fan commented on a change in pull request #28996:
URL: https://github.com/apache/spark/pull/28996#discussion_r452061943
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##
@@ -2656,6 +2656,14 @@ object SQLConf {
.checkValue(_ > 0, "The difference must be positive.")
.createWithDefault(4)
+ val ALLOW_MISSING_COLUMNS_IN_UNION_BY_NAME =
+buildConf("spark.sql.allowMissingColumnsInUnionByName")
+.doc("If this config is enabled, `Dataset.unionByName` allows different
set of column names " +
+ "between two Datasets. Missing columns at each side, will be filled with
null values.")
Review comment:
We can add an overload method instead of using default parameter value.
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[GitHub] [spark] cloud-fan commented on a change in pull request #28996: [SPARK-29358][SQL] Make unionByName optionally fill missing columns with nulls
cloud-fan commented on a change in pull request #28996:
URL: https://github.com/apache/spark/pull/28996#discussion_r451359616
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##
@@ -2656,6 +2656,14 @@ object SQLConf {
.checkValue(_ > 0, "The difference must be positive.")
.createWithDefault(4)
+ val ALLOW_MISSING_COLUMNS_IN_UNION_BY_NAME =
+buildConf("spark.sql.allowMissingColumnsInUnionByName")
+.doc("If this config is enabled, `Dataset.unionByName` allows different
set of column names " +
+ "between two Datasets. Missing columns at each side, will be filled with
null values.")
Review comment:
Seems like `Dataset` already has many APIs taking a boolean parameter.
I'm OK with adding a `allowMissingColumns` parameter to `union`.
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[GitHub] [spark] cloud-fan commented on a change in pull request #28996: [SPARK-29358][SQL] Make unionByName optionally fill missing columns with nulls
cloud-fan commented on a change in pull request #28996:
URL: https://github.com/apache/spark/pull/28996#discussion_r450162816
##
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##
@@ -2656,6 +2656,14 @@ object SQLConf {
.checkValue(_ > 0, "The difference must be positive.")
.createWithDefault(4)
+ val ALLOW_MISSING_COLUMNS_IN_UNION_BY_NAME =
+buildConf("spark.sql.allowMissingColumnsInUnionByName")
+.doc("If this config is enabled, `Dataset.unionByName` allows different
set of column names " +
+ "between two Datasets. Missing columns at each side, will be filled with
null values.")
Review comment:
It seems not a breaking change if this case fails before and now we
allow it by filling missing columns with nulls. Do we really need a config? cc
@gatorsmile @bart-samwel
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