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new 767a52d5db35 [SPARK-47528][SQL] Add UserDefinedType support to
DataTypeUtils.canWrite
767a52d5db35 is described below
commit 767a52d5db354786d5ca07ddc4192d0eb8e8be80
Author: Liang-Chi Hsieh <vii...@gmail.com>
AuthorDate: Sun Mar 24 01:27:52 2024 -0700
[SPARK-47528][SQL] Add UserDefinedType support to DataTypeUtils.canWrite
### What changes were proposed in this pull request?
This patch adds `UserDefinedType` handling to `DataTypeUtils.canWrite`.
### Why are the changes needed?
Our customer hits an issue recently when they tries to save a DataFrame
containing some UDTs as table (`saveAsTable`). The error looks like:
```
- Cannot write 'xxx': struct<...> is incompatible with struct<...>
```
The catalog strings between two sides are actually same which makes the
customer confused.
It is because `DataTypeUtils.canWrite` doesn't handle `UserDefinedType`. If
the `UserDefinedType`'s underlying sql type is same as read side, `canWrite`
should return true for two sides.
### Does this PR introduce _any_ user-facing change?
Yes. Write side column with `UserDefinedType` can be written into read side
column with same sql data type.
### How was this patch tested?
Unit test
### Was this patch authored or co-authored using generative AI tooling?
No
Closes #45678 from viirya/udt_dt_write.
Authored-by: Liang-Chi Hsieh <vii...@gmail.com>
Signed-off-by: Dongjoon Hyun <dh...@apple.com>
---
.../spark/sql/catalyst/types/DataTypeUtils.scala | 14 ++-
.../types/DataTypeWriteCompatibilitySuite.scala | 134 +++++++++++++++++++++
2 files changed, 147 insertions(+), 1 deletion(-)
diff --git
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/DataTypeUtils.scala
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/DataTypeUtils.scala
index 01fb86bf2957..cf8e903f03a3 100644
---
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/DataTypeUtils.scala
+++
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/DataTypeUtils.scala
@@ -22,7 +22,7 @@ import org.apache.spark.sql.catalyst.util.TypeUtils.toSQLId
import org.apache.spark.sql.errors.QueryCompilationErrors
import org.apache.spark.sql.internal.SQLConf.StoreAssignmentPolicy
import org.apache.spark.sql.internal.SQLConf.StoreAssignmentPolicy.{ANSI,
STRICT}
-import org.apache.spark.sql.types.{ArrayType, AtomicType, DataType, Decimal,
DecimalType, MapType, NullType, StructField, StructType}
+import org.apache.spark.sql.types.{ArrayType, AtomicType, DataType, Decimal,
DecimalType, MapType, NullType, StructField, StructType, UserDefinedType}
import org.apache.spark.sql.types.DecimalType.{forType, fromDecimal}
object DataTypeUtils {
@@ -64,6 +64,8 @@ object DataTypeUtils {
* - Both types are structs and have the same number of fields. The type and
nullability of each
* field from read/write is compatible. If byName is true, the name of
each field from
* read/write needs to be the same.
+ * - It is user defined type and its underlying sql type is same as the read
type, or the read
+ * type is user defined type and its underlying sql type is same as the
write type.
* - Both types are atomic and the write type can be safely cast to the read
type.
*
* Extra fields in write-side structs are not allowed to avoid accidentally
writing data that
@@ -180,6 +182,16 @@ object DataTypeUtils {
case (w, r) if DataTypeUtils.sameType(w, r) && !w.isInstanceOf[NullType]
=>
true
+ // If write-side data type is a user-defined type, check with its
underlying data type.
+ case (w, r) if w.isInstanceOf[UserDefinedType[_]] &&
!r.isInstanceOf[UserDefinedType[_]] =>
+ canWrite(tableName, w.asInstanceOf[UserDefinedType[_]].sqlType, r,
byName, resolver,
+ context, storeAssignmentPolicy, addError)
+
+ // If read-side data type is a user-defined type, check with its
underlying data type.
+ case (w, r) if r.isInstanceOf[UserDefinedType[_]] &&
!w.isInstanceOf[UserDefinedType[_]] =>
+ canWrite(tableName, w, r.asInstanceOf[UserDefinedType[_]].sqlType,
byName, resolver,
+ context, storeAssignmentPolicy, addError)
+
case (w, r) =>
throw
QueryCompilationErrors.incompatibleDataToTableCannotSafelyCastError(
tableName, context, w.catalogString, r.catalogString
diff --git
a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeWriteCompatibilitySuite.scala
b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeWriteCompatibilitySuite.scala
index 7aaa69a0a5dd..8c9196cc33ca 100644
---
a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeWriteCompatibilitySuite.scala
+++
b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeWriteCompatibilitySuite.scala
@@ -510,6 +510,140 @@ abstract class DataTypeWriteCompatibilityBaseSuite
extends SparkFunSuite {
"Should allow map of int written to map of long column")
}
+ test("SPARK-47528: Check udt: underlying sql type is same") {
+ val udtType = new UserDefinedType[Any] {
+ override def sqlType: DataType = StructType(Seq(
+ StructField("col1", FloatType, nullable = false),
+ StructField("col2", FloatType, nullable = false)))
+
+ override def userClass: java.lang.Class[Any] = null
+
+ override def serialize(obj: Any): Any = null
+
+ override def deserialize(datum: Any): Any = null
+ }
+
+ val sqlType = StructType(Seq(
+ StructField("col1", FloatType, nullable = false),
+ StructField("col2", FloatType, nullable = false)))
+
+ assertAllowed(udtType, sqlType, "m",
+ "Should allow udt with same sqlType written to struct column")
+
+ assertAllowed(sqlType, udtType, "m",
+ "Should allow udt with same sqlType written to struct column")
+ }
+
+ test("SPARK-47528: Check udt: underlying sql type is same but different
nullability") {
+ val udtType = new UserDefinedType[Any] {
+ override def sqlType: DataType = StructType(Seq(
+ StructField("col1", FloatType, nullable = false),
+ StructField("col2", FloatType, nullable = false)))
+
+ override def userClass: java.lang.Class[Any] = null
+
+ override def serialize(obj: Any): Any = null
+
+ override def deserialize(datum: Any): Any = null
+ }
+
+ val sqlType = StructType(Seq(
+ StructField("col1", FloatType, nullable = false),
+ StructField("col2", FloatType, nullable = true)))
+
+ assertAllowed(udtType, sqlType, "m",
+ "Should allow udt with same sqlType written to struct column")
+
+ val errs = new mutable.ArrayBuffer[String]()
+ checkError(
+ exception = intercept[AnalysisException] (
+ DataTypeUtils.canWrite("", sqlType, udtType, true,
+ analysis.caseSensitiveResolution, "t", storeAssignmentPolicy, errMsg
=> errs += errMsg)
+ ),
+ errorClass = "INCOMPATIBLE_DATA_FOR_TABLE.NULLABLE_COLUMN",
+ parameters = Map(
+ "tableName" -> "``",
+ "colName" -> "`t`.`col2`"
+ )
+ )
+ }
+
+ test("SPARK-47528: Check udt: write underlying sql type is not same") {
+ val udtType = new UserDefinedType[Any] {
+ override def sqlType: DataType = StructType(Seq(
+ StructField("col1", FloatType, nullable = false),
+ StructField("col2", FloatType, nullable = false)))
+
+ override def userClass: java.lang.Class[Any] = null
+
+ override def serialize(obj: Any): Any = null
+
+ override def deserialize(datum: Any): Any = null
+ }
+
+ val sqlType = StructType(Seq(
+ StructField("col1", FloatType, nullable = false),
+ StructField("col2", IntegerType, nullable = false)))
+
+ if (canCast(udtType.sqlType, sqlType)) {
+ assertAllowed(udtType, sqlType, "m",
+ "Should allow udt with compatible sqlType written to struct column")
+ } else {
+ val errs = new mutable.ArrayBuffer[String]()
+ checkError(
+ exception = intercept[AnalysisException](
+ DataTypeUtils.canWrite("", udtType, sqlType, true,
+ analysis.caseSensitiveResolution, "t", storeAssignmentPolicy,
errMsg => errs += errMsg)
+ ),
+ errorClass = "INCOMPATIBLE_DATA_FOR_TABLE.CANNOT_SAFELY_CAST",
+ parameters = Map(
+ "tableName" -> "``",
+ "colName" -> "`t`.`col2`",
+ "srcType" -> "\"FLOAT\"",
+ "targetType" -> "\"INT\""
+ )
+ )
+ }
+ }
+
+ test("SPARK-47528: Check udt: read side underlying sql type is not same") {
+ val udtType = new UserDefinedType[Any] {
+ override def sqlType: DataType = StructType(Seq(
+ StructField("col1", FloatType, nullable = false),
+ StructField("col2", IntegerType, nullable = false)))
+
+ override def userClass: java.lang.Class[Any] = null
+
+ override def serialize(obj: Any): Any = null
+
+ override def deserialize(datum: Any): Any = null
+ }
+
+ val sqlType = StructType(Seq(
+ StructField("col1", FloatType, nullable = false),
+ StructField("col2", FloatType, nullable = false)))
+
+ if (canCast(sqlType, udtType.sqlType)) {
+ assertAllowed(sqlType, udtType, "m",
+ "Should allow udt with compatible sqlType written to struct column")
+ } else {
+ val errs = new mutable.ArrayBuffer[String]()
+ checkError(
+ exception = intercept[AnalysisException](
+ DataTypeUtils.canWrite("", sqlType, udtType, true,
+ analysis.caseSensitiveResolution, "t", storeAssignmentPolicy,
errMsg => errs += errMsg)
+ ),
+ errorClass = "INCOMPATIBLE_DATA_FOR_TABLE.CANNOT_SAFELY_CAST",
+ parameters = Map(
+ "tableName" -> "``",
+ "colName" -> "`t`.`col2`",
+ "srcType" -> "\"FLOAT\"",
+ "targetType" -> "\"INT\""
+ )
+ )
+ }
+ }
+
test("Check types with multiple errors") {
val readType = StructType(Seq(
StructField("a", ArrayType(DoubleType, containsNull = false)),
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