[GitHub] [flink] wuchong commented on a change in pull request #10667: [FLINK-15313][table] Fix can't insert decimal data into sink using TypeInformation

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wuchong commented on a change in pull request #10667: [FLINK-15313][table] Fix can't insert decimal data into sink using TypeInformation
URL: https://github.com/apache/flink/pull/10667#discussion_r361116539
 
 

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 File path: flink-table/flink-table-planner-blink/src/main/scala/org/apache/flink/table/planner/sinks/TableSinkUtils.scala
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 @@ -18,67 +18,99 @@
 
 package org.apache.flink.table.planner.sinks
 
-import org.apache.flink.table.api.ValidationException
-import org.apache.flink.table.catalog.ObjectIdentifier
+import org.apache.flink.api.java.tuple.{Tuple2 => JTuple2}
+import org.apache.flink.api.java.typeutils.{GenericTypeInfo, TupleTypeInfo}
+import org.apache.flink.api.scala.typeutils.CaseClassTypeInfo
+import org.apache.flink.table.api.{TableException, TableSchema, Types, ValidationException}
+import org.apache.flink.table.catalog.{CatalogTable, ObjectIdentifier}
+import org.apache.flink.table.dataformat.BaseRow
 import org.apache.flink.table.operations.CatalogSinkModifyOperation
-import org.apache.flink.table.runtime.types.LogicalTypeDataTypeConverter.fromDataTypeToLogicalType
-import org.apache.flink.table.runtime.types.PlannerTypeUtils
-import org.apache.flink.table.sinks.{PartitionableTableSink, TableSink}
+import org.apache.flink.table.planner.calcite.FlinkTypeFactory
+import org.apache.flink.table.planner.plan.utils.RelOptUtils
+import org.apache.flink.table.sinks._
+import org.apache.flink.table.types.DataType
+import org.apache.flink.table.types.inference.TypeTransformations.{legacyDecimalToDefaultDecimal, toNullable}
+import org.apache.flink.table.types.logical.utils.{LogicalTypeCasts, LogicalTypeChecks}
+import org.apache.flink.table.types.logical.{LegacyTypeInformationType, RowType}
+import org.apache.flink.table.types.utils.DataTypeUtils
+import org.apache.flink.table.types.utils.TypeConversions.{fromLegacyInfoToDataType, fromLogicalToDataType}
+import org.apache.flink.table.utils.{TableSchemaUtils, TypeMappingUtils}
+import org.apache.flink.types.Row
+import org.apache.calcite.rel.RelNode
 
 import scala.collection.JavaConversions._
 
 object TableSinkUtils {
 
   /**
-    * Checks if the given [[CatalogSinkModifyOperation]]'s query can be written to
-    * the given [[TableSink]]. It checks if the names & the field types match. If the table
-    * sink is a [[PartitionableTableSink]], also check that the partitions are valid.
+    * Checks if the given query can be written into the given sink. It checks the field types
+    * should be compatible (types should equal including precisions). If types are not compatible,
+    * but can be implicitly casted, a cast projection will be applied. Otherwise, an exception will
+    * be thrown.
+    *
+    * @param query the query to be checked
+    * @param sinkSchema the schema of sink to be checked
+    * @param typeFactory type factory
+    * @return the query RelNode which may be applied the implicitly cast projection.
+    */
+  def validateSchemaAndApplyImplicitCast(
+      query: RelNode,
+      sinkSchema: TableSchema,
+      typeFactory: FlinkTypeFactory,
+      sinkIdentifier: Option[String] = None): RelNode = {
+
+    val queryLogicalType = DataTypeUtils
+      // convert type to nullable, because we ignore nullability when writing query into sink
 
 Review comment:
   It is equivalent no matter `toNullable` or not, because if the nullability is not the same, we will add a implicit cast. The purpose to ignore nullable at earlier stage is in order to avoid cast project as much as possible, otherwise, we have to update a lot of XML plan files. 

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