Automatic type widening in MERGE

spark/src/main/scala/org/apache/spark/sql/delta/ResolveDeltaMergeInto.scala

@@ -234,7 +234,7 @@ object ResolveDeltaMergeInto {
       // schema before merging it with the target schema. We don't consider NOT MATCHED BY SOURCE
       // clauses since these can't by definition reference source columns and thus can't introduce
       // new columns in the target schema.
-      val actions = (matchedClauses ++ notMatchedClauses).flatMap(_.actions)
+      val actions = (resolvedMatchedClauses ++ resolvedNotMatchedClauses).flatMap(_.actions)
       val assignments = actions.collect { case a: DeltaMergeAction => a.targetColNameParts }
       val containsStarAction = actions.exists {
         case _: UnresolvedStar => true
@@ -278,14 +278,22 @@ object ResolveDeltaMergeInto {
         })
 
       val migrationSchema = filterSchema(source.schema, Seq.empty)
+      val allowTypeWidening = target.exists {
+        case DeltaTable(fileIndex) =>
+          TypeWidening.isEnabled(fileIndex.protocol, fileIndex.metadata)
+        case _ => false
+      }
+
       // The implicit conversions flag allows any type to be merged from source to target if Spark
       // SQL considers the source type implicitly castable to the target. Normally, mergeSchemas
       // enforces Parquet-level write compatibility, which would mean an INT source can't be merged
       // into a LONG target.
       SchemaMergingUtils.mergeSchemas(
         target.schema,
         migrationSchema,
-        allowImplicitConversions = true)
+        allowImplicitConversions = true,
+        allowTypeWidening = allowTypeWidening
+      )
     } else {
       target.schema
     }

spark/src/main/scala/org/apache/spark/sql/delta/TypeWidening.scala

@@ -62,6 +62,19 @@ object TypeWidening {
       case _ => false
     }
 
+  /**
+   * Returns whether the given type change can be applied during schema evolution. Only a
+   * subset of supported type changes are considered for schema evolution.
+   */
+  def isTypeChangeSupportedForSchemaEvolution(fromType: AtomicType, toType: AtomicType): Boolean =
+    (fromType, toType) match {
+      case (from, to) if !isTypeChangeSupported(from, to) => false
+      case (from, to) if from == to => true
+      case (ByteType, ShortType) => true
+      case (ByteType | ShortType, IntegerType) => true
+      case _ => false
+    }
+
   /**
    * Filter the given list of files to only keep files that were written before the latest type
    * change, if any. These older files contain a column or field with a type that is different than

spark/src/main/scala/org/apache/spark/sql/delta/schema/ImplicitMetadataOperation.scala

@@ -122,7 +122,14 @@ trait ImplicitMetadataOperation extends DeltaLogging {
       if (rearrangeOnly) {
         throw DeltaErrors.unexpectedDataChangeException("Change the Delta table schema")
       }
-      txn.updateMetadata(txn.metadata.copy(schemaString = mergedSchema.json
+
+      val schemaWithTypeWideningMetadata = TypeWideningMetadata.addTypeWideningMetadata(
+        txn,
+        schema = mergedSchema,
+        oldSchema = txn.metadata.schema
+      )
+
+      txn.updateMetadata(txn.metadata.copy(schemaString = schemaWithTypeWideningMetadata.json
       ))
     } else if (isNewSchema || isNewPartitioning
         ) {
@@ -201,7 +208,8 @@ object ImplicitMetadataOperation {
       SchemaMergingUtils.mergeSchemas(
         txn.metadata.schema,
         dataSchema,
-        fixedTypeColumns = fixedTypeColumns)
+        fixedTypeColumns = fixedTypeColumns,
+        allowTypeWidening = TypeWidening.isEnabled(txn.protocol, txn.metadata))
     }
   }
 

spark/src/main/scala/org/apache/spark/sql/delta/schema/SchemaMergingUtils.scala

@@ -20,14 +20,13 @@ import java.util.Locale
 
 import scala.util.control.NonFatal
 
-import org.apache.spark.sql.delta.DeltaAnalysisException
+import org.apache.spark.sql.delta.{DeltaAnalysisException, TypeWidening}
 
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.analysis.{Resolver, TypeCoercion, UnresolvedAttribute}
 import org.apache.spark.sql.catalyst.expressions.Literal
-import org.apache.spark.sql.catalyst.plans.logical.DeltaMergeInto
 import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
-import org.apache.spark.sql.types.{ArrayType, ByteType, DataType, DecimalType, IntegerType, MapType, NullType, ShortType, StructField, StructType}
+import org.apache.spark.sql.types._
 
 /**
  * Utils to merge table schema with data schema.
@@ -168,6 +167,7 @@ object SchemaMergingUtils {
       dataSchema: StructType,
       allowImplicitConversions: Boolean = false,
       keepExistingType: Boolean = false,
+      allowTypeWidening: Boolean = false,
       fixedTypeColumns: Set[String] = Set.empty,
       caseSensitive: Boolean = false): StructType = {
     checkColumnNameDuplication(dataSchema, "in the data to save", caseSensitive)
@@ -232,6 +232,9 @@ object SchemaMergingUtils {
         // Simply keeps the existing type for primitive types
         case (current, update) if keepExistingType => current
 
+        case (current: AtomicType, update: AtomicType) if allowTypeWidening &&
+          TypeWidening.isTypeChangeSupportedForSchemaEvolution(current, update) => update
+
         // If implicit conversions are allowed, that means we can use any valid implicit cast to
         // perform the merge.
         case (current, update)

spark/src/test/scala/org/apache/spark/sql/delta/DeltaErrorsSuite.scala

@@ -1432,7 +1432,12 @@ trait DeltaErrorsSuiteBase
       val e = intercept[DeltaAnalysisException] {
         val s1 = StructType(Seq(StructField("c0", IntegerType, true)))
         val s2 = StructType(Seq(StructField("c0", StringType, false)))
-        SchemaMergingUtils.mergeSchemas(s1, s2, false, false, Set("c0"))
+        SchemaMergingUtils.mergeSchemas(s1, s2,
+          allowImplicitConversions = false,
+          keepExistingType = false,
+          allowTypeWidening = false,
+          Set("c0")
+        )
       }
       checkErrorMessage(e, Some("DELTA_GENERATED_COLUMNS_DATA_TYPE_MISMATCH"), Some("42K09"),
         Some("Column c0 is a generated column or a column used by a generated " +

spark/src/test/scala/org/apache/spark/sql/delta/DeltaTestUtils.scala

@@ -41,10 +41,11 @@ import org.apache.spark.scheduler.{JobFailed, SparkListener, SparkListenerJobEnd
 import org.apache.spark.sql.{AnalysisException, DataFrame, SparkSession}
 import org.apache.spark.sql.catalyst.TableIdentifier
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-import org.apache.spark.sql.catalyst.util.quietly
+import org.apache.spark.sql.catalyst.util.{quietly, FailFastMode}
 import org.apache.spark.sql.execution.{FileSourceScanExec, QueryExecution, RDDScanExec, SparkPlan, WholeStageCodegenExec}
 import org.apache.spark.sql.execution.aggregate.HashAggregateExec
 import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.util.QueryExecutionListener
 import org.apache.spark.util.Utils
 
@@ -467,6 +468,8 @@ trait DeltaDMLTestUtils
   with BeforeAndAfterEach {
   self: SharedSparkSession =>
 
+  import testImplicits._
+
   protected var tempDir: File = _
 
   protected var deltaLog: DeltaLog = _
@@ -515,6 +518,23 @@ trait DeltaDMLTestUtils
     }
   }
 
+  /**
+   * Parse the input JSON data into a dataframe, one row per input element.
+   * Throws an exception on malformed inputs or records that don't comply with the provided schema.
+   */
+  protected def readFromJSON(data: Seq[String], schema: StructType = null): DataFrame = {
+    if (schema != null) {
+      spark.read
+        .schema(schema)
+        .option("mode", FailFastMode.name)
+        .json(data.toDS)
+    } else {
+      spark.read
+        .option("mode", FailFastMode.name)
+        .json(data.toDS)
+    }
+  }
+
   protected def readDeltaTable(path: String): DataFrame = {
     spark.read.format("delta").load(path)
   }