Remove the metadata used to mark optional columns for merged Parquet …

…schema.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala

@@ -575,9 +575,9 @@ class Analyzer(
     //     |- view2 (defaultDatabase = db2)
     //        |- view3 (defaultDatabase = db3)
     //   |- view4 (defaultDatabase = db4)
-    // In this case, the view `view1` is a nested view, it directly references `table2`、`view2`
+    // In this case, the view `view1` is a nested view, it directly references `table2`, `view2`
     // and `view4`, the view `view2` references `view3`. On resolving the table, we look up the
-    // relations `table2`、`view2`、`view4` using the default database `db1`, and look up the
+    // relations `table2`, `view2`, `view4` using the default database `db1`, and look up the
     // relation `view3` using the default database `db2`.
     //
     // Note this is compatible with the views defined by older versions of Spark(before 2.2), which

sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala

@@ -402,13 +402,6 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
 @InterfaceStability.Stable
 object StructType extends AbstractDataType {
 
-  /**
-   * A key used in field metadata to indicate that the field comes from the result of merging
-   * two different StructTypes that do not always contain the field. That is to say, the field
-   * might be missing (optional) from one of the StructTypes.
-   */
-  private[sql] val metadataKeyForOptionalField = "_OPTIONAL_"
-
   override private[sql] def defaultConcreteType: DataType = new StructType
 
   override private[sql] def acceptsType(other: DataType): Boolean = {
@@ -463,8 +456,6 @@ object StructType extends AbstractDataType {
 
       case (StructType(leftFields), StructType(rightFields)) =>
         val newFields = ArrayBuffer.empty[StructField]
-        // This metadata will record the fields that only exist in one of two StructTypes
-        val optionalMeta = new MetadataBuilder()
 
         val rightMapped = fieldsMap(rightFields)
         leftFields.foreach {
@@ -476,8 +467,7 @@ object StructType extends AbstractDataType {
                   nullable = leftNullable || rightNullable)
               }
               .orElse {
-                optionalMeta.putBoolean(metadataKeyForOptionalField, value = true)
-                Some(leftField.copy(metadata = optionalMeta.build()))
+                Some(leftField)
               }
               .foreach(newFields += _)
         }
@@ -486,8 +476,7 @@ object StructType extends AbstractDataType {
         rightFields
           .filterNot(f => leftMapped.get(f.name).nonEmpty)
           .foreach { f =>
-            optionalMeta.putBoolean(metadataKeyForOptionalField, value = true)
-            newFields += f.copy(metadata = optionalMeta.build())
+            newFields += f
           }
 
         StructType(newFields)

sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala

@@ -132,55 +132,6 @@ class DataTypeSuite extends SparkFunSuite {
     assert(mapped === expected)
   }
 
-  test("merge where right is empty") {
-    val left = StructType(
-      StructField("a", LongType) ::
-      StructField("b", FloatType) :: Nil)
-
-    val right = StructType(List())
-    val merged = left.merge(right)
-
-    assert(DataType.equalsIgnoreCompatibleNullability(merged, left))
-    assert(merged("a").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-    assert(merged("b").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-  }
-
-  test("merge where left is empty") {
-
-    val left = StructType(List())
-
-    val right = StructType(
-      StructField("a", LongType) ::
-      StructField("b", FloatType) :: Nil)
-
-    val merged = left.merge(right)
-
-    assert(DataType.equalsIgnoreCompatibleNullability(merged, right))
-    assert(merged("a").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-    assert(merged("b").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-  }
-
-  test("merge where both are non-empty") {
-    val left = StructType(
-      StructField("a", LongType) ::
-      StructField("b", FloatType) :: Nil)
-
-    val right = StructType(
-      StructField("c", LongType) :: Nil)
-
-    val expected = StructType(
-      StructField("a", LongType) ::
-      StructField("b", FloatType) ::
-      StructField("c", LongType) :: Nil)
-
-    val merged = left.merge(right)
-
-    assert(DataType.equalsIgnoreCompatibleNullability(merged, expected))
-    assert(merged("a").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-    assert(merged("b").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-    assert(merged("c").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-  }
-
   test("merge where right contains type conflict") {
     val left = StructType(
       StructField("a", LongType) ::

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFileFormat.scala

@@ -109,9 +109,7 @@ class ParquetFileFormat
 
     // We want to clear this temporary metadata from saving into Parquet file.
     // This metadata is only useful for detecting optional columns when pushdowning filters.
-    val dataSchemaToWrite = StructType.removeMetadata(StructType.metadataKeyForOptionalField,
-      dataSchema).asInstanceOf[StructType]
-    ParquetWriteSupport.setSchema(dataSchemaToWrite, conf)
+    ParquetWriteSupport.setSchema(dataSchema, conf)
 
     // Sets flags for `CatalystSchemaConverter` (which converts Catalyst schema to Parquet schema)
     // and `CatalystWriteSupport` (writing actual rows to Parquet files).
@@ -307,11 +305,7 @@ class ParquetFileFormat
       ParquetWriteSupport.SPARK_ROW_SCHEMA,
       ParquetSchemaConverter.checkFieldNames(requiredSchema).json)
 
-    // We want to clear this temporary metadata from saving into Parquet file.
-    // This metadata is only useful for detecting optional columns when pushdowning filters.
-    val dataSchemaToWrite = StructType.removeMetadata(StructType.metadataKeyForOptionalField,
-      requiredSchema).asInstanceOf[StructType]
-    ParquetWriteSupport.setSchema(dataSchemaToWrite, hadoopConf)
+    ParquetWriteSupport.setSchema(requiredSchema, hadoopConf)
 
     // Sets flags for `CatalystSchemaConverter`
     hadoopConf.setBoolean(

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala

@@ -169,23 +169,14 @@ private[parquet] object ParquetFilters {
   }
 
   /**
-   * Returns a map from name of the column to the data type, if predicate push down applies
-   * (i.e. not an optional field).
-   *
-   * SPARK-11955: The optional fields will have metadata StructType.metadataKeyForOptionalField.
-   * These fields only exist in one side of merged schemas. Due to that, we can't push down filters
-   * using such fields, otherwise Parquet library will throw exception (PARQUET-389).
-   * Here we filter out such fields.
+   * Returns a map from name of the column to the data type, if predicate push down applies.
    */
   private def getFieldMap(dataType: DataType): Map[String, DataType] = dataType match {
     case StructType(fields) =>
       // Here we don't flatten the fields in the nested schema but just look up through
       // root fields. Currently, accessing to nested fields does not push down filters
       // and it does not support to create filters for them.
-      fields.filter { f =>
-        !f.metadata.contains(StructType.metadataKeyForOptionalField) ||
-          !f.metadata.getBoolean(StructType.metadataKeyForOptionalField)
-      }.map(f => f.name -> f.dataType).toMap
+      fields.map(f => f.name -> f.dataType).toMap
     case _ => Map.empty[String, DataType]
   }
 

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala

@@ -368,76 +368,36 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
   }
 
 
-  test("SPARK-11103: Filter applied on merged Parquet schema with new column fails") {
+  test("Filter applied on merged Parquet schema with new column should work") {
     import testImplicits._
     Seq("true", "false").map { vectorized =>
       withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true",
         SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "true",
         SQLConf.PARQUET_VECTORIZED_READER_ENABLED.key -> vectorized) {
         withTempPath { dir =>
-          val pathOne = s"${dir.getCanonicalPath}/table1"
-          (1 to 3).map(i => (i, i.toString)).toDF("a", "b").write.parquet(pathOne)
-          val pathTwo = s"${dir.getCanonicalPath}/table2"
-          (1 to 3).map(i => (i, i.toString)).toDF("c", "b").write.parquet(pathTwo)
-
-          // If the "c = 1" filter gets pushed down, this query will throw an exception which
-          // Parquet emits. This is a Parquet issue (PARQUET-389).
-          val df = spark.read.parquet(pathOne, pathTwo).filter("c = 1").selectExpr("c", "b", "a")
+          val path1 = s"${dir.getCanonicalPath}/table1"
+          (1 to 3).map(i => (i, i.toString)).toDF("a", "b").write.parquet(path1)
+          val path2 = s"${dir.getCanonicalPath}/table2"
+          (1 to 3).map(i => (i, i.toString)).toDF("c", "b").write.parquet(path2)
+
+          // No matter "c = 1" gets pushed down or not, this query should work without exception.
+          val df = spark.read.parquet(path1, path2).filter("c = 1").selectExpr("c", "b", "a")
           checkAnswer(
             df,
             Row(1, "1", null))
 
-          // The fields "a" and "c" only exist in one Parquet file.
-          assert(df.schema("a").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-          assert(df.schema("c").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-
-          val pathThree = s"${dir.getCanonicalPath}/table3"
-          df.write.parquet(pathThree)
-
-          // We will remove the temporary metadata when writing Parquet file.
-          val schema = spark.read.parquet(pathThree).schema
-          assert(schema.forall(!_.metadata.contains(StructType.metadataKeyForOptionalField)))
-
-          val pathFour = s"${dir.getCanonicalPath}/table4"
+          val path3 = s"${dir.getCanonicalPath}/table3"
           val dfStruct = sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
-          dfStruct.select(struct("a").as("s")).write.parquet(pathFour)
+          dfStruct.select(struct("a").as("s")).write.parquet(path3)
 
-          val pathFive = s"${dir.getCanonicalPath}/table5"
+          val path4 = s"${dir.getCanonicalPath}/table4"
           val dfStruct2 = sparkContext.parallelize(Seq((1, 1))).toDF("c", "b")
-          dfStruct2.select(struct("c").as("s")).write.parquet(pathFive)
+          dfStruct2.select(struct("c").as("s")).write.parquet(path4)
 
-          // If the "s.c = 1" filter gets pushed down, this query will throw an exception which
-          // Parquet emits.
-          val dfStruct3 = spark.read.parquet(pathFour, pathFive).filter("s.c = 1")
+          // No matter "s.c = 1" gets pushed down or not, this query should work without exception.
+          val dfStruct3 = spark.read.parquet(path3, path4).filter("s.c = 1")
             .selectExpr("s")
           checkAnswer(dfStruct3, Row(Row(null, 1)))
-
-          // The fields "s.a" and "s.c" only exist in one Parquet file.
-          val field = dfStruct3.schema("s").dataType.asInstanceOf[StructType]
-          assert(field("a").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-          assert(field("c").metadata.getBoolean(StructType.metadataKeyForOptionalField))
-
-          val pathSix = s"${dir.getCanonicalPath}/table6"
-          dfStruct3.write.parquet(pathSix)
-
-          // We will remove the temporary metadata when writing Parquet file.
-          val forPathSix = spark.read.parquet(pathSix).schema
-          assert(forPathSix.forall(!_.metadata.contains(StructType.metadataKeyForOptionalField)))
-
-          // sanity test: make sure optional metadata field is not wrongly set.
-          val pathSeven = s"${dir.getCanonicalPath}/table7"
-          (1 to 3).map(i => (i, i.toString)).toDF("a", "b").write.parquet(pathSeven)
-          val pathEight = s"${dir.getCanonicalPath}/table8"
-          (4 to 6).map(i => (i, i.toString)).toDF("a", "b").write.parquet(pathEight)
-
-          val df2 = spark.read.parquet(pathSeven, pathEight).filter("a = 1").selectExpr("a", "b")
-          checkAnswer(
-            df2,
-            Row(1, "1"))
-
-          // The fields "a" and "b" exist in both two Parquet files. No metadata is set.
-          assert(!df2.schema("a").metadata.contains(StructType.metadataKeyForOptionalField))
-          assert(!df2.schema("b").metadata.contains(StructType.metadataKeyForOptionalField))
         }
       }
     }