diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 63a62cd0cbfe..208688efa21e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -516,7 +516,8 @@ object PushProjectionThroughUnion extends Rule[LogicalPlan] with PredicateHelper
 object ColumnPruning extends Rule[LogicalPlan] {
   private def sameOutput(output1: Seq[Attribute], output2: Seq[Attribute]): Boolean =
     output1.size == output2.size &&
-      output1.zip(output2).forall(pair => pair._1.semanticEquals(pair._2))
+      output1.zip(output2).forall(pair =>
+        pair._1.semanticEquals(pair._2) && pair._1.name == pair._2.name)
 
   def apply(plan: LogicalPlan): LogicalPlan = removeProjectBeforeFilter(plan transform {
     // Prunes the unused columns from project list of Project/Aggregate/Expand
@@ -680,7 +681,12 @@ object CollapseProject extends Rule[LogicalPlan] {
     // e.g., 'SELECT c + 1 FROM (SELECT a + b AS C ...' produces 'SELECT a + b + 1 ...'
     // Use transformUp to prevent infinite recursion.
     val rewrittenUpper = upper.map(_.transformUp {
-      case a: Attribute => aliases.getOrElse(a, a)
+      case a: Attribute => if (aliases.contains(a)) {
+        val alias = aliases.get(a).get
+        alias.copy(name = a.name)(alias.exprId, alias.qualifier, alias.explicitMetadata)
+      } else {
+        a
+      }
     })
     // collapse upper and lower Projects may introduce unnecessary Aliases, trim them here.
     rewrittenUpper.map { p =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 01dc28d70184..4f734c79e0f4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -24,6 +24,7 @@ import java.util.concurrent.atomic.AtomicBoolean
 
 import org.apache.spark.{AccumulatorSuite, SparkException}
 import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart}
+import org.apache.spark.sql.catalyst.TableIdentifier
 import org.apache.spark.sql.catalyst.util.StringUtils
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.execution.aggregate.{HashAggregateExec, SortAggregateExec}
@@ -2853,6 +2854,81 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     }
   }
 
+  test("Insert overwrite table command should output correct schema: basic") {
+    withTable("tbl", "tbl2") {
+      withView("view1") {
+        val df = spark.range(10).toDF("id")
+        df.write.format("parquet").saveAsTable("tbl")
+        spark.sql("CREATE VIEW view1 AS SELECT id FROM tbl")
+        spark.sql("CREATE TABLE tbl2(ID long) USING parquet")
+        spark.sql("INSERT OVERWRITE TABLE tbl2 SELECT ID FROM view1")
+        val identifier = TableIdentifier("tbl2", Some("default"))
+        val location = spark.sessionState.catalog.getTableMetadata(identifier).location.toString
+        val expectedSchema = StructType(Seq(StructField("ID", LongType, true)))
+        assert(spark.read.parquet(location).schema == expectedSchema)
+        checkAnswer(spark.table("tbl2"), df)
+      }
+    }
+  }
+
+  test("Insert overwrite table command should output correct schema: complex") {
+    withTable("tbl", "tbl2") {
+      withView("view1") {
+        val df = spark.range(10).map(x => (x, x.toInt, x.toInt)).toDF("col1", "col2", "col3")
+        df.write.format("parquet").saveAsTable("tbl")
+        spark.sql("CREATE VIEW view1 AS SELECT * FROM tbl")
+        spark.sql("CREATE TABLE tbl2(COL1 long, COL2 int, COL3 int) USING parquet PARTITIONED " +
+          "BY (COL2) CLUSTERED BY (COL3) INTO 3 BUCKETS")
+        spark.sql("INSERT OVERWRITE TABLE tbl2 SELECT COL1, COL2, COL3 " +
+          "FROM view1 CLUSTER BY COL3")
+        val identifier = TableIdentifier("tbl2", Some("default"))
+        val location = spark.sessionState.catalog.getTableMetadata(identifier).location.toString
+        val expectedSchema = StructType(Seq(
+          StructField("COL1", LongType, true),
+          StructField("COL3", IntegerType, true),
+          StructField("COL2", IntegerType, true)))
+        assert(spark.read.parquet(location).schema == expectedSchema)
+        checkAnswer(spark.table("tbl2"), df)
+      }
+    }
+  }
+
+  test("Create table as select command should output correct schema: basic") {
+    withTable("tbl", "tbl2") {
+      withView("view1") {
+        val df = spark.range(10).toDF("id")
+        df.write.format("parquet").saveAsTable("tbl")
+        spark.sql("CREATE VIEW view1 AS SELECT id FROM tbl")
+        spark.sql("CREATE TABLE tbl2 USING parquet AS SELECT ID FROM view1")
+        val identifier = TableIdentifier("tbl2", Some("default"))
+        val location = spark.sessionState.catalog.getTableMetadata(identifier).location.toString
+        val expectedSchema = StructType(Seq(StructField("ID", LongType, true)))
+        assert(spark.read.parquet(location).schema == expectedSchema)
+        checkAnswer(spark.table("tbl2"), df)
+      }
+    }
+  }
+
+  test("Create table as select command should output correct schema: complex") {
+    withTable("tbl", "tbl2") {
+      withView("view1") {
+        val df = spark.range(10).map(x => (x, x.toInt, x.toInt)).toDF("col1", "col2", "col3")
+        df.write.format("parquet").saveAsTable("tbl")
+        spark.sql("CREATE VIEW view1 AS SELECT * FROM tbl")
+        spark.sql("CREATE TABLE tbl2 USING parquet PARTITIONED BY (COL2) " +
+          "CLUSTERED BY (COL3) INTO 3 BUCKETS AS SELECT COL1, COL2, COL3 FROM view1")
+        val identifier = TableIdentifier("tbl2", Some("default"))
+        val location = spark.sessionState.catalog.getTableMetadata(identifier).location.toString
+        val expectedSchema = StructType(Seq(
+          StructField("COL1", LongType, true),
+          StructField("COL3", IntegerType, true),
+          StructField("COL2", IntegerType, true)))
+        assert(spark.read.parquet(location).schema == expectedSchema)
+        checkAnswer(spark.table("tbl2"), df)
+      }
+    }
+  }
+
   test("SPARK-25144 'distinct' causes memory leak") {
     val ds = List(Foo(Some("bar"))).toDS
     val result = ds.flatMap(_.bar).distinct