diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index bd986d0138..276d5d29bf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -825,8 +825,16 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
   def orc(paths: String*): DataFrame = format("orc").load(paths: _*)
 
   /**
-   * Returns the specified table as a `DataFrame`.
+   * Returns the specified table/view as a `DataFrame`. If it's a table, it must support batch
+   * reading and the returned DataFrame is the batch scan query plan of this table. If it's a view,
+   * the returned DataFrame is simply the query plan of the view, which can either be a batch or
+   * streaming query plan.
    *
+   * @param tableName is either a qualified or unqualified name that designates a table or view.
+   *                  If a database is specified, it identifies the table/view from the database.
+   *                  Otherwise, it first attempts to find a temporary view with the given name
+   *                  and then match the table/view from the current database.
+   *                  Note that, the global temporary view database is also valid here.
    * @since 1.4.0
    */
   def table(tableName: String): DataFrame = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala b/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala
index 592f209475..d738d617f2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala
@@ -573,7 +573,10 @@ class SparkSession private(
   @transient lazy val catalog: Catalog = new CatalogImpl(self)
 
   /**
-   * Returns the specified table/view as a `DataFrame`.
+   * Returns the specified table/view as a `DataFrame`. If it's a table, it must support batch
+   * reading and the returned DataFrame is the batch scan query plan of this table. If it's a view,
+   * the returned DataFrame is simply the query plan of the view, which can either be a batch or
+   * streaming query plan.
    *
    * @param tableName is either a qualified or unqualified name that designates a table or view.
    *                  If a database is specified, it identifies the table/view from the database.
@@ -583,11 +586,7 @@ class SparkSession private(
    * @since 2.0.0
    */
   def table(tableName: String): DataFrame = {
-    table(sessionState.sqlParser.parseMultipartIdentifier(tableName))
-  }
-
-  private[sql] def table(multipartIdentifier: Seq[String]): DataFrame = {
-    Dataset.ofRows(self, UnresolvedRelation(multipartIdentifier))
+    read.table(tableName)
   }
 
   private[sql] def table(tableIdent: TableIdentifier): DataFrame = {