[SPARK-30280][DOC] Update docs for make Hive 2.3 dependency by default

### What changes were proposed in this pull request?

This PR update document for make Hive 2.3 dependency by default.

### Why are the changes needed?

The documentation is incorrect.

### Does this PR introduce any user-facing change?

No.

### How was this patch tested?

N/A

Closes apache#26919 from wangyum/SPARK-30280.

Authored-by: Yuming Wang <[email protected]>
Signed-off-by: Dongjoon Hyun <[email protected]>

docs/building-spark.md

@@ -83,13 +83,10 @@ Example:
 
 To enable Hive integration for Spark SQL along with its JDBC server and CLI,
 add the `-Phive` and `-Phive-thriftserver` profiles to your existing build options.
-By default, Spark will use Hive 1.2.1 with the `hadoop-2.7` profile, and Hive 2.3.6 with the `hadoop-3.2` profile.
-
-    # With Hive 1.2.1 support
-    ./build/mvn -Pyarn -Phive -Phive-thriftserver -DskipTests clean package
+By default Spark will build with Hive 2.3.6.
 
     # With Hive 2.3.6 support
-    ./build/mvn -Pyarn -Phive -Phive-thriftserver -Phadoop-3.2 -DskipTests clean package
+    ./build/mvn -Pyarn -Phive -Phive-thriftserver -DskipTests clean package
 
 ## Packaging without Hadoop Dependencies for YARN
 

docs/sql-data-sources-hive-tables.md

@@ -119,15 +119,15 @@ One of the most important pieces of Spark SQL's Hive support is interaction with
 which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary
 build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below.
 Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL
-will compile against Hive 1.2.1 and use those classes for internal execution (serdes, UDFs, UDAFs, etc).
+will compile against built-in Hive and use those classes for internal execution (serdes, UDFs, UDAFs, etc).
 
 The following options can be used to configure the version of Hive that is used to retrieve metadata:
 
 <table class="table">
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
   <tr>
     <td><code>spark.sql.hive.metastore.version</code></td>
-    <td><code>1.2.1</code></td>
+    <td><code>2.3.6</code></td>
     <td>
       Version of the Hive metastore. Available
       options are <code>0.12.0</code> through <code>2.3.6</code> and <code>3.0.0</code> through <code>3.1.2</code>.
@@ -141,9 +141,9 @@ The following options can be used to configure the version of Hive that is used
       property can be one of three options:
       <ol>
         <li><code>builtin</code></li>
-        Use Hive 1.2.1, which is bundled with the Spark assembly when <code>-Phive</code> is
+        Use Hive 2.3.6, which is bundled with the Spark assembly when <code>-Phive</code> is
         enabled. When this option is chosen, <code>spark.sql.hive.metastore.version</code> must be
-        either <code>1.2.1</code> or not defined.
+        either <code>2.3.6</code> or not defined.
         <li><code>maven</code></li>
         Use Hive jars of specified version downloaded from Maven repositories. This configuration
         is not generally recommended for production deployments.

docs/sql-distributed-sql-engine.md

@@ -29,7 +29,7 @@ without the need to write any code.
 ## Running the Thrift JDBC/ODBC server
 
 The Thrift JDBC/ODBC server implemented here corresponds to the [`HiveServer2`](https://cwiki.apache.org/confluence/display/Hive/Setting+Up+HiveServer2)
-in Hive 1.2.1. You can test the JDBC server with the beeline script that comes with either Spark or Hive 1.2.1.
+in built-in Hive. You can test the JDBC server with the beeline script that comes with either Spark or compatible Hive.
 
 To start the JDBC/ODBC server, run the following in the Spark directory:
 

docs/sql-migration-guide.md

@@ -819,7 +819,7 @@ Python UDF registration is unchanged.
 ## Compatibility with Apache Hive
 
 Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs.
-Currently, Hive SerDes and UDFs are based on Hive 1.2.1,
+Currently, Hive SerDes and UDFs are based on built-in Hive,
 and Spark SQL can be connected to different versions of Hive Metastore
 (from 0.12.0 to 2.3.6 and 3.0.0 to 3.1.2. Also see [Interacting with Different Versions of Hive Metastore](sql-data-sources-hive-tables.html#interacting-with-different-versions-of-hive-metastore)).