[SPARK-15171][SQL] Remove the references to deprecated method dataset.registerTempTable

## What changes were proposed in this pull request?

Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`.

## How was this patch tested?

This PR only changes the unit test code, examples, and comments. It should be safe.
This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged.

Author: Sean Zhong <seanzhong@databricks.com>

Closes #13098 from clockfly/spark-15171-remove-deprecation.

7 files changed, 13 insertions, 13 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
index cf0167f13a..55e591d0ce 100644
--- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
@@ -73,11 +73,11 @@ public class JavaSparkSQL {
         }
       });
 
-    // Apply a schema to an RDD of Java Beans and register it as a table.
+    // Apply a schema to an RDD of Java Beans and create a temporary view
     Dataset<Row> schemaPeople = spark.createDataFrame(people, Person.class);
     schemaPeople.createOrReplaceTempView("people");
 
-    // SQL can be run over RDDs that have been registered as tables.
+    // SQL can be run over RDDs which backs a temporary view.
     Dataset<Row> teenagers = spark.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
 
     // The results of SQL queries are DataFrames and support all the normal RDD operations.
@@ -101,7 +101,7 @@ public class JavaSparkSQL {
     // The result of loading a parquet file is also a DataFrame.
     Dataset<Row> parquetFile = spark.read().parquet("people.parquet");
 
-    //Parquet files can also be registered as tables and then used in SQL statements.
+    // A temporary view can be created by using Parquet files and then used in SQL statements.
     parquetFile.createOrReplaceTempView("parquetFile");
     Dataset<Row> teenagers2 =
       spark.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
@@ -130,7 +130,7 @@ public class JavaSparkSQL {
     //  |-- age: IntegerType
     //  |-- name: StringType
 
-    // Register this DataFrame as a table.
+    // Creates a temporary view using the DataFrame
     peopleFromJsonFile.createOrReplaceTempView("people");
 
     // SQL statements can be run by using the sql methods provided by `spark`
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java
index 5130522770..b8e9e125ba 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java
@@ -94,7 +94,7 @@ public final class JavaSqlNetworkWordCount {
         });
         Dataset<Row> wordsDataFrame = spark.createDataFrame(rowRDD, JavaRecord.class);
 
-        // Register as table
+        // Creates a temporary view using the DataFrame
         wordsDataFrame.createOrReplaceTempView("words");
 
         // Do word count on table using SQL and print it
diff --git a/examples/src/main/python/sql.py b/examples/src/main/python/sql.py
index 234024063f..ac7246938d 100644
--- a/examples/src/main/python/sql.py
+++ b/examples/src/main/python/sql.py
@@ -66,7 +66,7 @@ if __name__ == "__main__":
     #  |-- age: long (nullable = true)
     #  |-- name: string (nullable = true)
 
-    # Register this DataFrame as a temporary table.
+    # Creates a temporary view using the DataFrame.
     people.createOrReplaceTempView("people")
 
     # SQL statements can be run by using the sql methods provided by `spark`
diff --git a/examples/src/main/python/streaming/sql_network_wordcount.py b/examples/src/main/python/streaming/sql_network_wordcount.py
index 25e821534e..398ac8d2d8 100644
--- a/examples/src/main/python/streaming/sql_network_wordcount.py
+++ b/examples/src/main/python/streaming/sql_network_wordcount.py
@@ -70,7 +70,7 @@ if __name__ == "__main__":
             rowRdd = rdd.map(lambda w: Row(word=w))
             wordsDataFrame = spark.createDataFrame(rowRdd)
 
-            # Register as table
+            # Creates a temporary view using the DataFrame.
             wordsDataFrame.createOrReplaceTempView("words")
 
             # Do word count on table using SQL and print it
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
index d1bda0ff84..1b019fbb51 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
@@ -35,8 +35,8 @@ object RDDRelation {
     import spark.implicits._
 
     val df = spark.createDataFrame((1 to 100).map(i => Record(i, s"val_$i")))
-    // Any RDD containing case classes can be registered as a table.  The schema of the table is
-    // automatically inferred using scala reflection.
+    // Any RDD containing case classes can be used to create a temporary view.  The schema of the
+    // view is automatically inferred using scala reflection.
     df.createOrReplaceTempView("records")
 
     // Once tables have been registered, you can run SQL queries over them.
@@ -66,7 +66,7 @@ object RDDRelation {
     // Queries can be run using the DSL on parquet files just like the original RDD.
     parquetFile.where($"key" === 1).select($"value".as("a")).collect().foreach(println)
 
-    // These files can also be registered as tables.
+    // These files can also be used to create a temporary view.
     parquetFile.createOrReplaceTempView("parquetFile")
     spark.sql("SELECT * FROM parquetFile").collect().foreach(println)
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
index a15cf5ded0..7293cb51b2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
@@ -70,9 +70,9 @@ object HiveFromSpark {
       case Row(key: Int, value: String) => s"Key: $key, Value: $value"
     }
 
-    // You can also register RDDs as temporary tables within a HiveContext.
+    // You can also use RDDs to create temporary views within a HiveContext.
     val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i")))
-    rdd.toDF().registerTempTable("records")
+    rdd.toDF().createOrReplaceTempView("records")
 
     // Queries can then join RDD data with data stored in Hive.
     println("Result of SELECT *:")
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala
index 688c5b23c2..787bbec73b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/SqlNetworkWordCount.scala
@@ -66,7 +66,7 @@ object SqlNetworkWordCount {
       // Convert RDD[String] to RDD[case class] to DataFrame
       val wordsDataFrame = rdd.map(w => Record(w)).toDF()
 
-      // Register as table
+      // Creates a temporary view using the DataFrame
       wordsDataFrame.createOrReplaceTempView("words")
 
       // Do word count on table using SQL and print it