[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.

2 files changed, 5 insertions, 5 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