fix-15171, remove the references to deprecated method registerTempTable

docs/sql-programming-guide.md

@@ -703,7 +703,7 @@ val rowRDD = people.map(_.split(",")).map(p => Row(p(0), p(1).trim))
 // Apply the schema to the RDD.
 val peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema)
 
-// Register the DataFrames as a table.
+// Creates a temporary view.
 peopleDataFrame.createOrReplaceTempView("people")
 
 // SQL statements can be run by using the sql methods provided by sqlContext.
@@ -771,10 +771,10 @@ JavaRDD<Row> rowRDD = people.map(
 // Apply the schema to the RDD.
 DataFrame peopleDataFrame = sqlContext.createDataFrame(rowRDD, schema);
 
-// Register the DataFrame as a table.
+// Creates a temporary view.
 peopleDataFrame.createOrReplaceTempView("people");
 
-// SQL can be run over RDDs that have been registered as tables.
+// SQL can be run over a temporary view.
 DataFrame results = sqlContext.sql("SELECT name FROM people");
 
 // The results of SQL queries are DataFrames and support all the normal RDD operations.
@@ -824,7 +824,7 @@ schema = StructType(fields)
 # Apply the schema to the RDD.
 schemaPeople = sqlContext.createDataFrame(people, schema)
 
-# Register the DataFrame as a table.
+# Creates a temporary view
 schemaPeople.createOrReplaceTempView("people")
 
 # SQL can be run over DataFrames that have been registered as a table.
@@ -1072,7 +1072,7 @@ people.write.parquet("people.parquet")
 // The result of loading a Parquet file is also a DataFrame.
 val parquetFile = sqlContext.read.parquet("people.parquet")
 
-//Parquet files can also be registered as tables and then used in SQL statements.
+//Parquet files can also be used to create a temporary view and then used in SQL statements.
 parquetFile.createOrReplaceTempView("parquetFile")
 val teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
 teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
@@ -1094,7 +1094,7 @@ schemaPeople.write().parquet("people.parquet");
 // The result of loading a parquet file is also a DataFrame.
 DataFrame parquetFile = sqlContext.read().parquet("people.parquet");
 
-// Parquet files can also be registered as tables and then used in SQL statements.
+// Parquet files can also be used to create a temporary view and then used in SQL statements.
 parquetFile.createOrReplaceTempView("parquetFile");
 DataFrame teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
 List<String> teenagerNames = teenagers.javaRDD().map(new Function<Row, String>() {
@@ -1120,7 +1120,7 @@ schemaPeople.write.parquet("people.parquet")
 # The result of loading a parquet file is also a DataFrame.
 parquetFile = sqlContext.read.parquet("people.parquet")
 
-# Parquet files can also be registered as tables and then used in SQL statements.
+# Parquet files can also be used to create a temporary view and then used in SQL statements.
 parquetFile.createOrReplaceTempView("parquetFile");
 teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
 teenNames = teenagers.map(lambda p: "Name: " + p.name)
@@ -1144,7 +1144,7 @@ write.parquet(schemaPeople, "people.parquet")
 # The result of loading a parquet file is also a DataFrame.
 parquetFile <- read.parquet(sqlContext, "people.parquet")
 
-# Parquet files can also be registered as tables and then used in SQL statements.
+# Parquet files can also be used to create a temporary view and then used in SQL statements.
 registerTempTable(parquetFile, "parquetFile")
 teenagers <- sql(sqlContext, "SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
 schema <- structType(structField("name", "string"))
@@ -1544,7 +1544,7 @@ people.printSchema();
 //  |-- age: long (nullable = true)
 //  |-- name: string (nullable = true)
 
-// Register this DataFrame as a table.
+// Creates a temporary view
 people.createOrReplaceTempView("people");
 
 // SQL statements can be run by using the sql methods provided by sqlContext.
@@ -1582,7 +1582,7 @@ people.printSchema()
 #  |-- age: long (nullable = true)
 #  |-- name: string (nullable = true)
 
-# Register this DataFrame as a table.
+# Creates a temporary view.
 people.createOrReplaceTempView("people")
 
 # SQL statements can be run by using the sql methods provided by `sqlContext`.

docs/streaming-programming-guide.md

@@ -1606,7 +1606,7 @@ words.foreachRDD(
       });
       DataFrame wordsDataFrame = sqlContext.createDataFrame(rowRDD, JavaRow.class);
 
-      // Register as table
+      // Creates a temporary view
       wordsDataFrame.createOrReplaceTempView("words");
 
       // Do word count on table using SQL and print it
@@ -1646,7 +1646,7 @@ def process(time, rdd):
         rowRdd = rdd.map(lambda w: Row(word=w))
         wordsDataFrame = sqlContext.createDataFrame(rowRdd)
 
-        # Register as table
+        # Creates a temporary view
         wordsDataFrame.createOrReplaceTempView("words")
 
         # Do word count on table using SQL and print it

examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java

@@ -73,11 +73,11 @@ public Person call(String line) {
         }
       });
 
-    // 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 String call(Row row) {
     // 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 String call(Row row) {
     //  |-- age: IntegerType
     //  |-- name: StringType
 
-    // Register this DataFrame as a table.
+    // Creates a temporary view
     peopleFromJsonFile.createOrReplaceTempView("people");
 
     // SQL statements can be run by using the sql methods provided by `spark`

examples/src/main/java/org/apache/spark/examples/streaming/JavaSqlNetworkWordCount.java

@@ -94,7 +94,7 @@ public JavaRecord call(String word) {
         });
         Dataset<Row> wordsDataFrame = spark.createDataFrame(rowRDD, JavaRecord.class);
 
-        // Register as table
+        // Creates a temporary view
         wordsDataFrame.createOrReplaceTempView("words");
 
         // Do word count on table using SQL and print it

examples/src/main/python/sql.py

@@ -66,7 +66,7 @@
     #  |-- age: long (nullable = true)
     #  |-- name: string (nullable = true)
 
-    # Register this DataFrame as a temporary table.
+    # Creates a temporary view.
     people.createOrReplaceTempView("people")
 
     # SQL statements can be run by using the sql methods provided by `spark`

examples/src/main/python/streaming/sql_network_wordcount.py

@@ -70,7 +70,7 @@ def process(time, rdd):
             rowRdd = rdd.map(lambda w: Row(word=w))
             wordsDataFrame = spark.createDataFrame(rowRdd)
 
-            # Register as table
+            # Creates a temporary view
             wordsDataFrame.createOrReplaceTempView("words")
 
             # Do word count on table using SQL and print it

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)
 

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
       wordsDataFrame.createOrReplaceTempView("words")
 
       // Do word count on table using SQL and print it

sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala

@@ -1912,7 +1912,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       Row(1, 1, 1, 1) :: Row(1, 2, 2, 1) :: Row(2, 1, 1, 2) :: Row(2, 2, 2, 2) ::
         Row(3, 1, 1, 3) :: Row(3, 2, 2, 3) :: Nil)
 
-    // Try with a registered table.
+    // Try with a temporary view
     sql("select struct(a, b) as record from testData2").createOrReplaceTempView("structTable")
     checkAnswer(
       sql("SELECT record.* FROM structTable"),
@@ -1977,7 +1977,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       nestedStructData.select($"record.r1.*"),
       Row(1, 1) :: Row(1, 2) :: Row(2, 1) :: Row(2, 2) :: Row(3, 1) :: Row(3, 2) :: Nil)
 
-    // Try with a registered table
+    // Try with a temporary view
     withTempTable("nestedStructTable") {
       nestedStructData.createOrReplaceTempView("nestedStructTable")
       checkAnswer(

sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala

@@ -685,7 +685,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
   createQueryTest("case sensitivity when query Hive table",
     "SELECT srcalias.KEY, SRCALIAS.value FROM sRc SrCAlias WHERE SrCAlias.kEy < 15")
 
-  test("case sensitivity: registered table") {
+  test("case sensitivity: created temporary view") {
     val testData =
       TestHive.sparkContext.parallelize(
         TestData(1, "str1") ::