[SPARK-2739][SQL] Rename registerAsTable to registerTempTable

There have been user complaints that the difference between `registerAsTable` and `saveAsTable` is too subtle.  This PR addresses this by renaming `registerAsTable` to `registerTempTable`, which more clearly reflects what is happening.  `registerAsTable` remains, but will cause a deprecation warning.

Author: Michael Armbrust <michael@databricks.com>

Closes #1743 from marmbrus/registerTempTable and squashes the following commits:

d031348 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into registerTempTable
4dff086 [Michael Armbrust] Fix .java files too
89a2f12 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into registerTempTable
0b7b71e [Michael Armbrust] Rename registerAsTable to registerTempTable

3 files changed, 7 insertions, 7 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 607df3eddd..898297dc65 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
@@ -74,7 +74,7 @@ public class JavaSparkSQL {
 
     // Apply a schema to an RDD of Java Beans and register it as a table.
     JavaSchemaRDD schemaPeople = sqlCtx.applySchema(people, Person.class);
-    schemaPeople.registerAsTable("people");
+    schemaPeople.registerTempTable("people");
 
     // SQL can be run over RDDs that have been registered as tables.
     JavaSchemaRDD teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
@@ -100,7 +100,7 @@ public class JavaSparkSQL {
     JavaSchemaRDD parquetFile = sqlCtx.parquetFile("people.parquet");
 
     //Parquet files can also be registered as tables and then used in SQL statements.
-    parquetFile.registerAsTable("parquetFile");
+    parquetFile.registerTempTable("parquetFile");
     JavaSchemaRDD teenagers2 =
       sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
     teenagerNames = teenagers2.map(new Function<Row, String>() {
@@ -128,7 +128,7 @@ public class JavaSparkSQL {
     //  |-- name: StringType
 
     // Register this JavaSchemaRDD as a table.
-    peopleFromJsonFile.registerAsTable("people");
+    peopleFromJsonFile.registerTempTable("people");
 
     // SQL statements can be run by using the sql methods provided by sqlCtx.
     JavaSchemaRDD teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
@@ -158,7 +158,7 @@ public class JavaSparkSQL {
     //  |    |-- state: StringType
     //  |-- name: StringType
 
-    peopleFromJsonRDD.registerAsTable("people2");
+    peopleFromJsonRDD.registerTempTable("people2");
 
     JavaSchemaRDD peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2");
     List<String> nameAndCity = peopleWithCity.map(new Function<Row, String>() {
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 63db688bfb..d56d64c564 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
@@ -36,7 +36,7 @@ object RDDRelation {
     val rdd = sc.parallelize((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.
-    rdd.registerAsTable("records")
+    rdd.registerTempTable("records")
 
     // Once tables have been registered, you can run SQL queries over them.
     println("Result of SELECT *:")
@@ -66,7 +66,7 @@ object RDDRelation {
     parquetFile.where('key === 1).select('value as 'a).collect().foreach(println)
 
     // These files can also be registered as tables.
-    parquetFile.registerAsTable("parquetFile")
+    parquetFile.registerTempTable("parquetFile")
     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 dc5290fb4f..12530c8490 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
@@ -56,7 +56,7 @@ object HiveFromSpark {
 
     // You can also register RDDs as temporary tables within a HiveContext.
     val rdd = sc.parallelize((1 to 100).map(i => Record(i, s"val_$i")))
-    rdd.registerAsTable("records")
+    rdd.registerTempTable("records")
 
     // Queries can then join RDD data with data stored in Hive.
     println("Result of SELECT *:")