2 files changed, 10 insertions, 9 deletions
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java
index adb96dd8bf..82bb284ea3 100644
--- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java
@@ -25,8 +25,6 @@ import java.util.List;
 import java.util.Properties;
 
 // $example on:basic_parquet_example$
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.MapFunction;
 import org.apache.spark.sql.Encoders;
 // $example on:schema_merging$
@@ -217,12 +215,11 @@ public class JavaSQLDataSourceExample {
     // +------+
 
     // Alternatively, a DataFrame can be created for a JSON dataset represented by
-    // an RDD[String] storing one JSON object per string.
+    // an Dataset[String] storing one JSON object per string.
     List<String> jsonData = Arrays.asList(
             "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
-    JavaRDD<String> anotherPeopleRDD =
-            new JavaSparkContext(spark.sparkContext()).parallelize(jsonData);
-    Dataset<Row> anotherPeople = spark.read().json(anotherPeopleRDD);
+    Dataset<String> anotherPeopleDataset = spark.createDataset(jsonData, Encoders.STRING());
+    Dataset<Row> anotherPeople = spark.read().json(anotherPeopleDataset);
     anotherPeople.show();
     // +---------------+----+
     // |        address|name|
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala b/examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala
index 66f7cb1b53..381e69cda8 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala
@@ -111,6 +111,10 @@ object SQLDataSourceExample {
 
   private def runJsonDatasetExample(spark: SparkSession): Unit = {
     // $example on:json_dataset$
+    // Primitive types (Int, String, etc) and Product types (case classes) encoders are
+    // supported by importing this when creating a Dataset.
+    import spark.implicits._
+
     // A JSON dataset is pointed to by path.
     // The path can be either a single text file or a directory storing text files
     val path = "examples/src/main/resources/people.json"
@@ -135,10 +139,10 @@ object SQLDataSourceExample {
     // +------+
 
     // Alternatively, a DataFrame can be created for a JSON dataset represented by
-    // an RDD[String] storing one JSON object per string
-    val otherPeopleRDD = spark.sparkContext.makeRDD(
+    // an Dataset[String] storing one JSON object per string
+    val otherPeopleDataset = spark.createDataset(
       """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
-    val otherPeople = spark.read.json(otherPeopleRDD)
+    val otherPeople = spark.read.json(otherPeopleDataset)
     otherPeople.show()
     // +---------------+----+
     // |        address|name|