[SPARK-16303][DOCS][EXAMPLES] Updated SQL programming guide and examples

- Hard-coded Spark SQL sample snippets were moved into source files under examples sub-project.
- Removed the inconsistency between Scala and Java Spark SQL examples
- Scala and Java Spark SQL examples were updated

The work is still in progress. All involved examples were tested manually. An additional round of testing will be done after the code review.

![image](https://cloud.githubusercontent.com/assets/6235869/16710314/51851606-462a-11e6-9fbe-0818daef65e4.png)

Author: aokolnychyi <okolnychyyanton@gmail.com>

Closes #14119 from aokolnychyi/spark_16303.

9 files changed, 1228 insertions, 806 deletions

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index e838a13af7..2076b29a86 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -65,14 +65,14 @@ Throughout this document, we will often refer to Scala/Java Datasets of `Row`s a
 
 The entry point into all functionality in Spark is the [`SparkSession`](api/scala/index.html#org.apache.spark.sql.SparkSession) class. To create a basic `SparkSession`, just use `SparkSession.builder()`:
 
-{% include_example init_session scala/org/apache/spark/examples/sql/RDDRelation.scala %}
+{% include_example init_session scala/org/apache/spark/examples/sql/SparkSqlExample.scala %}
 </div>
 
 <div data-lang="java" markdown="1">
 
 The entry point into all functionality in Spark is the [`SparkSession`](api/java/index.html#org.apache.spark.sql.SparkSession) class. To create a basic `SparkSession`, just use `SparkSession.builder()`:
 
-{% include_example init_session java/org/apache/spark/examples/sql/JavaSparkSQL.java %}
+{% include_example init_session java/org/apache/spark/examples/sql/JavaSparkSqlExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -105,14 +105,7 @@ from a Hive table, or from [Spark data sources](#data-sources).
 
 As an example, the following creates a DataFrame based on the content of a JSON file:
 
-{% highlight scala %}
-val spark: SparkSession // An existing SparkSession.
-val df = spark.read.json("examples/src/main/resources/people.json")
-
-// Displays the content of the DataFrame to stdout
-df.show()
-{% endhighlight %}
-
+{% include_example create_df scala/org/apache/spark/examples/sql/SparkSqlExample.scala %}
 </div>
 
 <div data-lang="java" markdown="1">
@@ -121,14 +114,7 @@ from a Hive table, or from [Spark data sources](#data-sources).
 
 As an example, the following creates a DataFrame based on the content of a JSON file:
 
-{% highlight java %}
-SparkSession spark = ...; // An existing SparkSession.
-Dataset<Row> df = spark.read().json("examples/src/main/resources/people.json");
-
-// Displays the content of the DataFrame to stdout
-df.show();
-{% endhighlight %}
-
+{% include_example create_df java/org/apache/spark/examples/sql/JavaSparkSqlExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -169,110 +155,20 @@ Here we include some basic examples of structured data processing using Datasets
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
-{% highlight scala %}
-val spark: SparkSession // An existing SparkSession
-
-// Create the DataFrame
-val df = spark.read.json("examples/src/main/resources/people.json")
-
-// Show the content of the DataFrame
-df.show()
-// age  name
-// null Michael
-// 30   Andy
-// 19   Justin
-
-// Print the schema in a tree format
-df.printSchema()
-// root
-// |-- age: long (nullable = true)
-// |-- name: string (nullable = true)
-
-// Select only the "name" column
-df.select("name").show()
-// name
-// Michael
-// Andy
-// Justin
-
-// Select everybody, but increment the age by 1
-df.select(df("name"), df("age") + 1).show()
-// name    (age + 1)
-// Michael null
-// Andy    31
-// Justin  20
-
-// Select people older than 21
-df.filter(df("age") > 21).show()
-// age name
-// 30  Andy
-
-// Count people by age
-df.groupBy("age").count().show()
-// age  count
-// null 1
-// 19   1
-// 30   1
-{% endhighlight %}
+{% include_example untyped_ops scala/org/apache/spark/examples/sql/SparkSqlExample.scala %}
 
 For a complete list of the types of operations that can be performed on a Dataset refer to the [API Documentation](api/scala/index.html#org.apache.spark.sql.Dataset).
 
 In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/scala/index.html#org.apache.spark.sql.functions$).
-
-
 </div>
 
 <div data-lang="java" markdown="1">
-{% highlight java %}
-SparkSession spark = ...; // An existing SparkSession
-
-// Create the DataFrame
-Dataset<Row> df = spark.read().json("examples/src/main/resources/people.json");
-
-// Show the content of the DataFrame
-df.show();
-// age  name
-// null Michael
-// 30   Andy
-// 19   Justin
-
-// Print the schema in a tree format
-df.printSchema();
-// root
-// |-- age: long (nullable = true)
-// |-- name: string (nullable = true)
-
-// Select only the "name" column
-df.select("name").show();
-// name
-// Michael
-// Andy
-// Justin
-
-// Select everybody, but increment the age by 1
-df.select(df.col("name"), df.col("age").plus(1)).show();
-// name    (age + 1)
-// Michael null
-// Andy    31
-// Justin  20
-
-// Select people older than 21
-df.filter(df.col("age").gt(21)).show();
-// age name
-// 30  Andy
-
-// Count people by age
-df.groupBy("age").count().show();
-// age  count
-// null 1
-// 19   1
-// 30   1
-{% endhighlight %}
+
+{% include_example untyped_ops java/org/apache/spark/examples/sql/JavaSparkSqlExample.java %}
 
 For a complete list of the types of operations that can be performed on a Dataset refer to the [API Documentation](api/java/org/apache/spark/sql/Dataset.html).
 
 In addition to simple column references and expressions, Datasets also have a rich library of functions including string manipulation, date arithmetic, common math operations and more. The complete list is available in the [DataFrame Function Reference](api/java/org/apache/spark/sql/functions.html).
-
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -353,19 +249,13 @@ In addition to simple column references and expressions, DataFrames also have a
 <div data-lang="scala"  markdown="1">
 The `sql` function on a `SparkSession` enables applications to run SQL queries programmatically and returns the result as a `DataFrame`.
 
-{% highlight scala %}
-val spark = ... // An existing SparkSession
-val df = spark.sql("SELECT * FROM table")
-{% endhighlight %}
+{% include_example run_sql scala/org/apache/spark/examples/sql/SparkSQLExample.scala %}
 </div>
 
 <div data-lang="java" markdown="1">
 The `sql` function on a `SparkSession` enables applications to run SQL queries programmatically and returns the result as a `Dataset<Row>`.
 
-{% highlight java %}
-SparkSession spark = ... // An existing SparkSession
-Dataset<Row> df = spark.sql("SELECT * FROM table")
-{% endhighlight %}
+{% include_example run_sql java/org/apache/spark/examples/sql/JavaSparkSqlExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -397,53 +287,11 @@ the bytes back into an object.
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
-
-{% highlight scala %}
-// Encoders for most common types are automatically provided by importing spark.implicits._
-val ds = Seq(1, 2, 3).toDS()
-ds.map(_ + 1).collect() // Returns: Array(2, 3, 4)
-
-// Encoders are also created for case classes.
-case class Person(name: String, age: Long)
-val ds = Seq(Person("Andy", 32)).toDS()
-
-// DataFrames can be converted to a Dataset by providing a class. Mapping will be done by name.
-val path = "examples/src/main/resources/people.json"
-val people = spark.read.json(path).as[Person]
-
-{% endhighlight %}
-
+{% include_example create_ds scala/org/apache/spark/examples/sql/SparkSqlExample.scala %}
 </div>
 
 <div data-lang="java" markdown="1">
-
-{% highlight java %}
-SparkSession spark = ... // An existing SparkSession
-
-// Encoders for most common types are provided in class Encoders.
-Dataset<Integer> ds = spark.createDataset(Arrays.asList(1, 2, 3), Encoders.INT());
-ds.map(new MapFunction<Integer, Integer>() {
-  @Override
-  public Integer call(Integer value) throws Exception {
-    return value + 1;
-  }
-}, Encoders.INT()); // Returns: [2, 3, 4]
-
-Person person = new Person();
-person.setName("Andy");
-person.setAge(32);
-
-// Encoders are also created for Java beans.
-Dataset<Person> ds = spark.createDataset(
-  Collections.singletonList(person),
-  Encoders.bean(Person.class)
-);
-
-// DataFrames can be converted to a Dataset by providing a class. Mapping will be done by name.
-String path = "examples/src/main/resources/people.json";
-Dataset<Person> people = spark.read().json(path).as(Encoders.bean(Person.class));
-{% endhighlight %}
-
+{% include_example create_ds java/org/apache/spark/examples/sql/JavaSparkSqlExample.java %}
 </div>
 </div>
 
@@ -470,38 +318,7 @@ reflection and become the names of the columns. Case classes can also be nested
 types such as `Seq`s or `Array`s. This RDD can be implicitly converted to a DataFrame and then be
 registered as a table. Tables can be used in subsequent SQL statements.
 
-{% highlight scala %}
-val spark: SparkSession // An existing SparkSession
-// this is used to implicitly convert an RDD to a DataFrame.
-import spark.implicits._
-
-// Define the schema using a case class.
-// Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
-// you can use custom classes that implement the Product interface.
-case class Person(name: String, age: Int)
-
-// Create an RDD of Person objects and register it as a temporary view.
-val people = sc
-  .textFile("examples/src/main/resources/people.txt")
-  .map(_.split(","))
-  .map(p => Person(p(0), p(1).trim.toInt))
-  .toDF()
-people.createOrReplaceTempView("people")
-
-// SQL statements can be run by using the sql methods provided by spark.
-val teenagers = spark.sql("SELECT name, age FROM people WHERE age >= 13 AND age <= 19")
-
-// The columns of a row in the result can be accessed by field index:
-teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
-
-// or by field name:
-teenagers.map(t => "Name: " + t.getAs[String]("name")).collect().foreach(println)
-
-// row.getValuesMap[T] retrieves multiple columns at once into a Map[String, T]
-teenagers.map(_.getValuesMap[Any](List("name", "age"))).collect().foreach(println)
-// Map("name" -> "Justin", "age" -> 19)
-{% endhighlight %}
-
+{% include_example schema_inferring scala/org/apache/spark/examples/sql/SparkSqlExample.scala %}
 </div>
 
 <div data-lang="java"  markdown="1">
@@ -513,68 +330,7 @@ does not support JavaBeans that contain `Map` field(s). Nested JavaBeans and `Li
 fields are supported though. You can create a JavaBean by creating a class that implements
 Serializable and has getters and setters for all of its fields.
 
-{% highlight java %}
-
-public static class Person implements Serializable {
-  private String name;
-  private int age;
-
-  public String getName() {
-    return name;
-  }
-
-  public void setName(String name) {
-    this.name = name;
-  }
-
-  public int getAge() {
-    return age;
-  }
-
-  public void setAge(int age) {
-    this.age = age;
-  }
-}
-
-{% endhighlight %}
-
-
-A schema can be applied to an existing RDD by calling `createDataFrame` and providing the Class object
-for the JavaBean.
-
-{% highlight java %}
-SparkSession spark = ...; // An existing SparkSession
-
-// Load a text file and convert each line to a JavaBean.
-JavaRDD<Person> people = spark.sparkContext.textFile("examples/src/main/resources/people.txt").map(
-  new Function<String, Person>() {
-    public Person call(String line) throws Exception {
-      String[] parts = line.split(",");
-
-      Person person = new Person();
-      person.setName(parts[0]);
-      person.setAge(Integer.parseInt(parts[1].trim()));
-
-      return person;
-    }
-  });
-
-// Apply a schema to an RDD of JavaBeans and register it as a table.
-Dataset<Row> schemaPeople = spark.createDataFrame(people, Person.class);
-schemaPeople.createOrReplaceTempView("people");
-
-// SQL can be run over RDDs that have been registered as tables.
-Dataset<Row> teenagers = spark.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
-
-// The columns of a row in the result can be accessed by ordinal.
-List<String> teenagerNames = teenagers.map(new MapFunction<Row, String>() {
-  public String call(Row row) {
-    return "Name: " + row.getString(0);
-  }
-}).collectAsList();
-
-{% endhighlight %}
-
+{% include_example schema_inferring java/org/apache/spark/examples/sql/JavaSparkSqlExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -628,43 +384,8 @@ a `DataFrame` can be created programmatically with three steps.
 by `SparkSession`.
 
 For example:
-{% highlight scala %}
-val spark: SparkSession // An existing SparkSession
-
-// Create an RDD
-val people = sc.textFile("examples/src/main/resources/people.txt")
-
-// The schema is encoded in a string
-val schemaString = "name age"
-
-// Import Row.
-import org.apache.spark.sql.Row;
-
-// Import Spark SQL data types
-import org.apache.spark.sql.types.{StructType, StructField, StringType};
-
-// Generate the schema based on the string of schema
-val schema = StructType(schemaString.split(" ").map { fieldName =>
-  StructField(fieldName, StringType, true)
-})
-
-// Convert records of the RDD (people) to Rows.
-val rowRDD = people.map(_.split(",")).map(p => Row(p(0), p(1).trim))
-
-// Apply the schema to the RDD.
-val peopleDataFrame = spark.createDataFrame(rowRDD, schema)
-
-// Creates a temporary view using the DataFrame.
-peopleDataFrame.createOrReplaceTempView("people")
-
-// SQL statements can be run by using the sql methods provided by spark.
-val results = spark.sql("SELECT name FROM people")
-
-// The columns of a row in the result can be accessed by field index or by field name.
-results.map(t => "Name: " + t(0)).collect().foreach(println)
-{% endhighlight %}
-
 
+{% include_example programmatic_schema scala/org/apache/spark/examples/sql/SparkSqlExample.scala %}
 </div>
 
 <div data-lang="java"  markdown="1">
@@ -681,62 +402,8 @@ a `Dataset<Row>` can be created programmatically with three steps.
 by `SparkSession`.
 
 For example:
-{% highlight java %}
-import org.apache.spark.api.java.function.Function;
-// Import factory methods provided by DataTypes.
-import org.apache.spark.sql.types.DataTypes;
-// Import StructType and StructField
-import org.apache.spark.sql.types.StructType;
-import org.apache.spark.sql.types.StructField;
-// Import Row.
-import org.apache.spark.sql.Row;
-// Import RowFactory.
-import org.apache.spark.sql.RowFactory;
-
-SparkSession spark = ...; // An existing SparkSession.
-JavaSparkContext sc = spark.sparkContext
-
-// Load a text file and convert each line to a JavaBean.
-JavaRDD<String> people = sc.textFile("examples/src/main/resources/people.txt");
-
-// The schema is encoded in a string
-String schemaString = "name age";
-
-// Generate the schema based on the string of schema
-List<StructField> fields = new ArrayList<>();
-for (String fieldName: schemaString.split(" ")) {
-  fields.add(DataTypes.createStructField(fieldName, DataTypes.StringType, true));
-}
-StructType schema = DataTypes.createStructType(fields);
-
-// Convert records of the RDD (people) to Rows.
-JavaRDD<Row> rowRDD = people.map(
-  new Function<String, Row>() {
-    public Row call(String record) throws Exception {
-      String[] fields = record.split(",");
-      return RowFactory.create(fields[0], fields[1].trim());
-    }
-  });
-
-// Apply the schema to the RDD.
-Dataset<Row> peopleDataFrame = spark.createDataFrame(rowRDD, schema);
-
-// Creates a temporary view using the DataFrame.
-peopleDataFrame.createOrReplaceTempView("people");
-
-// SQL can be run over a temporary view created using DataFrames.
-Dataset<Row> results = spark.sql("SELECT name FROM people");
-
-// The results of SQL queries are DataFrames and support all the normal RDD operations.
-// The columns of a row in the result can be accessed by ordinal.
-List<String> names = results.javaRDD().map(new Function<Row, String>() {
-  public String call(Row row) {
-    return "Name: " + row.getString(0);
-  }
-}).collect();
-
-{% endhighlight %}
 
+{% include_example programmatic_schema java/org/apache/spark/examples/sql/JavaSparkSqlExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -805,23 +472,11 @@ In the simplest form, the default data source (`parquet` unless otherwise config
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
-
-{% highlight scala %}
-val df = spark.read.load("examples/src/main/resources/users.parquet")
-df.select("name", "favorite_color").write.save("namesAndFavColors.parquet")
-{% endhighlight %}
-
+{% include_example generic_load_save_functions scala/org/apache/spark/examples/sql/SqlDataSourceExample.scala %}
 </div>
 
 <div data-lang="java"  markdown="1">
-
-{% highlight java %}
-
-Dataset<Row> df = spark.read().load("examples/src/main/resources/users.parquet");
-df.select("name", "favorite_color").write().save("namesAndFavColors.parquet");
-
-{% endhighlight %}
-
+{% include_example generic_load_save_functions java/org/apache/spark/examples/sql/JavaSqlDataSourceExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -852,23 +507,11 @@ using this syntax.
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
-
-{% highlight scala %}
-val df = spark.read.format("json").load("examples/src/main/resources/people.json")
-df.select("name", "age").write.format("parquet").save("namesAndAges.parquet")
-{% endhighlight %}
-
+{% include_example manual_load_options scala/org/apache/spark/examples/sql/SqlDataSourceExample.scala %}
 </div>
 
 <div data-lang="java"  markdown="1">
-
-{% highlight java %}
-
-Dataset<Row> df = spark.read().format("json").load("examples/src/main/resources/people.json");
-df.select("name", "age").write().format("parquet").save("namesAndAges.parquet");
-
-{% endhighlight %}
-
+{% include_example manual_load_options java/org/apache/spark/examples/sql/JavaSqlDataSourceExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -895,18 +538,11 @@ file directly with SQL.
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
-
-{% highlight scala %}
-val df = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
-{% endhighlight %}
-
+{% include_example direct_sql scala/org/apache/spark/examples/sql/SqlDataSourceExample.scala %}
 </div>
 
 <div data-lang="java"  markdown="1">
-
-{% highlight java %}
-Dataset<Row> df = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`");
-{% endhighlight %}
+{% include_example direct_sql java/org/apache/spark/examples/sql/JavaSqlDataSourceExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -997,53 +633,11 @@ Using the data from the above example:
 <div class="codetabs">
 
 <div data-lang="scala"  markdown="1">
-
-{% highlight scala %}
-// spark from the previous example is used in this example.
-// This is used to implicitly convert an RDD to a DataFrame.
-import spark.implicits._
-
-val people: RDD[Person] = ... // An RDD of case class objects, from the previous example.
-
-// The RDD is implicitly converted to a DataFrame by implicits, allowing it to be stored using Parquet.
-people.write.parquet("people.parquet")
-
-// Read in the parquet file created above. Parquet files are self-describing so the schema is preserved.
-// The result of loading a Parquet file is also a DataFrame.
-val parquetFile = spark.read.parquet("people.parquet")
-
-// Parquet files can also be used to create a temporary view and then used in SQL statements.
-parquetFile.createOrReplaceTempView("parquetFile")
-val teenagers = spark.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
-teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
-{% endhighlight %}
-
+{% include_example basic_parquet_example scala/org/apache/spark/examples/sql/SqlDataSourceExample.scala %}
 </div>
 
 <div data-lang="java"  markdown="1">
-
-{% highlight java %}
-// spark from the previous example is used in this example.
-
-Dataset<Row> schemaPeople = ... // The DataFrame from the previous example.
-
-// DataFrames can be saved as Parquet files, maintaining the schema information.
-schemaPeople.write().parquet("people.parquet");
-
-// Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved.
-// The result of loading a parquet file is also a DataFrame.
-Dataset<Row> parquetFile = spark.read().parquet("people.parquet");
-
-// Parquet files can also be used to create a temporary view and then used in SQL statements.
-parquetFile.createOrReplaceTempView("parquetFile");
-Dataset<Row> teenagers = spark.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
-List<String> teenagerNames = teenagers.javaRDD().map(new Function<Row, String>() {
-  public String call(Row row) {
-    return "Name: " + row.getString(0);
-  }
-}).collect();
-{% endhighlight %}
-
+{% include_example basic_parquet_example java/org/apache/spark/examples/sql/JavaSqlDataSourceExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -1172,34 +766,11 @@ turned it off by default starting from 1.5.0. You may enable it by
 <div class="codetabs">
 
 <div data-lang="scala"  markdown="1">
+{% include_example schema_merging scala/org/apache/spark/examples/sql/SqlDataSourceExample.scala %}
+</div>
 
-{% highlight scala %}
-// spark from the previous example is used in this example.
-// This is used to implicitly convert an RDD to a DataFrame.
-import spark.implicits._
-
-// Create a simple DataFrame, stored into a partition directory
-val df1 = sc.makeRDD(1 to 5).map(i => (i, i * 2)).toDF("single", "double")
-df1.write.parquet("data/test_table/key=1")
-
-// Create another DataFrame in a new partition directory,
-// adding a new column and dropping an existing column
-val df2 = sc.makeRDD(6 to 10).map(i => (i, i * 3)).toDF("single", "triple")
-df2.write.parquet("data/test_table/key=2")
-
-// Read the partitioned table
-val df3 = spark.read.option("mergeSchema", "true").parquet("data/test_table")
-df3.printSchema()
-
-// The final schema consists of all 3 columns in the Parquet files together
-// with the partitioning column appeared in the partition directory paths.
-// root
-// |-- single: int (nullable = true)
-// |-- double: int (nullable = true)
-// |-- triple: int (nullable = true)
-// |-- key : int (nullable = true)
-{% endhighlight %}
-
+<div data-lang="java"  markdown="1">
+{% include_example schema_merging java/org/apache/spark/examples/sql/JavaSqlDataSourceExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -1280,8 +851,8 @@ metadata.
 <div data-lang="scala"  markdown="1">
 
 {% highlight scala %}
-// spark is an existing HiveContext
-spark.refreshTable("my_table")
+// spark is an existing SparkSession
+spark.catalog.refreshTable("my_table")
 {% endhighlight %}
 
 </div>
@@ -1289,8 +860,8 @@ spark.refreshTable("my_table")
 <div data-lang="java"  markdown="1">
 
 {% highlight java %}
-// spark is an existing HiveContext
-spark.refreshTable("my_table")
+// spark is an existing SparkSession
+spark.catalog().refreshTable("my_table");
 {% endhighlight %}
 
 </div>
@@ -1402,33 +973,7 @@ Note that the file that is offered as _a json file_ is not a typical JSON file.
 line must contain a separate, self-contained valid JSON object. As a consequence,
 a regular multi-line JSON file will most often fail.
 
-{% highlight scala %}
-val spark: SparkSession // An existing SparkSession
-
-// 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"
-val people = spark.read.json(path)
-
-// The inferred schema can be visualized using the printSchema() method.
-people.printSchema()
-// root
-//  |-- age: long (nullable = true)
-//  |-- name: string (nullable = true)
-
-// Creates a temporary view using the DataFrame
-people.createOrReplaceTempView("people")
-
-// SQL statements can be run by using the sql methods provided by spark.
-val teenagers = spark.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
-
-// Alternatively, a DataFrame can be created for a JSON dataset represented by
-// an RDD[String] storing one JSON object per string.
-val anotherPeopleRDD = sc.parallelize(
-  """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
-val anotherPeople = spark.read.json(anotherPeopleRDD)
-{% endhighlight %}
-
+{% include_example json_dataset scala/org/apache/spark/examples/sql/SqlDataSourceExample.scala %}
 </div>
 
 <div data-lang="java"  markdown="1">
@@ -1440,33 +985,7 @@ Note that the file that is offered as _a json file_ is not a typical JSON file.
 line must contain a separate, self-contained valid JSON object. As a consequence,
 a regular multi-line JSON file will most often fail.
 
-{% highlight java %}
-// sc is an existing JavaSparkContext.
-SparkSession spark = new org.apache.spark.sql.SparkSession(sc);
-
-// A JSON dataset is pointed to by path.
-// The path can be either a single text file or a directory storing text files.
-Dataset<Row> people = spark.read().json("examples/src/main/resources/people.json");
-
-// The inferred schema can be visualized using the printSchema() method.
-people.printSchema();
-// root
-//  |-- age: long (nullable = true)
-//  |-- name: string (nullable = true)
-
-// Creates a temporary view using the DataFrame
-people.createOrReplaceTempView("people");
-
-// SQL statements can be run by using the sql methods provided by spark.
-Dataset<Row> teenagers = spark.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
-
-// Alternatively, a DataFrame can be created for a JSON dataset represented by
-// an RDD[String] storing one JSON object per string.
-List<String> jsonData = Arrays.asList(
-  "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
-JavaRDD<String> anotherPeopleRDD = sc.parallelize(jsonData);
-Dataset<Row> anotherPeople = spark.read().json(anotherPeopleRDD);
-{% endhighlight %}
+{% include_example json_dataset java/org/apache/spark/examples/sql/JavaSqlDataSourceExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -1561,18 +1080,7 @@ the `hive.metastore.warehouse.dir` property in `hive-site.xml` is deprecated sin
 Instead, use `spark.sql.warehouse.dir` to specify the default location of database in warehouse.
 You may need to grant write privilege to the user who starts the spark application.
 
-{% highlight scala %}
-// warehouse_location points to the default location for managed databases and tables
-val conf = new SparkConf().setAppName("HiveFromSpark").set("spark.sql.warehouse.dir", warehouse_location)
-val spark = SparkSession.builder.config(conf).enableHiveSupport().getOrCreate()
-
-spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
-spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
-
-// Queries are expressed in HiveQL
-spark.sql("FROM src SELECT key, value").collect().foreach(println)
-{% endhighlight %}
-
+{% include_example spark_hive scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala %}
 </div>
 
 <div data-lang="java"  markdown="1">
@@ -1587,17 +1095,7 @@ the `hive.metastore.warehouse.dir` property in `hive-site.xml` is deprecated sin
 Instead, use `spark.sql.warehouse.dir` to specify the default location of database in warehouse.
 You may need to grant write privilege to the user who starts the spark application.
 
-{% highlight java %}
-SparkSession spark = SparkSession.builder().appName("JavaSparkSQL").getOrCreate();
-
-spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)");
-spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
-
-// Queries are expressed in HiveQL.
-List<Row> results = spark.sql("FROM src SELECT key, value").collectAsList();
-
-{% endhighlight %}
-
+{% include_example spark_hive java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java %}
 </div>
 
 <div data-lang="python"  markdown="1">
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
deleted file mode 100644
index 7fc6c007b6..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
+++ /dev/null
@@ -1,186 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.examples.sql;
-
-import java.io.Serializable;
-import java.util.Arrays;
-import java.util.List;
-
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.function.Function;
-
-import org.apache.spark.sql.Dataset;
-import org.apache.spark.sql.Row;
-// $example on:init_session$
-import org.apache.spark.sql.SparkSession;
-// $example off:init_session$
-
-public class JavaSparkSQL {
-  public static class Person implements Serializable {
-    private String name;
-    private int age;
-
-    public String getName() {
-      return name;
-    }
-
-    public void setName(String name) {
-      this.name = name;
-    }
-
-    public int getAge() {
-      return age;
-    }
-
-    public void setAge(int age) {
-      this.age = age;
-    }
-  }
-
-  public static void main(String[] args) throws Exception {
-    // $example on:init_session$
-    SparkSession spark = SparkSession
-      .builder()
-      .appName("JavaSparkSQL")
-      .config("spark.some.config.option", "some-value")
-      .getOrCreate();
-    // $example off:init_session$
-
-    System.out.println("=== Data source: RDD ===");
-    // Load a text file and convert each line to a Java Bean.
-    String file = "examples/src/main/resources/people.txt";
-    JavaRDD<Person> people = spark.read().textFile(file).javaRDD().map(
-      new Function<String, Person>() {
-        @Override
-        public Person call(String line) {
-          String[] parts = line.split(",");
-
-          Person person = new Person();
-          person.setName(parts[0]);
-          person.setAge(Integer.parseInt(parts[1].trim()));
-
-          return person;
-        }
-      });
-
-    // 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 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.
-    // The columns of a row in the result can be accessed by ordinal.
-    List<String> teenagerNames = teenagers.toJavaRDD().map(new Function<Row, String>() {
-      @Override
-      public String call(Row row) {
-        return "Name: " + row.getString(0);
-      }
-    }).collect();
-    for (String name: teenagerNames) {
-      System.out.println(name);
-    }
-
-    System.out.println("=== Data source: Parquet File ===");
-    // DataFrames can be saved as parquet files, maintaining the schema information.
-    schemaPeople.write().parquet("people.parquet");
-
-    // Read in the parquet file created above.
-    // Parquet files are self-describing so the schema is preserved.
-    // The result of loading a parquet file is also a DataFrame.
-    Dataset<Row> parquetFile = spark.read().parquet("people.parquet");
-
-    // 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");
-    teenagerNames = teenagers2.toJavaRDD().map(new Function<Row, String>() {
-      @Override
-      public String call(Row row) {
-          return "Name: " + row.getString(0);
-      }
-    }).collect();
-    for (String name: teenagerNames) {
-      System.out.println(name);
-    }
-
-    System.out.println("=== Data source: JSON Dataset ===");
-    // A JSON dataset is pointed by path.
-    // The path can be either a single text file or a directory storing text files.
-    String path = "examples/src/main/resources/people.json";
-    // Create a DataFrame from the file(s) pointed by path
-    Dataset<Row> peopleFromJsonFile = spark.read().json(path);
-
-    // Because the schema of a JSON dataset is automatically inferred, to write queries,
-    // it is better to take a look at what is the schema.
-    peopleFromJsonFile.printSchema();
-    // The schema of people is ...
-    // root
-    //  |-- age: IntegerType
-    //  |-- name: StringType
-
-    // Creates a temporary view using the DataFrame
-    peopleFromJsonFile.createOrReplaceTempView("people");
-
-    // SQL statements can be run by using the sql methods provided by `spark`
-    Dataset<Row> teenagers3 = spark.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
-
-    // The results of SQL queries are DataFrame and support all the normal RDD operations.
-    // The columns of a row in the result can be accessed by ordinal.
-    teenagerNames = teenagers3.toJavaRDD().map(new Function<Row, String>() {
-      @Override
-      public String call(Row row) { return "Name: " + row.getString(0); }
-    }).collect();
-    for (String name: teenagerNames) {
-      System.out.println(name);
-    }
-
-    // Alternatively, a DataFrame can be created for a JSON dataset represented by
-    // a RDD[String] storing one JSON object per string.
-    List<String> jsonData = Arrays.asList(
-          "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
-    JavaRDD<String> anotherPeopleRDD = spark
-      .createDataFrame(jsonData, String.class).toJSON().javaRDD();
-    Dataset<Row> peopleFromJsonRDD = spark.read().json(anotherPeopleRDD);
-
-    // Take a look at the schema of this new DataFrame.
-    peopleFromJsonRDD.printSchema();
-    // The schema of anotherPeople is ...
-    // root
-    //  |-- address: StructType
-    //  |    |-- city: StringType
-    //  |    |-- state: StringType
-    //  |-- name: StringType
-
-    peopleFromJsonRDD.createOrReplaceTempView("people2");
-
-    Dataset<Row> peopleWithCity = spark.sql("SELECT name, address.city FROM people2");
-    List<String> nameAndCity = peopleWithCity.toJavaRDD().map(new Function<Row, String>() {
-      @Override
-      public String call(Row row) {
-        return "Name: " + row.getString(0) + ", City: " + row.getString(1);
-      }
-    }).collect();
-    for (String name: nameAndCity) {
-      System.out.println(name);
-    }
-
-    spark.stop();
-  }
-}
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSqlExample.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSqlExample.java
new file mode 100644
index 0000000000..586d6e3a3e
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSqlExample.java
@@ -0,0 +1,336 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.examples.sql;
+
+// $example on:programmatic_schema$
+import java.util.ArrayList;
+import java.util.List;
+// $example off:programmatic_schema$
+// $example on:create_ds$
+import java.util.Arrays;
+import java.util.Collections;
+import java.io.Serializable;
+// $example off:create_ds$
+
+// $example on:schema_inferring$
+// $example on:programmatic_schema$
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.function.Function;
+// $example off:programmatic_schema$
+// $example on:create_ds$
+import org.apache.spark.api.java.function.MapFunction;
+// $example on:create_df$
+// $example on:run_sql$
+// $example on:programmatic_schema$
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+// $example off:programmatic_schema$
+// $example off:create_df$
+// $example off:run_sql$
+import org.apache.spark.sql.Encoder;
+import org.apache.spark.sql.Encoders;
+// $example off:create_ds$
+// $example off:schema_inferring$
+import org.apache.spark.sql.RowFactory;
+// $example on:init_session$
+import org.apache.spark.sql.SparkSession;
+// $example off:init_session$
+// $example on:programmatic_schema$
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+// $example off:programmatic_schema$
+
+// $example on:untyped_ops$
+// col("...") is preferable to df.col("...")
+import static org.apache.spark.sql.functions.col;
+// $example off:untyped_ops$
+
+public class JavaSparkSqlExample {
+  // $example on:create_ds$
+  public static class Person implements Serializable {
+    private String name;
+    private int age;
+
+    public String getName() {
+      return name;
+    }
+
+    public void setName(String name) {
+      this.name = name;
+    }
+
+    public int getAge() {
+      return age;
+    }
+
+    public void setAge(int age) {
+      this.age = age;
+    }
+  }
+  // $example off:create_ds$
+
+  public static void main(String[] args) {
+    // $example on:init_session$
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("Java Spark SQL Example")
+      .config("spark.some.config.option", "some-value")
+      .getOrCreate();
+    // $example off:init_session$
+
+    runBasicDataFrameExample(spark);
+    runDatasetCreationExample(spark);
+    runInferSchemaExample(spark);
+    runProgrammaticSchemaExample(spark);
+
+    spark.stop();
+  }
+
+  private static void runBasicDataFrameExample(SparkSession spark) {
+    // $example on:create_df$
+    Dataset<Row> df = spark.read().json("examples/src/main/resources/people.json");
+
+    // Displays the content of the DataFrame to stdout
+    df.show();
+    // +----+-------+
+    // | age|   name|
+    // +----+-------+
+    // |null|Michael|
+    // |  30|   Andy|
+    // |  19| Justin|
+    // +----+-------+
+    // $example off:create_df$
+
+    // $example on:untyped_ops$
+    // Print the schema in a tree format
+    df.printSchema();
+    // root
+    // |-- age: long (nullable = true)
+    // |-- name: string (nullable = true)
+
+    // Select only the "name" column
+    df.select("name").show();
+    // +-------+
+    // |   name|
+    // +-------+
+    // |Michael|
+    // |   Andy|
+    // | Justin|
+    // +-------+
+
+    // Select everybody, but increment the age by 1
+    df.select(col("name"), col("age").plus(1)).show();
+    // +-------+---------+
+    // |   name|(age + 1)|
+    // +-------+---------+
+    // |Michael|     null|
+    // |   Andy|       31|
+    // | Justin|       20|
+    // +-------+---------+
+
+    // Select people older than 21
+    df.filter(col("age").gt(21)).show();
+    // +---+----+
+    // |age|name|
+    // +---+----+
+    // | 30|Andy|
+    // +---+----+
+
+    // Count people by age
+    df.groupBy("age").count().show();
+    // +----+-----+
+    // | age|count|
+    // +----+-----+
+    // |  19|    1|
+    // |null|    1|
+    // |  30|    1|
+    // +----+-----+
+    // $example off:untyped_ops$
+
+    // $example on:run_sql$
+    // Register the DataFrame as a SQL temporary view
+    df.createOrReplaceTempView("people");
+
+    Dataset<Row> sqlDF = spark.sql("SELECT * FROM people");
+    sqlDF.show();
+    // +----+-------+
+    // | age|   name|
+    // +----+-------+
+    // |null|Michael|
+    // |  30|   Andy|
+    // |  19| Justin|
+    // +----+-------+
+    // $example off:run_sql$
+  }
+
+  private static void runDatasetCreationExample(SparkSession spark) {
+    // $example on:create_ds$
+    // Create an instance of a Bean class
+    Person person = new Person();
+    person.setName("Andy");
+    person.setAge(32);
+
+    // Encoders are created for Java beans
+    Encoder<Person> personEncoder = Encoders.bean(Person.class);
+    Dataset<Person> javaBeanDS = spark.createDataset(
+      Collections.singletonList(person),
+      personEncoder
+    );
+    javaBeanDS.show();
+    // +---+----+
+    // |age|name|
+    // +---+----+
+    // | 32|Andy|
+    // +---+----+
+
+    // Encoders for most common types are provided in class Encoders
+    Encoder<Integer> integerEncoder = Encoders.INT();
+    Dataset<Integer> primitiveDS = spark.createDataset(Arrays.asList(1, 2, 3), integerEncoder);
+    Dataset<Integer> transformedDS = primitiveDS.map(new MapFunction<Integer, Integer>() {
+      @Override
+      public Integer call(Integer value) throws Exception {
+        return value + 1;
+      }
+    }, integerEncoder);
+    transformedDS.collect(); // Returns [2, 3, 4]
+
+    // DataFrames can be converted to a Dataset by providing a class. Mapping based on name
+    String path = "examples/src/main/resources/people.json";
+    Dataset<Person> peopleDS = spark.read().json(path).as(personEncoder);
+    peopleDS.show();
+    // +----+-------+
+    // | age|   name|
+    // +----+-------+
+    // |null|Michael|
+    // |  30|   Andy|
+    // |  19| Justin|
+    // +----+-------+
+    // $example off:create_ds$
+  }
+
+  private static void runInferSchemaExample(SparkSession spark) {
+    // $example on:schema_inferring$
+    // Create an RDD of Person objects from a text file
+    JavaRDD<Person> peopleRDD = spark.read()
+      .textFile("examples/src/main/resources/people.txt")
+      .javaRDD()
+      .map(new Function<String, Person>() {
+        @Override
+        public Person call(String line) throws Exception {
+          String[] parts = line.split(",");
+          Person person = new Person();
+          person.setName(parts[0]);
+          person.setAge(Integer.parseInt(parts[1].trim()));
+          return person;
+        }
+      });
+
+    // Apply a schema to an RDD of JavaBeans to get a DataFrame
+    Dataset<Row> peopleDF = spark.createDataFrame(peopleRDD, Person.class);
+    // Register the DataFrame as a temporary view
+    peopleDF.createOrReplaceTempView("people");
+
+    // SQL statements can be run by using the sql methods provided by spark
+    Dataset<Row> teenagersDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
+
+    // The columns of a row in the result can be accessed by field index
+    Encoder<String> stringEncoder = Encoders.STRING();
+    Dataset<String> teenagerNamesByIndexDF = teenagersDF.map(new MapFunction<Row, String>() {
+      @Override
+      public String call(Row row) throws Exception {
+        return "Name: " + row.getString(0);
+      }
+    }, stringEncoder);
+    teenagerNamesByIndexDF.show();
+    // +------------+
+    // |       value|
+    // +------------+
+    // |Name: Justin|
+    // +------------+
+
+    // or by field name
+    Dataset<String> teenagerNamesByFieldDF = teenagersDF.map(new MapFunction<Row, String>() {
+      @Override
+      public String call(Row row) throws Exception {
+        return "Name: " + row.<String>getAs("name");
+      }
+    }, stringEncoder);
+    teenagerNamesByFieldDF.show();
+    // +------------+
+    // |       value|
+    // +------------+
+    // |Name: Justin|
+    // +------------+
+    // $example off:schema_inferring$
+  }
+
+  private static void runProgrammaticSchemaExample(SparkSession spark) {
+    // $example on:programmatic_schema$
+    // Create an RDD
+    JavaRDD<String> peopleRDD = spark.sparkContext()
+      .textFile("examples/src/main/resources/people.txt", 1)
+      .toJavaRDD();
+
+    // The schema is encoded in a string
+    String schemaString = "name age";
+
+    // Generate the schema based on the string of schema
+    List<StructField> fields = new ArrayList<>();
+    for (String fieldName : schemaString.split(" ")) {
+      StructField field = DataTypes.createStructField(fieldName, DataTypes.StringType, true);
+      fields.add(field);
+    }
+    StructType schema = DataTypes.createStructType(fields);
+
+    // Convert records of the RDD (people) to Rows
+    JavaRDD<Row> rowRDD = peopleRDD.map(new Function<String, Row>() {
+      @Override
+      public Row call(String record) throws Exception {
+        String[] attributes = record.split(",");
+        return RowFactory.create(attributes[0], attributes[1].trim());
+      }
+    });
+
+    // Apply the schema to the RDD
+    Dataset<Row> peopleDataFrame = spark.createDataFrame(rowRDD, schema);
+
+    // Creates a temporary view using the DataFrame
+    peopleDataFrame.createOrReplaceTempView("people");
+
+    // SQL can be run over a temporary view created using DataFrames
+    Dataset<Row> results = spark.sql("SELECT name FROM people");
+
+    // The results of SQL queries are DataFrames and support all the normal RDD operations
+    // The columns of a row in the result can be accessed by field index or by field name
+    Dataset<String> namesDS = results.map(new MapFunction<Row, String>() {
+      @Override
+      public String call(Row row) throws Exception {
+        return "Name: " + row.getString(0);
+      }
+    }, Encoders.STRING());
+    namesDS.show();
+    // +-------------+
+    // |        value|
+    // +-------------+
+    // |Name: Michael|
+    // |   Name: Andy|
+    // | Name: Justin|
+    // +-------------+
+    // $example off:programmatic_schema$
+  }
+}
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
new file mode 100644
index 0000000000..4db5e1b0af
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSqlDataSourceExample.java
@@ -0,0 +1,217 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.examples.sql;
+
+// $example on:schema_merging$
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.List;
+// $example off:schema_merging$
+
+// $example on:basic_parquet_example$
+import org.apache.spark.api.java.function.MapFunction;
+import org.apache.spark.sql.Encoders;
+// import org.apache.spark.sql.Encoders;
+// $example on:schema_merging$
+// $example on:json_dataset$
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+// $example off:json_dataset$
+// $example off:schema_merging$
+// $example off:basic_parquet_example$
+import org.apache.spark.sql.SparkSession;
+
+public class JavaSqlDataSourceExample {
+
+  // $example on:schema_merging$
+  public static class Square implements Serializable {
+    private int value;
+    private int square;
+
+    // Getters and setters...
+    // $example off:schema_merging$
+    public int getValue() {
+      return value;
+    }
+
+    public void setValue(int value) {
+      this.value = value;
+    }
+
+    public int getSquare() {
+      return square;
+    }
+
+    public void setSquare(int square) {
+      this.square = square;
+    }
+    // $example on:schema_merging$
+  }
+  // $example off:schema_merging$
+
+  // $example on:schema_merging$
+  public static class Cube implements Serializable {
+    private int value;
+    private int cube;
+
+    // Getters and setters...
+    // $example off:schema_merging$
+    public int getValue() {
+      return value;
+    }
+
+    public void setValue(int value) {
+      this.value = value;
+    }
+
+    public int getCube() {
+      return cube;
+    }
+
+    public void setCube(int cube) {
+      this.cube = cube;
+    }
+    // $example on:schema_merging$
+  }
+  // $example off:schema_merging$
+
+  public static void main(String[] args) {
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("Java Spark SQL Data Sources Example")
+      .config("spark.some.config.option", "some-value")
+      .getOrCreate();
+
+    runBasicDataSourceExample(spark);
+    runBasicParquetExample(spark);
+    runParquetSchemaMergingExample(spark);
+    runJsonDatasetExample(spark);
+
+    spark.stop();
+  }
+
+  private static void runBasicDataSourceExample(SparkSession spark) {
+    // $example on:generic_load_save_functions$
+    Dataset<Row> usersDF = spark.read().load("examples/src/main/resources/users.parquet");
+    usersDF.select("name", "favorite_color").write().save("namesAndFavColors.parquet");
+    // $example off:generic_load_save_functions$
+    // $example on:manual_load_options$
+    Dataset<Row> peopleDF = spark.read().format("json").load("examples/src/main/resources/people.json");
+    peopleDF.select("name", "age").write().format("parquet").save("namesAndAges.parquet");
+    // $example off:manual_load_options$
+    // $example on:direct_sql$
+    Dataset<Row> sqlDF = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`");
+    // $example off:direct_sql$
+  }
+
+  private static void runBasicParquetExample(SparkSession spark) {
+    // $example on:basic_parquet_example$
+    Dataset<Row> peopleDF = spark.read().json("examples/src/main/resources/people.json");
+
+    // DataFrames can be saved as Parquet files, maintaining the schema information
+    peopleDF.write().parquet("people.parquet");
+
+    // Read in the Parquet file created above.
+    // Parquet files are self-describing so the schema is preserved
+    // The result of loading a parquet file is also a DataFrame
+    Dataset<Row> parquetFileDF = spark.read().parquet("people.parquet");
+
+    // Parquet files can also be used to create a temporary view and then used in SQL statements
+    parquetFileDF.createOrReplaceTempView("parquetFile");
+    Dataset<Row> namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19");
+    Dataset<String> namesDS = namesDF.map(new MapFunction<Row, String>() {
+      public String call(Row row) {
+        return "Name: " + row.getString(0);
+      }
+    }, Encoders.STRING());
+    namesDS.show();
+    // +------------+
+    // |       value|
+    // +------------+
+    // |Name: Justin|
+    // +------------+
+    // $example off:basic_parquet_example$
+  }
+
+  private static void runParquetSchemaMergingExample(SparkSession spark) {
+    // $example on:schema_merging$
+    List<Square> squares = new ArrayList<>();
+    for (int value = 1; value <= 5; value++) {
+      Square square = new Square();
+      square.setValue(value);
+      square.setSquare(value * value);
+      squares.add(square);
+    }
+
+    // Create a simple DataFrame, store into a partition directory
+    Dataset<Row> squaresDF = spark.createDataFrame(squares, Square.class);
+    squaresDF.write().parquet("data/test_table/key=1");
+
+    List<Cube> cubes = new ArrayList<>();
+    for (int value = 6; value <= 10; value++) {
+      Cube cube = new Cube();
+      cube.setValue(value);
+      cube.setCube(value * value * value);
+      cubes.add(cube);
+    }
+
+    // Create another DataFrame in a new partition directory,
+    // adding a new column and dropping an existing column
+    Dataset<Row> cubesDF = spark.createDataFrame(cubes, Cube.class);
+    cubesDF.write().parquet("data/test_table/key=2");
+
+    // Read the partitioned table
+    Dataset<Row> mergedDF = spark.read().option("mergeSchema", true).parquet("data/test_table");
+    mergedDF.printSchema();
+
+    // The final schema consists of all 3 columns in the Parquet files together
+    // with the partitioning column appeared in the partition directory paths
+    // root
+    // |-- value: int (nullable = true)
+    // |-- square: int (nullable = true)
+    // |-- cube: int (nullable = true)
+    // |-- key : int (nullable = true)
+    // $example off:schema_merging$
+  }
+
+  private static void runJsonDatasetExample(SparkSession spark) {
+    // $example on:json_dataset$
+    // A JSON dataset is pointed to by path.
+    // The path can be either a single text file or a directory storing text files
+    Dataset<Row> people = spark.read().json("examples/src/main/resources/people.json");
+
+    // The inferred schema can be visualized using the printSchema() method
+    people.printSchema();
+    // root
+    //  |-- age: long (nullable = true)
+    //  |-- name: string (nullable = true)
+
+    // Creates a temporary view using the DataFrame
+    people.createOrReplaceTempView("people");
+
+    // SQL statements can be run by using the sql methods provided by spark
+    Dataset<Row> namesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19");
+    namesDF.show();
+    // +------+
+    // |  name|
+    // +------+
+    // |Justin|
+    // +------+
+    // $example off:json_dataset$
+  }
+
+}
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java b/examples/src/main/java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java
new file mode 100644
index 0000000000..493d759a91
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/sql/hive/JavaSparkHiveExample.java
@@ -0,0 +1,131 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.examples.sql.hive;
+
+// $example on:spark_hive$
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.List;
+
+import org.apache.spark.api.java.function.MapFunction;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Encoders;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SparkSession;
+// $example off:spark_hive$
+
+public class JavaSparkHiveExample {
+
+  // $example on:spark_hive$
+  public static class Record implements Serializable {
+    private int key;
+    private String value;
+
+    public int getKey() {
+      return key;
+    }
+
+    public void setKey(int key) {
+      this.key = key;
+    }
+
+    public String getValue() {
+      return value;
+    }
+
+    public void setValue(String value) {
+      this.value = value;
+    }
+  }
+  // $example off:spark_hive$
+
+  public static void main(String[] args) {
+    // $example on:spark_hive$
+    // warehouseLocation points to the default location for managed databases and tables
+    String warehouseLocation = "file:" + System.getProperty("user.dir") + "spark-warehouse";
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("Java Spark Hive Example")
+      .config("spark.sql.warehouse.dir", warehouseLocation)
+      .enableHiveSupport()
+      .getOrCreate();
+
+    spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)");
+    spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
+
+    // Queries are expressed in HiveQL
+    spark.sql("SELECT * FROM src").show();
+    // +---+-------+
+    // |key|  value|
+    // +---+-------+
+    // |238|val_238|
+    // | 86| val_86|
+    // |311|val_311|
+    // ...
+
+    // Aggregation queries are also supported.
+    spark.sql("SELECT COUNT(*) FROM src").show();
+    // +--------+
+    // |count(1)|
+    // +--------+
+    // |    500 |
+    // +--------+
+
+    // The results of SQL queries are themselves DataFrames and support all normal functions.
+    Dataset<Row> sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key");
+
+    // The items in DaraFrames are of type Row, which lets you to access each column by ordinal.
+    Dataset<String> stringsDS = sqlDF.map(new MapFunction<Row, String>() {
+      @Override
+      public String call(Row row) throws Exception {
+        return "Key: " + row.get(0) + ", Value: " + row.get(1);
+      }
+    }, Encoders.STRING());
+    stringsDS.show();
+    // +--------------------+
+    // |               value|
+    // +--------------------+
+    // |Key: 0, Value: val_0|
+    // |Key: 0, Value: val_0|
+    // |Key: 0, Value: val_0|
+    // ...
+
+    // You can also use DataFrames to create temporary views within a HiveContext.
+    List<Record> records = new ArrayList<>();
+    for (int key = 1; key < 100; key++) {
+      Record record = new Record();
+      record.setKey(key);
+      record.setValue("val_" + key);
+      records.add(record);
+    }
+    Dataset<Row> recordsDF = spark.createDataFrame(records, Record.class);
+    recordsDF.createOrReplaceTempView("records");
+
+    // Queries can then join DataFrames data with data stored in Hive.
+    spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show();
+    // +---+------+---+------+
+    // |key| value|key| value|
+    // +---+------+---+------+
+    // |  2| val_2|  2| val_2|
+    // |  2| val_2|  2| val_2|
+    // |  4| val_4|  4| val_4|
+    // ...
+    // $example off:spark_hive$
+
+    spark.stop();
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/SparkSqlExample.scala b/examples/src/main/scala/org/apache/spark/examples/sql/SparkSqlExample.scala
new file mode 100644
index 0000000000..cf3f864267
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/SparkSqlExample.scala
@@ -0,0 +1,254 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.examples.sql
+
+// $example on:schema_inferring$
+import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.apache.spark.sql.Encoder
+// $example off:schema_inferring$
+import org.apache.spark.sql.Row
+// $example on:init_session$
+import org.apache.spark.sql.SparkSession
+// $example off:init_session$
+// $example on:programmatic_schema$
+import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.types.StructField
+import org.apache.spark.sql.types.StructType
+// $example off:programmatic_schema$
+
+object SparkSqlExample {
+
+  // $example on:create_ds$
+  // Note: Case classes in Scala 2.10 can support only up to 22 fields. To work around this limit,
+  // you can use custom classes that implement the Product interface
+  case class Person(name: String, age: Long)
+  // $example off:create_ds$
+
+  def main(args: Array[String]) {
+    // $example on:init_session$
+    val spark = SparkSession
+      .builder()
+      .appName("Spark SQL Example")
+      .config("spark.some.config.option", "some-value")
+      .getOrCreate()
+
+    // For implicit conversions like converting RDDs to DataFrames
+    import spark.implicits._
+    // $example off:init_session$
+
+    runBasicDataFrameExample(spark)
+    runDatasetCreationExample(spark)
+    runInferSchemaExample(spark)
+    runProgrammaticSchemaExample(spark)
+
+    spark.stop()
+  }
+
+  private def runBasicDataFrameExample(spark: SparkSession): Unit = {
+    // $example on:create_df$
+    val df = spark.read.json("examples/src/main/resources/people.json")
+
+    // Displays the content of the DataFrame to stdout
+    df.show()
+    // +----+-------+
+    // | age|   name|
+    // +----+-------+
+    // |null|Michael|
+    // |  30|   Andy|
+    // |  19| Justin|
+    // +----+-------+
+    // $example off:create_df$
+
+    // $example on:untyped_ops$
+    // This import is needed to use the $-notation
+    import spark.implicits._
+    // Print the schema in a tree format
+    df.printSchema()
+    // root
+    // |-- age: long (nullable = true)
+    // |-- name: string (nullable = true)
+
+    // Select only the "name" column
+    df.select("name").show()
+    // +-------+
+    // |   name|
+    // +-------+
+    // |Michael|
+    // |   Andy|
+    // | Justin|
+    // +-------+
+
+    // Select everybody, but increment the age by 1
+    df.select($"name", $"age" + 1).show()
+    // +-------+---------+
+    // |   name|(age + 1)|
+    // +-------+---------+
+    // |Michael|     null|
+    // |   Andy|       31|
+    // | Justin|       20|
+    // +-------+---------+
+
+    // Select people older than 21
+    df.filter($"age" > 21).show()
+    // +---+----+
+    // |age|name|
+    // +---+----+
+    // | 30|Andy|
+    // +---+----+
+
+    // Count people by age
+    df.groupBy("age").count().show()
+    // +----+-----+
+    // | age|count|
+    // +----+-----+
+    // |  19|    1|
+    // |null|    1|
+    // |  30|    1|
+    // +----+-----+
+    // $example off:untyped_ops$
+
+    // $example on:run_sql$
+    // Register the DataFrame as a SQL temporary view
+    df.createOrReplaceTempView("people")
+
+    val sqlDF = spark.sql("SELECT * FROM people")
+    sqlDF.show()
+    // +----+-------+
+    // | age|   name|
+    // +----+-------+
+    // |null|Michael|
+    // |  30|   Andy|
+    // |  19| Justin|
+    // +----+-------+
+    // $example off:run_sql$
+  }
+
+  private def runDatasetCreationExample(spark: SparkSession): Unit = {
+    import spark.implicits._
+    // $example on:create_ds$
+    // Encoders are created for case classes
+    val caseClassDS = Seq(Person("Andy", 32)).toDS()
+    caseClassDS.show()
+    // +----+---+
+    // |name|age|
+    // +----+---+
+    // |Andy| 32|
+    // +----+---+
+
+    // Encoders for most common types are automatically provided by importing spark.implicits._
+    val primitiveDS = Seq(1, 2, 3).toDS()
+    primitiveDS.map(_ + 1).collect() // Returns: Array(2, 3, 4)
+
+    // DataFrames can be converted to a Dataset by providing a class. Mapping will be done by name
+    val path = "examples/src/main/resources/people.json"
+    val peopleDS = spark.read.json(path).as[Person]
+    peopleDS.show()
+    // +----+-------+
+    // | age|   name|
+    // +----+-------+
+    // |null|Michael|
+    // |  30|   Andy|
+    // |  19| Justin|
+    // +----+-------+
+    // $example off:create_ds$
+  }
+
+  private def runInferSchemaExample(spark: SparkSession): Unit = {
+    // $example on:schema_inferring$
+    // For implicit conversions from RDDs to DataFrames
+    import spark.implicits._
+
+    // Create an RDD of Person objects from a text file, convert it to a Dataframe
+    val peopleDF = spark.sparkContext
+      .textFile("examples/src/main/resources/people.txt")
+      .map(_.split(","))
+      .map(attributes => Person(attributes(0), attributes(1).trim.toInt))
+      .toDF()
+    // Register the DataFrame as a temporary view
+    peopleDF.createOrReplaceTempView("people")
+
+    // SQL statements can be run by using the sql methods provided by Spark
+    val teenagersDF = spark.sql("SELECT name, age FROM people WHERE age BETWEEN 13 AND 19")
+
+    // The columns of a row in the result can be accessed by field index
+    teenagersDF.map(teenager => "Name: " + teenager(0)).show()
+    // +------------+
+    // |       value|
+    // +------------+
+    // |Name: Justin|
+    // +------------+
+
+    // or by field name
+    teenagersDF.map(teenager => "Name: " + teenager.getAs[String]("name")).show()
+    // +------------+
+    // |       value|
+    // +------------+
+    // |Name: Justin|
+    // +------------+
+
+    // No pre-defined encoders for Dataset[Map[K,V]], define explicitly
+    implicit val mapEncoder = org.apache.spark.sql.Encoders.kryo[Map[String, Any]]
+    // Primitive types and case classes can be also defined as
+    implicit val stringIntMapEncoder: Encoder[Map[String, Int]] = ExpressionEncoder()
+
+    // row.getValuesMap[T] retrieves multiple columns at once into a Map[String, T]
+    teenagersDF.map(teenager => teenager.getValuesMap[Any](List("name", "age"))).collect()
+    // Array(Map("name" -> "Justin", "age" -> 19))
+    // $example off:schema_inferring$
+  }
+
+  private def runProgrammaticSchemaExample(spark: SparkSession): Unit = {
+    import spark.implicits._
+    // $example on:programmatic_schema$
+    // Create an RDD
+    val peopleRDD = spark.sparkContext.textFile("examples/src/main/resources/people.txt")
+
+    // The schema is encoded in a string
+    val schemaString = "name age"
+
+    // Generate the schema based on the string of schema
+    val fields = schemaString.split(" ")
+      .map(fieldName => StructField(fieldName, StringType, nullable = true))
+    val schema = StructType(fields)
+
+    // Convert records of the RDD (people) to Rows
+    val rowRDD = peopleRDD
+      .map(_.split(","))
+      .map(attributes => Row(attributes(0), attributes(1).trim))
+
+    // Apply the schema to the RDD
+    val peopleDF = spark.createDataFrame(rowRDD, schema)
+
+    // Creates a temporary view using the DataFrame
+    peopleDF.createOrReplaceTempView("people")
+
+    // SQL can be run over a temporary view created using DataFrames
+    val results = spark.sql("SELECT name FROM people")
+
+    // The results of SQL queries are DataFrames and support all the normal RDD operations
+    // The columns of a row in the result can be accessed by field index or by field name
+    results.map(attributes => "Name: " + attributes(0)).show()
+    // +-------------+
+    // |        value|
+    // +-------------+
+    // |Name: Michael|
+    // |   Name: Andy|
+    // | Name: Justin|
+    // +-------------+
+    // $example off:programmatic_schema$
+  }
+}
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
new file mode 100644
index 0000000000..61dea6ad2c
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/SqlDataSourceExample.scala
@@ -0,0 +1,148 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.examples.sql
+
+import org.apache.spark.sql.SparkSession
+
+object SqlDataSourceExample {
+
+  case class Person(name: String, age: Long)
+
+  def main(args: Array[String]) {
+    val spark = SparkSession
+      .builder()
+      .appName("Spark SQL Data Soures Example")
+      .config("spark.some.config.option", "some-value")
+      .getOrCreate()
+
+    runBasicDataSourceExample(spark)
+    runBasicParquetExample(spark)
+    runParquetSchemaMergingExample(spark)
+    runJsonDatasetExample(spark)
+
+    spark.stop()
+  }
+
+  private def runBasicDataSourceExample(spark: SparkSession): Unit = {
+    // $example on:generic_load_save_functions$
+    val usersDF = spark.read.load("examples/src/main/resources/users.parquet")
+    usersDF.select("name", "favorite_color").write.save("namesAndFavColors.parquet")
+    // $example off:generic_load_save_functions$
+    // $example on:manual_load_options$
+    val peopleDF = spark.read.format("json").load("examples/src/main/resources/people.json")
+    peopleDF.select("name", "age").write.format("parquet").save("namesAndAges.parquet")
+    // $example off:manual_load_options$
+    // $example on:direct_sql$
+    val sqlDF = spark.sql("SELECT * FROM parquet.`examples/src/main/resources/users.parquet`")
+    // $example off:direct_sql$
+  }
+
+  private def runBasicParquetExample(spark: SparkSession): Unit = {
+    // $example on:basic_parquet_example$
+    // Encoders for most common types are automatically provided by importing spark.implicits._
+    import spark.implicits._
+
+    val peopleDF = spark.read.json("examples/src/main/resources/people.json")
+
+    // DataFrames can be saved as Parquet files, maintaining the schema information
+    peopleDF.write.parquet("people.parquet")
+
+    // Read in the parquet file created above
+    // Parquet files are self-describing so the schema is preserved
+    // The result of loading a Parquet file is also a DataFrame
+    val parquetFileDF = spark.read.parquet("people.parquet")
+
+    // Parquet files can also be used to create a temporary view and then used in SQL statements
+    parquetFileDF.createOrReplaceTempView("parquetFile")
+    val namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19")
+    namesDF.map(attributes => "Name: " + attributes(0)).show()
+    // +------------+
+    // |       value|
+    // +------------+
+    // |Name: Justin|
+    // +------------+
+    // $example off:basic_parquet_example$
+  }
+
+  private def runParquetSchemaMergingExample(spark: SparkSession): Unit = {
+    // $example on:schema_merging$
+    // This is used to implicitly convert an RDD to a DataFrame.
+    import spark.implicits._
+
+    // Create a simple DataFrame, store into a partition directory
+    val squaresDF = spark.sparkContext.makeRDD(1 to 5).map(i => (i, i * i)).toDF("value", "square")
+    squaresDF.write.parquet("data/test_table/key=1")
+
+    // Create another DataFrame in a new partition directory,
+    // adding a new column and dropping an existing column
+    val cubesDF = spark.sparkContext.makeRDD(6 to 10).map(i => (i, i * i * i)).toDF("value", "cube")
+    cubesDF.write.parquet("data/test_table/key=2")
+
+    // Read the partitioned table
+    val mergedDF = spark.read.option("mergeSchema", "true").parquet("data/test_table")
+    mergedDF.printSchema()
+
+    // The final schema consists of all 3 columns in the Parquet files together
+    // with the partitioning column appeared in the partition directory paths
+    // root
+    // |-- value: int (nullable = true)
+    // |-- square: int (nullable = true)
+    // |-- cube: int (nullable = true)
+    // |-- key : int (nullable = true)
+    // $example off:schema_merging$
+  }
+
+  private def runJsonDatasetExample(spark: SparkSession): Unit = {
+    // $example on:json_dataset$
+    // 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"
+    val peopleDF = spark.read.json(path)
+
+    // The inferred schema can be visualized using the printSchema() method
+    peopleDF.printSchema()
+    // root
+    //  |-- age: long (nullable = true)
+    //  |-- name: string (nullable = true)
+
+    // Creates a temporary view using the DataFrame
+    peopleDF.createOrReplaceTempView("people")
+
+    // SQL statements can be run by using the sql methods provided by spark
+    val teenagerNamesDF = spark.sql("SELECT name FROM people WHERE age BETWEEN 13 AND 19")
+    teenagerNamesDF.show()
+    // +------+
+    // |  name|
+    // +------+
+    // |Justin|
+    // +------+
+
+    // 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(
+      """{"name":"Yin","address":{"city":"Columbus","state":"Ohio"}}""" :: Nil)
+    val otherPeople = spark.read.json(otherPeopleRDD)
+    otherPeople.show()
+    // +---------------+----+
+    // |        address|name|
+    // +---------------+----+
+    // |[Columbus,Ohio]| Yin|
+    // +---------------+----+
+    // $example off:json_dataset$
+  }
+
+}
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
deleted file mode 100644
index 2343f98c8d..0000000000
--- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-// scalastyle:off println
-package org.apache.spark.examples.sql.hive
-
-import java.io.File
-
-import com.google.common.io.{ByteStreams, Files}
-
-import org.apache.spark.sql._
-
-object HiveFromSpark {
-  case class Record(key: Int, value: String)
-
-  // Copy kv1.txt file from classpath to temporary directory
-  val kv1Stream = HiveFromSpark.getClass.getResourceAsStream("/kv1.txt")
-  val kv1File = File.createTempFile("kv1", "txt")
-  kv1File.deleteOnExit()
-  ByteStreams.copy(kv1Stream, Files.newOutputStreamSupplier(kv1File))
-
-  def main(args: Array[String]) {
-    // When working with Hive, one must instantiate `SparkSession` with Hive support, including
-    // connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined
-    // functions. Users who do not have an existing Hive deployment can still enable Hive support.
-    // When not configured by the hive-site.xml, the context automatically creates `metastore_db`
-    // in the current directory and creates a directory configured by `spark.sql.warehouse.dir`,
-    // which defaults to the directory `spark-warehouse` in the current directory that the spark
-    // application is started.
-    val spark = SparkSession.builder
-      .appName("HiveFromSpark")
-      .enableHiveSupport()
-      .getOrCreate()
-
-    import spark.implicits._
-    import spark.sql
-
-    sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
-    sql(s"LOAD DATA LOCAL INPATH '${kv1File.getAbsolutePath}' INTO TABLE src")
-
-    // Queries are expressed in HiveQL
-    println("Result of 'SELECT *': ")
-    sql("SELECT * FROM src").collect().foreach(println)
-
-    // Aggregation queries are also supported.
-    val count = sql("SELECT COUNT(*) FROM src").collect().head.getLong(0)
-    println(s"COUNT(*): $count")
-
-    // The results of SQL queries are themselves RDDs and support all normal RDD functions.  The
-    // items in the RDD are of type Row, which allows you to access each column by ordinal.
-    val rddFromSql = sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")
-
-    println("Result of RDD.map:")
-    val rddAsStrings = rddFromSql.rdd.map {
-      case Row(key: Int, value: String) => s"Key: $key, Value: $value"
-    }
-
-    // You can also use RDDs to create temporary views within a HiveContext.
-    val rdd = spark.sparkContext.parallelize((1 to 100).map(i => Record(i, s"val_$i")))
-    rdd.toDF().createOrReplaceTempView("records")
-
-    // Queries can then join RDD data with data stored in Hive.
-    println("Result of SELECT *:")
-    sql("SELECT * FROM records r JOIN src s ON r.key = s.key").collect().foreach(println)
-
-    spark.stop()
-  }
-}
-// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala
new file mode 100644
index 0000000000..e897c2d066
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/SparkHiveExample.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.examples.sql.hive
+
+// $example on:spark_hive$
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.SparkSession
+// $example off:spark_hive$
+
+object SparkHiveExample {
+
+  // $example on:spark_hive$
+  case class Record(key: Int, value: String)
+  // $example off:spark_hive$
+
+  def main(args: Array[String]) {
+    // When working with Hive, one must instantiate `SparkSession` with Hive support, including
+    // connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined
+    // functions. Users who do not have an existing Hive deployment can still enable Hive support.
+    // When not configured by the hive-site.xml, the context automatically creates `metastore_db`
+    // in the current directory and creates a directory configured by `spark.sql.warehouse.dir`,
+    // which defaults to the directory `spark-warehouse` in the current directory that the spark
+    // application is started.
+
+    // $example on:spark_hive$
+    // warehouseLocation points to the default location for managed databases and tables
+    val warehouseLocation = "file:${system:user.dir}/spark-warehouse"
+
+    val spark = SparkSession
+      .builder()
+      .appName("Spark Hive Example")
+      .config("spark.sql.warehouse.dir", warehouseLocation)
+      .enableHiveSupport()
+      .getOrCreate()
+
+    import spark.implicits._
+    import spark.sql
+
+    sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
+    sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
+
+    // Queries are expressed in HiveQL
+    sql("SELECT * FROM src").show()
+    // +---+-------+
+    // |key|  value|
+    // +---+-------+
+    // |238|val_238|
+    // | 86| val_86|
+    // |311|val_311|
+    // ...
+
+    // Aggregation queries are also supported.
+    sql("SELECT COUNT(*) FROM src").show()
+    // +--------+
+    // |count(1)|
+    // +--------+
+    // |    500 |
+    // +--------+
+
+    // The results of SQL queries are themselves DataFrames and support all normal functions.
+    val sqlDF = sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")
+
+    // The items in DaraFrames are of type Row, which allows you to access each column by ordinal.
+    val stringsDS = sqlDF.map {
+      case Row(key: Int, value: String) => s"Key: $key, Value: $value"
+    }
+    stringsDS.show()
+    // +--------------------+
+    // |               value|
+    // +--------------------+
+    // |Key: 0, Value: val_0|
+    // |Key: 0, Value: val_0|
+    // |Key: 0, Value: val_0|
+    // ...
+
+    // You can also use DataFrames to create temporary views within a HiveContext.
+    val recordsDF = spark.createDataFrame((1 to 100).map(i => Record(i, s"val_$i")))
+    recordsDF.createOrReplaceTempView("records")
+
+    // Queries can then join DataFrame data with data stored in Hive.
+    sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show()
+    // +---+------+---+------+
+    // |key| value|key| value|
+    // +---+------+---+------+
+    // |  2| val_2|  2| val_2|
+    // |  2| val_2|  2| val_2|
+    // |  4| val_4|  4| val_4|
+    // ...
+    // $example off:spark_hive$
+
+    spark.stop()
+  }
+}