To make this recipe one should know about its main ingredient and that is case classes. These are special classes in Scala and the main spice of this ingredient is that all the grunt work which is needed in Java can be done in case classes in one code line. Spark uses reflection on case classes to infer schema.

Recipe for this is given below

1. Start the spark shell and give it some additional memory:

 $ spark-shell --driver-memory 1G

2. Import for the implicit conversations:

 scala> import sqlContext. implicits._

3. Create a person case class:

 scala> case class Person (first_name:String,last_name: String,age:Int)

4. In another shell, create some sample data to be put in HDFS:

$ mkdir person
$ echo "Barack,Obama,53" >> person/person.txt
$ echo "George,Bush,68" >> person/person.txt
$ echo "Bill,Clinton,68" >> person/person.txt
$ hdfs dfs -put person person

5. Load the person directly as on RDD:

 scala> val p = sc.textFile ("hdfs://localhost:9000/user/hduser/person")

6. Split each line into an array of string, based on a comma, as a delimiter:

 val pmap = ( line => line.split (","))

7. Convert the RDD of Array[string] into the RDD of person case objects:

 scala> val personRDD = ( p => Person (p(0), p(1), p(2). toInt))

8. Convert the personRDD into the personDF DataFrame:

 scala> val personDF = personRDD. toDF

9. Register the personDF as a table:

 scala> personDF.registerTempTable ("person")

10. Run a SQL query against it:

 scala> val people = SQL ("select * from person")

11. Get the output values from persons:

 scala> people.collect.foreach (printIn)