Download or create the people.txt file.

Copy people.txt into HDFS:

cd /usr/hdp/current/spark-client/conf/

hadoop dfs -put examples/src/main/resources/people.txt people.txt

Create and populate a Hive table:

**# Import ORC support and Spark SQL libraries
import org.apache.spark.sql.hive.orc._ 
import org.apache.spark.sql._ 
 
**# Prepare the Spark table**
 
**# Create a hiveContext
**# sc is an existing SparkContext 
val hiveContext = new org.apache.spark.sql.hive.HiveContext(sc)
 
**# Create an RDD of "people" objects
val people = sc.textFile("people.txt")
 
**# Specify the schema as a string 
val schemaString = "name age" 
 
**# Create the schema based on the schemaString
val schema = 
 StructType(
 schemaString.split(" ").map(fieldName => 
 {if(fieldName == "name")
 StructField(fieldName, StringType, true) else
 StructField(fieldName, IntegerType, true)})) 
 
**# Convert records in people to rows
val rowRDD = people.map(_.split(",")).map(p => Row(p(0), new Integer(p(1).trim))) 
 
**# Apply the schema to the RDD 
val peopleSchemaRDD = hiveContext.applySchema(rowRDD, schema) 
 
**# Register the people SchemaRdd as a table
peopleSchemaRDD.registerTempTable("people") 
val results = hiveContext.sql("SELECT * FROM people")
 
**# List query results
results.map(t => "Name: " + t.toString).collect().foreach(println) 

Create and populate an ORC table from people:

**# ORC-specific section **
            
**# Save people as an ORC-format file
peopleSchemaRDD.saveAsOrcFile("people.orc") 

**# Import "people.orc" into a Spark SQL table called "morePeople"
val morePeople = hiveContext.orcFile("people.orc")

**# Register morePeople as a table 
**# This allows you to run standard SQL queries on morePeople
morePeople.registerTempTable("morePeople") 

**# Display all rows
hiveContext.sql("SELECT * from morePeople").collect.foreach(println)