Join two ordinary RDDs with/without Spark SQL - scala

Question

I need to join two ordinary RDDs on one/more columns. Logically this operation is equivalent to the database join operation of two tables. I wonder if this is possible only through Spark SQL or there are other ways of doing it.
As a concrete example, consider
RDD r1 with primary key ITEM_ID:
(ITEM_ID, ITEM_NAME, ITEM_UNIT, COMPANY_ID)
and RDD r2 with primary key COMPANY_ID:
(COMPANY_ID, COMPANY_NAME, COMPANY_CITY)
I want to join r1 and r2.
How can this be done?

Answer 1

Soumya Simanta gave a good answer. However, the values in joined RDD are Iterable, so the results may not be very similar to ordinary table joining.
Alternatively, you can:
val mappedItems = items.map(item => (item.companyId, item))
val mappedComp = companies.map(comp => (comp.companyId, comp))
mappedItems.join(mappedComp).take(10).foreach(println)
The output would be:
(c1,(Item(1,first,2,c1),Company(c1,company-1,city-1)))
(c1,(Item(2,second,2,c1),Company(c1,company-1,city-1)))
(c2,(Item(3,third,2,c2),Company(c2,company-2,city-2)))

Answer 2

(Using Scala)
Let say you have two RDDs:
emp: (empid, ename, dept)
dept: (dname, dept)
Following is another way:
//val emp = sc.parallelize(Seq((1,"jordan",10), (2,"ricky",20), (3,"matt",30), (4,"mince",35), (5,"rhonda",30)))
val emp = sc.parallelize(Seq(("jordan",10), ("ricky",20), ("matt",30), ("mince",35), ("rhonda",30)))
val dept = sc.parallelize(Seq(("hadoop",10), ("spark",20), ("hive",30), ("sqoop",40)))
//val shifted_fields_emp = emp.map(t => (t._3, t._1, t._2))
val shifted_fields_emp = emp.map(t => (t._2, t._1))
val shifted_fields_dept = dept.map(t => (t._2,t._1))
shifted_fields_emp.join(shifted_fields_dept)
// Create emp RDD
val emp = sc.parallelize(Seq((1,"jordan",10), (2,"ricky",20), (3,"matt",30), (4,"mince",35), (5,"rhonda",30)))
// Create dept RDD
val dept = sc.parallelize(Seq(("hadoop",10), ("spark",20), ("hive",30), ("sqoop",40)))
// Establishing that the third field is to be considered as the Key for the emp RDD
val manipulated_emp = emp.keyBy(t => t._3)
// Establishing that the second field need to be considered as the Key for dept RDD
val manipulated_dept = dept.keyBy(t => t._2)
// Inner Join
val join_data = manipulated_emp.join(manipulated_dept)
// Left Outer Join
val left_outer_join_data = manipulated_emp.leftOuterJoin(manipulated_dept)
// Right Outer Join
val right_outer_join_data = manipulated_emp.rightOuterJoin(manipulated_dept)
// Full Outer Join
val full_outer_join_data = manipulated_emp.fullOuterJoin(manipulated_dept)
// Formatting the Joined Data for better understandable (using map)
val cleaned_joined_data = join_data.map(t => (t._2._1._1, t._2._1._2, t._1, t._2._2._1))
This will give the output as:
// Print the output cleaned_joined_data on the console
scala> cleaned_joined_data.collect()
res13: Array[(Int, String, Int, String)] = Array((3,matt,30,hive), (5,rhonda,30,hive), (2,ricky,20,spark), (1,jordan,10,hadoop))

Answer 3

Something like this should work.
scala> case class Item(id:String, name:String, unit:Int, companyId:String)
scala> case class Company(companyId:String, name:String, city:String)
scala> val i1 = Item("1", "first", 2, "c1")
scala> val i2 = i1.copy(id="2", name="second")
scala> val i3 = i1.copy(id="3", name="third", companyId="c2")
scala> val items = sc.parallelize(List(i1,i2,i3))
items: org.apache.spark.rdd.RDD[Item] = ParallelCollectionRDD[14] at parallelize at <console>:20
scala> val c1 = Company("c1", "company-1", "city-1")
scala> val c2 = Company("c2", "company-2", "city-2")
scala> val companies = sc.parallelize(List(c1,c2))
scala> val groupedItems = items.groupBy( x => x.companyId)
groupedItems: org.apache.spark.rdd.RDD[(String, Iterable[Item])] = ShuffledRDD[16] at groupBy at <console>:22
scala> val groupedComp = companies.groupBy(x => x.companyId)
groupedComp: org.apache.spark.rdd.RDD[(String, Iterable[Company])] = ShuffledRDD[18] at groupBy at <console>:20
scala> groupedItems.join(groupedComp).take(10).foreach(println)
14/12/12 00:52:32 INFO DAGScheduler: Job 5 finished: take at <console>:35, took 0.021870 s
(c1,(CompactBuffer(Item(1,first,2,c1), Item(2,second,2,c1)),CompactBuffer(Company(c1,company-1,city-1))))
(c2,(CompactBuffer(Item(3,third,2,c2)),CompactBuffer(Company(c2,company-2,city-2))))

Answer 4

Spark SQL can perform join on SPARK RDDs.
Below code performs SQL join on Company and Items RDDs
object SparkSQLJoin {
case class Item(id:String, name:String, unit:Int, companyId:String)
case class Company(companyId:String, name:String, city:String)
def main(args: Array[String]) {
val sparkConf = new SparkConf()
val sc= new SparkContext(sparkConf)
val sqlContext = new SQLContext(sc)
import sqlContext.createSchemaRDD
val i1 = Item("1", "first", 1, "c1")
val i2 = Item("2", "second", 2, "c2")
val i3 = Item("3", "third", 3, "c3")
val c1 = Company("c1", "company-1", "city-1")
val c2 = Company("c2", "company-2", "city-2")
val companies = sc.parallelize(List(c1,c2))
companies.registerAsTable("companies")
val items = sc.parallelize(List(i1,i2,i3))
items.registerAsTable("items")
val result = sqlContext.sql("SELECT * FROM companies C JOIN items I ON C.companyId= I.companyId").collect
result.foreach(println)
}
}
Output is displayed as
[c1,company-1,city-1,1,first,1,c1]
[c2,company-2,city-2,2,second,2,c2]