使用/不使用Spark SQL加入两个普通RDD

Question

我需要RDDs在一个/多个列上加入两个普通的列.逻辑上,此操作等效于两个表的数据库连接操作.我想知道这是否只有通过Spark SQL或其他方式可行.

作为一个具体示例,请考虑r1使用主键的RDD ITEM_ID:

(ITEM_ID, ITEM_NAME, ITEM_UNIT, COMPANY_ID)

和r2主键的RDD COMPANY_ID:

(COMPANY_ID, COMPANY_NAME, COMPANY_CITY)

我想加入r1和r2.

如何才能做到这一点？

Answer 1

Soumya Simanta给出了一个很好的答案.但是,连接的RDD中的值是Iterable,因此结果可能与普通表连接不太相似.

或者,您可以:

val mappedItems = items.map(item => (item.companyId, item))
val mappedComp = companies.map(comp => (comp.companyId, comp))
mappedItems.join(mappedComp).take(10).foreach(println)

输出将是:

(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

(使用Scala)假设您有两个RDD:

• emp :( empid,ename,dept)

• 部门:(dname,dept)

以下是另一种方式:

//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))

这将输出为:

//在控制台上打印输出cleaning_joined_data

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

这样的事情应该有效.

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))))