我需要在一个/多个列上加入两个普通的RDDs。逻辑上这个操作相当于两个表的数据库连接操作。我想知道这是否只能通过Spark SQL 才能实现，或者还有其他方法。
作为一个具体的例子，考虑
RDD r1 主键 ITEM_ID:
(ITEM_ID, ITEM_NAME, ITEM_UNIT, COMPANY_ID)
和带有主键COMPANY_ID的RDD r2：
(COMPANY_ID, COMPANY_NAME, COMPANY_CITY)
我想加入r1 和r2。
如何做到这一点？
【问题讨论】：
标签： scala join apache-spark rdd apache-spark-sql
Soumya Simanta 给出了一个很好的答案。但是joined RDD里面的值是Iterable，所以结果可能和普通的表join不太一样。
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)))
【讨论】：
items.keyBy{_.companyId}、companies.keyBy{_.companyId} 相同。既然它们是 Spark 的一部分，那么有可能会更有效率吗？
def keyBy[K](f: T => K): RDD[(K, T)] = { map(x => (f(x), x)) } 所以你的解决方案和@virya 解决方案完全一样
（使用 Scala） 假设你有两个 RDD：
emp: (empid, ename, dept)
部门：（dname，部门）
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))
这将给出如下输出：
// 在控制台打印输出cleaned_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))
【讨论】：
这样的事情应该可以工作。
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))))
【讨论】：
Spark SQL 可以在 SPARK RDD 上执行连接。
以下代码对 Company 和 Items RDD 执行 SQL 连接
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)
}
}
输出显示为
[c1,company-1,city-1,1,first,1,c1]
[c2,company-2,city-2,2,second,2,c2]
【讨论】：