spark常用的算子总结（7）—— join

join是根据key做两张表全连接

("B", 2)连接了("B", "B1")就变成了(B,(2,B1))

val arr = List(("A", 1), ("B", 2), ("A", 2), ("B", 3))
val arr1 = List(("A", "A1"), ("B", "B1"), ("A", "A2"), ("B", "B2"))
val rdd = sc.parallelize(arr, 3)
val rdd1 = sc.parallelize(arr1, 3)
val groupByKeyRDD = rdd.join(rdd1)
groupByKeyRDD.foreach(println)

# (B,(2,B1))
# (B,(2,B2))
# (B,(3,B1))
# (B,(3,B2))
 
# (A,(1,A1))
# (A,(1,A2))
# (A,(2,A1))
# (A,(2,A2

 

LeftOutJoin

左外连接，如果右边没有与之匹配的用None表示，有就有some

 

//省略
val arr = List(("A", 1), ("B", 2), ("A", 2), ("B", 3),("C",1))
val arr1 = List(("A", "A1"), ("B", "B1"), ("A", "A2"), ("B", "B2"))
val rdd = sc.parallelize(arr, 3)
val rdd1 = sc.parallelize(arr1, 3)
val leftOutJoinRDD = rdd.leftOuterJoin(rdd1)
leftOutJoinRDD .foreach(println)
sc.stop

# (B,(2,Some(B1)))
# (B,(2,Some(B2)))
# (B,(3,Some(B1)))
# (B,(3,Some(B2)))
# (C,(1,None))
# (A,(1,Some(A1)))
# (A,(1,Some(A2)))
# (A,(2,Some(A1)))
# (A,(2,Some(A2)))