Spark算子：transformation之键值转换join、cogroup

1、join

1）def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))]
2）def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))]
3）def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))]

join相当于SQL中的内关联join，只返回两个RDD根据K可以关联上的结果，join只能用于两个RDD之间的关联，如果要多个RDD关联，多关联几次即可。参数numPartitions指分区数，partitioner指分区函数。

var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
 
scala> rdd1.join(rdd2).collect
res10: Array[(String, (String, String))] = Array((A,(1,a)), (C,(3,c)))

2、cogroup

（1）参数为一个RDD

1）def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]
2）def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))]
3）def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W]))]

（2）参数是两个RDD

1）def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]
2）def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]
3）def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]

（3）参数是三个RDD

1）def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]
2）def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]
3）def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))]

cogroup相当于SQL中的全外关联full outer join，返回左右RDD中的记录，关联不上的为空。参数numPartitions指分区数，partitioner指分区函数。
 示例1：参数为一个RDD

var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
 
scala> var rdd3 = rdd1.cogroup(rdd2)
rdd3: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String]))] = MapPartitionsRDD[12] at cogroup at :25
 
scala> rdd3.partitions.size
res3: Int = 2
 
scala> rdd3.collect
res1: Array[(String, (Iterable[String], Iterable[String]))] = Array(
(B,(CompactBuffer(2),CompactBuffer())), 
(D,(CompactBuffer(),CompactBuffer(d))), 
(A,(CompactBuffer(1),CompactBuffer(a))), 
(C,(CompactBuffer(3),CompactBuffer(c)))
)
 
 
scala> var rdd4 = rdd1.cogroup(rdd2,3)
rdd4: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String]))] = MapPartitionsRDD[14] at cogroup at :25
 
scala> rdd4.partitions.size
res5: Int = 3
 
scala> rdd4.collect
res6: Array[(String, (Iterable[String], Iterable[String]))] = Array(
(B,(CompactBuffer(2),CompactBuffer())), 
(C,(CompactBuffer(3),CompactBuffer(c))), 
(A,(CompactBuffer(1),CompactBuffer(a))), 
(D,(CompactBuffer(),CompactBuffer(d))))

示例2：参数为两个RDD

var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
var rdd3 = sc.makeRDD(Array(("A","A"),("E","E")),2)
 
scala> var rdd4 = rdd1.cogroup(rdd2,rdd3)
rdd4: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String], Iterable[String]))] = 
MapPartitionsRDD[17] at cogroup at :27
 
scala> rdd4.partitions.size
res7: Int = 2
 
scala> rdd4.collect
res9: Array[(String, (Iterable[String], Iterable[String], Iterable[String]))] = Array(
(B,(CompactBuffer(2),CompactBuffer(),CompactBuffer())), 
(D,(CompactBuffer(),CompactBuffer(d),CompactBuffer())), 
(A,(CompactBuffer(1),CompactBuffer(a),CompactBuffer(A))), 
(C,(CompactBuffer(3),CompactBuffer(c),CompactBuffer())), 
(E,(CompactBuffer(),CompactBuffer(),CompactBuffer(E))))

示例3：参数为三个RDD，代码同示例2，不赘述。