cogroup

##参数为1个RDD

def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))]

def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))]

def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W]))]

##参数为2个RDD

def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]

def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]

def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner): RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))]

##参数为3个RDD

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]))]

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]))]

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用于指定结果的分区数。

参数partitioner用于指定分区函数。

##参数为1个RDD的例子

1. var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
2. var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
3.  
4. scala> var rdd3 = rdd1.cogroup(rdd2)
5. rdd3: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String]))] = MapPartitionsRDD[12] at cogroup at :25
6.  
7. scala> rdd3.partitions.size
8. res3: Int = 2
9.  
10. scala> rdd3.collect
11. res1: Array[(String, (Iterable[String], Iterable[String]))] = Array(
12. (B,(CompactBuffer(2),CompactBuffer())),
13. (D,(CompactBuffer(),CompactBuffer(d))),
14. (A,(CompactBuffer(1),CompactBuffer(a))),
15. (C,(CompactBuffer(3),CompactBuffer(c)))
16. )
17.  
18.  
19. scala> var rdd4 = rdd1.cogroup(rdd2,3)
20. rdd4: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String]))] = MapPartitionsRDD[14] at cogroup at :25
21.  
22. scala> rdd4.partitions.size
23. res5: Int = 3
24.  
25. scala> rdd4.collect
26. res6: Array[(String, (Iterable[String], Iterable[String]))] = Array(
27. (B,(CompactBuffer(2),CompactBuffer())),
28. (C,(CompactBuffer(3),CompactBuffer(c))),
29. (A,(CompactBuffer(1),CompactBuffer(a))),
30. (D,(CompactBuffer(),CompactBuffer(d))))
31.  

##参数为2个RDD的例子

1. var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
2. var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
3. var rdd3 = sc.makeRDD(Array(("A","A"),("E","E")),2)
4.  
5. scala> var rdd4 = rdd1.cogroup(rdd2,rdd3)
6. rdd4: org.apache.spark.rdd.RDD[(String, (Iterable[String], Iterable[String], Iterable[String]))] =
7. MapPartitionsRDD[17] at cogroup at :27
8.  
9. scala> rdd4.partitions.size
10. res7: Int = 2
11.  
12. scala> rdd4.collect
13. res9: Array[(String, (Iterable[String], Iterable[String], Iterable[String]))] = Array(
14. (B,(CompactBuffer(2),CompactBuffer(),CompactBuffer())),
15. (D,(CompactBuffer(),CompactBuffer(d),CompactBuffer())),
16. (A,(CompactBuffer(1),CompactBuffer(a),CompactBuffer(A))),
17. (C,(CompactBuffer(3),CompactBuffer(c),CompactBuffer())),
18. (E,(CompactBuffer(),CompactBuffer(),CompactBuffer(E))))
19.  
20.  

##参数为3个RDD示例略，同上。

join

def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))]

def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))]

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用于指定结果的分区数

参数partitioner用于指定分区函数

1. var rdd1 = sc.makeRDD(Array(("A","1"),("B","2"),("C","3")),2)
2. var rdd2 = sc.makeRDD(Array(("A","a"),("C","c"),("D","d")),2)
3.  
4. scala> rdd1.join(rdd2).collect
5. res10: Array[(String, (String, String))] = Array((A,(1,a)), (C,(3,c)))
6.