Spark join算子学习使用详解

join:

     关联查询

     Spark 中的Rdd关联可以通过 4种方式

     1、Join

     2、leftOuterJoin

     3、rightOuterJoin

     4、fullOuterJoin

示例代码：

def my_join():
    rdd_a = sc.parallelize([('A','a1'),('C','c1'),('D','d1'),('F','f1'),('F','f2')])
    rdd_b = sc.parallelize([('A','a2'),('C','c2'),('C','c3'),('E','e1')])
    result = rdd_a.join(rdd_b) 

 print(result.collect())
    result_left_join = rdd_a.leftOuterJoin(rdd_b) 

 print(result_left_join.collect())
    result_right_join = rdd_a.rightOuterJoin(rdd_b) 

 print(result_right_join.collect())
    result_full_join = rdd_a.fullOuterJoin(rdd_b) 

 print(result_full_join.collect())

输出结果：

[('A', ('a1', 'a2')), ('C', ('c1', 'c2')), ('C', ('c1', 'c3'))]
[('A', ('a1', 'a2')), ('F', ('f1', None)), ('F', ('f2', None)), ('D', ('d1', None)), ('C', ('c1', 'c2')), ('C', ('c1', 'c3'))]
[('A', ('a1', 'a2')), ('C', ('c1', 'c2')), ('C', ('c1', 'c3')), ('E', (None, 'e1'))]
[('A', ('a1', 'a2')), ('F', ('f1', None)), ('F', ('f2', None)), ('D', ('d1', None)), ('C', ('c1', 'c2')), ('C', ('c1', 'c3')), ('E', (None, 'e1'))]