再谈炸裂函数
前两篇，给大家分享了Lateral View、Lateral View Outer、explode和posexplode的用法，但是在群里大家有更加深入的讨论，讨论posexplode的应用场景，感觉之前的分享不是很尽兴，于是再加一篇。
1. 测试数据
为了更加完成更加复杂的测试，我们把基础数据调整一下，数据如下：
create table test(id int, items array<string>,cnts array<string>);
insert into test values
(1, array('apple', 'banana', 'pear'),array('1','2','3')),
(2,array('watermelon', 'orange'),array('5','6')),
(3,array(null),array(null));+----------+----------------------------+----------------+
| test.id | test.items | test.cnts |
+----------+----------------------------+----------------+
| 1 | ["apple","banana","pear"] | ["1","2","3"] |
| 2 | ["watermelon","orange"] | ["5","6"] |
| 3 | NULL | NULL |
+----------+----------------------------+----------------+2.两列数组内容笛卡尔积
我们使用两次lateral view explode，可以计算出两列的笛卡尔积，SQL如下
select
id,items,cnts,item,cnt
from test
lateral view explode(items) t_item as item
lateral view explode(cnts) t_cnts as cnt
结果会是：
+-----+----------------------------+----------------+-------------+------+
| id | items | cnts | item | cnt |
+-----+----------------------------+----------------+-------------+------+
| 1 | ["apple","banana","pear"] | ["1","2","3"] | apple | 1 |
| 1 | ["apple","banana","pear"] | ["1","2","3"] | apple | 2 |
| 1 | ["apple","banana","pear"] | ["1","2","3"] | apple | 3 |
| 1 | ["apple","banana","pear"] | ["1","2","3"] | banana | 1 |
| 1 | ["apple","banana","pear"] | ["1","2","3"] | banana | 2 |
| 1 | ["apple","banana","pear"] | ["1","2","3"] | banana | 3 |
| 1 | ["apple","banana","pear"] | ["1","2","3"] | pear | 1 |
| 1 | ["apple","banana","pear"] | ["1","2","3"] | pear | 2 |
| 1 | ["apple","banana","pear"] | ["1","2","3"] | pear | 3 |
| 2 | ["watermelon","orange"] | ["5","6"] | watermelon | 5 |
| 2 | ["watermelon","orange"] | ["5","6"] | watermelon | 6 |
| 2 | ["watermelon","orange"] | ["5","6"] | orange | 5 |
| 2 | ["watermelon","orange"] | ["5","6"] | orange | 6 |
+-----+----------------------------+----------------+-------------+------+上面我们可以看到：item列和cnt两列进行了笛卡尔积计算，在需要计算笛卡尔积的时候，可以这样完成。
2. posexplode用法
可是，如果我们的items和cnts是有对应关系的，即 items中的apple对应cnts中的1，banana对应2，pear对应3，我们就需要有对应的关系，这个时候posexplode就闪亮登场了
select
id,item_idx,item,cnt_idx,cnt
from test
lateral view posexplode(items) t_item as item_idx,item
lateral view posexplode(cnts) t_cnts as cnt_idx,cnt结果会是：
+-----+-----------+-------------+----------+------+
| id | item_idx | item | cnt_idx | cnt |
+-----+-----------+-------------+----------+------+
| 1 | 0 | apple | 0 | 1 |
| 1 | 0 | apple | 1 | 2 |
| 1 | 0 | apple | 2 | 3 |
| 1 | 1 | banana | 0 | 1 |
| 1 | 1 | banana | 1 | 2 |
| 1 | 1 | banana | 2 | 3 |
| 1 | 2 | pear | 0 | 1 |
| 1 | 2 | pear | 1 | 2 |
| 1 | 2 | pear | 2 | 3 |
| 2 | 0 | watermelon | 0 | 5 |
| 2 | 0 | watermelon | 1 | 6 |
| 2 | 1 | orange | 0 | 5 |
| 2 | 1 | orange | 1 | 6 |
+-----+-----------+-------------+----------+------+我们可以限定item_idx和cnt_idx 相等，得到对应的数据：
select
id,item_idx,item,cnt_idx,cnt
from test
lateral view posexplode(items) t_item as item_idx,item
lateral view posexplode(cnts) t_cnts as cnt_idx,cnt
where item_idx=cnt_idx查询结果如下
+-----+-----------+-------------+----------+------+
| id | item_idx | item | cnt_idx | cnt |
+-----+-----------+-------------+----------+------+
| 1 | 0 | apple | 0 | 1 |
| 1 | 1 | banana | 1 | 2 |
| 1 | 2 | pear | 2 | 3 |
| 2 | 0 | watermelon | 0 | 5 |
| 2 | 1 | orange | 1 | 6 |
+-----+-----------+-------------+----------+------+我们可以看到只留下了对应关系的数据。
3.map_from_arrays()
然而，此时群里的朋友提出了新的解法，引入了一个新的函数 map_from_arrays()，具体写法如下：
select
id,items,cnts,map_from_arrays(items,cnts) as col3
from test可是结果出现了意外，语句报错说没有map_from_arrays函数。
于是我去spark集群上进行了测试
可以得到如下结果，可以拿到对应关系结果。
于是大家学到了一个新的方法map_from_arrays()。