hive lateral view 与 explode详解

1.explode

Then running the query:

SELECT explode(myCol) AS myNewCol FROM myTable;
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will produce: 
 
The usage with Maps is similar:

SELECT explode(myMap) AS (myMapKey, myMapValue) FROM myMapTable;
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总结起来一句话：explode就是将hive一行中复杂的array或者map结构拆分成多行。

使用实例： 
xxx表中有一个字段mvt为string类型，数据格式如下：

[{“eid”:”38”,”ex”:”affirm_time_Android”,”val”:”1”,”vid”:”31”,”VR”:”var1”},{“eid”:”42”,”ex”:”new_comment_Android”,”val”:”1”,”vid”:”34”,”vr”:”var1”},{“eid”:”40”,”ex”:”new_rpname_Android”,”val”:”1”,”vid”:”1”,”vr”:”var1”},{“eid”:”19”,”ex”:”hotellistlpage_Android”,”val”:”1”,”vid”:”1”,”vr”:”var01”},{“eid”:”29”,”ex”:”bookhotelpage_Android”,”val”:”0”,”vid”:”1”,”vr”:”var01”},{“eid”:”17”,”ex”:”trainMode_Android”,”val”:”1”,”vid”:”1”,”vr”:”mode_Android”},{“eid”:”44”,”ex”:”ihotelList_Android”,”val”:”1”,”vid”:”36”,”vr”:”var1”},{“eid”:”47”,”ex”:”ihotelDetail_Android”,”val”:”0”,”vid”:”38”,”vr”:”var1”}]

用explode小试牛刀一下：

select explode(split(regexp_replace(mvt,'\\[|\\]',''),'\\},\\{')) from ods_mvt_hourly where day=20160710 limit 10;
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最后出来的结果如下： 
{“eid”:”38”,”ex”:”affirm_time_Android”,”val”:”1”,”vid”:”31”,”vr”:”var1” 
“eid”:”42”,”ex”:”new_comment_Android”,”val”:”1”,”vid”:”34”,”vr”:”var1” 
“eid”:”40”,”ex”:”new_rpname_Android”,”val”:”1”,”vid”:”1”,”vr”:”var1” 
“eid”:”19”,”ex”:”hotellistlpage_Android”,”val”:”1”,”vid”:”1”,”vr”:”var01” 
“eid”:”29”,”ex”:”bookhotelpage_Android”,”val”:”0”,”vid”:”1”,”vr”:”var01” 
“eid”:”17”,”ex”:”trainMode_Android”,”val”:”1”,”vid”:”1”,”vr”:”mode_Android” 
“eid”:”44”,”ex”:”ihotelList_Android”,”val”:”1”,”vid”:”36”,”vr”:”var1” 
“eid”:”47”,”ex”:”ihotelDetail_Android”,”val”:”0”,”vid”:”38”,”vr”:”var1”} 
{“eid”:”38”,”ex”:”affirm_time_Android”,”val”:”1”,”vid”:”31”,”vr”:”var1” 
“eid”:”42”,”ex”:”new_comment_Android”,”val”:”1”,”vid”:”34”,”vr”:”var1”

2.lateral view

hive wiki 上的解释如下：

Example

Consider the following base table named pageAds. It has two columns: pageid (name of the page) and adid_list (an array of ads appearing on the page) 

An example table with two rows: 

and the user would like to count the total number of times an ad appears across all pages. 
A lateral view with explode() can be used to convert adid_list into separate rows using the query:

SELECT pageid, adidFROM pageAds LATERAL VIEW explode(adid_list) adTable AS adid;
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The resulting output will be 
 
Then in order to count the number of times a particular ad appears, count/group by can be used:

SELECT adid, count(1)FROM pageAds LATERAL VIEW explode(adid_list) adTable AS adidGROUP BY adid;
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The resulting output will be 

由此可见，lateral view与explode等udtf就是天生好搭档，explode将复杂结构一行拆成多行，然后再用lateral view做各种聚合。

3.实例

还是第一部分的例子，上面我们explode出来以后的数据，不是标准的json格式，我们通过lateral view与explode组合解析出标准的json格式数据:

SELECT ecrd, CASE WHEN instr(mvtstr,'{')=0    AND instr(mvtstr,'}')=0 THEN concat('{',mvtstr,'}') WHEN instr(mvtstr,'{')=0    AND instr(mvtstr,'}')>0 THEN concat('{',mvtstr) WHEN instr(mvtstr,'}')=0    AND instr(mvtstr,'{')>0 THEN concat(mvtstr,'}') ELSE mvtstr END AS mvt      FROM ods.ods_mvt_hourly LATERAL VIEW explode(split(regexp_replace(mvt,'\\[|\\]',''),'\\},\\{')) addTable AS mvtstr        WHERE DAY='20160710' and ecrd is not null limit 10
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查询出来的结果： 
xxx 
{“eid”:”38”,”ex”:”affirm_time_Android”,”val”:”1”,”vid”:”31”,”vr”:”var1”} 
xxx 
{“eid”:”42”,”ex”:”new_comment_Android”,”val”:”1”,”vid”:”34”,”vr”:”var1”} 
xxx 
{“eid”:”40”,”ex”:”new_rpname_Android”,”val”:”1”,”vid”:”1”,”vr”:”var1”} 
xxx 
{“eid”:”19”,”ex”:”hotellistlpage_Android”,”val”:”1”,”vid”:”1”,”vr”:”var01”} 
xxx 
{“eid”:”29”,”ex”:”bookhotelpage_Android”,”val”:”0”,”vid”:”1”,”vr”:”var01” 
xxx 
{“eid”:”17”,”ex”:”trainMode_Android”,”val”:”1”,”vid”:”1”,”vr”:”mode_Android”} 
xxx 
{“eid”:”44”,”ex”:”ihotelList_Android”,”val”:”1”,”vid”:”36”,”vr”:”var1”} 
xxx 
{“eid”:”47”,”ex”:”ihotelDetail_Android”,”val”:”1”,”vid”:”38”,”vr”:”var1”} 
xxx 
{“eid”:”38”,”ex”:”affirm_time_Android”,”val”:”1”,”vid”:”31”,”vr”:”var1”} 
xxx 
{“eid”:”42”,”ex”:”new_comment_Android”,”val”:”1”,”vid”:”34”,”vr”:”var1”}

4.Ending

Lateral View通常和UDTF一起出现，为了解决UDTF不允许在select字段的问题。 
Multiple Lateral View可以实现类似笛卡尔乘积。 
Outer关键字可以把不输出的UDTF的空结果，输出成NULL，防止丢失数据。