hive函数之~hive当中的lateral view 与 explode

1、使用explode函数将hive表中的Map和Array字段数据进行拆分

  lateral view用于和split、explode等UDTF一起使用的，能将一行数据拆分成多行数据，在此基础上可以对拆分的数据进行聚合，lateral view首先为原始表的每行调用UDTF，UDTF会把一行拆分成一行或者多行，lateral view在把结果组合，产生一个支持别名表的虚拟表。

  其中explode还可以用于将hive一列中复杂的array或者map结构拆分成多行。

需求：现在有数据格式如下

zhangsan     child1,child2,child3,child4      k1:v1,k2:v2
lisi  child5,child6,child7,child8      k3:v3,k4:v4

  字段之间使用 分割，需求将所有的child进行拆开成为一列

+----------+--+
| mychild  |
+----------+--+
| child1   |
| child2   |
| child3   |
| child4   |
| child5   |
| child6   |
| child7   |
| child8   |
+----------+--+

将map的key和value也进行拆开，成为如下结果

+-----------+-------------+--+
| mymapkey  | mymapvalue  |
+-----------+-------------+--+
| k1        | v1          |
| k2        | v2          |
| k3        | v3          |
| k4        | v4          |
+-----------+-------------+--+

第一步：创建hive数据库

创建hive数据库

hive (default)> create database hive_explode;
hive (default)> use hive_explode;

第二步：创建hive表，然后使用explode拆分map和array

hive (hive_explode)> create  table t3(name string,children array<string>,address Map<string,string>)
                    row format delimited fields terminated by '	'
                    collection items terminated by ','
                    map keys terminated by ':' stored as textFile;

第三步：加载数据

node03执行以下命令创建表数据文件

mkdir -p /export/servers/hivedatas/
cd /export/servers/hivedatas/
vim maparray

zhangsan     child1,child2,child3,child4      k1:v1,k2:v2
lisi  child5,child6,child7,child8      k3:v3,k4:v4

hive表当中加载数据

hive (hive_explode)> load data local inpath '/export/servers/hivedatas/maparray' into table t3;

第四步：使用explode将hive当中数据拆开

将array当中的数据拆分开

hive (hive_explode)> SELECT explode(children) AS myChild FROM t3;

将map当中的数据拆分开

hive (hive_explode)> SELECT explode(address) AS (myMapKey, myMapValue) FROM t3;

2、使用explode拆分json字符串

需求：现在有一些数据格式如下：

a:shandong,b:beijing,c:hebei|1,2,3,4,5,6,7,8,9|[{"source":"7fresh","monthSales":4900,"userCount":1900,"score":"9.9"},{"source":"jd","monthSales":2090,"userCount":78981,"score":"9.8"},{"source":"jdmart","monthSales":6987,"userCount":1600,"score":"9.0"}]

其中字段与字段之间的分隔符是 |

我们要解析得到所有的monthSales对应的值为以下这一列（行转列）

4900
2090
6987

第一步：创建hive表

hive (hive_explode)> create table explode_lateral_view
                   (`area` string,
                   `goods_id` string,
                   `sale_info` string)
                   ROW FORMAT DELIMITED
                   FIELDS TERMINATED BY '|'
                   STORED AS textfile;

第二步：准备数据并加载数据

准备数据如下

cd /export/servers/hivedatas
vim explode_json

a:shandong,b:beijing,c:hebei|1,2,3,4,5,6,7,8,9|[{"source":"7fresh","monthSales":4900,"userCount":1900,"score":"9.9"},{"source":"jd","monthSales":2090,"userCount":78981,"score":"9.8"},{"source":"jdmart","monthSales":6987,"userCount":1600,"score":"9.0"}]

加载数据到hive表当中去

hive (hive_explode)> load data local inpath '/export/servers/hivedatas/explode_json' overwrite into table explode_lateral_view;

第三步：使用explode拆分Array

hive (hive_explode)> select explode(split(goods_id,',')) as goods_id from explode_lateral_view;

第四步：使用explode拆解Map

hive (hive_explode)> select explode(split(area,',')) as area from explode_lateral_view;

5．创建hive表并导入数据

创建hive表并加载数据

hive (hive_explode)> create table person_info(
                    name string,
                    constellation string,
                    blood_type string)
                    row format delimited fields terminated by "	";
加载数据
hive (hive_explode)> load data local inpath '/export/servers/hivedatas/constellation.txt' into table person_info;

第五步：拆解json字段

hive (hive_explode)> select explode(split(regexp_replace(regexp_replace(sale_info,'\[\{',''),'}]',''),'},\{')) as  sale_info from explode_lateral_view;

然后我们想用get_json_object来获取key为monthSales的数据：

hive (hive_explode)> select get_json_object(explode(split(regexp_replace(regexp_replace(sale_info,'\[\{',''),'}]',''),'},\{')),'$.monthSales') as  sale_info from explode_lateral_view;

然后挂了FAILED: SemanticException [Error 10081]: UDTF's are not supported outside the SELECT clause, nor nested in expressions

UDTF explode不能写在别的函数内

如果你这么写，想查两个字段，select explode(split(area,',')) as area,good_id from explode_lateral_view;

会报错FAILED: SemanticException 1:40 Only a single expression in the SELECT clause is supported with UDTF's. Error encountered near token 'good_id'

使用UDTF的时候，只支持一个字段，这时候就需要LATERAL VIEW出场了

3、配合LATERAL  VIEW使用

配合lateral view查询多个字段

hive (hive_explode)> select goods_id2,sale_info from explode_lateral_view LATERAL VIEW explode(split(goods_id,','))goods as goods_id2;

其中LATERAL VIEW explode(split(goods_id,','))goods相当于一个虚拟表，与原表explode_lateral_view笛卡尔积关联。

也可以多重使用

hive (hive_explode)> select goods_id2,sale_info,area2
                    from explode_lateral_view
                    LATERAL VIEW explode(split(goods_id,','))goods as goods_id2
                    LATERAL VIEW explode(split(area,','))area as area2;
也是三个表笛卡尔积的结果

最终，我们可以通过下面的句子，把这个json格式的一行数据，完全转换成二维表的方式展现

hive (hive_explode)> select get_json_object(concat('{',sale_info_1,'}'),'$.source') as source,
                    get_json_object(concat('{',sale_info_1,'}'),'$.monthSales') as monthSales,
                    get_json_object(concat('{',sale_info_1,'}'),'$.userCount') as monthSales,
                    get_json_object(concat('{',sale_info_1,'}'),'$.score') as monthSales from explode_lateral_view
                    LATERAL VIEW explode(split(regexp_replace(regexp_replace(sale_info,'\[\{',''),'}]',''),'},\{'))sale_info as sale_info_1;

总结：

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。