Hive解析多重嵌套JSON数组
Hive解析多重嵌套JSON数组
最近在工作中遇到一个很难解析的JSON,他是一个嵌套的JSON数组的JSON,要使用Hive来进行解析,用Presto写了一次,逻辑就很清晰,因为Presto自带了JSON数据类型,转换数组就很方便,而Hive解析完JSON数组后是一个字符串,只能使用split
方法来对string
类型的数据进行切分,所以如果遇到多层嵌套的数组,要注意切分方法,不然就会乱套。
需要解析的JSON
{ "base": { "code": "xm", "name": "project" }, "list": [{ "ACode": "cp1", "AName": "Product1", "BList": [{ "BCode": "gn1", "BName": "Feature1" }, { "BCode": "gn2", "BName": "Feature2" }] }, { "ACode": "cp2", "AName": "Product2", "BList": [{ "BCode": "gn1", "BName": "Feature1" }] }]}
解析出来的结果应该如下表所示
code | name | ACode | Aname | Bcode | Bname |
---|---|---|---|---|---|
xm | project | cp1 | Product1 | gn1 | Feature1 |
xm | project | cp1 | Product1 | gn2 | Feature2 |
xm | project | cp2 | Product2 | gn1 | Feature1 |
解决方案
首先使用get_json_object
方法,把需要解析的数组解析出来,然后使用regexp_replace
将}]},{
替换成}]}||{
,然后再使用split
方法对||
进行分割,分割成数组后,使用lateral view explode
方法对其进行展开成多列即刻。
SELECT code , name , ai.ACode , ai.AName , bi.BCode , bi.BNameFROM ( SELECT get_json_object(t.value, '$.base.code') AS code , get_json_object(t.value, '$.base.name') AS name , get_json_object(t.value, '$.list') AS list FROM ( SELECT '{"base":{"code":"xm","name":"project"},"list":[{"ACode":"cp1","AName":"Product1","BList":[{"BCode":"gn1","BName":"Feature1"},{"BCode":"gn2","BName":"Feature2"}]},{"ACode":"cp2","AName":"Product2","BList":[{"BCode":"gn1","BName":"Feature1"}]}]}' as value ) t ) t lateral view explode(split(regexp_replace(regexp_extract(list,'^\\[(.+)\\]$',1),'\\}\\]\\}\\,\\{', '\\}\\]\\}\\|\\|\\{'),'\\|\\|')) list as a lateral view json_tuple(a,'ACode','AName','BList') ai as ACode , AName , BList lateral view explode(split(regexp_replace(regexp_extract(BList,'^\\[(.+)\\]$',1),'\\}\\,\\{', '\\}\\|\\|\\{'),'\\|\\|')) BList as b lateral view json_tuple(b,'BCode','BName') bi as BCode , BName;
执行完
xm project cp1 Product1 gn1 Feature1xm project cp1 Product1 gn2 Feature2xm project cp2 Product2 gn1 Feature1Time taken: 0.787 seconds, Fetched: 3 row(s)
总结
- 尝试切分为数组后,使用
lateral view posexplode
方案,逐层解析,但这样会导致笛卡尔。所以必须一次性全部解析好,而不是套用多个子查询逐层解析; - 使用
OUTER
字段,能使LATERAL VIEW
不忽略NULL
include
OUTER
in the query to get rows with NULL valuessomething like,
select * FROM table LATERAL VIEW OUTER explode ( split ( email ,',' ) ) email AS email_id;
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