我有一个配置单元表作为我的源表。 我还有另外一个作为目标的配置单元表。 源表和目标表的DDL相同，只是在目标表中添加了一些日记记录列。 以下是DDL： 来源：
CREATE EXTERNAL TABLE source.customer_detail(
id string,
name string,
city string,
properties_owned array<struct<property_addr:string, location:string>>
)
ROW FORMAT SERDE
'org.apache.hive.hcatalog.data.JsonSerDe'
STORED AS TEXTFILE
LOCATION
'/user/aiman/customer_detail';
目标：
CREATE EXTERNAL TABLE target.customer_detail(
id string,
name string,
city string,
properties_owned array<struct<property_addr:string, location:string>>
audit_insterted_ts timestamp,
audit_dml_action char(1)
)
PARTITIONED BY (audit_active_flag char(1))
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\u0001'
STORED AS ORC
LOCATION
'/user/aiman/target/customer_detail';
来源数据：
+---------------------+--------------------------+-------------------------+--------------------------------------------------------------------------------------------------------------------------------------+
| customer_detail.id | customer_detail.name | customer_detail.city | customer_detail.properties_owned |
+---------------------+--------------------------+-------------------------+--------------------------------------------------------------------------------------------------------------------------------------+
| 1 | Aiman Sarosh | kolkata | [{"property_addr":"H1 Block Saltlake","location":"kolkata"},{"property_addr":"New Property Added Saltlake","location":"kolkata"}] |
| 2 | Justin | delhi | [{"property_addr":"some address in delhi","location":"delhi"}] |
+---------------------+--------------------------+-------------------------+--------------------------------------------------------------------------------------------------------------------------------------+
目标数据：
+---------------------+--------------------------+-------------------------+------------------------------------------------------------------+--------------------------------------+-----------------------------------+------------------------------------+
| customer_detail.id | customer_detail.name | customer_detail.city | customer_detail.properties_owned | customer_detail.audit_insterted_ts | customer_detail.audit_dml_action | customer_detail.audit_active_flag |
+---------------------+--------------------------+-------------------------+------------------------------------------------------------------+--------------------------------------+-----------------------------------+------------------------------------+
| 1 | Aiman Sarosh | kolkata | [{"property_addr":"H1 Block Saltlake","location":"kolkata"}] | 2018-09-04 06:55:12.361 | I | A |
| 2 | Justin | delhi | [{"property_addr":"some address in delhi","location":"delhi"}] | 2018-09-05 08:36:39.023 | I | A |
+---------------------+--------------------------+-------------------------+---------------------------------------------------------------------------------------------------------+-----------------------------------+------------------------------------+
当我运行以下查询时，它应该为我获取1条已修改的记录，即：
+---------------------+--------------------------+-------------------------+------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+-----------------------------------+------------------------------------+
| customer_detail.id | customer_detail.name | customer_detail.city | customer_detail.properties_owned | customer_detail.audit_insterted_ts | customer_detail.audit_dml_action | customer_detail.audit_active_flag |
+---------------------+--------------------------+-------------------------+------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+-----------------------------------+------------------------------------+
| 1 | Aiman Sarosh | kolkata | [{"property_addr":"H1 Block Saltlake","location":"kolkata"},{"property_addr":"New Property Added Saltlake","location":"kolkata"}] | 2018-09-05 07:15:10.321 | U | A |
+---------------------+--------------------------+-------------------------+------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------+-----------------------------------+------------------------------------+
基本上，{"property_addr":"New Property Added Saltlake","location":"kolkata"}元素已添加到properties_owned中记录ID为1的数组列source中。
查询：
SELECT --fetch modified/updated records in source
source.id AS id,
source.name AS name,
source.city AS city,
source.properties_owned AS properties_owned,
current_timestamp() AS audit_insterted_ts,
'U' AS audit_dml_action,
'A' AS audit_active_flag
FROM source.customer_detail source
INNER JOIN target.customer_detail jrnl
ON source.id=jrnl.id
WHERE source.name!=jrnl.name
OR source.city!=jrnl.city
OR source.properties_owned!=jrnl.properties_owned
但是它抛出错误：
Error: Error while compiling statement: FAILED: SemanticException [Error 10016]: Line 14:3 Argument type mismatch 'properties_owned': The 1st argument of NOT EQUAL is expected to a primitive type, but list is found (state=42000,code=10016)
当我使用JOINS时，如何比较WHERE子句中具有复杂数据类型的两列？
我可以使用.POS和.ITEM，但这将无济于事，因为我的列是结构数组，并且数组的长度可以不同。
答案 0 :(得分：1)
处理复杂类型的一种方法是将它们转换为String，例如Json string。有brickhouse项目带有有用的第三方Hive UDF。它具有to_json函数，可以将任何复杂类型转换为json字符串。首先，克隆并构建jar：
git clone https://github.com/klout/brickhouse.git
cd brickhouse
mvn clean package
然后将Brickhouse jar复制到HDFS并将该jar添加到Hive：
add jar hdfs://<your_path>/brickhouse-0.7.1-SNAPSHOT.jar;
在Hive中注册to_json UDF
create temporary function to_json as 'brickhouse.udf.json.ToJsonUDF';
现在您可以使用它了，例如，
hive> select to_json(ARRAY(MAP('a',1), MAP('b',2)));
OK
[{"a":1},{"b":2}]
因此，在您的情况下，您需要将列转换为json字符串，然后在where子句中进行比较。请记住，to_json会原样转换复杂的值。例如，在您的情况下，两个数组
[{"property_addr":"H1 Block Saltlake","location":"kolkata"},{"property_addr":"New Property Added Saltlake","location":"kolkata"}]
和
[{"property_addr":"New Property Added Saltlake","location":"kolkata"},{"property_addr":"H1 Block Saltlake","location":"kolkata"}]
将有所不同。
答案 1 :(得分：1)
我使用LATERAL VIEW explode()修复了该问题。
然后在爆炸列上结合使用concat_ws()和collect_list(array<string>)方法，最终给了我一个string，我进行了比较：
SELECT --fetch modified/updated records in source
source.id AS id,
source.name AS name,
source.city AS city,
source.properties_owned AS properties_owned,
current_timestamp() AS audit_insterted_ts,
'U' AS audit_dml_action,
'A' AS audit_active_flag
FROM source.customer_detail source
INNER JOIN target.customer_detail jrnl
ON source.id=jrnl.id
WHERE source.id IN
(
SELECT t1.id
FROM
(
SELECT src.id,concat_ws(',', collect_list(src.property_addr),collect_list(src.location)) newcol
FROM
(
SELECT id, prop_owned.property_addr AS property_addr, prop_owned.location AS location
FROM source.customer_detail LATERAL VIEW explode(properties_owned) exploded_tab AS prop_owned
) src
GROUP BY src.id
) t1
INNER JOIN
(
SELECT trg.id,concat_ws(',', collect_list(trg.property_addr),collect_list(trg.location)) newcol
FROM
(
SELECT id, prop_owned.property_addr AS property_addr, prop_owned.location AS location
FROM target.customer_detail LATERAL VIEW explode(properties_owned) exploded_tab AS prop_owned
) trg
GROUP BY trg.id
) t2
ON t1.id=t2.id
WHERE t1.newcol!=t2.newcol
希望有人觉得这很有帮助。 ：-）
答案 2 :(得分：0)
问题：您正在尝试比较列表而不是原始类型
当前情况：无法使用内置的Hive udfs直接比较复杂对象的列表（有一些字符串列表的解决方法）。
解决方法：您将需要一些第三方UDF来帮助您。有一些有趣的udfs here（我之前没有测试过）