Hive分区表修改（增删）列

2023-05-16

文章目录

一、环境及测试数据
二、删除列
- 2.1 测试表test2
- 2.2 DDL删除列？
- 2.3 代码连接Hive Metastore删除列
- - 2.3.1 同时更新表与分区元数据
三、添加列
结论
参考链接

一、环境及测试数据

环境：CDH6.3.0，Hive 2.1.1-cdh6.3.0

基础数据分区表test1，包含a,b,c,d共4列加分区列p_day，向其中插入两行数据

create table test1(a int,b bigint,c float,d string) partitioned by(p_day string) stored as parquet;

insert into test1 partition(p_day) values(1,11,1.1,'str1','2022-11-19'),(2,22,2.2,'str2','2022-11-19');

表中数据及parquet文件信息如下：

0: jdbc:hive2://dev-master1:10000 > select * from test1;
+----------+----------+----------+----------+--------------+
| test1.a  | test1.b  | test1.c  | test1.d  | test1.p_day  |
+----------+----------+----------+----------+--------------+
| 1        | 11       | 1.1      | str1     | 2022-11-19   |
| 2        | 22       | 2.2      | str2     | 2022-11-19   |
+----------+----------+----------+----------+--------------+

[hive@dev-master1 tmp]$ hdfs dfs -get /user/hive/warehouse/debug_test.db/test1/p_day=2022-11-19/000000_0 ./
[hive@dev-master1 tmp]$ parquet-tools schema 000000_0
message hive_schema {
  optional int32 a;
  optional int64 b;
  optional float c;
  optional binary d (STRING);
}

[hive@dev-master1 tmp]$ parquet-tools cat 000000_0
a = 1
b = 11
c = 1.1
d = str1

a = 2
b = 22
c = 2.2
d = str2

二、删除列

2.1 测试表test2

create table test2(b bigint,a int,c float) partitioned by(p_day string) stored as parquet;

test2表直接使用test1表的文件：

[hive@dev-master1 tmp]$ hdfs dfs -mkdir /user/hive/warehouse/debug_test.db/test2/p_day=2022-11-19
[hive@dev-master1 tmp]$ hdfs dfs -cp /user/hive/warehouse/debug_test.db/test1/p_day=2022-11-19/000000_0 /user/hive/warehouse/debug_test.db/test2/p_day=2022-11-19/

修复分区并查询数据

msck repair table test2;
select * from test2;
+----------+----------+----------+--------------+
| test2.b  | test2.a  | test2.c  | test2.p_day  |
+----------+----------+----------+--------------+
| 11       | 1        | 1.1      | 2022-11-19   |
| 22       | 2        | 2.2      | 2022-11-19   |
+----------+----------+----------+--------------+

2.2 DDL删除列？

删除test2表的a列，看起来只有通过replace columns实现，但是运行报错，根据官方文档，只有表的序列化方式为native SerDe（DynamicSerDe, MetadataTypedColumnsetSerDe, LazySimpleSerDe and ColumnarSerDe）才能执行。

0: jdbc:hive2://dev-master1:10000> alter table test2 replace columns(b bigint,c float);
INFO  : Compiling command(queryId=hive_20221119180121_23e7971f-7b2f-4693-90b9-469ec44a97bd): alter table test2 replace columns(b bigint,c float)
INFO  : Semantic Analysis Completed
INFO  : Returning Hive schema: Schema(fieldSchemas:null, properties:null)
INFO  : Completed compiling command(queryId=hive_20221119180121_23e7971f-7b2f-4693-90b9-469ec44a97bd); Time taken: 1.479 seconds
INFO  : Executing command(queryId=hive_20221119180121_23e7971f-7b2f-4693-90b9-469ec44a97bd): alter table test2 replace columns(b bigint,c float)
INFO  : Starting task [Stage-0:DDL] in serial mode
ERROR : FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. Replacing columns cannot drop columns for table debug_test.test2. SerDe may be incompatible
INFO  : Completed executing command(queryId=hive_20221119180121_23e7971f-7b2f-4693-90b9-469ec44a97bd); Time taken: 0.018 seconds
Error: Error while processing statement: FAILED: Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. Replacing columns cannot drop columns for table debug_test.test2. SerDe may be incompatible (state=42000,code=1)

2.3 代码连接Hive Metastore删除列

主要Maven依赖：

用代码连接Hive MetaStore修改可以成功:

package com.bigdata.databasetest.hive.metastore;

import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.metastore.HiveMetaStoreClient;
import org.apache.hadoop.hive.metastore.api.FieldSchema;
import org.apache.hadoop.hive.metastore.api.Table;
import org.apache.thrift.TException;

import java.util.List;
import java.util.stream.Collectors;


/**
 * ClassName： HiveMetaStoreClientTest
 * Description：
 *
 * @author 0x3E6
 * @version 1.0.0
 * @date 2022/11/19 17:12
 */
public class HiveMetaStoreClientTest {

    public static void main(String[] args) throws TException {
        HiveConf hiveConf = new HiveConf();
        System.setProperty("HADOOP_USER_NAME", "hive");
        hiveConf.set(HiveConf.ConfVars.METASTOREURIS.varname, "thrift://dev-master1:9083");
        try (HiveMetaStoreClient client = new HiveMetaStoreClient(hiveConf)) {
            Table table = client.getTable("debug_test", "test2");
            List<FieldSchema> cols = table.getSd().getCols();
            cols = cols.stream().filter(fieldSchema -> !"a".equalsIgnoreCase(fieldSchema.getName())).collect(Collectors.toList());
            table.getSd().setCols(cols);
            client.alter_table("debug_test", "test2", table);
        }
    }
}

这样查询数据有问题，不论sql是否带分区：

select * from test2;
select * from test2 where p_day='2022-11-19';

查询结果为：

+----------+----------+--------------+
| test2.b  | test2.c  | test2.p_day  |
+----------+----------+--------------+
| 11       | NULL     | 2022-11-19   |
| 22       | NULL     | 2022-11-19   |
+----------+----------+--------------+

因为只改了表的元数据，而未改分区的元数据。

0: jdbc:hive2://dev-master1:10000> desc test2;
+--------------------------+-----------------------+-----------------------+
|         col_name         |       data_type       |        comment        |
+--------------------------+-----------------------+-----------------------+
| b                        | bigint                |                       |
| c                        | float                 |                       |
| p_day                    | string                |                       |
|                          | NULL                  | NULL                  |
| # Partition Information  | NULL                  | NULL                  |
| # col_name               | data_type             | comment               |
|                          | NULL                  | NULL                  |
| p_day                    | string                |                       |
+--------------------------+-----------------------+-----------------------+
0: jdbc:hive2://dev-master1:10000> desc test2 partition(p_day='2022-11-19');
+--------------------------+-----------------------+-----------------------+
|         col_name         |       data_type       |        comment        |
+--------------------------+-----------------------+-----------------------+
| b                        | bigint                |                       |
| a                        | int                   |                       |
| c                        | float                 |                       |
| p_day                    | string                |                       |
|                          | NULL                  | NULL                  |
| # Partition Information  | NULL                  | NULL                  |
| # col_name               | data_type             | comment               |
|                          | NULL                  | NULL                  |
| p_day                    | string                |                       |
+--------------------------+-----------------------+-----------------------+

具体原理及原因还未分析，但可以修复，只需要保持分区的列与表的列顺序一致，无法执行REPLACE COLUMNS语句，通过CHANGE COLUMNS语句将c列移动到a列之前：

alter table test2 partition(p_day='2022-11-19') change column c c float after b;
select * from test2 where p_day='2022-11-19';
+----------+----------+--------------+
| test2.b  | test2.c  | test2.p_day  |
+----------+----------+--------------+
| 11       | 1.1      | 2022-11-19   |
| 22       | 2.2      | 2022-11-19   |
+----------+----------+--------------+

2.3.1 同时更新表与分区元数据

ALTER TABLE语句可以添加CASCADE，更新表元数据的同时级联更新分区元数据，Hive MetaStoreClient API也可以使用带CASCADE的方法，前面的代码调用的HiveMetaStoreClient的alter_table(String dbname, String tbl_name, Table new_tbl)方法，可以调用alter_table(String defaultDatabaseName, String tblName, Table table, boolean cascade)方法，新建表test3与test2结构一致，也同样把test1的数据拷贝到表test3，再通过代码删除表test3的列a：

package com.bigdata.databasetest.hive.metastore;

import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.metastore.HiveMetaStoreClient;
import org.apache.hadoop.hive.metastore.api.FieldSchema;
import org.apache.hadoop.hive.metastore.api.Table;
import org.apache.thrift.TException;

import java.util.List;
import java.util.stream.Collectors;


/**
 * ClassName： HiveMetaStoreClientTest
 * Description：
 *
 * @author 0x3E6
 * @version 1.0.0
 * @date 2022/11/19 17:12
 */
public class HiveMetaStoreClientTest {

    public static void main(String[] args) throws TException {
        HiveConf hiveConf = new HiveConf();
        System.setProperty("HADOOP_USER_NAME", "hive");
        hiveConf.set(HiveConf.ConfVars.METASTOREURIS.varname, "thrift://dev-master1:9083");
        try (HiveMetaStoreClient client = new HiveMetaStoreClient(hiveConf)) {
            Table table = client.getTable("debug_test", "test3");
            List<FieldSchema> cols = table.getSd().getCols();
            cols = cols.stream().filter(fieldSchema -> !"a".equalsIgnoreCase(fieldSchema.getName())).collect(Collectors.toList());
            table.getSd().setCols(cols);
//            client.alter_table("debug_test", "test2", table);
            client.alter_table("debug_test", "test3", table, true);
        }
    }
}

更新后表test3可正常查询：

0: jdbc:hive2://dev-master1:10000> select * from test3;
+----------+----------+--------------+
| test3.b  | test3.c  | test3.p_day  |
+----------+----------+--------------+
| 11       | 1.1      | 2022-11-19   |
| 22       | 2.2      | 2022-11-19   |
+----------+----------+--------------+

表与分区元数据也相同：

0: jdbc:hive2://dev-master1:10000> desc test3;
+--------------------------+-----------------------+-----------------------+
|         col_name         |       data_type       |        comment        |
+--------------------------+-----------------------+-----------------------+
| b                        | bigint                |                       |
| c                        | float                 |                       |
| p_day                    | string                |                       |
|                          | NULL                  | NULL                  |
| # Partition Information  | NULL                  | NULL                  |
| # col_name               | data_type             | comment               |
|                          | NULL                  | NULL                  |
| p_day                    | string                |                       |
+--------------------------+-----------------------+-----------------------+
0: jdbc:hive2://dev-master1:10000> desc test3 partition(p_day='2022-11-19');
+--------------------------+-----------------------+-----------------------+
|         col_name         |       data_type       |        comment        |
+--------------------------+-----------------------+-----------------------+
| b                        | bigint                |                       |
| c                        | float                 |                       |
| p_day                    | string                |                       |
|                          | NULL                  | NULL                  |
| # Partition Information  | NULL                  | NULL                  |
| # col_name               | data_type             | comment               |
|                          | NULL                  | NULL                  |
| p_day                    | string                |                       |
+--------------------------+-----------------------+-----------------------+

三、添加列

根据Hive文档，可通过ADD COLUMNS语句添加列，ADD COLUMNS语句会将指定的列添加到（除分区列外）其他列后面。

ALTER TABLE table_name 
  [PARTITION partition_spec]                 -- (Note: Hive 0.14.0 and later)
  ADD|REPLACE COLUMNS (col_name data_type [COMMENT col_comment], ...)
  [CASCADE|RESTRICT]                         -- (Note: Hive 1.1.0 and later)

如果使用Hive MetaStoreClient API不小心将列加到了（除分区列）外其他列之间，且造成了表与分区列顺序不同，也可结合CHANGE COLUMNS语句修改表或分区的列顺序恢复即可：

ALTER TABLE table_name [PARTITION partition_spec] CHANGE [COLUMN] col_old_name col_new_name column_type
  [COMMENT col_comment] [FIRST|AFTER column_name] [CASCADE|RESTRICT];

结论

可以通过Hive MetaStoreClient API增删Hive列，但必须保持Hive表与各分区元数据的列顺序一致。

参考链接

Hive LanguageManual DDL
Parquet格式表重命名列名后Hive查询列数据显示NULL异常分析
Java调用Hive-metastore接口

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