问题分析：
- hive中分区表其底层就是HDFS中的多个目录下的单个文件，hive导出数据本质是将HDFS中的文件导出
- hive中的分区表，因为分区字段（静态分区）不在文件中，所以在sqoop导出的时候，无法将分区字段进行直接导出
思路：在hive中创建一个临时表，将分区表复制过去后分区字段转换为普通字段，然后再用sqoop将tmp表导出即实现需求
步凑如下：
1.创建目标表（分区表）
hive> CREATE TABLE `dept_partition`(
`deptno` int,
`dname` string,
`loc` string)
PARTITIONED BY (`month` string) row format delimited fields terminated by '\t';
1.1查看表结构
hive> show create table dept_partition;
+----------------------------------------------------+--+
| createtab_stmt |
+----------------------------------------------------+--+
| CREATE TABLE `dept_partition`( |
| `deptno` int, |
| `dname` string, |
| `loc` string) |
| PARTITIONED BY ( |
| `month` string)
2.导入数据
hive> load data inpath '/user/hive/hive_db/data/dept.txt' into table dept_partition;
10 ACCOUNTING 1700
20 RESEARCH 1800
30 SALES 1900
40 OPERATIONS 1700
3.查询表dept_partition
hive> select * from dept_partition;
+------------------------+-----------------------+---------------------+-----------------------+--+
| dept_partition.deptno | dept_partition.dname | dept_partition.loc | dept_partition.month |
+------------------------+-----------------------+---------------------+-----------------------+--+
| 10 | ACCOUNTING | 1700 | 2019-10-19 |
| 20 | RESEARCH | 1800 | 2019-10-19 |
| 30 | SALES | 1900 | 2019-10-19 |
| 40 | OPERATIONS | 1700 | 2019-10-19 |
| 10 | ACCOUNTING | 1700 | 2019-10-20 |
| 20 | RESEARCH | 1800 | 2019-10-20 |
| 30 | SALES | 1900 | 2019-10-20 |
| 40 | OPERATIONS | 1700 | 2019-10-20 |
+------------------------+-----------------------+---------------------+-----------------------+--+
4.创建临时表 tmp_dept_partition
hive> create table tmp_dept_partition as select * from dept_partition;
5.查询临时表
hive> select * from tmp_dept_partition;
+----------------------------+---------------------------+-------------------------+---------------------------+--+
| tmp_dept_partition.deptno | tmp_dept_partition.dname | tmp_dept_partition.loc | tmp_dept_partition.month |
+----------------------------+---------------------------+-------------------------+---------------------------+--+
| 10 | ACCOUNTING | 1700 | 2019-10-19 |
| 20 | RESEARCH | 1800 | 2019-10-19 |
| 30 | SALES | 1900 | 2019-10-19 |
| 40 | OPERATIONS | 1700 | 2019-10-19 |
| 10 | ACCOUNTING | 1700 | 2019-10-20 |
| 20 | RESEARCH | 1800 | 2019-10-20 |
| 30 | SALES | 1900 | 2019-10-20 |
| 40 | OPERATIONS | 1700 | 2019-10-20 |
+----------------------------+---------------------------+-------------------------+---------------------------+--+
6.查看表结构(这个时候分区表已经转换为非分区表了)
hive> show create table tmp_dept_partition;
+----------------------------------------------------+--+
| createtab_stmt |
+----------------------------------------------------+--+
| CREATE TABLE `tmp_dept_partition`( |
| `deptno` int, |
| `dname` string, |
| `loc` string, |
| `month` string)
7.MySQL中建表 dept_partition
mysql> drop table if exists dept_partition;
create table dept_partition(
`deptno` int,
`dname` varchar(20),
`loc` varchar(20),
`month` varchar(50))
8.使用sqoop导入到MySQL
bin/sqoop export \
--connect jdbc:mysql://hadoop01:3306/partitionTb \
--username root \
--password 123456 \
--table dept_partition \
--num-mappers 1 \
--export-dir /user/hive/warehouse/hive_db.db/tmp_dept_partition \
--input-fields-terminated-by "\001"
9.Mysql查询验证是否成功导出
mysql> select * from dept_partition;
+--------+------------+------+------------+
| deptno | dname | loc | month |
+--------+------------+------+------------+
| 10 | ACCOUNTING | 1700 | 2019-10-19 |
| 20 | RESEARCH | 1800 | 2019-10-19 |
| 30 | SALES | 1900 | 2019-10-19 |
| 40 | OPERATIONS | 1700 | 2019-10-19 |
| 10 | ACCOUNTING | 1700 | 2019-10-20 |
| 20 | RESEARCH | 1800 | 2019-10-20 |
| 30 | SALES | 1900 | 2019-10-20 |
| 40 | OPERATIONS | 1700 | 2019-10-20 |
+--------+------------+------+------------+
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