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 ' ';
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 " 01"
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 | +--------+------------+------+------------+