Sqoop数据的导入导出

作者: 小猪Harry | 来源:发表于2018-10-05 12:07 被阅读0次

数据导入
1、导入数据库表数据到hdfs
mysql创建表，插入数据，为了使用方便复制了如下

mysql> use test
Reading table information for completion of table and column names
You can turn off this feature to get a quicker startup with -A

Database changed
mysql>CREATE TABLE `emp` (
  `id` int(32) NOT NULL AUTO_INCREMENT,
  `name` varchar(255) NOT NULL,
  `deg` varchar(255) NOT NULL,
  `salary` int(11) NOT NULL,
  `dept` varchar(32) NOT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
Query OK, 0 rows affected (0.03 sec)

mysql> show tables;
+----------------+
| Tables_in_test |
+----------------+
| emp            |
| t_user         |
+----------------+
2 rows in set (0.01 sec)

mysql> desc emp;
+--------+--------------+------+-----+---------+----------------+
| Field  | Type         | Null | Key | Default | Extra          |
+--------+--------------+------+-----+---------+----------------+
| id     | int(32)      | NO   | PRI | NULL    | auto_increment |
| name   | varchar(255) | NO   |     | NULL    |                |
| deg    | varchar(255) | NO   |     | NULL    |                |
| salary | int(11)      | NO   |     | NULL    |                |
| dept   | varchar(32)  | NO   |     | NULL    |                |
+--------+--------------+------+-----+---------+----------------+
5 rows in set (0.02 sec)

mysql> INSERT INTO `test`.`emp` (`id`, `name`, `deg`, `salary`, `dept`) VALUES ('1', 'zhangsan', 'manager', '30000', 'AA');
Query OK, 1 row affected (0.02 sec)

mysql> INSERT INTO `test`.`emp` (`id`, `name`, `deg`, `salary`, `dept`) VALUES ('2', 'lisi', 'programmer', '20000', 'AA');
Query OK, 1 row affected (0.01 sec)

mysql> INSERT INTO `test`.`emp` (`id`, `name`, `deg`, `salary`, `dept`) VALUES ('2', 'wangwu', 'programmer', '15000', 'BB');
ERROR 1062 (23000): Duplicate entry '2' for key 'PRIMARY'
mysql> INSERT INTO `test`.`emp` (`id`, `name`, `deg`, `salary`, `dept`) VALUES ('3', 'wangwu', 'programmer', '15000', 'BB');
Query OK, 1 row affected (0.00 sec)

mysql> INSERT INTO `test`.`emp` (`id`, `name`, `deg`, `salary`, `dept`) VALUES ('4', 'hund', 'programmer', '5000', 'CC');
Query OK, 1 row affected (0.01 sec)

mysql> select * from emp;
+----+----------+------------+--------+------+
| id | name     | deg        | salary | dept |
+----+----------+------------+--------+------+
|  1 | zhangsan | manager    |  30000 | AA   |
|  2 | lisi     | programmer |  20000 | AA   |
|  3 | wangwu   | programmer |  15000 | BB   |
|  4 | hund     | programmer |   5000 | CC   |
+----+----------+------------+--------+------+

使用下面的命令将test数据库中的emp表导入到hdfs（有默认目录）

bin/sqoop import   \
--connect jdbc:mysql://192.168.25.127:3306/test   \
--username root  \
--password 123456   \
--table emp   \
--m 1

数据库ip，使用的数据库
mysql用户名
mysql密码
要导入的表
注:m 1 表示使用一个mapreduce
程序在执行的时候能看到是跑了mapreduce程序的。
执行完毕后页面进行查看（/user/root是默认默认目录，我用的是root用户）

image.png

查看文件内容（数据间逗号隔开的）

[root@mini1 sqoop]# hadoop fs -ls /user/root/emp
Found 2 items
-rw-r--r--   2 root supergroup          0 2017-10-26 09:49 /user/root/emp/_SUCCESS
-rw-r--r--   2 root supergroup        110 2017-10-26 09:49 /user/root/emp/part-m-00000
[root@mini1 sqoop]# hadoop fs -cat  /user/root/emp/part-m-00000
1,zhangsan,manager,30000,AA
2,lisi,programmer,20000,AA
3,wangwu,programmer,15000,BB
4,hund,programmer,5000,CC

注：执行导入的时候很大可能出现下面的异常

java.sql.SQLException: Access denied for user 'root'@'mini1' (using password: YES)
        at com.mysql.jdbc.SQLError.createSQLException(SQLError.java:1086)
     ...
        at org.apache.sqoop.Sqoop.runTool(Sqoop.java:227)
        at org.apache.sqoop.Sqoop.main(Sqoop.java:236)
17/10/26 00:01:46 ERROR tool.ImportTool: Encountered IOException running import job: java.io.IOException: No columns to generate for ClassWriter

这基本就是没授权导致的，给mini1授权即可如下

mysql> grant all privileges on *.* to root@mini1 identified by "123456";
Query OK, 0 rows affected (0.01 sec)

mysql> FLUSH PRIVILEGES;
Query OK, 0 rows affected (0.00 sec)
mysql> show grants for root@mini1;
+------------------------------------------------------------------------------------------------------------------------------------+
| Grants for root@mini1                                                                                                              |
+------------------------------------------------------------------------------------------------------------------------------------+
| GRANT ALL PRIVILEGES ON *.* TO 'root'@'mini1' IDENTIFIED BY PASSWORD '*6BB4837EB74329105EE4568DDA7DC67ED2CA2AD9' WITH GRANT OPTION |
| GRANT PROXY ON ''@'' TO 'root'@'mini1' WITH GRANT OPTION                                                                           |
+------------------------------------------------------------------------------------------------------------------------------------+

2、emp表数据导入到hive表中
其实是先导入到hdfs，再由hdfs导入到hive（属于剪切粘贴）

先将前面生成的目录删了

[root@mini2 ~]# hadoop fs -rm -r  /user/root

执行以下命令导入emp表数据到hive表（表名也是emp）

[root@mini1 sqoop]# bin/sqoop import   \
> --connect jdbc:mysql://192.168.25.127:3306/test   \
> --username root  \
> --password 123456   \
> --table emp   \
> --hive-import \
> --m 1
...
17/10/26 10:04:13 INFO mapreduce.Job: Job job_1508930025306_0022 running in uber mode : false
17/10/26 10:04:13 INFO mapreduce.Job:  map 0% reduce 0%
17/10/26 10:04:17 INFO mapreduce.Job:  map 100% reduce 0%
17/10/26 10:04:18 INFO mapreduce.Job: Job job_1508930025306_0022 completed successfully
17/10/26 10:04:19 INFO mapreduce.Job: Counters: 30
        File System Counters
                FILE: Number of bytes read=0
                FILE: Number of bytes written=124217
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=87
                HDFS: Number of bytes written=110
                HDFS: Number of read operations=4
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        Job Counters 
                Launched map tasks=1
                Other local map tasks=1
                Total time spent by all maps in occupied slots (ms)=2926
                Total time spent by all reduces in occupied slots (ms)=0
                Total time spent by all map tasks (ms)=2926
                Total vcore-milliseconds taken by all map tasks=2926
                Total megabyte-milliseconds taken by all map tasks=2996224
...
17/10/26 10:04:21 INFO hive.HiveImport: It's highly recommended that you fix the library with 'execstack -c <libfile>', or link it with '-z noexecstack'.
17/10/26 10:04:27 INFO hive.HiveImport: OK
17/10/26 10:04:27 INFO hive.HiveImport: Time taken: 1.649 seconds
17/10/26 10:04:27 INFO hive.HiveImport: Loading data to table default.emp
17/10/26 10:04:28 INFO hive.HiveImport: Table default.emp stats: [numFiles=1, totalSize=110]
17/10/26 10:04:28 INFO hive.HiveImport: OK
17/10/26 10:04:28 INFO hive.HiveImport: Time taken: 0.503 seconds
17/10/26 10:04:28 INFO hive.HiveImport: Hive import complete.
17/10/26 10:04:28 INFO hive.HiveImport: Export directory is contains the _SUCCESS file only, removing the directory.

将重要的输出信息都粘贴了下来，可见是先导入到hdfs的文件中，再移动到hive中的。
去hive中查看是否创建了该表导入了数据

hive> select * from emp;
OK
1       zhangsan        manager 30000   AA
2       lisi    programmer      20000   AA
3       wangwu  programmer      15000   BB
4       hund    programmer      5000    CC
Time taken: 0.641 seconds, Fetched: 4 row(s)

3、导入数据到hdfs指定目录
跟导入数据到hdfs查了句指定目录

[root@mini1 sqoop]# bin/sqoop import   \
> --connect jdbc:mysql://192.168.25.127:3306/test   \
> --username root  \
> --password 123456   \
> --table emp   \
> --target-dir /queryresult \
> --m 1

执行后查看

[root@mini3 ~]# hadoop fs -ls /queryresult 
Found 2 items
-rw-r--r--   2 root supergroup          0 2017-10-26 10:14 /queryresult/_SUCCESS
-rw-r--r--   2 root supergroup        110 2017-10-26 10:14 /queryresult/part-m-00000
[root@mini3 ~]# hadoop fs -cat /queryresult/part-m-00000
1,zhangsan,manager,30000,AA
2,lisi,programmer,20000,AA
3,wangwu,programmer,15000,BB
4,hund,programmer,5000,CC

4、导入表数据子集
有时候不是整张表都要导入，那么可以按照需要来进行导入。
比如只导入id，name，salary三个字段，且要求deg=programmer

如下

bin/sqoop import \
--connect jdbc:mysql://192.168.25.127:3306/test  \
--username root \
--password 123456 \
--target-dir /wherequery2 \
--query 'select id,name,deg from emp WHERE  deg = "programmer" and $CONDITIONS' \
--split-by id \
--fields-terminated-by '\t' \
--m 1

split-by id表示按照id切片，fields-terminated-by ‘\t’表示导入到文件系统中的数据分隔符为”\t”,默认是”,”

[root@mini3 ~]# hadoop fs -ls /wherequery2
Found 2 items
-rw-r--r--   2 root supergroup          0 2017-10-26 10:21 /wherequery2/_SUCCESS
-rw-r--r--   2 root supergroup         56 2017-10-26 10:21 /wherequery2/part-m-00000
[root@mini3 ~]# hadoop fs -cat /wherequery2/part-m-00000
2       lisi    programmer
3       wangwu  programmer
4       hund    programmer

--split-by原理

1)--split-by的原理
设置并行--num-mappers=4，加--split-by的情况会根据主键先查最大值和最小值,即：select min(key_id),max(key_id) from tb_oracle_stock_info_key。

如tb_oracle_stock_info_key(股票信息表)中 key_id(主键)最小值为300，最大值为400，那么4个并行度的切片情况如下：

并行度实现的sql如下：

select * from tb_oracle_stock_info_key where key_id between 300 and 325;

select * from tb_oracle_stock_info_key where key_id between 325 and 350;

select * from tb_oracle_stock_info_key where key_id between 351 and 375;

select * from tb_oracle_stock_info_key where key_id between 376 and 400;

综上所述，加--split-by参数后，使用大于1个并行时，效果理论上优于没有加--split-by参数作业。

2)数据倾斜

假设oracle的表tb_oracle_stock_info_key(股票信息表)主键为key_id，sqoop根据max（key_id）来平均分配4份。假设min(key_id)=1,max（key_id）=400，那么导数的时候会按400切割生4份，即 :

select * from tb_oracle_stock_info_key where key_id between 1  and 100;

select * from tb_oracle_stock_info_key where key_id between 101 and 200;

select * from tb_oracle_stock_info_key where key_id between 201 and 300;

select * from tb_oracle_stock_info_key where key_id between 301 and 400;

但是由于数据特殊的原因，key_id=[1,100]分区内自由1条数据，key_id=[101,300]内完全没有数据，99%数据都是key_id=[301,400],这样就会产生数据倾斜，也就是4个并行中，有3个不耗费时间，有1个花了大部分时间，这样的并行效果相当的不好：

因此，在使用并行度的时候需要了解主键的分布情况是是否有必要的。

5、增量导入
增量导入这里是仅导入新增加的表中的行，比如emp表有4条记录，但是我们新表中只需要导入id为3和4的记录进去
使用以下命令

bin/sqoop import \
--connect jdbc:mysql://192.168.25.127:3306/test \
--username root \
--password 123456 \
--table emp --m 1 \
--incremental append \
--check-column id \ 
--last-value 2

[root@mini1 sqoop]# bin/sqoop import \
> --connect jdbc:mysql://192.168.25.127:3306/test \
> --username root \
> --password 123456 \
> --table emp --m 1 \
> --incremental append \
> --check-column id \
> --last-value 2
[root@mini1 sqoop]# hadoop fs -ls /user/root/emp
Found 1 items
-rw-r--r--   2 root supergroup         55 2017-10-26 10:28 /user/root/emp/part-m-00000
[root@mini1 sqoop]# hadoop fs -cat /user/root/emp/part-m-00000
3,wangwu,programmer,15000,BB
4,hund,programmer,5000,CC

数据导出
将hdfs上数据导入到mysql数据库表中
注：需要将mysql上数据库和表创建出来才能导出
继续使用上面的emp表，但是将数据清空

mysql> select * from emp;
+----+----------+------------+--------+------+
| id | name     | deg        | salary | dept |
+----+----------+------------+--------+------+
|  1 | zhangsan | manager    |  30000 | AA   |
|  2 | lisi     | programmer |  20000 | AA   |
|  3 | wangwu   | programmer |  15000 | BB   |
|  4 | hund     | programmer |   5000 | CC   |
+----+----------+------------+--------+------+
4 rows in set (0.00 sec)

mysql> truncate emp;
Query OK, 0 rows affected (0.05 sec)

mysql> select * from emp;
Empty set (0.00 sec)

使用以下命令，将数据从hdfs上指定目录数据导出到mysql指定的数据库和表上

bin/sqoop export \
--connect jdbc:mysql://192.168.25.127:3306/test \
--username root \
--password 123456 \
--table emp \
--export-dir /user/root/emp/

执行完之后查看表emp数据

mysql> select * from emp;
+----+--------+------------+--------+------+
| id | name   | deg        | salary | dept |
+----+--------+------------+--------+------+
|  3 | wangwu | programmer |  15000 | BB   |
|  4 | hund   | programmer |   5000 | CC   |
+----+--------+------------+--------+------+
2 rows in set (0.00 sec)

导出完成

Sqoop数据的导入导出

--split-by原理

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