这篇文章主要介绍“Sqoop1.x的安装配置方法”，在日常操作中，相信很多人在Sqoop1.x的安装配置方法问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”Sqoop1.x的安装配置方法”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
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一、安装hadoop
hadoop:
sqoop2.x:
http://my.oschina.net/u/204498/blog/518941
二、安装sqoop1.x
1.选择对应的版本
[hadoop@hftclclw0001 ~]$ pwd
/home/hadoop
[hadoop@hftclclw0001 ~]$ wget
[hadoop@hftclclw0001 ~]$ tar -zxvf sqoop-1.4.6.bin__hadoop-2.0.4-alpha.tar.gz
[hadoop@hftclclw0001 ~]$ cd sqoop-1.4.6.bin__hadoop-2.0.4-alpha/conf
[hadoop@hftclclw0001 conf]$ ls -al
total 44
drwx------ 2 hadoop root 4096 Nov 25 04:32 .
drwx------ 9 hadoop root 4096 Nov 25 04:20 ..
-rw------- 1 hadoop root 818 Apr 27 2015 .gitignore
-rw------- 1 hadoop root 3895 Apr 27 2015 oraoop-site-template.xml
-rw------- 1 hadoop root 1404 Apr 27 2015 sqoop-env-template.cmd
-rwx------ 1 hadoop root 1345 Apr 27 2015 sqoop-env-template.sh
-rw------- 1 hadoop root 5531 Apr 27 2015 sqoop-site-template.xml
-rw------- 1 hadoop root 5531 Apr 27 2015 sqoop-site.xml
[hadoop@hftclclw0001 conf]$ cp sqoop-env-template.sh sqoop-env.sh
[hadoop@hftclclw0001 conf]$ vim sqoop-env.sh
export HADOOP_COMMON_HOME=/home/hadoop/hadoop-2.7.1
#Set path to where hadoop-*-core.jar is available
export HADOOP_MAPRED_HOME=/home/hadoop/hadoop-2.7.1/share/hadoop/mapreduce
#set the path to where bin/hbase is available => 可以不用，当使用到HBASE时再配置
#export HBASE_HOME=/home/hadoop/hbase-1.0.1.1
#Set the path to where bin/hive is available => 可以不使用，当使用的HIVE时再配置
#export HIVE_HOME=/home/hadoop/apache-hive-1.2.1-bin
#Set the path for where zookeper config dir is
#export ZOOCFGDIR=
export JAVA_HOME=/usr/java/latest => 要安装JDK，之前安装的JRE，使用时会有问题
2.添加对应的jdbc 驱动，我使用的是MySQL
[hadoop@hftclclw0001 lib]$ pwd
/home/hadoop/sqoop-1.4.6.bin__hadoop-2.0.4-alpha/lib
[hadoop@hftclclw0001 lib]$ ls -al | grep mysql
-rw------- 1 hadoop root 848401 Nov 3 06:41 mysql-connector-java-5.1.25-bin.jar
三、Sqoop 1.x 语法
1.安装mysql(配置相应的repo)
[root@hftclclw0001 opt] yum install mysql-server mysql mysql-client
2.启动并测试，并给root用户添加密码
[root@hftclclw0001 opt] service mysqld start
[root@hftclclw0001 opt]# netstat -apn|grep 3306
tcp 0 0 0.0.0.0:3306 0.0.0.0:* LISTEN 24540/mysqld
[root@hftclclw0001 opt]# mysql -u root -p
Enter password:
mysql>
3.准备测试数据
我参考的是Apache Sqoop Cookbook 使用的mysql的
https://github.com/jarcec/Apache-Sqoop-Cookbook
使用github上面的mysql文件，创建sqoop用户，创建sqoop数据库，并新增对应的tables。并给sqoop用户赋予相应的权限
mysql> show databases;
+--------------------+
| Database |
+--------------------+
| information_schema |
| mysql |
| performance_schema |
| sqoop |
+--------------------+
4 rows in set (0.00 sec)
mysql> use sqoop
mysql> show tables;
+-----------------+
| Tables_in_sqoop |
+-----------------+
| cities |
| countries |
| normcities |
| staging_cities |
| visits |
+-----------------+
5 rows in set (0.00 sec)
chapter2 importing data
sqoop list:
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop-list-tables --connect jdbc:mysql://{ip}:{por}/sqoop \
> --username sqoop \
> --password sqoop
...
...
cities
countries
normcities
staging_cities
visits => 这些tables就是之前mysql中的新建的sqoop import:全表导入(transferring an entire table)
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop import \
> --connect jdbc:mysql://{ip}:{port}/sqoop \
> --username sqoop \
> --password sqoop \
> --table cities
... => 会调用MR，读取mysql，并写入文件中(默认理解是当前用户下，table名称对应的木)
总共三条记录，生成了三个文件
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ hadoop dfs -ls /user/hadoop/cities
-rw-r--r-- 3 hadoop supergroup 0 2015-11-25 05:29 /user/hadoop/cities/_SUCCESS
-rw-r--r-- 3 hadoop supergroup 16 2015-11-25 05:29 /user/hadoop/cities/part-m-00000
-rw-r--r-- 3 hadoop supergroup 22 2015-11-25 05:29 /user/hadoop/cities/part-m-00001
-rw-r--r-- 3 hadoop supergroup 16 2015-11-25 05:29 /user/hadoop/cities/part-m-00002
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ hadoop dfs -cat cities/part-m-00000
1,USA,Palo Altosqoop import:指定路径(specifying a target directory)
--target-dir 指定路径不能存在(针对单表使用的)
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop import \
> --connect jdbc:mysql://{ip}:{port}/sqoop \
> --username sqoop \
> --password sqoop \
> --table cities \
> --target-dir /tmp/cities
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ hadoop dfs -ls /tmp/cities
-rw-r--r-- 3 hadoop supergroup 0 2015-11-25 05:29 /user/hadoop/cities/_SUCCESS
-rw-r--r-- 3 hadoop supergroup 16 2015-11-25 05:29 /user/hadoop/cities/part-m-00000
-rw-r--r-- 3 hadoop supergroup 22 2015-11-25 05:29 /user/hadoop/cities/part-m-00001
-rw-r--r-- 3 hadoop supergroup 16 2015-11-25 05:29 /user/hadoop/cities/part-m-00002
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ hadoop dfs -cat cities/part-m-00000
1,USA,Palo Alto
当多表导入是，可以使用--warehouse-dir 会再指定目录下，再生成以table表名称的目录sqoop import:带where条件的sql,即子集 (importing only a subset of data)
mysql> select * from sqoop.cities;
+----+----------------+-----------+
| id | country | city |
+----+----------------+-----------+
| 1 | USA | Palo Alto |
| 2 | Czech Republic | Brno |
| 3 | USA | Sunnyvale |
+----+----------------+-----------+
3 rows in set (0.00 sec)
mysql> select * from sqoop.cities where country = 'USA';
+----+---------+-----------+
| id | country | city |
+----+---------+-----------+
| 1 | USA | Palo Alto |
| 3 | USA | Sunnyvale |
+----+---------+-----------+
2 rows in set (0.00 sec)
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ hadoop dfs -rmr /tmp/cities
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop import \
> --connect jdbc:mysql://{ip}:{port}/sqoop \
> --username sqoop \
> --password sqoop \
> --table cities \
> --where "country = 'USA'" \
> --target-dir /tmp/citiessqoop import:(protecting your password)
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop import \
> --connect jdbc:mysql://{ip}:{port}/sqoop \
> --username sqoop \
> --table cities \
> --where "country = 'USA'" \
> --target-dir /tmp/cities \
> -P => 命令行输入
>--password-file my-sqoop-password => 指定密码文件
sqoop import:(Using a File Format Other Than CSV)
默认生成的是CSV文件，字段间使用tab间隔
--as-sequencefile
--as-avrodatafile
sqoop import:(Compressing imported data)
--compress
--compression-codec org.apache.hadoop.io.compress.BZip2Codec =>指定压缩算法sqoop import:(speeding up transfers)
默认mr的inputformat是通过jdbc的形式读取数据，效率低，可以使用数据库提供的一些工具，如mysql的 mysqldump等
--direct
chapter3 Incremental Import
mysql> select * from sqoop.visits;
+----+----------+---------------------+
| id | city | last_update_date |
+----+----------+---------------------+
| 1 | Freemont | 1983-05-22 01:01:01 |
| 2 | Jicin | 1987-02-02 02:02:02 |
+----+----------+---------------------+
2 rows in set (0.00 sec)
importing only new data
表中有个id的主键(int类型的) 我们导入>1的数据
--check-column => 检查那个字段
--last-value => 检查的字段，上次的值是多少，这次会 +1 开始导入
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop import \
>--connect jdbc:mysql://{ip}:{port}/sqoop \
>--username sqoop \
>--password sqoop \
>--table visits \
>--target-dir /tmp/visits \
>--incremental append \ => incremental 模式是append 即追加
>--check-column id \ => append模式下， 需要一个递增的主键
>--last-value 1 => 会从 id>1开始导入
注意这边在执行的时候是输出以下日志，提示下次增量import是last-value 2 (即本次导入的最后一条记录)
并提示你最好使用 sqoop job --create 来处理类似的定时增量导入
15/11/25 06:05:28 INFO tool.ImportTool: Incremental import complete! To run another incremental import of all data following this import, supply the following arguments:
15/11/25 06:05:28 INFO tool.ImportTool: --incremental append
15/11/25 06:05:28 INFO tool.ImportTool: --check-column id
15/11/25 06:05:28 INFO tool.ImportTool: --last-value 2
15/11/25 06:05:28 INFO tool.ImportTool: (Consider saving this with 'sqoop job --create')
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ hadoop dfs -ls /tmp/visits
-rw-r--r-- 3 hadoop supergroup 30 2015-11-25 06:05 /tmp/visits/part-m-00000
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ hadoop dfs -cat /tmp/visits/part-m-00000
2,Jicin,1987-02-02 02:02:02.0
incrementally importing mutable dataSqoop Job:
http://shiyanjun.cn/archives/621.html
我们使用Sqoop1.x是，在rdbms和hadoop/hive进行数据同步时，如果是用了--incremental append模式，我们要记录--last-value.如果每次执行同步脚步时候，都需要从日志中解析出一个--last-value的值，然后重新设置脚步的参数，才能正确的保证同步正确。
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop job \
>--create visits-sync-job \ => 创建job： job-id(visits-sync-job)
>-- \
>import \
>--connect jdbc:mysql://10.224.243.124:3306/sqoop \
>--username sqoop \
>--password sqopp \
>--table visits \
>--incremental append \
>--check-column id \
>--last-value 1
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop job --list
15/11/25 06:40:00 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
Available jobs:
visits-sync-job
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop job --show visits-sync-job
15/11/25 06:40:10 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
Enter password:
Job: visits-sync-job
Tool: import
...
...
incremental.last.value = 1
...
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop job --exec visits-sync-job
Enter password:
执行job后，我们在show job
[hadoop@hftclclw0001 sqoop-1.4.6.bin__hadoop-2.0.4-alpha]$ ./bin/sqoop job --show visits-sync-job
15/11/25 06:44:52 INFO sqoop.Sqoop: Running Sqoop version: 1.4.6
Enter password:
incremental.last.value = 2 => last_value已经被记录下，下次再执行的时候就会读取该值，再执行
chapter 4 Free-Form Query Import
sqoop import:(importing data from two tables)
mysql> select * from sqoop.cities;
+----+----------------+-----------+
| id | country | city |
+----+----------------+-----------+
| 1 | USA | Palo Alto |
| 2 | Czech Republic | Brno |
| 3 | USA | Sunnyvale |
+----+----------------+-----------+
3 rows in set (0.00 sec)
mysql> select * from sqoop.countries;
+------------+----------------+
| country_id | country |
+------------+----------------+
| 1 | USA |
| 2 | Czech Republic |
+------------+----------------+
2 rows in set (0.00 sec)
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