hadoop的安装配置这里就不讲了。
Sqoop的安装也很简单。
完成sqoop的安装后，可以这样测试是否可以连接到mysql(注意：mysql的jar包要放到 SQOOP_HOME/lib 下)：
sqoop list-databases --connect jdbc:mysql://192.168.1.109:3306/ --username root --password 19891231
结果如下
即说明sqoop已经可以正常使用了。
下面，要将mysql中的数据导入到hadoop中。
我准备的是一个300万条数据的身份证数据表：
先启动hive（使用命令行：hive 即可启动）
然后使用sqoop导入数据到hive：
sqoop import --connect jdbc:mysql://192.168.1.109:3306/hadoop --username root --password 19891231 --table test_sfz --hive-import
sqoop 会启动job来完成导入工作。
完成导入用了2分20秒，还是不错的。
在hive中可以看到刚刚导入的数据表：
我们来一句sql测试一下数据：
select * from test_sfz where id < 10;
可以看到，hive完成这个任务用了将近25秒，确实是挺慢的（在mysql中几乎是不费时间），但是要考虑到hive是创建了job在hadoop中跑，时间当然多。
接下来，我们会对这些数据进行复杂查询的测试：
我机子的配置如下：
hadoop 是运行在虚拟机上的伪分布式，虚拟机OS是ubuntu12.04 64位，配置如下：
TEST 1 计算平均年龄
测试数据：300.8 W
1. 计算广东的平均年龄
mysql：select (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz where address like ‘广东%‘;
用时： 0.877s
hive：select (sum(year(‘2014-10-01‘) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz where address like ‘广东%‘;
用时：25.012s
2. 对每个城市的的平均年龄进行从高到低的排序
mysql：select address, (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz GROUP BY address order by ageAvge desc;
用时：2.949s
hive：select address, (sum(year(‘2014-10-01‘) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz GROUP BY address order by ageAvge desc;
用时：51.29s
可以看到，在耗时上面，hive的增长速度较mysql慢。
TEST 2
测试数据：1200W
mysql 引擎： MyISAM（为了加快查询速度）
导入到hive：
1. 计算广东的平均年龄
mysql：select (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 where address like ‘广东%‘;
用时： 5.642s
hive：select (sum(year(‘2014-10-01‘) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 where address like ‘广东%‘;
用时：168.259s
2. 对每个城市的的平均年龄进行从高到低的排序
mysql：select address, (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 GROUP BY address order by ageAvge desc;
用时：11.964s
hive：select address, (sum(year(‘2014-10-01‘) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 GROUP BY address order by ageAvge desc;
用时：311.714s
TEST 3
测试数据：2000W
mysql 引擎： MyISAM（为了加快查询速度）
导入到hive：
（这次用的时间很短！可能是因为TEST2中的导入时，我的主机在做其他耗资源的工作..）
1. 计算广东的平均年龄
mysql：select (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 where address like ‘广东%‘;
用时： 6.605s
hive：select (sum(year(‘2014-10-01‘) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 where address like ‘广东%‘;
用时：188.206s
2. 对每个城市的的平均年龄进行从高到低的排序
mysql：select address, (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 GROUP BY address order by ageAvge desc;
用时：19.926s
hive：select address, (sum(year(‘2014-10-01‘) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 GROUP BY address order by ageAvge desc;
用时：411.816s
使用sqoop将mysql数据导入到hadoop
原文：http://blog.csdn.net/ssrc0604hx/article/details/41809087
