hadoop的安裝配置這里就不講了。

Sqoop的安裝也很簡單。

完成sqoop的安裝后，可以這樣測試是否可以連接到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

結果如下

即說明sqoop已經可以正常使用了。

下面，要將mysql中的數據導入到hadoop中。

我準備的是一個300萬條數據的身份證數據表：

先啟動hive(使用命令行：hive 即可啟動)

然后使用sqoop導入數據到hive：

sqoop import --connect jdbc:mysql://192.168.1.109:3306/hadoop --username root --password 19891231 --table test_sfz --hive-import

sqoop 會啟動job來完成導入工作。

完成導入用了2分20秒，還是不錯的。

在hive中可以看到剛剛導入的數據表：

我們來一句sql測試一下數據：

select * from test_sfz where id < 10;

可以看到，hive完成這個任務用了將近25秒，確實是挺慢的(在mysql中幾乎是不費時間)，但是要考慮到hive是創建了job在hadoop中跑，時間當然多。

接下來，我們會對這些數據進行復雜查詢的測試：

我機子的配置如下：

hadoop 是運行在虛擬機上的偽分布式，虛擬機OS是ubuntu12.04 64位，配置如下：

TEST 1 計算平均年齡

測試數據：300.8 W

1. 計算廣東的平均年齡

mysql：select (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz where address like '廣東%';

用時： 0.877s

hive：select (sum(year('2014-10-01') - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz where address like '廣東%';

用時：25.012s

2. 對每個城市的的平均年齡進行從高到低的排序

mysql：select address, (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz GROUP BY address order by ageAvge desc;

用時：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;

用時：51.29s

可以看到，在耗時上面，hive的增長速度較mysql慢。

TEST 2

測試數據：1200W

mysql 引擎： MyISAM(為了加快查詢速度)

導入到hive：

1. 計算廣東的平均年齡

mysql：select (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 where address like '廣東%';

用時： 5.642s

hive：select (sum(year('2014-10-01') - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 where address like '廣東%';

用時：168.259s

2. 對每個城市的的平均年齡進行從高到低的排序

mysql：select address, (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 GROUP BY address order by ageAvge desc;

用時：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;

用時：311.714s

TEST 3

測試數據：2000W

mysql 引擎： MyISAM(為了加快查詢速度)

導入到hive：

(這次用的時間很短！可能是因為TEST2中的導入時，我的主機在做其他耗資源的工作..)

1. 計算廣東的平均年齡

mysql：select (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 where address like '廣東%';

用時： 6.605s

hive：select (sum(year('2014-10-01') - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 where address like '廣東%';

用時：188.206s

2. 對每個城市的的平均年齡進行從高到低的排序

mysql：select address, (sum(year(NOW()) - SUBSTRING(borth,1,4))/count(*)) as ageAvge from test_sfz2 GROUP BY address order by ageAvge desc;

用時：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;

用時：411.816s

總結

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