使用sqoop將mysql數據導入到hadoop

使用sqoop將mysql數據導入到hadoop

hadoop的安裝配置這裡就不講了。

Sqoop的安裝也很簡單。 完成sqoop的安裝後，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。 我准備的是一個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） 然後使用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。

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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