1安装Hive
1.1下载解压
wget http://mirrors.cnnic.cn/apache/hive/stable/hive-0.10.0.tar.gz
tar xzvfhive-0.10.0.tar.gz
1.2配置环境变量
exportHIVE_HOME=/usr/local/src/hive-0.10.0
export PATH=$HIVE_HOME/bin:$PATH
1.3建立Hive仓库目录
hadoop fs -mkdir/tmp
hadoop fs -mkdir/user/hive/warehouse
hadoop fs -chmodg+w /tmp
hadoop fs -chmodg+w /user/hive/warehouse
1.4启动命令行
通过hive命令进入命令行，操作与MySQL的命令行类似：
2安装Sqoop
2.1下载解压
下载适合Hadoop 0.20版本的Sqoop：
wget http://mirrors.cnnic.cn/apache/sqoop/1.4.3/sqoop-1.4.3.bin__hadoop-0.20.tar.gz
tar -xvf sqoop-1.4.3.bin__hadoop-0.20.tar.gz
2.2配置环境变量
export SQOOP_HOME=/usr/local/src/sqoop-1.4.3.bin__hadoop-0.20
export PATH=$SQOOP_HOME/bin:$PATH
export HADOOP_COMMON_HOME=/home/admin/hadoop-0.20.2
export HADOOP_MAPRED_HOME=/home/admin/hadoop-0.20.2
3用Sqoop导入数据到HIVE
3.1导入HDFS
我们从MySQL数据库中导入一张表的数据来测试一下Sqoop是否配置成功。首先上传mysql-connector-java-5.1.23.jar到sqoop的lib文件夹下，然后在sqoop/bin下执行下列命令：
sqoop import--connect jdbc:mysql://ip/database --table tb1 --username user -P
===============================================================================
Warning: /usr/lib/hbase does not exist!HBase imports will fail.
Please set $HBASE_HOME to the root of yourHBase installation.
Enter password:
13/06/07 16:51:46 INFOmanager.MySQLManager: Preparing to use a MySQL streaming resultset.
13/06/07 16:51:46 INFO tool.CodeGenTool: Beginning codegeneration
13/06/07 16:51:48 INFO manager.SqlManager:Executing SQL statement: SELECT t.* FROM `tb1` AS t LIMIT 1
13/06/07 16:51:48 INFO manager.SqlManager:Executing SQL statement: SELECT t.* FROM `tb1` AS t LIMIT 1
13/06/07 16:51:48 INFOorm.CompilationManager: HADOOP_MAPRED_HOME is /home/admin/hadoop-0.20.2
13/06/07 16:51:48 INFOorm.CompilationManager: Found hadoop core jar at:/home/admin/hadoop-0.20.2/hadoop-0.20.2-core.jar
Note:/tmp/sqoop-root/compile/44c4b6c5ac57de04b487eb90633ac33e/tb1.java uses oroverrides a deprecated API.
Note: Recompile with -Xlint:deprecation fordetails.
13/06/07 16:51:54 INFO orm.CompilationManager:Writing jar file:/tmp/sqoop-root/compile/44c4b6c5ac57de04b487eb90633ac33e/tb1.jar
13/06/07 16:51:54 WARNmanager.MySQLManager: It looks like you are importing from mysql.
13/06/07 16:51:54 WARNmanager.MySQLManager: This transfer can be faster! Use the --direct
13/06/07 16:51:54 WARNmanager.MySQLManager: option to exercise a MySQL-specific fast path.
13/06/07 16:51:54 INFOmanager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
13/06/07 16:51:54 INFO mapreduce.ImportJobBase:Beginning import of tb1
13/06/07 16:51:57 INFOdb.DataDrivenDBInputFormat: BoundingValsQuery: SELECT MIN(`id`), MAX(`id`) FROM`tb1`
13/06/07 16:51:59 INFO mapred.JobClient:Running job: job_201306071651_0001
13/06/07 16:52:00 INFOmapred.JobClient: map 0% reduce 0%
13/06/07 16:52:38 INFOmapred.JobClient: map 50% reduce 0%
13/06/07 16:52:44 INFOmapred.JobClient: map 100% reduce 0%
13/06/07 16:52:46 INFO mapred.JobClient:Job complete: job_201306071651_0001
13/06/07 16:52:46 INFO mapred.JobClient:Counters: 5
13/06/07 16:52:46 INFOmapred.JobClient: Job Counters
13/06/07 16:52:46 INFOmapred.JobClient: Launched map tasks=2
13/06/07 16:52:46 INFOmapred.JobClient: FileSystemCounters
13/06/07 16:52:46 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=212
13/06/07 16:52:46 INFOmapred.JobClient: Map-Reduce Framework
13/06/07 16:52:46 INFOmapred.JobClient: Map input records=2
13/06/07 16:52:46 INFOmapred.JobClient: Spilled Records=0
13/06/07 16:52:46 INFO mapred.JobClient: Map output records=2
13/06/07 16:52:46 INFOmapreduce.ImportJobBase: Transferred 212 bytes in 51.383 seconds (4.1259bytes/sec)
13/06/07 16:52:46 INFOmapreduce.ImportJobBase: Retrieved 2 records.
===============================================================================
数据文件默认被导入到当前用户文件夹下表名对应的文件夹了：
Sqoop默认会同时启动四个Map任务来加速数据导入，可以通过-m 1命令来强制只启动一个map任务，这样就只会在HDFS中生成一个数据文件了。因为tb1表目前就两条数据，所以一共产生两个文件，查看下生成的文件内容：
3.2创建Hive表
首先在hive命令行中创建tb1表。注意hive支持的数据类型有限，并且 一定要设置表的分隔符为逗号 ，否则Hive默认分隔符为Ctrl+A。
CREATE TABLE tb1(
id int,
......
) row format delimited fields terminated by ‘,’ ;
也可以通过下面的命令让Sqoop根据MySQL表结构自动创建出Hive表：
sqoop create-hive-table --connect jdbc:mysql://ip/database --table tb1 --hive-table tb1 --username user -P
3.3导入Hive
现在导入HDFS中的文件到Hive，注意Hive从HDFS导入数据后，会将HDFS中的文件/user/root/tb1移动到/user/hive/tb1：
LOADDATA INPATH '/user/root/tb1/part-m-*' OVERWRITE INTO TABLE tb1
3.4一条强大的命令
上面的从MySQL导出数据到HDFS、创建Hive表格、导入数据到Hive三步，可以直接用一条Sqoop命令完成：
sqoop import--connect jdbc:mysql://ip/database --table tb1 --username user -P --hive-import
4用HiveQL做分析
待续......
参考资料
Hive安装
https://cwiki.apache.org/confluence/display/Hive/GettingStarted
http://sqoop.apache.org/docs/1.99.1/Installation.html
