hive安装配置（0.11版本）

1 假设Hadoop已经安装并配置正确，MySQL已经正确安装

2 为支持Hive的多用户多会话需求，需要使用一个独立的数据库存储元数据。
  这里选择MySQL存储Hive的元数据，现在为Hive创建元数据库：
mysql> create database hive;
mysql> create user 'hive' identified by '123456';
mysql> grant all privileges on *.* to 'hive'@'%' with grant option;
flush privileges; 刷新权限重新加载授权表
或
mysql> create database metastore_db;
mysql> create user 'hive'@'localhost' identified by 'hive';
mysql> grant all on metastore_db.* to 'hive'@'localhost';
flush privileges;
Hive配置信息：  Hive仅需要在Master节点安装配置即可
将hive配置到系统配置文件中，基本上任何软件都需要在此进行配置才可运行
vi /etc/profile  
#export PATH USER LOGNAME MAIL HOSTNAME HISTSIZE HISTCONTROL
export JAVA_HOME=/usr/java/jdk1.7.0_51 配置到java的安装目录即可
export PATH=$JAVA_HOME/bin:$PATH:/usr/hive/hive-0.11.0  配置到hadoop的安装目录即可
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export HIVE_HOME=/usr/hive/hive-0.11.0  配置到hive的安装目录即可

将hadoop路径添加到hive配置文件中，以使hive可以找到hadoop，因hive是基于hadoop的，同时hadoop又是基于java虚拟的。修改bin目录下hive-config.sh文件：
HIVE_CONF_DIR="${HIVE_CONF_DIR:-$HIVE_HOME/conf}"
export HADOOP_HOME=/home/hadoop/hadoop/hadoop-1.2.1
export HIVE_CONF_DIR=$HIVE_CONF_DIR
export HIVE_AUX_JARS_PATH=$HIVE_AUX_JARS_PATH

作为附加的, 你必须在创建Hive库表前，在HDFS上创建/tmp和/user/hive/warehouse，并且将它们的权限设置为chmod g+w,修改hdfs文件
cd /usr/local/hadoop 进入hadoop操作目录，对hdfs文件系统进行操作：
bin/hadoop fs -mkdir /tmp
bin/hadoop fs -mkdir /user/hive/warehouse
bin/hadoop fs -chmod g+w /tmp
bin/hadoop fs -chmod g+w /user/hive/warehouse

cp hive-default.xml.template hive-site.xml

vi conf/hive-site.xml

<configuration>
<property>
  <name>hive.metastore.local</name>
  <value>true</value>
</property>
<property>
  <name>javax.jdo.option.ConnectionURL</name>
  <value>jdbc:mysql://localhost:3306/metastore_db?createDatabaseIfNotExist=true</value>
  <description>JDBC connect string FOR a JDBC metastore</description>
</property>
<property>
  <name>javax.jdo.option.ConnectionDriverName</name>
  <value>com.mysql.jdbc.Driver</value>
  <description>Driver class name FOR a JDBC metastore</description> </property>
<property>
  <name>javax.jdo.option.ConnectionUserName</name>
  <value>hive</value>
  <description>username TOUSE against metastore database</description>
</property>
<property>
  <name>javax.jdo.option.ConnectionPassword</name>
  <value>hive</value>
  <description>password TOUSE against metastore database</description>
</property>
</configuration>

# MySQL 驱动package：
下载mysql-connector-java-5.1.18-bin.jar文件，并放到$HIVE_HOME/lib目录下

# Hive测试（进入hive的bin文件输入hive进入hive环境）：
[root@sysimages bin]# hive
Logging initialized using configuration in jar:file:/opt/hive-0.9.0/lib/hive-common-0.9.0.jar!/hive-log4j.properties
Hive history file=/tmp/root/hive_job_log_root_201211061923_2139056245.txt
hive> show tables;
OK
Time taken: 4.837 seconds

# Hive 创建数据库表、加载数据、查询数据：
hive> create table records (year string, temperature int, quality int)  # 创建数据库表
    > row format delimited
    > fields terminated by ',';
OK
Time taken: 0.292 seconds

hive> load data local inpath '/opt/hive-0.9.0/temp.txt'    # 加载数据
    > overwrite into table records;
Copying data from file:/opt/hive-0.9.0/temp.txt
Copying file: file:/opt/hive-0.9.0/temp.txt
Loading data to table default.records
Deleted hdfs://sysimages:9100/user/hive/warehouse/records
OK
Time taken: 0.486 seconds

hive> select year, max(temperature)    # 查询数据
    > from records
    > where temperature != 9999
    > group by year;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
  set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
  set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
  set mapred.reduce.tasks=<number>
Starting Job = job_201210151705_0018, Tracking URL = http://sysimages:50030/jobdetails.jsp?jobid=job_201210151705_0018
Kill Command = /opt/hadoop-0.20.2/bin/../bin/hadoop job  -Dmapred.job.tracker=sysimages:9200 -kill job_201210151705_0018
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2012-11-06 19:43:23,897 Stage-1 map = 0%,  reduce = 0%
2012-11-06 19:43:26,923 Stage-1 map = 100%,  reduce = 0%
2012-11-06 19:43:35,978 Stage-1 map = 100%,  reduce = 100%
Ended Job = job_201210151705_0018
MapReduce Jobs Launched:
Job 0: Map: 1  Reduce: 1   HDFS Read: 79 HDFS Write: 53 SUCCESS
Total MapReduce CPU Time Spent: 0 msec
OK
drfdf    234234
drwe    234234
rwerwer    42342
sdfsdf    234234
Time taken: 590.055 seconds
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