转载自：
http://blog.fens.me/hadoop-hive-intro/
本人根据实际情况稍微做了一些改编。
让Hadoop跑在云端系列文章，介绍了如何整合虚拟化和Hadoop，让Hadoop集群跑在VPS虚拟主机上，通过云向用户提供存储和计算的服务。
现在硬件越来越便宜，一台非品牌服务器，2颗24核CPU，配48G内存，2T的硬盘，已经降到2万块人民币以下了。这种配置如果简单地放几个web应用，显然是奢侈的浪费。就算是用来实现单节点的hadoop，对计算资源浪费也是非常高的。对于这么高性能的计算机，如何有效利用计算资源，就成为成本控制的一项重要议题了。
通过虚拟化技术，我们可以将一台服务器，拆分成12台VPS，每台2核CPU，4G内存，40G硬盘，并且支持资源重新分配。多么伟大的技术啊！现在我们有了12个节点的hadoop集群， 让Hadoop跑在云端，让世界加速。
关于作者：
- 张丹(Conan), 程序员Java,R,PHP,Javascript
- weibo：@Conan_Z
- blog: http://blog.fens.me
- email: bsspirit@gmail.com
转载请注明出处：
http://blog.fens.me/hadoop-hive-intro/
前言
Hive是Hadoop一个程序接口，Hive让数据分析人员快速上手，Hive使用了类SQL的语法，Hive让JAVA的世界变得简单而轻巧，Hive让Hadoop普及到了程序员以外的人。
从Hive开始，让分析师们也能玩转大数据。
目录
- Hive的安装
- Hive的hwi安装
- Hive的基本使用:CRUD
- Hive交互式模式
- 数据导入
- 数据导出
- Hive查询HiveQL
- Hive视图
- Hive分区表
1. Hive的安装
系统环境
装好hadoop的环境后，我们可以把Hive装在namenode机器上(c1)。
hadoop的环境，请参考：让Hadoop跑在云端系列文章，RHadoop实践系列之一:Hadoop环境搭建
下载: hive-0.9.0.tar.gz
解压到： /home/cos/toolkit/hive-0.9.0
hive配置
~ cd /home/cos/toolkit/hive-0.9.0
~ cp hive-default.xml.template hive-site.xml
~ cp hive-log4j.properties.template hive-log4j.properties
修改hive-site.xml配置文件
把Hive的元数据存储到MySQL中
~ vi conf/hive-site.xml
<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://localhost:3306/hive_metadata?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 to use against metastore database</description>
</property>
<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>hive</value>
<description>password to use against metastore database</description>
</property>
<property>
<name>hive.metastore.warehouse.dir</name>
<value>/user/hive/warehouse</value>
<description>location of default database for the warehouse</description>
</property>
这里选择的是单机的部署方式，即mysql跟hive是安装在同一台机器上的，当然也可以将mysql安装在另一台机器上，然后通过远程连接的方式操作，但是本人再实践的时候总出现一些奇怪的错误，后面也就放弃尝试了。装在单机上的话，直接配置localhost就可以了。
至于安装mysql的话，我用的是ubuntu，直接使用suod apt-get install mysql-server安装就可以了，还有mysql客户端，直接打开ubuntu的软件中心找找就可以下载方便。
ubuntu上安装mysql非常简单只需要几条命令就可以完成。
1. sudo apt-get install mysql-server 2. apt-get isntall mysql-client3. sudo apt-get install libmysqlclient-dev 安装过程中会提示设置密码什么的，注意设置了不要忘了，安装完成之后可以使用如下命令来检查是否安装成功： sudo netstat -tap | grep mysql 通过上述命令检查之后，如果看到有mysql 的socket处于 listen 状态则表示安装成功。 登陆mysql数据库可以通过如下命令： mysql -u root -p -u 表示选择登陆的用户名， -p 表示登陆的用户密码，上面命令输入之后会提示输入密码，此时输入密码就可以登录到mysql。
修改hive-log4j.properties
#log4j.appender.EventCounter=org.apache.hadoop.metrics.jvm.EventCounter
log4j.appender.EventCounter=org.apache.hadoop.log.metrics.EventCounter设置环境变量
~ sudo vi /etc/environment
PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/home/cos/toolkit/ant184/bin:/home/cos/toolkit/jdk16/bin:/home/cos/toolkit/maven3/bin:/home/cos/toolkit/hadoop-1.0.3/bin:/home/cos/toolkit/hive-0.9.0/bin"
JAVA_HOME=/home/cos/toolkit/jdk16
ANT_HOME=/home/cos/toolkit/ant184
MAVEN_HOME=/home/cos/toolkit/maven3
HADOOP_HOME=/home/cos/toolkit/hadoop-1.0.3
HIVE_HOME=/home/cos/toolkit/hive-0.9.0
CLASSPATH=/home/cos/toolkit/jdk16/lib/dt.jar:/home/cos/toolkit/jdk16/lib/tools.jar
在hdfs上面，创建目录
$HADOOP_HOME/bin/hadoop fs -mkidr /tmp
$HADOOP_HOME/bin/hadoop fs -mkidr /user/hive/warehouse
$HADOOP_HOME/bin/hadoop fs -chmod g+w /tmp
$HADOOP_HOME/bin/hadoop fs -chmod g+w /user/hive/warehouse
在MySQL中创建数据库
create database hive_metadata;
grant all on hive_metadata.* to hive@'%' identified by 'hive';
grant all on hive_metadata.* to hive@localhost identified by 'hive';
ALTER DATABASE hive_metadata CHARACTER SET latin1;
手动上传mysql的jdbc库到hive/lib
~ ls /home/cos/toolkit/hive-0.9.0/lib
mysql-connector-java-5.1.22-bin.jar
启动hive
#启动metastore服务
~ bin/hive --service metastore &
Starting Hive Metastore Server
#启动hiveserver服务
~ bin/hive --service hiveserver &
Starting Hive Thrift Server
#启动hive客户端
~ bin/hive shell
Logging initialized using configuration in file:/root/hive-0.9.0/conf/hive-log4j.properties
Hive history file=/tmp/root/hive_job_log_root_201211141845_1864939641.txt
hive> show tables
OK
查询MySQL数据库中的元数据
~ mysql -uroot -p
mysql> use hive_metadata;
Database changed
mysql> show tables;
+-------------------------+
| Tables_in_hive_metadata |
+-------------------------+
| BUCKETING_COLS |
| CDS |
| COLUMNS_V2 |
| DATABASE_PARAMS |
| DBS |
| IDXS |
| INDEX_PARAMS |
| PARTITIONS |
| PARTITION_KEYS |
| PARTITION_KEY_VALS |
| PARTITION_PARAMS |
| PART_COL_PRIVS |
| PART_PRIVS |
| SDS |
| SD_PARAMS |
| SEQUENCE_TABLE |
| SERDES |
| SERDE_PARAMS |
| SORT_COLS |
| TABLE_PARAMS |
| TBLS |
| TBL_COL_PRIVS |
| TBL_PRIVS |
+-------------------------+
23 rows in set (0.00 sec)
Hive已经成功安装，下面是hive的使用攻略。
2.Hive的hwi安装
hwi是hive的图形化管理工具，但是hwi只允许执行查询操作，不过hive的网络接口可以更方便，更直观的操作。 1.分离查询的执行 在命令行（CLI）下，要执行多个查询就要打开多个终端，而通过网络接口，可以同时执行多个查询。 2.不用本地安装Hive 用户不需要本地安装Hive就可以通过网络游览器访问Hive并进行操作。 修改配置文件：hive-site.xml 增加如下三个参数项：保存退出 <property>
<name>hive.hwi.listen.host</name>
<value>0.0.0.0</value>
</property>
<property>
<name>hive.hwi.listen.port</name>
<value>9999</value>
</property>
<property>
<name>hive.hwi.war.file</name>
<value>lib/hive-hwi-0.13.1.war</value>
</property>即可。
启动hwi: hive --service hwi
如有下报错：
Problem accessing /hwi/. Reason:
Unable to find a javac compiler;
com.sun.tools.javac.Main is not on the classpath.
Perhaps JAVA_HOME does not point to the JDK.
It is currently set to "/usr/java/jdk1.7.0_55/jre"
解决办法：
cp /usr/java/jdk1.7.0_55/lib/tools.jar /usr/local/hive/lib/
hive --service hwi 重启即可。
3.Hive的数据类型
1.简单数据类型常用的简单数据类型有int，boolean，double，string，varchar，char 这里补充下varchar和char的区别： varchar(20)：最大20个字符，占用空间根据实际情况 char(20)：最大20个字符，不管长度怎样都是占用20个字符
2.复杂类型
hive提供了复合数据类型：
Structs： structs内部的数据可以通过DOT（.）来存取，例如，表中一列c的类型为STRUCT{a INT; b INT}，我们可以通过c.a来访问域a
Maps（K-V对）：访问指定域可以通过["指定域名称"]进行，例如，一个Map M包含了一个group-》gid的kv对，gid的值可以通过M['group']来获取
Arrays：array中的数据为相同类型，例如，假如array A中元素['a','b','c']，则A[1]的值为'b'
Struct使用
建表：
- hive> create table student_test(id INT, info struct<name:STRING, age:INT>)
- > ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
- > COLLECTION ITEMS TERMINATED BY ':';
- OK
- Time taken: 0.446 seconds
'FIELDS TERMINATED BY' ：字段与字段之间的分隔符
''COLLECTION ITEMS TERMINATED BY' ：一个字段各个item的分隔符
导入数据：
- $ cat test5.txt
- 1,zhou:30
- 2,yan:30
- 3,chen:20
- 4,li:80
- hive> LOAD DATA LOCAL INPATH '/home/work/data/test5.txt' INTO TABLE student_test;
- Copying data from file:/home/work/data/test5.txt
- Copying file: file:/home/work/data/test5.txt
- Loading data to table default.student_test
- OK
- Time taken: 0.35 seconds
查询：
- hive> select info.age from student_test;
- Total MapReduce jobs = 1
- ......
- Total MapReduce CPU Time Spent: 490 msec
- OK
- 30
- 30
- 20
- 80
- Time taken: 21.677 seconds
Array使用
建表：
- hive> create table class_test(name string, student_id_list array<INT>)
- > ROW FORMAT DELIMITED
- > FIELDS TERMINATED BY ','
- > COLLECTION ITEMS TERMINATED BY ':';
- OK
- Time taken: 0.099 seconds
导入数据：
- $ cat test6.txt
- 034,1:2:3:4
- 035,5:6
- 036,7:8:9:10
- hive> LOAD DATA LOCAL INPATH '/home/work/data/test6.txt' INTO TABLE class_test ;
- Copying data from file:/home/work/data/test6.txt
- Copying file: file:/home/work/data/test6.txt
- Loading data to table default.class_test
- OK
- Time taken: 0.198 seconds
查询：
- hive> select student_id_list[3] from class_test;
- Total MapReduce jobs = 1
- ......
- Total MapReduce CPU Time Spent: 480 msec
- OK
- 4
- NULL
- 10
- Time taken: 21.574 seconds
Map使用
建表：
- hive> create table employee(id string, perf map<string, int>)
- > ROW FORMAT DELIMITED
- > FIELDS TERMINATED BY '\t'
- > COLLECTION ITEMS TERMINATED BY ','
- > MAP KEYS TERMINATED BY ':';
- OK
- Time taken: 0.144 seconds
‘MAP KEYS TERMINATED BY’ ：key value分隔符
导入数据：
- $ cat test7.txt
- 1 job:80,team:60,person:70
- 2 job:60,team:80
- 3 job:90,team:70,person:100
- hive> LOAD DATA LOCAL INPATH '/home/work/data/test7.txt' INTO TABLE employee;
查询：
- hive> select perf['person'] from employee;
- Total MapReduce jobs = 1
- ......
- Total MapReduce CPU Time Spent: 460 msec
- OK
- 70
- NULL
- 100
- Time taken: 20.902 seconds
- hive> select perf['person'] from employee where perf['person'] is not null;
- Total MapReduce jobs = 1
- .......
- Total MapReduce CPU Time Spent: 610 msec
- OK
- 70
- 100
- Time taken: 21.989 seconds
- hive>
- <span style="font-family:Arial, Helvetica, sans-serif;"><span style="white-space: normal;">
- </span></span>
3.特殊类型
4. Hive的基本使用
1. 进入hive控制台
~ cd /home/cos/toolkit/hive-0.9.0
~ bin/hive shell
Logging initialized using configuration in file:/home/cos/toolkit/hive-0.9.0/conf/hive-log4j.properties
Hive history file=/tmp/cos/hive_job_log_cos_201307160003_95040367.txt
hive>
新建表
#创建数据(文本以tab分隔)
~ vi /home/cos/demo/t_hive.txt
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
#创建新表
hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
OK
Time taken: 0.489 seconds
#导入数据t_hive.txt到t_hive表
hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hive.txt' OVERWRITE INTO TABLE t_hive ;
Copying data from file:/home/cos/demo/t_hive.txt
Copying file: file:/home/cos/demo/t_hive.txt
Loading data to table default.t_hive
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
OK
Time taken: 0.397 seconds
查看表和数据
#查看表
hive> show tables;
OK
t_hive
Time taken: 0.099 seconds
#正则匹配表名
hive>show tables '*t*';
OK
t_hive
Time taken: 0.065 seconds
#查看表数据
hive> select * from t_hive;
OK
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
Time taken: 0.264 seconds
#查看表结构
hive> desc t_hive;
OK
a int
b int
c int
Time taken: 0.1 seconds
修改表
#增加一个字段
hive> ALTER TABLE t_hive ADD COLUMNS (new_col String);
OK
Time taken: 0.186 seconds
hive> desc t_hive;
OK
a int
b int
c int
new_col string
Time taken: 0.086 seconds
#重命令表名
~ ALTER TABLE t_hive RENAME TO t_hadoop;
OK
Time taken: 0.45 seconds
hive> show tables;
OK
t_hadoop
Time taken: 0.07 seconds
删除表
hive> DROP TABLE t_hadoop;
OK
Time taken: 0.767 seconds
hive> show tables;
OK
Time taken: 0.064 seconds
删除表中数据
Hive是不支持更新操作的，你可以这样子做，从Hive表中查询出你想保留的数据，并且输入到原来的表中。insert overwrite table t_table1 select * from t_table1 where XXXX;
其中xxx是你需要保留的数据的查询条件。
如果清空表，如下：
insert overwrite table t_table1 select * from t_table where 1=0;
insert 语法格式为：
1. 基本的插入语法：
insert overwrite table tablename [partition(partcol1=val1,partclo2=val2)] select_statement;
insert into table tablename [partition(partcol1=val1,partclo2=val2)] select_statement;
eg：
insert overwrite table test_insert select * from test_table;
insert into table test_insert select * from test_table;注：
overwrite重写，into追加。
2. 对多个表进行插入操作：
from source_table
insert overwrite table tablename1 [partition (partcol1=val1,partclo2=val2)] select_statement1
insert overwrite table tablename2 [partition (partcol1=val1,partclo2=val2)] select_statement2eg:
from test_table
insert overwrite table test_insert1
select key
insert overwrite table test_insert2
select value;注：hive不支持用insert语句一条一条的进行插入操作，也不支持update操作。数据是以load的方式加载到建立好的表中，数据一旦导入就不可以修改。
2.通过查询将数据保存到filesysteminsert overwrite [local] directory 'directory' select_statement;
eg:
（1）导入数据到本地目录：
insert overwrite local directory '/home/hadoop/data' select * from test_insert1;
产生的文件会覆盖指定目录中的其他文件，即将目录中已经存在的文件进行删除。
只能用overwrite，into错误！
（2）导出数据到HDFS中：
insert overwrite directory '/user/hive/warehouse/table' select value from test_table;
只能用overwrite，into错误！（3）同一个查询结果可以同时插入到多个表或者多个目录中：
from source_table
insert overwrite local directory '/home/hadoop/data' select *
insert overwrite directory '/user/hive/warehouse/table' select value;
3. 小结：
（1）insert命令主要用于将hive中的数据导出，导出的目的地可以是hdfs或本地filesysytem，导入什么数据在于书写的select语句。
（2）overwrite与into：
insert overwrite/into table 可以搭配；
insert overwrite directory 可以搭配；
from t_table1 where 1=0;
3. Hive交互式模式
- quit,exit: 退出交互式shell
- reset: 重置配置为默认值
- set <key>=<value> : 修改特定变量的值(如果变量名拼写错误，不会报错)
- set : 输出用户覆盖的hive配置变量
- set -v : 输出所有Hadoop和Hive的配置变量
- add FILE[S] *, add JAR[S] *, add ARCHIVE[S] * : 添加 一个或多个 file, jar, archives到分布式缓存
- list FILE[S], list JAR[S], list ARCHIVE[S] : 输出已经添加到分布式缓存的资源。
- list FILE[S] *, list JAR[S] *,list ARCHIVE[S] * : 检查给定的资源是否添加到分布式缓存
- delete FILE[S] *,delete JAR[S] *,delete ARCHIVE[S] * : 从分布式缓存删除指定的资源
- ! <command> : 从Hive shell执行一个shell命令
- dfs <dfs command> : 从Hive shell执行一个dfs命令
- <query string> : 执行一个Hive 查询，然后输出结果到标准输出
- source FILE <filepath>: 在CLI里执行一个hive脚本文件
4. 数据导入
还以刚才的t_hive为例。
#创建表结构
hive> CREATE TABLE t_hive (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
从操作本地文件系统加载数据(LOCAL)
hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hive.txt' OVERWRITE INTO TABLE t_hive ;
Copying data from file:/home/cos/demo/t_hive.txt
Copying file: file:/home/cos/demo/t_hive.txt
Loading data to table default.t_hive
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
OK
Time taken: 0.612 seconds
#在HDFS中查找刚刚导入的数据
~ hadoop fs -cat /user/hive/warehouse/t_hive/t_hive.txt
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
从HDFS加载数据
创建表t_hive2
hive> CREATE TABLE t_hive2 (a int, b int, c int) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t';
#从HDFS加载数据
hive> LOAD DATA INPATH '/user/hive/warehouse/t_hive/t_hive.txt' OVERWRITE INTO TABLE t_hive2;
Loading data to table default.t_hive2
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive2
OK
Time taken: 0.325 seconds
#查看数据
hive> select * from t_hive2;
OK
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
Time taken: 0.287 seconds
从其他表导入数据
hive> INSERT OVERWRITE TABLE t_hive2 SELECT * FROM t_hive ;
Total MapReduce jobs = 2
Launching Job 1 out of 2
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0002, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0002
Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 10:32:41,979 Stage-1 map = 0%, reduce = 0%
2013-07-16 10:32:48,034 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:49,050 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:50,068 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:51,082 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:52,093 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:53,102 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.03 sec
2013-07-16 10:32:54,112 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 1.03 sec
MapReduce Total cumulative CPU time: 1 seconds 30 msec
Ended Job = job_201307131407_0002
Ended Job = -314818888, job is filtered out (removed at runtime).
Moving data to: hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-32-31_323_5732404975764014154/-ext-10000
Loading data to table default.t_hive2
Deleted hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive2
Table default.t_hive2 stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 56, raw_data_size: 0]
7 Rows loaded to t_hive2
MapReduce Jobs Launched:
Job 0: Map: 1 Cumulative CPU: 1.03 sec HDFS Read: 273 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 30 msec
OK
Time taken: 23.227 seconds
hive> select * from t_hive2;
OK
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
Time taken: 0.134 seconds
创建表并从其他表导入数据
#删除表
hive> DROP TABLE t_hive;
#创建表并从其他表导入数据
hive> CREATE TABLE t_hive AS SELECT * FROM t_hive2 ;
Total MapReduce jobs = 2
Launching Job 1 out of 2
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0003, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0003
Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0003
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 10:36:48,612 Stage-1 map = 0%, reduce = 0%
2013-07-16 10:36:54,648 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:55,657 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:56,666 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:57,673 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:58,683 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.13 sec
2013-07-16 10:36:59,691 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 1.13 sec
MapReduce Total cumulative CPU time: 1 seconds 130 msec
Ended Job = job_201307131407_0003
Ended Job = -670956236, job is filtered out (removed at runtime).
Moving data to: hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-36-39_986_1343249562812540343/-ext-10001
Moving data to: hdfs://c1.wtmart.com:9000/user/hive/warehouse/t_hive
Table default.t_hive stats: [num_partitions: 0, num_files: 1, num_rows: 0, total_size: 56, raw_data_size: 0]
7 Rows loaded to hdfs://c1.wtmart.com:9000/tmp/hive-cos/hive_2013-07-16_10-36-39_986_1343249562812540343/-ext-10000
MapReduce Jobs Launched:
Job 0: Map: 1 Cumulative CPU: 1.13 sec HDFS Read: 272 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 1 seconds 130 msec
OK
Time taken: 20.13 seconds
hive> select * from t_hive;
OK
16 2 3
61 12 13
41 2 31
17 21 3
71 2 31
1 12 34
11 2 34
Time taken: 0.109 seconds
仅复制表结构不导数据
hive> CREATE TABLE t_hive3 LIKE t_hive;
hive> select * from t_hive3;
OK
Time taken: 0.077 seconds
从MySQL数据库导入数据
我们将在介绍Sqoop时讲。
5. 数据导出
从HDFS复制到HDFS其他位置
~ hadoop fs -cp /user/hive/warehouse/t_hive /
~ hadoop fs -ls /t_hive
Found 1 items
-rw-r--r-- 1 cos supergroup 56 2013-07-16 10:41 /t_hive/000000_0
~ hadoop fs -cat /t_hive/000000_0
1623
611213
41231
17213
71231
11234
11234
通过Hive导出到本地文件系统
hive> INSERT OVERWRITE LOCAL DIRECTORY '/tmp/t_hive' SELECT * FROM t_hive;
Total MapReduce jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_201307131407_0005, Tracking URL = http://c1.wtmart.com:50030/jobdetails.jsp?jobid=job_201307131407_0005
Kill Command = /home/cos/toolkit/hadoop-1.0.3/libexec/../bin/hadoop job -Dmapred.job.tracker=hdfs://c1.wtmart.com:9001 -kill job_201307131407_0005
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 0
2013-07-16 10:46:24,774 Stage-1 map = 0%, reduce = 0%
2013-07-16 10:46:30,823 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:31,833 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:32,844 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:33,856 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:34,865 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:35,873 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 0.87 sec
2013-07-16 10:46:36,884 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 0.87 sec
MapReduce Total cumulative CPU time: 870 msec
Ended Job = job_201307131407_0005
Copying data to local directory /tmp/t_hive
Copying data to local directory /tmp/t_hive
7 Rows loaded to /tmp/t_hive
MapReduce Jobs Launched:
Job 0: Map: 1 Cumulative CPU: 0.87 sec HDFS Read: 271 HDFS Write: 56 SUCCESS
Total MapReduce CPU Time Spent: 870 msec
OK
Time taken: 23.369 seconds
#查看本地操作系统
hive> ! cat /tmp/t_hive/000000_0;
hive> 1623
611213
41231
17213
71231
11234
11234
6. Hive查询HiveQL
注：以下代码将去掉map,reduce的日志输出部分。
普通查询：排序，列别名，嵌套子查询
hive> FROM (
> SELECT b,c as c2 FROM t_hive
> ) t
> SELECT t.b, t.c2
> WHERE b>2
> LIMIT 2;
12 13
21 3
连接查询：JOIN
hive> SELECT t1.a,t1.b,t2.a,t2.b
> FROM t_hive t1 JOIN t_hive2 t2 on t1.a=t2.a
> WHERE t1.c>10;
1 12 1 12
11 2 11 2
41 2 41 2
61 12 61 12
71 2 71 2
聚合查询1：count, avg
hive> SELECT count(*), avg(a) FROM t_hive;
7 31.142857142857142
聚合查询2：count, distinct
hive> SELECT count(DISTINCT b) FROM t_hive;
3
聚合查询3：GROUP BY, HAVING
#GROUP BY
hive> SELECT avg(a),b,sum(c) FROM t_hive GROUP BY b,c
16.0 2 3
56.0 2 62
11.0 2 34
61.0 12 13
1.0 12 34
17.0 21 3
#HAVING
hive> SELECT avg(a),b,sum(c) FROM t_hive GROUP BY b,c HAVING sum(c)>30
56.0 2 62
11.0 2 34
1.0 12 34
7. Hive视图
Hive视图和数据库视图的概念是一样的，我们还以t_hive为例。
hive> CREATE VIEW v_hive AS SELECT a,b FROM t_hive where c>30;
hive> select * from v_hive;
41 2
71 2
1 12
11 2
删除视图
hive> DROP VIEW IF EXISTS v_hive;
OK
Time taken: 0.495 seconds
8. Hive分区表
分区表是数据库的基本概念，但很多时候数据量不大，我们完全用不到分区表。Hive是一种OLAP数据仓库软件，涉及的数据量是非常大的，所以分区表在这个场景就显得非常重要！！
下面我们重新定义一个数据表结构：t_hft
创建数据
~ vi /home/cos/demo/t_hft_20130627.csv
000001,092023,9.76
000002,091947,8.99
000004,092002,9.79
000005,091514,2.2
000001,092008,9.70
000001,092059,9.45
~ vi /home/cos/demo/t_hft_20130628.csv
000001,092023,9.76
000002,091947,8.99
000004,092002,9.79
000005,091514,2.2
000001,092008,9.70
000001,092059,9.45
创建数据表
DROP TABLE IF EXISTS t_hft;
CREATE TABLE t_hft(
SecurityID STRING,
tradeTime STRING,
PreClosePx DOUBLE
) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
创建分区数据表
根据业务：按天和股票ID进行分区设计
DROP TABLE IF EXISTS t_hft;
CREATE TABLE t_hft(
SecurityID STRING,
tradeTime STRING,
PreClosePx DOUBLE
) PARTITIONED BY (tradeDate INT)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
导入数据
#20130627
hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hft_20130627.csv' OVERWRITE INTO TABLE t_hft PARTITION (tradeDate=20130627);
Copying data from file:/home/cos/demo/t_hft_20130627.csv
Copying file: file:/home/cos/demo/t_hft_20130627.csv
Loading data to table default.t_hft partition (tradedate=20130627)
#20130628
hive> LOAD DATA LOCAL INPATH '/home/cos/demo/t_hft_20130628.csv' OVERWRITE INTO TABLE t_hft PARTITION (tradeDate=20130628);
Copying data from file:/home/cos/demo/t_hft_20130628.csv
Copying file: file:/home/cos/demo/t_hft_20130628.csv
Loading data to table default.t_hft partition (tradedate=20130628)
查看分区表
hive> SHOW PARTITIONS t_hft;
tradedate=20130627
tradedate=20130628
Time taken: 0.082 seconds
查询数据
hive> select * from t_hft where securityid='000001';
000001 092023 9.76 20130627
000001 092008 9.7 20130627
000001 092059 9.45 20130627
000001 092023 9.76 20130628
000001 092008 9.7 20130628
000001 092059 9.45 20130628
hive> select * from t_hft where tradedate=20130627 and PreClosePx<9;
000002 091947 8.99 20130627
000005 091514 2.2 20130627
Hive基于使用完成，这些都是日常的操作。后面我会继续讲一下，HiveQL优化及Hive的运维。