Hive——环境搭建

Hive——环境搭建

相关hadoop和mysql环境已经搭建好。我博客中也有相关搭建的博客。

一、下载Hive并解压到指定目录（本次使用版本hive-1.1.0-cdh5.7.0，下载地址：http://archive.cloudera.com/cdh5/cdh/5/）

tar zxvf ./hive-1.1.0-cdh5.7.0.tar.gz -C ~/app/

二、Hive配置：参考官网：https://cwiki.apache.org/confluence/display/Hive/GettingStarted#GettingStarted-InstallationandConfiguration

1、配置环境变量

1)vi .bash_profile

    export HIVE_HOME=/home/hadoop/app/hive-1.1.0-cdh5.7.0
    export PATH=$HIVE_HOME/bin:$PATH

2)source .bash_profile

source .bash_profile

2、hive-1.1.0-cdh5.7.0/conf/hive-env.sh

1)cp hive-env.sh.template hive-env.sh

cp hive-env.sh.template hive-env.sh

 2)vi hive-env.sh 添加HADOOP_HOME

    HADOOP_HOME=/home/hadoop/app/hadoop-2.6.0-cdh5.7.0

3、hive-1.1.0-cdh5.7.0/conf/hive-site.xml(自己创建配置)

（mysql驱动包需要自己手动拷贝到hive-1.1.0-cdh5.7.0/lib中）。

 <configuration> 
        <!-- 配置连接串 -->
        <property>
            <name>javax.jdo.option.ConnectionURL</name>
            <!-- 数据库名称：zhaotao_hive -->
            <!-- createDatabaseIfNotExist=true：当数据库不存在的时候，自动帮你创建 -->
            <value>jdbc:mysql://localhost:3306/rdb_hive?createDatabaseIfNotExist=true</value>
        </property>
        <!-- mysql的driver类 -->
        <property>
            <name>javax.jdo.option.ConnectionDriverName</name>
            <value>com.mysql.jdbc.Driver</value>
        </property>
        <!-- 用户名 -->
        <property>
            <name>javax.jdo.option.ConnectionUserName</name>
            <value>root</value>
        </property>
        <!-- 密码 -->
        <property>
            <name>javax.jdo.option.ConnectionPassword</name>
            <value>root</value>
        </property>
    </configuration>

三、启动hive

hive-1.1.0-cdh5.7.0/bin/hive

启动日志：

[hadoop@hadoop01 bin]$ ./hive
which: no hbase in (/home/hadoop/app/hive-1.1.0-cdh5.7.0/bin:/home/hadoop/app/hadoop-2.6.0-cdh5.7.0
/bin:/home/hadoop/app/jdk1.8.0_131/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/home/hadoop/
.local/bin:/home/hadoop/bin)
Logging initialized using configuration in jar:file:/home/hadoop/app/hive-1.1.0-cdh5.7.0/lib/
hive-common-1.1.0-cdh5.7.0.jar!/hive-log4j.properties
WARNING: Hive CLI is deprecated and migration to Beeline is recommended.
hive>

启动后会自动在mysql库上建立数据库和表：

mysql> show tables;
+--------------------+
| Tables_in_rdb_hive |
+--------------------+
| CDS                |
| DATABASE_PARAMS    |
| DBS                |
| FUNCS              |
| FUNC_RU            |
| GLOBAL_PRIVS       |
| PARTITIONS         |
| PART_COL_STATS     |
| ROLES              |
| SDS                |
| SEQUENCE_TABLE     |
| SERDES             |
| SKEWED_STRING_LIST |
| TAB_COL_STATS      |
| TBLS               |
| VERSION            |
+--------------------+

四、hive简单入门

使用hive实现wordcount。

1、创建表：create table hive_wordcount(context string);

hive> create table hive_wordcount(context string);
OK
Time taken: 1.203 seconds
hive> show tables;
OK
hive_wordcount
Time taken: 0.19 seconds, Fetched: 1 row(s)

2、导入数据：load data local inpath '/home/hadoop/data/hello.txt' into table hive_wordcount;

hive> load data local inpath '/home/hadoop/data/hello.txt' into table hive_wordcount;
Loading data to table default.hive_wordcount
Table default.hive_wordcount stats: [numFiles=1, totalSize=44]
OK
Time taken: 2.294 seconds

3、查询表数据看是否导成功：select * from hive_wordcount;

hello.txt内容：

Deer Bear River
Car Car River
Deer Car Bear

hive> select * from hive_wordcount;
OK
Deer Bear River
Car Car River
Deer Car Bear
Time taken: 0.588 seconds, Fetched: 3 row(s)

4、使用sql实现wordcount:select word,count(1) from hive_wordcount lateral view explode(split(context,' ')) wc as word group by word;

hive> select word,count(1) from hive_wordcount lateral view explode(split(context,' ')) wc as 
word group by word;
Query ID = hadoop_20180904070404_b23d8c2e-161b-4e65-a2cc-206ce343d9e8
Total 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 mapreduce.job.reduces=<number>
Starting Job = job_1536010835653_0002, 
Kill Command = /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/bin/hadoop job  -kill job_1536010835653_0002
Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1
2018-09-04 07:05:49,279 Stage-1 map = 0%,  reduce = 0%
2018-09-04 07:06:01,893 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 1.95 sec
2018-09-04 07:06:10,804 Stage-1 map = 100%,  reduce = 100%, Cumulative CPU 3.44 sec
MapReduce Total cumulative CPU time: 3 seconds 440 msec
Ended Job = job_1536010835653_0002
MapReduce Jobs Launched:
Stage-Stage-1: Map: 1  Reduce: 1   Cumulative CPU: 3.44 sec   HDFS Read: 8797 HDFS 
Write: 28 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 440 msec
OK
Bear    2
Car     3
Deer    2
River   2
Time taken: 37.441 seconds, Fetched: 4 row(s)

可以看到结果：

Bear    2
Car     3
Deer    2
River   2

注意：在创建表的时候遇到一个错误：

Execution Error, return code 1 from org.apache.hadoop.hive.ql.exec.DDLTask. MetaException(message:
For direct MetaStore DB connections, we don't support retries at the client level.)

从字面意思是是连接msql有问题。从网上查询大概有两种解决办法：

1、换mysql jdbc驱动包，比如换成  mysql-connector-java-5.1.34-bin.jar，但我试过，我这里没有解决

2、换对应mysq 上MetaStore 数据库的编码，换成 latin1,亲测，解决。