Hive的安装与使用-1

1.下载tar包，在客户端窗口上执行
wget -c http://archive.cloudera.com/cdh5/cdh/5/hive-1.1.0-cdh5.7.0.tar.gz
2.解压到app目录下
tar -zxvf hive-1.1.0-cdh5.7.0.tar.gz -C ~/app
3.配置~/.bash_profile

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

4.环境配置

cd hive-1.1.0-cdh5.7.0/conf/
cp hive-env.sh.template hive-env.sh
vi hive-env.sh

# Set HADOOP_HOME to point to a specific hadoop install directory
# HADOOP_HOME=${bin}/../../hadoop

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

5.安装mysql，yum install mysql-xxx
6.配置hive-site.xml文件

        javax.jdo.option.ConnectionURL
        jdbc:mysql://hadoop000:3306/sparksql?createDatabaseIfNotExist=true



        javax.jdo.option.ConnectionDriverName
        com.mysql.jdbc.Driver
        Driver class name for a JDBC metastore


        javax.jdo.option.ConnectionUserName
        root
        username to use against metastore database


        javax.jdo.option.ConnectionPassword
        root
        password to use against metastore database

7.拷贝数据库驱动到hive-1.1.0-cdh5.7.0/lib目录下
cp /home/hadoop/software/mysql-connector-java-5.1.27-bin.jar
8.启动hive，bin/hive

image.png

9.到mysql数据库里查看sparksql

mysql> show databases;
+--------------------+
| Database           |
+--------------------+
| information_schema |
| azkaban            |
| hive               |
| mysql              |
| spark              |
| sparksql           |
| sqoop              |
| ss                 |
| test               |
+--------------------+
9 rows in set (0.00 sec)

mysql> use sparksql
Database changed
mysql> show tables;
+---------------------------+
| Tables_in_sparksql        |
+---------------------------+
| BUCKETING_COLS            |
| CDS                       |
| COLUMNS_V2                |
| DATABASE_PARAMS           |
| DBS                       |
| FUNCS                     |
| FUNC_RU                   |
| GLOBAL_PRIVS              |
| PARTITIONS                |
| PARTITION_KEYS            |
| PARTITION_KEY_VALS        |
| PART_COL_STATS            |
| ROLES                     |
| SDS                       |
| SEQUENCE_TABLE            |
| SERDES                    |
| SKEWED_COL_NAMES          |
| SKEWED_COL_VALUE_LOC_MAP  |
| SKEWED_STRING_LIST        |
| SKEWED_STRING_LIST_VALUES |
| SKEWED_VALUES             |
| SORT_COLS                 |
| TAB_COL_STATS             |
| TBLS                      |
| VERSION                   |
+---------------------------+
25 rows in set (0.00 sec)

10.在Hive中建表，
create table hive_count(context string );
报错：

FAILED: 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.)

解决：

这是由于字符集的问题，需要配置MySQL的字符集：
mysql> alter database hive character set latin1;

11.建好表以后，查看sparksql里的TBLS和COLUMNS_V2

image.png

12.查看hive官方文档

https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML#LanguageManualDML-Loadingfilesintotables
从外部文件加载数据

LOAD DATA [LOCAL] INPATH 'filepath' [OVERWRITE] INTO TABLE tablename [PARTITION (partcol1=val1, partcol2=val2 ...)]

13.在/home/hadoop/data/data.log下面有外部数据，把它导入到hive表里
LOAD DATA LOCAL INPATH '/home/hadoop/data/data.log' INTO TABLE hive_count;

14.select下结果：

image.png

15.hive ql提交执行后生成MR作业，并在yarn上运行。

select word,count(1) from hive_count lateral view explode(split(context,'\t')) wc as word group by word;

lateral view explode(split(context,'\t'))
:把每行记录按照指定分割符进行分解。

16.确认结果

image.png

yarn页面,

http://hostname:8088/cluster/apps

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