Hive安装与简单使用并集成SparkSQL

## Hive环境搭建
1. hive下载：http://archive-primary.cloudera.com/cdh5/cdh/5/hive-1.1.0-cdh5.7.0.tar.gz
wget http://archive-primary.cloudera.com/cdh5/cdh/5/hive-1.1.0-cdh5.7.0.tar.gz

2. 解压
tar -zxvf hive-1.1.0-cdh5.7.0.tar.gz -C ../apps/

3. 系统环境变量（vim ~/.bash_profile）
```
export HIVE_HOME=/root/apps/hive-1.1.0-cdh5.7.0
export PATH=$HIVE_HOME/bin:$PATH
source ~/.bash_profile
```

4. 配置

```
4.1 $HIVE_HOME/conf/hive-env.sh 中导出Hadoop_Home
4.2 拷贝mysql 驱动架包到$HIVE_HOME/lib
```

4.3 vim hive-site.xml

```
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://spark003:3306/hive?createDatabaseIfNotExist=true</value>
</property>
<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
</property>
<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>root</value>
</property>
<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>123456</value>
</property>
</configuration>
```

5. 启动Hive： $HIVE_HOME/bin/hive

## Hive的基本使用
创建表

> create table test_table(name string);

加载本地数据到hive表【local方式】

> load data local inpath '/home/hadoop/data/hello.txt' into table test_table;

查询，统计，词频的个数：
select * from test_table;

> select word, count(1) from test_table lateral view explode(split(name),' ') wc as word group by word;

### 小案例
create table emp(
empno int,
ename string,
job string,
mgr int,
sal double,
comm double,
deptno int
)row format delimited fields terminated by ' ';

create table dept(
deptno int,
dname string,
location string
)row format delimited fields terminated by ' ';

load data local inpath '/home/hadoop/data/emp.txt' into table emp;
load data local inpath '/home/hadoop/data/dept.txt' into table dept;

统计分析：
求每个部门的人数：
select deptno,count(1) from emp group by deptno;

## Spark SQL 与Hive集成（spark-shell）

1. 将hive的配置文件hive-site.xml拷贝到spark conf目录，同时添加metastore的url配置。

```
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
<property>
<name>hive.metastore.uris</name>
<value>thrift://spark001:9083</value>
</property>
</configuration>
```
2. mysql jar包到 spark 的 lib 目录下

```
[root@spark001 lib]# pwd
/root/apps/spark-2.2.0-bin-2.6.0-cdh5.7.0/lib
[root@spark001 lib]# ll
total 972
-rw-r--r--. 1 root root 992805 Oct 23 23:59 mysql-connector-java-5.1.41.jar

```

3. 修改spark-env.sh 文件中的配置

操作： vim spark-env.sh，添加如下内容：

```
export JAVA_HOME=/root/apps/jdk1.8.0_144
export SPARK_HOME=/root/apps/spark-2.2.0-bin-2.6.0-cdh5.7.0
export SCALA_HOME=/root/apps/scala-2.11.8
#新添加下面的这一条
export HADOOP_CONF_DIR=/root/apps/spark-2.2.0-bin-2.6.0-cdh5.7.0/etc/hadoop
```
4. 启动服务
启动hadoop start-all.sh
启动saprk start-all.sh
启动mysql元数据库 service mysqld restart
启动hive metastore服务 hive --service metastore
启动hive命令行 hive
启动spark-shell命令行 spark-shell

5. 简单测试
创建本地文件 test.csv，内容如下：
0001,spark
0002,hive
0003,hbase
0004,hadoop
> 执行hive命令：

hive> show databases;
hive> create database databases1;
hive> create table if not exists test(userid string,username string)ROW FORMAT DELIMITED FIELDS TERMINATED BY ' ' STORED AS textfile;
hive> load data local inpath "/root/test.csv" into table test;
hive>select * from test;

> 执行Spark-shell命令：

spark.sql("select * from databases1.test").show