spark和hive结合
1.安装mysql
2.在spark/conf中创建一个hive-site.xml
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
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<configuration>
<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:
mysql://192.168.224.132:3306/hive?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>root</value>
<description>username to use against metastore database</description>
</property>
<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>root</value>
<description>password to use against metastore database</description>
</property>
</configuration>
3.把mysql的jar包放入spark的jars目录里
有两种方法让spark照hdfs中hive表的数据
3.修改etc/profile
export HADOOP_CONF_DIR=/home/xss/java/hadoop/etc/hadoop
3.在spark的conf目录的spark-env.sh 中加入 export HADOOP_CONF_DIR=/usr/local/hadoop/etc/hadoop/
（以上三点效果都一样，所以执行哪个都可以，只需要执行一个就行）
4.启动spark-sql(用命令行的方式) ****运行指令之前需要先启动 start-spark.sh
bin/spark-sql --master
spark://192.168.224.132:7077 --driver-class-path /usr/local/spark/mysql.jar（
在jars中放mysql包之后就不用之后的dirver）
5.修改mysql中hive数据库中的DBS ***没有操作
把数据存放的路径改为HDFS目录
