Hive beeline和Spark SQL兼容Hive – 配置

Hive beeline和Spark SQL兼容Hive – 配置

备注:如果你是用hive的api去连,那么就需要打开Hive beeline,如果是用spark SQL就需要开启spark的ThriftServer

Hive beeline

上面安装后直接输入hive是CLI的操作方式,在后面可能会被舍弃掉,换用beeline方式去连接,因为目前使用CLI的人比较多,所以还暂未舍弃
1.打开hiveserver2的服务：nohup $HIVE_HOME/bin/hiveserver2 > /tmp/hiveserver2.log 2>&1 &
2.执行beeline脚本：$HIVE_HOME/bin/beeline
3.!connect jdbc:hive2://master:10000 连接hiveserver2服务, 输入用户名:hadoop-jrq，不需要密码
4.如果报如下的错，则在hdfs的core-site.xml中增加两个属性配置

WARN jdbc.HiveConnection: Failed to connect to master:10000
Error: Could not open client transport with JDBC Uri: jdbc:hive2://master:10000: Failed to open new session : java.lang.RuntimeException: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.authorize.Au thorizationException): User: hadoop-jrq is not allowed to impersonate anonymous (state=08S01,code=0)

		
                hadoop.proxyuser.hadoop-jrq.hosts
                *
        
        
                hadoop.proxyuser.hadoop-jrq.groups
                *
        

注意:三个集群都需要配置,配置好后需要重启集群,并且需要重启hiveserver2

5.如果报如下的错，则执行hadoop dfsadmin -safemode leave

6.如果没有报错的话，则可以执行查询语句sql了

一个java操作hive的例子

依赖包

        
            org.apache.hive
            hive-jdbc
            1.2.1
        

public class HiveJdbcClient {
    private static String driverName = "org.apache.hive.jdbc.HiveDriver";
    
    public static void main(String[] args) throws SQLException {
        try {
            // 注册驱动
            Class.forName(driverName);
        } catch (ClassNotFoundException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
            System.exit(1);
        }
        //replace "hive" here with the name of the user the queries should run as
        // 获取连接,不是在安全模式下,所以密码是空的
        Connection con =
                DriverManager.getConnection("jdbc:hive2://master:10000", "hadoop-jrq", "");

        // 创建编译对象,之后就可以用SQL的方式对hive进行操作了
        Statement stmt = con.createStatement();

        // 数据库的名字
        String tableName = "jrq.tracker_session";
		
		// 创建数据库,存在则不创建,不存在则创建
        stmt.execute("CREATE DATABASE IF NOT EXISTS jrq");
		
		// 创建一张表
        stmt.execute("CREATE TABLE IF NOT EXISTS " + tableName + " (\n" +
                " session_id string,\n" +
                " session_server_time string,\n" +
                " cookie string,\n" +
                " cookie_label string,\n" +
                " ip string,\n" +
                " landing_url string,\n" +
                " pageview_count int,\n" +
                " click_count int,\n" +
                " domain string,\n" +
                " domain_label string)\n" +
                "STORED AS PARQUET");
        // show tables
        String sql = "show tables '" + tableName + "'";
        System.out.println("Running: " + sql);
        // 提取结果集
        ResultSet res = stmt.executeQuery(sql);
        if (res.next()) {
            System.out.println(res.getString(1));
        }
        // describe table   看table的行和列
        sql = "describe " + tableName;
        System.out.println("Running: " + sql);
        res = stmt.executeQuery(sql);
        while (res.next()) {
            System.out.println(res.getString(1) + "\t" + res.getString(2));
        }

        // load data into table
        // 不写的话默认就是hdfs
        // 数据导入成功后,这个目录都会被删除
        sql =
                "LOAD DATA INPATH 'hdfs://slave1:8020/user/hadoop-jrq/example/output/trackerSession'" +
                        " OVERWRITE INTO TABLE " + tableName;
        System.out.println("Running: " + sql);
        stmt.execute(sql);

        // select * query
        sql = "select * from " + tableName;
        System.out.println("Running: " + sql);
        // 获取结果,循环打印
        res = stmt.executeQuery(sql);
        while (res.next()) {
            System.out.println(String.valueOf(res.getString(1)) + "\t" + res.getString(2));
        }

        // regular hive query
        sql = "select count(1) from " + tableName;
        System.out.println("Running: " + sql);
        res = stmt.executeQuery(sql);
        while (res.next()) {
            System.out.println(res.getString(1));
        }
		//操作完成后需要关闭连接
        res.close();
        stmt.close();
        con.close();
    }
}

Spark SQL兼容Hive – 配置

1.将$HIVE_HOME/conf/hive-site.conf拷贝到$SPARK_HOME/conf下

cp $HIVE_HOME/conf/hive-site.xml $SPARK_HOME/conf

2.将mysql-connector-java-x.x.x-bin.jar上传到$SPARK_HOME/jars下

3.重启spark集群

4.$SPARK_HOME/bin/spark-sql脚本操作Hive
这个就相当于hive的CLI操作

Spark SQL兼容Hive – ThriftServer

1.开启spark SQL HiveThriftServer
服务端执行:

 $SPARK_HOME/sbin/start-thriftserver.sh \
--hiveconf hive.server2.thrift.port=10000 \
--hiveconf hive.server2.thrift.bind.host=master \
--master spark://master:7077

注意这里有个坑点,请移步我的另一篇文章https://blog.csdn.net/weixin_42411818/article/details/100116987

执行后利用jps可以看到多了个sparksubmit进程

2.Spark SQL beeline：
执行$SPARK_HOME/bin/beeline
然后输入!connect jdbc:hive2://master:10000
账号是你目前机器的账号,密码无

3.代码中JDBC的方式访问Spark SQL
客户端JDBC依赖的是1.2.1版本，
jdbc url为：jdbc:hive2/master:10000

   
	org.apache.hive  
   hive-jdbc    
   1.2.1
 

4.Spark SQL读写数据源中的table和saveAsTable
spark.read.table读一张表,saveAsTable保存一张新表