java通过jdbc连接hive并实时获取日志（转）

转载：https://blog.csdn.net/weixin_43455443/article/details/115343995

4、通过java代码连接hive on spark，使用hive-jdbc
引入pom文件

<dependency>
<groupId>org.apache.hive</groupId>
<artifactId>hive-jdbc</artifactId>
<version>2.1.1</version>
</dependency>

 

 

import java.sql.*;
import java.util.Scanner;

public class HiveJDBC {
    public static void main(String[] args) throws ClassNotFoundException, SQLException, InterruptedException {
        String driverName = "org.apache.hive.jdbc.HiveDriver";
        String url = "jdbc:hive2://hadoop162:10000";
        String dbName = "default";
        Connection con = null;
        Statement state = null;
        ResultSet res = null;

        Class.forName(driverName);
        //这里必须指定用户名和密码，密码可以为空字符串，如果不指定则或报错启动sparksession失败
        con= DriverManager.getConnection(url+"/"+dbName,"hive","");
        state = con.createStatement();
        Scanner scan = new Scanner(System.in);
        String sql=null;
        //创建临时udf，可以不创建
        state.execute("create temporary function pinjie as 'com.topnet.MyUDF' using jar 'hdfs:///user/hive/udf/hiveUDF-1.0.jar'");
        while (true){
            System.out.println("亲输入sql：");
            if(scan.hasNext()){
                sql=scan.nextLine();
            }
            System.out.println(sql);
            res = state.executeQuery(sql);
            while (res.next()) {
                System.out.println(res.getString(1));
            }
            Thread.sleep(100);
        }
    }
}

 

5、运行sql的执行日志获取
使用hive-jdbc运行时，如果想获取sql的执行日志，则可以通过这几个方法获取运行的日志信息。List<String> getQueryLog()，List<String> getQueryLog(boolean incremental, int fetchSize)和boolean hasMoreLogs()三个方法，在进行hive的sql查询时，有时一个sql可能需要运行很长时间，借助这三个方法，还可以实时显示sql 的查询进度。

想要实时的显示sql查询进度，则需要再开启一个线程进行日志获取打印。

 

public class HiveJDBC {
    public static void main(String[] args) throws ClassNotFoundException, SQLException, InterruptedException {
        Logger log = LoggerFactory.getLogger(HiveJDBC.class);
        String driverName = "org.apache.hive.jdbc.HiveDriver";
        String url = "jdbc:hive2://hadoop162:10000";
        String dbName = "default";
        Connection con = null;
        Statement state = null;
        ResultSet res = null;

        Class.forName(driverName);
        //这里必须指定用户名和密码，密码可以为空字符串，如果不指定则或报错启动sparksession失败
        con = DriverManager.getConnection(url + "/" + dbName, "hive", "");
        state = con.createStatement();
        Scanner scan = new Scanner(System.in);
        String sql = null;
        
        //开启线程获取sql执行日志
        Thread logThread = new Thread(new HiveLog((HiveStatement) state));
        logThread.setDaemon(true);
        logThread.start();

        //注册临时udf函数，可以不创建
        state.execute("create temporary function pinjie as 'com.topnet.MyUDF' using jar 'hdfs:///user/hive/udf/hiveUDF-1.0.jar'");
        while (true) {
            System.out.println("亲输入sql：");
            if (scan.hasNext()) {
                sql = scan.nextLine();
            }
            log.error("打印日志sql语句：" + sql);
            res = state.executeQuery(sql);
            while (res.next()) {
                System.out.println(res.getString(1));
            }
            Thread.sleep(100);
        }
    }

    static class HiveLog extends Thread {
        private final HiveStatement state;

        HiveLog(HiveStatement state) {
            this.state = state;
        }
        
        private void updateQueryLog() {
            try {
                List<String> queryLogs = state.getQueryLog();
                for (String log : queryLogs) {
                    System.out.println("进度信息-->" + log);
                }
            } catch (Exception e) {
            }
        }
        
        @Override
        public void run() {
            try {
                //循环不断的获取sql执行的日志
                while (true) {
                    if (state.hasMoreLogs()) {
                        updateQueryLog();
                    }
                    Thread.sleep(100);
                }
            } catch (InterruptedException e) {
                e.getStackTrace();
            }
        }
    }
}