Mapreduce program running in cluster mode, word statistics case

Mapreduce program running in cluster mode, word statistics case

Local mode operation: https://blog.csdn.net/weixin_43614067/article/details/108386389
Local submission to the cluster to run: https://blog.csdn.net/weixin_43614067/article/details/108401227
map, reduce and local The mode runs the same.

Yarn high-availability cluster environment construction
1. Configure hadoop-2.6.5/etc/hadoop/mapred-site.xml

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
 </configuration>

2. Place hadoop-2.6.5 /etc/hadoop/ yarn-site.xml

<configuration>

    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>

    <!--启用resourcemanager ha-->
    <property>
        <name>yarn.resourcemanager.ha.enabled</name>
        <value>true</value>
    </property>
 
    <!--声明两台resourcemanager的地址-->
    <property>
        <name>yarn.resourcemanager.cluster-id</name>
        <value>cluster-yarn1</value>
    </property>

    <property>
        <name>yarn.resourcemanager.ha.rm-ids</name>
        <value>rm1,rm2</value>
    </property>

    <property>
        <name>yarn.resourcemanager.hostname.rm1</name>
        <value>node001</value>
    </property>

    <property>
        <name>yarn.resourcemanager.hostname.rm2</name>
        <value>node002</value>
    </property>
 
    <!--指定zookeeper集群的地址--> 
    <property>
        <name>yarn.resourcemanager.zk-address</name>
        <value>node002:2181,node003:2181,node004:2181</value>
    </property>

    <!--启用自动恢复--> 
    <property>
        <name>yarn.resourcemanager.recovery.enabled</name>
        <value>true</value>
    </property>
 
    <!--指定resourcemanager的状态信息存储在zookeeper集群--> 
    <property>
        <name>yarn.resourcemanager.store.class</name>            
        <value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
    </property>
</configuration>

3. Synchronize the configuration on other cluster machines
4. Start yarn, others need to be manually started on other machines

sbin/start-yarn.sh

5. Check whether the startup is successful
(1) Visit: node001:8088, it will be automatically switched to node002:8088
(2) Command view

 bin/yarn rmadmin -getServiceState rm1
 active
 bin/yarn rmadmin -getServiceState rm2
 standby

Modify Runner

package com.bjsxt.wc;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WCRunner {
    
    
    public static void main(String[] args) throws Exception {
    
    
        //创建配置对象
        Configuration conf = new Configuration();
        //创建Job对象
        Job job = Job.getInstance(conf, "wordCount");
        //设置mapper类
        job.setMapperClass(WCMapper.class);
        //设置 Reduce类
        job.setReducerClass(WCReducer.class);
        //设置运行job类
        job.setJarByClass(WCRunner.class);
        //设置map输出的key,value类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        //设置reduce输出的key,value类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        //设置输入路径金额输出路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        long startTime = System.currentTimeMillis();
        try {
    
    
       		//提交job
            boolean b = job.waitForCompletion(true);
            if (b) {
    
    
                System.out.println("单词统计完成!");
            }
        } finally {
    
    
            // 结束的毫秒数
            long endTime = System.currentTimeMillis();
            System.out.println("Job<" + job.getJobName() + ">是否执行成功:" + job.isSuccessful() + "; 开始时间:" + startTime + "; 结束时间:" + endTime + "; 用时:" + (endTime - startTime) + "ms");
        }
    }
}

After compiling and packaging, upload to any directory on the server
, for example, /root/project/wordcount.jar hdfs to create a directory, put the word text to be counted in the input directory, such as word.txt

hdfs dfs -mkdir -p /wordcount/input
hdfs dfs -put test.txt /wordcount/input

Note: The output directory does not need to be created

Execute wordcount jar

hadoop jar /root/project/wordcount.jar com/bjsxt/wc/WCRunner /wordcount/input /wordcount/output

wordcount.jar: the jar after the project is compiled and packaged.
com/bjsxt/wc/WCRunner: is the full path of the Runner class
/wordcount/input: is the data input path in hdfs
/wordcount/output: is the data output path in hdfs

Verify after execution

hdfs dfs -cat /wordcount/output/*

Local mode operation: https://blog.csdn.net/weixin_43614067/article/details/108386389
Local submission to the cluster for operation: https://blog.csdn.net/weixin_43614067/article/details/108401227