看的16年的学习视频，却忽略了这些年的技术更新，有些命令也随之发生了变化，在这个上边吃了大亏，特此做记录。
想要运行MapReduce程序，首先需要用javaApi先写一些脚本代码：
首先需要的是Mapper类与Reducer类，在此我将两个类以及main函数都写在一个类里，需要读取的文件为手机流量例子。
public class FlowCount {
/*
* Mapper
* */
static class FlowCountMapper extends Mapper<LongWritable,Text,Text,FlowBean>{
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//将一行内容转成string
String line = value.toString();
//切分字段
String[] fields = line.split("\t");
//取出手机号
String phoneNum = fields[1];
//取出上行流量下行流量
long upFlow = Long.parseLong(fields[fields.length-3]);
long downFlow = Long.parseLong(fields[fields.length-2]);
context.write(new Text(phoneNum),new FlowBean(upFlow,downFlow));
}
}
/*
Reducer
*/
static class FlowCountReducer extends Reducer<Text,FlowBean,Text,FlowBean>{
//<183323,bean1><183323,bean2><183323,bean3><183323,bean4>
@Override
protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
long sum_upFlow = 0;
long sum_dFlow = 0;
//遍历所有的Bean,将其中的上行流量，下行流量分别累加
for (FlowBean bean:values){
sum_upFlow += bean.getUpFlow();
sum_dFlow += bean.getDownFlow();
}
FlowBean resultBean = new FlowBean(sum_upFlow, sum_dFlow);
context.write(key,resultBean);
}
}
public static void main(String[] args)throws Exception{
Configuration conf = new Configuration();
conf.set("fs.defaultFS","hdfs://min1:9000/");
conf.set("mapreduce.framework.name", "yarn");
conf.set("yarn.resourcemanager.hostname", "min1");
conf.set("yarn.resourcemanager.address", "min1"+":"+8032);
conf.set("yarn.resourcemanager.scheduler.address", "min1"+":"+8030);
//运行集群模式，就是把程序提交到yarn中去运行
//要想运行为集群模式，以下3个参数要指定为集群上的值
/*conf.set("mapreduce.framework.name", "yarn");
conf.set("yarn.resourcemanager.hostname", "mini1");
conf.set("fs.defaultFS", "hdfs://mini1:9000/");*/
Job job = Job.getInstance(conf,"wordcount");
// job.setJar("c:/wc.jar");
//指定本程序的jar包所在的本地路径
job.setJarByClass(FlowCount.class);
//指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(FlowCountMapper.class);
job.setReducerClass(FlowCountReducer.class);
//指定mapper输出数据的kv类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
//指定最终输出的数据的kv类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
//指定job的输入原始文件所在目录
FileInputFormat.setInputPaths(job, new Path("/flowsum/input"));
//指定job的输出结果所在目录
FileOutputFormat.setOutputPath(job, new Path("/flowsum/output"));
//将job中配置的相关参数，以及job所用的java类所在的jar包，提交给yarn去运行
/*job.submit();*/
boolean res = job.waitForCompletion(true);
System.exit(res?0:1);
}
}我们将读取的数据进行封装，封装成一个FlowBean类
package mrFlowSum;
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class FlowBean implements Writable {
private long upFlow;
private long downFlow;
private long sumFlow;
//反序列化时需要反射调用空参构造函数，所以要显式定义一个
public FlowBean() {
}
public FlowBean(long upFlow, long downFlow) {
this.upFlow = upFlow;
this.downFlow = downFlow;
this.sumFlow = downFlow+upFlow;
}
public long getSumFlow() {
return sumFlow;
}
public void setSumFlow(long sumFlow) {
this.sumFlow = sumFlow;
}
public long getUpFlow() {
return upFlow;
}
public void setUpFlow(long upFlow) {
this.upFlow = upFlow;
}
public long getDownFlow() {
return downFlow;
}
public void setDownFlow(long downFlow) {
this.downFlow = downFlow;
}
/*
序列化方法
*/
@Override
public void write(DataOutput dataOutput) throws IOException {
dataOutput.writeLong(upFlow);
dataOutput.writeLong(downFlow);
dataOutput.writeLong(sumFlow);
}
/*
反序列化方法
注意：反序列化的顺序跟序列化的顺序完全一致
*/
@Override
public void readFields(DataInput dataInput) throws IOException {
long upFlow = dataInput.readLong();
long downFlow = dataInput.readLong();
long sumFlow = dataInput.readLong();
}
@Override
public String toString() {
return upFlow + "\t" + downFlow + "\t" + sumFlow;
}
}Flow.data例子：
1363157985066 13726230503 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157995052 13826544101 5C-0E-8B-C7-F1-E0:CMCC 120.197.40.4 4 0 264 0 200
1363157991076 13926435656 20-10-7A-28-CC-0A:CMCC 120.196.100.99 2 4 132 1512 200
1363154400022 13926251106 5C-0E-8B-8B-B1-50:CMCC 120.197.40.4 4 0 240 0 200
1363157993044 18211575961 94-71-AC-CD-E6-18:CMCC-EASY 120.196.100.99 iface.qiyi.com 视频网站 15 12 1527 2106 200
1363157995074 84138413 5C-0E-8B-8C-E8-20:7DaysInn 120.197.40.4 122.72.52.12 20 16 4116 1432 200
1363157993055 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
1363157995033 15920133257 5C-0E-8B-C7-BA-20:CMCC 120.197.40.4 sug.so.360.cn 信息安全 20 20 3156 2936 200
1363157983019 13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82 4 0 240 0 200
1363157984041 13660577991 5C-0E-8B-92-5C-20:CMCC-EASY 120.197.40.4 s19.cnzz.com 站点统计 24 9 6960 690 200
1363157973098 15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com 搜索引擎 28 27 3659 3538 200
1363157986029 15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com 站点统计 3 3 1938 180 200
1363157992093 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 15 9 918 4938 200
1363157986041 13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4 3 3 180 180 200
1363157984040 13602846565 5C-0E-8B-8B-B6-00:CMCC 120.197.40.4 2052.flash2-http.qq.com 综合门户 15 12 1938 2910 200
1363157995093 13922314466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn 12 12 3008 3720 200
1363157982040 13502468823 5C-0A-5B-6A-0B-D4:CMCC-EASY 120.196.100.99 y0.ifengimg.com 综合门户 57 102 7335 110349 200
1363157986072 18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99 input.shouji.sogou.com 搜索引擎 21 18 9531 2412 200
1363157990043 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 200
1363157988072 13760778710 00-FD-07-A4-7B-08:CMCC 120.196.100.82 2 2 120 120 200
1363157985066 13726238888 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157993055 13560436666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
在IDEA上进行jar包打包，上传至Linux服务器，将例子文件(flow.data)也上传至服务器，而后使用
hadoop fs -put flow.data /flowsum/input 这条命令将文件放入HDFS的/flowsum/input 输入文件夹内
使用命令hadoop jar mapreduce.jar /flowsum/input /flowsum/output2 运行jar包运行程序。 （在这里栽了很大的跟头，之前跟着学习视频使用命令 hadoop jar mapreduce.jar mrFlowSum.FlowCount /flowsum/input /flowsum/output2，里边多了一个主类函数名称，而我的主类名称早就在pom中定义好了，所以无需加这个主类名称）
运行成功后会在HDFS中生成一个output文件夹，文件夹中生成_SUCCESS文件以及part-r-0000xx文件，后者即为我们想要的最终结果，
我们可以使用命令
hadoop fs -cat /flowsum/output/part-r-0000xx查看最终结果
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