我最近刚学习hadoop（我使用hadoop2.7.3＆amp; java 1.7.0.89）。我写了一些代码来分析不同的电视广播＆amp;对不同网站的评论。我重写了filesinputformat类。但是当我在eclipse上运行我的代码时，有很多例外。我尝试在eclipse上调试。只是找到找到mapper或reducer有一些问题..但我不知道出了什么问题..
这里是日期示例，辅助数据是网站的昵称
truelove 3 3678 0 0 0 1
truelove 2 39155 0 0 173 438
truelove 1 142208 1 2 1 1
truelove 1 142208 1 2 1 1
truelove 1 142208 1 2 1 1
frink 2 950 0 0 0 0
frink 2 800 0 0 0 0
daughter 4 4489850 0 0 0 0
daughter 4 1161 0 0 0 0
princess 2 33593 0 0 0 3
princess 2 36118 0 0 0 2
princess 2 38608 0 0 0 1
princess 3 2542 0 0 0 0
princess 1 35322 2 4 0 1
然后我重写了inputformat
自定义数据格式
package com.hadoop.mapreduce;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
/*1-5means：1youku2souhu3tudou4aiqiyi5xunlei
princess 2 33593 0 0 0 3
princess 2 36118 0 0 0 2
princess 2 38608 0 0 0 1
princess 3 2542 0 0 0 0
princess 1 35322 2 4 0 1*/
public class TVplaydata implements WritableComparable<Object>{
//private String tvname;
private int tvplaynum;
private int tvfavorite;
private int tvcomment;
private int tvdown;
private int tvvote;
public TVplaydata(){}
public void set(int tvplaynum,int tvfavorite,int tvcomment,int tvdown,int tvvote){
this.tvplaynum = tvplaynum;
this.tvfavorite = tvfavorite;
this.tvcomment = tvcomment;
this.tvdown = tvdown;
this.tvvote = tvvote;
}
//source get set
public void setTvpalynum(int tvplaynum) {
this.tvplaynum = tvplaynum;
}
public int getTvpalynum() {
return tvplaynum;
}
public int getTvfavorite() {
return tvfavorite;
}
public void setTvfavorite(int tvfavorite) {
this.tvfavorite = tvfavorite;
}
public int getTvcomment() {
return tvcomment;
}
public void setTvcomment(int tvcomment) {
this.tvcomment = tvcomment;
}
public int getTvdown() {
return tvdown;
}
public void setTvdown(int tvdown) {
this.tvdown = tvdown;
}
public int getTvvote() {
return tvvote;
}
public void setTvvote(int tvvote) {
this.tvvote = tvvote;
}
@Override
public void readFields(DataInput in) throws IOException {
// TODO Auto-generated method stub
tvplaynum = in.readInt();
tvfavorite = in.readInt();
tvcomment = in.readInt();
tvdown = in.readInt();
tvvote = in.readInt();
}
@Override
public void write(DataOutput out) throws IOException {
// TODO Auto-generated method stub
out.writeInt(tvplaynum);
out.writeInt(tvfavorite);
out.writeInt(tvcomment);
out.writeInt(tvdown);
out.writeInt(tvvote);
}
@Override
public int compareTo(Object o) {
// TODO Auto-generated method stub
return 0;
}
}
然后重写inputformat。
package com.hadoop.mapreduce;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.util.LineReader;
class PlayinputFormat extends FileInputFormat<Text, TVplaydata>{
@Override
public RecordReader<Text, TVplaydata> createRecordReader(InputSplit input, TaskAttemptContext context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
return new TvplayRecordReader();
}
class TvplayRecordReader extends RecordReader<Text, TVplaydata>{
public LineReader in;
public Text lineKey;
public TVplaydata lineValue;
public Text line;
@Override
public void close() throws IOException {
// TODO Auto-generated method stub
if(in !=null){
in.close();
}
}
@Override
public Text getCurrentKey() throws IOException, InterruptedException {
// TODO Auto-generated method stub
return lineKey;
}
@Override
public TVplaydata getCurrentValue() throws IOException, InterruptedException {
// TODO Auto-generated method stub
return lineValue;
}
@Override
public float getProgress() throws IOException, InterruptedException {
// TODO Auto-generated method stub
return 0;
}
@Override
public void initialize(InputSplit input, TaskAttemptContext context) throws IOException, InterruptedException {
// TODO Auto-generated method stub
FileSplit split=(FileSplit)input;
Configuration job=context.getConfiguration();
Path file=split.getPath();
FileSystem fs=file.getFileSystem(job);
FSDataInputStream filein=fs.open(file); //open
in=new LineReader(filein,job);
line=new Text();
lineKey=new Text();
lineValue = new TVplaydata();
}
@Override
public boolean nextKeyValue() throws IOException, InterruptedException {
// TODO Auto-generated method stub
int linesize=in.readLine(line);
if(linesize==0) return false;
String[] pieces = line.toString().split("\t");
if(pieces.length != 7){
throw new IOException("Invalid record received");
}
lineKey.set(pieces[0]+"\t"+pieces[1]);
lineValue.set(Integer.parseInt(pieces[2]),Integer.parseInt(pieces[3]),Integer.parseInt(pieces[4])
,Integer.parseInt(pieces[5]),Integer.parseInt(pieces[6]));
return true;
}
}
}
最后编写了运行Mapper＆amp; Reducer
的主要方法package com.hadoop.mapreduce;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class TVPlay extends Configured implements Tool{
public static void main(String[] args) throws Exception {
String[] paths = {"hdfs://wang:9000/mapreduce/tvplay.txt","hdfs://wang:9000/mapreduce/tvout"};
int ec = ToolRunner.run(new Configuration(), new TVPlay(), paths);
System.exit(ec);
}
//mapper
public class TVmapper extends Mapper<Text, TVplaydata, Text, TVplaydata> {
public void map(Text key,TVplaydata value, Context context) throws IOException, InterruptedException {
context.write(key, value);
}
}
public class TVreducer extends Reducer<Text, TVplaydata, Text, Text>{
private Text m_key = new Text();
private Text m_value = new Text();
private MultipleOutputs<Text, Text> mos;
protected void setup(Context context) throws IOException,
InterruptedException {
mos = new MultipleOutputs<Text, Text>(context);
}
public void reduce(Text key,Iterable<TVplaydata> values, Context context) throws IOException, InterruptedException{
int tvplaynum = 0;
int tvfavorite = 0;
int tvcomment = 0;
int tvvote = 0;
int tvdown = 0;
for (TVplaydata tv:values) {
tvplaynum += tv.getTvpalynum();
tvfavorite += tv.getTvfavorite();
tvcomment += tv.getTvcomment();
tvvote += tv.getTvvote();
tvdown += tv.getTvdown();
}
String[] records = key.toString().split("\t");
String source = records[1];
m_key.set(records[0]);
m_value.set(tvplaynum+"\t"+tvfavorite+"\t"+tvcomment+"\t"+tvdown+"\t"+tvvote);
if(source.equals("1")){
mos.write("youku", m_key, m_value);
}else if (source.equals("2")) {
mos.write("souhu", m_key, m_value);
}else if (source.equals("3")) {
mos.write("tudou",m_key, m_value);
}else if (source.equals("4")) {
mos.write("aiqiyi", m_key, m_value);
}else if (source.equals("5")) {
mos.write("xunlei", m_key, m_value);
}else{
mos.write("other", m_key, m_value);
}
}
protected void cleanup(Context context) throws IOException ,InterruptedException{
mos.close();
}
}
@Override
public int run(String[] arg0) throws Exception {
// TODO Auto-generated method stub
Configuration conf = new Configuration();
Path path = new Path(arg0[1]);
FileSystem hdfs = path.getFileSystem(conf);
if(hdfs.isDirectory(path)){
hdfs.delete(path, true);
}
Job job = new Job(conf,"tvplay");
job.setJarByClass(TVPlay.class);
// set InputFormatClass
job.setInputFormatClass(PlayinputFormat.class);
// set mapper
job.setMapperClass(TVmapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(TVplaydata.class);
// set reduce
job.setReducerClass(TVreducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path(arg0[0]));
FileOutputFormat.setOutputPath(job,new Path(arg0[1]));
MultipleOutputs.addNamedOutput(job, "youku", TextOutputFormat.class,
Text.class, Text.class);
MultipleOutputs.addNamedOutput(job, "souhu", TextOutputFormat.class,
Text.class, Text.class);
MultipleOutputs.addNamedOutput(job, "tudou", TextOutputFormat.class,
Text.class, Text.class);
MultipleOutputs.addNamedOutput(job, "aiqiyi", TextOutputFormat.class,
Text.class, Text.class);
MultipleOutputs.addNamedOutput(job, "xunlei", TextOutputFormat.class,
Text.class, Text.class);
MultipleOutputs.addNamedOutput(job, "other", TextOutputFormat.class,
Text.class, Text.class);
return job.waitForCompletion(true)?0:1;
}
}
以下是例外
2017-06-20 23:03:26,848 INFO [org.apache.hadoop.conf.Configuration.deprecation] - session.id is deprecated. Instead, use dfs.metrics.session-id
2017-06-20 23:03:26,854 INFO [org.apache.hadoop.metrics.jvm.JvmMetrics] - Initializing JVM Metrics with processName=JobTracker, sessionId=
2017-06-20 23:03:27,874 WARN [org.apache.hadoop.mapreduce.JobResourceUploader] - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2017-06-20 23:03:28,186 WARN [org.apache.hadoop.mapreduce.JobResourceUploader] - No job jar file set. User classes may not be found. See Job or Job#setJar(String).
2017-06-20 23:03:28,236 INFO [org.apache.hadoop.mapreduce.lib.input.FileInputFormat] - Total input paths to process : 1
2017-06-20 23:03:28,639 INFO [org.apache.hadoop.mapreduce.JobSubmitter] - number of splits:1
2017-06-20 23:03:29,389 INFO [org.apache.hadoop.mapreduce.JobSubmitter] - Submitting tokens for job: job_local622257889_0001
2017-06-20 23:03:30,552 INFO [org.apache.hadoop.mapreduce.Job] - The url to track the job: http://localhost:8080/
2017-06-20 23:03:30,556 INFO [org.apache.hadoop.mapreduce.Job] - Running job: job_local622257889_0001
2017-06-20 23:03:30,607 INFO [org.apache.hadoop.mapred.LocalJobRunner] - OutputCommitter set in config null
2017-06-20 23:03:30,630 INFO [org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter] - File Output Committer Algorithm version is 1
2017-06-20 23:03:30,670 INFO [org.apache.hadoop.mapred.LocalJobRunner] - OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
2017-06-20 23:03:31,562 INFO [org.apache.hadoop.mapreduce.Job] - Job job_local622257889_0001 running in uber mode : false
2017-06-20 23:03:31,567 INFO [org.apache.hadoop.mapreduce.Job] - map 0% reduce 0%
2017-06-20 23:03:31,569 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Waiting for map tasks
2017-06-20 23:03:31,571 INFO [org.apache.hadoop.mapred.LocalJobRunner] - Starting task: attempt_local622257889_0001_m_000000_0
2017-06-20 23:03:31,667 INFO [org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter] - File Output Committer Algorithm version is 1
2017-06-20 23:03:31,691 INFO [org.apache.hadoop.yarn.util.ProcfsBasedProcessTree] - ProcfsBasedProcessTree currently is supported only on Linux.
2017-06-20 23:03:34,256 INFO [org.apache.hadoop.mapred.Task] - Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@26cd629f
2017-06-20 23:03:34,259 INFO [org.apache.hadoop.mapred.LocalJobRunner] - map task executor complete.
2017-06-20 23:03:34,485 WARN [org.apache.hadoop.mapred.LocalJobRunner] - job_local622257889_0001
java.lang.Exception: java.lang.RuntimeException: java.lang.NoSuchMethodException: com.hadoop.mapreduce.TVPlay$TVmapper.<init>()
at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462)
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:522)
Caused by: java.lang.RuntimeException: java.lang.NoSuchMethodException: com.hadoop.mapreduce.TVPlay$TVmapper.<init>()
at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:134)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:745)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:243)
at java.util.concurrent.Executors$RunnableAdapter.call(Unknown Source)
at java.util.concurrent.FutureTask.run(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
at java.lang.Thread.run(Unknown Source)
Caused by: java.lang.NoSuchMethodException: com.hadoop.mapreduce.TVPlay$TVmapper.<init>()
at java.lang.Class.getConstructor0(Unknown Source)
at java.lang.Class.getDeclaredConstructor(Unknown Source)
at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:128)
... 8 more
2017-06-20 23:03:34,574 INFO [org.apache.hadoop.mapreduce.Job] - Job job_local622257889_0001 failed with state FAILED due to: NA
2017-06-20 23:03:34,598 INFO [org.apache.hadoop.mapreduce.Job] - Counters: 0
我不知道这意味着什么。想想你的帮助！
答案 0 :(得分：0)
您已将作业配置放在reducer的run()方法中，这是错误的。最简单的解决方案是将所有代码移到main()的{{1}}方法中。
除非你清楚自己在做什么，否则你通常不应该覆盖TVplay方法，所以我会从reduce类中完全删除它。
答案 1 :(得分：0)
最后我发现mapper＆amp; reducer不是 static 方法。当我将它们改为静态方法时。它会没问题。
Why declaring Mapper and Reducer classes as static?
Hadoop使用反射为每个地图创建一个类的实例，或者减少运行的任务。创建的新实例需要一个零参数构造函数（否则它将如何知道要传递的内容）。
通过在不使用static关键字的情况下声明内部映射器或reduce类，java编译实际上创建了一个构造函数，该构造函数期望在构造时传递父类的实例。
您应该可以通过对生成的类文件运行javap命令来查看此内容
此外，static关键字在父类声明中使用时无效（这就是为什么你从未在顶层看到它，但只在子类中看到它）