我的wordcount代码：
1 package com.walloce.wordcount; 2 3 import java.io.IOException; 4 import org.apache.hadoop.conf.Configuration; 5 import org.apache.hadoop.conf.Configured; 6 import org.apache.hadoop.fs.FileSystem; 7 import org.apache.hadoop.fs.Path; 8 import org.apache.hadoop.io.IntWritable; 9 import org.apache.hadoop.io.LongWritable; 10 import org.apache.hadoop.io.Text; 11 import org.apache.hadoop.mapreduce.Job; 12 import org.apache.hadoop.mapreduce.Mapper; 13 import org.apache.hadoop.mapreduce.Reducer; 14 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; 15 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 16 import org.apache.hadoop.util.Tool; 17 import org.apache.hadoop.util.ToolRunner; 18 19 import com.walloce.wordcount.MyMapReduce.myMap.myReduce; 20 21 public class MyMapReduce extends Configured implements Tool { 22 23 /** 24 * KEYIN LongWritable 传入的key类型(偏移量) 25 * VALUEIN Text 传入的value类型(文本) 26 * KEYOUT 传出的key类型 27 * VALUEOUT 传出的value类型 28 * @author Walloce 29 * 2018 30 */ 31 static class myMap extends Mapper<LongWritable, Text, Text, IntWritable> { 32 //输出功效的key 33 Text text = new Text(); 34 //输出功效的value 35 IntWritable mr_value = new IntWritable(1); 36 37 int line_count = 1; 38 39 @Override 40 protected void map(LongWritable key, Text value, Context context) 41 throws IOException, InterruptedException { 42 43 System.out.println("map阶段开始..."); 44 //将获取的文本类型转为字符串类型 45 String line = value.toString(); 46 47 System.out.println("第 "+ line_count +" 行的字符串的偏移量为：" + key.get()); 48 49 //将的到的一行字符串拆解为多个单词的字符串数组 50 String[] words = line.split(" "); 51 52 //遍历得到的所有word 53 for (String word : words) { 54 text.set(word); 55 context.write(text, mr_value); 56 } 57 line_count++; 58 } 59 60 /** 61 * Text, IntWritable, Text, IntWritable 62 * reduce时输入的key-value和输出的key-value. 63 * eg:(hello,2) 64 * @author Walloce 65 * 2018 66 */ 67 static class myReduce extends Reducer<Text, IntWritable, Text, IntWritable> { 68 69 private IntWritable result = new IntWritable(); 70 71 int reduce_time = 0; 72 73 @Override 74 protected void reduce(Text key, Iterable<IntWritable> values, Context context) 75 throws IOException, InterruptedException { 76 System.out.println("这是第"+ reduce_time +"次reduce"); 77 System.out.println("Reduce阶段开始...."); 78 79 int sum = 0; 80 for (IntWritable value : values) { 81 sum += value.get(); 82 } 83 result.set(sum); 84 context.write(key, result); 85 reduce_time++; 86 } 87 88 } 89 90 } 91 92 public int run(String[] args) throws Exception { 93 94 //hadoop的八股文 95 Configuration conf = this.getConf(); 96 Job job = Job.getInstance(conf, this.getClass().getSimpleName()); 97 //对job进行具体的配置 98 99 //当你本地运行，这个设置可以不写，不会报错 100 //当提价到集群上面运行的时候，，这个设置不写，会报类找不到的异常 101 job.setJarByClass(MyMapReduce.class); 102 103 //写一个输入路径 104 Path input = new Path(args[0]); 105 FileInputFormat.addInputPath(job, input); 106 //写一个输出路径 107 Path output = new Path(args[1]); 108 FileOutputFormat.setOutputPath(job, output); 109 110 //执行前先判断输出路径是否存在，存在就删除 111 FileSystem fs = output.getFileSystem(conf); 112 if(fs.exists(output)){ 113 fs.delete(output,true); 114 } 115 116 //设置运行的map类的相关参数 117 job.setMapperClass(myMap.class); 118 job.setMapOutputKeyClass(Text.class); 119 job.setMapOutputValueClass(IntWritable.class); 120 //==============shuffle======================= 121 // job.setCombinerClass(MyCombiner.class); 122 123 124 //==============shuffle======================= 125 //设置运行的Reduce的相关参数 126 job.setReducerClass(myReduce.class); 127 job.setOutputKeyClass(Text.class); 128 job.setOutputValueClass(IntWritable.class); 129 130 boolean isSuccess = job.waitForCompletion(true); 131 132 133 return isSuccess?0:1; 134 } 135 136 public static void main(String[] args) { 137 // TODO Auto-generated method stub 138 Configuration conf = new Configuration(); 139 args = new String[]{ 140 "hdfs://bigdata-study-104:8020/testdata/word.txt", 141 "hdfs://bigdata-study-104:8020/testresult/output/" 142 }; 143 144 try { 145 ToolRunner.run(conf, new MyMapReduce(), args); 146 } catch (Exception e) { 147 // TODO Auto-generated catch block 148 e.printStackTrace(); 149 } 150 } 151 152 }
异常信息：