Hadoop MapReduce：context.write更改值

Question

我是Hadoop和编写MapReduce作业的新手，我遇到了一个问题，似乎reducers context.write方法正在将正确的值改为不正确的值。

• 计数字数总数（int wordCount)
• 计算不同字的数目(int counter_dist)
• 计数以“Z”或“Z”开头的字数(int counter_startZ)
• 计算出现次数少于4次的单词数(int counter_less4)

所有这些都必须在单个MapReduce作业中完成。

正在分析的文本文件

Hello how zou zou zou zou how are you

public class WordCountMapper extends Mapper <Object, Text, Text, IntWritable> {

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    @Override
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        StringTokenizer itr = new StringTokenizer(value.toString());
        while (itr.hasMoreTokens()) {
            String hasKey = itr.nextToken();
            word.set(hasKey);
            context.write(word, one);
        }

    }
}

public class WordCountReducer extends Reducer <Text, IntWritable, Text, IntWritable> {

    int wordCount = 0; // Total number of words
    int counter_dist = 0; // Number of distinct words in the corpus
    int counter_startZ = 0; // Number of words that start with letter Z
    int counter_less4 = 0; // Number of words that appear less than 4 

    @Override
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int repeatedWords = 0;
        System.out.println("###Reduce method starts");
        System.out.println("Values: wordCount:" + wordCount + " counter_dist:" + counter_dist + " counter_startZ:" + counter_startZ + " counter_less4:" + counter_less4 + " (start)");
        for (IntWritable val : values){
            System.out.println("Key: " + key.toString());
            repeatedWords++;
            wordCount += val.get();
            if(key.toString().startsWith("z") || key.toString().startsWith("Z")){
            counter_startZ++;
            }
            System.out.println("Values: wordCount:" + wordCount + " counter_dist:" + counter_dist + " counter_startZ:" + counter_startZ + " counter_less4:" + counter_less4 + " (end of loop)");
        }
        counter_dist++;

        if(repeatedWords < 4){
            counter_less4++;
        }

        System.out.println("Values: wordCount:" + wordCount + " counter_dist:" + counter_dist + " counter_startZ:" + counter_startZ + " counter_less4:" + counter_less4 + " repeatedWords:" + repeatedWords + " (end)");
        System.out.println("###Reduce method ends\n");
    }


    @Override
    public void cleanup(Context context) throws IOException, InterruptedException{
        System.out.println("###CLEANUP: wordCount: " + wordCount);
        System.out.println("###CLEANUP: counter_dist: " + counter_dist);
        System.out.println("###CLEANUP: counter_startZ: " + counter_startZ);
        System.out.println("###CLEANUP: counter_less4: " + counter_less4);

        context.write(new Text("Total words: "), new IntWritable(wordCount));
        context.write(new Text("Distinct words: "), new IntWritable(counter_dist));
        context.write(new Text("Starts with Z: "), new IntWritable(counter_startZ));
        context.write(new Text("Appears less than 4 times:"), new IntWritable(counter_less4));
    }


}

Stdout日志，我正在使用它进行调试

###Reduce method starts
Values: wordCount:0 counter_dist:0 counter_startZ:0 counter_less4:0 (start)
Key: Hello
Values: wordCount:1 counter_dist:0 counter_startZ:0 counter_less4:0 (end of loop)
Values: wordCount:1 counter_dist:1 counter_startZ:0 counter_less4:1 repeatedWords:1 (end)
###Reduce method ends

###Reduce method starts
Values: wordCount:1 counter_dist:1 counter_startZ:0 counter_less4:1 (start)
Key: are
Values: wordCount:2 counter_dist:1 counter_startZ:0 counter_less4:1 (end of loop)
Values: wordCount:2 counter_dist:2 counter_startZ:0 counter_less4:2 repeatedWords:1 (end)
###Reduce method ends

###Reduce method starts
Values: wordCount:2 counter_dist:2 counter_startZ:0 counter_less4:2 (start)
Key: how
Values: wordCount:3 counter_dist:2 counter_startZ:0 counter_less4:2 (end of loop)
Key: how
Values: wordCount:4 counter_dist:2 counter_startZ:0 counter_less4:2 (end of loop)
Values: wordCount:4 counter_dist:3 counter_startZ:0 counter_less4:3 repeatedWords:2 (end)
###Reduce method ends

###Reduce method starts
Values: wordCount:4 counter_dist:3 counter_startZ:0 counter_less4:3 (start)
Key: you
Values: wordCount:5 counter_dist:3 counter_startZ:0 counter_less4:3 (end of loop)
Values: wordCount:5 counter_dist:4 counter_startZ:0 counter_less4:4 repeatedWords:1 (end)
###Reduce method ends

###Reduce method starts
Values: wordCount:5 counter_dist:4 counter_startZ:0 counter_less4:4 (start)
Key: zou
Values: wordCount:6 counter_dist:4 counter_startZ:1 counter_less4:4 (end of loop)
Key: zou
Values: wordCount:7 counter_dist:4 counter_startZ:2 counter_less4:4 (end of loop)
Key: zou
Values: wordCount:8 counter_dist:4 counter_startZ:3 counter_less4:4 (end of loop)
Key: zou
Values: wordCount:9 counter_dist:4 counter_startZ:4 counter_less4:4 (end of loop)
Values: wordCount:9 counter_dist:5 counter_startZ:4 counter_less4:4 repeatedWords:4 (end)
###Reduce method ends

###CLEANUP: wordCount: 9
###CLEANUP: counter_dist: 5
###CLEANUP: counter_startZ: 4
###CLEANUP: counter_less4: 4

从日志中可以看出，所有的值都是正确的，而且一切正常。然而，当我打开HDFS中的输出目录并读取“part-r-00000”文件时，在那里写入的context.write的输出完全不同。

Total words: 22
Distinct words: 4
Starts with Z: 0
Appears less than 4 times: 4

Answer 1

对于关键的程序逻辑，绝不能依赖cleanup()方法。每次撕下JVM时都会调用cleanup()方法。因此，根据JVM（您无法预测）生成和终止的数量，您的逻辑会变得不稳定。

将初始化和写入上下文都移动到reduce方法中。

即。

int wordCount = 0; // Total number of words
int counter_dist = 0; // Number of distinct words in the corpus
int counter_startZ = 0; // Number of words that start with letter Z
int counter_less4 = 0; // Number of words that appear less than 4 

   context.write(new Text("Total words: "), new IntWritable(wordCount));
    context.write(new Text("Distinct words: "), new IntWritable(counter_dist));
    context.write(new Text("Starts with Z: "), new IntWritable(counter_startZ));
    context.write(new Text("Appears less than 4 times:"), new IntWritable(counter_less4));

public class WordCountMapper extends Mapper <Object, Text, Text, IntWritable> {

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    @Override
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        StringTokenizer itr = new StringTokenizer(value.toString());
        while (itr.hasMoreTokens()) {
            String hasKey = itr.nextToken();
            word.set(hasKey);
            context.getCounter("my_counters", "TOTAL_WORDS").increment(1);
            if(hasKey.toUpperCase().startsWith("Z")){
            context.getCounter("my_counters", "Z_WORDS").increment(1);
            }
            context.write(word, one);
        }
    }
}

可在reducer计数器中计数出现少于4次的非重复字和字的数目。

public class WordCountReducer extends Reducer <Text, IntWritable, Text, IntWritable> {

    @Override
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int wordCount= 0;
        context.getCounter("my_counters", "DISTINCT_WORDS").increment(1);
        for (IntWritable val : values){
            wordCount += val.get();
        }
        if(wordCount < 4{
           context.getCounter("my_counters", "WORDS_LESS_THAN_4").increment(1);
        }
    }
}

在Driver类中获取计数器。下面的代码位于提交作业的行之后

CounterGroup group = job.getCounters().getGroup("my_counters");

for (Counter counter : group) {
   System.out.println(counter.getName() + "=" + counter.getValue());
}