mapreduce scalar代码

MapReduce scalar代码可以用来实现分布式（distributed）计算，分析大量数据，并生成结果。它使用MapReduce算法和一组用户定义的函数来处理数据，使用简单的抽象层来处理复杂性。

相关问题

MapReduce排序代码

以下是一个基于MapReduce的排序代码的示例：

Mapper函数：

def mapper(key, value):
    # 将每一行数据拆分成key和value
    line = value.split(",")
    # 将key设置为数据本身
    key = int(line[0])
    # 将value设置为空
    value = ""
    # 将key和value返回
    yield key, value

Reducer函数：

def reducer(key, values):
    # 将key和空值拼接起来即可得到排序结果
    result = str(key) + "\n"
    # 将结果返回
    yield result, None

在MapReduce中，排序可以通过将数据拆分成key和value，将key设置为数据本身，将value设置为空来实现。然后，使用MapReduce的排序功能对key进行排序，最后将排序结果以key和空值的形式返回即可。

MapReduce常用代码

MapReduce是一种分布式计算框架，主要用于大规模数据处理和分析。下面是一些常用的MapReduce代码示例：

1. Word Count示例：

Map函数：

public void map(LongWritable key, Text value, Context context)
    throws IOException, InterruptedException {
    String line = value.toString();
    StringTokenizer tokenizer = new StringTokenizer(line);
    while (tokenizer.hasMoreTokens()) {
        word.set(tokenizer.nextToken());
        context.write(word, one);
    }
}

Reduce函数：

public void reduce(Text key, Iterable<IntWritable> values, Context context)
    throws IOException, InterruptedException {
    int sum = 0;
    for (IntWritable value : values) {
        sum += value.get();
    }
    result.set(sum);
    context.write(key, result);
}

2. Inverted Index示例：

Map函数：

public void map(LongWritable key, Text value, Context context)
    throws IOException, InterruptedException {
    String line = value.toString();
    String[] words = line.split("\\s+");
    for (String word : words) {
        wordDoc.set(word + "@" + key.toString());
        context.write(wordDoc, one);
    }
}

Reduce函数：

public void reduce(Text key, Iterable<IntWritable> values, Context context)
    throws IOException, InterruptedException {
    StringBuilder builder = new StringBuilder();
    for (IntWritable value : values) {
        builder.append(value.get() + ",");
    }
    String[] wordDoc = key.toString().split("@");
    result.set(wordDoc[1] + ":" + builder.toString());
    context.write(new Text(wordDoc[0]), result);
}

3. Join示例：

Map函数：

public void map(Object key, Text value, Context context)
    throws IOException, InterruptedException {
    String line = value.toString();
    String[] words = line.split(",");
    if (words.length == 2) {
        context.write(new Text(words[0]), new Text("A:" + words[1]));
    } else if (words.length == 3) {
        context.write(new Text(words[1]), new Text("B:" + words[0] + "," + words[2]));
    }
}

Reduce函数：

public void reduce(Text key, Iterable<Text> values, Context context)
    throws IOException, InterruptedException {
    List<String> listA = new ArrayList<String>();
    List<String> listB = new ArrayList<String>();
    for (Text value : values) {
        String[] words = value.toString().split(":");
        if (words[0].equals("A")) {
            listA.add(words[1]);
        } else if (words[0].equals("B")) {
            listB.add(words[1]);
        }
    }
    for (String a : listA) {
        for (String b : listB) {
            context.write(key, new Text(a + "," + b));
        }
    }
}

这些代码示例只是MapReduce的冰山一角，它还有很多其他的应用场景和代码实现方式。