如何将MapReduce技术提升到实战进阶水平？

MapReduce实战进阶

MapReduce是一种编程模型，用于大规模数据集（尤其是大于1TB的数据）的并行运算，它借鉴了函数式编程语言中的“映射”和“归约”概念，并结合了矢量编程语言的特性，极大地方便了编程人员在不会分布式并行编程的情况下，将自己的程序运行在分布式系统上。

实战详解

以下是几个常见的MapReduce实战案例及其实现步骤：

1、成绩统计

需求描述：对一组学生的成绩进行统计，计算每个学生的总分。

Mapper类：读取每一行输入数据，将学生姓名作为key，分数作为value输出。

     public static class ScoreMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
         private final static IntWritable one = new IntWritable(1);
         private Text word = new Text();
         public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
             StringTokenizer itr = new StringTokenizer(value.toString(), "
");
             while (itr.hasMoreTokens()) {
                 String[] str = itr.nextToken().split(" ");
                 String name = str[0];
                 int score = Integer.parseInt(str[1]);
                 word.set(name);
                 context.write(word, new IntWritable(score));
             }
         }
     }

Reducer类：对相同key的值进行累加，得到每个学生的总分。

     public static class ScoreReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
         private IntWritable result = new IntWritable();
         public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
             int sum = 0;
             for (IntWritable val : values) {
                 sum += val.get();
             }
             result.set(sum);
             context.write(key, result);
         }
     }

Driver类：配置作业，设置Mapper和Reducer类。

     public static void main(String[] args) throws Exception {
         Configuration conf = new Configuration();
         Job job = Job.getInstance(conf, "Score Count");
         job.setJarByClass(ScoreCount.class);
         job.setMapperClass(ScoreMapper.class);
         job.setCombinerClass(ScoreReducer.class);
         job.setReducerClass(ScoreReducer.class);
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(IntWritable.class);
         FileInputFormat.addInputPath(job, new Path(args[0]));
         FileOutputFormat.setOutputPath(job, new Path(args[1]));
         System.exit(job.waitForCompletion(true) ? 0 : 1);
     }

2、合并去重

需求描述：合并多个文件中的内容，去除重复行。

Mapper类：读取每一行输入数据，直接输出。

     public static class LineMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
         private Text line = new Text();
         public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
             line.set(value);
             context.write(line, NullWritable.get());
         }
     }

Reducer类：对相同key的值进行去重，只保留一个。

     public static class LineReducer extends Reducer<Text, NullWritable, Text, NullWritable> {
         private HashSet<Text> set = new HashSet<>();
         public void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
             if (set.add(key)) {
                 context.write(key, NullWritable.get());
             }
         }
     }

Driver类：配置作业，设置Mapper和Reducer类。

     public static void main(String[] args) throws Exception {
         Configuration conf = new Configuration();
         Job job = Job.getInstance(conf, "Line Deduplication");
         job.setJarByClass(LineDeduplication.class);
         job.setMapperClass(LineMapper.class);
         job.setReducerClass(LineReducer.class);
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(NullWritable.class);
         FileInputFormat.addInputPath(job, new Path(args[0]));
         FileOutputFormat.setOutputPath(job, new Path(args[1]));
         System.exit(job.waitForCompletion(true) ? 0 : 1);
     }

3、信息挖掘 挖掘父子关系

需求描述：从一个社交网络的用户关系数据中，找出具有共同好友的用户对及其共同好友。

Mapper类：读取每一行输入数据，将好友作为key，用户作为value输出。

     public static class CommonFriendStep1Mapper extends Mapper<LongWritable, Text, Text, Text> {
         @Override
         protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
             String[] split = value.toString().split(":");
             String user = split[0];
             String[] friends = split[1].split(",");
             for (String f : friends) {
                 context.write(new Text(f), new Text(user));
             }
         }
     }

Reducer类：将拥有共同好友的用户两两组合，输出用户对及其共同好友。

     public static class CommonFriendStep1Reducer extends Reducer<Text, Text, Text, Text> {
         @Override
         protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
             ArrayList<Text> users = new ArrayList<>();
             for (Text u : values) {
                 users.add(u);
             }
             Collections.sort(users); // 排序以避免重复组合
             for (int i = 0; i < users.size()  1; i++) {
                 for (int j = i + 1; j < users.size(); j++) {
                     context.write(new Text(users.get(i) + "" + users.get(j)), new Text(key));
                 }
             }
         }
     }

Driver类：配置作业，设置Mapper和Reducer类。

     public static void main(String[] args) throws Exception {
         Configuration conf = new Configuration();
         Job job = Job.getInstance(conf, "Common Friends");
         job.setJarByClass(CommonFriends.class);
         job.setMapperClass(CommonFriendStep1Mapper.class);
         job.setReducerClass(CommonFriendStep1Reducer.class);
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(Text.class);
         FileInputFormat.addInputPath(job, new Path(args[0]));
         FileOutputFormat.setOutputPath(job, new Path(args[1]));
         System.exit(job.waitForCompletion(true) ? 0 : 1);
     }

FAQs（常见问题解答）

1、什么是MapReduce？

MapReduce是一种编程模型，用于处理大规模数据集的并行运算，它将任务分解为两个主要阶段：Map阶段和Reduce阶段，在Map阶段，数据被分解成键值对；在Reduce阶段，这些键值对被聚合处理以生成最终结果。

2、如何优化MapReduce的性能？

优化方法包括：选择合适的数据结构、优化Shuffle过程、使用Combiner来减少数据传输量、配置多个reducer以平衡负载、设置合适的分片大小以及避免数据倾斜等，通过这些优化手段，可以显著提高MapReduce作业的执行效率。