使用Partitioner

src/main/java/com/fjy/hadoop/mapreduce/WordCountPartitionerApp.java

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+package com.fjy.hadoop.mapreduce;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.Text;
+import org.apache.hadoop.mapreduce.Job;
+import org.apache.hadoop.mapreduce.Mapper;
+import org.apache.hadoop.mapreduce.Partitioner;
+import org.apache.hadoop.mapreduce.Reducer;
+import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
+import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
+
+import java.io.IOException;
+
+/**
+ * 使用MapReduce开发Partitioner组件应用
+ * @author F嘉阳
+ * @date 2018-04-20
+ */
+public class WordCountPartitionerApp {
+    /**
+     * Map：读取输入文件
+     * Text:类似字符串
+     */
+    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
+
+        LongWritable one = new LongWritable(1);
+
+        /**
+         * @param key     偏移量
+         * @param value   每行的字符串
+         * @param context 上下文
+         * @throws IOException
+         * @throws InterruptedException
+         */
+        @Override
+        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
+            /*super.map(key, value, context);*/
+
+            //接收到每一行数据
+            String line = value.toString();
+
+            String[] words = line.split(" ");//以空格分隔
+
+                //每一个空格是一个手机品牌，另一个是销售数量
+                context.write(new Text(words[0]), new LongWritable(Long.parseLong(words[1])));
+
+        }
+    }
+
+    /**
+     * Reduce 归并操作
+     * LongWritable：文本出现的次数/求和后的次数
+     */
+    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
+        /**
+         * @param key
+         * @param values  相同偏移量的集合
+         * @param context
+         * @throws IOException
+         * @throws InterruptedException
+         */
+        @Override
+        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
+            //super.reduce(key, values, context);
+
+            long sum = 0;
+
+            for (LongWritable value : values) {
+                //求key出现的次数和总和
+                sum += value.get();
+            }
+            //统计结果的输出
+            context.write(key, new LongWritable(sum));
+
+        }
+    }
+
+    /**
+     * Partitioner处理类
+     */
+    public static class MyPartitioner extends Partitioner<Text,LongWritable>{
+         @Override
+         public int getPartition(Text key, LongWritable value, int i) {
+
+             if ("xiaomi".equals(key.toString())) {
+                 return 0;//若为xiaomi则交由0 ReduceTask处理
+             }
+             if ("huawei".equals(key.toString())) {
+                 return 1;//若为huawei则交由1 ReduceTask处理
+             }
+             if ("iphone".equals(key.toString())) {
+                 return 2;//若为iphone则交由2 ReduceTask处理
+             }
+
+             return 3;//若为其他，则交由3 ReduceTask处理
+         }
+     }
+
+    /**
+     * 定义Driver,封装MapReduce作业的所有信息
+     */
+    public static void main(String[] args) throws Exception {
+
+        //创建Configuration
+        Configuration configuration = new Configuration();
+
+        //清理已存在的输出目录
+        Path outputPath = new Path(args[1]);
+        FileSystem fileSystem = FileSystem.get(configuration);
+        if (fileSystem.exists(outputPath)){
+            fileSystem.delete(outputPath);
+            System.out.println("The exist files had been deleted!");
+        }
+
+        //创建作业
+        Job job = Job.getInstance(configuration, "wordcount");
+
+        //设置作业的主类
+        job.setJarByClass(WordCountPartitionerApp.class);
+
+        //作业处理的输入路径
+        FileInputFormat.setInputPaths(job, new Path(args[0]));
+
+        //设置map相关参数
+        job.setMapperClass(MyMapper.class);
+
+        //设置map输出的key的类型
+        job.setMapOutputKeyClass(Text.class);
+
+        //设置map输出的value的类型
+        job.setMapOutputValueClass(LongWritable.class);
+
+        //设置Reduce相关参数
+        job.setReducerClass(MyReducer.class);
+        job.setOutputKeyClass(Text.class);
+        job.setOutputValueClass(LongWritable.class);
+
+        //设置job的Partition
+        job.setPartitionerClass(MyPartitioner.class);
+        //设置四个reducer，每个分区一个，否则Partitioner配置不生效
+        job.setNumReduceTasks(4);
+
+        //设置作业处理输出结果的输出路径
+        FileOutputFormat.setOutputPath(job, new Path(args[1]));
+
+        //作业提交
+        job.waitForCompletion(true);
+
+        //作业完成后退出
+        System.exit(job.waitForCompletion(true) ? 0 : 1);
+
+    }
+
+
+}