java hadoop（五） hadoop mapreduce 排序和序列化案例

墨蓝 2022-10-05 14:46 89阅读 0赞

### **什么是序列化与反序列化：** ###

**序列化**（Serialization）是指把结构化对象转化为字节流。

**反序列化**（Deserialization）是序列化的逆过程。把字节流转为结构化对象。

### **mapreduce中的序列化：** ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNzcxNTY3_size_16_color_FFFFFF_t_70][]

**实现这个接口即可**

**第一个方法是进行序列化**

**第二个方法是反序列化**

### **mapreduce中的排序：** ###

![20210609170824994.png][]

WritableComparable 接口包含了排序以及序列化，如果我们序列化的同时还需要排序，就实现这个接口即可。

### **需求：** ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNzcxNTY3_size_16_color_FFFFFF_t_70 1][]

### **代码实现：** ###

**目录结构：**

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQwNzcxNTY3_size_16_color_FFFFFF_t_70 2][]

**包装类：通过继承实现排序与序列化**

package sortserializedemo;
    
    import org.apache.hadoop.io.WritableComparable;
    
    import java.io.DataInput;
    import java.io.DataOutput;
    import java.io.IOException;
    
    public class SortBean implements WritableComparable<SortBean> {
        private String word;
        private int num;
    
        public SortBean() {
        }
    
        public SortBean(String word, int num) {
            this.word = word;
            this.num = num;
        }
    
        public String getWord() {
            return word;
        }
    
        public void setWord(String word) {
            this.word = word;
        }
    
        public int getNum() {
            return num;
        }
    
        public void setNum(int num) {
            this.num = num;
        }
    
        //排序
        public int compareTo(SortBean o) {
            //按字典顺序排
            int res = this.word.compareTo(o.word);
            //如果大小相同  比较num
            if(res==0){
                return this.num - o.num;
            }
            return res;
        }
    
        @Override
        public String toString() {
            return "SortBean{" +
                    "word='" + word + '\'' +
                    ", num=" + num +
                    '}';
        }
    
        //实现序列化
        public void write(DataOutput out) throws IOException {
    
            out.writeUTF(word);
            out.writeInt(num);
        }
    
        //实现反序列化
        public void readFields(DataInput in) throws IOException {
            this.word=in.readUTF();
            this.num=in.readInt();
        }
    }

**Mapper：**

package sortserializedemo;
    
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.NullWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapred.OutputCollector;
    import org.apache.hadoop.mapred.Reporter;
    import org.apache.hadoop.mapreduce.Mapper;
    
    import java.io.IOException;
    
    public class MyMapper extends Mapper<LongWritable, Text,SortBean, NullWritable> {
    
        public void map(LongWritable longWritable, Text text, OutputCollector<SortBean, NullWritable> outputCollector, Reporter reporter) throws IOException {
            //分隔文本内容 取出K2
            String[] split = text.toString().split("\t");
            //包装
            SortBean sortBean = new SortBean(split[0], Integer.parseInt(split[1]));
            //K2 sortBean V2 为空 后边基于K2进行操作
            outputCollector.collect(sortBean,NullWritable.get());
        }
    }

**Reducer：**

package sortserializedemo;
    
    import org.apache.hadoop.io.NullWritable;
    import org.apache.hadoop.mapreduce.Reducer;
    
    import java.io.IOException;
    
    public class MyReducer extends Reducer<SortBean, NullWritable,SortBean,NullWritable> {
    
        @Override
        protected void reduce(SortBean key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
            context.write(key, NullWritable.get());
        }
    }

**Main：**

package sortserializedemo;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.NullWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
    import partitiondemo.MyMapper;
    import partitiondemo.MyPartitioner;
    import partitiondemo.MyReducer;
    import partitiondemo.ReducerMain;
    
    public class ReduceMain {
    
        public static void main(String[] args) throws Exception{
            //创建job
            Configuration configuration = new Configuration();
            Job job = Job.getInstance(configuration, "my-partition");
            //指定job所在jar包
            job.setJarByClass(ReducerMain.class);
            //指定文件读取方式 路径
            job.setInputFormatClass(TextInputFormat.class);
            TextInputFormat.addInputPath(job, new Path("hdfs://192.168.40.150:9000/test.txt"));
            //指定mapper
            job.setMapperClass(MyMapper.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(NullWritable.class);
    
    
            //指定reducer
            job.setReducerClass(MyReducer.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(NullWritable.class);
    
            //指定输出方式类以及输出路径 目录必须不存在
            job.setOutputFormatClass(TextOutputFormat.class);
            TextOutputFormat.setOutputPath(job, new Path("hdfs://192.168.40.150:9000/lgy_test/res"));
    
            //将job提交到yarn集群
            boolean bl = job.waitForCompletion(true);
            System.exit(bl?0:1);
        }
    }

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[20210609170824994.png]: /images/20221005/05e87b09a7a84eaead3d155ef8337554.png
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