1.需求

2.思路

3.代码实现

3.1MyWeather 类代码：

这个类主要是用来定义hadoop的配置，在执行计算程序时所需加载的一些类。

package com.hadoop.mr.weather;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.TestMapReduceLazyOutput.TestMapper;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MyWeather {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        Configuration conf =new Configuration(true);

        Job job = Job.getInstance(conf);

        job.setJarByClass(MyWeather.class);

        //----------conf-----------------------

        //---begin Map :

        //输入格式化类

//        job.setInputFormatClass(ooxx.class);

        //设置mapper类

        job.setMapperClass(TMapper.class);

        job.setMapOutputKeyClass(TQ.class);

        job.setMapOutputValueClass(IntWritable.class);

        //设置partitioner类

        job.setPartitionerClass(TPartitioner.class);

        //设置排序比较器类

        job.setSortComparatorClass(TSortComparator.class);

        //设置combiner类

//        job.setCombinerClass(TCombiner.class);

        //----end Map

        //----begin Reduce:

        //设置组比较器的类

        job.setGroupingComparatorClass(TGroupingComparator.class);

        //设置reducer类

        job.setReducerClass(TReducer.class);

        //-----end Reduce:

        //设置输入数据的路径

        Path input = new Path("/data/tq/input");

        FileInputFormat.addInputPath(job, input);

        //设置输出数据的路径

        Path output=new Path("/data/tq/output");

        if(output.getFileSystem(conf).exists(output)){

            //如果目录存在递归删除

            output.getFileSystem(conf).delete(output,true);

        }

        FileOutputFormat.setOutputPath(job, output);

        //设置reduceTask的数量 和 partitions数量对应

        job.setNumReduceTasks(2);

        //-------------------------------------

        job.waitForCompletion(true);

    }

}

3.2Tmapper类代码

该类继承Mapper类他的主要作用是对输入的文件做一些预处理工作。

package com.hadoop.mr.weather;

import java.io.IOException;

import java.text.ParseException;

import java.text.SimpleDateFormat;

import java.util.Calendar;

import java.util.Date;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.util.StringUtils;

//TextInputFormat.class  --key类型是 longWritable 偏移量  --value是Text类型

public class TMapper extends Mapper<LongWritable, Text, TQ, IntWritable>{

    //创建map的 k v 对象

    TQ mkey=new TQ();  // map --->key

    IntWritable mval=new IntWritable(); //map --->value

    //重写map方法

    @Override

    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, TQ, IntWritable>.Context context)

            throws IOException, InterruptedException {

        /**

        1949-10-01 14:21:02     34c

        1949-10-01 19:21:02        38c

        1949-10-02 14:01:02        36c

        1950-01-01 11:21:02        32c

        1950-10-01 12:21:02        37c

        **/

        try {

            String[] strs = StringUtils.split(value.toString(),'\t');//对文本将制表符切分

            SimpleDateFormat sdf= new SimpleDateFormat("yyyy-MM-dd");

            Date date = sdf.parse(strs[0]);

            Calendar cal= Calendar.getInstance();

            cal.setTime(date);

            mkey.setYear(cal.get(Calendar.YEAR));

            mkey.setMonth(cal.get(Calendar.MONTH)+1); //第一个月默认从0开始所以加1

            mkey.setDay(cal.get(Calendar.DAY_OF_MONTH));

            int wd = Integer.parseInt(strs[1].substring(0, strs[1].length()-1));//获取温度字符串并强转为int类型

            mkey.setWd(wd);

            mval.set(wd);

            context.write(mkey, mval);

        } catch (ParseException e) {

            // TODO Auto-generated catch block

            e.printStackTrace();

        }

    }

}

3.3TQ类代码

该类实现WritableComparable接口他的作用是给生成相关的属性并重写 写入，读取，比较的方法，

package com.hadoop.mr.weather;

import java.io.DataInput;

import java.io.DataOutput;

import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;

public class TQ implements WritableComparable<TQ> {

    //定义属性

    private int year;

    private int month;

    private int day;

    private int wd;  //温度属性

    public int getYear() {

        return year;

    }

    public void setYear(int year) {

        this.year = year;

    }

    public int getMonth() {

        return month;

    }

    public void setMonth(int month) {

        this.month = month;

    }

    public int getDay() {

        return day;

    }

    public void setDay(int day) {

        this.day = day;

    }

    public int getWd() {

        return wd;

    }

    public void setWd(int wd) {

        this.wd = wd;

    }

    @Override

    public void write(DataOutput out) throws IOException {

        out.writeInt(year);

        out.writeInt(month);

        out.writeInt(day);

        out.writeInt(wd);

    }

    @Override

    public void readFields(DataInput in) throws IOException {

        // TODO Auto-generated method stub

        this.year=in.readInt();

        this.month=in.readInt();

        this.day=in.readInt();

        this.wd=in.readInt();

    }

    @Override

    public int compareTo(TQ that) {

        //compare方法返回值说明the value 0 if x == y; a value less than 0 if x < y; and a value greater than 0 if x > y

        // 日期正序 ,使用这年和那年比较 -.-

        int c1=Integer.compare(this.year, that.getYear());

        // 如果年份相同比较天

        if(c1==0){

            int c2=Integer.compare(this.month, that.getMonth());

            //如果是同一天返回0

            if(c2==0){

                return Integer.compare(this.day, that.getDay());

            }

            return c2;

        }

        return 0;

    }

}

3.4Tpartitioner类代码

该类的作用，是定义输出文件的分布规则，避免产生数据倾斜

package com.hadoop.mr.weather;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.mapreduce.Partitioner;

public class TPartitioner extends Partitioner<TQ, IntWritable> {

    //约定成俗规则：避免数据倾斜，将少的数据都放在一个reduce任务组里，将数据量大的单独放一个任务组里。

    @Override

    public int getPartition(TQ key, IntWritable value, int numPartitions) {

        return key.hashCode() % numPartitions;

    }

}

3.5TSortComparator类代码：

该类的作用是定义一个排序比较器

package com.hadoop.mr.weather;

import org.apache.hadoop.io.WritableComparable;

import org.apache.hadoop.io.WritableComparator;

public class TSortComparator extends WritableComparator{

    public TSortComparator() {

        super(TQ.class,true);

    }

    @Override

    public int compare(WritableComparable a, WritableComparable b) {

        TQ t1=(TQ) a;

        TQ t2=(TQ) b;

        int c1 = Integer.compare(t1.getYear(), t2.getYear());

        if(c1==0){

            int c2= Integer.compare(t1.getMonth(), t2.getMonth());

            if(c2==0){

                return -Integer.compare(t1.getWd(), t2.getWd());// -号表示返回温度的倒序排列

            }

        }

        return super.compare(a, b);

    }

}

3.6TGroupingComparator类代码：

该类的作用是根据年月两个维度做分组

package com.hadoop.mr.weather;

import org.apache.hadoop.io.WritableComparable;

import org.apache.hadoop.io.WritableComparator;

public class TGroupingComparator extends WritableComparator {

    public TGroupingComparator() {

        super(TQ.class,true);

    }

    @Override

    public int compare(WritableComparable a, WritableComparable b) {

        TQ t1=(TQ) a;

        TQ t2=(TQ) b;

        int c1 = Integer.compare(t1.getYear(), t2.getYear());

        if(c1==0){

            return Integer.compare(t1.getMonth(), t2.getMonth()); //返回月份的比较结果来分组

        }

        return c1;

    }

}

3.7TReducer 类代码

该类的作用是定义数据的输出格式和内容

package com.hadoop.mr.weather;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class TReducer extends Reducer<TQ, IntWritable, Text, IntWritable>{

    Text rkey=new Text();

    IntWritable rval=new IntWritable();

    /* (non-Javadoc)

     * @see org.apache.hadoop.mapreduce.Reducer#reduce(KEYIN, java.lang.Iterable, org.apache.hadoop.mapreduce.Reducer.Context)

     */

    @Override

    protected void reduce(TQ key, Iterable<IntWritable> values, Context context)

            throws IOException, InterruptedException {

        //相同的key为一组。。。。

        //1970 01 01 88   88

        //1970 01 11 78   78

        //1970 01 21 68   68

        //1970 01 01 58   58

        int flag=0; //迭代的次数

        int day=0;  

        for (IntWritable v : values) {

            if(flag==0){

                //将reduce的key格式化成1970-01-01:88

                rkey.set(key.getYear()+"-"+key.getMonth()+"-"+key.getDay()+":"+key.getWd());

                //将reduce的value设置为温度

                rval.set(key.getWd());

                flag++;

                day=key.getDay();

                context.write(rkey, rval);

            }

            //如果迭代次数不为0且当前的天不等于迭代得到的天就将新的天气数据赋值给reduce的 kv

            if(flag!=0 && day!=key.getDay()){

                //将reduce的key格式化成1970-01-01:88

                rkey.set(key.getYear()+"-"+key.getMonth()+"-"+key.getDay()+":"+key.getWd());

                //将reduce的value设置为温度

                rval.set(key.getWd());

                context.write(rkey, rval);

                break;

            }

        }

    }

}

4.执行程序

4.1将包导出为jar包 上传至服务器

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" alt="" />
 

4.2创建hdfs文件输入路径

hdfs dfs -mkdir -p /data/tq/input

4.3上传测试文件到创建的hdfs目录下

[root@node01 ~]# cat tq.txt

-- ::    34c

-- ::    38c

-- ::    36c

-- ::    32c

-- ::    37c

-- ::    23c

-- ::    41c

-- ::    27c

-- ::    45c

-- ::    46c

-- ::    47c

[root@node01 ~]# hdfs dfs -put tq.txt /data/tq/input
 

4.4服务端执行程序

[root@node01 ~]# hadoop jar Myweather.jar com.hadoop.mr.weather.MyWeather

-- ::, INFO client.ConfiguredRMFailoverProxyProvider: Failing over to rm2

-- ::, WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.

-- ::, INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /user/root/.staging/job_1546092355023_0004

-- ::, INFO input.FileInputFormat: Total input files to process :

-- ::, INFO mapreduce.JobSubmitter: number of splits:

-- ::, INFO Configuration.deprecation: yarn.resourcemanager.zk-address is deprecated. Instead, use hadoop.zk.address

-- ::, INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled

-- ::, INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1546092355023_0004

-- ::, INFO mapreduce.JobSubmitter: Executing with tokens: []

-- ::, INFO conf.Configuration: resource-types.xml not found

-- ::, INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.

-- ::, INFO impl.YarnClientImpl: Submitted application application_1546092355023_0004

-- ::, INFO mapreduce.Job: The url to track the job: http://node04:8088/proxy/application_1546092355023_0004/

-- ::, INFO mapreduce.Job: Running job: job_1546092355023_0004

-- ::, INFO mapreduce.Job: Job job_1546092355023_0004 running in uber mode : false

-- ::, INFO mapreduce.Job:  map % reduce %

-- ::, INFO mapreduce.Job:  map % reduce %

-- ::, INFO mapreduce.Job:  map % reduce %

-- ::, INFO mapreduce.Job:  map % reduce %

-- ::, INFO mapreduce.Job: Job job_1546092355023_0004 completed successfully

-- ::, INFO mapreduce.Job: Counters:

    File System Counters

        FILE: Number of bytes read=

        FILE: Number of bytes written=

        FILE: Number of read operations=

        FILE: Number of large read operations=

        FILE: Number of write operations=

        HDFS: Number of bytes read=

        HDFS: Number of bytes written=

        HDFS: Number of read operations=

        HDFS: Number of large read operations=

        HDFS: Number of write operations=

    Job Counters

        Launched map tasks=

        Launched reduce tasks=

        Rack-local map tasks=

        Total time spent by all maps in occupied slots (ms)=

        Total time spent by all reduces in occupied slots (ms)=

        Total time spent by all map tasks (ms)=

        Total time spent by all reduce tasks (ms)=

        Total vcore-milliseconds taken by all map tasks=

        Total vcore-milliseconds taken by all reduce tasks=

        Total megabyte-milliseconds taken by all map tasks=

        Total megabyte-milliseconds taken by all reduce tasks=

    Map-Reduce Framework

        Map input records=

        Map output records=

        Map output bytes=

        Map output materialized bytes=

        Input split bytes=

        Combine input records=

        Combine output records=

        Reduce input groups=

        Reduce shuffle bytes=

        Reduce input records=

        Reduce output records=

        Spilled Records=

        Shuffled Maps =

        Failed Shuffles=

        Merged Map outputs=

        GC time elapsed (ms)=

        CPU time spent (ms)=

        Physical memory (bytes) snapshot=

        Virtual memory (bytes) snapshot=

        Total committed heap usage (bytes)=

        Peak Map Physical memory (bytes)=

        Peak Map Virtual memory (bytes)=

        Peak Reduce Physical memory (bytes)=

        Peak Reduce Virtual memory (bytes)=

    Shuffle Errors

        BAD_ID=

        CONNECTION=

        IO_ERROR=

        WRONG_LENGTH=

        WRONG_MAP=

        WRONG_REDUCE=

    File Input Format Counters

        Bytes Read=

    File Output Format Counters

        Bytes Written=
 

4.5将hdfs上生成的输出文件 拉取到本地

[root@node01 ~]# hdfs dfs -get  /data/tq/output/* ./test
 

4.6查看输出文件

[root@node01 test]# ls

part-r-  part-r-  _SUCCESS

[root@node01 test]# cat part-r-

[root@node01 test]# cat part-r-

--:

--:

--:

--:

--:

--:

--:

--:

--:
 
0分区是空的  1分区有程序定义的k v输出。这就发生了数据倾斜，可能上面的Tpartitioner类的代码对数据分布规则定义的不恰当导致的。

5.Combiner说明

由于数据量比较少，这边没有对combiner类做扩展

每一个map都可能会产生大量的本地输出，Combiner的作用就是对map端的输出先做一次合并，以减少在map和reduce节点之间的数据传输量，以提高网络IO性能，是MapReduce的一种优化手段之一，其具体的作用如下所述。

（1）Combiner最基本是实现本地key的聚合，对map输出的key排序，value进行迭代。如下所示：

　　map: (K1, V1) → list(K2, V2)

　　combine: (K2, list(V2)) → list(K2, V2)

　　reduce: (K2, list(V2)) → list(K3, V3)

（2）Combiner还有本地reduce功能（其本质上就是一个reduce），例如Hadoop自带的wordcount的例子和找出value的最大值的程序，combiner和reduce完全一致，如下所示：

　　map: (K1, V1) → list(K2, V2)

　　combine: (K2, list(V2)) → list(K3, V3)

　　reduce: (K3, list(V3)) → list(K4, V4)

如果在wordcount中不用combiner，那么所有的结果都是reduce完成，效率会相对低下。使用combiner之后，先完成的map会在本地聚合，提升速度。对于hadoop自带的wordcount的例子，value就是一个叠加的数字，所以map一结束就可以进行reduce的value叠加，而不必要等到所有的map结束再去进行reduce的value叠加。

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