Mapreduce的排序(全局排序、分区加排序、Combiner优化)

一、MR排序的分类

  1.部分排序:MR会根据自己输出记录的KV对数据进行排序,保证输出到每一个文件内存都是经过排序的;

  2.全局排序;

  3.辅助排序:再第一次排序后经过分区再排序一次;

  4.二次排序:经过一次排序后又根据业务逻辑再次进行排序。

 

二、MR排序的接口——WritableComparable

  该接口继承了Hadoop的Writable接口和Java的Comparable接口,实现该接口要重写write、readFields、compareTo三个方法。

 

三、流量统计案例的排序与分区

/**
 * @author: PrincessHug
 * @date: 2019/3/24, 15:36
 * @Blog: https://www.cnblogs.com/HelloBigTable/
 */
public class FlowSortBean implements WritableComparable<FlowSortBean> {
    private long upFlow;
    private long dwFlow;
    private long flowSum;

    public FlowSortBean() {
    }

    public FlowSortBean(long upFlow, long dwFlow) {
        this.upFlow = upFlow;
        this.dwFlow = dwFlow;
        this.flowSum = upFlow + dwFlow;
    }

    public long getUpFlow() {
        return upFlow;
    }

    public void setUpFlow(long upFlow) {
        this.upFlow = upFlow;
    }

    public long getDwFlow() {
        return dwFlow;
    }

    public void setDwFlow(long dwFlow) {
        this.dwFlow = dwFlow;
    }

    public long getFlowSum() {
        return flowSum;
    }

    public void setFlowSum(long flowSum) {
        this.flowSum = flowSum;
    }

    @Override
    public void write(DataOutput out) throws IOException {
        out.writeLong(upFlow);
        out.writeLong(dwFlow);
        out.writeLong(flowSum);
    }

    @Override
    public void readFields(DataInput in) throws IOException {
        upFlow = in.readLong();
        dwFlow = in.readLong();
        flowSum = in.readLong();
    }

    @Override
    public String toString() {
        return upFlow + "\t" + dwFlow + "\t" + flowSum;
    }

    @Override
    public int compareTo(FlowSortBean o) {
        return this.flowSum > o.getFlowSum() ? -1:1;
    }
}

public class FlowSortMapper extends Mapper<LongWritable, Text,FlowSortBean,Text> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        //获取数据
        String line = value.toString();

        //切分数据
        String[] fields = line.split("\t");

        //封装数据
        long upFlow = Long.parseLong(fields[1]);
        long dwFlow = Long.parseLong(fields[2]);

        //传输数据
        context.write(new FlowSortBean(upFlow,dwFlow),new Text(fields[0]));
    }
}

public class FlowSortReducer extends Reducer<FlowSortBean,Text,Text,FlowSortBean> {
    @Override
    protected void reduce(FlowSortBean key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        context.write(values.iterator().next(),key);
    }
}

public class FlowSortPartitioner extends Partitioner<FlowSortBean, Text> {
    @Override
    public int getPartition(FlowSortBean key, Text value, int i) {
        String phoneNum = value.toString().substring(0, 3);

        int partition = 4;
        if ("135".equals(phoneNum)){
            return 0;
        }else if ("137".equals(phoneNum)){
            return 1;
        }else if ("138".equals(phoneNum)){
            return 2;
        }else if ("139".equals(phoneNum)){
            return 3;
        }
        return partition;
    }
}

public class FlowSortDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        //设置配置,初始化Job类
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        //设置执行类
        job.setJarByClass(FlowSortDriver.class);

        //设置Mapper、Reducer类
        job.setMapperClass(FlowSortMapper.class);
        job.setReducerClass(FlowSortReducer.class);

        //设置Mapper输出数据类型
        job.setMapOutputKeyClass(FlowSortBean.class);
        job.setMapOutputValueClass(Text.class);

        //设置Reducer输出数据类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowSortBean.class);

        //设置自定义分区
        job.setPartitionerClass(FlowSortPartitioner.class);
        job.setNumReduceTasks(5);

        //设置文件输入输出类型
        FileInputFormat.setInputPaths(job,new Path("G:\\mapreduce\\flow\\flowsort\\in"));
        FileOutputFormat.setOutputPath(job,new Path("G:\\mapreduce\\flow\\flowsort\\partitionout"));

        //提交任务
        if (job.waitForCompletion(true)){
            System.out.println("运行完成!");
        }else {
            System.out.println("运行失败!");
        }

    }
}

  注意:再写Mapper类的时候,要注意KV对输出的数据类型,Key的类型一定要为FlowSortBean,因为在Mapper和Reducer之间进行的排序(只是排序)是通过Mapper输出的Key来进行排序的,而分区可以指定是通过Key或者Value。

 

四、Combiner合并

  Combiner是在MR之外的一个组件,可以用来在maptask输出到环形缓冲区溢写之后,分区排序完成时进行局部的汇总,可以减少网络传输量,进而优化MR程序。

  Combiner是用在当数据量到达一定规模之后的,小的数据量并不是很明显。

  例如WordCount程序,当单词文件的大小到达一定程度,可以使用自定义Combiner进行优化:

public class WordCountCombiner extends Reducer<Text,IntWritable,Text,IntWritable>{
	protected void reduce(Text key,Iterable<IntWritable> values,Context context){
		//计数
		int count = 0;
		
		//累加求和
		for(IntWritable v:values){
			count += v.get();
		}
		//输出
		context.write(key,new IntWritable(count));
	}
}

  然后再Driver类中设置使用Combiner类

job.setCombinerClass(WordCountCombiner.class);

  如果仔细观察,WordCount的自定义Combiner类与Reducer类是完全相同的,因为他们的逻辑是相同的,即在maptask之后的分区内先进行一次累加求和,然后到reducer后再进行总的累加求和,所以在设置Combiner时也可以这样:

job.setCombinerClass(WordCountReducer.class);

 

  注意:Combiner的应用一定要注意不能影响最终业务逻辑的情况下使用,比如在求平均值的时候:

  mapper输出两个分区:3,5,7  =>avg=5

            2,6    =>avg=4

  reducer合并输出:  5,4     =>avg=4.5  但是实际应该为4.6,错误!

  所以在使用Combiner时要注意其不会影响最中的结果!!!

 

    原文作者:MapReduce
    原文地址: https://www.cnblogs.com/HelloBigTable/p/10591267.html
    本文转自网络文章,转载此文章仅为分享知识,如有侵权,请联系博主进行删除。
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