MapReduce切片(Split)和分区(Partitioner)

MapReduce中,分片、分区、排序和分组(Group)的关系图:

img

分片大小

对于HDFS中存储的一个文件,要进行Map处理前,需要将它切分成多个块,才能分配给不同的MapTask去执行。 分片的数量等于启动的MapTask的数量。默认情况下,分片的大小就是HDFS的blockSize。

Map阶段的对数据文件的切片,使用如下判断逻辑:

  protected long computeSplitSize(long blockSize, long minSize,
                                  long maxSize) {
    return Math.max(minSize, Math.min(maxSize, blockSize));
  }

blockSize:默认大小是128M(dfs.blocksize

minSize:默认是1byte(mapreduce.input.fileinputformat.split.minsize):

img

maxSize:默认值是Long.MaxValue(mapreduce.input.fileinputformat.split.minsize) img

由此可以看出两个可以自定义的值(minSize和maxSize)与blockSize之间的关系如下:

当blockSize位于minSize和maxSize 之间时,认blockSize:

img

当maxSize小于blockSize时,认maxSize:

img

当minSize大于blockSize时,认minSize:

img

另外一个极端的情况,maxSize小于minSize时,认minsize,可以理解为minSize的优先级比maxSize大:

img

实际使用中,建议不要去修改maxSize,通过调整minSize(使他大于blockSize)就可以设定分片(Split)的大小了。

总之通过minSize和maxSize的来设置切片大小,使之在blockSize的上下自由调整。

什么时候需要调整分片的大小

首先要明白,HDFS的分块其实是指HDFS在存储文件时的一个参数。而这里分片的大小是为了业务逻辑用的。分片的大小直接影响到MapTask的数量,你可以根据实际的业务需求来调整分片的大小。

分区

在Reduce过程中,可以根据实际需求(比如按某个维度进行归档,类似于数据库的分组),把Map完的数据Reduce到不同的文件中。分区的设置需要与ReduceTaskNum配合使用。比如想要得到5个分区的数据结果。那么就得设置5个ReduceTask。

自定义Partitioner:

public class URLResponseTimePartitioner extends Partitioner<Text, LongWritable>{

    @Override
    public int getPartition(Text key, LongWritable value, int numPartitions) {
        String accessPath = key.toString();
        if(accessPath.endsWith(".do")) {
            return 0;
        }
        return 1;
    }

}

然后可以在job中设置partitioner:

        job.setPartitionerClass(URLResponseTimePartitioner.class);
        //URLResponseTimePartitioner returns 1 or 0,so num of reduce task must be 2
        job.setNumReduceTasks(2);

两个分区会产生两个最终结果文件:

[root@centos01 ~]# hadoop fs -ls /access/log/response-time
17/12/19 14:53:55 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Found 3 items
-rw-r--r--   2 root supergroup          0 2017-12-19 14:49 /access/log/response-time/_SUCCESS
-rw-r--r--   2 root supergroup       7769 2017-12-19 14:49 /access/log/response-time/part-r-00000
-rw-r--r--   2 root supergroup      18183 2017-12-19 14:49 /access/log/response-time/part-r-00001

其中00000中存放着.do的统计结果,00001则存放其他访问路径的统计结果。

[root@centos01 ~]# hadoop fs -cat /access/log/response-time/part-r-00001 |more
17/12/19 14:55:41 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
//MyAdmin/scripts/setup.php     3857
//css/console.css       356
//css/result_html.css   628
//images/male.png       268
//js/tooltipster/css/plugins/tooltipster/sideTip/themes/tooltipster-sideTip-borderless.min.css  1806
//js/tooltipster/css/tooltipster.bundle.min.css 6495
//myadmin/scripts/setup.php     3857
//phpMyAdmin/scripts/setup.php  3857
//phpmyadmin/scripts/setup.php  3857
//pma/scripts/setup.php 3857
/404/search_children.js 3827
/Dashboard.action       3877
/Homepage.action        3877
/My97DatePicker/WdatePicker.js  9371
/My97DatePicker/calendar.js     22044
/My97DatePicker/lang/zh-cn.js   1089
/My97DatePicker/skin/WdatePicker.css    158
/My97DatePicker/skin/default/datepicker.css     3486
/My97DatePicker/skin/default/img.gif    475

排序

要想最终结果中按某个特性排序,则需要在Map阶段,通过Key的排序来实现。

例如,想让上述平均响应时间的统计结果按降序排列,实现如下:

关键就在于这个用于OUTKey的Bean。它实现了Comparable接口,所以输出的结果就是按compareTo的结果有序。

由于这个类会作为Key,所以它的equals方法很重要,会作为,需要按实际情况重写。这里重写的逻辑是url相等则表示是同一个Key。(虽然Key相同的情况其实没有,因为之前的responseTime统计结果已经把url做了group,但是这里还是要注意有这么个逻辑。)

排序并不是依赖于key的equals!

    public class URLResponseTime implements WritableComparable<URLResponseTime>{
    String url;
    long avgResponseTime;

    public void write(DataOutput out) throws IOException {
        out.writeUTF(url);
        out.writeLong(avgResponseTime);
    }

    public void readFields(DataInput in) throws IOException {
        this.url = in.readUTF();
        this.avgResponseTime = in.readLong();
    }

    public int compareTo(URLResponseTime urt) {
        return this.avgResponseTime > urt.avgResponseTime ? -1 : 1;
    }

    public String getUrl() {
        return url;
    }

    public void setUrl(String url) {
        this.url = url;
    }

    public long getAvgResponseTime() {
        return avgResponseTime;
    }

    public void setAvgResponseTime(long avgResponseTime) {
        this.avgResponseTime = avgResponseTime;
    }

    @Override
    public int hashCode() {
        final int prime = 31;
        int result = 1;
        result = prime * result + ((url == null) ? 0 : url.hashCode());
        return result;
    }

    @Override
    public boolean equals(Object obj) {
        if (this == obj)
            return true;
        if (obj == null)
            return false;
        if (getClass() != obj.getClass())
            return false;
        URLResponseTime other = (URLResponseTime) obj;
        if (url == null) {
            if (other.url != null)
                return false;
        } else if (!url.equals(other.url))
            return false;
        return true;
    }

    @Override
    public String toString() {
        return url;
    }

}

然后就简单了,在Map和Reduce分别执行简单的写和读操作就行了,没有更多的处理,依赖于Hadoop MapReduce框架自身的特点就实现了排序:

public class URLResponseTimeSortMapper extends Mapper<LongWritable,Text,URLResponseTime,LongWritable>{


    //make a member property to avoid new instance every time when map function invoked.
    URLResponseTime key = new URLResponseTime();
    LongWritable value = new LongWritable();

    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {

        String line = value.toString();
        String[] logs = line.split("\t");
        String url = logs[0];
        String responseTimeStr = logs[1];

        long responseTime = Long.parseLong(responseTimeStr);


        this.key.setUrl(url);
        this.key.setAvgResponseTime(responseTime);
        this.value.set(responseTime);
        context.write(this.key,this.value);
    }


}
public class URLResponseTimeSortReducer extends Reducer<URLResponseTime, LongWritable, URLResponseTime, LongWritable> {


    @Override
    protected void reduce(URLResponseTime key, Iterable<LongWritable> values,
            Context ctx) throws IOException, InterruptedException {
        ctx.write(key, values.iterator().next());
    }


}

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