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Hadoop架構之——Mapreduce序列化1 序列化概述2.自定義bean對象實作序列化接口（Writable）3 序列化案例實操

文章目錄

  • 1 序列化概述
    • 1.1什麼是序列化
    • 1.2為什麼要序列化
    • 1.3為什麼不用Java的序列化
  • 2.自定義bean對象實作序列化接口（Writable）
  • 3 序列化案例實操
    • 1. 需求
    • **2．需求分析**
    • **3．編寫MapReduce程式**
    • 4.打包到叢集測試
    • 5,結果為：

1 序列化概述

1.1什麼是序列化

序列化就是把記憶體中的對象,轉換成位元組序列(或其他資料傳輸協定)以便于存儲到磁盤(持久化)和網絡傳輸。

反序列化就是将收到位元組序列(或其他資料傳輸協定)或者是磁盤的持久化資料，轉換成記憶體中的對象。

1.2為什麼要序列化

一般來說，“活的” 對象隻生存在記憶體裡，關機斷電就沒有了。而且“活的”對象隻能由本地的程序使用，不能被發送到網絡上的另外-台計算機。然而序列化可以存儲“活的”對象，可以将“活的”對象發送到遠端計算機。

1.3為什麼不用Java的序列化

Java的序列化是-個重量級序列化架構(Serializable) ，-一個對象被序列化後，會附帶很多額外的資訊(各種校驗資訊, Header, 繼承體系等)，不便于在網絡中高效傳輸。是以, Hadoop自己開發了一套序列化機制 (Writable) 。

Hadoop序列化特點:

(1)緊湊:高效使用存儲空間。

(2)快速:讀寫資料的額外開銷小。

(3)可擴充:随着通信協定的更新而可更新(4)互操作:支援多語言的互動

2.自定義bean對象實作序列化接口（Writable）

在企業開發中往往常用的基本序列化類型不能滿足所有需求，比如在Hadoop架構内部傳遞一個bean對象，那麼該對象就需要實作序列化接口。

具體實作bean對象序列化步驟如下7步。

（1）必須實作Writable接口

（2）反序列化時，需要反射調用空參構造函數，是以必須有空參構造

public FlowBean() {
	super();
}

（3）重寫序列化方法

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

（4）重寫反序列化方法

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

（5）注意反序列化的順序和序列化的順序完全一緻

（6）要想把結果顯示在檔案中，需要重寫toString()，可用”\t”分開，友善後續用。

（7）如果需要将自定義的bean放在key中傳輸，則還需要實作Comparable接口，因為MapReduce框中的Shuffle過程要求對key必須能排序。詳見後面排序案例。

@Override
public int compareTo(FlowBean o) {
	// 倒序排列，從大到小
	return this.sumFlow > o.getSumFlow() ? -1 : 1;
}

3 序列化案例實操

1. 需求

統計每一個手機号耗費的總上行流量、下行流量、總流量

檔案名phone_data.txt

1	13736230513	192.196.100.1	www.atguigu.com	2481	24681	200
2	13846544121	192.196.100.2					264			0	200
3 	13956435636	192.196.100.3					132		1512	200
4 	13966251146	192.168.100.1					240			0	404
5 	18271575951	192.168.100.2	www.atguigu.com	1527	2106	200
6 	84188413	192.168.100.3	www.atguigu.com	4116	1432	200
7 	13590439668	192.168.100.4					1116	954		200
8 	15910133277	192.168.100.5	www.hao123.com	3156	2936	200
9 	13729199489	192.168.100.6					240		   0	200
10 	13630577991	192.168.100.7	www.shouhu.com	6960	690		200
11 	15043685818	192.168.100.8	www.baidu.com	3659	3538	200
12 	15959002129	192.168.100.9	www.atguigu.com	1938	180		500
13 	13560439638	192.168.100.10					918		4938	200
14 	13470253144	192.168.100.11					180		180		200
15 	13682846555	192.168.100.12	www.qq.com		1938	2910	200
16 	13992314666	192.168.100.13	www.gaga.com	3008	3720	200
17 	13509468723	192.168.100.14	www.qinghua.com	7335	110349	404
18 	18390173782	192.168.100.15	www.sogou.com	9531	2412	200
19 	13975057813	192.168.100.16	www.baidu.com	11058	48243	200
20 	13768778790	192.168.100.17					120		120		200
21 	13568436656	192.168.100.18	www.alibaba.com	2481	24681	200
22 	13568436656	192.168.100.19					1116	954		200

（2）輸入資料格式：

7 	13560436666	120.196.100.99		1116		 954			200
id	手機号碼		網絡ip			  上行流量       下行流量     網絡狀态碼

（3）期望輸出資料格式

13560436666 		1116		      954 			2070
手機号碼		        上行流量        下行流量		    總流量

2．需求分析

Hadoop架構之——Mapreduce序列化1 序列化概述2.自定義bean對象實作序列化接口（Writable）3 序列化案例實操

3．編寫MapReduce程式

（1）編寫流量統計的Bean對象

public class FlowBean implements Writable {

    private long upFlow;  //上行流量
    private long downFlow; //下行流量
    private long sumFlow; //總流量

    //TODO 反序列化時，需要反射調用空參構造函數，是以必須有
    public FlowBean() { }

    public FlowBean(long upFlow, long downFlow) {
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        this.sumFlow = upFlow + downFlow;
    }

    //TODO 序列化
    @Override
    public void write(DataOutput out) throws IOException {
        out.writeLong( upFlow );
        out.writeLong( downFlow );
        out.writeLong( sumFlow );
    }

    //TODO 反序列化方法
    //TODO 反序列化方法讀順序必須和寫序列化方法的寫順序必須一緻
    @Override
    public void readFields(DataInput in) throws IOException {
        upFlow = in.readLong();
        downFlow = in.readLong();
        sumFlow = in.readLong();
    }

    public long getUpFlow() {
        return upFlow;
    }

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

    public long getDownFlow() {
        return downFlow;
    }

    public void setDownFlow(long downFlow) {
        this.downFlow = downFlow;
    }

    public long getSumFlow() {
        return sumFlow;
    }

    public void setSumFlow(long sumFlow) {
        this.sumFlow = sumFlow;
    }

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


    public void set(long upFlow, long downFlow) {
        this.upFlow = upFlow;
        this.downFlow = downFlow;
        this.sumFlow = upFlow + downFlow;

    }
}

（2）編寫Mapper類

public class FlowCountMapper extends Mapper<LongWritable, Text,Text,FlowBean> {

    Text k = new Text();
    FlowBean v = new FlowBean();

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

        //TODO 擷取值和切分
        String[] splits = value.toString().split( "\t" );

        //TODO 封裝手機号
        k.set( splits[1]);

        //TODO 反方向封裝
        //TODO 減去上行流量和下行流量
        long upFlow  = Long.parseLong( splits[splits.length - 3] );
        long downFlow = Long.parseLong( splits[splits.length - 2] );

        //TODO 拿到FlowBean類的方法，指派
        v.setUpFlow( upFlow );
        v.setDownFlow( downFlow );
        v.set( upFlow,downFlow );

        //TODO 寫出
        context.write( k,v );
    }
}

（3）編寫Reducer類

public class FlowCountReducer extends Reducer<Text,FlowBean,Text,FlowBean> {

    FlowBean v = new FlowBean();
    @Override
    protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
        //TODO 定義累加變量
        long sum_unFlow = 0;
        long sum_downFlow = 0;
       // TODO  周遊所用bean，将其中的上行流量，下行流量分别累加
        for (FlowBean value : values) {
            sum_unFlow += value.getUpFlow();
            sum_downFlow += value.getDownFlow();
        }

        //TODO 封裝對象
        v.set( sum_unFlow,sum_downFlow );

        //TODO 寫出
        context.write( key,v );
    }

}

（4）編寫Driver驅動類

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

        //TODO 擷取job
        Configuration conf= new Configuration();
        Job job = Job.getInstance( conf );
        //TODO 擷取jar
        job.setJarByClass( FlowsumDriver.class );
        //TODO 擷取Map Reduce
        job.setMapperClass( FlowCountMapper.class );
        job.setReducerClass( FlowCountReducer.class );
        //TODO 擷取Map端輸出類型
        job.setMapOutputKeyClass( Text.class );
        job.setMapOutputValueClass( FlowBean.class );
        //TODO 擷取總輸出類型
        job.setOutputKeyClass( Text.class );
        job.setOutputValueClass( FlowBean.class );
        //TODO 設定輸入路徑和輸出路徑
        FileInputFormat.setInputPaths( job,new Path( args[0] ) );
        FileOutputFormat.setOutputPath( job,new Path( args[1] ) );
        //TODO 送出
        boolean result = job.waitForCompletion( true );
        System.exit( result ? 0:1 );
    }
}

4.打包到叢集測試 

[[email protected] jar]# yarn jar FlowSum.jar com.huan.flowsun.FlowsumDriver /huan/input1/phone_data.txt /huan/output2
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/hadoop/hadoop-2.7.2/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/hbase/hbase/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
20/08/04 02:12:46 INFO client.RMProxy: Connecting to ResourceManager at huan01/192.168.168.234:8032
20/08/04 02:12:49 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
20/08/04 02:12:51 INFO input.FileInputFormat: Total input paths to process : 1
20/08/04 02:12:51 INFO lzo.GPLNativeCodeLoader: Loaded native gpl library from the embedded binaries
20/08/04 02:12:51 INFO lzo.LzoCodec: Successfully loaded & initialized native-lzo library [hadoop-lzo rev 52decc77982b58949890770d22720a91adce0c3f]
20/08/04 02:12:51 INFO mapreduce.JobSubmitter: number of splits:1
20/08/04 02:12:52 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1596447552943_0002
20/08/04 02:12:53 INFO impl.YarnClientImpl: Submitted application application_1596447552943_0002
20/08/04 02:12:54 INFO mapreduce.Job: The url to track the job: http://huan01:8088/proxy/application_1596447552943_0002/
20/08/04 02:12:54 INFO mapreduce.Job: Running job: job_1596447552943_0002
20/08/04 02:13:26 INFO mapreduce.Job: Job job_1596447552943_0002 running in uber mode : false
20/08/04 02:13:26 INFO mapreduce.Job:  map 0% reduce 0%
20/08/04 02:13:59 INFO mapreduce.Job:  map 100% reduce 0%
20/08/04 02:14:19 INFO mapreduce.Job:  map 100% reduce 100%
20/08/04 02:14:20 INFO mapreduce.Job: Job job_1596447552943_0002 completed successfully
20/08/04 02:14:21 INFO mapreduce.Job: Counters: 49
	File System Counters
		FILE: Number of bytes read=839
		FILE: Number of bytes written=237431
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=1288
		HDFS: Number of bytes written=550
		HDFS: Number of read operations=6
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
	Job Counters 
		Launched map tasks=1
		Launched reduce tasks=1
		Data-local map tasks=1
		Total time spent by all maps in occupied slots (ms)=27015
		Total time spent by all reduces in occupied slots (ms)=17082
		Total time spent by all map tasks (ms)=27015
		Total time spent by all reduce tasks (ms)=17082
		Total vcore-milliseconds taken by all map tasks=27015
		Total vcore-milliseconds taken by all reduce tasks=17082
		Total megabyte-milliseconds taken by all map tasks=27663360
		Total megabyte-milliseconds taken by all reduce tasks=17491968
	Map-Reduce Framework
		Map input records=22
		Map output records=22
		Map output bytes=789
		Map output materialized bytes=839
		Input split bytes=110
		Combine input records=0
		Combine output records=0
		Reduce input groups=21
		Reduce shuffle bytes=839
		Reduce input records=22
		Reduce output records=21
		Spilled Records=44
		Shuffled Maps =1
		Failed Shuffles=0
		Merged Map outputs=1
		GC time elapsed (ms)=679
		CPU time spent (ms)=7390
		Physical memory (bytes) snapshot=325103616
		Virtual memory (bytes) snapshot=4132458496
		Total committed heap usage (bytes)=214433792
	Shuffle Errors
		BAD_ID=0
		CONNECTION=0
		IO_ERROR=0
		WRONG_LENGTH=0
		WRONG_MAP=0
		WRONG_REDUCE=0
	File Input Format Counters 
		Bytes Read=1178
	File Output Format Counters 
		Bytes Written=550

5,結果為： 

[[email protected] ~]# hadoop fs -cat /huan/output2/part-r-00000
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/hadoop/hadoop-2.7.2/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/hbase/hbase/lib/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
13470253144		180		180		360
13509468723		7335 	110349	117684
13560439638		918		4938	5856
13568436656		3597	25635	29232
13590439668		1116	954		2070
13630577991		6960	690		7650
13682846555		1938	2910	4848
13729199489		240		0		240
13736230513		2481	24681	27162
13768778790		120		120		240
13846544121		264		0		264
13956435636		132		1512	1644
13966251146		240		0		240
13975057813		11058	48243	59301
13992314666		3008	3720	6728
15043685818		3659	3538	7197
15910133277		3156	2936	6092
15959002129		1938	180		2118
18271575951		1527	2106	3633
18390173782		9531	2412	11943
84188413		4116	1432	5548
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