Hadoop MapReduce编程：计算极值

现在，我们不是计算一个最大值了（想了解如何计算最大值，可以参考Hadoop MapReduce编程：计算最大值），而是计算一个最大值和一个最小值。实际上，实现Mapper和Reducer也是非常简单的，但是我们要输出结果，需要能够区分出最大值和最小值，并同时输出结果，这就需要自定义自己的输出类型，以及需要定义输出格式。

测试数据

数据格式，如下所示：

01	SG 253654006139495 253654006164392 619850464

02	KG 253654006225166 253654006252433 743485698

03	UZ 253654006248058 253654006271941 570409379

04	TT 253654006282019 253654006286839 23236775

05	BE 253654006276984 253654006301435 597874033

06	BO 253654006293624 253654006315946 498265375

07	SR 253654006308428 253654006330442 484613339

08	SV 253654006320312 253654006345405 629640166

09	LV 253654006330384 253654006359891 870680704

10	FJ 253654006351709 253654006374468 517965666

上面文本数据一行一行存储，一行包含4部分，分别表示：

1. 国家代码
2. 起始时间
3. 截止时间
4. 随机成本/权重估值

各个字段之间以空格号分隔。我们要计算的结果是，求各个国家（以国家代码标识）的成本估值的极值（最大值和最小值）。

编程实现

首先，我们应该考虑需要实现哪些内容，才能满足我们的编程需要。
Mapper中输入数据类型分别为：K1=>LongWritable，V1=>Text，K1表示文本文件中行偏移量，V1表示一行的文本内容；输出数据类型分别为：K2=>Text，V2=>LongWritable，K2表示我们解析出来的国家代码，是字符串类型，V2表示成本估算值，是一个数字类型。
而Reducer的输入即为<K2, list>，输出我们设计为，K3=>Text，是国家代码，V3是我们自己定义的类型，这个类中应该包含最大值和最小值。
另外，因为Reducer运行阶段我们得到最终的输出，而且是输出到HDFS中。我们设计了输出对象的类型，但是还需要设计一个用来描述输出数据的格式，其中包含了应该如何将最终的数据写入到HDFS中。
有关Mapper的实现，可以参考Hadoop MapReduce编程：计算最大值，这里，为了计算极值，需要实现如下内容：

1. Reducer输出类型
2. Reducer输出规格说明的定义
3. Mapper实现
4. Reducer实现
5. 配置Job

下面，我们详细说明如何去实现：

• Reducer输出类型

Reducer的输出Value类型应该定义两个字段：最大值和最小值，这样才能在最终的输出中同时看到同一个国家代码对应的极值数据。我们定义了Extremum类来代表极值，实现代码如下所示：

01	package org.shirdrn.kodz.inaction.hadoop.extremum;

02

03	import java.io.DataInput;

04	import java.io.DataOutput;

05	import java.io.IOException;

06

07	import org.apache.hadoop.io.LongWritable;

08	import org.apache.hadoop.io.Writable;

09

10	public class Extremum implements Writable {

11

12	private LongWritable maxValue;

13	private LongWritable minValue;

14

15	public Extremum() {

16

super();

17	maxValue = new LongWritable(0);

18	minValue = new LongWritable(0);

19

}

20

21	public Extremum(long min, long max) {

22

super();

23	this.minValue = new LongWritable(min);

24	this.maxValue = new LongWritable(max);

25

}

26

27

@Override

28	public void readFields(DataInput in) throws IOException {

29	minValue.readFields(in);

30	maxValue.readFields(in);

31

}

32

33

@Override

34	public void write(DataOutput out) throws IOException {

35	minValue.write(out);

36	maxValue.write(out);

37

}

38

39	public LongWritable getMaxValue() {

40	return maxValue;

41

}

42

43	public void setMaxValue(LongWritable maxValue) {

44	this.maxValue = maxValue;

45

}

46

47	public LongWritable getMinValue() {

48	return minValue;

49

}

50

51	public void setMinValue(LongWritable minValue) {

52	this.minValue = minValue;

53

}

54

55

@Override

56	public String toString() {

57	StringBuilder builder = new StringBuilder();

58	builder.append(minValue.get()).append("\t").append(maxValue.get());

59	return builder.toString();

60

}

61

}

我们自己定义的类型必须实现Hadoop定义的Writable接口，这样才能够实用Hadoop的序列化机制，最终将数据写入到HDFS。该接口定义了两个方法，分别对应于序列化和反序列化操作。
这个自定义类型中，封装了最大值和最小值两个字段。

• Reducer输出规格说明的定义

Reducer输出，实际上就是我们写个这个Job的输出。我们定义了ExtremumOutputFormat类，该类描述Reducer输出规格的，你可以参考Hadoop自带的TextOutputFormat类，重写自带的getRecordWriter方法，来实现我们自己输出结果的操作。ExtremumOutputFormat类实现代码如下所示：

01	package org.shirdrn.kodz.inaction.hadoop.extremum;

02

03	import java.io.DataOutputStream;

04	import java.io.IOException;

05	import java.io.UnsupportedEncodingException;

06

07	import org.apache.hadoop.conf.Configuration;

08	import org.apache.hadoop.fs.FSDataOutputStream;

09	import org.apache.hadoop.fs.FileSystem;

10	import org.apache.hadoop.fs.Path;

11	import org.apache.hadoop.io.Text;

12	import org.apache.hadoop.io.compress.CompressionCodec;

13	import org.apache.hadoop.io.compress.GzipCodec;

14	import org.apache.hadoop.mapreduce.RecordWriter;

15	import org.apache.hadoop.mapreduce.TaskAttemptContext;

16	import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

17	import org.apache.hadoop.util.ReflectionUtils;

18

19	public class ExtremumOutputFormat extends TextOutputFormat<Text, Extremum> {

20

21

@Override

22	public RecordWriter<Text, Extremum> getRecordWriter(TaskAttemptContext job)

23	throws IOException, InterruptedException {

24

25	Configuration conf = job.getConfiguration();

26	boolean isCompressed = getCompressOutput(job); // 是否压缩输出结果

27	String fieldSeparator= conf.get("mapred.textoutputformat.separator", "\t");

28	CompressionCodec codec = null;

29	String extension = "";

30	if (isCompressed) {

31	Class<? extends CompressionCodec> codecClass = getOutputCompressorClass(job, GzipCodec.class);

32	codec = (CompressionCodec) ReflectionUtils.newInstance(codecClass, conf);

33	extension = codec.getDefaultExtension();

34

}

35	Path file = getDefaultWorkFile(job, extension);

36	FileSystem fs = file.getFileSystem(conf);

37	FSDataOutputStream fileOut = fs.create(file, false);

38	if (!isCompressed) {

39	return new ExtremumRecordWriter(fileOut, fieldSeparator);

40

} else {

41	DataOutputStream out = newDataOutputStream(codec.createOutputStream(fileOut));

42	return new ExtremumRecordWriter(out, fieldSeparator);

43

}

44

}

45

46	public static class ExtremumRecordWriter extends RecordWriter<Text, Extremum> {

47

48	private static final String CHARSET = "UTF-8";

49	protected DataOutputStream out;

50	private final byte[] fieldSeparator;

51	private static final byte[] NEWLINE;

52

static {

53

try {

54	NEWLINE = "\n".getBytes(CHARSET);

55	} catch (UnsupportedEncodingException uee) {

56	throw new IllegalArgumentException("can't find " + CHARSET + " encoding.");

57

}

58

}

59

60	public ExtremumRecordWriter(DataOutputStream out) {

61	this(out, "\t");

62

}

63

64	public ExtremumRecordWriter(DataOutputStream out, String fieldSeparator) {

65

super();

66	this.out = out;

67

try {

68	this.fieldSeparator = fieldSeparator.getBytes(CHARSET);

69	} catch (UnsupportedEncodingException e) {

70	throw new IllegalArgumentException("can't find " + CHARSET + " encoding.");

71

}

72

}

73

74

@Override

75	public synchronized void close(TaskAttemptContext context) throwsIOException,

76	InterruptedException {

77	out.close();

78

}

79

80

@Override

81	public synchronized void write(Text key, Extremum value) throws IOException,

82	InterruptedException {

83	if(key != null) {

84	out.write(key.getBytes(), 0, key.getLength());

85	out.write(fieldSeparator);

86

}

87	if(value !=null) {

88	out.write(value.getMinValue().toString().getBytes());

89	out.write(fieldSeparator);

90	out.write(value.getMaxValue().toString().getBytes());

91

}

92	out.write(NEWLINE);

93

}

94

95

}

96

}

我们实现的ExtremumOutputFormat类，在getRecordWriter方法中返回一个ExtremumRecordWriter实例，这个实例就是用来执行写入输出结果的，上面输出结果的格式就是“国家代码最小值最大值”，各个字段时间使用TAB分隔，一共三列。

• Mapper实现

Mapper实现就是解析一行文本数据，抽取出国家代码和成本估值，直接列出我们实现的ExtremunGlobalCostMapper累代码，如下所示：

01	package org.shirdrn.kodz.inaction.hadoop.extremum;

02

03	import java.io.IOException;

04

05	import org.apache.hadoop.io.LongWritable;

06	import org.apache.hadoop.io.Text;

07	import org.apache.hadoop.mapreduce.Mapper;

08

09	public class ExtremunGlobalCostMapper extends

10	Mapper<LongWritable, Text, Text, LongWritable> {

11

12	private LongWritable costValue = new LongWritable(1);

13	private Text code = new Text();

14

15

@Override

16	protected void map(LongWritable key, Text value, Context context)

17	throws IOException, InterruptedException {

18	// a line, such as 'SG 253654006139495 253654006164392 619850464'

19	String line = value.toString();

20	String[] array = line.split("\\s");

21	if (array.length == 4) {

22	String countryCode = array[0];

23	String strCost = array[3];

24	long cost = 0L;

25

try {

26	cost = Long.parseLong(strCost);

27	} catch (NumberFormatException e) {

28	cost = 0L;

29

}

30	if (cost != 0) {

31	code.set(countryCode);

32	costValue.set(cost);

33	context.write(code, costValue);

34

}

35

}

36

}

37

38

}

• Reducer实现

Reducer实现也不是很复杂，需要注意的是，计算出最小值和最大值之后，将它们包装到非Hadoop定义的类型的实例中，这里是Extremum类。我们实现的ExtremumGlobalCostReducer类，代码如下所示：

01	package org.shirdrn.kodz.inaction.hadoop.extremum;

02

03	import java.io.IOException;

04	import java.util.Iterator;

05

06	import org.apache.hadoop.io.LongWritable;

07	import org.apache.hadoop.io.Text;

08	import org.apache.hadoop.mapreduce.Reducer;

09

10	public class ExtremumGlobalCostReducer extends

11	Reducer<Text, LongWritable, Text, Extremum> {

12

13

@Override

14	protected void reduce(Text key, Iterable<LongWritable> values,

15	Context context) throws IOException, InterruptedException {

16	long max = 0L;

17	long min = Long.MAX_VALUE;

18	Iterator<LongWritable> iter = values.iterator();

19	while (iter.hasNext()) {

20	LongWritable current = iter.next();

21	if (current.get() > max) {

22	max = current.get();

23

}

24	if (current.get() < min) {

25	min = current.get();

26

}

27

}

28	Extremum extremum = new Extremum(min, max);

29	context.write(key, extremum);

30

}

31

}

• 配置Job

配置Job，需要按照我们定义的输出值类型，以及输出规格说明来进行配置，我们实现的MapReduce程序的配置逻辑，实现类为ExtremumCostDriver，代码如下所示：

01	package org.shirdrn.kodz.inaction.hadoop.extremum;

02

03	import java.io.IOException;

04

05	import org.apache.hadoop.conf.Configuration;

06	import org.apache.hadoop.fs.Path;

07	import org.apache.hadoop.io.LongWritable;

08	import org.apache.hadoop.io.Text;

09	import org.apache.hadoop.mapreduce.Job;

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

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

12	import org.apache.hadoop.util.GenericOptionsParser;

13

14	public class ExtremumCostDriver {

15

16	public static void main(String[] args) throws IOException,

17	InterruptedException, ClassNotFoundException {

18

19	Configuration conf = new Configuration();

20	String[] otherArgs = new GenericOptionsParser(conf, args)

21	.getRemainingArgs();

22	if (otherArgs.length != 2) {

23	System.err.println("Usage: extremumcost <in> <out>");

24	System.exit(2);

25

}

26

27	Job job = new Job(conf, "extremum cost");

28

29	job.setJarByClass(ExtremumCostDriver.class);

30	job.setMapperClass(ExtremunGlobalCostMapper.class);

31	job.setReducerClass(ExtremumGlobalCostReducer.class);

32

33	job.setMapOutputKeyClass(Text.class);

34	job.setMapOutputValueClass(LongWritable.class);

35	job.setOutputKeyClass(Text.class);

36	job.setOutputValueClass(Extremum.class);

37	job.setOutputFormatClass(ExtremumOutputFormat.class);

38

39	job.setNumReduceTasks(2);

40

41	FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

42	FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

43

44	int exitFlag = job.waitForCompletion(true) ? 0 : 1;

45	System.exit(exitFlag);

46

}

47

}

这里，一定要正确设置对应阶段的Key和Value输出类型，以及我们定义的输出规格描述类型。另外，我们设置了启动2个Reduce任务，最终会输出2个结果文件。

运行程序

下面看运行程序的过程：

• 编译代码（我直接使用Maven进行），打成jar文件

1	shirdrn@SYJ:~/programs/eclipse-jee-juno/workspace/kodz-all/kodz-hadoop/target/classes$ jar -cvf global-extremum-cost.jar -C ./ org

• 拷贝上面生成的jar文件，到NameNode环境中

1	scp shirdrn@172.0.8.212:~/programs/eclipse-jee-juno/workspace/kodz-all/kodz-hadoop/target/classes/global-extremum-cost.jar ./xiaoxiang@ubuntu3:/opt/stone/cloud/hadoop-1.0.3$ bin/hadoop fs -copyFromLocal /opt/stone/cloud/dataset/data_10m /user/xiaoxiang/datasets/cost/

• 上传待处理的数据文件

1	xiaoxiang@ubuntu3:/opt/stone/cloud/hadoop-1.0.3$ bin/hadoop fs -copyFromLocal /opt/stone/cloud/dataset/data_10m /user/xiaoxiang/datasets/cost/

• 运行我们编写MapReduce任务，计算极值

1	xiaoxiang@ubuntu3:/opt/stone/cloud/hadoop-1.0.3$ bin/hadoop jar global-extremum-cost.jar org.shirdrn.kodz.inaction.hadoop.extremum.ExtremumCostDriver /user/xiaoxiang/datasets/cost /user/xiaoxiang/output/extremum/cost

• 运行输出

运行过程控制台输出内容，如下所示：

01	13/03/22 21:38:46 INFO input.FileInputFormat: Total input paths to process : 1

02	13/03/22 21:38:46 INFO util.NativeCodeLoader: Loaded the native-hadoop library

03	13/03/22 21:38:46 WARN snappy.LoadSnappy: Snappy native library not loaded

04	13/03/22 21:38:46 INFO mapred.JobClient: Running job: job_201303111631_0012

05	13/03/22 21:38:47 INFO mapred.JobClient: map 0% reduce 0%

06	13/03/22 21:39:03 INFO mapred.JobClient: map 21% reduce 0%

07	13/03/22 21:39:06 INFO mapred.JobClient: map 28% reduce 0%

08	13/03/22 21:39:18 INFO mapred.JobClient: map 48% reduce 4%

09	13/03/22 21:39:21 INFO mapred.JobClient: map 57% reduce 9%

10	13/03/22 21:39:34 INFO mapred.JobClient: map 78% reduce 14%

11	13/03/22 21:39:37 INFO mapred.JobClient: map 85% reduce 19%

12	13/03/22 21:39:49 INFO mapred.JobClient: map 100% reduce 23%

13	13/03/22 21:39:52 INFO mapred.JobClient: map 100% reduce 28%

14	13/03/22 21:40:01 INFO mapred.JobClient: map 100% reduce 30%

15	13/03/22 21:40:04 INFO mapred.JobClient: map 100% reduce 33%

16	13/03/22 21:40:07 INFO mapred.JobClient: map 100% reduce 66%

17	13/03/22 21:40:10 INFO mapred.JobClient: map 100% reduce 100%

18	13/03/22 21:40:15 INFO mapred.JobClient: Job complete: job_201303111631_0012

19	13/03/22 21:40:15 INFO mapred.JobClient: Counters: 29

20	13/03/22 21:40:15 INFO mapred.JobClient: Job Counters

21	13/03/22 21:40:15 INFO mapred.JobClient: Launched reduce tasks=2

22	13/03/22 21:40:15 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=98141

23	13/03/22 21:40:15 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0

24	13/03/22 21:40:15 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0

25	13/03/22 21:40:15 INFO mapred.JobClient: Launched map tasks=7

26	13/03/22 21:40:15 INFO mapred.JobClient: Data-local map tasks=7

27	13/03/22 21:40:15 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=111222

28	13/03/22 21:40:15 INFO mapred.JobClient: File Output Format Counters

29	13/03/22 21:40:15 INFO mapred.JobClient: Bytes Written=4339

30	13/03/22 21:40:15 INFO mapred.JobClient: FileSystemCounters

31	13/03/22 21:40:15 INFO mapred.JobClient: FILE_BYTES_READ=260079964

32	13/03/22 21:40:15 INFO mapred.JobClient: HDFS_BYTES_READ=448913653

33	13/03/22 21:40:15 INFO mapred.JobClient: FILE_BYTES_WRITTEN=390199096

34	13/03/22 21:40:15 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=4339

35	13/03/22 21:40:15 INFO mapred.JobClient: File Input Format Counters

36	13/03/22 21:40:15 INFO mapred.JobClient: Bytes Read=448912799

37	13/03/22 21:40:15 INFO mapred.JobClient: Map-Reduce Framework

38	13/03/22 21:40:15 INFO mapred.JobClient: Map output materialized bytes=130000084

39	13/03/22 21:40:15 INFO mapred.JobClient: Map input records=10000000

40	13/03/22 21:40:15 INFO mapred.JobClient: Reduce shuffle bytes=116610358

41	13/03/22 21:40:15 INFO mapred.JobClient: Spilled Records=30000000

42	13/03/22 21:40:15 INFO mapred.JobClient: Map output bytes=110000000

43	13/03/22 21:40:15 INFO mapred.JobClient: CPU time spent (ms)=121520

44	13/03/22 21:40:15 INFO mapred.JobClient: Total committed heap usage (bytes)=1763442688

45	13/03/22 21:40:15 INFO mapred.JobClient: Combine input records=0

46	13/03/22 21:40:15 INFO mapred.JobClient: SPLIT_RAW_BYTES=854

47	13/03/22 21:40:15 INFO mapred.JobClient: Reduce input records=10000000

48	13/03/22 21:40:15 INFO mapred.JobClient: Reduce input groups=233

49	13/03/22 21:40:15 INFO mapred.JobClient: Combine output records=0

50	13/03/22 21:40:15 INFO mapred.JobClient: Physical memory (bytes) snapshot=1973850112

51	13/03/22 21:40:15 INFO mapred.JobClient: Reduce output records=233

52	13/03/22 21:40:15 INFO mapred.JobClient: Virtual memory (bytes) snapshot=4880068608

53	13/03/22 21:40:15 INFO mapred.JobClient: Map output records=10000000

• 验证输出结果

001	xiaoxiang@ubuntu3:/opt/stone/cloud/hadoop-1.0.3$ bin/hadoop fs -cat/user/xiaoxiang/output/extremum/cost/part-r-00000

002

121

003	xiaoxiang@ubuntu3:/opt/stone/cloud/hadoop-1.0.3$ bin/hadoop fs -cat/user/xiaoxiang/output/extremum/cost/part-r-00001

004

112

005	xiaoxiang@ubuntu3:/opt/stone/cloud/hadoop-1.0.3$ bin/hadoop fs -cat/user/xiaoxiang/output/extremum/cost/part-r-00000

006	AD 43328 999974516

007	AF 11795 999996180

008	AL 11148 999998489

009	AR 33649 999953989

010	AT 5051 999999909

011	AZ 3726 999996557

012	BA 89066 999949773

013	BE 28187 999925057

014	BG 64672 999971528

015	BI 50687 999978516

016	BM 14127 999991925

017	BO 61786 999995482

018	BS 52428 999980931

019	BW 78739 999935985

020	BY 39216 999998496

021	CD 51328 999978139

022	CF 5084 999995342

023	CH 17996 999997524

024	CL 359 999967083

025	CN 17985 999975367

026	CR 17435 999971685

027	CV 16166 999990543

028	CX 74232 999987579

029	CZ 1400 999993908

030	DE 1676 999985416

031	DK 84072 999963312

032	DM 57727 999941706

033	DO 9162 999945597

034	ER 60594 999980425

035	ET 21273 999987033

036	FI 19268 999966243

037	FK 39074 999966573

038	FM 9493 999972146

039	FO 47655 999988472

040	GB 12629 999970658

041	GD 7713 999996318

042	GF 42623 999982024

043	GH 9904 999941039

044	GL 16512 999948726

045	GN 2779 999951804

046	GP 64525 999904645

047	GR 44233 999999672

048	GT 159 999972984

049	HK 24464 999970084

050	HU 23165 999997568

051	ID 19812 999994762

052	IL 14553 999982184

053	IN 25241 999914991

054	IR 5173 999986780

055	IT 19399 999997239

056	JM 30194 999982209

057	JO 5149 999977276

058	KH 5010 999975644

059	KN 597 999991068

060	KP 61560 999967939

061	KR 13295 999992162

062	KZ 5565 999992835

063	LA 4002 999989151

064	LC 8509 999962233

065	LI 54653 999986863

066	LK 12556 999989876

067	LS 6702 999957706

068	LU 17627 999999823

069	LY 41618 999992365

070	MD 8494 999996042

071	MH 1050 999989668

072	ML 30858 999990079

073	MN 4236 999969051

074	MP 8422 999995234

075	MR 14023 999982303

076	MT 91203 999982604

077	MV 15186 999961206

078	MX 15807 999978066

079	MZ 14800 999981189

080	NA 7673 999961177

081	NC 9467 999961053

082	NE 1802 999990091

083	NG 5189 999985037

084	NI 29440 999965733

085	NO 20466 999993122

086	NU 17175 999987046

087	PA 51054 999924435

088	PE 10286 999981176

089	PG 119327 999987347

090	PK 1041 999954268

091	PM 1435 999998975

092	PW 40353 999991278

093	PY 9586 999985509

094	RE 5640 999952291

095	RO 7139 999994148

096	RS 1342 999999923

097	RU 11319 999894985

098	RW 815 999980184

099	SB 15446 999972832

100	SD 14060 999963744

101	SH 1276 999983638

102	SL 35087 999999269

103	SN 117358 999990278

104	SR 12974 999975964

105	ST 3796 999980447

106	SV 20563 999999945

107	SX 41237 999903445

108	SZ 18993 999992537

109	TC 46396 999969540

110	TG 58484 999977640

111	TK 11880 999971131

112	TM 25103 999958998

113	TO 40829 999947915

114	TW 5437 999975092

115	UZ 2620 999982762

116	VA 774 999975548

117	VC 9514 999991495

118	VE 12156 999997971

119	VG 32832 999949690

120	VI 895 999990063

121	VU 1375 999953162

122	WF 62709 999947666

123	YT 29640 999994707

124	ZA 8399 999998692

125	ZM 25699 999973392

126	ZW 77984 999928087

127	xiaoxiang@ubuntu3:/opt/stone/cloud/hadoop-1.0.3$ bin/hadoop fs -cat/user/xiaoxiang/output/extremum/cost/part-r-00001

128	AE 1870 999938630

129	AG 7701 999991085

130	AI 8609 999989595

131	AM 16681 999976746

132	AO 17126 999989628

133	AQ 40493 999995031

134	AS 3096 999935982

135	AU 13311 999937089

136	AW 1734 999965784

137	BB 27334 999987345

138	BD 27928 999992272

139	BF 20745 999999220

140	BH 4980 999994900

141	BJ 11385 999977886

142	BN 33440 999986630

143	BR 56601 999989947

144	BT 84033 999977488

145	BZ 27250 999975972

146	CA 54534 999978275

147	CC 753 999968311

148	CG 10644 999788112

149	CI 17263 999998864

150	CK 7710 999968719

151	CM 12402 999998369

152	CO 7616 999999167

153	CU 30200 999976352

154	CW 6597 999987713

155	CY 5686 999982925

156	DJ 30348 999997438

157	DZ 31633 999973610

158	EC 15342 999920447

159	EE 3834 999949534

160	EG 60885 999980522

161	ES 540 999949155

162	FJ 14016 999990686

163	FR 14682 999988342

164	GA 54051 999982099

165	GE 25641 999991970

166	GI 9387 999995295

167	GM 12637 999967823

168	GQ 13033 999988635

169	GU 68211 999919056

170	GW 34868 999962551

171	GY 4633 999999881

172	HN 18264 999972628

173	HR 18192 999986688

174	HT 2732 999970913

175	IE 23339 999996686

176	IM 47842 999987831

177	IO 2457 999968575

178	IQ 75 999990126

179	IS 24153 999973585

180	JP 8463 999983684

181	KE 45373 999996012

182	KG 5037 999991556

183	KI 1146 999994328

184	KM 41461 999989895

185	KW 20441 999924295

186	KY 23539 999977105

187	LB 15169 999963014

188	LR 76821 999897202

189	LT 346 999999688

190	LV 17147 999945411

191	MA 22225 999922726

192	MC 4131 999978886

193	MG 15255 999996602

194	MK 21709 999968900

195	MM 33329 999987977

196	MO 19139 999977975

197	MQ 57391 999913110

198	MS 38541 999974690

199	MU 51654 999988632

200	MW 16153 999991903

201	MY 1575 999995010

202	NF 9028 999989399

203	NL 22375 999949789

204	NP 15809 999972410

205	NR 11740 999956464

206	NZ 4921 999998214

207	OM 20128 999967428

208	PF 8544 999959978

209	PH 58869 999981534

210	PL 7105 999996619

211	PR 34529 999906386

212	PT 50257 999993404

213	QA 33588 999995061

214	SA 54320 999973822

215	SC 64674 999973271

216	SE 13044 999972256

217	SG 20304 999977637

218	SI 17186 999980580

219	SK 7472 999998152

220	SM 5636 999941188

221	SO 52133 999973175

222	SY 36761 999988858

223	TD 98111 999999303

224	TH 11173 999968746

225	TJ 19322 999983666

226	TN 40668 999963035

227	TP 40353 999986796

228	TR 4495 999995112

229	TT 32136 999984435

230	TV 40558 999971989

231	TZ 5852 999992734

232	UA 12571 999970993

233	UG 4142 999976267

234	UM 5465 999998377

235	US 52833 999912229

236	UY 12782 999989662

237	VN 45107 999974393

238	WS 19808 999970242

239	YE 3291 999984650

可见，结果符合预期。