Test_1.java
/** * Hadoop网络课程模板程序* 编写者：James*/ import java.io.IOException; import java.text.DateFormat; import java.text.SimpleDateFormat; import java.util.Date;import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.*; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner;/** * 无Reducer版本*/ public class Test_1 extends Configured implements Tool { /** * 计数器* 用于计数各种异常数据*/ enum Counter {LINESKIP, //出错的行 }/** * MAP任务*/ public static class Map extends Mapper<LongWritable, Text, NullWritable, Text> {public void map ( LongWritable key, Text value, Context context ) throws IOException, InterruptedException {String line = value.toString(); //读取源数据try{//数据处理String [] lineSplit = line.split(" ");String month = lineSplit[0];String time = lineSplit[1];String mac = lineSplit[6];Text out = new Text(month + ' ' + time + ' ' + mac);context.write( NullWritable.get(), out); //输出 }catch ( java.lang.ArrayIndexOutOfBoundsException e ){context.getCounter(Counter.LINESKIP).increment(1); //出错令计数器+1return;}}}@Overridepublic int run(String[] args) throws Exception {Configuration conf = getConf();Job job = new Job(conf, "Test_1"); //任务名job.setJarByClass(Test_1.class); //指定Class FileInputFormat.addInputPath( job, new Path(args[0]) ); //输入路径FileOutputFormat.setOutputPath( job, new Path(args[1]) ); //输出路径 job.setMapperClass( Map.class ); //调用上面Map类作为Map任务代码job.setOutputFormatClass( TextOutputFormat.class );job.setOutputKeyClass( NullWritable.class ); //指定输出的KEY的格式job.setOutputValueClass( Text.class ); //指定输出的VALUE的格式 job.waitForCompletion(true);//输出任务完成情况System.out.println( "任务名称：" + job.getJobName() );System.out.println( "任务成功：" + ( job.isSuccessful()?"是":"否" ) );System.out.println( "输入行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS").getValue() );System.out.println( "输出行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_OUTPUT_RECORDS").getValue() );System.out.println( "跳过的行：" + job.getCounters().findCounter(Counter.LINESKIP).getValue() );return job.isSuccessful() ? 0 : 1;}/** * 设置系统说明* 设置MapReduce任务*/ public static void main(String[] args) throws Exception {//判断参数个数是否正确//如果无参数运行则显示以作程序说明if ( args.length != 2 ){System.err.println("");System.err.println("Usage: Test_1 < input path > < output path > ");System.err.println("Example: hadoop jar ~/Test_1.jar hdfs://localhost:9000/home/james/Test_1 hdfs://localhost:9000/home/james/output");System.err.println("Counter:");System.err.println("\t"+"LINESKIP"+"\t"+"Lines which are too short");System.exit(-1);}//记录开始时间DateFormat formatter = new SimpleDateFormat( "yyyy-MM-dd HH:mm:ss" );Date start = new Date();//运行任务int res = ToolRunner.run(new Configuration(), new Test_1(), args);//输出任务耗时Date end = new Date();float time = (float) (( end.getTime() - start.getTime() ) / 60000.0) ;System.out.println( "任务开始：" + formatter.format(start) );System.out.println( "任务结束：" + formatter.format(end) );System.out.println( "任务耗时：" + String.valueOf( time ) + " 分钟" ); System.exit(res);} }
Test_1数据
Apr 23 11:49:54 hostapd: wlan0: STA 14:7d:c5:9e:fb:84 Apr 23 11:49:52 hostapd: wlan0: STA 74:e5:0b:04:28:f2 Apr 23 11:49:50 hostapd: wlan0: STA cc:af:78:cc:d5:5d Apr 23 11:49:44 hostapd: wlan0: STA cc:af:78:cc:d5:5d Apr 23 11:49:43 hostapd: wlan0: STA 74:e5:0b:04:28:f2 Apr 23 11:49:42 hostapd: wlan0: STA 14:7d:c5:9e:fb:84
Test_2.java
/** * Hadoop网络课程模板程序* 编写者：James*/ import java.io.IOException; import java.text.DateFormat; import java.text.SimpleDateFormat; import java.util.Date;import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.*; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner;/** * 有Reducer版本*/ public class Test_2 extends Configured implements Tool { /** * 计数器* 用于计数各种异常数据*/ enum Counter {LINESKIP, //出错的行 }/** * MAP任务*/ public static class Map extends Mapper<LongWritable, Text, Text, Text> {public void map ( LongWritable key, Text value, Context context ) throws IOException, InterruptedException {String line = value.toString(); //读取源数据try{//数据处理String [] lineSplit = line.split(" ");String anum = lineSplit[0];String bnum = lineSplit[1];context.write( new Text(bnum), new Text(anum) ); //输出 }catch ( java.lang.ArrayIndexOutOfBoundsException e ){context.getCounter(Counter.LINESKIP).increment(1); //出错令计数器+1return;}}}/** * REDUCE任务*/ public static class Reduce extends Reducer<Text, Text, Text, Text> {public void reduce ( Text key, Iterable<Text> values, Context context ) throws IOException, InterruptedException{String valueString;String out = "";for ( Text value : values ){valueString = value.toString();out += valueString + "|";}context.write( key, new Text(out) );}}@Overridepublic int run(String[] args) throws Exception {Configuration conf = getConf();Job job = new Job(conf, "Test_2"); //任务名job.setJarByClass(Test_2.class); //指定Class FileInputFormat.addInputPath( job, new Path(args[0]) ); //输入路径FileOutputFormat.setOutputPath( job, new Path(args[1]) ); //输出路径 job.setMapperClass( Map.class ); //调用上面Map类作为Map任务代码job.setReducerClass ( Reduce.class ); //调用上面Reduce类作为Reduce任务代码job.setOutputFormatClass( TextOutputFormat.class );job.setOutputKeyClass( Text.class ); //指定输出的KEY的格式job.setOutputValueClass( Text.class ); //指定输出的VALUE的格式 job.waitForCompletion(true);//输出任务完成情况System.out.println( "任务名称：" + job.getJobName() );System.out.println( "任务成功：" + ( job.isSuccessful()?"是":"否" ) );System.out.println( "输入行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS").getValue() );System.out.println( "输出行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_OUTPUT_RECORDS").getValue() );System.out.println( "跳过的行：" + job.getCounters().findCounter(Counter.LINESKIP).getValue() );return job.isSuccessful() ? 0 : 1;}/** * 设置系统说明* 设置MapReduce任务*/ public static void main(String[] args) throws Exception {//判断参数个数是否正确//如果无参数运行则显示以作程序说明if ( args.length != 2 ){System.err.println("");System.err.println("Usage: Test_2 < input path > < output path > ");System.err.println("Example: hadoop jar ~/Test_2.jar hdfs://localhost:9000/home/james/Test_2 hdfs://localhost:9000/home/james/output");System.err.println("Counter:");System.err.println("\t"+"LINESKIP"+"\t"+"Lines which are too short");System.exit(-1);}//记录开始时间DateFormat formatter = new SimpleDateFormat( "yyyy-MM-dd HH:mm:ss" );Date start = new Date();//运行任务int res = ToolRunner.run(new Configuration(), new Test_2(), args);//输出任务耗时Date end = new Date();float time = (float) (( end.getTime() - start.getTime() ) / 60000.0) ;System.out.println( "任务开始：" + formatter.format(start) );System.out.println( "任务结束：" + formatter.format(end) );System.out.println( "任务耗时：" + String.valueOf( time ) + " 分钟" ); System.exit(res);} }
Test_2数据
13599999999 10086 13899999999 120 13944444444 13800138000 13722222222 13800138000 18800000000 120 13722222222 10086 18944444444 10086
Exercise_1.java
/** * Hadoop网络课程作业程序* 编写者：James*/ import java.io.IOException; import java.text.DateFormat; import java.text.SimpleDateFormat; import java.util.Date;import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.*; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner;public class Exercise_1 extends Configured implements Tool { /** * 计数器* 用于计数各种异常数据*/ enum Counter {LINESKIP, //出错的行 }/** * MAP任务*/ public static class Map extends Mapper<LongWritable, Text, NullWritable, Text> {public void map ( LongWritable key, Text value, Context context ) throws IOException, InterruptedException {String line = value.toString(); //读取源数据try{//数据处理String [] lineSplit = line.split(" ");String month = lineSplit[0];String time = lineSplit[1];String mac = lineSplit[6];/** 需要注意的部分 **/ String name = context.getConfiguration().get("name");Text out = new Text(name + ' ' + month + ' ' + time + ' ' + mac);/** 需要注意的部分 **/ context.write( NullWritable.get(), out); //输出 }catch ( java.lang.ArrayIndexOutOfBoundsException e ){context.getCounter(Counter.LINESKIP).increment(1); //出错令计数器+1return;}}}@Overridepublic int run(String[] args) throws Exception {Configuration conf = getConf();/** 需要注意的部分 **/ conf.set("name", args[2]);/** 需要注意的部分 **/ Job job = new Job(conf, "Exercise_1"); //任务名job.setJarByClass(Exercise_1.class); //指定Class FileInputFormat.addInputPath( job, new Path(args[0]) ); //输入路径FileOutputFormat.setOutputPath( job, new Path(args[1]) ); //输出路径 job.setMapperClass( Map.class ); //调用上面Map类作为Map任务代码job.setOutputFormatClass( TextOutputFormat.class );job.setOutputKeyClass( NullWritable.class ); //指定输出的KEY的格式job.setOutputValueClass( Text.class ); //指定输出的VALUE的格式 job.waitForCompletion(true);//输出任务完成情况System.out.println( "任务名称：" + job.getJobName() );System.out.println( "任务成功：" + ( job.isSuccessful()?"是":"否" ) );System.out.println( "输入行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS").getValue() );System.out.println( "输出行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_OUTPUT_RECORDS").getValue() );System.out.println( "跳过的行：" + job.getCounters().findCounter(Counter.LINESKIP).getValue() );return job.isSuccessful() ? 0 : 1;}/** * 设置系统说明* 设置MapReduce任务*/ public static void main(String[] args) throws Exception {//判断参数个数是否正确//如果无参数运行则显示以作程序说明if ( args.length != 3 ){System.err.println("");System.err.println("Usage: Test_1 < input path > < output path > < name >");System.err.println("Example: hadoop jar ~/Test_1.jar hdfs://localhost:9000/home/james/Test_1
hdfs://localhost:9000/home/james/output hadoop");System.err.println("Counter:");System.err.println("\t"+"LINESKIP"+"\t"+"Lines which are too short");System.exit(-1);}//记录开始时间DateFormat formatter = new SimpleDateFormat( "yyyy-MM-dd HH:mm:ss" );Date start = new Date();//运行任务int res = ToolRunner.run(new Configuration(), new Exercise_1(), args);//输出任务耗时Date end = new Date();float time = (float) (( end.getTime() - start.getTime() ) / 60000.0) ;System.out.println( "任务开始：" + formatter.format(start) );System.out.println( "任务结束：" + formatter.format(end) );System.out.println( "任务耗时：" + String.valueOf( time ) + " 分钟" ); System.exit(res);} }
result_1
hadoop Apr 23 14:7d:c5:9e:fb:84 hadoop Apr 23 74:e5:0b:04:28:f2 hadoop Apr 23 cc:af:78:cc:d5:5d hadoop Apr 23 cc:af:78:cc:d5:5d hadoop Apr 23 74:e5:0b:04:28:f2 hadoop Apr 23 14:7d:c5:9e:fb:84
Exercise_2.java
/** * Hadoop网络课程作业程序* 编写者：James*/ import java.io.IOException; import java.text.DateFormat; import java.text.SimpleDateFormat; import java.util.Date;import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.*; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner;public class Exercise_2 extends Configured implements Tool { /** * 计数器* 用于计数各种异常数据*/ enum Counter {LINESKIP, //出错的行 }/** * MAP任务*/ public static class Map extends Mapper<LongWritable, Text, NullWritable, Text> {/** 需要注意的部分 **/private String name;public void setup ( Context context ){this.name = context.getConfiguration().get("name"); //读取名字 }/** 需要注意的部分 **/public void map ( LongWritable key, Text value, Context context ) throws IOException, InterruptedException {String line = value.toString(); //读取源数据try{//数据处理String [] lineSplit = line.split(" ");String month = lineSplit[0];String time = lineSplit[1];String mac = lineSplit[6];/** 需要注意的部分 **/ Text out = new Text(this.name + ' ' + month + ' ' + time + ' ' + mac);/** 需要注意的部分 **/ context.write( NullWritable.get(), out); //输出 }catch ( java.lang.ArrayIndexOutOfBoundsException e ){context.getCounter(Counter.LINESKIP).increment(1); //出错令计数器+1return;}}}@Overridepublic int run(String[] args) throws Exception {Configuration conf = getConf();/** 需要注意的部分 **/ conf.set("name", args[2]);/** 需要注意的部分 **/ Job job = new Job(conf, "Exercise_2"); //任务名job.setJarByClass(Exercise_2.class); //指定Class FileInputFormat.addInputPath( job, new Path(args[0]) ); //输入路径FileOutputFormat.setOutputPath( job, new Path(args[1]) ); //输出路径 job.setMapperClass( Map.class ); //调用上面Map类作为Map任务代码job.setOutputFormatClass( TextOutputFormat.class );job.setOutputKeyClass( NullWritable.class ); //指定输出的KEY的格式job.setOutputValueClass( Text.class ); //指定输出的VALUE的格式 job.waitForCompletion(true);//输出任务完成情况System.out.println( "任务名称：" + job.getJobName() );System.out.println( "任务成功：" + ( job.isSuccessful()?"是":"否" ) );System.out.println( "输入行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS").getValue() );System.out.println( "输出行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_OUTPUT_RECORDS").getValue() );System.out.println( "跳过的行：" + job.getCounters().findCounter(Counter.LINESKIP).getValue() );return job.isSuccessful() ? 0 : 1;}/** * 设置系统说明* 设置MapReduce任务*/ public static void main(String[] args) throws Exception {//判断参数个数是否正确//如果无参数运行则显示以作程序说明if ( args.length != 3 ){System.err.println("");System.err.println("Usage: Test_1 < input path > < output path > < name >");System.err.println("Example: hadoop jar ~/Test_1.jar hdfs://localhost:9000/home/james/Test_1
hdfs://localhost:9000/home/james/output hadoop");System.err.println("Counter:");System.err.println("\t"+"LINESKIP"+"\t"+"Lines which are too short");System.exit(-1);}//记录开始时间DateFormat formatter = new SimpleDateFormat( "yyyy-MM-dd HH:mm:ss" );Date start = new Date();//运行任务int res = ToolRunner.run(new Configuration(), new Exercise_2(), args);//输出任务耗时Date end = new Date();float time = (float) (( end.getTime() - start.getTime() ) / 60000.0) ;System.out.println( "任务开始：" + formatter.format(start) );System.out.println( "任务结束：" + formatter.format(end) );System.out.println( "任务耗时：" + String.valueOf( time ) + " 分钟" ); System.exit(res);} }
改写test_2
/** * Hadoop网络课程模板程序* 编写者：James*/ import java.io.IOException; import java.text.DateFormat; import java.text.SimpleDateFormat; import java.util.Date;import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.*; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner;/** * 有Reducer版本*/ public class Test_2 extends Configured implements Tool { /** * 计数器* 用于计数各种异常数据*/ enum Counter {LINESKIP, //出错的行 }/** * MAP任务*/ public static class Map extends Mapper<LongWritable, Text, Text, Text> {public void map ( LongWritable key, Text value, Context context ) throws IOException, InterruptedException {String line = value.toString(); //读取源数据try{//数据处理String [] lineSplit = line.split(" ");String anum = lineSplit[0];String bnum = lineSplit[1];context.write( new Text(bnum), new Text(anum) ); //输出 }catch ( java.lang.ArrayIndexOutOfBoundsException e ){context.getCounter(Counter.LINESKIP).increment(1); //出错令计数器+1return;}}}/** * REDUCE任务*/ public static class Reduce extends Reducer<Text, Text, Text, Text> {public void reduce ( Text key, Iterable<Text> values, Context context ) throws IOException, InterruptedException{String valueString;String out = "";String name = context.getConfiguration().get("name");for ( Text value : values ){valueString = value.toString();out += valueString + "|";}context.write( key, new Text(out) + "|" + name );}}@Overridepublic int run(String[] args) throws Exception {Configuration conf = getConf();conf.set("name", args[2]);Job job = new Job(conf, "Test_2"); //任务名job.setJarByClass(Test_2.class); //指定Class FileInputFormat.addInputPath( job, new Path(args[0]) ); //输入路径FileOutputFormat.setOutputPath( job, new Path(args[1]) ); //输出路径 job.setMapperClass( Map.class ); //调用上面Map类作为Map任务代码job.setReducerClass ( Reduce.class ); //调用上面Reduce类作为Reduce任务代码job.setOutputFormatClass( TextOutputFormat.class );job.setOutputKeyClass( Text.class ); //指定输出的KEY的格式job.setOutputValueClass( Text.class ); //指定输出的VALUE的格式 job.waitForCompletion(true);//输出任务完成情况System.out.println( "任务名称：" + job.getJobName() );System.out.println( "任务成功：" + ( job.isSuccessful()?"是":"否" ) );System.out.println( "输入行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS").getValue() );System.out.println( "输出行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_OUTPUT_RECORDS").getValue() );System.out.println( "跳过的行：" + job.getCounters().findCounter(Counter.LINESKIP).getValue() );return job.isSuccessful() ? 0 : 1;}/** * 设置系统说明* 设置MapReduce任务*/ public static void main(String[] args) throws Exception {//判断参数个数是否正确//如果无参数运行则显示以作程序说明if ( args.length != 3 ){System.err.println("");System.err.println("Usage: Test_2 < input path > < output path > ");System.err.println("Example: hadoop jar ~/Test_2.jar hdfs://localhost:9000/home/james/Test_2 hdfs://localhost:9000/home/james/output hadoop");System.err.println("Counter:");System.err.println("\t"+"LINESKIP"+"\t"+"Lines which are too short");System.exit(-1);}//记录开始时间DateFormat formatter = new SimpleDateFormat( "yyyy-MM-dd HH:mm:ss" );Date start = new Date();//运行任务int res = ToolRunner.run(new Configuration(), new Test_2(), args);//输出任务耗时Date end = new Date();float time = (float) (( end.getTime() - start.getTime() ) / 60000.0) ;System.out.println( "任务开始：" + formatter.format(start) );System.out.println( "任务结束：" + formatter.format(end) );System.out.println( "任务耗时：" + String.valueOf( time ) + " 分钟" ); System.exit(res);} }
result_2
10086 13599999999|13722222222|18944444444|hadoop 120 18800000000|hadoop 13800138000 13944444444|13722222222|hadoop
