Hadoop MapReduce的一些相关代码Code

MapReduce是一种分布式计算模型（distributed programming model），由Google于2004年左右提出，主要用于搜索领域，解决海量数据的计算问题。

MapReduce由两个阶段组成：即Map阶段和Reduce阶段，用户需要实现map()函数和reduce()函数，用于实现分布式计算。

1、WordCountApp.java

package cmd;import java.net.URI;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;public class WordCountApp extends Configured implements Tool{static String INPUT_PATH = "";static String OUT_PATH = "";@Overridepublic int run(String[] arg0) throws Exception {INPUT_PATH = arg0[0];OUT_PATH = arg0[1];Configuration conf = new Configuration();final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);final Path outPath = new Path(OUT_PATH);if(fileSystem.exists(outPath)){fileSystem.delete(outPath, true);}final Job job = new Job(conf , WordCountApp.class.getSimpleName());//打包运行必须执行的秘密方法job.setJarByClass(WordCountApp.class);//1.1指定读取的文件位于哪里FileInputFormat.setInputPaths(job, INPUT_PATH);//指定如何对输入文件进行格式化，把输入文件每一行解析成键值对//job.setInputFormatClass(TextInputFormat.class);//1.2 指定自定义的map类job.setMapperClass(MyMapper.class);//map输出的<k,v>类型。如果<k3,v3>的类型与<k2,v2>类型一致，则可以省略//job.setMapOutputKeyClass(Text.class);//job.setMapOutputValueClass(LongWritable.class);//1.3 分区//job.setPartitionerClass(HashPartitioner.class);//有一个reduce任务运行//job.setNumReduceTasks(1);//1.4 TODO 排序、分组//1.5 TODO 规约//2.2 指定自定义reduce类job.setReducerClass(MyReducer.class);//指定reduce的输出类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(LongWritable.class);//2.3 指定写出到哪里FileOutputFormat.setOutputPath(job, outPath);//指定输出文件的格式化类//job.setOutputFormatClass(TextOutputFormat.class);//把job提交给JobTracker运行job.waitForCompletion(true);return 0;}public static void main(String[] args) throws Exception {ToolRunner.run(new WordCountApp(), args);}/** * KEYIN即k1表示行的偏移量 * VALUEIN即v1表示行文本内容 * KEYOUT即k2表示行中出现的单词 * VALUEOUT即v2表示行中出现的单词的次数，固定值1 */static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{protected void map(LongWritable k1, Text v1, Context context) throws java.io.IOException ,InterruptedException {final String[] splited = v1.toString().split("\t");for (String word : splited) {context.write(new Text(word), new LongWritable(1));}};}/** * KEYIN即k2表示行中出现的单词 * VALUEIN即v2表示行中出现的单词的次数 * KEYOUT即k3表示文本中出现的不同单词 * VALUEOUT即v3表示文本中出现的不同单词的总次数 * */static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{protected void reduce(Text k2, java.lang.Iterable<LongWritable> v2s, Context ctx) throws java.io.IOException ,InterruptedException {long times = 0L;for (LongWritable count : v2s) {times += count.get();}ctx.write(k2, new LongWritable(times));};}}

2、GroupApp.java

package group;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import java.net.URI;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.RawComparator;import org.apache.hadoop.io.Text;import org.apache.hadoop.io.WritableComparable;import org.apache.hadoop.io.WritableComparator;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;public class GroupApp {static final String INPUT_PATH = "hdfs://cloud4:9000/input";static final String OUT_PATH = "hdfs://cloud4:9000/out";public static void main(String[] args) throws Exception{final Configuration configuration = new Configuration();final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), configuration);if(fileSystem.exists(new Path(OUT_PATH))){fileSystem.delete(new Path(OUT_PATH), true);}final Job job = new Job(configuration, GroupApp.class.getSimpleName());//1.1 指定输入文件路径FileInputFormat.setInputPaths(job, INPUT_PATH);//指定哪个类用来格式化输入文件job.setInputFormatClass(TextInputFormat.class);//1.2指定自定义的Mapper类job.setMapperClass(MyMapper.class);//指定输出<k2,v2>的类型job.setMapOutputKeyClass(NewK2.class);job.setMapOutputValueClass(LongWritable.class);//1.3 指定分区类job.setPartitionerClass(HashPartitioner.class);job.setNumReduceTasks(1);//1.4 TODO 排序、分区job.setGroupingComparatorClass(MyGroupingComparator.class);//1.5  TODO （可选）合并//2.2 指定自定义的reduce类job.setReducerClass(MyReducer.class);//指定输出<k3,v3>的类型job.setOutputKeyClass(LongWritable.class);job.setOutputValueClass(LongWritable.class);//2.3 指定输出到哪里FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));//设定输出文件的格式化类job.setOutputFormatClass(TextOutputFormat.class);//把代码提交给JobTracker执行job.waitForCompletion(true);}static class MyMapper extends Mapper<LongWritable, Text, NewK2, LongWritable>{protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,NewK2,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {final String[] splited = value.toString().split("\t");final NewK2 k2 = new NewK2(Long.parseLong(splited[0]), Long.parseLong(splited[1]));final LongWritable v2 = new LongWritable(Long.parseLong(splited[1]));context.write(k2, v2);};}static class MyReducer extends Reducer<NewK2, LongWritable, LongWritable, LongWritable>{protected void reduce(NewK2 k2, java.lang.Iterable<LongWritable> v2s, org.apache.hadoop.mapreduce.Reducer<NewK2,LongWritable,LongWritable,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {long min = Long.MAX_VALUE;for (LongWritable v2 : v2s) {if(v2.get()<min){min = v2.get();}}context.write(new LongWritable(k2.first), new LongWritable(min));};}/** * 问：为什么实现该类？ * 答：因为原来的v2不能参与排序，把原来的k2和v2封装到一个类中，作为新的k2 * */static class  NewK2 implements WritableComparable<NewK2>{Long first;Long second;public NewK2(){}public NewK2(long first, long second){this.first = first;this.second = second;}@Overridepublic void readFields(DataInput in) throws IOException {this.first = in.readLong();this.second = in.readLong();}@Overridepublic void write(DataOutput out) throws IOException {out.writeLong(first);out.writeLong(second);}/** * 当k2进行排序时，会调用该方法. * 当第一列不同时，升序；当第一列相同时，第二列升序 */@Overridepublic int compareTo(NewK2 o) {final long minus = this.first - o.first;if(minus !=0){return (int)minus;}return (int)(this.second - o.second);}@Overridepublic int hashCode() {return this.first.hashCode()+this.second.hashCode();}@Overridepublic boolean equals(Object obj) {if(!(obj instanceof NewK2)){return false;}NewK2 oK2 = (NewK2)obj;return (this.first==oK2.first)&&(this.second==oK2.second);}}/** * 问：为什么自定义该类？ * 答：业务要求分组是按照第一列分组，但是NewK2的比较规则决定了不能按照第一列分。只能自定义分组比较器。 */static class MyGroupingComparator implements RawComparator<NewK2>{@Overridepublic int compare(NewK2 o1, NewK2 o2) {return (int)(o1.first - o2.first);}/** * @param arg0 表示第一个参与比较的字节数组 * @param arg1 表示第一个参与比较的字节数组的起始位置 * @param arg2 表示第一个参与比较的字节数组的偏移量 *  * @param arg3 表示第二个参与比较的字节数组 * @param arg4 表示第二个参与比较的字节数组的起始位置 * @param arg5 表示第二个参与比较的字节数组的偏移量 */@Overridepublic int compare(byte[] arg0, int arg1, int arg2, byte[] arg3,int arg4, int arg5) {return WritableComparator.compareBytes(arg0, arg1, 8, arg3, arg4, 8);}}}

3、data

#当第一列相同时，求出第二列的最小值333231222111-------------------312111

4、KpiApp.java

package mapreduce;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.io.Writable;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;public class KpiApp {static final String INPUT_PATH = "hdfs://cloud4:9000/wlan";static final String OUT_PATH = "hdfs://cloud4:9000/out";public static void main(String[] args) throws Exception{final Job job = new Job(new Configuration(), KpiApp.class.getSimpleName());//1.1 指定输入文件路径FileInputFormat.setInputPaths(job, INPUT_PATH);//指定哪个类用来格式化输入文件job.setInputFormatClass(TextInputFormat.class);//1.2指定自定义的Mapper类job.setMapperClass(MyMapper.class);//指定输出<k2,v2>的类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(KpiWritable.class);//1.3 指定分区类job.setPartitionerClass(HashPartitioner.class);job.setNumReduceTasks(1);//1.4 TODO 排序、分区//1.5  TODO （可选）合并//2.2 指定自定义的reduce类job.setReducerClass(MyReducer.class);//指定输出<k3,v3>的类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(KpiWritable.class);//2.3 指定输出到哪里FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));//设定输出文件的格式化类job.setOutputFormatClass(TextOutputFormat.class);//把代码提交给JobTracker执行job.waitForCompletion(true);}static class MyMapper extends Mapper<LongWritable, Text, Text, KpiWritable>{protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {final String[] splited = value.toString().split("\t");final String msisdn = splited[1];final Text k2 = new Text(msisdn);final KpiWritable v2 = new KpiWritable(splited[6],splited[7],splited[8],splited[9]);context.write(k2, v2);};}static class MyReducer extends Reducer<Text, KpiWritable, Text, KpiWritable>{/** * @paramk2表示整个文件中不同的手机号码 * @paramv2s表示该手机号在不同时段的流量的集合 */protected void reduce(Text k2, java.lang.Iterable<KpiWritable> v2s, org.apache.hadoop.mapreduce.Reducer<Text,KpiWritable,Text,KpiWritable>.Context context) throws IOException ,InterruptedException {long upPackNum = 0L;long downPackNum = 0L;long upPayLoad = 0L;long downPayLoad = 0L;for (KpiWritable kpiWritable : v2s) {upPackNum += kpiWritable.upPackNum;downPackNum += kpiWritable.downPackNum;upPayLoad += kpiWritable.upPayLoad;downPayLoad += kpiWritable.downPayLoad;}final KpiWritable v3 = new KpiWritable(upPackNum+"", downPackNum+"", upPayLoad+"", downPayLoad+"");context.write(k2, v3);};}}class KpiWritable implements Writable{long upPackNum;long downPackNum;long upPayLoad;long downPayLoad;public KpiWritable(){}public KpiWritable(String upPackNum, String downPackNum, String upPayLoad, String downPayLoad){this.upPackNum = Long.parseLong(upPackNum);this.downPackNum = Long.parseLong(downPackNum);this.upPayLoad = Long.parseLong(upPayLoad);this.downPayLoad = Long.parseLong(downPayLoad);}@Overridepublic void readFields(DataInput in) throws IOException {this.upPackNum = in.readLong();this.downPackNum = in.readLong();this.upPayLoad = in.readLong();this.downPayLoad = in.readLong();}@Overridepublic void write(DataOutput out) throws IOException {out.writeLong(upPackNum);out.writeLong(downPackNum);out.writeLong(upPayLoad);out.writeLong(downPayLoad);}@Overridepublic String toString() {return upPackNum + "\t" + downPackNum + "\t" + upPayLoad + "\t" + downPayLoad;}}

5、SortApp.java

package sort;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import java.net.URI;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.io.WritableComparable;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;public class SortApp {static final String INPUT_PATH = "hdfs://cloud4:9000/input";static final String OUT_PATH = "hdfs://cloud4:9000/out";public static void main(String[] args) throws Exception{final Configuration configuration = new Configuration();final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), configuration);if(fileSystem.exists(new Path(OUT_PATH))){fileSystem.delete(new Path(OUT_PATH), true);}final Job job = new Job(configuration, SortApp.class.getSimpleName());//1.1 指定输入文件路径FileInputFormat.setInputPaths(job, INPUT_PATH);//指定哪个类用来格式化输入文件job.setInputFormatClass(TextInputFormat.class);//1.2指定自定义的Mapper类job.setMapperClass(MyMapper.class);//指定输出<k2,v2>的类型job.setMapOutputKeyClass(NewK2.class);job.setMapOutputValueClass(LongWritable.class);//1.3 指定分区类job.setPartitionerClass(HashPartitioner.class);job.setNumReduceTasks(1);//1.4 TODO 排序、分区//1.5  TODO （可选）合并//2.2 指定自定义的reduce类job.setReducerClass(MyReducer.class);//指定输出<k3,v3>的类型job.setOutputKeyClass(LongWritable.class);job.setOutputValueClass(LongWritable.class);//2.3 指定输出到哪里FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));//设定输出文件的格式化类job.setOutputFormatClass(TextOutputFormat.class);//把代码提交给JobTracker执行job.waitForCompletion(true);}static class MyMapper extends Mapper<LongWritable, Text, NewK2, LongWritable>{protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,NewK2,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {final String[] splited = value.toString().split("\t");final NewK2 k2 = new NewK2(Long.parseLong(splited[0]), Long.parseLong(splited[1]));final LongWritable v2 = new LongWritable(Long.parseLong(splited[1]));context.write(k2, v2);};}static class MyReducer extends Reducer<NewK2, LongWritable, LongWritable, LongWritable>{protected void reduce(NewK2 k2, java.lang.Iterable<LongWritable> v2s, org.apache.hadoop.mapreduce.Reducer<NewK2,LongWritable,LongWritable,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {context.write(new LongWritable(k2.first), new LongWritable(k2.second));};}/** * 问：为什么实现该类？ * 答：因为原来的v2不能参与排序，把原来的k2和v2封装到一个类中，作为新的k2 * */static class  NewK2 implements WritableComparable<NewK2>{Long first;Long second;public NewK2(){}public NewK2(long first, long second){this.first = first;this.second = second;}@Overridepublic void readFields(DataInput in) throws IOException {this.first = in.readLong();this.second = in.readLong();}@Overridepublic void write(DataOutput out) throws IOException {out.writeLong(first);out.writeLong(second);}/** * 当k2进行排序时，会调用该方法. * 当第一列不同时，升序；当第一列相同时，第二列升序 */@Overridepublic int compareTo(NewK2 o) {final long minus = this.first - o.first;if(minus !=0){return (int)minus;}return (int)(this.second - o.second);}@Overridepublic int hashCode() {return this.first.hashCode()+this.second.hashCode();}@Overridepublic boolean equals(Object obj) {if(!(obj instanceof NewK2)){return false;}NewK2 oK2 = (NewK2)obj;return (this.first==oK2.first)&&(this.second==oK2.second);}}}

6、TopKApp.java

package suanfa;import java.net.URI;import mapreduce.WordCountApp;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.FileSplit;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;/** * 作业：求最大的100个值 */public class TopKApp {static final String INPUT_PATH = "hdfs://cloud4:9000/input";static final String OUT_PATH = "hdfs://cloud4:9000/out";public static void main(String[] args) throws Exception {Configuration conf = new Configuration();final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);final Path outPath = new Path(OUT_PATH);if(fileSystem.exists(outPath)){fileSystem.delete(outPath, true);}final Job job = new Job(conf , WordCountApp.class.getSimpleName());FileInputFormat.setInputPaths(job, INPUT_PATH);job.setMapperClass(MyMapper.class);job.setReducerClass(MyReducer.class);job.setOutputKeyClass(LongWritable.class);job.setOutputValueClass(NullWritable.class);FileOutputFormat.setOutputPath(job, outPath);job.waitForCompletion(true);}static class MyMapper extends Mapper<LongWritable, Text, LongWritable, NullWritable>{long max = Long.MIN_VALUE;protected void map(LongWritable k1, Text v1, Context context) throws java.io.IOException ,InterruptedException {final long temp = Long.parseLong(v1.toString());if(temp>max){max = temp;}};protected void cleanup(org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,LongWritable, NullWritable>.Context context) throws java.io.IOException ,InterruptedException {context.write(new LongWritable(max), NullWritable.get());};}static class MyReducer extends Reducer<LongWritable, NullWritable, LongWritable, NullWritable>{long max = Long.MIN_VALUE;protected void reduce(LongWritable k2, java.lang.Iterable<NullWritable> arg1, org.apache.hadoop.mapreduce.Reducer<LongWritable,NullWritable,LongWritable,NullWritable>.Context arg2) throws java.io.IOException ,InterruptedException {final long temp = k2.get();if(temp>max){max = temp;}};protected void cleanup(org.apache.hadoop.mapreduce.Reducer<LongWritable,NullWritable,LongWritable,NullWritable>.Context context) throws java.io.IOException ,InterruptedException {context.write(new LongWritable(max), NullWritable.get());};}}