hadoop常用算法简单实例

实例一、对以下数据进行排序，根据收入减去支出得到最后结余从大到小排序，数据如下：

SumStep运行之后结果如下：

SortStep运行之后结果为上图根据结余从大到小排序。

代码如下：

public class InfoBean implements WritableComparable<InfoBean>{private String account;private double income;private double expenses;private double surplus;public void set(String account, double income, double expenses){this.account = account;this.income = income;this.expenses = expenses;this.surplus = income - expenses;}@Overridepublic String toString() {return this.income + "\t" + this.expenses + "\t" + this.surplus;}/** * serialize */public void write(DataOutput out) throws IOException {out.writeUTF(account);out.writeDouble(income);out.writeDouble(expenses);out.writeDouble(surplus);}/** * deserialize */public void readFields(DataInput in) throws IOException {this.account = in.readUTF();this.income = in.readDouble();this.expenses = in.readDouble();this.surplus = in.readDouble();}public int compareTo(InfoBean o) {if(this.income == o.getIncome()){return this.expenses > o.getExpenses() ? 1 : -1; } else {return this.income > o.getIncome() ? -1 : 1;}}public String getAccount() {return account;}public void setAccount(String account) {this.account = account;}public double getIncome() {return income;}public void setIncome(double income) {this.income = income;}public double getExpenses() {return expenses;}public void setExpenses(double expenses) {this.expenses = expenses;}public double getSurplus() {return surplus;}public void setSurplus(double surplus) {this.surplus = surplus;}}

public class SumStep {public static void main(String[] args) throws Exception {Configuration conf = new Configuration();Job job = Job.getInstance(conf);job.setJarByClass(SumStep.class);job.setMapperClass(SumMapper.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(InfoBean.class);FileInputFormat.setInputPaths(job, new Path(args[0]));job.setReducerClass(SumReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(InfoBean.class);FileOutputFormat.setOutputPath(job, new Path(args[1]));job.waitForCompletion(true);}public static class SumMapper extends Mapper<LongWritable, Text, Text, InfoBean>{private InfoBean bean = new InfoBean();private Text k = new Text();@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {// split String line = value.toString();String[] fields = line.split("\t");// get useful fieldString account = fields[0];double income = Double.parseDouble(fields[1]);double expenses = Double.parseDouble(fields[2]);k.set(account);bean.set(account, income, expenses);context.write(k, bean);}}public static class SumReducer extends Reducer<Text, InfoBean, Text, InfoBean>{private InfoBean bean = new InfoBean();@Overrideprotected void reduce(Text key, Iterable<InfoBean> v2s, Context context)throws IOException, InterruptedException {double in_sum = 0;double out_sum = 0;for(InfoBean bean : v2s){in_sum += bean.getIncome();out_sum += bean.getExpenses();}bean.set("", in_sum, out_sum);context.write(key, bean);}}}

此处的输入为SumStep的输出而不是源文件作为输入，当然也可以将两个job合并到一起执行，此处不再讨论。

public class SortStep {public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {Configuration conf = new Configuration();Job job = Job.getInstance(conf);job.setJarByClass(SortStep.class);job.setMapperClass(SortMapper.class);job.setMapOutputKeyClass(InfoBean.class);job.setMapOutputValueClass(NullWritable.class);FileInputFormat.setInputPaths(job, new Path(args[0]));job.setReducerClass(SortReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(InfoBean.class);FileOutputFormat.setOutputPath(job, new Path(args[1]));job.waitForCompletion(true);}public static class SortMapper extends Mapper<LongWritable, Text, InfoBean, NullWritable>{private InfoBean bean = new InfoBean();@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {String line = value.toString();String[] fields = line.split("\t");String account = fields[0];double income = Double.parseDouble(fields[1]);double expenses = Double.parseDouble(fields[2]);bean.set(account, income, expenses);context.write(bean, NullWritable.get());}}public static class SortReducer extends Reducer<InfoBean, NullWritable, Text, InfoBean>{private Text k = new Text();@Overrideprotected void reduce(InfoBean bean, Iterable<NullWritable> v2s, Context context)throws IOException, InterruptedException {String account = bean.getAccount();k.set(account);context.write(k, bean);}}}

实例二、倒排索引，过程如下：

Map阶段<0,"hello tom">....context.write("hello->a.txt",1);context.write("hello->a.txt",1);context.write("hello->a.txt",1);context.write("hello->a.txt",1);context.write("hello->a.txt",1);context.write("hello->b.txt",1);context.write("hello->b.txt",1);context.write("hello->b.txt",1);--------------------------------------------------------combiner阶段<"hello->a.txt",1><"hello->a.txt",1><"hello->a.txt",1><"hello->a.txt",1><"hello->a.txt",1><"hello->b.txt",1><"hello->b.txt",1><"hello->b.txt",1>context.write("hello","a.txt->5");context.write("hello","b.txt->3");--------------------------------------------------------Reducer阶段<"hello",{"a.txt->5","b.txt->3"}>context.write("hello","a.txt->5 b.txt->3");-------------------------------------------------------hello"a.txt->5 b.txt->3"tom"a.txt->2 b.txt->1"kitty"a.txt->1".......

代码如下：

public class InverseIndex {public static void main(String[] args) throws Exception {Configuration conf = new Configuration();Job job = Job.getInstance(conf);//设置jarjob.setJarByClass(InverseIndex.class);//设置Mapper相关的属性job.setMapperClass(IndexMapper.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(Text.class);FileInputFormat.setInputPaths(job, new Path(args[0]));//words.txt//设置Reducer相关属性job.setReducerClass(IndexReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);FileOutputFormat.setOutputPath(job, new Path(args[1]));job.setCombinerClass(IndexCombiner.class);//提交任务job.waitForCompletion(true);}public static class IndexMapper extends Mapper<LongWritable, Text, Text, Text>{private Text k = new Text();private Text v = new Text();@Overrideprotected void map(LongWritable key, Text value,Mapper<LongWritable, Text, Text, Text>.Context context)throws IOException, InterruptedException {String line = value.toString();String[] fields = line.split(" ");FileSplit inputSplit = (FileSplit) context.getInputSplit();Path path = inputSplit.getPath();String name = path.getName();for(String f : fields){k.set(f + "->" + name);v.set("1");context.write(k, v);}}}public static class IndexCombiner extends Reducer<Text, Text, Text, Text>{private Text k = new Text();private Text v = new Text();@Overrideprotected void reduce(Text key, Iterable<Text> values,Reducer<Text, Text, Text, Text>.Context context)throws IOException, InterruptedException {String[] fields = key.toString().split("->");long sum = 0;for(Text t : values){sum += Long.parseLong(t.toString());}k.set(fields[0]);v.set(fields[1] + "->" + sum);context.write(k, v);}}public static class IndexReducer extends Reducer<Text, Text, Text, Text>{private Text v = new Text();@Overrideprotected void reduce(Text key, Iterable<Text> values,Reducer<Text, Text, Text, Text>.Context context)throws IOException, InterruptedException {String value = "";for(Text t : values){value += t.toString() + " ";}v.set(value);context.write(key, v);}}}

实例三、使用Partitioner使相同或者相似的数据传递到相同的reduce：

数据格式如下，分别代表手机号，上行流量，下行流量：

代码如下：

public class DataBean implements Writable {private String telNo;private long upPayLoad;private long downPayLoad;private long totalPayLoad;public DataBean(){}public DataBean(String telNo, long upPayLoad, long downPayLoad) {super();this.telNo = telNo;this.upPayLoad = upPayLoad;this.downPayLoad = downPayLoad;this.totalPayLoad = upPayLoad + downPayLoad;}@Overridepublic String toString() {return this.upPayLoad + "\t" + this.downPayLoad + "\t" + this.totalPayLoad;}/** * 序列化 * 注意：1.类型 2.顺序 */public void write(DataOutput out) throws IOException {out.writeUTF(telNo);out.writeLong(upPayLoad);out.writeLong(downPayLoad);out.writeLong(totalPayLoad);}/** * 反序列化 */public void readFields(DataInput in) throws IOException {this.telNo = in.readUTF();this.upPayLoad = in.readLong();this.downPayLoad = in.readLong();this.totalPayLoad = in.readLong();}public String getTelNo() {return telNo;}public void setTelNo(String telNo) {this.telNo = telNo;}public long getUpPayLoad() {return upPayLoad;}public void setUpPayLoad(long upPayLoad) {this.upPayLoad = upPayLoad;}public long getDownPayLoad() {return downPayLoad;}public void setDownPayLoad(long downPayLoad) {this.downPayLoad = downPayLoad;}public long getTotalPayLoad() {return totalPayLoad;}public void setTotalPayLoad(long totalPayLoad) {this.totalPayLoad = totalPayLoad;}}

public class DataCount {public static void main(String[] args) throws Exception {Configuration conf = new Configuration();Job job = Job.getInstance(conf);job.setJarByClass(DataCount.class);job.setMapperClass(DCMapper.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(DataBean.class);FileInputFormat.setInputPaths(job, new Path(args[0]));job.setReducerClass(DCReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(DataBean.class);FileOutputFormat.setOutputPath(job, new Path(args[1]));//设置reduce默认的Partitionerjob.setPartitionerClass(ServiceProviderPartitioner.class);//此处需要设置reduce的数量job.setNumReduceTasks(Integer.parseInt(args[2]));job.waitForCompletion(true);}public static class DCMapper extends Mapper<LongWritable, Text, Text, DataBean>{@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {//接收数据String line = value.toString();//分割数据String[] fields = line.split("\t");//获取有效字段，封装到对象里面//手机号String telNo = fields[0];//上行流量long up = Long.parseLong(fields[1]);//下行流量long down = Long.parseLong(fields[2]);//封装数据，new DataBeanDataBean bean = new DataBean(telNo, up, down);//输出context.write(new Text(telNo), bean);}}public static class DCReducer extends Reducer<Text, DataBean, Text, DataBean>{@Overrideprotected void reduce(Text key, Iterable<DataBean> v2s, Context context)throws IOException, InterruptedException {//定义计数器long up_sum = 0;long down_sum = 0;//迭代v2s，进行求和for(DataBean bean : v2s){up_sum += bean.getUpPayLoad();down_sum += bean.getDownPayLoad();}//封装数据DataBean bean = new DataBean("", up_sum, down_sum);//输出context.write(key, bean);}}public static class ServiceProviderPartitioner extends Partitioner<Text, DataBean>{private static Map<String, Integer> providerMap = new HashMap<String, Integer>(); static {providerMap.put("139", 1);providerMap.put("138", 2);providerMap.put("159", 3);}@Overridepublic int getPartition(Text key, DataBean value, int number) {String telNo = key.toString();String pcode = telNo.substring(0, 3);Integer p = providerMap.get(pcode);if(p == null){p = 0;}return p;}}}

实例四、实现以下简单算法，其中mr程序涉及到了reduce分组等概念：

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

public class GroupApp {static final String INPUT_PATH = "hdfs://xxx:9000/input";static final String OUT_PATH = "hdfs://xxx: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 = Job.getInstance(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>. 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;}public void readFields(DataInput in) throws IOException {this.first = in.readLong();this.second = in.readLong();}public 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 */public 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>{public 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 */public int compare(byte[] arg0, int arg1, int arg2, byte[] arg3,int arg4, int arg5) {return WritableComparator.compareBytes(arg0, arg1, 8, arg3, arg4, 8);}}}

实例五：利用MapReduce求解海量数据文件中的最大值，利用Mapper类中的cleanup()函数，因为cleanup()函数是在所有的map（）完成之后才执行的。

public class TopKApp {static final String INPUT_PATH = "hdfs://xxx:9000/input";static final String OUT_PATH = "hdfs://xxx: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 = Job.getInstance(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());};}}

实力六、计数器：

public class WordCountApp {static final String INPUT_PATH = "hdfs://xxx:9000/hello";static final String OUT_PATH = "hdfs://xxx: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 =  Job.getInstance(conf , WordCountApp.class.getSimpleName());//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);}/** * 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 Counter helloCounter = context.getCounter("Sensitive Words", "hello");final String line = v1.toString();if(line.contains("hello")){//记录敏感词出现在一行中helloCounter.increment(1L);}final String[] splited = line.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));};}}