大数据学习day7------hadoop04----1 流量案例 2 电影案例(统计每部电影的均分，统计每个人的均分，统计电影的评论次数，***统计每部电影评分最高的N条记录(Integer.max)，统计评论次数最多的n部电影（全局排序）) 3 line线段重叠次数案例 4.索引案例

1. 案例一： 流量案例

字段一：手机号

字段二：url

字段三：上行流量

字段四：下行流量

 1.1 统计每个人的访问量的总流量

思路：以电话这个字段聚合，即以key聚合

map阶段代码如下

public class ViewsMapper extends Mapper<LongWritable, Text, Text, LongWritable>{
    Text k = new Text();
    LongWritable v = new LongWritable();
    long sumFlow = 0L;
    @Override
    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context)
            throws IOException, InterruptedException {
        String line = value.toString();
        String[] split = line.split("\s+");
        sumFlow = Long.parseLong(split[2]) + Long.parseLong(split[3]);
        k.set(split[0]);
        v.set(sumFlow);
        context.write(k, v);
    }
}

reduce阶段代码

public class ViewsReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
    LongWritable v = new LongWritable();
    @Override
    protected void reduce(Text key, Iterable<LongWritable> iters,
            Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {
        long sum = 0L;
        for (LongWritable longWritable : iters) {
            sum += longWritable.get();
        }
        v.set(sum);
        context.write(key, v);
    }
}

JobDriver类

public class JobDriver {
    public static void main(String[] args) throws Exception {
         // 获取MR程序运行时的初始化配置
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        // 设置map和reduce类，调用类中自定义的map  reduce方法的业务逻辑
        job.setMapperClass(ViewsMapper.class);
        job.setReducerClass(ViewsReducer.class);
        // 设置map端输出key-value的类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        // 设置reduce端输出key-value的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        // 处理的文件的路径
        FileInputFormat.setInputPaths(job, new Path("E:/wc/flow.txt"));
        // 结果输出路径
        FileOutputFormat.setOutputPath(job, new Path("E:/wc/flow/"));
        // 提交任务，参数   等待执行
        job.waitForCompletion(true);
    }
}

此处要注意的是：设置的key-value的类型要与map和reduce阶段的一致

1.2 统计每个url的总流量

思路同上，只是以url聚合，代码类似

2. 电影案例

movie：电影id

rate：用户评分

timeStamp:评分时间

uid：用户id

 此数据是json数据，处理数据的时候尽量转换成java对象，便于操作

 2.1 统计每部电影的均分

 此处map和reduce部分的代码可以合并到一块去，如下

public class MovieAvgRate {
    static Text k = new Text();
    static DoubleWritable v = new DoubleWritable();
    // mapper部分
    static class MovieMapper extends Mapper<LongWritable, Text, Text, DoubleWritable>{
        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, DoubleWritable>.Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            MovieBean m = JSON.parseObject(line, MovieBean.class);
            String movie = m.getMovie();
            double rate = m.getRate();
            k.set(movie);
            v.set(rate);
            context.write(k, v);
        }
    }
    // Reducer部分
    static class MovieReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable>{
        
        @Override
        protected void reduce(Text key, Iterable<DoubleWritable> iters,
                Reducer<Text, DoubleWritable, Text, DoubleWritable>.Context context)
                throws IOException, InterruptedException {
            double sumRate = 0;
            int count = 0;
            for (DoubleWritable doubleWritable : iters) {
                double rate = doubleWritable.get();
                sumRate += rate;
                count++;
            }
            double avg = sumRate/count;
            v.set(avg);
            context.write(key, v);
        }
    }
    // JOB驱动
    public static void main(String[] args) throws Exception {
         // 获取MR程序运行时的初始化配置
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        // 设置map和reduce类，调用类中自定义的map  reduce方法的业务逻辑
        job.setMapperClass(MovieMapper.class);
        job.setReducerClass(MovieReducer.class);
        // 设置map端输出key-value的类型，当map和reduce的输出值类型一致时，可以省略map处值类型的设置
//        job.setMapOutputKeyClass(Text.class);
//        job.setMapOutputValueClass(LongWritable.class);
        // 设置reduce端输出key-value的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);
        // 处理的文件的路径
        FileInputFormat.setInputPaths(job, new Path("E:/javafile/movie.txt"));
        // 结果输出路径
        FileOutputFormat.setOutputPath(job, new Path("E:/wc/movie/res2"));
        // 提交任务，参数   等待执行
        job.waitForCompletion(true);
    }
}

注意：

 

 2.2  统计每个人的均分

 代码和上面每部电影均分相似，只是以uid为key进行聚合

 2.3 统计电影的评论次数

 以movie为key进行聚合，评论的次数直接用1来代替

 代码如下

public class MovieCommentCount {
    static Text k = new Text();
    static IntWritable v = new IntWritable();
    static class ComentMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            MovieBean mb = JSON.parseObject(line, MovieBean.class);
            String movie = mb.getMovie();
            k.set(movie);
            v.set(1);
            context.write(k, v);
        }
    }
    static class ComentReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
        @Override
        protected void reduce(Text key, Iterable<IntWritable> iters,
                Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            int count = 0;
            for (IntWritable intWritable : iters) {
                int i = intWritable.get();
                count += i;
            }
            k.set(key);
            v.set(count);
            context.write(k, v);
        }
    }
    public static void main(String[] args) throws Exception, IOException {
         // 获取MR程序运行时的初始化配置
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        // 设置map和reduce类，调用类中自定义的map  reduce方法的业务逻辑
        job.setMapperClass(ComentMapper.class);
        job.setReducerClass(ComentReducer.class);
        // 设置map端输出key-value的类型，当map和reduce的输出值类型一致时，可以省略map处值类型的设置
//        job.setMapOutputKeyClass(Text.class);
//        job.setMapOutputValueClass(LongWritable.class);
        // 设置reduce端输出key-value的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        // 处理的文件的路径
        FileInputFormat.setInputPaths(job, new Path("E:/javafile/movie.txt"));
        // 结果输出路径
        FileOutputFormat.setOutputPath(job, new Path("E:/wc/movie/res3"));
        // 提交任务，参数   等待执行
        job.waitForCompletion(true);
    }
}

 2.4 统计每部电影评分最高的n条记录

 以movie为key进行聚合，最后在reducer部分输出key为MovieBean  值为NullWritable

 代码

（1）MovieBean

public class MovieBean implements Writable{
    private String movie;
    private double rate;
    private String timeStamp;
    private String uid;
    public String getMovie() {
        return movie;
    }
    public void setMovie(String movie) {
        this.movie = movie;
    }
    public double getRate() {
        return rate;
    }
    public void setRate(double rate) {
        this.rate = rate;
    }
    public String getTimeStamp() {
        return timeStamp;
    }
    public void setTimeStamp(String timeStamp) {
        this.timeStamp = timeStamp;
    }
    public String getUid() {
        return uid;
    }
    public void setUid(String uid) {
        this.uid = uid;
    }
    @Override
    public String toString() {
        return "MovieBean [movie=" + movie + ", rate=" + rate + ", timeStamp=" + timeStamp + ", uid=" + uid + "]";
    }
    // 读  反序列化
    @Override
    public void readFields(DataInput din) throws IOException {
        this.movie = din.readUTF();
        this.rate = din.readDouble();
        this.timeStamp = din.readUTF();
        this.uid = din.readUTF();
        
    }
    // 写 序列化
    @Override
    public void write(DataOutput dout) throws IOException {
        dout.writeUTF(movie);
        dout.writeDouble(rate);
        dout.writeUTF(timeStamp);
        dout.writeUTF(uid);
    }

此处的注意点：当使用自定义的类作为mapper或者reducer的key或者value时，需要对其实现hdp内部的序列化，否则汇报如下的错误

2019-11-08 10:42:50,720 INFO  [main] Configuration.deprecation (Configuration.java:logDeprecation(1285)) - session.id is deprecated. Instead, use dfs.metrics.session-id
2019-11-08 10:42:50,724 INFO  [main] jvm.JvmMetrics (JvmMetrics.java:init(79)) - Initializing JVM Metrics with processName=JobTracker, sessionId=
2019-11-08 10:42:52,101 WARN  [main] mapreduce.JobResourceUploader (JobResourceUploader.java:uploadFiles(64)) - Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2019-11-08 10:42:52,136 WARN  [main] mapreduce.JobResourceUploader (JobResourceUploader.java:uploadFiles(171)) - No job jar file set.  User classes may not be found. See Job or Job#setJar(String).
2019-11-08 10:42:52,146 INFO  [main] input.FileInputFormat (FileInputFormat.java:listStatus(289)) - Total input files to process : 1
2019-11-08 10:42:52,176 INFO  [main] mapreduce.JobSubmitter (JobSubmitter.java:submitJobInternal(200)) - number of splits:1
2019-11-08 10:42:52,271 INFO  [main] mapreduce.JobSubmitter (JobSubmitter.java:printTokens(289)) - Submitting tokens for job: job_local933787924_0001
2019-11-08 10:42:52,483 INFO  [main] mapreduce.Job (Job.java:submit(1345)) - The url to track the job: http://localhost:8080/
2019-11-08 10:42:52,484 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1390)) - Running job: job_local933787924_0001
2019-11-08 10:42:52,489 INFO  [Thread-3] mapred.LocalJobRunner (LocalJobRunner.java:createOutputCommitter(498)) - OutputCommitter set in config null
2019-11-08 10:42:52,497 INFO  [Thread-3] output.FileOutputCommitter (FileOutputCommitter.java:<init>(123)) - File Output Committer Algorithm version is 1
2019-11-08 10:42:52,498 INFO  [Thread-3] output.FileOutputCommitter (FileOutputCommitter.java:<init>(138)) - FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
2019-11-08 10:42:52,498 INFO  [Thread-3] mapred.LocalJobRunner (LocalJobRunner.java:createOutputCommitter(516)) - OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
2019-11-08 10:42:52,554 INFO  [Thread-3] mapred.LocalJobRunner (LocalJobRunner.java:runTasks(475)) - Waiting for map tasks
2019-11-08 10:42:52,555 INFO  [LocalJobRunner Map Task Executor #0] mapred.LocalJobRunner (LocalJobRunner.java:run(251)) - Starting task: attempt_local933787924_0001_m_000000_0
2019-11-08 10:42:52,588 INFO  [LocalJobRunner Map Task Executor #0] output.FileOutputCommitter (FileOutputCommitter.java:<init>(123)) - File Output Committer Algorithm version is 1
2019-11-08 10:42:52,588 INFO  [LocalJobRunner Map Task Executor #0] output.FileOutputCommitter (FileOutputCommitter.java:<init>(138)) - FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
2019-11-08 10:42:52,600 INFO  [LocalJobRunner Map Task Executor #0] util.ProcfsBasedProcessTree (ProcfsBasedProcessTree.java:isAvailable(168)) - ProcfsBasedProcessTree currently is supported only on Linux.
2019-11-08 10:42:52,663 INFO  [LocalJobRunner Map Task Executor #0] mapred.Task (Task.java:initialize(619)) -  Using ResourceCalculatorProcessTree : org.apache.hadoop.yarn.util.WindowsBasedProcessTree@88da7d9
2019-11-08 10:42:52,668 INFO  [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:runNewMapper(756)) - Processing split: file:/E:/javafile/movie.txt:0+1019
2019-11-08 10:42:52,721 INFO  [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:setEquator(1205)) - (EQUATOR) 0 kvi 26214396(104857584)
2019-11-08 10:42:52,721 INFO  [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(998)) - mapreduce.task.io.sort.mb: 100
2019-11-08 10:42:52,721 INFO  [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(999)) - soft limit at 83886080
2019-11-08 10:42:52,721 INFO  [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(1000)) - bufstart = 0; bufvoid = 104857600
2019-11-08 10:42:52,721 INFO  [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:init(1001)) - kvstart = 26214396; length = 6553600
2019-11-08 10:42:52,745 WARN  [LocalJobRunner Map Task Executor #0] mapred.MapTask (MapTask.java:createSortingCollector(411)) - Unable to initialize MapOutputCollector org.apache.hadoop.mapred.MapTask$MapOutputBuffer
java.lang.NullPointerException
    at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.init(MapTask.java:1011)
    at org.apache.hadoop.mapred.MapTask.createSortingCollector(MapTask.java:402)
    at org.apache.hadoop.mapred.MapTask.access$100(MapTask.java:81)
    at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:698)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:770)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
    at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:270)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
2019-11-08 10:42:52,753 INFO  [Thread-3] mapred.LocalJobRunner (LocalJobRunner.java:runTasks(483)) - map task executor complete.
2019-11-08 10:42:52,755 WARN  [Thread-3] mapred.LocalJobRunner (LocalJobRunner.java:run(587)) - job_local933787924_0001
java.lang.Exception: java.io.IOException: Initialization of all the collectors failed. Error in last collector was :null
    at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:489)
    at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:549)
Caused by: java.io.IOException: Initialization of all the collectors failed. Error in last collector was :null
    at org.apache.hadoop.mapred.MapTask.createSortingCollector(MapTask.java:414)
    at org.apache.hadoop.mapred.MapTask.access$100(MapTask.java:81)
    at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:698)
    at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:770)
    at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
    at org.apache.hadoop.mapred.LocalJobRunner$Job$MapTaskRunnable.run(LocalJobRunner.java:270)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.NullPointerException
    at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.init(MapTask.java:1011)
    at org.apache.hadoop.mapred.MapTask.createSortingCollector(MapTask.java:402)
    ... 10 more
2019-11-08 10:42:53,488 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1411)) - Job job_local933787924_0001 running in uber mode : false
2019-11-08 10:42:53,490 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1418)) -  map 0% reduce 0%
2019-11-08 10:42:53,491 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1431)) - Job job_local933787924_0001 failed with state FAILED due to: NA
2019-11-08 10:42:53,497 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1436)) - Counters: 0

序列化代码如下：

 

 （2）业务代码（MovieRateTopN）

public class MovieRateTopN {
    static Text k = new Text();
    static class RateTopNMapper extends Mapper<LongWritable, Text, Text, MovieBean>{
        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, MovieBean>.Context context)
                throws IOException, InterruptedException {
            try {
                String line = value.toString();
                MovieBean mb = JSON.parseObject(line, MovieBean.class);
                String movie = mb.getMovie();
                k.set(movie);
                context.write(k, mb);
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    }
    static class RateTopNReducer extends Reducer<Text, MovieBean, MovieBean, NullWritable>{
        @Override
        protected void reduce(Text key, Iterable<MovieBean> iters,
                Reducer<Text, MovieBean, MovieBean, NullWritable>.Context context) throws IOException, InterruptedException {
            try {
                List<MovieBean> list = new ArrayList<>();
                for (MovieBean movieBean : iters) {
                    MovieBean mb = new MovieBean();
                    // 将迭代器中得到的MovieBean中的属性复制到新创建的对象中去
                    BeanUtils.copyProperties(mb,movieBean);
                    list.add(mb);
                }
                // 对list中的数据按rate降序排列
                Collections.sort(list, new Comparator<MovieBean>() {
                    @Override
                    public int compare(MovieBean o1, MovieBean o2) {
                        return Double.compare(o2.getRate(), o1.getRate());
                    }
                });
                
                // 输出前3个数据
                for(int i = 0 ; i < Integer.min(3, list.size()) ; i++) {
                    context.write(list.get(i), NullWritable.get());
                }
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    }
    public static void main(String[] args) throws Exception, IOException {
         // 获取MR程序运行时的初始化配置
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        // 设置map和reduce类，调用类中自定义的map  reduce方法的业务逻辑
        job.setMapperClass(RateTopNMapper.class);
        job.setReducerClass(RateTopNReducer.class);
        // 设置map端输出key-value的类型，当map和reduce的输出值类型一致时，可以省略map处值类型的设置
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(MovieBean.class);
        // 设置reduce端输出key-value的类型
        job.setOutputKeyClass(MovieBean.class);
        job.setOutputValueClass(NullWritable.class);
        // 处理的文件的路径
        FileInputFormat.setInputPaths(job, new Path("E:/javafile/movie.txt"));
        // 结果输出路径
        FileOutputFormat.setOutputPath(job, new Path("E:/wc/movie/res15"));
        // 提交任务，参数     等待执行
        job.waitForCompletion(true);
    
    }
}

需要注意的部分1：

第一处

 第一点：reducer部分的输出value的类型可以为NullWritable

第二点：迭代器返回的的对象是同一个对象（内存空间的地址是一样的），所以需要创建一个对象来获取从迭代器中获得的对象的属性，若是第二点处直接使用如下代码

list.add(movieBean);

这样列表中存放的为同一个对象（属性值刚好为从迭代器最后出来的一个movieBean的属性）

 第二处

 

 知识点：Integer.max(2, 3) ------>得到的结果为3

 　　　　Integer.min(2, 3) ------>得到的结果为2

 需要注意的部分2

 

 排序形式：

• (1.2-3.3)>0?-1:1 
• 包装类型直接调用conparaTo方法
• Double.compare(2.3 , 2,1)

 需要注意的部分3

 在map端和reduce端处理数据的时候一定要try  catch  否则程序出现数据解析异常的时候mr程序就会失败（空指针异常）

 2.5 统计评论次数最多的n部电影

 思路：此题属于全局排序，首先以movie为key聚合，然后在reduce处得到movie以及对应的评论次数，这个时候再对这个数据进行全局排序，即创建一个map，以movie为key，评论的次数为value放进map中，然后将map以value降序排序（cleanup方法）

cleanup方法在reduce方法执行之后 ，最后执行一次，全局数据的操作，用于收尾工作

具体代码如下：

public class MovieViewsTopN {
    static Text k = new Text();
    static IntWritable v = new IntWritable();
    // map部分，以movie为key聚合
    static class MovieViewsMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            try {
                String line = value.toString();
                MovieBean mb = JSON.parseObject(line, MovieBean.class);
                k.set(mb.getMovie());
                v.set(1);
                context.write(k, v);
            } catch (Exception e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }
    }
    // reduce部分  输出值:  movie-评论次数
    static class MovieViewsReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
        Map<String, Integer> map = new HashMap<>();
        @Override
        protected void reduce(Text key, Iterable<IntWritable> iters,
                Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
            int count = 0;
            for (IntWritable intWritable : iters) {
                int i = intWritable.get();
                count += i;
            }
            map.put(key.toString(), count);
        }
        // 进行全局排序
        @Override
        protected void cleanup(Reducer<Text, IntWritable, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            Set<Entry<String,Integer>> set = map.entrySet();
            ArrayList<Entry<String,Integer>> list = new ArrayList<>(set);
            Collections.sort(list, new Comparator<Entry<String, Integer>>(){
                @Override
                public int compare(Entry<String, Integer> o1, Entry<String, Integer> o2) {
                    return o2.getValue().compareTo(o1.getValue());
                }
            });
            
            for(int i=0;i<Integer.min(3, list.size()); i++) {
                k.set(list.get(i).getKey());
                v.set(list.get(i).getValue());
                context.write(k, v);
            }
        }
    }
    
    public static void main(String[] args) throws Exception {
         // 获取MR程序运行时的初始化配置
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        // 设置map和reduce类，调用类中自定义的map  reduce方法的业务逻辑
        job.setMapperClass(MovieViewsMapper.class);
        job.setReducerClass(MovieViewsReducer.class);
        // 设置reduce端输出key-value的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        // 处理的文件的路径
        FileInputFormat.setInputPaths(job, new Path("E:/javafile/movie.txt"));
        // 结果输出路径
        FileOutputFormat.setOutputPath(job, new Path("E:/wc/movie/res20"));
        // 提交任务，参数   等待执行
        job.waitForCompletion(true);
    }
}

3.  line线段重叠次数案例

 题意：有如下文件（1，4表示1，2，3，4），记录各个数字的重叠次数（如1出现了几次，2出现了几次）

 代码

public class Line {

    static class LineMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable> {
        @Override
        protected void map(LongWritable key, Text value,
                Mapper<LongWritable, Text, IntWritable, IntWritable>.Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            String[] split = line.split(","); // 1,4
            int x1 = Integer.parseInt(split[0]);// 1
            int x2 = Integer.parseInt(split[1]);// 4
            //
            for (int i = x1; i <= x2; i++) {
                context.write(new IntWritable(i), new IntWritable(1));// 11 21 31 41
            }
        }
    }

    static class LineReducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {
        @Override
        protected void reduce(IntWritable key, Iterable<IntWritable> iters,
                Reducer<IntWritable, IntWritable, IntWritable, IntWritable>.Context context)
                throws IOException, InterruptedException {
            int count = 0;
            for (IntWritable intWritable : iters) {
                count++;
            }
            context.write(key, new IntWritable(count));
        }

    }

    public static void main(String[] args) throws Exception {

        // 获取mr程序运行时的初始化配置
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        // 设置map和reduce类 调用类中自定义的map reduce方法的业务逻辑
        job.setMapperClass(LineMapper.class);
        job.setReducerClass(LineReducer.class);
        // 设置map端输出的key-value的类型
        /*
         * job.setMapOutputKeyClass(Text.class);
         * job.setMapOutputValueClass(IntWritable.class);
         */
        // 设置reduce的key-value的类型 结果的最终输出
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(IntWritable.class);

        // 设置reducetask的个数 默认1个
        // job.setNumReduceTasks(3);

        // 处理的文件的路径
        FileInputFormat.setInputPaths(job, new Path("D:\data\line\input"));
        // 结果输出路径
        FileOutputFormat.setOutputPath(job, new Path("D:\data\line\res"));
        // 提交任务 参数等待执行
        job.waitForCompletion(true);

    }
}

4. 索引案例

 题意：有三个文件  a.html    b.html   c.html 其内容如下（只列出一个文件，其它类似）

 需求：单词在每个文件夹中的数量（以java为例，格式如下： java  a.html-3  c.html-3  b.html-1    ）

代码分两部分：

第一部分：为了得到如下形式的输出数据（还是以java单词为例）：

 java  a.html-1     java  a.html-1    java  b.html-1

Index1代码

public class Index1 {
    static Text k = new Text();
    static IntWritable v = new IntWritable();
    static class Index1Mapper extends Mapper<LongWritable, Text, Text, IntWritable>{
        String name = null;
        // 获取相应的文件名
        @Override
        protected void setup(Mapper<LongWritable, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            FileSplit fs = (FileSplit)context.getInputSplit();
            name = fs.getPath().getName();
        }
        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context)
                throws IOException, InterruptedException {
            try {
                String line = value.toString();
                String[] split = line.split("\s");
                for (String word : split) {
                    // word-文件名 数量
                    k.set(word + "-" + name);
                    v.set(1);
                    context.write(k, v);// java-a.html 1 java-b.html 1
                }
            } catch (Exception e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
        }
    }
    static class Index1Reducer extends Reducer<Text, IntWritable, Text, IntWritable>{
        @Override
        protected void reduce(Text key, Iterable<IntWritable> iters,
                Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
            int count = 0;
            for (IntWritable intWritable : iters) {
                int i = intWritable.get();
                count += i;
            }
            v.set(count);
            context.write(key, v);
        }
    }
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setMapperClass(Index1Mapper.class);
        job.setReducerClass(Index1Reducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // 输出好输入的数据路径

        FileInputFormat.setInputPaths(job, new Path("E:/wc/input/"));
        FileOutputFormat.setOutputPath(job, new Path("E:/wc/output1/"));
        // true 执行成功
        boolean b = job.waitForCompletion(true);
        // 退出程序的状态码 404 200 500
        System.exit(b ? 0 : -1);
    }
}

第二部分：将第一部分得到的数据再次处理，从而得到想要的数据格式

public class Index2 {

    static class Index2Mapper extends Mapper<LongWritable, Text, Text, Text> {
        Text k = new Text();
        Text v = new Text();

        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)
                throws IOException, InterruptedException {
            // 获取第一个mapreduce任务处理的数据的每行
            String line = value.toString(); // a-a.html 	 3
            String[] split = line.split("\s");
            //a-a.html
            String wordAndFileName = split[0];
            // 3
            String count = split[1];
            //处理  a-a.html  切割
            String[] split2 = wordAndFileName.split("-");
            //获取单词  a 
            String word = split2[0];
            // 获取文件名  a.html 
            String fileName = split2[1];
            // 组装文件名和单词个数   a.html-3
            String valueString = fileName + "-" + count; // a.html-3
            // 单词设置到key
            k.set(word);
            v.set(valueString); // java a.html-3
            // 输出kv  单词   v就是 文件名-个数
            context.write(k, v);
        }

    }

    static class Index2Reducer extends Reducer<Text, Text, Text, Text> {
        Text v = new  Text() ;
        @Override
        protected void reduce(Text key, Iterable<Text> iters, Reducer<Text, Text, Text, Text>.Context context)
                throws IOException, InterruptedException {
            /*
             * hello  a.html-3
             * hello  b.html-2
             * hello  c.html-3
             *         ---->  a.html-3 b.html-2 c.html-3 
             */
            // 创建一个String对象  追加每个单词在每个文中出现的次数
            StringBuilder sb = new StringBuilder();
            for (Text text : iters) {//a.html-3  b.html-2 c.html-3 
                sb.append(text.toString()+" ") ;//a.html-3 b.html-2 c.html-3 
            }
            // 字符串
            v.set(sb.toString().trim());
            //输出结果
            context.write(key, v);

        }

    }

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);

        job.setMapperClass(Index2Mapper.class);
        job.setReducerClass(Index2Reducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        // 输出好输入的数据路径

        FileInputFormat.setInputPaths(job, new Path("E:/wc/output1/"));
        FileOutputFormat.setOutputPath(job, new Path("E:/wc/index/res1"));
        // true 执行成功
        boolean b = job.waitForCompletion(true);
        // 退出程序的状态码 404 200 500
        System.exit(b ? 0 : -1);
    }
}

最终得到的结果如下图

 此处遇到的问题：

 答案：

在TextInputFormat的父类（FileInputFormat）中做了一个通用的处理，即过滤掉以“_”，"." 开头的文件，所以就会只读取part-r-00000文件，这是一个通用的处理，一个job产生的结果，经常要作为下一个job的输入，如果它在读取时，不过滤掉这些“附加”文件，那就会很麻烦。具体源码如下