利用美国人口统计数据文件，使用mapreduce算法，完成下列任务： 1）统计数据文件中每一年每个州的总人口数量； 2）统计2008年所有州人口密度，并按降序排列。

好的，我知道你的问题了。不用担心，我会保密的。对于你的问题，可以使用Hadoop的MapReduce框架来进行处理。以下是实现这两个任务的步骤：

1. 统计数据文件中每一年每个州的总人口数量

首先，需要编写一个Mapper程序，将数据文件中的每一行按照年份和州分组，输出键值对<年份_州, 人口数量>。代码示例如下：

public class PopulationMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
    private Text outputKey = new Text();
    private LongWritable outputValue = new LongWritable();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] fields = value.toString().split(",");
        String year = fields[0];
        String state = fields[1];
        long population = Long.parseLong(fields[2]);

        outputKey.set(year + "_" + state);
        outputValue.set(population);

        context.write(outputKey, outputValue);
    }
}

然后，需要编写一个Reducer程序，将Mapper输出的键值对按照年份和州再次分组，并将人口数量相加得到每一年每个州的总人口数量。代码示例如下：

public class PopulationReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
    private LongWritable outputValue = new LongWritable();

    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        long sum = 0;
        for (LongWritable value : values) {
            sum += value.get();
        }

        outputValue.set(sum);

        context.write(key, outputValue);
    }
}

最后，在Driver程序中配置MapReduce作业的输入输出路径、Mapper和Reducer类，并提交作业到Hadoop集群运行即可。代码示例如下：

public class PopulationJob {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "PopulationJob");

        job.setJarByClass(PopulationJob.class);

        job.setMapperClass(PopulationMapper.class);
        job.setReducerClass(PopulationReducer.class);

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

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

2. 统计2008年所有州人口密度，并按降序排列

同样地，需要编写一个Mapper程序，将数据文件中的每一行按照州分组，输出键值对<州, 人口数量>。代码示例如下：

public class DensityMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
    private Text outputKey = new Text();
    private LongWritable outputValue = new LongWritable();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] fields = value.toString().split(",");
        String year = fields[0];
        String state = fields[1];
        long population = Long.parseLong(fields[2]);

        if ("2008".equals(year)) {
            outputKey.set(state);
            outputValue.set(population);

            context.write(outputKey, outputValue);
        }
    }
}

然后，需要编写一个Reducer程序，将Mapper输出的键值对按照州分组，并将人口数量相加得到每个州的总人口数量。代码示例如下：

public class DensityReducer extends Reducer<Text, LongWritable, Text, DoubleWritable> {
    private DoubleWritable outputValue = new DoubleWritable();

    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        long sum = 0;
        for (LongWritable value : values) {
            sum += value.get();
        }

        double density = (double) sum / 1000000; // 人口密度 = 总人口数量 / 平方英里
        outputValue.set(density);

        context.write(key, outputValue);
    }
}

最后，在Driver程序中配置MapReduce作业的输入输出路径、Mapper和Reducer类，并将Reducer输出的键值对按照值降序排列输出到文件中。代码示例如下：

public class DensityJob {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "DensityJob");

        job.setJarByClass(DensityJob.class);

        job.setMapperClass(DensityMapper.class);
        job.setReducerClass(DensityReducer.class);

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

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.waitForCompletion(true);

        Job sortJob = Job.getInstance(conf, "DensitySortJob");

        sortJob.setJarByClass(DensityJob.class);

        sortJob.setMapperClass(InverseMapper.class);
        sortJob.setReducerClass(DescendingSortReducer.class);

        sortJob.setOutputKeyClass(DoubleWritable.class);
        sortJob.setOutputValueClass(Text.class);

        FileInputFormat.addInputPath(sortJob, new Path(args[1]));
        FileOutputFormat.setOutputPath(sortJob, new Path(args[2]));

        sortJob.waitForCompletion(true);
    }
}

注意，为了按照降序排列，还需要编写一个InverseMapper类和一个DescendingSortReducer类，代码示例如下：

public class InverseMapper extends Mapper<LongWritable, Text, DoubleWritable, Text> {
    private DoubleWritable outputKey = new DoubleWritable();
    private Text outputValue = new Text();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] fields = value.toString().split("\t");
        String state = fields[0];
        double density = Double.parseDouble(fields[1]);

        outputKey.set(-density); // 取相反数以实现降序排列
        outputValue.set(state);

        context.write(outputKey, outputValue);
    }
}

public class DescendingSortReducer extends Reducer<DoubleWritable, Text, Text, DoubleWritable> {
    private Text outputKey = new Text();

    @Override
    protected void reduce(DoubleWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        double density = -key.get(); // 取相反数还原为原来的值
        for (Text value : values) {
            outputKey.set(value);
            context.write(outputKey, new DoubleWritable(density));
        }
    }
}

这样，就完成了两个任务的处理。希望这些代码对你有所帮助！