一个Hadoop工程关于温度的二次排序程序

可以使用MapReduce框架实现二次排序，具体步骤如下：

1. 自定义一个WritableComparable类，用于存储温度和时间戳两个属性，并实现compareTo方法，以便进行二次排序。

2. 自定义一个Mapper类，将输入的数据按照温度和时间戳分别作为key和value输出。

3. 自定义一个Partitioner类，根据key的温度属性将数据分发到不同的Reducer中。

4. 自定义一个GroupingComparator类，用于将相同温度的数据分到同一个组中，以便进行二次排序。

5. 自定义一个Reducer类，将同一温度下的数据按照时间戳进行排序，并输出结果。

完整代码如下：

public class Temperature implements WritableComparable<Temperature> {

private double temp;
private long timestamp;

public Temperature() {}

public Temperature(double temp, long timestamp) {
this.temp = temp;
this.timestamp = timestamp;
}

@Override
public void write(DataOutput out) throws IOException {
out.writeDouble(temp);
out.writeLong(timestamp);
}

@Override
public void readFields(DataInput in) throws IOException {
temp = in.readDouble();
timestamp = in.readLong();
}

@Override
public int compareTo(Temperature o) {
int cmp = Double.compare(temp, o.temp);
if (cmp != 0) {
return cmp;
}
return Long.compare(timestamp, o.timestamp);
}

public double getTemp() {
return temp;
}

public long getTimestamp() {
return timestamp;
}
}

public class TemperatureMapper extends Mapper<LongWritable, Text, Temperature, NullWritable> {

private Temperature temperature = new Temperature();

@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] fields = value.toString().split(",");
double temp = Double.parseDouble(fields[0]);
long timestamp = Long.parseLong(fields[1]);
temperature = new Temperature(temp, timestamp);
context.write(temperature, NullWritable.get());
}
}

public class TemperaturePartitioner extends Partitioner<Temperature, NullWritable> {

@Override
public int getPartition(Temperature key, NullWritable value, int numPartitions) {
return (int) (key.getTemp() * 100) % numPartitions;
}
}

public class TemperatureGroupingComparator extends WritableComparator {

protected TemperatureGroupingComparator() {
super(Temperature.class, true);
}

@Override
public int compare(WritableComparable a, WritableComparable b) {
Temperature t1 = (Temperature) a;
Temperature t2 = (Temperature) b;
return Double.compare(t1.getTemp(), t2.getTemp());
}
}

public class TemperatureReducer extends Reducer<Temperature, NullWritable, Text, NullWritable> {

@Override
protected void reduce(Temperature key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
for (NullWritable value : values) {
context.write(new Text(key.getTemp() + "," + key.getTimestamp()), NullWritable.get());
}
}
}

在Driver类中，需要设置自定义的WritableComparable类、Mapper类、Partitioner类、GroupingComparator类和Reducer类，并指定输入输出路径等参数。