使用hadoop分析气象数据完整版（附带完整代码）

### 数据准备 在使用Hadoop进行气象数据分析之前，首先需要准备好数据。通常气象数据可以从公开的数据源获取，例如NOAA（National Oceanic and Atmospheric Administration）提供的全球气象数据。这些数据通常以文本文件的形式存在，包含各种气象参数，如温度、湿度、风速等。 #### 示例数据格式 假设我们有一个简单的气象数据文件，每行记录包含日期、地点、最高温度和最低温度： ``` 2023-01-01,New York,25,15 2023-01-01,Los Angeles,30,20 2023-01-02,New York,28,18 2023-01-02,Los Angeles,32,22 ``` ### MapReduce 实现 接下来，我们将编写一个MapReduce程序来分析这些气象数据，计算每个城市的平均最高温度和最低温度。 #### Mapper 类 ```java import java.io.IOException; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.Mapper; public class WeatherMapper extends Mapper<LongWritable, Text, Text, DoubleWritable> { private static final int MAX_TEMPERATURE = 0; private static final int MIN_TEMPERATURE = 1; @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String[] parts = value.toString().split(","); if (parts.length == 4) { String city = parts[1]; double maxTemp = Double.parseDouble(parts[2]); double minTemp = Double.parseDouble(parts[3]); context.write(new Text(city + "_max"), new DoubleWritable(maxTemp)); context.write(new Text(city + "_min"), new DoubleWritable(minTemp)); } } } ``` #### Reducer 类 ```java import java.io.IOException; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.Reducer; public class WeatherReducer extends Reducer<Text, DoubleWritable, Text, DoubleWritable> { @Override public void reduce(Text key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException { double sum = 0.0; int count = 0; for (DoubleWritable value : values) { sum += value.get(); count++; } double average = sum / count; context.write(key, new DoubleWritable(average)); } } ``` #### Driver 类 ```java import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WeatherAnalysis { public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "Weather Analysis"); job.setJarByClass(WeatherAnalysis.class); job.setMapperClass(WeatherMapper.class); job.setReducerClass(WeatherReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(DoubleWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } ``` ### 运行 MapReduce 程序 编译并打包上述代码为JAR文件后，可以使用以下命令运行MapReduce作业： ```bash hadoop jar weather-analysis.jar WeatherAnalysis /input/path /output/path ``` ### 结果展示 运行完成后，输出目录中将包含每个城市及其对应的平均最高温度和最低温度。例如： ``` New York_max 26.5 New York_min 16.5 Los Angeles_max 31.0 Los Angeles_min 21.0 ``` ###