Программа MapReduce - Анализ погодных данных для анализа жарких и холодных дней

Опубликовано: 18 Февраля, 2022

Здесь мы напишем программу Map-Reduce для анализа наборов данных о погоде, чтобы понять ее программную модель обработки данных. Датчики погоды собирают информацию о погоде по всему миру в большом объеме данных журнала. Эти данные о погоде частично структурированы и ориентированы на записи.

Эти данные хранятся в строчно-ориентированном формате ASCII, где каждая строка представляет собой отдельную запись. В каждой строке есть много полей, таких как долгота, широта, максимальная и минимальная дневная температура, среднесуточная температура и т. Д. Для простоты мы сосредоточимся на основном элементе, то есть температуре. Мы будем использовать данные Национальных центров экологической информации (NCEI). Он содержит огромное количество исторических данных о погоде, которые мы можем использовать для анализа данных.

Постановка задачи:

 Анализ погодных данных Фэрбенкса, Аляска, чтобы найти холодные и жаркие дни с помощью MapReduce Hadoop.

Шаг 1:

Мы можем скачать набор данных по этой ссылке для разных городов в разные годы. выберите год по вашему выбору и выберите любой из текстовых файлов данных для анализа. В моем случае я выбрал набор данных CRND0103-2020-AK_Fairbanks_11_NE.txt для анализа жарких и холодных дней в Фэрбенксе, Аляска.

Мы можем получить информацию о данных из файла README.txt, доступного на сайте NCEI.

Шаг 2:

Ниже приведен пример нашего набора данных, где столбец 6 и столбец 7 показывают максимальную и минимальную температуру соответственно.

Шаг 3:

Make a project in Eclipse with below steps:

First Open Eclipse -> then select File -> New -> Java Project ->Name it MyProject -> then select use an execution environment -> choose JavaSE-1.8 then next -> Finish.
In this Project Create Java class with name MyMaxMin -> then click Finish

Copy the below source code to this MyMaxMin java class

// importing Libraries 
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.conf.Configuration;
  
public class MyMaxMin {
  
      
    // Mapper
      
    /*MaxTemperatureMapper class is static
     * and extends Mapper abstract class 
     * having four Hadoop generics type 
     * LongWritable, Text, Text, Text.
    */
      
      
    public static class MaxTemperatureMapper extends
            Mapper<LongWritable, Text, Text, Text> {
          
        /**
        * @method map
        * This method takes the input as a text data type.
        * Now leaving the first five tokens, it takes 
        * 6th token is taken as temp_max and
        * 7th token is taken as temp_min. Now 
        * temp_max > 30 and temp_min < 15 are
        * passed to the reducer.
        */
  
    // the data in our data set with
    // this value is inconsistent data
    public static final int MISSING = 9999;
          
    @Override
        public void map(LongWritable arg0, Text Value, Context context)
                throws IOException, InterruptedException {
  
        // Convert the single row(Record) to 
        // String and store it in String
        // variable name line
              
        String line = Value.toString();
              
            // Check for the empty line
            if (!(line.length() == 0)) {
                  
                // from character 6 to 14 we have
                // the date in our dataset
                String date = line.substring(6, 14);
  
                // similarly we have taken the maximum 
                // temperature from 39 to 45 characters
                float temp_Max = Float.parseFloat(line.substring(39, 45).trim());
                  
                // similarly we have taken the minimum 
                // temperature from 47 to 53 characters
                  
                float temp_Min = Float.parseFloat(line.substring(47, 53).trim());
  
                // if maximum temperature is
                // greater than 30, it is a hot day
                if (temp_Max > 30.0) {
                      
                    // Hot day
                    context.write(new Text("The Day is Hot Day :" + date),
                                         new Text(String.valueOf(temp_Max)));
                }
  
                // if the minimum temperature is 
                // less than 15, it is a cold day
                if (temp_Min < 15) {
                      
                    // Cold day
                    context.write(new Text("The Day is Cold Day :" + date),
                            new Text(String.valueOf(temp_Min)));
                }
            }
        }
  
    }
  
// Reducer
      
    /*MaxTemperatureReducer class is static 
      and extends Reducer abstract class
      having four Hadoop generics type 
      Text, Text, Text, Text.
    */
      
    public static class MaxTemperatureReducer extends
            Reducer<Text, Text, Text, Text> {
  
        /**
        * @method reduce
        * This method takes the input as key and
        * list of values pair from the mapper, 
        * it does aggregation based on keys and 
        * produces the final context.
        */
          
        public void reduce(Text Key, Iterator<Text> Values, Context context)
                throws IOException, InterruptedException {
  
              
            // putting all the values in 
            // temperature variable of type String
            String temperature = Values.next().toString();
            context.write(Key, new Text(temperature));
        }
  
    }
  
  
  
    /**
    * @method main
    * This method is used for setting
    * all the configuration properties.
    * It acts as a driver for map-reduce
    * code.
    */
      
    public static void main(String[] args) throws Exception {
  
        // reads the default configuration of the 
        // cluster from the configuration XML files
        Configuration conf = new Configuration();
          
        // Initializing the job with the 
        // default configuration of the cluster     
        Job job = new Job(conf, "weather example");
          
        // Assigning the driver class name
        job.setJarByClass(MyMaxMin.class);
  
        // Key type coming out of mapper
        job.setMapOutputKeyClass(Text.class);
          
        // value type coming out of mapper
        job.setMapOutputValueClass(Text.class);
  
        // Defining the mapper class name
        job.setMapperClass(MaxTemperatureMapper.class);
          
        // Defining the reducer class name
        job.setReducerClass(MaxTemperatureReducer.class);
  
        // Defining input Format class which is 
        // responsible to parse the dataset 
        // into a key value pair
        job.setInputFormatClass(TextInputFormat.class);
          
        // Defining output Format class which is
        // responsible to parse the dataset
        // into a key value pair
        job.setOutputFormatClass(TextOutputFormat.class);
  
        // setting the second argument
        // as a path in a path variable
        Path OutputPath = new Path(args[1]);
  
        // Configuring the input path
        // from the filesystem into the job
        FileInputFormat.addInputPath(job, new Path(args[0]));
  
        // Configuring the output path from 
        // the filesystem into the job
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
  
        // deleting the context path automatically
        // from hdfs so that we don"t have 
        // to delete it explicitly
        OutputPath.getFileSystem(conf).delete(OutputPath);
  
        // exiting the job only if the
        // flag value becomes false
        System.exit(job.waitForCompletion(true) ? 0 : 1);
  
    }
}

Now we need to add external jar for the packages that we have import. Download the jar package Hadoop Common and Hadoop MapReduce Core according to your Hadoop version.
You can check Hadoop Version:
```
hadoop version
```
Now we add these external jars to our MyProject. Right Click on MyProject -> then select Build Path-> Click on Configue Build Path and select Add External jars…. and add jars from it’s download location then click -> Apply and Close.
Now export the project as jar file. Right-click on MyProject choose Export.. and go to Java -> JAR file click -> Next and choose your export destination then click -> Next.
choose Main Class as MyMaxMin by clicking -> Browse and then click -> Finish -> Ok.

Step 4:

Start our Hadoop Daemons

start-dfs.sh

start-yarn.sh

Step 5:

Move your dataset to the Hadoop HDFS.

Syntax:

hdfs dfs -put /file_path /destination

In below command / shows the root directory of our HDFS.

hdfs dfs -put /home/dikshant/Downloads/CRND0103-2020-AK_Fairbanks_11_NE.txt /

Check the file sent to our HDFS.

hdfs dfs -ls /

Step 6:

Now Run your Jar File with below command and produce the output in MyOutput File.

Syntax:

hadoop jar /jar_file_location /dataset_location_in_HDFS /output-file_name

Command:

hadoop jar /home/dikshant/Documents/Project.jar /CRND0103-2020-AK_Fairbanks_11_NE.txt /MyOutput

Step 7:

Now Move to localhost:50070/, under utilities select Browse the file system and download part-r-00000 in /MyOutput directory to see result.

Step 8:

See the result in the Downloaded File.

In the above image, you can see the top 10 results showing the cold days. The second column is a day in yyyy/mm/dd format. For Example, 20200101 means

year = 2020
month = 01
Date = 01

Hadoop