Hadoop Project on NCDC ( National Climate Data Center – NOAA ) Dataset

Hadoop Project on NCDC ( National Climate Data Center - NOAA ) Dataset

Hadoop Project on NCDC ( National Climate Data Center - NOAA ) DatasetNOAA’s National Climatic Data Center (NCDC) is responsible for preserving, monitoring, assessing, and providing public access to weather data.

NCDC provides access to daily data from the U.S. Climate Reference Network / U.S. Regional Climate Reference Network (USCRN/USRCRN) via anonymous ftp at:

Datasets : ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01
Readme : ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/README.txt

All daily data are calculated over the station’s 24-hour LST day.

Field#  Name                           Units


1    WBANNO                         XXXXX

2    LST_DATE                       YYYYMMDD

3    CRX_VN                         XXXXXX

4    LONGITUDE                      Decimal_degrees

5    LATITUDE                       Decimal_degrees

6    T_DAILY_MAX                    Celsius

7    T_DAILY_MIN                    Celsius

8    T_DAILY_MEAN                   Celsius

9    T_DAILY_AVG                    Celsius

10   P_DAILY_CALC                   mm

11   SOLARAD_DAILY                  MJ/m^2

12   SUR_TEMP_DAILY_TYPE            X

13   SUR_TEMP_DAILY_MAX             Celsius

14   SUR_TEMP_DAILY_MIN             Celsius

15   SUR_TEMP_DAILY_AVG             Celsius

16   RH_DAILY_MAX                   %

17   RH_DAILY_MIN                   %

18   RH_DAILY_AVG                   %

19   SOIL_MOISTURE_5_DAILY          m^3/m^3

20   SOIL_MOISTURE_10_DAILY         m^3/m^3

21   SOIL_MOISTURE_20_DAILY         m^3/m^3

22   SOIL_MOISTURE_50_DAILY         m^3/m^3

23   SOIL_MOISTURE_100_DAILY        m^3/m^3

24   SOIL_TEMP_5_DAILY              Celsius

25   SOIL_TEMP_10_DAILY             Celsius

26   SOIL_TEMP_20_DAILY             Celsius

27   SOIL_TEMP_50_DAILY             Celsius

28   SOIL_TEMP_100_DAILY            Celsius

After going through wordcount mapreduce guide, you now have the basic idea of how a mapreduce program works. So, let us see a complex mapreduce program on weather dataset. Here I am using one of the dataset of year 2015 of  Austin, Texas . We will do analytics on the dataset and classify whether it was a hot day or a cold day depending on the temperature recorded by NCDC.

NCDC gives us all the weather data we need for this mapreduce project.

The dataset which we will be using looks like below snapshot.

Step 1
Download the complete project using below link.


Below is the complete code

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 {

	*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 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 > 35 and temp_min < 10 are passed to the reducer.

		public void map(LongWritable arg0, Text Value, Context context)
				throws IOException, InterruptedException {

		//Converting the record (single line) to String and storing it in a String variable line
			String line = Value.toString();
		//Checking if the line is not empty
			if (!(line.length() == 0)) {
				String date = line.substring(6, 14);

				//maximum temperature
				float temp_Max = Float
						.parseFloat(line.substring(39, 45).trim());
				//minimum temperature
				float temp_Min = Float
						.parseFloat(line.substring(47, 53).trim());

			//if maximum temperature is greater than 35 , its a hot day
				if (temp_Max > 35.0) {
					// Hot day
					context.write(new Text("Hot Day " + date),
							new Text(String.valueOf(temp_Max)));

				//if minimum temperature is less than 10 , its a cold day
				if (temp_Min < 10) {
					// Cold day
					context.write(new Text("Cold Day " + date),
							new Text(String.valueOf(temp_Min)));


	*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 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 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

		//Key type coming out of mapper
		//value type coming out of mapper

		//Defining the mapper class name
		//Defining the reducer class name

		//Defining input Format class which is responsible to parse the dataset into a key value pair
		//Defining output Format class which is responsible to parse the dataset into a key value pair

		//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 delete it explicitly

		//exiting the job only if the flag value becomes false
		System.exit(job.waitForCompletion(true) ? 0 : 1);


Step 2
Import the project in eclipse IDE in the same way it was told in earlier guide and change the jar paths with the jar files present in the lib directory of this project.

Step 3
When the project is not having any error, we will export it as a jar file, same as we did in wordcount mapreduce guide. Right Click on the Project file and click on Export. Select jar file.
Give the path where you want to save the file.
Select the mail file by clicking on browse.
Click on Finish to export.
Step 4
You can download the jar file directly using below link



Download Dataset used by me using below link



Step 5
Before running the mapreduce program to check what it does, see that your cluster is up and all the hadoop daemons are running.

Command: jps
Step 6
Send the weather dataset on to HDFS.

Command: hdfs dfs -put Downloads/weather_data.txt /

Command: hdfs dfs -ls /

Step 7
Run the jar file.

Command: hadoop jar temperature.jar /weather_data.txt /output_hotandcold10


Step 8
Check output_hotandcold directory in HDFS.
Now inside part-r-00000 you will get your output.
Depending my dataset , only two days crossed 35 in max temperature.
The Project has been successfully executed!!


  1. Think this code will have a problem with negative, so the date 20150303 should be classified as a cold day as both the max and min temp are -9999. However post running the map phase, the -9999 becomes 9999 and hence is classified as a hot day. I am not sure if anyone else encountered this as well?

  2. do we need to firstly create an hadoop cluster before running the mapreduce program. and how to create that sir please help me out

  3. #add this line in the mapper class:
    public static final int MISSING=9999;

    #append to the if statements in the mapper class
    if (temp_Max > 35.0 && temp_Max !=MISSING)
    if (temp_Min < 10 && temp_Max !=MISSING)


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