Here, we will write a Map-Reduce program for analyzing weather datasets to understand its data processing programming model. Weather sensors are collecting weather information across the globe in a large volume of log data. This weather data is semi-structured and record-oriented.
This data is stored in a line-oriented ASCII format, where each row represents a single record. Each row has lots of fields like longitude, latitude, daily max-min temperature, daily average temperature, etc. for easiness, we will focus on the main element, i.e. temperature. We will use the data from the National Centres for Environmental Information(NCEI). It has a massive amount of historical weather data that we can use for our data analysis.
Problem Statement:
Analyzing weather data of Fairbanks, Alaska to find cold and hot days using MapReduce Hadoop.
Step 1:
We can download the dataset from this Link, For various cities in different years. choose the year of your choice and select any one of the data text-file for analyzing. In my case, I have selected CRND0103-2020-AK_Fairbanks_11_NE.txt dataset for analysis of hot and cold days in Fairbanks, Alaska.
We can get information about data from README.txt file available on the NCEI website.
Step 2:
Below is the example of our dataset where column 6 and column 7 is showing Maximum and Minimum temperature, respectively.
Step 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 Configure 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