This project was bootstrapped with Create React App.
In the project directory, you can run:
Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.
The page will reload if you make edits.
You will also see any lint errors in the console.
Launches the test runner in the interactive watch mode.
See the section about running tests for more information.
Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.
The build is minified and the filenames include the hashes.
Your app is ready to be deployed!
See the section about deployment for more information.
Note: this is a one-way operation. Once you eject, you can’t go back!
If you aren’t satisfied with the build tool and configuration choices, you can eject at any time. This command will remove the single build dependency from your project.
Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except eject will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own.
You don’t have to ever use eject. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it.
You can learn more in the Create React App documentation.
To learn React, check out the React documentation.
BE SEM VII Computer Engineering (TSEC) Name : WORDCOUNT Program in haddop Step 1 : Create New Java Project in Eclipse Name project : WordCountJob click : finish Step 2: Right Click on project -> New -> Class create class files as Name : WordCount Name : WordMapper Name : WordReducer Add the respective program contents to the file which is given below : Run The program as java application Step 3 : Right Click Project -> build path -> add expertnal archievs -> filesystem -> usr -> lib -> hadoop-0.20 -> hadoop-core.jar click add Step 4 : export the jar file in same folder (training/workspace/WordCountJob/src) Step 5 : open terminal [training@localhost ~]$ ls -l Step 6 : create sample file [training@localhost ~]$ cat sample.txt hi how are you bye see you soon hi hi hi Put the file in Hadoop Step 7 : Put the content of sample.txt to samplehadoop.txt [training@localhost ~]$ hadoop fs -put sample.txt /user/training/samplehadoop.txt [training@localhost ~]$ hadoop fs -ls Step 8 : change the directory to [training@localhost ~]$ cd /home/training/workspace/WordCountJob/src Step 9: Listing the contents of that directory [training@localhost WordCount22]$ ls sample.txt wordCount.jar WordCount.java WordMapper.java WordReducer.java Step 10 : Run the wordcount program and collect the output in sampleoutdir [training@localhost src]$ hadoop jar wordCount.jar wordCount samplehadoop.txt sampleoutdir Step 11 : chk the output file [training@localhost src]$ hadoop fs -ls /user/training/sampleoutdir Found 3 items -rw-r--r-- 1 training supergroup 0 2019-08-21 00:40 /user/training/sampleoutdir/_SUCCESS drwxr-xr-x - training supergroup 0 2019-08-21 00:40 /user/training/sampleoutdir/_logs -rw-r--r-- 1 training supergroup 27 2019-08-21 00:40 /user/training/sampleoutdir/part-r-00000 Step 12: Displaying the Output [training@localhost src]$ hadoop fs -cat /user/training/sampleoutdir/part-r-00000 are 1 bye 1 hi 4 how 1 see 1
soon 1 you 2
Prog 1 : WordCount .java import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; 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.Job; public class wordCount { public static void main(String[] args) throws Exception { if (args.length != 2) { System.out.printf("Usage: WordCount \n"); System.exit(-1); } Job job = new Job(); job.setJarByClass(wordCount.class); job.setJobName("wordCount"); FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.setMapperClass(wordMapper.class); job.setReducerClass(wordReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); boolean success = job.waitForCompletion(true); System.exit(success ? 0 : 1); } }
Prog 2 : WordMapper.java import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class wordMapper extends Mapper<LongWritable, Text, Text, IntWritable> { @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); for (String word : line.split("\W+")) {
if (word.length() > 0) context.write(new Text(word), new IntWritable(1));
}
} } Program 3 : WordReducer.java import java.io.IOException;
import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class wordReducer extends Reducer<Text, IntWritable, Text, IntWritable> { @Override public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
int wordCount = 0; for (IntWritable value : values) wordCount += value.get(); context.write(key, new IntWritable(wordCount));
} }