Excellence is a drive from inside

Shared in the Cognizant Internal Mail Thread.

Excellence is a drive from inside

A tourist once visited a temple under construction where he saw a sculptor making an idol of God. Suddenly he noticed a similar idol lying nearby.

Surprised, he asked the sculptor, "Do you need two statues of the same idol?"

"No," said the sculptor without looking up, "We need only one, but the first one got damaged at the last stage."

The gentleman examined the idol and found no apparent damage.

"Where is the damage?" he asked.

"There is a scratch on the nose of the idol." said the sculptor, still busy with his work.

"Where are you going to install the idol?"

The sculptor replied that it would be installed on a pillar twenty feet high.

"If the idol is that far, who is going to know that there is a scratch on the nose?" the gentleman asked...

The sculptor stopped his work, looked up at the gentleman, smiled and said, "I will know it."

Moral :

Excellence is not for someone else to notice but for your own satisfaction and efficiency.

The desire to excel is exclusive of the fact whether someone else appreciates.

Writing MapReduce Program on Hadoop - Context

Map and Reduce

Map Reduce works by breaking the processing into two phases: Map phase and Reduce phase. Each phase has the key-value pair as input and type of key and value can be chosen by the programmer.

Data flow in the Map and Reduce:

Input ==> Map ==> Mapper Output ==> Sort and shuffle ==> Reduce ==> Final Output

Steps to Write the Hadoop Map Reduce in Java

Map Reduce program need three things: Map, Reduce and Some code to run job(Here we will call it as Invoker)

1). Create the Map(Any Name) class and map function was represented by org.apache.hadoop.mapreduce.Mapper.class which declares an abstract map() method.
[code lang="java"]import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class Map extends Mapper<;LongWritable, Text, Text,IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key,Text value,Context context) throws IOException, InterruptedException {
word.set(value.toString());
context.write(word, one);
}
}

[/code]
Explanation:
The Mapper class is the generic class with four formal parameters(input key, input value, output key and output value). Here input key is LongWritable(Long representation by hadoop), input value is the Text(String representation by hadoop), output key is text(keyword) and output value is Intwritable(int representation by hadoop). All above hadoop datatypes are same as java datatype expect that are optimized for network serialization.

2). Create the Reducer(Any Name) class and reduce function was represented by org.apache.hadoop.mapreduce.Reducer.class which declares an abstract reduce() method.
[code lang="java"]import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;
import java.util.Iterator;

public class Reduce extends Reducer<Text, IntWritable, Text,IntWritable> {
@Override
protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
int sum = 0;
for(IntWritable intWritable : values){
sum += intWritable.get();
}
context.write(key, new IntWritable(sum));
}
}
[/code]
Explanation:
The Reducer class is the generic class with four formal parameters(input key, input value, output key and output value). Here input key and input value type must match with Mapper output, output key is text(keyword) and output value is Intwritable(number of occurence).

3) We are ready with Map and Reduce implementation, then we need to have the invoker for confguring the Hadoop job and invoke the Map Reduce program.
[code lang="java"]import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class WordCount{
public static void main(String[] args) throws Exception {
Configuration configuration = new Configuration();
configuration.set("fs.default.name", "hdfs://localhost:10011");
configuration.set("mapred.job.tracker","localhost:10012");

Job job = new Job(configuration, "Word Count");

job.setJarByClass(WordCount.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(org.apache.hadoop.mapreduce.lib.input.TextInputFormat.class);
job.setOutputFormatClass(org.apache.hadoop.mapreduce.lib.output.TextOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));

//Submit the job to the cluster and wait for it to finish.
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
[/code]
4). Compile code by using following command

mkdir WordCount
javac -classpath ${HADOOP_HOME}/hadoop-0.20.2+228-core.jar -d WordCount path/*.java

5). Create the jar by using command

jar -cvf ~/WordCount.jar -C WordCount/ .

6). Create the input file in the local file system

Eg : mkdir /home/user1/wordcount/input

cd /wordcount/input
gedit file01
gedit file02

and so on..

7). Copy the input file in local file system to HDFS

$HADOOP_HOME/bin/hadoop fs -cp ~/wordcount/input/file01 /home/user1/dfs/input/file01
$HADOOP_HOME/bin/hadoop fs -cp ~/wordcount/input/file02 /home/user1/dfs/input/file02

8). Execute the jar as follows:

$HADOOP_HOME/bin/hadoop jar WordCount.jar WordCount /home/user1/dfs/input /home/user1/dfs/output

9). After execution get completed, below set of commands is used to view the reduce file that are generated

$HADOOP_HOME/bin/hadoop fs -ls /home/user1/dfs/output/

10). To view the output, use this below given command

$HADOOP_HOME/bin/hadoop fs -cat hdfs:///home/user1/dfs/output/part-00000
$HADOOP_HOME/bin/hadoop fs -cat hdfs:///home/user1/dfs/output/part-00001
$HADOOP_HOME/bin/hadoop fs -cat hdfs:///home/user1/dfs/output/part-00002

and so on...

Upcoming Post: Using Distributed Cache in Java Hadoop MapReduce.