Sunday, October 27, 2013

Hadoop FileSystem (HDFS) Tutorial 1

In this tutorial I will show some common commands for HDFS operations.
If you don't have Hadoop setup in your linux, you can follow Hadoop Setup Guide

Log into Linux, "hduser" is the login used in following examples.

Start Hadoop If it's not running
$ start-dfs.sh
....
$ start-yarn.sh
Create someFile.txt in your home directory
hduser@ubuntu:~$ vi someFile.txt

Paste any text you want in to the file and save it.
Create Home Directory In HDFS (If it doesn't exist)
hduser@ubuntu:~$ hadoop fs -mkdir -p /user/hduser
Copy file someFile.txt from local disk to the user’s directory in HDFS.
hduser@ubuntu:~$ hadoop fs -copyFromLocal someFile.txt someFile.txt
Get a directory listing of the user’s home directory in HDFS
hduser@ubuntu:~$ hadoop fs –ls


Found 1 items
-rw-r--r--   1 hduser supergroup          5 2013-10-27 17:57 someFile.txt

Display the contents of the HDFS file /user/hduser/someFile.txt
hduser@ubuntu:~$ hadoop fs –cat /user/hduser/someFile.txt
Get a directory listing of the HDFS root directory
hduser@ubuntu:~$ hadoop fs –ls /
copy that file to the local disk, named as someFile2.txt
hduser@ubuntu:~$ hadoop fs –copyToLocal /user/hduser/someFile.txt someFile2.txt
Delete the file from hadoop hdfs
hduser@ubuntu:~$ hadoop fs –rm someFile.txt

Deleted someFile.txt


For a full list of commands, Please visit HDFS FileSystem Shell Commands. Please feel free to leave me any comments or suggestions.

Monday, October 21, 2013

Setup newest Hadoop 2.x (2.2.0) on Ubuntu

In this tutorial I am going to guide you through setting up hadoop 2.2.0 environment on Ubuntu.

Prerequistive

$ sudo apt-get install openjdk-7-jdk
$ java -version
java version "1.7.0_25"
OpenJDK Runtime Environment (IcedTea 2.3.12) (7u25-2.3.12-4ubuntu3)
OpenJDK 64-Bit Server VM (build 23.7-b01, mixed mode)
$ cd /usr/lib/jvm
$ ln -s java-7-openjdk-amd64 jdk

$ sudo apt-get install openssh-server

Add Hadoop Group and User

$ sudo addgroup hadoop
$ sudo adduser --ingroup hadoop hduser
$ sudo adduser hduser sudo
After user is created, re-login into ubuntu using hduser

Setup SSH Certificate

$ ssh-keygen -t rsa -P ''
...
Your identification has been saved in /home/hduser/.ssh/id_rsa.
Your public key has been saved in /home/hduser/.ssh/id_rsa.pub.
...
$ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
$ ssh localhost

Download Hadoop 2.2.0

$ cd ~
$ wget http://www.trieuvan.com/apache/hadoop/common/hadoop-2.2.0/hadoop-2.2.0.tar.gz
$ sudo tar vxzf hadoop-2.2.0.tar.gz -C /usr/local
$ cd /usr/local
$ sudo mv hadoop-2.2.0 hadoop
$ sudo chown -R hduser:hadoop hadoop

Setup Hadoop Environment Variables

$cd ~
$vi .bashrc

paste following to the end of the file

#Hadoop variables
export JAVA_HOME=/usr/lib/jvm/jdk/
export HADOOP_INSTALL=/usr/local/hadoop
export PATH=$PATH:$HADOOP_INSTALL/bin
export PATH=$PATH:$HADOOP_INSTALL/sbin
export HADOOP_MAPRED_HOME=$HADOOP_INSTALL
export HADOOP_COMMON_HOME=$HADOOP_INSTALL
export HADOOP_HDFS_HOME=$HADOOP_INSTALL
export YARN_HOME=$HADOOP_INSTALL
###end of paste

$ cd /usr/local/hadoop/etc/hadoop
$ vi hadoop-env.sh

#modify JAVA_HOME
export JAVA_HOME=/usr/lib/jvm/jdk/
Re-login into Ubuntu using hdser and check hadoop version
$ hadoop version
Hadoop 2.2.0
Subversion https://svn.apache.org/repos/asf/hadoop/common -r 1529768
Compiled by hortonmu on 2013-10-07T06:28Z
Compiled with protoc 2.5.0
From source with checksum 79e53ce7994d1628b240f09af91e1af4
This command was run using /usr/local/hadoop-2.2.0/share/hadoop/common/hadoop-common-2.2.0.jar
At this point, hadoop is installed.

Configure Hadoop

$ cd /usr/local/hadoop/etc/hadoop
$ vi core-site.xml
#Paste following between <configuration>


   fs.default.name
   hdfs://localhost:9000



$ vi yarn-site.xml
#Paste following between <configuration>


   yarn.nodemanager.aux-services
   mapreduce_shuffle


   yarn.nodemanager.aux-services.mapreduce.shuffle.class
   org.apache.hadoop.mapred.ShuffleHandler



$ mv mapred-site.xml.template mapred-site.xml
$ vi mapred-site.xml
#Paste following between <configuration>


   mapreduce.framework.name
   yarn



$ cd ~
$ mkdir -p mydata/hdfs/namenode
$ mkdir -p mydata/hdfs/datanode
$ cd /usr/local/hadoop/etc/hadoop
$ vi hdfs-site.xml
Paste following between <configuration> tag


   dfs.replication
   1
 
 
   dfs.namenode.name.dir
   file:/home/hduser/mydata/hdfs/namenode
 
 
   dfs.datanode.data.dir
   file:/home/hduser/mydata/hdfs/datanode
 

Format Namenode

hduser@ubuntu40:~$ hdfs namenode -format

Start Hadoop Service

$ start-dfs.sh
....
$ start-yarn.sh
....

hduser@ubuntu40:~$ jps
If everything is sucessful, you should see following services running
2583 DataNode
2970 ResourceManager
3461 Jps
3177 NodeManager
2361 NameNode
2840 SecondaryNameNode

Run Hadoop Example

hduser@ubuntu: cd /usr/local/hadoop
hduser@ubuntu:/usr/local/hadoop$ hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar pi 2 5

Number of Maps  = 2
Samples per Map = 5
13/10/21 18:41:03 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Wrote input for Map #0
Wrote input for Map #1
Starting Job
13/10/21 18:41:04 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
13/10/21 18:41:04 INFO input.FileInputFormat: Total input paths to process : 2
13/10/21 18:41:04 INFO mapreduce.JobSubmitter: number of splits:2
13/10/21 18:41:04 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name
...

Note: ericduq has created a shell script (make-single-node.sh) for this setup and it is available at git repo at https://github.com/ericduq/hadoop-scripts.

What to read next
Hadoop FileSystem (HDFS) Tutorial 1
Hadoop 2.x Core (HDFS and YARN) Components Explained
Hadoop Wordcount example

Feel free to leave comments below. I will have more hadoop tutorials added regularly.

Friday, October 18, 2013

Hadoop WordCount with new map reduce api

There are so many version of WordCount hadoop example flowing around the web. However, a lot of them are using the older version of hadoop api. Following are example of word count using the newest hadoop map reduce api. The new map reduce api reside in org.apache.hadoop.mapreduce package instead of org.apache.hadoop.mapred.

WordMapper.java

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WordMapper extends Mapper<Object, Text, Text, IntWritable> {
 private Text word = new Text();
 private final static IntWritable one = new IntWritable(1);
 
 @Override
 public void map(Object key, Text value,
   Context contex) throws IOException, InterruptedException {
  // Break line into words for processing
  StringTokenizer wordList = new StringTokenizer(value.toString());
  while (wordList.hasMoreTokens()) {
   word.set(wordList.nextToken());
   contex.write(word, one);
  }
 }
}

SumReducer.java

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

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;



public class SumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
 
 private IntWritable totalWordCount = new IntWritable();
 
 @Override
 public void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
  int wordCount = 0;
  Iterator<IntWritable> it=values.iterator();
  while (it.hasNext()) {
   wordCount += it.next().get();
  }
  totalWordCount.set(wordCount);
  context.write(key, totalWordCount);
 }
}

WordCount.java (Driver)

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class WordCount {
 public static void main(String[] args) throws Exception {
        if (args.length != 2) {
          System.out.println("usage: [input] [output]");
          System.exit(-1);
        }
  
  
        Job job = Job.getInstance(new Configuration());
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        job.setMapperClass(WordMapper.class); 
        job.setReducerClass(SumReducer.class);  

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.setJarByClass(WordCount.class);

        job.submit();
        
        
        
  

  
 }
}