Interacting With HDFS

The VMware image will expose a single-node HDFS instance for your use in MapReduce applications. If you are logged in to the virtual machine, you can interact with HDFS using the command-line tools described in Module 2. You can also manipulate HDFS through the MapReduce plugin.
Using the Command Line

An interesting MapReduce task will require some external data to process: log files, web crawl results, etc. Before you can begin processing with MapReduce, data must be loaded into its distributed file system. In Module 2, you learned how to copy files from the local file system into HDFS. But this will copy files from the local file system of the VM into HDFS - not from the file system of your host computer.

To load data into HDFS in the virtual machine, you have several options available to you:

1. scp the files to the virtual machine, and then use the bin/hadoop fs -put ... syntax to copy the files from the VM's local file system into HDFS,
2. pipe the data from the local machine into a put command reading from stdin,
3. or install the Hadoop tools on the host system and configure it to communicate directly with the guest instance

We will review each of these in turn.

To load data into HDFS using the command line within the virtual machine, you can first send the data to the VM's local disk, then insert it into HDFS. You can send files to the VM using an scp client, such as the pscp component of putty, or WinSCP.

scp will allow you to copy files from one machine to another over the network. The scp command takes two arguments, both of the form [[username@]hostname]:filename. The scp command itself is of the form scp source dest, where source and dest are formatted as described above. By default, it will assume that paths are on the local host, and should be accessed using the current username. You can override the username and hostname to perform remote copies.

So supposing you have a file named foo.txt, and you would like to copy this into the virtual machine which has IP address, you can perform this operation with the command:

$ scp foo.txt hadoop-user@

If you are using the pscp program, substitute pscp instead of scp above. A copy of the "regular" scp can be run under cygwin by downloading the OpenSSH package. pscp is a utility by the makers of putty and does not require cygwin.

Note that since we did not specify a destination directory, it will go in /home/hadoop-user by default. To change the target directory, specify it after the hostname (e.g., hadoop-user@ You can also omit the destination filename, if you want it to be identical to the source filename. However, if you omit both the target directory and filename, you must not forget the colon (":") that follows the target hostname. Otherwise it will make a local copy of the file, with the name An equivalent correct command to copy foo.txt to /home/hadoop-user on the remote machine is:

$ scp foo.txt hadoop-user@

Windows users may be more inclined to use a GUI tool to perform scp commands. The free WinSCP program provides an FTP-like GUI interface over scp.

After you have copied files into the local disk of the virtual machine, you can log in to the virtual machine as hadoop-user and insert the files into HDFS using the standard Hadoop commands. For example,

hadoop-user@vm-instance:hadoop$ bin/hadoop dfs -put ~/foo.txt \

A second option available to upload individual files to HDFS from the host machine is to echo the file contents into a put command running via ssh. e.g., assuming you have the cat program (which comes with Linux or cygwin) to echo the contents of a file to the terminal output, you can connect its output to the input of a put command running over ssh like so:

you@host-machine$ cat somefile | ssh hadoop-user@vm-ip-addr \
"hadoop/bin/hadoop fs -put - destinationfile

The - as an argument to the put command instructs the system to use stdin as its input file. This will copy somefile on the host machine to destinationfile in HDFS on the virtual machine.

Finally, if you are running either Linux or cygwin, you can copy the /hadoop-0.18.0 directory on the CD to your local instance. You can then configure hadoop-site.xml to use the virtual machine as the default distributed file system (by setting the parameter). If you then run bin/hadoop fs -put ... commands on this machine (or any other hadoop commands, for that matter), they will interact with HDFS as served by the virtual machine. See the Hadoop quickstart for instructions on configuring a Hadoop installation, or Module 7 for a more thorough treatment.
Using the MapReduce Plugin For Eclipse

An easier way to manipulate files in HDFS may be through the Eclipse plugin. In the DFS location viewer, right-click on any folder to see a list of actions available. You can create new subdirectories, upload individual files or whole subdirectories, or download files and directories to the local disk.

If /user/hadoop-user does not exist, create that first. Right-click on the top-level directory and select "Create New Directory". Type "user" and click OK. You will then need to refresh the current directory view by right-clicking and selecting "Refresh" from the pop-up menu. Repeat this process to create the "hadoop-user" directory under "user."

Now, prepare some local files to upload. Somewhere on your hard drive, create a directory named "input" and find some text files to copy there. In the DFS explorer, right-click the "hadoop-user" directory and click "Upload Directory to DFS." Select your new input folder and click OK. Eclipse will copy the files directly into HDFS, bypassing the local drive of the virtual machine. You may have to refresh the directory view to see your changes. You should now have a directory hierarchy containing the /user/hadoop-user/input directory, which has at least one text file in it.
Running a Sample Program

While we have not yet formally introduced the programming style for Hadoop, we can still test whether a MapReduce program will run on our Hadoop virtual machine. This section walks you through the steps required to verify this.

The program that we will run is a word count utility. The program will read the files you uploaded to HDFS in the previous section, and determine how many times each word in the files appears.

If you have not already done so, start the virtual machine and Eclipse, and switch Eclipse to use the MapReduce perspective. Instructions are in the previous section.
Creating the Project

In the menu, click File * New * Project. Select "Map/Reduce Project" from the list and click Next.

You now need to select a project name. Any name will do, e.g., "WordCount". You will also need to specify the Hadoop Library Installation Path. This is the path where you made a copy of the /hadoop-0.18.0 folder on the CD. Since we have not yet configured this part of Eclipse, do so now by clicking "Configure Hadoop install directory..." and choosing the path where you copied Hadoop to. There should be a file named hadoop-0.18.0-core.jar in this directory. Creating a MapReduce Project instead of a generic Java project automatically adds the prerequisite jar files to the build path. If you create a regular Java project, you must add the Hadoop jar (and its dependencies) to the build path manually.

When you have completed these steps, click Finish.
Creating the Source Files

Our program needs three classes to run: a Mapper, a Reducer, and a Driver. The Driver tells Hadoop how to run the MapReduce process. The Mapper and Reducer operate on your data.

Right-click on the "src" folder under your project and select New * Other.... In the "Map/Reduce" folder on the resulting window, we can create Mapper, Reducer, and Driver classes based on pre-written stub code. Create classes named WordCountMapper, WordCountReducer, and WordCount that use the Mapper, Reducer, and Driver stubs respectively.

The code for each of these classes is shown here. You can copy this code into your files.

import java.util.StringTokenizer;

import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;

public class WordCountMapper extends MapReduceBase
implements Mapper {

private final IntWritable one = new IntWritable(1);
private Text word = new Text();

public void map(WritableComparable key, Writable value,
OutputCollector output, Reporter reporter) throws IOException {

String line = value.toString();
StringTokenizer itr = new StringTokenizer(line.toLowerCase());
while(itr.hasMoreTokens()) {
output.collect(word, one);

import java.util.Iterator;

import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;

public class WordCountReducer extends MapReduceBase
implements Reducer {

public void reduce(Text key, Iterator values,
OutputCollector output, Reporter reporter) throws IOException {

int sum = 0;
while (values.hasNext()) {
IntWritable value = (IntWritable);
sum += value.get(); // process value

output.collect(key, new IntWritable(sum));

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;

public class WordCount {

public static void main(String[] args) {
JobClient client = new JobClient();
JobConf conf = new JobConf(WordCount.class);

// specify output types

// specify input and output dirs
FileInputPath.addInputPath(conf, new Path("input"));
FileOutputPath.addOutputPath(conf, new Path("output"));

// specify a mapper

// specify a reducer

try {
} catch (Exception e) {

For now, don't worry about how these functions work; we will introduce how to write MapReduce programs in Module 4. We currently just want to establish that we can run jobs on the virtual machine.
Launching the Job

After the code has been entered, it is time to run it. You have already created a directory named input below /user/hadoop-user in HDFS. This will serve as the input files to this process. In the Project Explorer, right-click on the driver class, In the pop-up menu, select Run As * Run On Hadoop. A window will appear asking you to select a Hadoop location to run on. Select the VMware server that you configured earlier, and click Finish.

If all goes well, the progress output from Hadoop should appear in the console in Eclipse; it should look something like:

08/06/25 12:14:22 INFO mapred.FileInputFormat: Total input paths to process : 3
08/06/25 12:14:23 INFO mapred.JobClient: Running job: job_200806250515_0002
08/06/25 12:14:24 INFO mapred.JobClient: map 0% reduce 0%
08/06/25 12:14:31 INFO mapred.JobClient: map 50% reduce 0%
08/06/25 12:14:33 INFO mapred.JobClient: map 100% reduce 0%
08/06/25 12:14:42 INFO mapred.JobClient: map 100% reduce 100%
08/06/25 12:14:43 INFO mapred.JobClient: Job complete: job_200806250515_0002
08/06/25 12:14:43 INFO mapred.JobClient: Counters: 12
08/06/25 12:14:43 INFO mapred.JobClient: Job Counters
08/06/25 12:14:43 INFO mapred.JobClient: Launched map tasks=4
08/06/25 12:14:43 INFO mapred.JobClient: Launched reduce tasks=1
08/06/25 12:14:43 INFO mapred.JobClient: Data-local map tasks=4
08/06/25 12:14:43 INFO mapred.JobClient: Map-Reduce Framework
08/06/25 12:14:43 INFO mapred.JobClient: Map input records=211
08/06/25 12:14:43 INFO mapred.JobClient: Map output records=1609
08/06/25 12:14:43 INFO mapred.JobClient: Map input bytes=11627
08/06/25 12:14:43 INFO mapred.JobClient: Map output bytes=16918
08/06/25 12:14:43 INFO mapred.JobClient: Combine input records=1609
08/06/25 12:14:43 INFO mapred.JobClient: Combine output records=682
08/06/25 12:14:43 INFO mapred.JobClient: Reduce input groups=568
08/06/25 12:14:43 INFO mapred.JobClient: Reduce input records=682
08/06/25 12:14:43 INFO mapred.JobClient: Reduce output records=568

In the DFS Explorer, right-click on /user/hadoop-user and select "Refresh." You should now see an "output" directory containing a file named part-00000. This is the output of the job. Double-clicking this file will allow you to view it in Eclipse; you can see each word and its frequency in the documents. (You may receive a warning that this file is larger than 1 MB, first. Click OK.)

If you want to run the job again, you will need to delete the output directory first. Right-click the output directory in the DFS Explorer and click "Delete."

Congratulations! You should now have a functioning Hadoop development environment.


  1. This comment has been removed by a blog administrator.

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    i tried the method from and it gives an error : usage java datanode [-rollback]

    any advice would be appreciated! thanks

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