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Running Hadoop On Ubuntu Linux (Single-Node Cluster)

[日期:2014-04-10] 来源:  作者: [字体: ]

What we want to do

in this short tutorial, I will describe the required steps for setting up a single-node Hadoop cluster using the Hadoop Distributed File System (HDFS) on Ubuntu Linux.

Are you looking for the multi-node cluster tutorial? Just head over there.

Hadoop is a framework written in Java for running applications on large clusters of commodity hardware and incorporates features similar to those of the Google File System and of MapReduce. HDFS is a highly fault-tolerant distributed file system and like Hadoop designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications that have large data sets.

Figure 1: Cluster of machines running Hadoop at Yahoo! (Source: Yahoo!)

The main goal of this tutorial is to get a simple Hadoop installation up and running so that you can play around with the software and learn more about it.

This tutorial has been tested with the following software versions:

  • Ubuntu Linux 8.04, 7.10, 7.04
  • Hadoop 0.18.0, released August 2008 (also works with 0.13.x - 0.17.x)

You can find the time of the last document update at the very bottom of this page.

Prerequisites Sun Java 6

Hadoop requires a working Java 1.5.x (aka 5.0.x) installation. However, using Java 1.6.x (aka 6.0.x aka 6) is recommended for running Hadoop. For the sake of this tutorial, I will therefore describe the installation of Java 1.6. But if you still want Java 1.5 for whatever reason, simply use the package sun-java5-jdk and adjust the paths described below as needed.

Install Sun's Java Development Kit v1.6.0 aka "Sun Java(TM) Development Kit (JDK) 6" as it is named on Ubuntu via Synaptic (System > Administration > Synaptic Package Manager) or via apt-get: Install the package

sun-java6-jdk

for the full JDK which will be placed in /usr/lib/jvm/java-6-sun (well, this directory is actually a symlink on Ubuntu).

After installation, check if Sun's JDK is at the top of /etc/jvm. For example, mine looks like this:

# /etc/jvm

# # This file defines the default system JVM search order. Each

# JVM should list their JAVA_HOME compatible directory in this file.

# The default system JVM is the first one available from top to

# bottom.

/usr/lib/jvm/java-6-sun

/usr/lib/jvm/java-gcj

/usr/lib/jvm/ia32-java-1.5.0-sun

/usr/lib/jvm/java-1.5.0-sun /usr

Adding a dedicated Hadoop system user

We will use a dedicated Hadoop user account for running Hadoop. While that's not required it is recommended because it helps to separate the Hadoop installation from other software applications and user accounts running on the same machine (think: security, permissions, backups, etc).

$ sudo addgroup hadoop $ sudo adduser --ingroup hadoop hadoop

This will add the user hadoop and the group hadoop to your local machine.

Configuring SSH

Hadoop requires SSH access to manage its nodes, i.e. remote machines plus your local machine if you want to use Hadoop on it (which is what we want to do in this short tutorial). For our single-node setup of Hadoop, we therefore need to configure SSH access to localhost for the hadoop user we create in the previous section.

I assume that you have SSH up and running on your machine and configured it to allow SSH public key authentication. If not, there are several guides available.

First, we have to generate an SSH key for the hadoop user.

noll@ubuntu:~$ su - hadoop

hadoop@ubuntu:~$ ssh-keygen -t rsa -P "" Generating public/private rsa key pair.

Enter file in which to save the key (/home/hadoop/.ssh/id_rsa): Created directory '/home/hadoop/.ssh'. Your identification has been saved in /home/hadoop/.ssh/id_rsa. Your public key has been saved in /home/hadoop/.ssh/id_rsa.pub.

The key fingerprint is: 9d:47:ab:d7:22:54:f0:f9:b9:3b:64:93:12:75:81:27 hadoop@ubuntu hadoop@ubuntu:~$

The second line will create an RSA key pair with an empty password. Generally, using an empty password is not recommended, but in this case it is needed to unlock the key without your interaction (you don't want to enter the passphrase every time Hadoop interacts with its nodes).

Second, you have to enable SSH access to your local machine with this newly created key.

hadoop@ubuntu:~$ cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys

The final step is to test the SSH setup by connecting to your local machine with the hadoop user. The step is also needed to save your local machine's host key fingerprint to the hadoop user's known_hosts file. If you have any special SSH configuration for your local machine like a non-standard SSH port, you can define host-specific SSH options in $HOME/.ssh/config (see man ssh_config for more information).

hadoop@ubuntu:~$ ssh localhost The authenticity of host 'localhost (127.0.0.1)' can't be established. RSA key fingerprint is 76:d7:61:86:ea:86:8f:31:89:9f:68:b0:75:88:52:72. Are you sure you want to continue connecting (yes/no)? yes Warning: Permanently added 'localhost' (RSA) to the list of known hosts. Ubuntu 8.04

.. hadoop@ubuntu:~$

If the SSH connect should fail, these general tips might help:

  • Enable debugging with ssh -vvv localhost and investigate the error in detail.
  • Check the SSH server configuration in /etc/ssh/sshd_config, in particular the options PubkeyAuthentication (which should be set to yes) and AllowUsers (if this option is active, add the hadoop user to it). If you made any changes to the SSH server configuration file, you can force a configuration reload with sudo /etc/init.d/ssh reload.

Disabling IPv6

I have not found out yet how to configure Hadoop to listen on all IPv4 (again: IPv4) network interfaces. Using 0.0.0.0 for the various networking-related Hadoop configuration options will result in Hadoop binding to the IPv6 addresses on my Ubuntu box.

As a workaround (and realizing that there's no practical point in enabling IPv6 on a box when you are not connected to any IPv6 network), I simply disabled IPv6 on my Ubuntu machine.

To disable IPv6 on Ubuntu Linux, open /etc/modprobe.d/blacklist in the editor of your choice and add the following lines to the end of the file:

# disable IPv6 blacklist ipv6

You have to reboot your machine in order to make the changes take effect.

Hadoop Installation

You have to download Hadoop from the Apache Download Mirrors and extract the contents of the Hadoop package to a location of your choice. I picked /usr/local/hadoop. Make sure to change the owner of all the files to the hadoop user and group, for example:

$ cd /usr/local $ sudo tar xzf hadoop-0.18.0.tar.gz $ sudo mv hadoop-0.18.0 hadoop $ sudo chown -R hadoop:hadoop hadoop

(just to give you the idea, YMMV - personally, I create a symlink from hadoop-0.18.0 to hadoop)

Excursus: Hadoop Distributed File System (HDFS)

From The Hadoop Distributed File System: Architecture and Design:

The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS was originally built as infrastructure for the Apache Nutch web search engine project. HDFS is part of the Apache Hadoop project, which is part of the Apache Lucene project.

The following picture gives an overview of the most important HDFS components.

HDFS Architecture (source: http://hadoop.apache.org/core/docs/current/hdfs_design.html )

Configuration

Our goal in this tutorial is a single-node setup of Hadoop. More information of what we do in this section is available on the Hadoop Wiki.

hadoop-env.sh

The only required environment variable we have to configure for Hadoop in this tutorial is JAVA_HOME. Open <HADOOP_INSTALL>/conf/hadoop-env.sh in the editor of your choice (if you used the installation path in this tutorial, the full path is /usr/local/hadoop/conf/hadoop-env.sh) and set the JAVA_HOME environment variable to the Sun JDK/JRE 6 directory.

Change

# The java implementation to use. Required. # export JAVA_HOME=/usr/lib/j2sdk1.5-sun

to

# The java implementation to use. Required. export JAVA_HOME=/usr/lib/jvm/java-6-sun

If you chose to use Java 1.5, remember to put the correct paths in here!

hadoop-site.xml

Any site-specific configuration of Hadoop is configured in <HADOOP_INSTALL>/conf/hadoop-site.xml. Here we will configure the directory where Hadoop will store its data files, the ports it listens to, etc. Our setup will use Hadoop's Distributed File System, HDFS, even though our little "cluster" only contains our single local machine.

You can leave the settings below as is with the exception of the hadoop.tmp.dir variable which you have to change to the directory of your choice, for example /usr/local/hadoop-datastore/hadoop-${user.name}. Hadoop will expand ${user.name} to the system user which is running Hadoop, so in our case this will be hadoop and thus the final path will be /usr/local/hadoop-datastore/hadoop-hadoop.

Note: Depending on your choice of location, you might have to create the directory manually with sudo mkdir /your/path; sudo chown hadoop:hadoop /your/path in case the hadoop user does not have the required permissions to do so (otherwise, you will see a java.io.IOException when you try to format the name node in the next section).

<?xml version="1.0"?> <?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

 

<!-- Put site-specific property overrides in this file. --><configuration>

<property>

<name>hadoop.tmp.dir</name> <value>/your/path/to/hadoop/tmp/dir/hadoop-${user.name}</value> <description>A base for other temporary directories.</description>

</property>

<property><name>fs.default.name</name> <value>hdfs://localhost:54310</value> <description>The name of the default file system. A URI whose scheme and authority determine the FileSystem implementation. The uri's scheme determines the config property (fs.SCHEME.impl) naming the FileSystem implementation class. The uri's authority is used to determine the host, port, etc. for a filesystem.</description></property>

<property>

<name>mapred.job.tracker</name> <value>localhost:54311</value> <description>The host and port that the MapReduce job tracker runs at. If "local", then jobs are run in-process as a single map and reduce task. </description>

</property>

<property>

<name>dfs.replication</name> <value>1</value> <description>Default block replication. The actual number of replications can be specified when the file is created. The default is used if replication is not specified in create time. </description>

</property>

</configuration>

See Getting Started with Hadoop and the documentation in Hadoop's API Overview if you have any questions about Hadoop's configuration options.

Formatting the name node

The first step to starting up your Hadoop installation is formatting the Hadoop filesystem which is implemented on top of the local filesystem of your "cluster" (which includes only your local machine if you followed this tutorial). You need to do this the first time you set up a Hadoop cluster. Do not format a running Hadoop filesystem, this will cause all your data to be erased.

To format the filesystem (which simply initializes the directory specified by the dfs.name.dir variable), run the command

hadoop@ubuntu:~$ <HADOOP_INSTALL>/hadoop/bin/hadoop namenode -format

The output will look like this:

hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop namenode -format

07/09/21 12:00:25 INFO dfs.NameNode: STARTUP_MSG: /*********************************************************** STARTUP_MSG: Starting NameNode STARTUP_MSG: host = ubuntu/127.0.0.1 STARTUP_MSG: args = [-format]

**********************************************************/ 07/09/21 12:00:25 INFO dfs.Storage: Storage directory [...] has been successfully formatted.

07/09/21 12:00:25 INFO dfs.NameNode: SHUTDOWN_MSG: /*********************************************************** SHUTDOWN_MSG: Shutting down NameNode at ubuntu/127.0.0.1

**********************************************************/ hadoop@ubuntu:/usr/local/hadoop$

Starting your single-node cluster

Run the command:

hadoop@ubuntu:~$ <HADOOP_INSTALL>/bin/start-all.sh

This will startup a Namenode, Datanode, Jobtracker and a Tasktracker on your machine.

The output will look like this:

hadoop@ubuntu:/usr/local/hadoop$ bin/start-all.sh starting namenode, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-namenode-ubuntu.out localhost: starting datanode, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-datanode-ubuntu.out localhost: starting secondarynamenode, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-secondarynamenode-ubuntu.out starting jobtracker, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-jobtracker-ubuntu.out localhost: starting tasktracker, logging to /usr/local/hadoop/bin/../logs/hadoop-hadoop-tasktracker-ubuntu.out hadoop@ubuntu:/usr/local/hadoop$

A nifty tool for checking whether the expected Hadoop processes are running is jps (part of Sun's Java since v1.5.0). See also How to debug MapReduce programs.

hadoop@sea:/usr/local/hadoop/$ jps

19811 TaskTracker 19674 SecondaryNameNode 19735 JobTracker 19497 NameNode 20879 TaskTracker$Child 21810 Jps

You can also check with netstat if Hadoop is listening on the configured ports.

hadoop@ubuntu:~$ sudo netstat -plten | grep java tcp 0 0 0.0.0.0:50050 0.0.0.0:* LISTEN 1001 86234 23634/java tcp 0 0 127.0.0.1:54310 0.0.0.0:* LISTEN 1001 85800 23317/java tcp 0 0 127.0.0.1:54311 0.0.0.0:* LISTEN 1001 86383 23543/java tcp 0 0 0.0.0.0:50090 0.0.0.0:* LISTEN 1001 86119 23478/java tcp 0 0 0.0.0.0:50060 0.0.0.0:* LISTEN 1001 86233 23634/java tcp 0 0 0.0.0.0:50030 0.0.0.0:* LISTEN 1001 86393 23543/java tcp 0 0 0.0.0.0:50070 0.0.0.0:* LISTEN 1001 85964 23317/java tcp 0 0 0.0.0.0:50010 0.0.0.0:* LISTEN 1001 86045 23389/java tcp 0 0 0.0.0.0:50075 0.0.0.0:* LISTEN 1001 86102 23389/java hadoop@ubuntu:~$

If there are any errors, examine the log files in the <HADOOP_INSTALL>/logs/ directory.

Stopping your single-node cluster

Run the command

hadoop@ubuntu:~$ <HADOOP_INSTALL>/bin/stop-all.sh

to stop all the daemons running on your machine.

Exemplary output:

hadoop@ubuntu:/usr/local/hadoop$ bin/stop-all.sh stopping jobtracker localhost: Ubuntu 8.04 localhost: stopping tasktracker stopping namenode localhost: Ubuntu 8.04 localhost: stopping datanode localhost: Ubuntu 8.04 localhost: stopping secondarynamenode hadoop@ubuntu:/usr/local/hadoop$

Running a MapReduce job

We will now run your first Hadoop MapReduce job. We will use the WordCount example job which reads text files and counts how often words occur. The input is text files and the output is text files, each line of which contains a word and the count of how often it occurred, separated by a tab. More information of what happens behind the scenes is available at the Hadoop Wiki.

Download example input data

We will use three ebooks from Project Gutenberg for this example:

      • The Outline of Science, Vol. 1 (of 4) by J. Arthur Thomson
      • The Notebooks of Leonardo Da Vinci
      • Ulysses by James Joyce

 

Download each ebook as plain text files in us-ascii encoding and store the uncompressed files in a temporary directory of choice, for example /tmp/gutenberg.

hadoop@ubuntu:~$ ls -l /tmp/gutenberg/ total 3592 -rw-r--r-- 1 hadoop hadoop 674425 2007-01-22 12:56 20417-8.txt -rw-r--r-- 1 hadoop hadoop 1423808 2006-08-03 16:36 7ldvc10.txt -rw-r--r-- 1 hadoop hadoop 1561677 2004-11-26 09:48 ulyss12.txt hadoop@ubuntu:~$

Restart the Hadoop cluster

Restart your Hadoop cluster if it's not running already.

hadoop@ubuntu:~$ <HADOOP_INSTALL>/bin/start-all.sh

Copy local example data to HDFS

Before we run the actual MapReduce job, we first have to copy the files from our local file system to Hadoop's HDFS.

hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -copyFromLocal /tmp/gutenberg gutenberg hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls Found 1 items

/user/hadoop/gutenberg <dir> hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls gutenberg Found 3 items /user/hadoop/gutenberg/20417-8.txt <r 1> 674425 /user/hadoop/gutenberg/7ldvc10.txt <r 1> 1423808 /user/hadoop/gutenberg/ulyss12.txt <r 1> 1561677

Run the MapReduce job

Now, we actually run the WordCount example job.

hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar hadoop-0.18.0-examples.jar wordcount gutenberg gutenberg-output

This command will read all the files in the HDFS directory gutenberg, process it, and store the result in the HDFS directory gutenberg-output.

Exemplary output of the previous command in the console:

hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar hadoop-0.18.0-examples.jar wordcount gutenberg gutenberg-output

07/09/21 13:00:30 INFO mapred.FileInputFormat: Total input paths to process : 3 07/09/21 13:00:31 INFO mapred.JobClient: Running job: job_200709211255_0001 07/09/21 13:00:32 INFO mapred.JobClient: map 0% reduce 0% 07/09/21 13:00:42 INFO mapred.JobClient: map 66% reduce 0% 07/09/21 13:00:47 INFO mapred.JobClient: map 100% reduce 22% 07/09/21 13:00:54 INFO mapred.JobClient: map 100% reduce 100% 07/09/21 13:00:55 INFO mapred.JobClient: Job complete: job_200709211255_0001 07/09/21 13:00:55 INFO mapred.JobClient: Counters: 12 07/09/21 13:00:55 INFO mapred.JobClient: Job Counters 07/09/21 13:00:55 INFO mapred.JobClient: Launched map tasks=3 07/09/21 13:00:55 INFO mapred.JobClient: Launched reduce tasks=1 07/09/21 13:00:55 INFO mapred.JobClient: Data-local map tasks=3 07/09/21 13:00:55 INFO mapred.JobClient: Map-Reduce Framework 07/09/21 13:00:55 INFO mapred.JobClient: Map input records=77637 07/09/21 13:00:55 INFO mapred.JobClient: Map output records=628439 07/09/21 13:00:55 INFO mapred.JobClient: Map input bytes=3659910 07/09/21 13:00:55 INFO mapred.JobClient: Map output bytes=6061344 07/09/21 13:00:55 INFO mapred.JobClient: Combine input records=628439 07/09/21 13:00:55 INFO mapred.JobClient: Combine output records=103910 07/09/21 13:00:55 INFO mapred.JobClient: Reduce input groups=85096 07/09/21 13:00:55 INFO mapred.JobClient: Reduce input records=103910 07/09/21 13:00:55 INFO mapred.JobClient: Reduce output records=85096 hadoop@ubuntu:/usr/local/hadoop$

Check if the result is successfully stored in HDFS directory gutenberg-output:

hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls Found 2 items

/user/hadoop/gutenberg <dir> /user/hadoop/gutenberg-output <dir> hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -ls gutenberg-output Found  1 items /user/hadoop/gutenberg-output/part-00000 <r 1> 903193 hadoop@ubuntu:/usr/local/hadoop$

If you want to modify some Hadoop settings on the fly like increasing the number of Reduce tasks, you can use the "-D" option:

hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar hadoop-0.18.0-examples.jar wordcount -D mapred.reduce.tasks=16 gutenberg gutenberg-output

An important note about mapred.map.tasks: Hadoop does not honor mapred.map.tasks beyond considering it a hint. But it accepts the user specified mapred.reduce.tasks and doesn't manipulate that. You cannot force mapred.map.tasks but can specify mapred.reduce.tasks.

Retrieve the job result from HDFS

To inspect the file, you can copy it from HDFS to the local file system. Alternatively, you can use the command

hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -cat gutenberg-output/part-00000

to read the file directly from HDFS without copying it to the local file system. In this tutorial, we will copy the results to the local file system though.

hadoop@ubuntu:/usr/local/hadoop$ mkdir /tmp/gutenberg-output hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop dfs -copyToLocal gutenberg-output/part-00000 /tmp/gutenberg-output hadoop@ubuntu:/usr/local/hadoop$ head /tmp/gutenberg-output/part-00000 "(Lo)cra" 1 "1490 1 "1498," 1 "35" 1 "40," 1 "A 2 "AS-IS". 2 "A_ 1 "Absoluti 1 "Alack! 1 hadoop@ubuntu:/usr/local/hadoop$

Note that in this specific output the quote signs (") enclosing the words in the head output above have not been inserted by Hadoop. They are the result of the word tokenizer used in the WordCount example, and in this case they matched the beginning of a quote in the ebook texts. Just inspect the part-00000 file further to see it for yourself. Hadoop Web Interfaces

Hadoop comes with several web interfaces which are by default (see conf/hadoop-default.xml) available at these locations:

http://localhost:50030/ - web UI for MapReduce job tracker(s)

http://localhost:50060/ - web UI for task tracker(s)

http://localhost:50070/ - web UI for HDFS name node(s) 

These web interfaces provide concise information about what's happening in your Hadoop cluster. You might want to give them a try.

MapReduce Job Tracker Web Interface

The job tracker web UI provides information about general job statistics of the Hadoop cluster, running/completed/failed jobs and a job history log file. It also gives access to the local machine's Hadoop log files (the machine on which the web UI is running on).

By default, it's available at http://localhost:50030/.

Figure 2: A screenshot of Hadoop's Job Tracker web interface.

Task Tracker Web Interface

The task tracker web UI shows you running and non-running tasks. It also gives access to the local machine's Hadoop log files.

By default, it's available at http://localhost:50060/

Figure 3: A screenshot of Hadoop's Task Tracker web interface.

HDFS Name Node Web Interface

The name node web UI shows you a cluster summary including information about total/remaining capacity, live and dead nodes. Additionally, it allows you to browse the HDFS namespace and view the contents of its files in the web browser. It also gives access to the local machine's Hadoop log files.

By default, it's available at http://localhost:50070/.

Figure 4: A screenshot of Hadoop's Name Node web interface.

What's next?

If you're feeling comfortable, you can continue your Hadoop experience with my follow-up tutorial Running Hadoop On Ubuntu Linux (Multi-Node Cluster) where I describe how to build a Hadoop multi-node cluster with two Ubuntu boxes (this will increase your current cluster size by 100% :-P).

In addition, I wrote tutorial on how to code a simple MapReduce job in the Python programming language which can serve as the basis for writing your own MapReduce programs.

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