How to Install and Set Up a 3-Node Hadoop Cluster

What is Hadoop?

Hadoop is an open-source Apache project that allows creation of parallel processing applications on large data sets, distributed across networked nodes. It is composed of the Hadoop Distributed File System (HDFS™) that handles scalability and redundancy of data across nodes, and Hadoop YARN, a framework for job scheduling that executes data processing tasks on all nodes.

Before You Begin

Create 3 Linode Compute Instances. They’ll be referred to throughout this guide as node-master, node1, and node2. See our Getting Started with Linode and Creating a Compute Instance guides.
Add a Private IP Address to each Linode so that your Cluster can communicate with an additional layer of security.
Follow the Setting Up and Securing a Compute Instance guide to harden each of the three servers. It is recommended that you set the hostname of each Linode to match the naming convention used when creating them. Create a normal user for the Hadoop installation, and a user called hadoop for the Hadoop daemons. Do not create SSH keys for hadoop users. SSH keys will be addressed in a later section.
Install the JDK using the appropriate guide for your distribution, Debian, CentOS or Ubuntu, or install the latest JDK from Oracle.
The steps below use example IPs for each node. Adjust each example according to your configuration:
- node-master: 192.0.2.1
- node1: 192.0.2.2
- node2: 192.0.2.3
Note
This guide is written for a non-root user. Commands that require elevated privileges are prefixed with sudo. If you’re not familiar with the sudo command, see the Users and Groups guide. All commands in this guide are run with the hadoop user if not specified otherwise.

Architecture of a Hadoop Cluster

Before configuring the master and worker nodes, it’s important to understand the different components of a Hadoop cluster.

A master node maintains knowledge about the distributed file system, like the inode table on an ext3 filesystem, and schedules resources allocation. node-master will handle this role in this guide, and host two daemons:

The NameNode manages the distributed file system and knows where stored data blocks inside the cluster are.
The ResourceManager manages the YARN jobs and takes care of scheduling and executing processes on worker nodes.

Worker nodes store the actual data and provide processing power to run the jobs. They’ll be node1 and node2, and will host two daemons:

The DataNode manages the physical data stored on the node; it’s named, NameNode.
The NodeManager manages execution of tasks on the node.

Configure the System

Create Host File on Each Node

For each node to communicate with each other by name, edit the /etc/hosts file to add the private IP addresses of the three servers. Don’t forget to replace the sample IP with your IP:

File: /etc/hosts

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192.0.2.1    node-master
192.0.2.2    node1
192.0.2.3    node2

Distribute Authentication Key-pairs for the Hadoop User

The master node will use an SSH connection to connect to other nodes with key-pair authentication. This will allow the master node to actively manage the cluster.

Login to node-master as the hadoop user, and generate an SSH key:
```
ssh-keygen -b 4096
```
When generating this key, leave the password field blank so your Hadoop user can communicate unprompted.
View the node-master public key and copy it to your clipboard to use with each of your worker nodes.
```
less /home/hadoop/.ssh/id_rsa.pub
```
In each Linode, make a new file master.pub in the /home/hadoop/.ssh directory. Paste your public key into this file and save your changes.

Copy your key file into the authorized key store.

cat ~/.ssh/master.pub >> ~/.ssh/authorized_keys

Download and Unpack Hadoop Binaries

Log into node-master as the hadoop user, download the Hadoop tarball from Hadoop project page, and unzip it:

cd
wget http://apache.cs.utah.edu/hadoop/common/current/hadoop-3.1.2.tar.gz
tar -xzf hadoop-3.1.2.tar.gz
mv hadoop-3.1.2 hadoop

Set Environment Variables

Add Hadoop binaries to your PATH. Edit /home/hadoop/.profile and add the following line:
File: /home/hadoop/.profile
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PATH=/home/hadoop/hadoop/bin:/home/hadoop/hadoop/sbin:$PATH

Add Hadoop to your PATH for the shell. Edit .bashrc and add the following lines:

File: /home/hadoop/.bashrc

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export HADOOP_HOME=/home/hadoop/hadoop
export PATH=${PATH}:${HADOOP_HOME}/bin:${HADOOP_HOME}/sbin

Configure the Master Node

Configuration will be performed on node-master and replicated to other nodes.

Set JAVA_HOME

Find your Java installation path. This is known as JAVA_HOME. If you installed open-jdk from your package manager, you can find the path with the command:
```
update-alternatives --display java
```
Take the value of the current link and remove the trailing /bin/java. For example on Debian, the link is /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java, so JAVA_HOME should be /usr/lib/jvm/java-8-openjdk-amd64/jre.
If you installed java from Oracle, JAVA_HOME is the path where you unzipped the java archive.
Edit ~/hadoop/etc/hadoop/hadoop-env.sh and replace this line:
```
export JAVA_HOME=${JAVA_HOME}
```
with your actual java installation path. On a Debian 9 Linode with open-jdk-8 this will be as follows:
File: ~/hadoop/etc/hadoop/hadoop-env.sh
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export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/jre

Set NameNode Location

Update your ~/hadoop/etc/hadoop/core-site.xml file to set the NameNode location to node-master on port 9000:

File: ~/hadoop/etc/hadoop/core-site.xml

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<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
    <configuration>
        <property>
            <name>fs.default.name</name>
            <value>hdfs://node-master:9000</value>
        </property>
    </configuration>

Set path for HDFS

Edit hdfs-site.conf to resemble the following configuration:

File: ~/hadoop/etc/hadoop/hdfs-site.xml

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<configuration>
    <property>
            <name>dfs.namenode.name.dir</name>
            <value>/home/hadoop/data/nameNode</value>
    </property>

    <property>
            <name>dfs.datanode.data.dir</name>
            <value>/home/hadoop/data/dataNode</value>
    </property>

    <property>
            <name>dfs.replication</name>
            <value>1</value>
    </property>
</configuration>

The last property, dfs.replication, indicates how many times data is replicated in the cluster. You can set 2 to have all the data duplicated on the two nodes. Don’t enter a value higher than the actual number of worker nodes.

Set YARN as Job Scheduler

Edit the mapred-site.xml file, setting YARN as the default framework for MapReduce operations:

File: ~/hadoop/etc/hadoop/mapred-site.xml

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<configuration>
    <property>
            <name>mapreduce.framework.name</name>
            <value>yarn</value>
    </property>
    <property>
            <name>yarn.app.mapreduce.am.env</name>
            <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
    </property>
    <property>
            <name>mapreduce.map.env</name>
            <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
    </property>
    <property>
            <name>mapreduce.reduce.env</name>
            <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
    </property>
</configuration>

Configure YARN

Edit yarn-site.xml, which contains the configuration options for YARN. In the value field for the yarn.resourcemanager.hostname, replace 203.0.113.0 with the public IP address of node-master:

File: ~/hadoop/etc/hadoop/yarn-site.xml

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<configuration>
    <property>
            <name>yarn.acl.enable</name>
            <value>0</value>
    </property>

    <property>
            <name>yarn.resourcemanager.hostname</name>
            <value>203.0.113.0</value>
    </property>

    <property>
            <name>yarn.nodemanager.aux-services</name>
            <value>mapreduce_shuffle</value>
    </property>
</configuration>

Configure Workers

The file workers is used by startup scripts to start required daemons on all nodes. Edit ~/hadoop/etc/hadoop/workers to include both of the nodes:

File: ~/hadoop/etc/hadoop/workers

node1
node2

Configure Memory Allocation

Memory allocation can be tricky on low RAM nodes because default values are not suitable for nodes with less than 8GB of RAM. This section will highlight how memory allocation works for MapReduce jobs, and provide a sample configuration for 2GB RAM nodes.

The Memory Allocation Properties

A YARN job is executed with two kind of resources:

An Application Master (AM) is responsible for monitoring the application and coordinating distributed executors in the cluster.
Some executors that are created by the AM actually run the job. For a MapReduce jobs, they’ll perform map or reduce operation, in parallel.

Both are run in containers on worker nodes. Each worker node runs a NodeManager daemon that’s responsible for container creation on the node. The whole cluster is managed by a ResourceManager that schedules container allocation on all the worker-nodes, depending on capacity requirements and current charge.

Four types of resource allocations need to be configured properly for the cluster to work. These are:

How much memory can be allocated for YARN containers on a single node. This limit should be higher than all the others; otherwise, container allocation will be rejected and applications will fail. However, it should not be the entire amount of RAM on the node.
This value is configured in yarn-site.xml with yarn.nodemanager.resource.memory-mb.
How much memory a single container can consume and the minimum memory allocation allowed. A container will never be bigger than the maximum, or else allocation will fail and will always be allocated as a multiple of the minimum amount of RAM.
Those values are configured in yarn-site.xml with yarn.scheduler.maximum-allocation-mb and yarn.scheduler.minimum-allocation-mb.
How much memory will be allocated to the ApplicationMaster. This is a constant value that should fit in the container maximum size.
This is configured in mapred-site.xml with yarn.app.mapreduce.am.resource.mb.
How much memory will be allocated to each map or reduce operation. This should be less than the maximum size.
This is configured in mapred-site.xml with properties mapreduce.map.memory.mb and mapreduce.reduce.memory.mb.

The relationship between all those properties can be seen in the following figure:

Schema of memory allocation properties

Sample Configuration for 2GB Nodes

For 2GB nodes, a working configuration may be:

Property	Value
yarn.nodemanager.resource.memory-mb	1536
yarn.scheduler.maximum-allocation-mb	1536
yarn.scheduler.minimum-allocation-mb	128
yarn.app.mapreduce.am.resource.mb	512
mapreduce.map.memory.mb	256
mapreduce.reduce.memory.mb	256

Edit /home/hadoop/hadoop/etc/hadoop/yarn-site.xml and add the following lines:

File: ~/hadoop/etc/hadoop/yarn-site.xml

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<property>
        <name>yarn.nodemanager.resource.memory-mb</name>
        <value>1536</value>
</property>

<property>
        <name>yarn.scheduler.maximum-allocation-mb</name>
        <value>1536</value>
</property>

<property>
        <name>yarn.scheduler.minimum-allocation-mb</name>
        <value>128</value>
</property>

<property>
        <name>yarn.nodemanager.vmem-check-enabled</name>
        <value>false</value>
</property>

The last property disables virtual-memory checking which can prevent containers from being allocated properly with JDK8 if enabled.

Edit /home/hadoop/hadoop/etc/hadoop/mapred-site.xml and add the following lines:

File: ~/hadoop/etc/hadoop/mapred-site.xml

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<property>
        <name>yarn.app.mapreduce.am.resource.mb</name>
        <value>512</value>
</property>

<property>
        <name>mapreduce.map.memory.mb</name>
        <value>256</value>
</property>

<property>
        <name>mapreduce.reduce.memory.mb</name>
        <value>256</value>
</property>

Duplicate Config Files on Each Node

Copy the Hadoop binaries to worker nodes:

cd /home/hadoop/
scp hadoop-*.tar.gz node1:/home/hadoop
scp hadoop-*.tar.gz node2:/home/hadoop

Connect to node1 via SSH. A password isn’t required, thanks to the SSH keys copied above:
```
ssh node1
```
Unzip the binaries, rename the directory, and exit node1 to get back on the node-master:
```
tar -xzf hadoop-3.1.2.tar.gz
mv hadoop-3.1.2 hadoop
exit
```
Repeat steps 2 and 3 for node2.

Copy the Hadoop configuration files to the worker nodes:

for node in node1 node2; do
    scp ~/hadoop/etc/hadoop/* $node:/home/hadoop/hadoop/etc/hadoop/;
done

Format HDFS

HDFS needs to be formatted like any classical file system. On node-master, run the following command:

hdfs namenode -format

Your Hadoop installation is now configured and ready to run.

Run and monitor HDFS

This section will walk through starting HDFS on NameNode and DataNodes, and monitoring that everything is properly working and interacting with HDFS data.

Start and Stop HDFS

Start the HDFS by running the following script from node-master:
```
start-dfs.sh
```
This will start NameNode and SecondaryNameNode on node-master, and DataNode on node1 and node2, according to the configuration in the workers config file.
Check that every process is running with the jps command on each node. On node-master, you should see the following (the PID number will be different):
```
21922 Jps
21603 NameNode
21787 SecondaryNameNode
```
And on node1 and node2 you should see the following:
```
19728 DataNode
19819 Jps
```
To stop HDFS on master and worker nodes, run the following command from node-master:
```
stop-dfs.sh
```

Monitor your HDFS Cluster

You can get useful information about running your HDFS cluster with the hdfs dfsadmin command. Try for example:
```
hdfs dfsadmin -report
```
This will print information (e.g., capacity and usage) for all running DataNodes. To get the description of all available commands, type:
```
hdfs dfsadmin -help
```
You can also automatically use the friendlier web user interface. Point your browser to http://node-master-IP:9870, where node-master-IP is the IP address of your node-master, and you’ll get a user-friendly monitoring console.

Put and Get Data to HDFS

Writing and reading to HDFS is done with command hdfs dfs. First, manually create your home directory. All other commands will use a path relative to this default home directory:

hdfs dfs -mkdir -p /user/hadoop

Let’s use some textbooks from the Gutenberg project as an example.

Create a books directory in HDFS. The following command will create it in the home directory, /user/hadoop/books:
```
hdfs dfs -mkdir books
```

Grab a few books from the Gutenberg project:

cd /home/hadoop
wget -O alice.txt https://www.gutenberg.org/files/11/11-0.txt
wget -O holmes.txt https://www.gutenberg.org/files/1661/1661-0.txt
wget -O frankenstein.txt https://www.gutenberg.org/files/84/84-0.txt

Put the three books through HDFS, in the booksdirectory:

hdfs dfs -put alice.txt holmes.txt frankenstein.txt books

List the contents of the book directory:
```
hdfs dfs -ls books
```
Move one of the books to the local filesystem:
```
hdfs dfs -get books/alice.txt
```
You can also directly print the books from HDFS:
```
hdfs dfs -cat books/alice.txt
```

There are many commands to manage your HDFS. For a complete list, you can look at the Apache HDFS shell documentation, or print help with:

hdfs dfs -help

Run YARN

HDFS is a distributed storage system, and doesn’t provide any services for running and scheduling tasks in the cluster. This is the role of the YARN framework. The following section is about starting, monitoring, and submitting jobs to YARN.

Start and Stop YARN

Start YARN with the script:
```
start-yarn.sh
```
Check that everything is running with the jps command. In addition to the previous HDFS daemon, you should see a ResourceManager on node-master, and a NodeManager on node1 and node2.
To stop YARN, run the following command on node-master:
```
stop-yarn.sh
```

Monitor YARN

The yarn command provides utilities to manage your YARN cluster. You can also print a report of running nodes with the command:
```
yarn node -list
```
Similarly, you can get a list of running applications with command:
```
yarn application -list
```
To get all available parameters of the yarn command, see Apache YARN documentation.
As with HDFS, YARN provides a friendlier web UI, started by default on port 8088 of the Resource Manager. Point your browser to http://node-master-IP:8088, where node-master-IP is the IP address of your node-master, and browse the UI:

Submit MapReduce Jobs to YARN

YARN jobs are packaged into jar files and submitted to YARN for execution with the command yarn jar. The Hadoop installation package provides sample applications that can be run to test your cluster. You’ll use them to run a word count on the three books previously uploaded to HDFS.

Submit a job with the sample jar to YARN. On node-master, run:
```
yarn jar ~/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.2.jar wordcount "books/*" output
```
The last argument is where the output of the job will be saved - in HDFS.

After the job is finished, you can get the result by querying HDFS with hdfs dfs -ls output. In case of a success, the output will resemble:

Found 2 items
-rw-r--r--   2 hadoop supergroup          0 2019-05-31 17:21 output/_SUCCESS
-rw-r--r--   2 hadoop supergroup     789726 2019-05-31 17:21 output/part-r-00000

Print the result with:

hdfs dfs -cat output/part-r-00000 | less

Next Steps

Now that you have a YARN cluster up and running, you can:

Learn how to code your own YARN jobs with Apache documentation.
Install Spark on top on your YARN cluster with Linode Spark guide.
Secure your cluster with Apache YARN Secure containers.

More Information

You may wish to consult the following resources for additional information on this topic. While these are provided in the hope that they will be useful, please note that we cannot vouch for the accuracy or timeliness of externally hosted materials.