Hadoop Real-World Solutions Cookbook- Second Edition

Chapter 1. Getting Started with Hadoop 2.X

This chapter covers the following topics:

Installing a single-node Hadoop cluster
Installing a multi-node Hadoop cluster
Adding new nodes to existing Hadoop clusters
Executing the balancer command for uniform data distribution
Entering and exiting from the safe mode in a Hadoop cluster
Decommissioning DataNodes
Performing benchmarking on a Hadoop cluster

Installing a single-node Hadoop Cluster

In this recipe, we are going to learn how to install a single-node Hadoop cluster, which can be used for development and testing.

Getting ready

To install Hadoop, you need to have a machine with the UNIX operating system installed on it. You can choose from any well known UNIX OS such as Red Hat, CentOS, Ubuntu, Fedora, and Amazon Linux (this is in case you are using Amazon Web Service instances).

Here, we will be using the Ubuntu distribution for demonstration purposes.

How to do it...

Let's start installing Hadoop:

First of all, you need to download the required installers from the Internet. Here, we need to download Java and Hadoop installers. The following are the links to do this:
For the Java download, choose the latest version of the available JDK from http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html.
You can also use Open JDK instead of Oracle.
For the Hadoop 2.7 Download, go to
http://www.eu.apache.org/dist/hadoop/common/hadoop-2.7.0/hadoop-2.7.0.tar.gz.
We will first install Java. Here, I am using /usr/local as the installation directory and the root user for all installations. You can choose a directory of your choice.
Extract tar.gz like this:
```
tar -xzf java-7-oracle.tar.gz
```
Rename the extracted folder to give the shorter name Java instead of java-7-oracle. Doing this will help you remember the folder name easily.
Alternately, you can install Java using the apt-get package manager if your machine is connected to the Internet:
```
sudo apt-get update
sudo apt-get install openjdk-7-jdk
```
Similarly, we will extract and configure Hadoop. We will also rename the extracted folder for easier accessibility. Here, we will extract Hadoop to path /usr/local:
```
tar –xzf hadoop-2.7.0.tar.gz
mv hadoop-2.7.0 hadoop
```

Next, in order to use Java and Hadoop from any folder, we would need to add these paths to the ~/.bashrc file. The contents of the file get executed every time a user logs in:

cd ~
vi .bashrc

Once the file is open, append the following environment variable settings to it. These variables are used by Java and Hadoop at runtime:

export JAVA_HOME=/usr/local/java
export PATH=$PATH:$JAVA_HOME/bin
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
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_INSTALL/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_INSTALL/lib"

In order to verify whether our installation is perfect, close the terminal and restart it again. Also, check whether the Java and Hadoop versions can be seen:

$java -version
java version "1.7.0_45"
Java(TM) SE Runtime Environment (build 1.7.0_45-b18)
Java HotSpot(TM) Server VM (build 24.45-b08, mixed mode)

$ hadoop version
Hadoop 2.7.0
Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r d4c8d4d4d203c934e8074b31289a28724c0842cf
Compiled by jenkins on 2015-04-10T18:40Z
Compiled with protoc 2.5.0
From source with checksum a9e90912c37a35c3195d23951fd18f

This command was run using /usr/local/hadoop/share/hadoop/common/hadoop-common-2.7.0.jar.

Now that Hadoop and Java are installed and verified, we need to install ssh (Secure Shell) if it's not already available by default. If you are connected to the Internet, execute the following commands. SSH is used to secure data transfers between nodes:
```
sudo apt-get install openssh-client
sudo apt-get install openssh-server
```
Once the ssh installation is done, we need to execute the ssh configuration in order to avail a passwordless access to remote hosts. Note that even though we are installing Hadoop on a single node, we need to perform an ssh configuration in order to securely access the localhost.
First of all, we need to generate public and private keys by executing the following command:
```
ssh-keygen -t rsa -P ""
```
This will generate the private and public keys by default in the $HOME/.ssh folder. In order to provide passwordless access, we need to append the public key to authorized_keys file:
```
cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys
```
Let's check whether the ssh configuration is okay or not. To test it, execute and connect to the localhost like this:
```
ssh localhost
```
This will prompt you to confirm whether to add this connection to the known_hosts file. Type yes, and you should be connected to ssh without prompting for the password.
Once the ssh configuration is done and verified, we need to configure Hadoop. The Hadoop configuration begins with adding various configuration parameters to the following default files:
- hadoop-env.sh: This is where we need to perform the Java environment variable configuration.
- core-site.xml: This is where we need to perform NameNode-related configurations.
- yarn-site.xml: This is where we need to perform configurations related to Yet Another Resource Negotiator (YARN).
- mapred-site.xml: This is where we need to the map reduce engine as YARN.
- hdfs-site.xml: This is where we need to perform configurations related to Hadoop Distributed File System (HDFS).
These configuration files can be found in the /usr/local/hadoop/etc/hadoop folder. If you install Hadoop as the root user, you will have access to edit these files, but if not, you will first need to get access to this folder before editing.

So, let's take a look at the configurations one by one.

Configure hadoop-env.sh and update the Java path like this:

Export JAVA_HOME=/usr/local/java.
Edit core-site.xml, and add the host and port on which you wish to install NameNode. Here is the single node installation that we would need in order to add the localhost:
```
<configuration>
  <property>
    <name>fs.default.name</name>
    <value>hdfs://localhost:9000/</value>
  </property>
</configuration>
```
Edit yarn-site.xml, add the following properties to it:
```
<configuration>
   <property>
      <name>yarn.nodemanager.aux-services</name>
      <value>mapreduce_shuffle</value>
   </property>
   <property>
      <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
      <value>org.apache.hadoop.mapred.ShuffleHandler</value>
   </property>
</configuration>
```
The yarn.nodemanager.aux-services property tells NodeManager that an auxiliary service named mapreduce.shuffle is present and needs to be implemented. The second property tells NodeManager about the class by which means it needs to implement the shuffle auxiliary service. This specific configuration is needed as the MapReduce job involves shuffling of key value pairs.

Next, edit mapred-site.xml to set the map reduce processing engine as YARN:

<configuration>
   <property>
      <name>mapreduce.framework.name</name>
      <value>yarn</value>
   </property>
</configuration>

Edit hdfs-site.xml to set the folder paths that can be used by NameNode and datanode:

<configuration>
   <property>
      <name>yarn.nodemanager.aux-services</name>
      <value>mapreduce_shuffle</value>
   </property>
   <property>
      <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
      <value>org.apache.hadoop.mapred.ShuffleHandler</value>
   </property>
</configuration>

I am also setting the HDFS block replication factor to 1 as this is a single node cluster installation.
We also need to make sure that we create the previously mentioned folders and change their ownership to suit the current user. To do this, you can choose a folder path of your own choice:
```
sudo mkdir –p /usr/local/store/hdfs/namenode
sudo mkdir –p /usr/local/store/hdfs/datanode
sudo chown root:root –R /usr/local/store 
```
Now, it's time to format namenode so that it creates the required folder structure by default:
```
hadoop namenode -format
```
The final step involves starting Hadoop daemons; here, we will first execute two scripts to start HDFS daemons and then start YARN daemons:
```
/usr/local/hadoop/sbin/start-dfs.sh
```

This will start NameNode, the secondary NameNode, and then DataNode daemons:

/usr/local/hadoop/sbin/start-yarn.sh

This will start NodeManager and ResourceManager. You can execute the jps command to take a look at the running daemons:

$jps
2184 DataNode
2765 NodeManager
2835 Jps
2403 SecondaryNameNode
2025 NameNode
2606 ResourceManager

We can also access the web portals for HDFS and YARN by accessing the following URLs:

For HDFS: http://<hostname>:50070/
For YARN: http://<hostname>:8088/

How it works...

Hadoop 2.0 has been majorly reformed in order to solve issues of scalability and high-availability. Earlier in Hadoop 1.0, Map Reduce was the only means of processing data stored in HDFS. With advancement of YARN, Map Reduce is one of the ways of processing data on Hadoop. Here is a pictorial difference between Hadoop 1.x and Hadoop 2.x:

Now, let's try to understand how HDFS and YARN works.

Hadoop Distributed File System (HDFS)

HDFS is a redundant, reliable storage for Hadoop. It consists of three important parts: NameNode, the secondary NameNode, and DataNodes. When a file needs to be processed on Hadoop, it first needs to be saved on HDFS. HDFS distributes the file in chunks of 64/128 MB data blocks across the data nodes. The blocks are replicated across data nodes for reliability. NameNode stores the metadata in the blocks and replicas. After a certain period of time, the metadata is backed up on the secondary NameNode. The default time is 60 seconds. We can modify this by setting a property called dfs.namenode.checkpoint.check.period in hdfs-site.xml.

Yet Another Resource Negotiator (YARN)

YARN has been developed to address scalability issues and for the better management of jobs in Hadoop; till date, it has proved itself to be the perfect solution. It is responsible for the management of resources available in clusters. It consists of two important components: ResouceManager(Master) and NodeManager(Worker). NodeManager provides a node-level view of the cluster, while ResourceManager takes a view of a cluster. When an application is submitted by an application client, the following things happen:

The application talks to ResourceManager and provides details about it.
ResourceManager makes a container request on behalf of an application to any of the worker nodes and ApplicationMaster starts running within that container.
ApplicationMaster then makes subsequent requests for the containers to execute tasks on other nodes.
These tasks then take care of all the communication. Once all the tasks are complete, containers are deallocated and ApplicationMaster exits.
After this, the application client also exits.

There's more

Now that your single node Hadoop cluster is up and running, you can try some HDFS file operations on it, such as creating a directory, copying a file from a local machine to HDFS, and so on. Here some sample commands.

To list all the files in the HDFS root directory, take a look at this:

hadoop fs –ls /

To create a new directory, take a look at this:

hadoop fs –mkdir /input

To copy a file from the local machine to HDFS, take a look at this:

hadoop fs –copyFromLocal /usr/local/hadoop/LICENSE.txt /input

In order to access all the command options that are available, go to https://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/FileSystemShell.html.

Installing a multi-node Hadoop cluster

Now that we are comfortable with a single-node Hadoop installation, it's time to learn about a multi-node Hadoop installation.

Getting ready

In the previous recipe, we used a single Ubuntu machine for installation; in this recipe, we will be using three Ubuntu machines. If you are an individual trying to install Hadoop for your own purposes and you don't have three machines to try this recipe, I would suggest that you get three AWS EC2 Ubuntu machines. I am using the t2.small type of EC2 instances. For more information on this, go to https://aws.amazon.com/ec2/.

Apart from this, I've also performed the following configurations on all the EC2 instances that I have been using:

Create an AWS security group to allow access to traffic to EC2 instances, and add EC2 instances to this security group.
Change the hostname of EC2 instances to their public hostnames like this:
```
sudo hostname ec2-52-10-22-65.us-west-2.compute.amazonaws.com
```
Disable firewalls for EC2 Ubuntu instances:
```
sudo ufw disable
```

How to do it...

There are a lot of similarities between single-node and multi-node Hadoop installations, so instead of repeating the steps, I would suggest that you refer to earlier recipes as and when they're mentioned. So, let's start installing a multi-node Hadoop cluster:

Install Java and Hadoop, as discussed in the previous recipe, on the master and slave nodes. Refer to steps 1-5 in the previous recipe.
AWS EC2 has a built-in installation of ssh so there's no need to install it again. To configure it, we need to perform the following steps.
First, copy the PEM key with which you initiated EC2 instances to the master node. Next, you need to execute the following set of commands that will add an identity into the master's ssh configurations, which can be used to perform passwordless logins to slave machines:
```
eval `ssh-agent -s`
chmod 644 $HOME/.ssh/authorized_keys
chmod 400 <my-pem-key>.pem
ssh-add <my-pem-key>.pem
```
But if you are NOT using AWS EC2, then you need to generate the ssh key on the master, and this key needs to be copied to slave machines. Here is a sample command to do this:
```
ssh-keygen -t rsa -P ""
ssh-copy-id -i $HOME/.ssh/id_rsa.pub ubuntu@slave1
ssh-copy-id -i $HOME/.ssh/id_rsa.pub ubuntu@slave2
```
Next, we need to perform the Hadoop configurations—most of the configuration files will be same as they were in the case of the single-node installation. These configurations are the same for all the nodes in the cluster. Refer to step 8 from the previous recipe for hadoop-env.sh, mapred-site.xml, and hdfs-site.xml. For core-site.xml and yarn-site.xml, we need to add some more properties, as shown here:
Edit core-site.xml and add the host and port on which you wish to install NameNode. As this is a multi-node Hadoop cluster installation, we will add the master's hostname instead of the localhost:
```
<configuration>
<property>
    <name>fs.default.name</name>
    <value>hdfs://<master's-host-name>:9000/</value>
</property>
</configuration>
```
Edit yarn-site.xml and add the following properties. As this is a multi-node installation, we also need to provide the address of the machine where ResourceManager is running:
```
<configuration>
    <property>
      <name>yarn.nodemanager.aux-services</name>
      <value>mapreduce_shuffle</value>
    </property>
    <property>
      <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
      <value>org.apache.hadoop.mapred.ShuffleHandler</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value><master's-host-name></value>
    </property>
</configuration>
```
In the case of hdfs-site.xml, in the previous recipe, we set the replication factor to 1. As this is a multi-node cluster, we set it to 3. Don't forget to create storage folders configured in hdfs-site.xml.
These configurations need to be made on all the machines of the cluster.
Now that we are done with configurations, execute the namenode format command so that it creates the required subfolder structure:
```
hadoop namenode -format
```
Now, we need to start specific services on specific nodes in order to start the cluster.
On the master node, execute following:
```
/usr/local/hadoop/sbin/hadoop-daemon.sh start namenode
/usr/local/hadoop/sbin/hadoop-daemon.sh start secondarynamenode
/usr/local/hadoop/sbin/yarn-daemon.sh start resourcemanager
```
On all slave nodes, execute following:
```
/usr/local/hadoop/sbin/hadoop-daemon.sh start datanode
/usr/local/hadoop/sbin/yarn-daemon.sh start nodemanager
```
If everything goes well, you should be able to see the cluster running properly. You can also check out the web interfaces for NameNode and Resource Managers, for example, by going to http://<master-ip-hostname>:50070/.

For ResourceManager, go to http://<master-ip-hostname>/8088

How it works...

Refer to the How it works section from the previous recipe.

Adding new nodes to existing Hadoop clusters

Sometimes, it may happen that an existing Hadoop cluster's capacity is not adequate enough to handle all the data you may want to process. In this case, you can add new nodes to the existing Hadoop cluster without any downtime for the existing cluster. Hadoop supports horizontal scalability.

Getting ready

To perform this recipe, you should have a Hadoop cluster running. Also, you will need one more machine. If you are using AWS EC2, then you can launch an EC2 instance that's similar to what we did in the previous recipes. You will also need the same security group configurations in order to make the installation process smooth.

How to do it...

To add a new instance to an existing cluster, simply install and configure Hadoop the way we did for the previous recipe. Make sure that you put the same configurations in core-site.xml and yarn-site.xml, which will point to the correct master node.

Once all the configurations are done, simply execute commands to start the newly added datanode and nodemanager:

/usr/local/hadoop/sbin/hadoop-daemon.sh start datanode
/usr/local/hadoop/sbin/yarn-daemon.sh start nodemanager

If you take a look at the cluster again, you will find that the new node is registered. You can use the dfsadmin command to take a look at the number of nodes and amount of capacity that's been used:

hdfs dfsadmin -report

Here is a sample output for the preceding command:

How it works...

Hadoop supports horizontal scalability. If the resources that are being used are not enough, we can always go ahead and add new nodes to the existing cluster without hiccups. In Hadoop, it's always the slave that reports to the master. So, while making configurations, we always configure the details of the master and do nothing about the slaves. This architecture helps achieve horizontal scalability as at any point of time, we can add new nodes by only providing the configurations of the master, and everything else is taken care of by the Hadoop cluster. As soon as the daemons start, the master node realizes that a new node has been added and it becomes part of the cluster.

Executing the balancer command for uniform data distribution

Data in HDFS may not always be placed uniformly. There can be numerous reasons for this. One of the major reasons is the addition of new nodes to the cluster. In such a case, it's the Hadoop administrator's job to make sure that they execute the balancer command to rebalance the data load.

Getting ready

To perform this recipe, you should have performed earlier recipes.

How to do it...

In the previous recipe, we added a new node to the cluster while the other three nodes were already part of the cluster. When you execute the dfsadmin report command, you would have noticed that the data is not uniformly balanced because of the addition of a new node. In my case, here is the state of the new node versus the old node.

This is the code for the old node:

Name: 172.31.0.9:50010 (ip-172-31-0-9.us-west-2.compute.internal)
Hostname: ip-172-31-0-9.us-west-2.compute.internal
Decommission Status : Normal
Configured Capacity: 8309932032 (7.74 GB)
DFS Used: 67551232 (64.42 MB)
Non DFS Used: 2193256448 (2.04 GB)
DFS Remaining: 6049124352 (5.63 GB)
DFS Used%: 0.81%
DFS Remaining%: 72.79%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Thu Oct 08 08:57:23 UTC 2015

This is the code for the new node:

Name: 172.31.18.55:50010 (ip-172-31-18-55.us-west-2.compute.internal)
Hostname: ip-172-31-18-55.us-west-2.compute.internal
Decommission Status : Normal
Configured Capacity: 8309932032 (7.74 GB)
DFS Used: 1127585 (1.08 MB)
Non DFS Used: 2372033375 (2.21 GB)
DFS Remaining: 5936771072 (5.53 GB)
DFS Used%: 0.01%
DFS Remaining%: 71.44%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Thu Oct 08 08:57:25 UTC 2015

This means that the load on the cluster is not uniform. In this case, we can execute the balancer command to distribute the data uniformly throughout the data nodes:

hdfs balancer

This will initiate the block balancing activity across the cluster. By default, it will run the balancing activity to make sure that the block storage in the nodes does not differ by more than 10%. You can also decide on the threshold limit by setting an optional parameter called threshold:

hdfs balancer -threshold 5

This will execute the balancer command with 5% threshold. This is how the sample execution looks:

How it works...

The balancer command provides instructions to namenode so that it can rebalance the data uniformly across datanode. This balancing is done by repositioning the blocks placed in datanode. So, if a data node is over utilized, some the blocks from that node would be repositioned to the node that is underutilized.

There's more...

There are some options you can provide as arguments to this command:

Usage: hdfs balancer
        [-policy <policy>]      the balancing policy: datanode or blockpool
        [-threshold <threshold>]        Percentage of disk capacity
        [-exclude [-f <hosts-file> | <comma-separated list of hosts>]]  Excludes the specified datanodes.
        [-include [-f <hosts-file> | <comma-separated list of hosts>]]  Includes only the specified datanodes.
        [-idleiterations <idleiterations>]      Number of consecutive idle iterations (-1 for Infinite) before exit.

Decommissioning DataNodes

The Hadoop framework provides us with the option to remove certain nodes from the cluster if they are not needed any more. Here, we cannot simply shutdown the nodes that need to be removed as we might lose some of our data. They need to be decommissioned properly. In this recipe, we are going to learn how to decommission nodes from the Hadoop cluster.

Getting ready

To perform this recipe, you should have a Hadoop cluster, and you should have decided which node to decommission.

How to do it...

To decommission a node from the HDFS cluster, we need to perform the following steps:

Create a dfs.exclude file in a folder, say /usr/local/hadoop/etc/hadoop, and add the hostname of the node you wish to decommission.

Edit hdfs-site.xml on NameNode to append the following property:

    <property>
        <name>dfs.hosts.exclude</name>
        <value>/usr/local/hadoop/etc/hadoop/dfs.exclude</value>
    </property>

Next, we need to execute the refreshNodes command so that it rereads the HDFS configuration in order to start the decommissioning:
```
hdfs dfsadmin –refreshNodes
```

This will start the decommissioning, and once successful execution of the dfsadmin report command, you will see that the node's status is changed to Decommissioned from Normal:

hdfs dfsadmin –report
Name: 172.31.18.55:50010 (ip-172-31-18-55.us-west-2.compute.internal)
Hostname: ip-172-31-18-55.us-west-2.compute.internal
Decommission Status : Decommissioned
Configured Capacity: 8309932032 (7.74 GB)
DFS Used: 1179648 (1.13 MB)
Non DFS Used: 2371989504 (2.21 GB)
DFS Remaining: 5936762880 (5.53 GB)
DFS Used%: 0.01%
DFS Remaining%: 71.44%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Thu Oct 08 10:56:49 UTC 2015

Generally, the decommissioning takes time as it requires block replications on other nodes. Once the decommissioning is complete, the node will be added to the decommissioned nodes list.

How it works...

HDFS/Namenode reads the configurations from hdfs-site.xml. You can configure a file with the list of nodes to decommission and execute the refreshNodes command; it then rereads the configuration file. While doing this, it reads the configuration about the decommissioned nodes and will start rereplicating blocks to other available datanode. Depending on the size of datanode getting decommissioned, the time varies. Unless the completed decommissioning is not completed, it advisable for you to touch datanode.

Performing benchmarking on a Hadoop cluster

The Hadoop framework supports built-in libraries so that we can perform benchmarking in order to take a look at how the Hadoop cluster configurations/hardware are performing. There are plenty of tests available that will perform the benchmarking of various aspects of the Hadoop cluster. In this recipe, we are going to take a look at how to perform benchmarking and read the results.

Getting ready

To perform this recipe, you should have a Hadoop cluster up and running.

How to do it...

The Hadoop framework supports built-in support to benchmark various aspects. These tests are written in a library called hadoop-mapreduce-client-jobclient-2.7.0-tests.jar

To know the list of all the supported tests, you can execute the following command:

hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.7.0-tests.jar

The result of the command will be similar to what is shown in this screenshot:

TestDFSIO

This is one the major tests that you may want to do in order to see how DFS is performing. So, we are now going to take a look at how to use these tests to know how efficiently HDFS is able to write and read data.

As seen in the preceding screenshot, the library provides tools to test DFS through an option called TestDFSIO. Now, let's execute the write test in order to understand how efficiently HDFS is able to write big files. The following is the command to execute the write test:

hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.7.0-tests.jar TestDFSIO -write -nrFiles 2 -fileSize 1GB -resFile /tmp/TestDFSIOwrite.txt

Once you initiate the preceding command, a map reduce job will start, which will write two files to HDFS that are 1GB in size . You can choose any numbers based on your cluster size. These tests create data in HDFS under the /benchmarks directory. Once the execution is complete, you will see these results:

15/10/08 11:37:23 INFO fs.TestDFSIO: ----- TestDFSIO ----- : write
15/10/08 11:37:23 INFO fs.TestDFSIO:            Date & time: Thu Oct 08 11:37:23 UTC 2015
15/10/08 11:37:23 INFO fs.TestDFSIO:        Number of files: 2
15/10/08 11:37:23 INFO fs.TestDFSIO: Total MBytes processed: 2048.0
15/10/08 11:37:23 INFO fs.TestDFSIO:      Throughput mb/sec: 26.637185406776354
15/10/08 11:37:23 INFO fs.TestDFSIO: Average IO rate mb/sec: 26.63718605041504
15/10/08 11:37:23 INFO fs.TestDFSIO:  IO rate std deviation: 0.00829867575568246
15/10/08 11:37:23 INFO fs.TestDFSIO:     Test exec time sec: 69.023

The preceding data is calculated from the RAW data generated by the Map Reduce program. You can also view the raw data as follows:

hdfs dfs -cat /benchmarks/TestDFSIO/io_read/part*
f:rate  53274.37
f:sqrate        1419079.2
l:size  2147483648
l:tasks 2
l:time  76885

Tip

The following formulae are used to calculate throughput, the average IO rate, and standard deviation.

Throughput = size * 1000/time * 1048576

Average IO rate = rate/1000/tasks

Standard deviation = square root of (absolute value(sqrate/1000/tasks – Average IO Rate * Average IO Rate))

Similarly, you can perform benchmarking of HDFS read operations as well:

hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.7.0-tests.jar TestDFSIO -read -nrFiles 2 -fileSize 1GB -resFile /tmp/TestDFSIOread.txt

At the end of the execution, a reducer will collect the data from the RAW results, and you will see calculated numbers for the DFSIO reads:

15/10/08 11:41:01 INFO fs.TestDFSIO: ----- TestDFSIO ----- : read
15/10/08 11:41:01 INFO fs.TestDFSIO:            Date & time: Thu Oct 08 11:41:01 UTC 2015
15/10/08 11:41:01 INFO fs.TestDFSIO:        Number of files: 2
15/10/08 11:41:01 INFO fs.TestDFSIO: Total MBytes processed: 2048.0
15/10/08 11:41:01 INFO fs.TestDFSIO:      Throughput mb/sec: 33.96633220001659
15/10/08 11:41:01 INFO fs.TestDFSIO: Average IO rate mb/sec: 33.968116760253906
15/10/08 11:41:01 INFO fs.TestDFSIO:  IO rate std deviation: 0.24641533955938721
15/10/08 11:41:01 INFO fs.TestDFSIO:     Test exec time sec: 59.343

Here, we can take a look at the RAW data as well:

hdfs dfs -cat /benchmarks/TestDFSIO/io_read/part*
f:rate  67936.234
f:sqrate        2307787.2
l:size  2147483648
l:tasks 2
l:time  60295

The same formulae are used to calculate the throughput, average IO rate, and standard deviation.

This way, you can benchmark the DFSIO reads and writes.

NNBench

Similar to DFS IO, we can also perform benchmarking for NameNode:

hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.7.0-tests.jar nnbench -operation create_write

MRBench

MRBench helps us understand the average time taken for a job to execute for a given number of mappers and reducers. The following is a sample command to execute MRBench with default parameters:

hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.7.0-tests.jar mrbench

How it works...

Hadoop benchmark tests use the parameters and conditions provided by users. For every test, it executes a map reduce job and once complete, it displays the results on the screen. Generally, it is recommended that you run the benchmarking tests as soon as you have installed the Hadoop cluster in order to predict the performance of HDFS/Map Reduce and so on.

Most of the tests require a sequence in which they should be executed, for example, all write tests should be executed first, then read/delete, and so on.

Once the complete execution is done, make sure you clean up the data in the /benchmarks directory in HDFS.

Here is an example command to clean up the data generated by the TestDFSIO tests:

hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-jobclient-2.7.0-tests.jar TestDFSIO -clean

Amazon Customer Mar 01, 2017

Excellent book with lot of practical examples

Amazon Verified review

Thakur Jaiveer Singh Jun 09, 2018

Good one

Amazon Customer Jan 21, 2018

Good

Sudhir Chawla Aug 02, 2016

This is an interesting book on Hadoop, which explains the installation part along with how to setup load balancer. Further down it deep dives to explain about HDFS, Map reduce program. A very good aspect of this book is that it allows you to know about various analysis tools like Hive, Pig and Hbase, moverover it goes in more details to explain about data analysis using Hive.Further down are some Import and export tools to onboard data into HDFS.A good book for someone who has basic knowledge of Hadoop and want to enhance it further by using different tools. This book gives a good start to people with experience in Java and Unix environment.Some good points of the book are:- Examples given with very clear explanation.- The author tried to used practical exercises instead of just basics.- Tried to cover many tools and deep-dive into some of the key toolsDemerits :- This book is meant for advance level developers. This is already mentioned in the book to have basic knowledge but I believe it demands more than basic.

Hadoop Real-World Solutions Cookbook- Second Edition: Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout , Second Edition

What do you get with Print?

Contact Details

Shipping Address

Billing Address

Hadoop Distributed File System (HDFS)

Yet Another Resource Negotiator (YARN)

TestDFSIO

NNBench

MRBench

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with Print?

Contact Details

Shipping Address

Billing Address

Product Details

Packt Subscriptions

Frequently bought together

Table of Contents

Recommendations for you

Customer reviews

People who bought this also bought

About the author

FAQs