Category: hadoop

Hadoop – Multi-Node Cluster ”; Previous Next This chapter explains the setup of the Hadoop Multi-Node cluster on a distributed environment. As the whole cluster cannot be demonstrated, we are explaining the Hadoop cluster environment using three systems (one master and two slaves); given below are their IP addresses. Hadoop Master: 192.168.1.15 (hadoop-master) Hadoop Slave: 192.168.1.16 (hadoop-slave-1) Hadoop Slave: 192.168.1.17 (hadoop-slave-2) Follow the steps given below to have Hadoop Multi-Node cluster setup. Installing Java Java is the main prerequisite for Hadoop. First of all, you should verify the existence of java in your system using “java -version”. The syntax of java version command is given below. $ java -version If everything works fine it will give you the following output. java version “1.7.0_71″ Java(TM) SE Runtime Environment (build 1.7.0_71-b13) Java HotSpot(TM) Client VM (build 25.0-b02, mixed mode) If java is not installed in your system, then follow the given steps for installing java. Step 1 Download java (JDK <latest version> – X64.tar.gz) by visiting the following link www.oracle.com Then jdk-7u71-linux-x64.tar.gz will be downloaded into your system. Step 2 Generally you will find the downloaded java file in Downloads folder. Verify it and extract the jdk-7u71-linux-x64.gz file using the following commands. $ cd Downloads/ $ ls jdk-7u71-Linux-x64.gz $ tar zxf jdk-7u71-Linux-x64.gz $ ls jdk1.7.0_71 jdk-7u71-Linux-x64.gz Step 3 To make java available to all the users, you have to move it to the location “/usr/local/”. Open the root, and type the following commands. $ su password: # mv jdk1.7.0_71 /usr/local/ # exit Step 4 For setting up PATH and JAVA_HOME variables, add the following commands to ~/.bashrc file. export JAVA_HOME=/usr/local/jdk1.7.0_71 export PATH=PATH:$JAVA_HOME/bin Now verify the java -version command from the terminal as explained above. Follow the above process and install java in all your cluster nodes. Creating User Account Create a system user account on both master and slave systems to use the Hadoop installation. # useradd hadoop # passwd hadoop Mapping the nodes You have to edit hosts file in /etc/ folder on all nodes, specify the IP address of each system followed by their host names. # vi /etc/hosts enter the following lines in the /etc/hosts file. 192.168.1.109 hadoop-master 192.168.1.145 hadoop-slave-1 192.168.56.1 hadoop-slave-2 Configuring Key Based Login Setup ssh in every node such that they can communicate with one another without any prompt for password. # su hadoop $ ssh-keygen -t rsa $ ssh-copy-id -i ~/.ssh/id_rsa.pub tutorialspoint@hadoop-master $ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop_tp1@hadoop-slave-1 $ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop_tp2@hadoop-slave-2 $ chmod 0600 ~/.ssh/authorized_keys $ exit Installing Hadoop In the Master server, download and install Hadoop using the following commands. # mkdir /opt/hadoop # cd /opt/hadoop/ # wget http://apache.mesi.com.ar/hadoop/common/hadoop-1.2.1/hadoop-1.2.0.tar.gz # tar -xzf hadoop-1.2.0.tar.gz # mv hadoop-1.2.0 hadoop # chown -R hadoop /opt/hadoop # cd /opt/hadoop/hadoop/ Configuring Hadoop You have to configure Hadoop server by making the following changes as given below. core-site.xml Open the core-site.xml file and edit it as shown below. <configuration> <property> <name>fs.default.name</name> <value>hdfs://hadoop-master:9000/</value> </property> <property> <name>dfs.permissions</name> <value>false</value> </property> </configuration> hdfs-site.xml Open the hdfs-site.xml file and edit it as shown below. <configuration> <property> <name>dfs.data.dir</name> <value>/opt/hadoop/hadoop/dfs/name/data</value> <final>true</final> </property> <property> <name>dfs.name.dir</name> <value>/opt/hadoop/hadoop/dfs/name</value> <final>true</final> </property> <property> <name>dfs.replication</name> <value>1</value> </property> </configuration> mapred-site.xml Open the mapred-site.xml file and edit it as shown below. <configuration> <property> <name>mapred.job.tracker</name> <value>hadoop-master:9001</value> </property> </configuration> hadoop-env.sh Open the hadoop-env.sh file and edit JAVA_HOME, HADOOP_CONF_DIR, and HADOOP_OPTS as shown below. Note − Set the JAVA_HOME as per your system configuration. export JAVA_HOME=/opt/jdk1.7.0_17 export HADOOP_OPTS=-Djava.net.preferIPv4Stack=true export HADOOP_CONF_DIR=/opt/hadoop/hadoop/conf Installing Hadoop on Slave Servers Install Hadoop on all the slave servers by following the given commands. # su hadoop $ cd /opt/hadoop $ scp -r hadoop hadoop-slave-1:/opt/hadoop $ scp -r hadoop hadoop-slave-2:/opt/hadoop Configuring Hadoop on Master Server Open the master server and configure it by following the given commands. # su hadoop $ cd /opt/hadoop/hadoop Configuring Master Node $ vi etc/hadoop/masters hadoop-master Configuring Slave Node $ vi etc/hadoop/slaves hadoop-slave-1 hadoop-slave-2 Format Name Node on Hadoop Master # su hadoop $ cd /opt/hadoop/hadoop $ bin/hadoop namenode –format 11/10/14 10:58:07 INFO namenode.NameNode: STARTUP_MSG: /************************************************************ STARTUP_MSG: Starting NameNode STARTUP_MSG: host = hadoop-master/192.168.1.109 STARTUP_MSG: args = [-format] STARTUP_MSG: version = 1.2.0 STARTUP_MSG: build = https://svn.apache.org/repos/asf/hadoop/common/branches/branch-1.2 -r 1479473; compiled by ”hortonfo” on Mon May 6 06:59:37 UTC 2013 STARTUP_MSG: java = 1.7.0_71 ************************************************************/ 11/10/14 10:58:08 INFO util.GSet: Computing capacity for map BlocksMap editlog=/opt/hadoop/hadoop/dfs/name/current/edits …………………………………………………. …………………………………………………. …………………………………………………. 11/10/14 10:58:08 INFO common.Storage: Storage directory /opt/hadoop/hadoop/dfs/name has been successfully formatted. 11/10/14 10:58:08 INFO namenode.NameNode: SHUTDOWN_MSG: /************************************************************ SHUTDOWN_MSG: Shutting down NameNode at hadoop-master/192.168.1.15 ************************************************************/ Starting Hadoop Services The following command is to start all the Hadoop services on the Hadoop-Master. $ cd $HADOOP_HOME/sbin $ start-all.sh Adding a New DataNode in the Hadoop Cluster Given below are the steps to be followed for adding new nodes to a Hadoop cluster. Networking Add new nodes to an existing Hadoop cluster with some appropriate network configuration. Assume the following network configuration. For New node Configuration − IP address : 192.168.1.103 netmask : 255.255.255.0 hostname : slave3.in Adding User and SSH Access Add a User On a new node, add “hadoop” user and set password of Hadoop user to “hadoop123” or anything you want by using the following commands. useradd hadoop passwd hadoop Setup Password less connectivity from master to new slave. Execute the following on the master mkdir -p $HOME/.ssh chmod 700 $HOME/.ssh ssh-keygen -t rsa -P ”” -f $HOME/.ssh/id_rsa cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys chmod 644 $HOME/.ssh/authorized_keys Copy the public key to new slave node in hadoop user $HOME directory scp $HOME/.ssh/id_rsa.pub [email protected]:/home/hadoop/ Execute the following on the slaves Login to hadoop. If not, login to hadoop user. su hadoop ssh -X [email protected] Copy the content of public key into file “$HOME/.ssh/authorized_keys” and then change the permission for the same by executing the following commands. cd $HOME mkdir -p $HOME/.ssh chmod 700 $HOME/.ssh cat id_rsa.pub >>$HOME/.ssh/authorized_keys chmod 644 $HOME/.ssh/authorized_keys Check ssh login from the master machine. Now check if you can ssh to the new node without a password from the master. ssh [email protected] or hadoop@slave3 Set Hostname of New Node You can set hostname in file /etc/sysconfig/network On new slave3 machine NETWORKING =

Hadoop – Quick Guide ”; Previous Next Hadoop – Big Data Overview “90% of the world’s data was generated in the last few years.” Due to the advent of new technologies, devices, and communication means like social networking sites, the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of time till 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and in every ten minutes in 2013. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it is being neglected. What is Big Data? Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. What Comes Under Big Data? Big data involves the data produced by different devices and applications. Given below are some of the fields that come under the umbrella of Big Data. Black Box Data − It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft. Social Media Data − Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe. Stock Exchange Data − The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers. Power Grid Data − The power grid data holds information consumed by a particular node with respect to a base station. Transport Data − Transport data includes model, capacity, distance and availability of a vehicle. Search Engine Data − Search engines retrieve lots of data from different databases. Thus Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types. Structured data − Relational data. Semi Structured data − XML data. Unstructured data − Word, PDF, Text, Media Logs. Benefits of Big Data Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums. Using the information in the social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production. Using the data regarding the previous medical history of patients, hospitals are providing better and quick service. Big Data Technologies Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business. To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security. There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology − Operational Big Data This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored. NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement. Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure. Analytical Big Data These includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data. MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL, and a system based on MapReduce that can be scaled up from single servers to thousands of high and low end machines. These two classes of technology are complementary and frequently deployed together. Operational vs. Analytical Systems Operational Analytical Latency 1 ms – 100 ms 1 min – 100 min Concurrency 1000 – 100,000 1 – 10 Access Pattern Writes and Reads Reads Queries Selective Unselective Data Scope Operational Retrospective End User Customer Data Scientist Technology NoSQL MapReduce, MPP Database Big Data Challenges The major challenges associated with big data are as follows − Capturing data Curation Storage Searching Sharing Transfer Analysis Presentation To fulfill the above challenges, organizations normally take the help of enterprise servers. Hadoop – Big Data Solutions Traditional Approach In this approach, an enterprise will have a computer to store and process big data. For storage purpose, the programmers will take the help of their choice of database vendors such as Oracle, IBM, etc. In this approach, the user interacts with the application, which in turn handles the part of data storage and analysis. Limitation This approach works fine with those applications that process less voluminous data that can be accommodated by standard database servers, or up to the limit of the processor that is processing the data. But when it comes to dealing with huge amounts of scalable data, it is a hectic task to process such data through a single database bottleneck. Google’s Solution Google solved this problem using an algorithm called MapReduce. This algorithm divides the task into small parts and assigns them to many computers, and collects the results from them which when integrated, form the result dataset. Hadoop Using the solution provided by Google, Doug Cutting and his team developed an Open Source Project called HADOOP. Hadoop runs applications using the MapReduce

Hadoop – MapReduce ”; Previous Next MapReduce is a framework using which we can write applications to process huge amounts of data, in parallel, on large clusters of commodity hardware in a reliable manner. What is MapReduce? MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce task is always performed after the map job. The major advantage of MapReduce is that it is easy to scale data processing over multiple computing nodes. Under the MapReduce model, the data processing primitives are called mappers and reducers. Decomposing a data processing application into mappers and reducers is sometimes nontrivial. But, once we write an application in the MapReduce form, scaling the application to run over hundreds, thousands, or even tens of thousands of machines in a cluster is merely a configuration change. This simple scalability is what has attracted many programmers to use the MapReduce model. The Algorithm Generally MapReduce paradigm is based on sending the computer to where the data resides! MapReduce program executes in three stages, namely map stage, shuffle stage, and reduce stage. Map stage − The map or mapper’s job is to process the input data. Generally the input data is in the form of file or directory and is stored in the Hadoop file system (HDFS). The input file is passed to the mapper function line by line. The mapper processes the data and creates several small chunks of data. Reduce stage − This stage is the combination of the Shuffle stage and the Reduce stage. The Reducer’s job is to process the data that comes from the mapper. After processing, it produces a new set of output, which will be stored in the HDFS. During a MapReduce job, Hadoop sends the Map and Reduce tasks to the appropriate servers in the cluster. The framework manages all the details of data-passing such as issuing tasks, verifying task completion, and copying data around the cluster between the nodes. Most of the computing takes place on nodes with data on local disks that reduces the network traffic. After completion of the given tasks, the cluster collects and reduces the data to form an appropriate result, and sends it back to the Hadoop server. Inputs and Outputs (Java Perspective) The MapReduce framework operates on <key, value> pairs, that is, the framework views the input to the job as a set of <key, value> pairs and produces a set of <key, value> pairs as the output of the job, conceivably of different types. The key and the value classes should be in serialized manner by the framework and hence, need to implement the Writable interface. Additionally, the key classes have to implement the Writable-Comparable interface to facilitate sorting by the framework. Input and Output types of a MapReduce job − (Input) <k1, v1> → map → <k2, v2> → reduce → <k3, v3>(Output). Input Output Map <k1, v1> list (<k2, v2>) Reduce <k2, list(v2)> list (<k3, v3>) Terminology PayLoad − Applications implement the Map and the Reduce functions, and form the core of the job. Mapper − Mapper maps the input key/value pairs to a set of intermediate key/value pair. NamedNode − Node that manages the Hadoop Distributed File System (HDFS). DataNode − Node where data is presented in advance before any processing takes place. MasterNode − Node where JobTracker runs and which accepts job requests from clients. SlaveNode − Node where Map and Reduce program runs. JobTracker − Schedules jobs and tracks the assign jobs to Task tracker. Task Tracker − Tracks the task and reports status to JobTracker. Job − A program is an execution of a Mapper and Reducer across a dataset. Task − An execution of a Mapper or a Reducer on a slice of data. Task Attempt − A particular instance of an attempt to execute a task on a SlaveNode. Example Scenario Given below is the data regarding the electrical consumption of an organization. It contains the monthly electrical consumption and the annual average for various years. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Avg 1979 23 23 2 43 24 25 26 26 26 26 25 26 25 1980 26 27 28 28 28 30 31 31 31 30 30 30 29 1981 31 32 32 32 33 34 35 36 36 34 34 34 34 1984 39 38 39 39 39 41 42 43 40 39 38 38 40 1985 38 39 39 39 39 41 41 41 00 40 39 39 45 If the above data is given as input, we have to write applications to process it and produce results such as finding the year of maximum usage, year of minimum usage, and so on. This is a walkover for the programmers with finite number of records. They will simply write the logic to produce the required output, and pass the data to the application written. But, think of the data representing the electrical consumption of all the largescale industries of a particular state, since its formation. When we write applications to process such bulk data, They will take a lot of time to execute. There will be a heavy network traffic when we move data from source to network server and so on. To solve these problems, we have the MapReduce framework. Input Data The above data is saved as sample.txtand given as input. The input file looks as shown below. 1979 23 23 2 43 24 25 26 26 26 26 25 26 25 1980 26 27 28 28 28 30 31 31 31 30

Hadoop Questions and Answers ”; Previous Next Hadoop Questions and Answers has been designed with a special intention of helping students and professionals preparing for various Certification Exams and Job Interviews. This section provides a useful collection of sample Interview Questions and Multiple Choice Questions (MCQs) and their answers with appropriate explanations. Sr.No. Question/Answers Type 1 Hadoop Interview Questions This section provides a huge collection of Hadoop Interview Questions with their answers hidden in a box to challenge you to have a go at them before discovering the correct answer. 2 Hadoop Online Quiz This section provides a great collection of Hadoop Multiple Choice Questions (MCQs) on a single page along with their correct answers and explanation. If you select the right option, it turns green; else red. 3 Hadoop Online Test If you are preparing to appear for a Java and Hadoop Framework related certification exam, then this section is a must for you. This section simulates a real online test along with a given timer which challenges you to complete the test within a given time-frame. Finally you can check your overall test score and how you fared among millions of other candidates who attended this online test. 4 Hadoop Mock Test This section provides various mock tests that you can download at your local machine and solve offline. Every mock test is supplied with a mock test key to let you verify the final score and grade yourself. Print Page Previous Next Advertisements ”;

Hadoop – Big Data Solutions ”; Previous Next Traditional Approach In this approach, an enterprise will have a computer to store and process big data. For storage purpose, the programmers will take the help of their choice of database vendors such as Oracle, IBM, etc. In this approach, the user interacts with the application, which in turn handles the part of data storage and analysis. Limitation This approach works fine with those applications that process less voluminous data that can be accommodated by standard database servers, or up to the limit of the processor that is processing the data. But when it comes to dealing with huge amounts of scalable data, it is a hectic task to process such data through a single database bottleneck. Google’s Solution Google solved this problem using an algorithm called MapReduce. This algorithm divides the task into small parts and assigns them to many computers, and collects the results from them which when integrated, form the result dataset. Hadoop Using the solution provided by Google, Doug Cutting and his team developed an Open Source Project called HADOOP. Hadoop runs applications using the MapReduce algorithm, where the data is processed in parallel with others. In short, Hadoop is used to develop applications that could perform complete statistical analysis on huge amounts of data. Print Page Previous Next Advertisements ”;

Hadoop – Big Data Overview ”; Previous Next “90% of the world’s data was generated in the last few years.” Due to the advent of new technologies, devices, and communication means like social networking sites, the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of time till 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and in every ten minutes in 2013. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it is being neglected. What is Big Data? Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks. What Comes Under Big Data? Big data involves the data produced by different devices and applications. Given below are some of the fields that come under the umbrella of Big Data. Black Box Data − It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft. Social Media Data − Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe. Stock Exchange Data − The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers. Power Grid Data − The power grid data holds information consumed by a particular node with respect to a base station. Transport Data − Transport data includes model, capacity, distance and availability of a vehicle. Search Engine Data − Search engines retrieve lots of data from different databases. Thus Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types. Structured data − Relational data. Semi Structured data − XML data. Unstructured data − Word, PDF, Text, Media Logs. Benefits of Big Data Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums. Using the information in the social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production. Using the data regarding the previous medical history of patients, hospitals are providing better and quick service. Big Data Technologies Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business. To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security. There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology − Operational Big Data This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored. NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement. Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure. Analytical Big Data These includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data. MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL, and a system based on MapReduce that can be scaled up from single servers to thousands of high and low end machines. These two classes of technology are complementary and frequently deployed together. Operational vs. Analytical Systems Operational Analytical Latency 1 ms – 100 ms 1 min – 100 min Concurrency 1000 – 100,000 1 – 10 Access Pattern Writes and Reads Reads Queries Selective Unselective Data Scope Operational Retrospective End User Customer Data Scientist Technology NoSQL MapReduce, MPP Database Big Data Challenges The major challenges associated with big data are as follows − Capturing data Curation Storage Searching Sharing Transfer Analysis Presentation To fulfill the above challenges, organizations normally take the help of enterprise servers. Print Page Previous Next Advertisements ”;

Hadoop – Introduction ”; Previous Next Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. The Hadoop framework application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage. Hadoop Architecture At its core, Hadoop has two major layers namely − Processing/Computation layer (MapReduce), and Storage layer (Hadoop Distributed File System). MapReduce MapReduce is a parallel programming model for writing distributed applications devised at Google for efficient processing of large amounts of data (multi-terabyte data-sets), on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. The MapReduce program runs on Hadoop which is an Apache open-source framework. Hadoop Distributed File System The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is 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. It is highly fault-tolerant and is designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications having large datasets. Apart from the above-mentioned two core components, Hadoop framework also includes the following two modules − Hadoop Common − These are Java libraries and utilities required by other Hadoop modules. Hadoop YARN − This is a framework for job scheduling and cluster resource management. How Does Hadoop Work? It is quite expensive to build bigger servers with heavy configurations that handle large scale processing, but as an alternative, you can tie together many commodity computers with single-CPU, as a single functional distributed system and practically, the clustered machines can read the dataset in parallel and provide a much higher throughput. Moreover, it is cheaper than one high-end server. So this is the first motivational factor behind using Hadoop that it runs across clustered and low-cost machines. Hadoop runs code across a cluster of computers. This process includes the following core tasks that Hadoop performs − Data is initially divided into directories and files. Files are divided into uniform sized blocks of 128M and 64M (preferably 128M). These files are then distributed across various cluster nodes for further processing. HDFS, being on top of the local file system, supervises the processing. Blocks are replicated for handling hardware failure. Checking that the code was executed successfully. Performing the sort that takes place between the map and reduce stages. Sending the sorted data to a certain computer. Writing the debugging logs for each job. Advantages of Hadoop Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores. Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer. Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption. Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based. Print Page Previous Next Advertisements ”;

Hadoop – Enviornment Setup ”; Previous Next Hadoop is supported by GNU/Linux platform and its flavors. Therefore, we have to install a Linux operating system for setting up Hadoop environment. In case you have an OS other than Linux, you can install a Virtualbox software in it and have Linux inside the Virtualbox. Pre-installation Setup Before installing Hadoop into the Linux environment, we need to set up Linux using ssh (Secure Shell). Follow the steps given below for setting up the Linux environment. Creating a User At the beginning, it is recommended to create a separate user for Hadoop to isolate Hadoop file system from Unix file system. Follow the steps given below to create a user − Open the root using the command “su”. Create a user from the root account using the command “useradd username”. Now you can open an existing user account using the command “su username”. Open the Linux terminal and type the following commands to create a user. $ su password: # useradd hadoop # passwd hadoop New passwd: Retype new passwd SSH Setup and Key Generation SSH setup is required to do different operations on a cluster such as starting, stopping, distributed daemon shell operations. To authenticate different users of Hadoop, it is required to provide public/private key pair for a Hadoop user and share it with different users. The following commands are used for generating a key value pair using SSH. Copy the public keys form id_rsa.pub to authorized_keys, and provide the owner with read and write permissions to authorized_keys file respectively. $ ssh-keygen -t rsa $ cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys $ chmod 0600 ~/.ssh/authorized_keys Installing Java Java is the main prerequisite for Hadoop. First of all, you should verify the existence of java in your system using the command “java -version”. The syntax of java version command is given below. $ java -version If everything is in order, it will give you the following output. java version “1.7.0_71″ Java(TM) SE Runtime Environment (build 1.7.0_71-b13) Java HotSpot(TM) Client VM (build 25.0-b02, mixed mode) If java is not installed in your system, then follow the steps given below for installing java. Step 1 Download java (JDK <latest version> – X64.tar.gz) by visiting the following link www.oracle.com Then jdk-7u71-linux-x64.tar.gz will be downloaded into your system. Step 2 Generally you will find the downloaded java file in Downloads folder. Verify it and extract the jdk-7u71-linux-x64.gz file using the following commands. $ cd Downloads/ $ ls jdk-7u71-linux-x64.gz $ tar zxf jdk-7u71-linux-x64.gz $ ls jdk1.7.0_71 jdk-7u71-linux-x64.gz Step 3 To make java available to all the users, you have to move it to the location “/usr/local/”. Open root, and type the following commands. $ su password: # mv jdk1.7.0_71 /usr/local/ # exit Step 4 For setting up PATH and JAVA_HOME variables, add the following commands to ~/.bashrc file. export JAVA_HOME=/usr/local/jdk1.7.0_71 export PATH=$PATH:$JAVA_HOME/bin Now apply all the changes into the current running system. $ source ~/.bashrc Step 5 Use the following commands to configure java alternatives − # alternatives –install /usr/bin/java java usr/local/java/bin/java 2 # alternatives –install /usr/bin/javac javac usr/local/java/bin/javac 2 # alternatives –install /usr/bin/jar jar usr/local/java/bin/jar 2 # alternatives –set java usr/local/java/bin/java # alternatives –set javac usr/local/java/bin/javac # alternatives –set jar usr/local/java/bin/jar Now verify the java -version command from the terminal as explained above. Downloading Hadoop Download and extract Hadoop 2.4.1 from Apache software foundation using the following commands. $ su password: # cd /usr/local # wget http://apache.claz.org/hadoop/common/hadoop-2.4.1/ hadoop-2.4.1.tar.gz # tar xzf hadoop-2.4.1.tar.gz # mv hadoop-2.4.1/* to hadoop/ # exit Hadoop Operation Modes Once you have downloaded Hadoop, you can operate your Hadoop cluster in one of the three supported modes − Local/Standalone Mode − After downloading Hadoop in your system, by default, it is configured in a standalone mode and can be run as a single java process. Pseudo Distributed Mode − It is a distributed simulation on single machine. Each Hadoop daemon such as hdfs, yarn, MapReduce etc., will run as a separate java process. This mode is useful for development. Fully Distributed Mode − This mode is fully distributed with minimum two or more machines as a cluster. We will come across this mode in detail in the coming chapters. Installing Hadoop in Standalone Mode Here we will discuss the installation of Hadoop 2.4.1 in standalone mode. There are no daemons running and everything runs in a single JVM. Standalone mode is suitable for running MapReduce programs during development, since it is easy to test and debug them. Setting Up Hadoop You can set Hadoop environment variables by appending the following commands to ~/.bashrc file. export HADOOP_HOME=/usr/local/hadoop Before proceeding further, you need to make sure that Hadoop is working fine. Just issue the following command − $ hadoop version If everything is fine with your setup, then you should see the following result − Hadoop 2.4.1 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 It means your Hadoop”s standalone mode setup is working fine. By default, Hadoop is configured to run in a non-distributed mode on a single machine. Example Let”s check a simple example of Hadoop. Hadoop installation delivers the following example MapReduce jar file, which provides basic functionality of MapReduce and can be used for calculating, like Pi value, word counts in a given list of files, etc. $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.2.0.jar Let”s have an input directory where we will push a few files and our requirement is to count the total number of words in those files. To calculate the total number of words, we do not need to write our MapReduce, provided the .jar file contains the implementation for word count. You can try other examples using the same .jar file; just issue the following commands to check supported MapReduce functional programs by hadoop-mapreduce-examples-2.2.0.jar file. $ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduceexamples-2.2.0.jar Step 1 Create temporary content files in the input directory. You can create this input directory anywhere you would like to work. $ mkdir input $ cp $HADOOP_HOME/*.txt input $ ls -l