TechNewsLetter Vol:4

Official Ansible Content on Docker Hub

What happens when you steal a hackers computer

Introducing Universal SSL

A PROPER SERVER NAMING SCHEME

Five Things Every Developer Should Know About Syslog

Bash remote vulnerability

Everything you need to know about the Shellshock Bash bug

DOCKER HUB OFFICIAL REPOS: ANNOUNCING LANGUAGE STACKS

Why Loggly Chose AWS Route 53 over Elastic Load Balancing

Keyless SSL: The Nitty Gritty Technical Details

Simplifying and securing multicloud development

Twitter’s Mobile Developers developer conference

Marathon v0.7.0 — Running Dockers at Scale and More

When Logstash and Syslog go wrong

Docker Networking

Decoupling packages

JavaScript Memory Management Masterclass

Be a happier developer with Docker

Get your internal IPv4 address

The Dockerfile is not the source of truth for your image.

FBI Not Happy With Apple & Google’s Encryption Policy

Collaborative “Google Docs” for code

Multi-model database with particularly strong fault tolerance, performance, and operational ease.

An open-source graph database

Use Python in Excel without add-ins

Turn dynamic websites into APIs

Material Design for AngularJS Apps

PayPal’s open source node.js automation framework

TechNewsLetter vol:3

A list of awesome #AngularJs services, directives, utilities and resources.

node-shell-parser: Tiny and handy lib for parsing the usual space-separated tables we get as shell commands outputs

DOCKER & VMWARE: 1 + 1 = 3

Service discovery with Docker

A shell which places users into individual docker containers

Docker container orchestration platform

Multiple Docker containers logging to a single syslog

What Docker does right and what it doesn’t do right… yet

Building Docker images with Puppet

Apache Storm and Kafka Cluster with Docker

Docker Service Discovery Using Etcd and Haproxy

Squashing Docker Images

The fundamental problem of programming language package management

JavaScript library for simple HTML5 & CSS3 time sheets

Turn any writing surface into digital streaming and sharing. Powered by the HD camera & Rocketboard’s Computer Vision algorithms.

Tracy library is a useful PHP everyday programmer’s helper.

The Recki Compiler Toolkit for PHP

49ers’ stadium Wi-Fi served 25,000 concurrent users, 2.13TB in all

The Relative Cost of Bandwidth Around the World

Why I Am The Most Important #DevOps Thought Leader

Bringing the #Linux #Revolution to the Networking

Billion Messages – Art of Architecting scalable #ElastiCache #Redis tier

DNS server that lets you look up ec2 instances by instance name

Scumblr is a web application that allows performing periodic searches and storing / taking actions on the identified results.

A modern reverse proxy for node

Ship it!! A story of continuous delivery at theguardian.com

Facebook unveils Autoscale, its load-balancing system that achieves an average power saving of 10-15%

How Fast Can You Debug?

perfbar: Simple way to collect and look at your website performance metrics quickly, that supports budgeting and adding custom metrics.

Build HTML5 Mobile Apps with Bootstrap and Angular JS

Things I wished I knew while doing my tech bachelor / undergraduate (Slides and video)

Angular module for jQuery nestable plugin

What happens when a non-coder tries to learn Linux

TechNewsLetter: Vol2

Nine Tips on Configuring Elasticsearch for High Performance

Streaming File Synchronization

ClusterJS, clusterify your NodeJS applications and achieve zero-downtime deployments

A good talk (slides, video and a transcript) all about the inevitability of failure and why and how you should design your organisations and teams to deal with it.

TLS hardening

Horizontally Scaling Node.js and WebSockets with Redis

Uchiwa is a simple dashboard for the Sensu monitoring framework, built with node.js.

A Chef server written in Go, able to run entirely in memory, with optional persistence with saving the in-memory data to disk or using MySQL or Postgres as the data storage backend.

Development environment builder powered by Docker and buildpacks

PHPSpec – the only Design Tool you need.

 
Infrataster is another tool for writing unit tests for infrastructure. It’s designed to complement serverspec, by focusing on running tests from outside the virtual machine to test external interfaces like HTTP or SSH.

Petit is an open source log analysis tool

Dissecting Message Queues

40 Million hits a day on WordPress using a $10 VPS

How LinkedIn used PoPs and RUM to make dynamic content download 25% faster

Bitly: Lessons Learned Building A Distributed System That Handles 6 Billion Clicks A Month

Load Balancing with HAProxy

A great presentation entitled “how to debug anything”. Lots of examples stepping through issues mainly using strace.

 
MaestroNG is an orchestration tool for multi-host docker environments. The examples in the README give the best idea, showing YAML files describing the entire environment.

 
Anode is a utility for analyzing graphite metrics. It’s currently quite experimental, containing a single three sigma analyzer that takes a time series from graphite and creates new metrics alongside.

 
Log File Navigator is an advanced log file viewer for the small-scale. It’s only really useful for a single host but provides some impressive command line visualisation tools as well as a powerful SQL query engine.

 

Elasticsearch in 15 minutes

gulp-inject:- A javascript, stylesheet and webcomponent injection plugin for Gulp, i.e. inject file references into your index.html

Riemann – A network monitoring system

Vagrant plugin that redirects `notify-send` from guest to host machine and notifies provisioning status.

XMPP Technologies

More Responsive Single-Page Applications With AngularJS & Socket.IO: Creating the Library

Meteor.JS

Visual Website Monitoring.

I Do Not Know One Person Who Is Happy at Amazon.

 
Lots of short hands-on screencasts aiming to teach lots of systems administration basics. Everything from man pages, command line tools, LXC containers and running postmortems.

The unilateral direction of DevOPS

Haka is an open source security oriented language which allows to apply security policies on (live) captured traffic.

Snabb Switch: Fast open source packet processing

Get SSH authorized keys from Github API

ng-inspector:-The AngularJS inspector pane for your browser

What Twitter Isn’t Telling You About GIFs

SSH Tricks

GitHub badges as a service

 

TechNewsLetter: Vol1

There are some interesting stuffs out there. I hope it will help me and you keep up-to-date with the world of technology, rounding up the best new products and things you need to know about.

Whether you’re working with web applications or behind the scenes services, you’ll probably be using HTTP somewhere. So it’s worth noting that the HTTP/1.1 RFC has just been replaced, by a series of clearer and less ambiguous documents. Worth reading.  https://www.mnot.net/blog/2014/06/07/rfc2616_is_dead

ServerSpec:-  Write your spec for your Servers

http://serverspec.org/

https://github.com/serverspec/serverspec

EC2BOX:-

A web-based SSH console to execute commands and manage multiple EC2 instances simultaneously running on Amazon Web Services (AWS).

https://github.com/skavanagh/EC2Box

Changelog is a tool designed to answer the question “What changed in the last twenty minutes?”. It provides a simple HTTP api and a web interface and is intended for tracking everything from deployments, dns changes, reboots, creation of servers, etc.

http://engineering.prezi.com/blog/2014/05/28/changelog-a-tool-designed-to-help-you-recover-faster/

https://github.com/prezi/changelog

SteveJobs Resume :) http://www.landsnail.com/apple/local/steve-jobs-resume/Resume.html

ReadOps

A virtual bookclub for people interested in tech ops. ReadOps is an online book group all about technical operations

http://readops.com/

Overcast

A simple, SSH-based cloud management CLI.It is easy to spin up, configure and manage clusters of machines.

https://github.com/andrewchilds/overcast

Mambo-collector

An interesting tool that connects MySQL with statsd, simply write SQL queries and provide a metrics name and the script will do the rest.

https://github.com/banyek/Mambo-collector

envirius

Envirius is described as a universal virtual environments manager. Like virtualenv in python, rvm in ruby, nvm for node, etc. but without needing a separate tool for every language or framework.

etcd

A highly-available key value store for shared configuration and service discovery. etcd is inspired by Apache ZooKeeper and doozer

https://github.com/coreos/etcd

Mist.io

Mist.io helps you manage and monitor your virtual machines, across different clouds, using any device that can access the web.

Link1:- https://mist.io

Link2:- https://github.com/mistio/mist.io

GoShip

A simple tool for deploying code to servers.

https://github.com/gengo/goship

Depot

Used to push, it is a replacement for reprepro+s3cmd sync and whatnot.

It does incremental updates of a repo, so you don’t need to keep a full local copy of the repo anymore.

You just feed it each package as they are made and it updates all the various metadata files as needed.

https://github.com/coderanger/depot

wonton

If you want to transfer any tools from Amazon S3 to Rackspace or Vice Versa

https://github.com/rackerlabs/wonton

A good topic of conversation when it comes to growing teams and codebases is how to manage change. Pull requests are the default GitHub answer but this post makes some good points about the advantages of tools like Phabricator for different workflows

http://cramer.io/2014/05/03/on-pull-requests/

Packetbeat

It looks like a very nice new open source application monitoring and packet tracing system. A lovely Kibana based interface and agents which can detect various types of traffic once installed make for a very simple getting started experience.

http://packetbeat.com/

gu-who

A neat approach to managing users in a large GitHub organisation. Provides tools for auditing users, ensuring two factor authentication is enabled and providing reporting of issues.

https://github.com/guardian/gu-who

cpthook

Managing multiple different git hooks across multiple repositories for self-hosted git repos can be a pain. cpthook aims to make that easier, moving the configuration into a simple YAML file and providing a command line to manage the hooks.

http://cpthook.com/

A nice set of strong cipher settings for Apache, Nginx and Lighttpd. Designed to be copy and pasted by people who know what they are doing, but a good example to study if you’re just looking at SSL.

https://cipherli.st/

Nagios-Herald is a project that aims to make it easy to provide context in Nagios alerts.

https://github.com/etsy/nagios-herald

Openduty is an open source Alerting and incident escalation tool. It supports alerting with XMPP, email, SMS, Phone and Push notifications, and supports the Pagerduty API for triggering alerts.

https://github.com/ustream/openduty

Checkzilla is simple tool for letting you know about out-of-date software versions. It currently supports Rubygems and NPM, but it’s extensible for other package types. Being able to manage this in one place feels like a good thing.

https://github.com/mickey/checkzilla

AWS-ElasticIP-Swapping

Attach and Detach Public IP in AWS

This script is used for detach elastic ip from one server and attach it to the secondary private ip of the other server.

For example:-

We have two servers with same content named “server01″ and “server02″ with primary and secondary private IP in AWS and each instance have a public IP  (ie. Elastic IP). This two public IP’s are pointed to the DNS.

If “server01″ goes down, only you need to detach the elastic IP and attached it to the “server02″ to the secondary private IP.

You can find the script in the below link:-

Elastic-IP-Swap

 

 

 

 

 

Simple Nodejs WebServer

Node.js is built on Chrome’s V8 JavaScript Engine. It’s useful for a range of applications and it brings JavaScript to the server. Applications written in Node.js are event-driven. For example, if you wanted to create a file, you would provide a callback function to be called upon the file’s successful creation. In this sort of model, your app doesn’t become stuck waiting for events to finish executing.

I hope you have installed nodejs. Then you need to install “forever”

Installing forever

forever, developed by nodejitsu, has functions to create applications that are always running—”forever” running. If the application dies, forever brings it back. It has built-in functionality to monitor processes. It also offers an API you can use to incorporate its features into your own code.

forever can be installed using npm. npm should have been installed as part of Node.js:

forever

Running the app

Create the file index.js with the following contents:

index

Then run:

indexx

Point your browser to http://127.0.0.1 or http://localhost. It should display the index.html”

Now we can use forever to manage our application:, instead of running the above command.

foreverr

To check, open the browser and point it to the http://127.0.0.1 or http://localhost. It should display the index.html

Stopping the app is similar:

stop

 

 

Hadoop

HADOOP

Hadoop is an Apache Software Foundation project that importantly provides two things:

  1. A distributed filesystem called HDFS (Hadoop Distributed File System)
  2. A framework and API for building and running MapReduce jobs

HDFS

HDFS is structured similarly to a regular Unix filesystem except that data storage is distributed across several machines. It is not intended as a replacement to a regular filesystem, but rather as a filesystem-like layer for large distributed systems to use. It has in built mechanisms to handle machine outages, and is optimized for throughput rather than latency.

There are two and a half types of machine in a HDFS cluster:

  • Datanode – where HDFS actually stores the data, there are usually quite a few of these.
  • Namenode – the ‘master’ machine. It controls all the meta data for the cluster. Eg – what blocks make up a file, and what datanodes those blocks are stored on.
  • Secondary Namenode – this is NOT a backup namenode, but is a separate service that keeps a copy of both the edit logs, and filesystem image, merging them periodically to keep the size reasonable.
    • this is soon being deprecated in favor of the backup node and the checkpoint node, but the functionality remains similar (if not the same)

hdfs diagram

Data can be accessed using either the Java API, or the Hadoop command line client. Many operations are similar to their Unix counterparts.

Here are some simple examples:

list files in the root directory

hadoop fs -ls /

list files in my home directory

hadoop fs -ls ./

cat a file (decompressing if needed)

hadoop fs -text ./file.txt.gz

upload and retrieve a file

hadoop fs -put ./localfile.txt /home/vishnu/remotefile.txt

hadoop fs -get /home/vishnu/remotefile.txt ./local/file/path/file.txt

Note that HDFS is optimized differently than a regular file system. It is designed for non-realtime applications demanding high throughput instead of online applications demanding low latency. For example, files cannot be modified once written, and the latency of reads/writes is really bad by filesystem standards. On the flip side, throughput scales fairly linearly with the number of datanodes in a cluster, so it can handle workloads no single machine would ever be able to.

HDFS also has a bunch of unique features that make it ideal for distributed systems:

  • Failure tolerant - data can be duplicated across multiple datanodes to protect against machine failures. The industry standard seems to be a replication factor of 3 (everything is stored on three machines).
  • Scalability – data transfers happen directly with the datanodes so your read/write capacity scales fairly well with the number of datanodes
  • Space – need more disk space? Just add more datanodes and re-balance
  • Industry standard – Lots of other distributed applications build on top of HDFS (HBase, Map-Reduce)
  • HDFS Resources

For more information about the design of HDFS, you should read through apache documentation page. In particular the streaming and data access section has some really simple and informative diagrams on how data read/writes actually happen.

MapReduce

The second fundamental part of Hadoop is the MapReduce layer. This is made up of two sub components:

  • An API for writing MapReduce workflows in Java.
  • A set of services for managing the execution of these workflows.

The Map and Reduce APIs

The basic premise is this:

  1. Map tasks perform a transformation.
  2. Reduce tasks perform an aggregation.

In scala, a simplified version of a MapReduce job might look like this:

def map(lineNumber: Long, sentance: String) = {
  val words = sentance.split()
  words.foreach{word =>
    output(word, 1)
  }
}


def reduce(word: String, counts: Iterable[Long]) = {
  var total = 0l
  counts.foreach{count =>
    total += count
  }
  output(word, total)
}

Notice that the output to a map and reduce task is always a KEY, VALUE pair. You always output exactly one key, and one value. The input to a reduce is KEY, ITERABLE[VALUE]. Reduce is called exactly once for each key output by the map phase. The ITERABLE[VALUE] is the set of all values output by the map phase for that key.

So if you had map tasks that output

map1: key: foo, value: 1
map2: key: foo, value: 32

Your reducer would receive:

key: foo, values: [1, 32]

Counter intuitively, one of the most important parts of a MapReduce job is what happens between map and reduce, there are 3 other stages; Partitioning, Sorting, and Grouping. In the default configuration, the goal of these intermediate steps is to ensure this behavior; that the values for each key are grouped together ready for the reduce() function. APIs are also provided if you want to tweak how these stages work (like if you want to perform a secondary sort).

Here’s a diagram of the full workflow to try and demonstrate how these pieces all fit together, but really at this stage it’s more important to understand how map and reduce interact rather than understanding all the specifics of how that is implemented.

mapreduce diagram

What’s really powerful about this API is that there is no dependency between any two of the same task. To do it’s job a map() task does not need to know about other map task, and similarly a single reduce() task has all the context it needs to aggregate for any particular key, it does not share any state with other reduce tasks.

Taken as a whole, this design means that the stages of the pipeline can be easily distributed to an arbitrary number of machines. Workflows requiring massive datasets can be easily distributed across hundreds of machines because there are no inherent dependencies between the tasks requiring them to be on the same machine.

MapReduce API Resources

If you want to learn more about MapReduce (generally, and within Hadoop) I recommend you read the Google MapReduce paper, the Apache MapReduce documentation, or maybe even the hadoop book. Performing a web search for MapReduce tutorials also offers a lot of useful information.

To make things more interesting, many projects have been built on top of the MapReduce API to ease the development of MapReduce workflows. For example Hive lets you write SQL to query data on HDFS instead of Java.

The Hadoop Services for Executing MapReduce Jobs

Hadoop MapReduce comes with two primary services for scheduling and running MapReduce jobs. They are the Job Tracker (JT) and the Task Tracker (TT). Broadly speaking the JT is the master and is in charge of allocating tasks to task trackers and scheduling these tasks globally. A TT is in charge of running the Map and Reduce tasks themselves.

When running, each TT registers itself with the JT and reports the number of ‘map’ and ‘reduce’ slots it has available, the JT keeps a central registry of these across all TTs and allocates them to jobs as required. When a task is completed, the TT re-registers that slot with the JT and the process repeats.

Many things can go wrong in a big distributed system, so these services have some clever tricks to ensure that your job finishes successfully:

  • Automatic retries – if a task fails, it is retried N times (usually 3) on different task trackers.
  • Data locality optimizations – if you co-locate a TT with a HDFS Datanode (which you should) it will take advantage of data locality to make reading the data faster
  • Blacklisting a bad TT – if the JT detects that a TT has too many failed tasks, it will blacklist it. No tasks will then be scheduled on this task tracker.
  • Speculative Execution – the JT can schedule the same task to run on several machines at the same time, just in case some machines are slower than others. When one version finishes, the others are killed.

Here’s a simple diagram of a typical deployment with TTs deployed alongside datanodes. hadoop infra

MapReduce Service Resources

For more reading on the JobTracker and TaskTracker check out Wikipedia or the Hadoop book. I find the apache documentation pretty confusing when just trying to understand these things at a high level, so again doing a web-search can be pretty useful.

Cluster

A  cluster  is a  group of  computers  connected  via  a network. Similarly a Hadoop Cluster can also be a  combination of  a  number of  systems  connected  together  which  completes the picture of distributed computing. Hadoop uses  a master slave architecture.

Components  required  in the cluster

NameNodes

Name node is the master server of the cluster. It  doesnot store any file but knows where the blocks are stored in the child nodes and can give pointers and can re-assemble .Namenodes  comes up with  two  features  say Fsimage  and the edit log.FSImage   and edit log

Features

  1. Highly memory intensive
  2. Keeping it safe and isolated is necessary
  3. Manages the file system namespaces

DataNodes

Child nodes are attached to the main node.

Features:

  1. Data node  has  a configuration file to make itself  available in the cluster .Again they stores  data regarding storage capacity(Ex:5 out f 10 is available) of   that  particular data  node.
  2. Data nodes are independent ,since they are not pointing to any other data nodes.
  3. Manages the storage  attached to the  node.
  4. There  will be  multiple data nodes  in a cluster.

Job Tracker

  1. Schedules and assign task to the different datanodes.
  2. Work Flow
  3. Takes  the request.
  4. Assign the  task.
  5. Validate the requested work.
  6. Checks  whether  all the  data nodes  are working properly.
  7. If not, reschedule the tasks.

Task Tracker

Job Tracker and  task tracker   works   in  a master slave model. Every  datanode has got a  task tracker which  actually performs  the  task  which ever  assigned to it by the Job tracker.

Secondary Name Node

Secondaryname node  is not  a redundant  namenode but  this actually  provides  the  check pointing  and  housekeeping tasks  periodically.

Types of Hadoop Installations

  1. Standalone (local) mode:  It is used to run Hadoop directly on your local machine. By default Hadoop is configured to run in this mode. It is used for debugging purpose.
  2. Pseudo-distributed mode:  It is used to stimulate multi node installation using a single node setup. We can use a single server instead of installing Hadoop in different servers.
  3. Fully distributed mode:  In this mode Hadoop is installed in all the servers which is a part of the cluster. One machine need to be designated as NameNode and another one as JobTracker. The rest acts as DataNode and TaskTracker.

How to make a Single node Hadoop Cluster

A Single node cluster is a cluster where all the Hadoop daemons run on a single machine. The development can be described as several steps.

Prerequisites

OS Requirements

Hadoop is meant to be deployed on Linux based platforms which includes OS like Mackintosh. Larger Hadoop production deployments are mostly on Cent OS, Red hat etc.

GNU/Linux is using as the development and production platform. Hadoop has been demonstrated on Linux clusters with more than 4000 nodes.

Win32 can be used as a development platform, but is not used as a production platform. For developing cluster  in windows, we need Cygwin.

Since Ubuntu is a common Linux distribution and with interfaces similar to Windows, we’ll describe the details of Hadoop deployment on Ubuntu, it is better using the latest stable versions of OS.

This document deals with the development of cluster using Ubuntu Linux platform. Version is 12.04.1 LTS 64 bit.

Softwares Required

  • Java JDK

The recommended and tested versions of java are listed below, you can choose any of the following

Jdk 1.6.0_20

Jdk 1.6.0_21

Jdk 1.6.0_24

Jdk 1.6.0_26

Jdk 1.6.0_28

Jdk 1.6.0_31

*Source Apache Software Foundation wiki. Test resukts announced by Cloudera,MapR,HortonWorks

  • SSH must be installed.
  • SSHD must be running.

This is used by the Hadoop scripts to manage remote Hadoop daemons.

  • Download a latest stable version of Hadoop.

Here we are using Hadoop 1.0.3.

Now we are ready with a Linux machine and required softwares. So we can start the set up. Open the terminal and follow the steps described below

Step 1

Checking whether the OS is 64 bit or 32 bit

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>$ uname –a

If it is showing a 64, then all the softwares(Java, ssh) must be of 64 bit. If it is showing 32, then use the softwares for 32 bit. This is very important.

Step 2

Installing  Java.

For setting up hadoop, we need java. It is recommended to use sun java 1.6.

For checking whether the java is already installed or not

>$ java –version


This will show the details about java, if it is already installed.

If it is not there, we have to install.

Download a stable version of java as described above.

The downloaded file may be .bin file or .tar file

For installing a .bin file, go to the directory containing the binary file.

>$ sudo chmod u+x <filename>.bin

>$ ./<filename>.bin


If it is a tar ball

>$ sudo chmod u+x <filename>.tar

>$ sudo tar xzf <filename>.tar


Then set the JAVA_HOME in .bashrc file

Go to $HOME/.bashrc file

For editing .bashrc file

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>$ sudo nano $HOME/.bashrc
# Set Java Home
export JAVA_HOME=<path from root to that java directory>
export PATH=$PATH:$JAVA_HOME/bin

Now close the terminal, re-open again and check whether the java installation is correct.

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>$ java –version

This will show the details, if java is installed correct.

Now we are ready with java installed.

Step 3

Adding a user for using Hadoop

We have to create a separate user account for running Hadoop. This is recommended, because it isolates other softwares and other users on the same machine from hadoop installation.

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>$ sudo addgroup hadoop
>$ sudo adduser –ingroup hadoop user

Here we created a user “user” in a group “hadoop”.

Step 4

In the following steps,  If you are not able to do sudo with user.

Then add user to sudoers group.

For that

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>$ sudo nano /etc/sudoers

Then add the following

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%user ALL= (ALL)ALL

This will give user the root privileges.

If you are not interested in giving root privileges, edit the line in the sudoers file as below

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# Allow members of group sudo to execute any command
%sudo   ALL=(ALL:ALL) ALL

Step 5

Installing SSH server.

Hadoop requires SSH access to manage the nodes.

In case of multinode cluster, it is remote machines and local machine.

In single node cluster, SSH is needed to access the localhost for user user.

If ssh server is not installed, install it before going further.

Download the correct version (64bit or 32 bit) of open-ssh-server.

Here we are using 64 bit OS, So I downloaded open ssh server for 64 bit.

The download link is

http://www.ubuntuupdates.org/package/core/precise/main/base/openssh-server

The downloaded file may be a .deb file.

For installing a .deb file

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>$ sudo chmod u+x <filename>.deb
>$ sudo dpkg –I <filename>.deb

This will install the .deb file.

Step 6

Configuring SSH

Now we have SSH up and running.

As the first step, we have to generate an SSH key for the user

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<div>
user@ubuntu:~$ su - user
user@ubuntu:~$ ssh-keygen -t rsa -P ""
Generating public/private rsa key pair.
Enter file in which to save the key (/home/user/.ssh/id_rsa):
Created directory '/home/user/.ssh'.
Your identification has been saved in /home/user/.ssh/id_rsa.
Your public key has been saved in /home/user/.ssh/id_rsa.pub.
The key fingerprint is:
9d:47:ab:d7:22:54:f0:f9:b9:3b:64:93:12:75:81:27user@ubuntu
The key’s randomart image is:
[........]
user@ubuntu:~$

Here it is needed to unlock the key without our interaction, so we are creating an RSA keypair with an empty password. This is done in the second line. If empty password is not given, we have to enter the password every time when Hadoop interacts with its nodes. This is not desirable, so we are giving empty password.

The next step is to enable SSH access to our local machine with the key created in the previous step.

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user@ubuntu:~$ cat $HOME/.ssh/id_rsa.pub >> $HOME/.ssh/authorized_keys
</div>

The last step is to test SSH setup by connecting to our local machine with user. This step is necessary to save our local machine’s host key fingerprint to the useruser’sknown_hosts file.

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user@ubuntu:~$ sshlocalhost
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 12.04.1
...
user@ubuntu:~$

Step 7

Disabling IPv6

There is no use in enabling IPv6 on our Ubuntu Box, because we are not connected to any IPv6 network. So we can disable IPv6. The performance may vary.

For disabling IPv6 on Ubuntu , go to

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>$ cd /etc/

Open the file sysctl.conf

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>$ sudo nano sysctl.conf

Add the following lines to the end of this file

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#disable ipv6
net.ipv6.conf.all.disable_ipv6 = 1
net.ipv6.conf.default.disable_ipv6 = 1
net.ipv6.conf.lo.disable_ipv6 = 1

Reboot the machine to make the changes take effect

For checking whether IPv6 is enabled or not, we can use the following command.

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>$ cat  /proc/sys/net/ipv6/conf/all/disable_ipv6

If the value is ‘0’ , IPv6 is enabled.

If it is ‘1’ , IPv6 is disabled.

We need the value to be ‘1’.

The requirements for installing Hadoop is ready. So we can start hadoop installation.

Step 8

Hadoop Installation

Here I am using this version hadoop 1.0.3.

So we are using this tar ball.

We create a directory named ‘utilities’ in user.

Practically, you can choose any directory. It will be good if you are keeping a good and uniform directory structure while installation. It will be good and when you deal with multinode clusters.

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>$ cd utilities
>$ sudo tar -xvf  hadoop-1.0.3.tar.gz
>$ sudo   chown –R user:hadoop hadoop-1.0.3

Here the 2nd line will extract the tar ball.

The 3rd line will the permission(ownership)of hadoop-1.0.3 to user

Step 9

Setting HADOOP_HOME in $HOME/.bashrc

Add the following lines in the .bashrc file

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# Set Hadoop_Home
export HADOOP_HOME=/home/user/utilities/hadoop-1.0.3
# Adding bin/ directory to PATH
export PATH=$PATH:$HADOOP_HOME/bin

Note: If you are editing this $HOME/.bashrc  file, the user doing this only will get the benefit.

For making this affect globally to all users,

go to /etc/bash.bashrc file  and do the same changes.

Thus JAVA_HOME and HADOOP_HOME will be available to all users.

Do the same procedure while setting java also.

Step 10

Configuring Hadoop

In hadoop, we can find three configuration files core-site.xml, mapred-site.xml, hdfs-site.xml.

If we open this files, the only thing we can see is an empty configuration tag <configuration></configuration>

What actually happening behind the curtain is that, hadoop assumes default value to a lot of properties. If we want to override that, we can edit these configuration files.

The default values are available in three files

core-default.xml, mapred-default.xml, hdfs-default.xml

These are available in the locations

utilities/hadoop-1.0.3/src/core, utilities/hadoop-1.0.3/src/mapred,

utilities/hadoop-1.0.3/src/hdfs.

If we open these files, we can see all the default properties.</pre>
Setting JAVA_HOME for hadoop directly

Open hadoop-env.sh file, you can see a JAVA_HOME with a path.

The location of hadoop-env.sh file is

hadoop-1.0.3/conf/hadoop-env.sh

Edit that JAVA_HOME and give the correct path in which java is installed.

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>$ sudo  nano hadoop-1.0.3/conf/hadoop-env.sh
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#The Java Implementation to use
export JAVA_HOME=<path from root to java directory>

Editting the Configuration files

All these files are present in the directory

hadoop-1.0.3/conf/

Here we are configuring the directory where the hadoop stores its data files, the network ports is listens to…etc

By default Hadoop stores its local file system and HDFS in hadoop.tmp.dir .

Here we are using the directory /app/hadoop/tmp for storing  temparory directories.

For that create a directory and set the ownership and  permissions to user

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>$  sudo   mkdir –p /app/hadoop/tmp
>$ sudo   chownuser:hadoop /app/hadoop/tmp
>$ sudo   chmod 750 /app/hadoop/tmp

Here the first line will create the directory structure.

Second line will give the ownership of that directory to user

The third line will set the rwx permissions.

Setting the ownership and permission is very important, if you forget this, you will get into some exceptions while formatting the namenode.

1.       Core-site.xml

Open the core-site.xml file, you can see empty configuration tags.

Add the following lines between the configuration tags.

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<property>
<name>hadoop.tmp.dir</name>
<value>/app/hadoop/tmp</value>
<description>
A base for other temporary directories.
</description>
</property>
<property>
<name>fs.default.name</name>
<value>hdfs://localhost:9000</value>
<description>The name of the default file system.</description>
</property>
2.       Mapred-site.xml

In the mapred-site.xml add the following between the configuration tags.

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<property>
<name>mapred.job.tracker</name>
 <value>localhost:9001</value>
 <description> The host and port that the MapReduce job tracker runs </description>
</property>
3.       Hdfs-site.xml

In the hdfs-site.xml add the following between the configuration tags.

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<property>
<name>dfs.replication</name>
<value>1</value>
<description>Default block replication</description>
</property>

Here we are giving replication as 1, because we have only one machine.

We can increase this as the number of nodes increases.

Step 11

Formatting the Hadoop Distributed File System via  NameNode.

The first step for starting our Hadoop installation is to format the distributed file system. This should be done before first use. Be careful that, do not format an already running cluster, because all the data will be lost.

user@ubuntu:~$ $HADOOP_HOME/bin/hadoop namenode –format

The output will look like this

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09/10/12 12:52:54 INFO namenode.NameNode: STARTUP_MSG:
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG:   host = ubuntu/127.0.1.1
STARTUP_MSG:   args = [-format]
STARTUP_MSG:   version = 0.20.2
STARTUP_MSG:   build = https://svn.apache.org/repos/asf/hadoop/common/branches/branch-1.0.3 -r 911707; compiled by 'chrisdo' on Fri Feb 19 08:07:34 UTC 2010
************************************************************/
09/10/12 12:52:54 INFO namenode.FSNamesystem: fsOwner=user,hadoop
09/10/12 12:52:54 INFO namenode.FSNamesystem: supergroup=supergroup
09/10/12 12:52:54 INFO namenode.FSNamesystem: isPermissionEnabled=true
09/10/12 12:52:54 INFO common.Storage: Image file of size 96 saved in 0 seconds.
09/10/12 12:52:54 INFO common.Storage: Storage directory .../hadoop-user/dfs/name has been successfully formatted.
09/10/12 12:52:54 INFO namenode.NameNode: SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at ubuntu/127.0.1.1
************************************************************/

Step 12

Starting Our single-node Cluster

Here we have only one node. So all the hadoop daemons are running on a single machine.

So we can start all the daemons by running a shell script.

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user@ubuntu:~$ $HADOOP_HOME/bin/start-all.sh

This willstartup all the hadoop daemonsNamenode, Datanode, Jobtracker and Tasktracker on our machine.

The output when we run this is shown below.

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user@ubuntu:/home/user/utilities/hadoop-1.0.3$ bin/start-all.sh
startingnamenode, logging to /home/user/utilities/hadoop-1.0.3/bin/../logs/hadoop-user-namenode-ubuntu.out
localhost: starting datanode, logging to home/user/utilities/hadoop-1.0.3/bin/../logs/hadoop-user-datanode-ubuntu.out
localhost: starting secondarynamenode, logging to home/user/utilities/hadoop-1.0.3/bin/../logs/hadoop-user-secondarynamenode-ubuntu.out
startingjobtracker, logging to home/user/utilities/hadoop-1.0.3/bin/../logs/hadoop-user-jobtracker-ubuntu.out
localhost: starting tasktracker, logging to home/user/utilities/hadoop-1.0.3/bin/../logs/hadoop-user-tasktracker-ubuntu.out
user@ubuntu$

You can check the process running on the by using jps.

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user@ubuntu:/home/user/utilities/hadoop-1.0.3$ jps
1127 TaskTracker
2339 JobTracker
1943 DataNode
2098 SecondaryNameNode
2378 Jps
1455 NameNode

Note: If jps is not working, you can use another linux command.

ps –ef | grepuser

You can check for each daemon also

ps –ef | grep<daemonname>eg:namenode

Step 13

StoppingOur single-node Cluster

For stopping all the daemons running in the machine

Run the command

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>$stop-all.sh

The output will be like this

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user@ubuntu:~/utilities/hadoop-1.0.3$ bin/stop-all.sh
stoppingjobtracker
localhost: stopping tasktracker
stoppingnamenode
localhost: stopping datanode
localhost: stopping secondarynamenode
user@ubuntu:~/utilities/hadoop-1.0.3$

Then check with jps

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>$jps
2378 Jps

Step 14

Testing the set up

Now our installation part is complete

The next step is to test the installed set up.

Restart the hadoop cluster again by using start-all.sh

Checking with HDFS
  1. Make a directory in hdfs
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    </pre>
    </li>
    </ol>
    hadoop fs –mkdir  /user/user/trial

    If it is success list the created directory.

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    hadoop fs –ls /

    The output will be like this

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    drwxr-xr-x   - usersupergroup  0 2012-10-10 18:08 /user/user/trial

    If getting like this, the HDFS is working fine.

    1. Copy a file from local linux file system
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    hadoop fs –copyFromLocal  utilities/hadoop-1.0.3/conf/core-site.xml  /user/user/trial/

    Check for the file in HDFS

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    hadoop fs –ls /user/user/trial/
    -rw-r--r--   1 usersupergroup 557 2012-10-10 18:20 /user/user/trial/core-site.xml

    If the output is like this, it is success.

    Checking with a MapReduce job

    Mapreduce jars for testing are available with the hadoop itself.

    So we can use that jar. No need to import another.

    For checking with mapreduce, we can run a wordcountmapreduce job.

    Go to $HADOOP_HOME

    Then run

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    >$hadoop jar hadoop-examples-1.0.3.jar

    This output will be like this

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    An example program must be given as the first argument.
    Valid program names are:
    aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.
    aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.
    dbcount: An example job that count the pageview counts from a database.
    grep: A map/reduce program that counts the matches of a regex in the input.
    join: A job that effects a join over sorted, equally partitioned datasets
    multifilewc: A job that counts words from several files.
    pentomino: A map/reduce tile laying program to find solutions to pentomino problems.
    pi: A map/reduce program that estimates Pi using monte-carlo method.
    randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.
    randomwriter: A map/reduce program that writes 10GB of random data per node.
    secondarysort: An example defining a secondary sort to the reduce.
    sleep: A job that sleeps at each map and reduce task.
    sort: A map/reduce program that sorts the data written by the random writer.
    sudoku: A sudoku solver.
    teragen: Generate data for the terasort
    terasort: Run the terasort
    teravalidate: Checking results of terasort
    wordcount: A map/reduce program that counts the words in the input files.

    The above shown are the programs that are contained inside that jar, we can choose any program.

    Here we are  going to run the wordcount process.

    The input file using is the file that we already copied from local to HDFS.

    Run the following commands for executing the wordcount

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    >$ hadoop jar hadoop-examples-1.0.3.jar wordcount user/user/trial/core-site.xml user/user/trial/output/
    The output will be like this
    12/10/10 18:42:30 INFO input.FileInputFormat: Total input paths to process : 1
    12/10/10 18:42:30 INFO util.NativeCodeLoader: Loaded the native-hadoop library
    12/10/10 18:42:30 WARN snappy.LoadSnappy: Snappy native library not loaded
    12/10/10 18:42:31 INFO mapred.JobClient: Running job: job_201210041646_0003
    12/10/10 18:42:32 INFO mapred.JobClient:  map 0% reduce 0%
    12/10/10 18:42:46 INFO mapred.JobClient:  map 100% reduce 0%
    12/10/10 18:42:58 INFO mapred.JobClient:  map 100% reduce 100%
    12/10/10 18:43:03 INFO mapred.JobClient: Job complete: job_201210041646_0003
    12/10/10 18:43:03 INFO mapred.JobClient: Counters: 29
    12/10/10 18:43:03 INFO mapred.JobClient:   Job Counters
    12/10/10 18:43:03 INFO mapred.JobClient:     Launched reduce tasks=1
    12/10/10 18:43:03 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=12386
    12/10/10 18:43:03 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
    12/10/10 18:43:03 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
    12/10/10 18:43:03 INFO mapred.JobClient:     Launched map tasks=1
    12/10/10 18:43:03 INFO mapred.JobClient:     Data-local map tasks=1
    12/10/10 18:43:03 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=10083
    12/10/10 18:43:03 INFO mapred.JobClient:   File Output Format Counters
    12/10/10 18:43:03 INFO mapred.JobClient:     Bytes Written=617
    12/10/10 18:43:03 INFO mapred.JobClient:   FileSystemCounters
    12/10/10 18:43:03 INFO mapred.JobClient:     FILE_BYTES_READ=803
    12/10/10 18:43:03 INFO mapred.JobClient:     HDFS_BYTES_READ=688
    12/10/10 18:43:03 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=44801
    12/10/10 18:43:03 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=617
    12/10/10 18:43:03 INFO mapred.JobClient:   File Input Format Counters
    12/10/10 18:43:03 INFO mapred.JobClient:     Bytes Read=557
    12/10/10 18:43:03 INFO mapred.JobClient:   Map-Reduce Framework
    12/10/10 18:43:03 INFO mapred.JobClient:     Map output materialized bytes=803
    12/10/10 18:43:03 INFO mapred.JobClient:     Map input records=18
    12/10/10 18:43:03 INFO mapred.JobClient:     Reduce shuffle bytes=803
    12/10/10 18:43:03 INFO mapred.JobClient:     Spilled Records=90
    12/10/10 18:43:03 INFO mapred.JobClient:     Map output bytes=746
    12/10/10 18:43:03 INFO mapred.JobClient:     CPU time spent (ms)=3320
    12/10/10 18:43:03 INFO mapred.JobClient:     Total committed heap usage (bytes)=233635840
    12/10/10 18:43:03 INFO mapred.JobClient:     Combine input records=48
    12/10/10 18:43:03 INFO mapred.JobClient:     SPLIT_RAW_BYTES=131
    12/10/10 18:43:03 INFO mapred.JobClient:     Reduce input records=45
    12/10/10 18:43:03 INFO mapred.JobClient:     Reduce input groups=45
    12/10/10 18:43:03 INFO mapred.JobClient:     Combine output records=45
    12/10/10 18:43:03 INFO mapred.JobClient:     Physical memory (bytes) snapshot=261115904
    12/10/10 18:43:03 INFO mapred.JobClient:     Reduce output records=45
    12/10/10 18:43:03 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=2876592128
    12/10/10 18:43:03 INFO mapred.JobClient:     Map output records=48
    user@ubuntu:~/utilities/hadoop-1.0.3$

    If the program executed successfully, the output will be in

    user/user/trial/output/part-r-00000 file in hdfs

    Check the output

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    >$hadoop fs –cat user/user/trial/output/part-r-00000

    If output is coming, then our installation is success with mapreduce.

    Thus we checked our installation.

    So our single node hadoop cluster is ready