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Openstack Cloud Software

Wednesday, October 17th, 2012 | Posted in Cloud computing

OpenStack : The Mission

“ To produce the ubiquitous Open Source Cloud Computing platform that will meet the needs of public and private cloud providers regardless of size, by being simple to implement and massively scalable.”

OpenStack is a collection of open source software projects that enterprises/service providers can use to setup and run their cloud compute and storage infrastructure.Rackspace and NASA are the key initial contributors to the stack. Rackspace contributed their “Cloud Files” platform (code) to power the Object Storage part of the OpenStack, while NASA contributed their “Nebula” platform (code) to power the Compute part. OpenStack consortium has managed to have more than 150 members including Canonical, Dell, Citrix etc.

There are 5 main service families under OpenStack

• Nova – Compute Service

• Swift – Storage Service

• Glance – Imaging Service

• Keystone – Identity Service

• Horizon – UI Service

Open Stack Compute Infrastructure (Nova)

Nova is the Computing Fabric controller for the OpenStack Cloud. All activities needed to support the life cycle of instances within the OpenStack cloud are handled by Nova. This makes Nova a Management Platform that manages compute resources, networking, authorization, and scalability needs of the OpenStack cloud. But, Nova does not provide any virtualization capabilities by itself; instead, it uses libvirt API to interact with supported hypervisors. Nova exposes all its capabilities through a web services API that is compatible with the EC2 API of Amazon Web Services.

Functions and Features:

• Instance life cycle management

• Management of compute resources

• Networking and Authorization

• REST-based API

• Asynchronous eventually consistent communication

• Hypervisor agnostic : support for Xen, XenServer/XCP, KVM, UML, VMware vSphere and Hyper-V

OpenStack Storage Infrastructure (Swift)

Swift provides a distributed, eventually consistent virtual object store for OpenStack. It is analogous to Amazon Web Services – Simple Storage Service (S3). Swift is capable of storing billions of objects distributed across nodes. Swift has built-in redundancy and fail-over management and is capable of archiving and media streaming. It is extremely scalable in terms of both size (several petabytes) and capacity (number of objects).

Functions and Features

• Storage of large number of objects

• Storage of large sized objects

• Data Redundancy

• Archival capabilities – Work with large datasets

• Data container for virtual machines and cloud apps

• Media Streaming capabilities

• Secure storage of objects

• Backup and archival

• Extreme scalability

OpenStack Imaging Service (Glance)

OpenStack Imaging Service is a lookup and retrieval system for virtual machine images. It can be configured to use any one of the following storage backends:

• Local filesystem (default)

• OpenStack Object Store to store images

• S3 storage directly

• S3 storage with Object Store as the intermediate for S3 access.

• HTTP (read-only)

Functions and Features

• Provides imaging service

OpenStack Identity Service (Keystone)

Keystone provides identity and access policy services for all components in the OpenStack family. It implements it’s own REST based API (Identity API). It provides authentication and authorization for all components of OpenStack including (but not limited to) Swift, Glance, Nova. Authentication verifies that a request actually comes from who it says it does. Authorization is verifying whether the authenticated user has access to the services he/she is requesting for.

Keystone provides two ways of authentication. One is username/password based and the other is token based. Apart from that, keystone provides the following services:

• Token Service (that carries authorization information about an authenticated user)

• Catalog Service (that contains a list of available services at the users’ disposal)

• Policy Service (that let’s keystone manage access to specific services by specific users or groups).

Openstack Administrative Web-Interface (Horizon)

Horizon the web based dashboard can be used to manage /administer OpenStack services. It can be used to manage instances and images, create keypairs, attach volumes to instances, manipulate Swift containers etc. Apart from this, dashboard even gives the user access to instance console and can connect to an instance through VNC. Overall, Horizon

Features the following:

• Instance Management – Create or terminate instance, view console logs and connect through VNC, Attaching volumes, etc.

• Access and Security Management – Create security groups, manage keypairs, assign floating IPs, etc.

• Flavor Management – Manage different flavors or instance virtual hardware templates.

• Image Management – Edit or delete images.

• View service catalog.

• Manage users, quotas and usage for projects.

• User Management – Create user, etc.

• Volume Management – Creating Volumes and snapshots.

• Object Store Manipulation – Create, delete containers and objects.

• Downloading environment variables for a project.

INSTALLATING OPEN STACK

We can install open stack ESSEX very easily using StackGeek script. Login to your box and install git with apt-get. We’ll become root and do an update first.

sudo su
apt-get update
apt-get install git

Now checkout the StackGeek scripts from Github:

git clone git://github.com/StackGeek/openstackgeek.git   
cd openstackgeek

Install the Base Scripts

Be sure to take a look at the scripts before you run them. Keep in mind the scripts will periodically prompt you for input, either for confirming installation of a package, or asking you for information for configuration.

Start the installation by running the first script:

./openstack_base_1.sh

When the script finishes you’ll see instructions for manually configuring your network. You can edit the interfaces file by doing a:

vim /etc/network/interfaces

Copy and paste the network code provided by the script into the file and then edit:

auto eth0 
iface eth0 inet static
  address 192.168.1.48		
  network 192.168.1.0		
  netmask 255.255.255.0
 broadcast 192.168.1.255
  gateway 192.168.1.124			
  dns-nameservers 8.8.8.8  
auto eth1

Change the settings for your network configuration and then restart networking and run the next script:

/etc/init.d/networking restart

Then run the second script :

./openstack_base_2.sh

After the second script finishes, you’ll need to set up a logical volume for Nova to use for creating snapshots and volumes. Nova is OpenStack’s compute controller process.

Here’s the output from the format and volume creation process:-

root@manager-System-Product-Name:/openstackgeek# fdisk /dev/sda
Device contains neither a valid DOS partition table,nor Sun,SGI or OSF disklabel
Building a new DOS disklabel with disk identifier 0xb39fe7af.
Changes will remain in memory only, until you decide to write them.
After that, of course, the previous content won't be recoverable.

Warning: invalid flag 0x0000 of partition table 4 will be corrected by w(rite)

Command (m for help): n
Partition type:
   p   primary (0 primary, 0 extended, 4 free)
   e   extended
Select (default p): p Partition number (1-4, default 1): 3  
First sector (2048-62914559, default 2048): 
 Using default value 2048 Last sector,(2048-62914559,default 62914559): 
Using default value 62914559 
Command (m for help): w The partition table has been altered! 
Calling ioctl() to re-read partition table. Syncing disks.
root@manager-System-Product-Name:/openstackgeek# pvcreate -ff /dev/sda3
 Physical volume "/dev/sda3" successfully created
root@manager-System-Product-Name:/openstackgeek# vgcreate nova-volumes /dev/sda3
 Volume group "nova-volumes" successfully created

Note: Your device names may vary.

Installing MySql

The OpenStack components use MySQL for storing state information. Start the install script for MySQL by entering the following:

./openstack_mysql.sh

You’ll be prompted for a password used for each of the components to talk to MySQL:
Enter a password to be used for the OpenStack services
to talk to MySQL (users nova, glance, keystone): redhat
Note(Here “redhat” is the password given to nova,glance,keystone)

During the installation process you will be prompted for a root password for MySQL. In our install example we use the same password, ‘redhat’. At the end of the MySQL install you’ll be prompted for your root password again.

mysql start/running, process 8796
################################################################################ 
Creating OpenStack databases and users. 
Use your database password when prompted. 
 Run './openstack_keystone.sh' when the script exits. 
################################################################################
Enter password:
After MySQL is running, you should be able to login with any of the OpenStack 
users and/or the root admin account by doing the following:

mysql -u root -predhat
mysql -u nova -predhat nova
mysql -u keystone -predhat keystone
mysql -u glance -predhat glance

Installing Keystone

Keystone is OpenStack’s identity manager. Start the install of Keystone by doing:

./openstack_keystone.sh

You’ll be prompted for a token, the password you entered for OpenStack’s services, and your email address. The email address is used to populate the user’s information in the database.

Enter a token for the OpenStack services to auth wth keystone: redhattoken 
Enter the password you used for the MySQL users (nova, glance, keystone):redhat 
Enter the email address for accounts(nova,glance,keystone):user@company.com
You should be able to query Keystone at this point. 
You’ll need to source the“stackrc” file before you talk to Keystone:
 . ./stackrc   
 keystone user-list    
 Keystone should return a list of users:
+----------------------------------+---------+------------------------+--------+
|                id                | enabled |         email          |  name  |
+----------------------------------+---------+------------------------+--------+
| b32b9017fb954eeeacb10bebf14aceb3 | True    | user@company.com       | demo   |
| bfcbaa1425ae4cd2b8ff1ddcf95c907a | True    | user@company.com       | glance |
| c1ca1604c38443f2856e3818c4ceb4d4 | True    | user@company.com       | nova   |
| dd183fe2daac436682e0550d3c339dde | True    | user@company.com       | admin  |
+----------------------------------+---------+------------------------+--------+

Installing Glance

Glance is OpenStack’s image manager. Start the install of Glance by doing:

./openstack_glance.sh

The script will download an Ubuntu 12.04 LTS cloud image from StackGeek’s S3 bucket.Once it’s done, you should be able to get a list of images:

glance index

Here’s the expected output:

ID              :- 71b8b5d5-a972-48b3-b940-98a74b85ed6a 
Name            :- Ubuntu 12.04 LTS
Disk Format     :- qcow2 
Container Format:- ovf 
Size            :- 226426880

Installing Nova

We’re almost done installing! The last component is the most important one as well. Nova is OpenStack’s compute and network manager. It’s responsible for starting instances, creating snapshots and volumes, and managing the network. Start the Nova install by doing:

./openstack_nova.sh

You’ll immediately be prompted for a few items, including your existing network interface’s IP address, the fixed network address, and the floating pool addresses:

######################################################
The IP address for eth0 is probably 192.168.1.48.
Keep in mind you need an eth1 for this to work.
######################################################
Enter the primary ethernet interface IP: 192.168.1.48
Enter the fixed network (eg. 10.0.2.32/27): 192.168.1.0/24
Enter the fixed starting IP (eg. 10.0.2.33): 192.168.1.1
############################################################################
The floating range can be a subset of your current network. 
Configure your DHCP server to block out the range before you choose it here. 
An example would be 10.0.1.224-255
############################################################################
Enter the floating network (eg. 10.0.1.224/27):  
Enter the floating netowrk size (eg. 32):

The fixed network is a set of IP addresses which will be local to the compute nodes. Think of these addresses as being held and routed internally inside any of the compute node instances.

The floating network is a pool of addresses which can be assigned to the instances you are running. For example, you could start a web server and map an external IP to it for serving a site on the Internet.

Finish Installing Nova

Nova should finish installing after you enter all the network information. When it’s done, you should be able to get a list of images from Glance via Nova:

 nova image-list

And get the expected output we saw earlier from Glance:

root@manager-System-Product-Name:/openstackgeek# nova image-list
+--------------------------------------+------------------+--------+--------+
|                  ID                  |       Name       | Status | Server |
+--------------------------------------+------------------+--------+--------+
| 71b8b5d5-a972-48b3-b940-98a74b85ed6a | Ubuntu 12.04 LTS | ACTIVE |        |
+--------------------------------------+------------------+--------+--------+

Installing Horizon

Horizon is the UI and dashboard controller for OpenStack. Install it by doing:

./openstack_horizon.sh

When it’s done installing, you’ll be given a URL to access the dashboard. 
You’ll be able to login with the user ‘admin’ 
and whatever you entered earlier for your password. 
If you’ve forgotten it, simply grep for it in your environment:

env |grep OS_PASSWORD

The URL will be : http://192.168.1.48

You can login the Openstack dashboard by the following credentials

USER : admin

PASSWORD : redhat

From CAP, Puppet Now Chef, Evolution of Configuration Management Tools

Tuesday, May 8th, 2012 | Posted in Cloud computing

CHEF, PUPPET & CAPISTRANO are used basically for two purposes :

Application Deployment is all of the activities that make a software system available for use.

Configuration Management is software configuration management is the task of tracking and controlling changes in the software. Configuration management practices include revision control and the establishment of baselines.

Let me enlighten on how we evolved from the beginning when we were using tools like ssh, scp to the point where we began to abstract and began to equip our-self with these sophisticated yet simple to use tools. Earlier the following tools like

ssh which is used as a configuration management solution for admins.
scp act as a secure channel for application deployment.

The need for any other tools was out of question until things got complicated!!!

HISTORY

Earlier an Application Deployment was just a few steps away such as

scp app to production box
restart server (optional)
profit

And these software refreshing/updates were done

Manual (ssh)
with shell scripts living on the servers
or not done at all

CAPISTRANO
(Introduced by Jamis Buck, written in Ruby, initially for Rails project)

Capistrano is a developer tool for deploying web applications. It is typically installed on a workstation, and used to deploy code from your source code management (SCM) to one, or more servers.In its simplest form, Capistrano allows you to copy code from your source control repository (SVN or Git) to your server via SSH, and perform pre & post-deploy functions like restarting a webserver, busting cache, renaming files, running database migrations and so on.

Nice things cap introduced :

Automate deploys with one set of files
The files don’t have to live on the production server
The language (Ruby) allows some abstraction

Now application deployment step can be coded and tested like rest of the project. It has also become the de facto way to deploy the Ruby on Rails applications. It has also had tools like webistrano build on top of it to provide a graphical interface to the command line tool.

Drawback : The tool seems to be widely used but not well supported.

PUPPET

(Written in Ruby and evolved from cfengine)

Luke Kanies came up with the idea for Puppet in 2003 after getting fed up with existing server-management software in his career as a systems administrator. In 2005 he quit his job at BladeLogic, a maker of data-center management software, and spent the next 10 months writing code to automate the dozens of steps required to set up a server with the right software, storage space, and network configurations. The result: scores of templates for different kinds of servers, which let systems administrators become, in Kanies’s metaphor, puppet masters, pulling on strings to give computers particular personalities and behaviors. He formed Puppet Labs to begin consulting for some of the thousands of companies using the software—the list includes Google, Zynga, and Twitter etc

Puppet is typically used in a client server formation, with all your clients talking to one or more servers. Each client contacts the servers periodically (every half an hour by default), downloads the latest configuration and makes sure it is sync with that configuration.

The Server in Puppet is called Puppet Master.
Puppet Manifests contains all the configuration details which are declarative as opposed to imperative.

The DSL is not Ruby as you are not writing scripts you are writing definitions, Install order is determined through dependencies.
The Puppet Master is idempotent which will make sure the client machines match the definitions.This is good as you can implement changes across machines automatically just by updating the manifest in the Puppet Master.

CHEF
(written in ruby evolved from puppet)

CHEF is an open source configuration management tool using pure-Ruby, the chef domain specific language for writing system configuration related stuff (recipes and cookbook)
CHEF brings a new feel with its interesting naming conventions relating to cookery like Cookbooks (they contain codes for a software package installation and configuration in the form of Recipes), Knife (API tool), Databags (act like global variables) etc

Chef Server – deployment scripts called Cookbooks and Recipes, configuration instructions called Nodes, security details etc. The clients in the chef infrastructure are called Nodes. Chef recipes are imperative as opposed to declarative. The DSL is extended Ruby so you can write scripts as well as definitions. Install order is script order NO dependency checking.

CHEF & PUPPET

Chef and Puppet automatically set up and tweak the operating systems and programs that run in massive data centers and the new-age “cloud” services, designed to replace massive data centers.

Chef Recipes is more programmer friendly as it is easily understood by a developer unlike a Puppet Manifest.

And when it comes to features in comparison to puppet, chef is rather more intriguing .
For example “Chef’s ability to search an environment and use that information at run time is very appealing.

Knife is Chef’s powerful command line interface. Knife allows you to interact with your entire infrastructure and Chef code base. Use knife to bootstrap a server, build the scaffolding for a new cookbook, or apply a role to a set of nodes in your environment. You can use knife ssh to execute commands on any number of nodes in your environment. knife ssh + search is a very powerful combination.

The part of defining dependencies in Puppet was overly verbose and cumbersome. With Chef, order matters and dependencies would be met if we specified them in the proper order.

We can deploy additional software applications on virtual machine instances without dealing with the overhead of doing everything manually,” Stowe explains. “We can do it with code — recipes that define how various applications and libraries are deployed and configured.” According to Stowe, creating and deploying a new software image now takes minutes or hours rather than hours or weeks. They call this technique DevOps because it applies traditional programming techniques to system administration tasks. “It’s just treating IT operations as a software development problem, – Stowe, CEO of Cycle Computing, a Greenwich, Connecticut-based start-up that uses Chef to manage the software underpinning the online “supercomputing” service it offers to big businesses and academic outfits. “Before this, there were ways of configuring servers and managing them, but DevOps has gotten it right.”

Lets CATEGORIZE

Let me help you to know onto which buckets does the above tools fell into and other similar tools…

App Deploy	Capistrano, ControlTier, Fabric, Fun, mCollective
SysConfig	Chef, Puppet, cfengine, Smart Frog, Bcfg2
Cloud/VM	Xen, Ixc, openVZ, Eucalyptus, KVM
OS Install	Kickstart, Jumpstart, Cobbler, OpenQRM, xCAT

DevOps on EC2 using Capistrano

Thursday, April 5th, 2012 | Posted in Cloud computing

DevOps is the combination of development and operation processes. Cloud with your DevOps offers some fantastic properties. The ability to leverage all the advancements made in software development around repeatability and testability with your infrastructure. The ability to scale up as need be real time and among other things being able to harness the power of self healing systems.

The process piece of devops is about taking the principles behind Agile to the entire continuous software development process. The obvious step is bringing Agile ideas to the operations team, which is sorely needed. Traditionally in the enterprise, the application development team is in charge of gathering business requirements for a software program and writing code. The development team tests their program in an isolated development environment for quality assurance which is later handed over to the operations team. The operations team is tasked with deploying and maintaining the program. The problem with this paradigm is that when the two teams work separately, the development team may not be aware of operational roadblocks that prevent the program from working as anticipated.

Capistrano

Capistrano is a developer tool for running scripts on multiple servers, mainly used for deploying web applications on to the servers. It is typically installed on a workstation, and used to deploy code from your source code management to one, or more servers. Capistrano is originally called “SwitchTower”, the name was changed to Capistrano in March 2006 because of some trademark conflict. It is a time saving command line tool and it is very useful to AWS/EC2 servers because we can deploy the code to 1000’s of aws servers by using a single command. For the security of servers we are commonly using aws ssh key authentication. In capistrano we use this aws ssh key to deploy the web applications to the aws servers.

In Cloud Computing, deploying applications to production/live servers is always a delicate task. The whole process needs to be quick to minimize downtime. Automating the deployment process helps running repetitive tasks minimizing the possibility human error. It is also a good idea to have a proven and easy way to rollback to a previous version if something goes wrong.

It is a standalone utility that can also integrate nicely with Rails. We simply provide Capistrano with a deployment “recipe” or “formula” that describes our various servers and their roles. It is a single-command deployment. it even allows us to roll a bad version out of production and it revert back to the previous release very easily.

Capistrano Deployment

The main functionality of the Capistrano is to Deploy the rails application which we have already developed and we are using the “SVN” or “GIT” to manage the code. It will transfer all the files of our rails application which we have developed in our local host to aws servers directly by simply executing a simple command in our command prompt.

Steps to deploy a rails application

[shell]gem install capistrano[/shell]

Now,we need to capistranize our rails application using the following commands

[shell]capify .[/shell]

It will create two files

[shell]

config/deploy.rb
capfile .

[/shell]

How to set up deploy.rb file

[shell]

require ‘rubygems’
require ‘activesupport’
set :application, “<application name>”
set :scm_username/ “<username>”
set :use_sudo, false
set :repository, “http://#{scm_username}@www.example.com/svn/trunk”
set :deploy_to, “/var/www/#{application}”
set :deploy_via, :checkout
set :scm, :git
set :user, “root”
role :app, “<domain_name>”
role :web, “<domain_name>”
rold :db, “<domain_name>”, :primary => true
namespace :migrations do
desc “Run the Migrations”
task :up, :roles => :app do
run “cd #{current_path}; rake db:auto:migrate;”
end
task :down, :roles => :app do
run “cd #{current_path}; rake db:drop; rake
db:create”
end
end

[/shell]

where,

‘scm_username’ is your user name
‘application’ is an arbitrary name you create to identify your application on the server
‘use_sudo’ specifies to capistrano that it does not need to append ‘sudo’ before all the commands it will run
‘repository’ identifies where your subversion repository is located

If we aren’t deploying to server’s default path, we need to specify the actual location by using the ‘deploy_to’ variable as given below

[shell]
set :deploy_to, “/var/www/#{application}”
set :deploy_via, :checkout
[/shell]

If we are using the git to manage our source code, specify the SCM by using the ‘scm’ variable as given below

[shell]
set :scm, :git
set :user, “root”
role :app, “<domain_name>”
role :web, “<domain_name>”
rold :db, “<domain_name>”, :primary => true
[/shell]

Since most rails users will have the same domain name for their web,app and database, we can simply use our domain variable we set earlier.

[shell]
namespace :migrations do
desc “Run the Migrations”
task :up, :roles => :app do
run “cd #{current_path}; rake db:auto:migrate;”
end
task :down, :roles => :app do
run “cd #{current_path}; rake db:drop; rake
db:create”
end
end

[/shell]

After completion of our settings in the deploy.rb file, we need to commit the application by using “svn commit” command if we use svn.

Then we need to run the following command:

[shell]

cap deploy:setup

[/shell]

It is used to create the directory structure in server.

[shell]cap deploy:check[/shell]

It checks all the dependencies/things like directory permission and necessary utilities to deploy the application by using capistrano.

If everything is successful, you should see a message like:
“You appear to have all necessary dependencies installed”
And finally deploy the application by using the following command:

[shell]cap deploy[/shell]

Command finished successfully

To Clean up the releases directory, leaving the five most recent releases

[shell]Cap cleanup[/shell]

Prints the difference between what was last deployed, and what is currently in our repository

[shell]cap diff_from_last_deploy[/shell]

To Rolls back to the previously deployed version

[shell]cap deploy:rollback:code[/shell]

Amazon’s EC2 cloud cuts the requisition time of the order & delivery stages down to just minutes. This is already a 75% savings in deployment time! But, without automated deployment, you’ll still need a week to get your application installed.

DevOPS on AWS Cloud using Opscode Chef

Friday, January 13th, 2012 | Posted in Cloud computing

‘Rule the Cloud‘ with Chef
Chef is Infrastructure as Code,an API for your entire infrastructure. Assuming that you are well versed with cloud if not still you should have atleast heard of cloud computing and it is still an evolving paradigm and Cloud computing companies are the newest buzz in the IT sector. Chef is used in conjunction with cloud from cloud providers say Amazon’s AWS. If a software thats being developed is a mix of technology which is interdependent and works in perfect harmony then why not the people behind it, this thought has led to the emergence of a new cultral trend called DevOPS. Now if you setup a number of instances on the cloud then whats next – new instances on cloud are just like bare metal server and the configuration has to be done from scratch and it would be feasible to do so manually for couple of them what if the count just got bigger say 100 live instances with different unix distros, although a script could be written but still it will not suffice, in the long run considering management too. Here the CHEF comes into play

“chef is sysadmin robot performing configuration tasks automatically and much more quickly than a single admin could ever hope to” – Jesse Robbins, Opscode CEO.

CHEF is an open source configuration management tool using pure-Ruby,the chef domain specific language for writting system configuration related stuff (recipes and cookbook)

CHEF brings a new feel with its interesting naming conventions relating to cookery like Cookbooks (they contain codes for a software package installation and configuration in the form of Recipes), Knife (API tool), Databags (act like global variables) etc

Although there are many configuration management tools prevailing in the industry CHEF was able to secure its position in the race.

“CHEF take a step farther passes puppet and cfengine — like doing “LIVE SEARCH” within configuration management like loadbalancer can call out to get a list of the app servers you need to balance or an applicaton server can call out, get a reference to the master database server etc …..the centralised chef server is indexing all the information about your infrasturctre so that you could search in the command line using knife you know in real time so that application could lever that data..” by Seth Chisamore from the OPSCODE.

A techonology peak that isnt fluffy – Cloud
For those folks new to cloud- Its a whole bunch of activites which began as an innovation, recently given out as products and now they have become so widespread and so feature complete that they became suitable for utility services.

So if you dont want cloud in your business its like saying you dont want to use the electricity instead you built your own generator and use it according to your need. Now what do we loose if we continue with that is the competitive edge ie you get the pressure to keep your stuff upgraded inorder to find your place relative to the others in the ecosystem.

Cloud is API oriented, everything you see in cloud is ulitmately programmable.

Virtualization is the foundation of Cloud but virtualization is not Cloud by itself. It certainly enables many of the things we talk about when we talk Cloud but it is not necessary sufficient to be a cloud. Google app engine is a cloud that does not incorporate virtualization. One of the reasons that virtualization is great is because you can automate the procurement of new boxes.

A Culture thats on path to revolutionize IT – DevOPS
Devops is something that orginated in webshops predominantly and it require a kind of tools thats really not available except for home grown tools which the big webshops built over and over again. So the organisation who wanted to use devops started using the tools that enable this transition as most organisations depends on web as a source of revenue in a variety of different ways, even the enterprise desire to be as agile as the webshops. This has begun a revolution from the website permeate into the enterprise base more frequently.

Considering a real life example for Devops say facebook, the most popular social networking site here the developers/QA/operations – there is alot of communications, cross talk happening between them like the developers has to write codes, QA who has to make sure the good code goes out, the operations team has to make sure its up and running. Finally all of these has to be in records which altogether seems to be inefficient, this led to the evolving of the entire system. According to the conventional practices where the developers writes the code and throws it off to the testing. Once the testing is done then it moves to the operations etc. Contrary to that the developers , operations team are all involved in the entire lifecycle of the project as a team. This creates a symbiotic relationship. Now the operations people could understand what the engineers needs the most and the developers are able to see the value that operation people brings as they make architecture decisions.

Cloud with your DevOps offers some fantastic properties. The ability to leverage all the advancements made in software development around repeatability and testability with your infrastructure. The ability to scale up as need be real time (autoscaling) and among other things being able to harness the power of self healing systems. DevOps better with Cloud.

Configuration management say CHEF is one of the most fundamental elements allowing DevOps in the cloud. It allows you to have different VMs that have just enough OS that they can be provisioned, automatically through virtualization, and then through configuration management can be assigned to a distinct purpose within the cloud. The CM system handles turning the lightly provisioned VM into the type of server that it is intended to be.

DevOps & Chef
DevOps is nonthing but a cultural movement where everybody say the developers, QA, Operations, Testing etc get along. A project group formation with a mixed skillset that blurs the line between say a developer and sysadmin. This helps the project to meet its deadlines
and avoid unexpected situations. Cloud computing act like a catalyst to this movement. Thereby the CHEF also hops in.

Chef forms a critical layer in the Devops stack.Thanks to the concept of infrastructure as code and virtualization, we can define and build our infrastructure based on text files. Those files can be version-controlled and tested like regular code. The artifact (ami, image), can then be deployed on an infrastructure. The following image gives you an overview on the similarities.

Inadvertently the issues like “what if the application” or “what if the infrasturcture” are resolved, the fact is that application is the infrastructure and infrastructure is the application and we are here to enable business, also it helped bring peoples in the team into better alignment across the board.

Chef configuration is written in pure ruby.

Devops == Ruby

For those who think Bash is enough as a scripting language – Bash becomes a liability not an asset once your script exceeds 100 lines and a total nightmare if you need to parse or output HTML, CSV, XML, JSON, etc. A significant point to be noted is that Chef uses Ruby in its recipes unlike puppet where it uses its own configuration language that is based on Ruby although chef is heavily inspired from puppet. If you chose chef then you are effectively scripting your infrastructure with ruby.

Though Chef was only released on January 15th , 2009 it has gotten rapid adoption and gained a large number of contributors. According to the Opscode wiki there are 545 approved contributors to Opscode projects and 106 companies. Beyond that the #chef IRC channel is typically attended by over 100 users and Opscode staff, signs of a healthy, growing open source community.

Springsource division of VMware have signed on to contribute to the project. They are even being very public about it as seen in this endorsement:

“We are excited about the open source contributions the Springsource Division of VMware has made to Opscode Chef.” said Javier Soltero, CTO of Springsource Management Products at VMware. “Chef is an important tool for automating infrastructure management and we look forward to its continued growth and success.”

Moreover on my experience of using chef I really enjoyed the quick response I could get from the Opscode Support Team for all my queries and they had always being able to direct me towards a solution.

Automation Using Chef to create an Instance on Amazon Cloud Service Provider with Apache webserver configured in it.

Memo
chef-workstation – is the place where we customize our cookbooks and maintains the chef-repo
chef node – is the management node that we create using chef, it configures itself based on its runlist and downloaded cookbooks

The really cool thing with Chef is that you can rerun cookbooks against a node and it will not do anything it has already done i.e it will not change the end result on the target node as defined by the recipes being run against it. So you will always get the same outcome no matter what state the node and actions will not be taken if already done (and conversely run if detected it has not been run). When reading about Chef you will see this described as being idempotent (There I’ve saved you looking it up).

Prerequisites – an AWS account, EC2 API configured, OS – Ubuntu.

1. Sign up an account at http://www.opscode.com/hosted-chef/# , Here we use the OHC (opscode hosted chef) where we get to create upto 5 nodes for free!!

2.Verify your opscode account.

3.Download the files

Create an organization in the Console page at www.manage.opscode.com, and then download the following files:

Your Organization validation key. This is used to automatically register new Chef Clients (like servers you manage).
The Knife configuration file.
Your User key. This is used to authenticate your user with Hosted Chef.
Edit knife.rb to add aws access key and secret access key
knife[:aws_access_key_id] = “Your AWS Access Key”
knife[:aws_secret_access_key] = “Your AWS Secret Access Key”

At this stage I have a chef ready user environment, an OpsCode organisation set up and now I want to start by spinning up an ec2 instance. I will not be going into any depth regarding the ec2 specifics as that would make this post far too long.

4.Setting Up chef-Workstation

Install Ruby and Development Tools

#sudo apt-get update
#sudo apt-get install ruby ruby-dev libopenssl-ruby rdoc ri irb build-essential wget ssl-cert git-core
#sudo gem update –system

Install RubyGems

#cd /tmp
#wget http://production.cf.rubygems.org/rubygems/rubygems-1.8.10.tgz
#tar zxf rubygems-1.8.10.tgz
#cd rubygems-1.8.10
#sudo ruby setup.rb –no-format-executable

Install Chef

#sudo gem install chef

5.To verify chef installation

#chef-client -v

6.Build the chef repository

#cd ~
#git clone https://github.com/opscode/chef-repo.git

Knife reads configuration files in .chef. so we need to create those as well

#mkdir -p ~/chef-repo/.chef

Copy the keys and knife configuration you downloaded earlier into this directory:

#cp USERNAME.pem ~/chef-repo/.chef
#cp ORGANIZATION-validator.pem ~/chef-repo/.chef
#cp knife.rb ~/chef-repo/.chef

Run the following command to confirm knife is working with the Hosted Chef API.

#cd ~/chef-repo
#knife client list

output : “ORGANIZATION-validator”

7.Now i need to download the apache2 cookbook on to my workstation, customize if required and then upload it to my account on the opscode platform

#knife cookbook site install apache2

this will notify git and also pulls down the desired cookbook

8.Upload the cookbook using the following command

#knife cookbook upload apache2

9.Enter the following command, sit back and enjoy the show!!!

#knife ec2 server create -G default -I ami-1212ef7b -f m1.small -S <aws ssh key id> -i <ssh identity file> -x root -r ‘recipe[apache2]’

Before proceeding it would probably be a good idea to take time out and read the Opscode Chef Recipe wiki which has a nice clear explanation on cookbook name spaces. Also remind yourself of the components that make up a cookbook it’s worth noting that recipes manage resources and those resources will be executed in the order they occur.

Splunk on AWS EC2 CloudSplunk

Wednesday, January 11th, 2012 | Posted in Amazon EC2, Amazon S3, Amazon Web Services, Cloud computing

Whats is Splunk ?

Splunk is a log, monitoring and reporting tool for IT system administrators with search capabilities. It crawls logs, metrics, and other data from applications, servers and network devices and indexes it in a searchable repository from which it can generate graphs, SQL reports and alerts. Splunk can be easily set on the AWS machine archival storage as EBS volumes and periodically syncing the archive from EBS to S3 Bucket or taking EBS snapshots for backup of the logs for the future use.

Generally its hard to track the logs from the server. We do have different monitoring tools such as Nagios, Zabix, here is a new tool named Splunk, which is a kind of bigger solution for providing monitor the visibility inside all the dynamic and complex environment. For example you have an application seems to be very slow, its not because the app have some issue , its because of the lack of free memory on the server. Such kind of details can be obtained from inside the splunk server.

Why do we go for Splunk ?

In auto-scaled where the instances are running under load-balancer scenarios, the servers gets scale up and down, and also there are some situations like some instance gets terminated without any alert. During this situation it will be good to get the login sessions during the server-down state, also the server access logs, so that we can track the reason for the server down. Managing logs on server is really hard, and also the logs will be available on different location. Inorder to address this problem, here we have setup Splunk to listen on a TCP port for any network traffic passes all others servers log to this host, then you will have a centralized, indexed log repository for all of your services.

Here i will guide you on deploying the splunk on the AWS EC2 and configuring splunk forwarder on the remote machine. Splunk is very flexible and is easy to install on any servers. You can select the appropriate hardware capacity planning for your Splunk deployment from here.

Once you have installed the Splunk server , follow the steps given below to start the app:

Now start the Splunk using the command given below:
[NOTE: The here Splunk is installed in /opt location]

/opt/splunk/bin/splunk start

Now you can access the Splunk web UI using the URL given below:

http://domain.com:8000

The Splunk need to be configure in such a way that it should be able to receive the data from the remote machine. For this you will need is to follow the following steps:

1. Login to Splunk WebUI eg. http://10.10.10.35:8000
2. Go to Manager –> Forwarding and receiving –> Receive data
3. Click on New Button and add default port i.e. 9997
4. Click on save button to save the settings.
NOTE: Make sure that the port is opened for the server to accept the data from the remote machine.

Next you will need to install Splunk forwarder on the remote machine. Once you have installed the forwarder start the app as shown below:

/opt/splunk/bin/splunk start

Then enable the forwarder using the command and restart the Splunk app.

./splunk enable app SplunkLightForwarder -auth
Splunk username: admin
Password: changeme

./splunk add forward-server 10.10.10.35:9997 -auth admin
./splunk restart

Now after few minutes you can see the Splunk dashboard indexes all it logs on the realtime dashboard.

Generally in Splunk deployment , we have a deployment server which pushes the configuration on to the deployment client, grouped into server class. The Splunk deployment server is a centralized manager which manages several splunk instances known as deployment client. The deployment client is the Splunk instance installed on the remote machine and parse the log on to the Splunk deployment server.

The Splunk generally collects the data from the remote machine which contain the machine-to-machine and also from human-to-machine interaction. With these collected data it indexes to the engine and generates the reports and also drives alert. The email alert can be configured for the specific conditions like. For example we can configure the alert mail when it finds any log containing the error messages. The Splunk will access all these large volume of data and also provides the visibility and intelligence to IT and data ware house. And also will be able to perform the real-time and historic analysis of all the bulk data from the remote machine.

Its easy to use, also to install and also easier deploy method make this application different from others. The Splunk will be very useful for the developer team for finding and fixing the bugs and also helps to provide real time insights.

Mapreduce using Hadoop + pig/hive on AWS EC2 hadoop cluster

Tuesday, July 26th, 2011 | Posted in Amazon EC2, Amazon Web Services, Cloud computing

This article discuss about running mapreduce jobs using the apache tools called pig and hive.Before we can process the data we need to upload the files to be processed to HDFS/S3. We recommend uploading to hdfs and keeping the important files in s3 for backup is a better practice. s3 is easily accessible from commandline using tools like s3cmd. HDFS is a failover cluster filesystem which provides enough protection to your data over instance failures.

Mapreduce

MapReduce is a programming model and an associated implementation for processing and generating large data sets. We can specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key.

The main steps hadoop takes to run a job are

The client, which submits the MapReduce job.
The jobtracker, which coordinates the job run. The jobtracker is a Java application whose main class is JobTracker.
The tasktrackers, which run the tasks that the job has been split into. Tasktrackers are Java applications whose main class is TaskTracker.
The distributed filesystem (normally HDFS), which is used for sharing job files between the other entities.

Hadoop Map/Reduce is very powerful, but

o Requires a Java Programmer.

o Harder to write and also time consuming.

o Difficult to update frequently.

A solution is to Run jobs using pig(Piglatin)/hive(HiveQL).

Pig

• An engine for executing programs on top of Hadoop

• It provides a language, Pig Latin, to specify these programs

Pig has Two main parts:

– A high level language to express data analysis

– Compiler to generate mapreduce programs (which can run on top of Hadoop)

Pig Latin is the name of the language with which Pig scripts are written. Pig also provides an interactive shell for executing simple commands, called Grunt. Pig Latin is a high level language. Pig runs on top of Hadoop. It collect the data for processing from Hadoop HDFS filesystem and Submit the jobs to the Hadoop mapreduce system.

A sample mapreduce job (like a Hello World program) using pig is given below

It is assumed that you are on one of the machines which is a part of a hadoop cluster having NameNode/DataNode as well as JobTracker/TaskTracker setup.

We will be executing piglatin commands using grunt shell. Switch to hadoop user first .

Consider we have a file ‘users’ on our local filesystem which contain data to be processed.First we have to upload it to hdfs. Then

# pig -x mapreduce

this command will take you to grunt shell. Pig Latin statements are generally

organized in the following manner:

A LOAD statement reads data from the file system.Then we process the data.And writes output to the file system using STORE statement. A DUMP statement displays output to the screen.

grunt> Users = load ‘users’ as (name, age);

grunt> Fltrd = filter Users by age >= 18 and age <= 25;

grunt> Pages = load ‘pages’ as (user, url);

grunt> Jnd = join Fltrd by name, Pages by user;

grunt> Grpd = group Jnd by url;

grunt> Smmd = foreach Grpd generate group, COUNT(Jnd) as clicks;

grunt> Srtd = order Smmd by clicks desc;

grunt> Top5 = limit Srtd 5;

grunt> store Top5 into ‘top5sites’;

We can also view the progress of the job through the web interface http://<ipaddress of jobtracker machine>:50030.

Tools like PigPen (an eclipse plugin) are available that helps us create pig-scripts, test them using the example generator and then submit them to a hadoop cluster.

There is another tool called oozie – Oozie is a server based Workflow Engine specialized in running workflow jobs with actions that run Hadoop Map/Reduce and Pig jobs.

Pig tasks can be modeled as a workflow in oozie. These are deployed to the Oozie server using a command line utility. Once deployed, the workflows can be started and manipulated as necessary using the same utility. Once the workflow is started Oozie will run through each flow.. The web console for Oozie server can be used to monitor the progress of various workflow jobs being managed by the server.

Hive

Pig, was causing some slowdowns at Facebook company as it needed training to bring business intelligence users up to speed. So the development team decided to write Hive which has an SQL like syntax.

Apache Hive is a data warehouse infrastructure built on top of Apache Hadoop. It provides tools for querying and analysis of large data sets stored in Hadoop files. Hive defines a simple SQL-like query language, called HiveQL, that enables users familiar with SQL to query the data. Also it allows custom mappers and reducers to perform more sophisticated analysis that may not be supported by the built-in capabilities of the language.

Some of the queries in HiveQL are given below, which is very similar to the SQL.

# show tables;

# describe <tablename>;

# SELECT * FROM <tablename> LIMIT 10;

# CREATE TABLE table_name

# ALTER TABLE table_name RENAME TO new_table_name

# DROP TABLE table_name

NoSQL databases like Cassandra provide support for hadoop. Cassandra supports running Hadoop MapReduce jobs against the Cassandra cluster. With proper cluster configuration, MapReduce jobs can retrieve data from Cassandra and then output results either back into Cassandra, or into a file system.

HADOOP Cluster on AWS EC2 with hadoop-0.20 and ubuntu-10.04

Friday, June 17th, 2011 | Posted in Amazon EC2, Cloud computing

Let’s start with a small introduction- what is hadoop ?. Hadoop is an open-source project administered by the Apache Software Foundation. Apache Hadoop is a Java software framework that supports data-intensive distributed applications under a free license. It enables applications to work with thousands of nodes and petabytes of data. Hadoop was inspired by Google’s MapReduce and Google File System (GFS) papers.

Technically, Hadoop consists of two key services: reliable data storage using the Hadoop Distributed File System (HDFS) and high-performance parallel data processing using a technique called MapReduce.

Dealing with big data requires two things:

Inexpensive, reliable storage; and
New tools for analyzing unstructured and structured data.

Hadoop creates clusters of machines and coordinates work among them. Clusters can be built with inexpensive computers.If one fails, Hadoop continues to operate the cluster without losing data or interrupting work, by shifting work to the remaining machines in the cluster.

HDFS manages storage on the cluster by breaking incoming files into pieces, called “blocks,” and storing each of the blocks redundantly across the pool of servers.

The main services running in a hadoop cluster will be

1)namenode

2)jobtracker

3)secondarynamenode

These three will be running only on a single node(machine) ; that machine is the central machine which controls the cluster.

4)datanode

5)tasktracker

These two services will be running on all other nodes in the cluster.

HDFS has a master/slave architecture. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients. In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on.

Above the file systems comes the MapReduce engine, which consists of one Job Tracker, to which client applications submit MapReduce jobs. The Job Tracker pushes work out to available Task Tracker nodes in the cluster, striving to keep the work as close to the data as possible.

The only purpose of the secondary name-node is to perform periodic checkpoints. The secondary name-node periodically downloads current name-node image and edits log files, joins them into new image and uploads the new image back to the (primary and the only) name-node.

Now Let us have a look at how to build a hadoop cluster using Cloudera hadoop-0.20 on ubuntu-10.04

You should install sun –jdk first. Then add the following repositories to the apt sources list.

vim /etc/apt/sources.list.d/cloudera.list

[bash]

deb http://archive.cloudera.com/debian lucid-cdh3u0 contrib

deb-src http://archive.cloudera.com/debian lucid-cdh3u0 contrib

[/bash]

Import key

[bash]curl -s http://archive.cloudera.com/debian/archive.key | apt-key add -[/bash]

Then run

[bash]apt-get update[/bash]

For Namenode/Jobtracker ( These two services should run only on a single central machine in the cluster)

[bash]

apt-get install hadoop –yes

apt-get install hadoop-0.20-namenode

apt-get install hadoop-0.20-jobtracker

apt-get install hadoop-0.20-secondarynamenode

[/bash]

Configuration

vim /etc/hadoop/conf/hadoop-env.sh

Append these

[bash]

export JAVA_HOME=/usr/lib/jvm/java-6-sun-1.6.0.24/ ( your java home comes here )

export HADOOP_CONF_DIR=/etc/hadoop/conf

export HADOOP_HOME=/usr/lib/hadoop-0.20

export HADOOP_NAMENODE_USER=hdfs

export HADOOP_SECONDARYNAMENODE_USER=hdfs

export HADOOP_DATANODE_USER=hdfs

export HADOOP_JOBTRACKER_USER=mapred

export HADOOP_TASKTRACKER_USER=mapred

export HADOOP_IDENT_STRING=hadoop

[/bash]

vim /etc/hadoop/conf/core-site.xml

[bash]

<?xml version=”1.0″?>

<?xml-stylesheet type=”text/xsl” href=”configuration.xsl”?>

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

<name>fs.default.name</name>

</property>

</configuration>

[/bash]

vim /etc/hadoop/conf/hdfs-site.xml

[bash]

<?xml version=”1.0″?>

<?xml-stylesheet type=”text/xsl” href=”configuration.xsl”?>

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

<value>/var/lib/hadoop-0.20/name</value>

</property>

<value>/var/lib/hadoop-0.20/data</value>

</property>

<name>dfs.replication</name>

</property>

</configuration>

[/bash]

vim /etc/hadoop/conf/mapred-site.xml

[bash]

<?xml version=”1.0″?>

<?xml-stylesheet type=”text/xsl” href=”configuration.xsl”?>

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

<name>mapred.job.tracker</name>

</property>

<name>mapred.system.dir</name>

<value>/var/lib/hadoop-0.20/system</value>

</property>

<name>mapred.local.dir</name>

<value>/var/lib/hadoop-0.20/mapred</value>

</property>

</configuration>

[/bash]

——————————————————————————————————————————————

[bash]

mkdir / var/lib/hadoop-0.20/name

mkdir / var/lib/hadoop-0.20/data

mkdir / var/lib/hadoop-0.20/system

mkdir / var/lib/hadoop-0.20/mapred

chown -R hdfs /var/lib/hadoop-0.20/name

chown -R hdfs /var/lib/hadoop-0.20/data

chown -R mapred /var/lib/hadoop-0.20/mapred

[/bash]

Now format NameNode

[bash]yes Y | /usr/bin/hadoop namenode –format[/bash]

Start namenode

[bash]/etc/init.d/hadoop-0.20-namenode start[/bash]

Check the log Files for error:

less /usr/lib/hadoop-0.20/logs/hadoop-hadoop-namenode-<ip>.log

Also you can check whether the Namenode process is up or not using the command

[bash]# jps[/bash]

Start the SecondaryNamenode

[bash]/etc/init.d/hadoop-0.20-secondarynamenode start[/bash]

Log: less /usr/lib/hadoop-0.20/logs/hadoop-hadoop-secondarynamenode-<ip>.log

[bash]

sudo -u hdfs hadoop fs -mkdir /var/lib/hadoop-0.20/system

sudo -u hdfs hadoop fs -chown mapred /var/lib/hadoop-0.20/system

[/bash]

Now Start the JobTracker

[bash]/etc/init.d/hadoop-0.20-jobtracker start[/bash]

Log : less /usr/lib/hadoop-0.20/logs/hadoop-hadoop-jobtracker-ip-10-108-39-34.log

Now jps command will show the three processes up

# jps

19233 JobTracker

18994 SecondaryNameNode

18871 NameNode

For Datanode/Tasktracker ( These two services should be running on all the other machines in the cluster )

[bash]

apt-get install hadoop-0.20-datanode

apt-get install hadoop-0.20-tasktracker

[/bash]

Configuration

vim /etc/hadoop/conf/core-site.xml

[bash]

<?xml version=”1.0″?>

<?xml-stylesheet type=”text/xsl” href=”configuration.xsl”?>

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

<name>fs.default.name</name>

</property>

</configuration>

[/bash]

vim /etc/hadoop/conf/hdfs-site.xml

[bash]

<?xml version=”1.0″?>

<?xml-stylesheet type=”text/xsl” href=”configuration.xsl”?>

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

<value>/var/lib/hadoop-0.20/name</value>

</property>

<value>/var/lib/hadoop-0.20/data</value>

</property>

<name>dfs.replication</name>

</property>

</configuration>

[/bash]

vim /etc/hadoop/conf/mapred-site.xml

[bash]

<?xml version=”1.0″?>

<?xml-stylesheet type=”text/xsl” href=”configuration.xsl”?>

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

<name>mapred.job.tracker</name>

</property>

<name>mapred.system.dir</name>

<value>/var/lib/hadoop-0.20/system</value>

</property>

<name>mapred.local.dir</name>

<value>/var/lib/hadoop-0.20/mapred</value>

</property>

</configuration>

[/bash]

———————————————————————————————————————————————

[bash]

mkdir /var/lib/hadoop-0.20/data/

chown -R hdfs /var/lib/hadoop-0.20/data

mkdir /var/lib/hadoop-0.20/mapred

chown -R mapred /var/lib/hadoop-0.20/mapred

[/bash]

Start the DataNode

[bash]/etc/init.d/hadoop-0.20-datanode start[/bash]

Log : less /usr/lib/hadoop-0.20/logs/hadoop-hadoop-datanode-<ip>.log

Start the TaskTracker

[bash]/etc/init.d/hadoop-0.20-tasktracker start[/bash]

Log: less /usr/lib/hadoop-0.20/logs/hadoop-hadoop-tasktracker-<ip>.log

You can now check the interface

http://< namenode-ip >:50070 – for HDFS overview

and

http://< jobtracker –ip>:50030 – for Mapreduce overview

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