Apache Hadoop on Rackspace Private Cloud


Rackspace Private Cloud Software allows businesses to quickly and seamlessly implement a stable and reliable Apache Hadoop cluster quickly and simply in an open cloud solution.

Apache Hadoop and the Cloud

Two major trends in the technology zeitgeist include Cloud Computing and Big Data and it now makes sense to look at the both of them together. However, this combination is not without some challenges. Big Data technologies such as Hadoop are taxing on servers, storage and network requirements, while the cloud promises elasticity and agility. How can Hadoop take advantage of this framework and how can the cloud meet the needs of a demanding Hadoop cluster? 

This paper investigates the synergies and challenges presented by Apache Hadoop and its role in Rackspace Private Cloud powered by OpenStack.


OpenStack is an open cloud standard and implementation that can be used to build both public and private clouds. A private cloud is an on-demand and scalable server environment reserved for your data alone, whether you host it in your datacenter, Rackspace’s or any third party’s. Private cloud is intended for companies who want to host the servers themselves for security and compliancy requirements and any other reasons they may have.

The attraction of using a free and open source cloud operating system without the worry of vendor lock-in has already led several companies to power their public and private clouds with OpenStack.

Rackspace Private Cloud Software Powered by OpenStack

Rackspace Private Cloud Software (RPCS) is a free and open source software that can be utilized to launch a cloud powered by OpenStack. RPCS provides the same cloud platform that powers Rackspace’s public cloud, the largest open cloud deployment in the world.

Apache Hadoop

Born out of the large Internet properties such as Yahoo, Hadoop is an open source project that provides a platform to store and process massive amounts of data, including structured and complex, unstructured data. There are also a set of Apache projects such as Hive, Pig, HBase, HCatalog and Ambari that have grown up around Hadoop to provide tools to manipulate data and to manage and monitor this complex clustered environment. As it has grown and added key enterprise features, its popularity has exploded. This is due to its open source nature and the fact that it can cost-effectively scale across clusters of commodity hardware, while delivering high availability and reliability.

Why Not Virtualize Hadoop

Hadoop’s architecture makes certain assumptions about the underlying infrastructure. Hadoop is resilient and is architected to accommodate and rebalance stored data and processing as nodes (servers) are added or removed to and from the environment. This may sound a perfect fit for the elasticity of the cloud; however, the current scheduling and recovery mechanism found in Hadoop is built on a more static and predictable infrastructure. It makes it difficult for Hadoop to take advantage of the rapid dynamic nature of the cloud where machines can join or leave from the cluster depending on a particular workload. There are three main challenges with Hadoop in a virtualized environment:

  • Virtual disks will add IO overhead.
  • Virtual machines can be allocated on the same server, breaking Hadoop’s redundancy expectations.
  • Hadoop assumes a static infrastructure – machines can reboot or go away but generally recover. The correct approach to deal with a bad virtual machine in cloud is to provision a new one.

Why Apache Hadoop on Rackspace Private Cloud

Although Hadoop was originally architected for the world of big-iron, the choice of virtual Hadoop is a very appealing one for several reasons. With the increasing adoption of cloud, it’s very likely that your data is already stored in the cloud, or will be soon. In that case, doing the analysis on the data close to where it sits is very extremely cost-effective. With Hadoop as part of the private cloud powered by OpenStack, you can spin up a cluster in minutes in order to extend your current environment and there is no need to move data from internal resources to the cloud. While this may not be the case for every Hadoop project, it makes sense for many. 

While performance of a Hadoop cluster may be superior with dedicated hardware, the agility of running it in the cloud on demand may trump some of the limitations for some workloads.

This is one benefit, but there are many positives of running Hadoop in the cloud.


Additional benefits of Hadoop Openstack can be summarized as:

  • One-click setup and rapid deployment. You can go from bare-metal to an open cloud with Hadoop running on it within a matter of a couple of hours.
  • Ability to reuse physical infrastructure. 
  • Multi-purpose cloud infrastructure, that you can use not just for Hadoop but for other services like hosting your web application, or databases within the same environment. 
  • Shrink and expand cluster on demand, by adding/removing nodes from a cluster or resizing VMs.
  • Ability to clone a VM and boot new VMs off of snapshots.
  • OpenStack can provide persistent local disks for Hadoop to use as its permanent storage.
  • When the Hadoop cluster is idle, some machines can be decommissioned and reused for other purposes. 

As Hadoop and the cloud grow together, the benefits of the combined offer will only grow stronger. For instance, there is ongoing work in the Apache Hadoop community to make Hadoop virtualization-aware which will ensure optimal data placement and provide support for failures in a cloud environment. The future looks even brighter as this will enable a truly elastic and reliable Hadoop Cluster. This work is all being completed in the open source community.

Hortonworks Data Platform for Hadoop

Rackspace has partnered with Hortonworks to bring an enterprise-ready, 100% open source Hadoop platform to Rackspace Private Cloud powered by OpenStack.

OpenStack was created as a collaborative software project designed to create freely available code, badly needed standards, and common ground for the benefit of both cloud providers and cloud customers. In this environment, Hortonworks Data Platform (HDP) just makes sense.It is 100% open source and is freely available; standards based and better yet open to integrate with the ecosystem and other stack components. More importantly, core Hadoop is compute and storage and HDP provides the most stable and reliable distribution for this.

Hortonworks Data Platform (HDP) is the only 100% open source data management platform for Apache Hadoop. Built and packaged by the core architects, builders and operators of Hadoop, HDP includes all of the necessary components to manage a cluster at scale and uncover business insights from existing and new big data sources.  Not only is it the most stable and reliable Hadoop distribution, but it is also the most close to the open source trunk available and is distributed as 100% open source with no holdbacks or proprietary components. It is perfect for OpenStack.

Hadoop Software Installation/Setup Process

RPCS makes it easy to create a production-ready cloud powered by OpenStack within a few of hours. Once you have your Rackspace Private Cloud ready, you can provision Hadoop in the cluster.

There are a few ways to install Hadoop, but most of them are geared towards installing in a dedicated environment. You could try using any of the existing options like Apache Whirr and Ambari or manually install the RPMs. We chose to write our own Chef Cookbooks and a Knife plugin for OpenStack to make Hadoop installation easier for public or private clouds powered by OpenStack and even bare metal. Please follow the detailed installation docs of the Cookbooks and Knife plugin on how to use them.

Once the Chef cookbooks are installed onto a chef-server, the knife-alamo plugin can be used from a workstation to interact with the private cloud and create both master and data Hadoop nodes. On a proper setup, creating a new name node or data node for Hadoop is as easy as running one command:

Creating a Hadoop NameNode:

$ knife alamo server create --name hadoopmaster --image fc63cb81-aca2-47dd-896b-a7a2bf4a041a --flavor 1 --chefenv hdp

Creating a Hadoop DataNode:

$ knife alamo server create --name hadoopworker8 --image 51f0b7ff-0326-4092-8568-30699e34da87 --flavor 2 --chefenv hdp --runlist 'recipe[chef-client],role[hadoop-worker]'

The command itself is pretty self-explanatory. It will spin up an instance of flavor size 2, install chef-client, and add the role of Hadoop-worker to it. To run the chef-client on demand for the instance, just do this:

$ knife alamo server chefclient 51f0b7ff-0326-4092-8568-30699e34da87

Running a map-reduce job:

Login to your master node and launch a job:

$ cd /usr/lib/hadoop
$ hadoop jar hadoop-*-examples.jar pi 10 1000000

Job progress can be tracked by using the JobTracker’s web UI at http://master_node:50030/

Deleting a node:

When  done with  the cluster, just issue a delete for each of the instances on the cluster:

$ knife alamo server delete 51f0b7ff-0326-4092-8568-30699e34da87

Resources have been released and can be instantly provisioned for other purposes. 


In conclusion, Hadoop on OpenStack is a very compelling choice that brings benefits like agility, automation, ease of deployment, and multi-tenancy and security through isolation of resources. Combining the Rackspace Private Cloud and OpenStack with Hadoop and Hortonworks   creates an enterprise-ready Hadoop solution that can be deployed in minutes into the open cloud.

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