Virtualized Hadoop. A Dell Hadoop Whitepaper. By Joey Jablonski. A Dell Hadoop Whitepaper

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1 Virtualized Hadoop A Dell Hadoop Whitepaper By Joey Jablonski A Dell Hadoop Whitepaper

2 Introduction to Virtualized Hadoop Hadoop has become a standard within many organizations and data centers for its ability to store and analyze large, diverse data sets in an economical fashion. At the same time, virtualization technologies like VMware, Hyper- V and KVM have enabled IT organizations to consolidate many diverse workloads onto fewer servers, gaining efficiency in operations, space, power and cooling. Now that Apache Hadoop [4] has caught on in traditional IT spaces, many organizations look at the potential to run Hadoop in a virtualized environment. The reasons for this exploration are diverse and include interoperability with other workloads, ease of deployment or corporate standards to virtualize all workloads. This paper explores the considerations for virtualizing Hadoop, and the best target Hadoop workloads for virtualization. Virtualized Hadoop Deployment Categories Hadoop workloads can be broken into several key categories: Production Systems that are expected to have the highest level of availability, only scheduled outages and where SLAs are present for response time and performance. Test/Development Environments that regularly change for the purpose of enabling development and operations staff to zero on the next identifiable step for production environments. Pilot/POC Early stage environments that enable staff to get familiar with new technologies and how they integrate into existing environments and workflows. Multi- tenant Physical systems that support multiple groups of users with different requirements for performance, security and compliance. Virtualization is important and can have an enormous impact on environments with the following characteristics: Applications that underutilize servers Virtualization has the largest impact in environments with diverse workloads, where each individual application does not fully utilize the capacity in a server. Highly dynamic environment and capacity needs across applications Virtualization can assist dynamic environments by making sure that workloads can move between physical platforms in a rapid, repeatable fashion. Users or policies require logical application separation Many corporations have compliance and security policies that require specific data or applications to be isolated from one another. Virtualization is one method to create this separation and ensure data sets, users and applications do not operate in a way that violates policy. Most of these characteristics do not apply to Hadoop environments where the goal is full utilization at all times and where change is kept to a minimum to ensure the integrity of results. The separation of users based on needs is the most common use for virtualization Hadoop implementations. 2

3 Hadoop and virtualization are best matched where the workloads vary or users require different, isolated environments to operate from and the operations team wants to maximize investment in supporting infrastructure. Dell typically recommends virtualizing Hadoop in the following categories: Test/Development Test and development environments are optimal to virtualize because of the need to create and recreate them quickly and the need to have multiple environments at any given time. Development and test are not commonly needed to operate at full performance, but rather to test functionality. Multi- tenant The ability to share hardware across multiple tenants, whether staff, projects, customers or security domains, is a valuable place to virtualize Hadoop implementations. These multi- tenant environments can provide each separate group a full Hadoop implementation, and leverage shared hardware for easier scalability. There are certain considerations for all Hadoop environments, because of how Hadoop handles failover and availability, it is critical that we adjust certain configuration settings and design parameters when virtualizing Hadoop: Data Replication Factor Because of the underlying block data devices in virtualized environments commonly employing RAID technology for data protection, the replication factor can be changed within Hadoop to lower the capacity needs while still providing for data protection. Capacity Growth Planning for capacity growth in a virtualized environment can be different because of the separation of compute and drive capacity. When planning for growth items such as number of jobs, hypervisor overhead and network capacity should be taken into account. Network capacity will need to be carefully planned because of multiple Hadoop instances sitting on a single physical server and the additional traffic that will create. Failed Physical and Virtual Machines The movement of virtual machines, either planned or unplanned, will have repercussions within Hadoop because of its ability to replicate data and restart jobs to accommodate node level failures. It is important that timeout values within Hadoop be increased to accommodate any events that could occur because of the virtualized environment. Orchestration Virtualization does introduce an added layer of systems operation and management through a separate management tool. Staff should be properly trained on the intersection of virtual machine orchestration and Hadoop operations for simplified debugging, upgrades and system monitoring. Virtualized Hadoop Performance Considerations The biggest question that comes up related to virtualized Hadoop is the considerations around performance. Performance is critical in Hadoop deployments because of the volumes of data, and virtualization can impact performance if not planned for properly. Performance implications can include: Oversubscription - Any work with capacity planning should ensure that the virtualized environment is not oversubscribed to the point that job performance and user experience will be affected. This means that a mix of high utilization and low utilization workloads should be put together on the same physical hardware. Decoupling of compute and storage components Because of common virtualized architectures, it is common that a shared storage system like a SAN will be used, separating the compute and data portions of the environment into distinct pieces. The capacity on the fabric connecting these two needs to be large enough to support the read intensive nature of Hadoop. There can also be performance advantages of decoupling storage and compute for workloads that are very compute intensive with minimal data movement. VMware [2] has tested various workloads to document those that work best in virtualized, decoupled environments. 3

4 Shared infrastructure with other applications and peak load times Any other applications that share the physical systems with virtualized Hadoop instances should be of a nature that their performance will not impact Hadoop in a negative way. The best practice would be to mix similar environments like development, test or qualification together on similar physical hardware. Technologies like Sahara [3] provide the ability to easily build private cloud environments using OpenStack for various shared workloads, including Hadoop. Shared Traffic across a vswitch Because of the ability to deploy multiple Hadoop nodes on a single physical server in a virtualized environment, performance advantages can come from the effective use of vswitches, within a single physical node. If virtual nodes can be effectively grouped on a single switch, outside communication can be minimized and performance improved. Hypervisor Aware Hadoop Distributions Tools like Serengeti [1] can be leveraged to enable the Hadoop environment to have greater visibility into the underlying physical infrastructure in the virtualized environment. This visibility can be used for more intelligent workload distribution and tuning to accommodate changes in physical infrastructure. Hadoop has a growing set of software and associated ecosystem tools. As Hadoop growth continues and more use cases develop, more organizations are looking at how to deploy Hadoop within their existing frameworks for operations management, and they are actively evaluating virtualization for these deployments. Virtualized Hadoop has its place to ensure agile environments can evolve quickly, developers have their own rapidly deployed environments and compliance policies can be effectively enforced. Hadoop will continue to work most effectively in dedicated environments for production use cases. 4

5 Bibliography [1] [2[ Hadoop- Performance- vsphere5.pdf [3[ [4] This white paper is for informational purposes only, and may contain typographical errors and technical inaccuracies. The content is provided as is, without express or implied warranties of any kind Dell Inc. All rights reserved. Reproduction of this material in any manner whatsoever without the express written permission of Dell Inc. is strictly forbidden. For more information, contact Dell. To learn more To learn more about Dell cloud solutions, contact your Dell representative or visit: Dell Inc. All rights reserved. Trademarks and trade names may be used in this document to refer to either the entities claiming the marks and names or their products. Specifications are correct at date of publication but are subject to availability or change without notice at any time. Dell and its affiliates cannot be responsible for errors or omissions in typography or photography. Dell s Terms and Conditions of Sales and Service apply and are available on request. Dell service offerings do not affect consumer s statutory rights. Dell, the DELL logo, and the DELL badge, PowerConnect, and PowerVault are trademarks of Dell Inc. 5

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