Accelerate Hadoop MapReduce Performance using Dedicated OrangeFS Servers

By Nicole Hemsoth

September 9, 2013

Recent tests performed at Clemson University achieved a 25 percent improvement in Apache Hadoop Terasort run times by replacing Hadoop Distributed File System (HDFS) with an OrangeFS configuration using dedicated servers. Key components included extension of the MapReduce “FileSystem” class and a Java Native Interface (JNI) shim to the OrangeFS client. No modifications of Hadoop were required, and existing MapReduce jobs require no modification to utilize OrangeFS. The results also demonstrated the ability to deploy MapReduce with a general purpose High Performance file system in a High Performance Computing (HPC) environment, increasing potential for more flexible workflow.

The open source Hadoop MapReduce project has a traditional hardware architecture that differs from standard HPC architecture, where thin clients access remote, shared, and potentially distributed data servers. With HDFS, clients and data servers are paired together, running on the same hardware. Many HPC sites would like to extend their cluster use to support Hadoop MapReduce. With OrangeFS providing distributed storage as part of HPC clusters, they could leverage their existing investment in HPC to run Hadoop MapReduce workloads.

Through testing this configuration, a number of benefits emerged:

  • MapReduce clients accessing a dedicated OrangeFS storage cluster yielded a 25 percent faster combined run time than the traditional approach, where MapReduce clients access data locally for the three operations (teragen, terasort, and teravalidate).
  • OrangeFS and HDFS, without replication enabled, performed similarly under identical local (traditional HDFS) configurations (within 0.2 percent); however, OrangeFS adds the advantages of a general purpose, scale-out file system. With a general purpose file system, applications can read and write data to OrangeFS while it remains available for Hadoop MapReduce job input, improving run time by eliminating time-consuming HDFS stage-in and stage-out operations.
  • Doubling the number of compute nodes accessing the OrangeFS cluster results in ~300 percent improvement on Terasort job run time.
  • OrangeFS provides good results when clients significantly overcommit storage servers.

About OrangeFS

OrangeFS is a user-friendly, open-source, next-generation parallel file system for compute and storage clusters of the future. OrangeFS increases IO performance by storing a file in objects across multiple servers and accessing these objects in parallel. Offering more feature rich data access and manipulation than HDFS, OrangeFS is an ideal tool for storing, processing and analyzing data with MapReduce. A staff of developers support OrangeFS, improving stability and functionality for the base system and developing new interfaces.

OrangeFS has an object-based infrastructure. Each file and directory consists of two or more objects: one primarily containing file metadata, and the other(s) primarily containing file data. Objects may contain both data and metadata as needed to fulfill their role in the file system. This division and distribution of data to the servers is imperceptible to users, who see a traditional, logical file view. The OrangeFS distributed file structure provides outstanding scalability in performance and capacity.

OrangeFS client interfaces work with a range of operating systems, including Linux, Mac OS X and Windows. Compatible client interfaces include Direct Interface, WebDAV, S3, REST, FUSE, Hadoop and MPI-IO.

OrangeFS with Hadoop MapReduce

Hadoop’s abstract FileSystem class allows MapReduce to leverage file systems other than HDFS, with a configuration file that sets the designated file system. Hadoop MapReduce is written in Java, but OrangeFS’s client libraries are written in C. A Java Native Interface (JNI) shim allows data to be passed between programs, avoiding the overhead of memory copies with Java’s NIO Direct ByteBuffer. The JNI shim allows Java code to execute functions present in the OrangeFS Direct Client Interface. The OrangeFS Direct Client Interface Library is a collection of familiar POSIX-like and system standard input/output (stdio.h) library calls designed for parallel access to OrangeFS. OrangeFS differs from HDFS in that it allows modification of data after the initial write.

The Terasort benchmarks successfully explored potential for replacing HDFS with the prerelease version 2.8.8 of OrangeFS, working with the Hadoop 1.x stable release. The two Hadoop configurations which were evaluated are shown in Figure 1.

File System Test Configuration 

Figure 1 Test Configurations

Test Protocols

Hadoop MapReduce File System Test (Figure 2)

To test the impact of replacing HDFS with OrangeFS, developers performed a full terabyte (1 TB) Terasort benchmark on 8 nodes, each running both MapReduce and the file system shown in the first configuration above. The tests were performed on 8 Dell PowerEdge R720s with local SSDs for metadata and 12 2-TB drives for data. In this test, MapReduce ran locally on the same nodes, first over OrangeFS and then over HDFS, interconnected with 10Gb/s Ethernet. Both file systems used the compute nodes for storage as well.

Hadoop MapReduce Remote Client Test

Using the same benchmarks with typical HPC storage architecture, another test, “OFS Remote” in Figure 2, measured how MapReduce performs when data is stored on dedicated, network-connected storage nodes running OrangeFS. Eight additional nodes were used as MapReduce clients, and eight Dell PowerEdge R720s with local SSDs for metadata and 12 2-TB drives for data were used as storage nodes only, shown in the Remote Client Test Configuration in Figure 1.


OrangeFS decreased Terasort run time in the dedicated OrangeFS storage cluster architecture by about 25 percent over the traditional MapReduce architecture, where clients access data from local disks. OrangeFS and HDFS, without replication enabled, performed similarly under identical local (traditional HDFS) configurations (within 0.2 percent); however, OrangeFS adds the advantages of a general purpose, scale-out file system.

Figure 2 Hadoop MapReduce File System Test 

Figure 2 Hadoop MapReduce File System Test

Hadoop MapReduce over OrangeFS with Overcommitted Storage Servers (Figure 3)

A separate test evaluated MapReduce over OrangeFS, overcommitting the storage nodes and evaluating how well this approach scales with more MapReduce clients than storage nodes. The Terasort test was performed with an increasing number of clients utilizing a dedicated OrangeFS cluster composed of 16 Dell PowerEdge R720s with local SSDs for metadata and 12 2-TB drives for data. The Hadoop client nodes had only a single hard disk drive available for intermediate data storage purposes, increasing the time over previous tests where Hadoop clients possessed 12 disks. If the clients used a solid state drive (SSD) for storage and retrieval of intermediate data instead, the slowdown caused by the single disk compared to an array of disks would be alleviated to some extent.

Figure 3 Hadoop MapReduce over OrangeFS with Overcommitted Storage Servers 

Figure 3 Hadoop MapReduce over OrangeFS with Overcommitted Storage Servers


In testing 16, 32, and 64 compute nodes, doubling the number of compute nodes caused a ~300 percent improvement on Terasort job run time. OrangeFS provides good results when clients significantly overcommit the storage servers (4 to 1 in these tests). While providing improvements as a good general purpose file system for MapReduce, OrangeFS is also an excellent concurrent working file system to support the storage needs of other applications while simultaneously serving Hadoop MapReduce.


  • OrangeFS enables modification of data anywhere in a file, while HDFS requires copying data before modification, except in the case of Append in the Hadoop 2.x release.
  • OrangeFS replaces the HDFS single namenode with multiple OrangeFS metadata/data servers, reducing task time with improved scalability and eliminating this single point of contention.
  • Potentially, intermediate data can also be written to OrangeFS rather than a temporary folder on each Hadoop client disk, optionally retaining it for use in future jobs and further improving performance with OrangeFS serving the data to MapReduce.


  • Unlike HDFS, OrangeFS doesn’t currently support built-in replication. (OrangeFS can be run in High Availability (HA) mode, and plans for the 3.0 release of OrangeFS include integrated replication for both data and metadata.)
  • OrangeFS and Hadoop are separate installations which must be configured to work together. (Plans for the 2.8.8 release of OrangeFS include a more comprehensive documentation set, including instructions for using Hadoop’s MapReduce with the OrangeFS file system.)


The results demonstrated that replacing HDFS with OrangeFS produced better MapReduce performance for workloads with high volumes of intermediate data, i.e., terasort.

Separating MapReduce clients from storage servers can provide stability in the case of client failure, without the overhead of replication, and eases local disk contention during the reduce stage.

Hadoop MapReduce can leverage OrangeFS as its underlying storage system in an HPC environment. A Portable Batch System (PBS) or Sun Grid Engine (SGE) scheduled HPC environment can support on-demand Hadoop MapReduce clusters deployed and configured automatically, using the open source project “myHadoop.” Researchers could customize a version of myHadoop to schedule on-demand MapReduce clusters, with data persisting on OrangeFS, eliminating HDFS’s time consuming data stage-in and stage-out phases. (myHadoop scripts will be available with the next release of OrangeFS, for running jobs in a scheduled environment.)

Future evaluations may test how performance could be improved, since Hadoop Map and Reduce tasks could be patched to support reading and writing intermediate data to OrangeFS, rather than local disk, improving job run time with faster I/O rates.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Cray Introduces All Flash Lustre Storage Solution Targeting HPC

June 19, 2018

Citing the rise of IOPS-intensive workflows and more affordable flash technology, Cray today introduced the L300F, a scalable all-flash storage solution whose primary use case is to support high IOPS rates to/from a scra Read more…

By John Russell

Lenovo to Debut ‘Neptune’ Cooling Technologies at ISC

June 19, 2018

Lenovo today announced a set of cooling technologies, dubbed Neptune, that include direct to node (DTN) warm water cooling, rear door heat exchanger (RDHX), and hybrid solutions that combine air and liquid cooling. Lenov Read more…

By John Russell

World Cup is Lame Compared to This Competition

June 18, 2018

So you think World Cup soccer is a big deal? While I’m sure it’s very compelling to watch a bunch of athletes kick a ball around, World Cup misses the boat because it doesn’t include teams putting together their ow Read more…

By Dan Olds

HPE Extreme Performance Solutions

HPC and AI Convergence is Accelerating New Levels of Intelligence

Data analytics is the most valuable tool in the digital marketplace – so much so that organizations are employing high performance computing (HPC) capabilities to rapidly collect, share, and analyze endless streams of data. Read more…

IBM Accelerated Insights

Banks Boost Infrastructure to Tackle GDPR

As banks become more digital and data-driven, their IT managers are challenged with fast growing data volumes and lines-of-businesses’ (LoBs’) seemingly limitless appetite for analytics. Read more…

IBM Demonstrates Deep Neural Network Training with Analog Memory Devices

June 18, 2018

From smarter, more personalized apps to seemingly-ubiquitous Google Assistant and Alexa devices, AI adoption is showing no signs of slowing down – and yet, the hardware used for AI is far from perfect. Currently, GPUs Read more…

By Oliver Peckham

Cray Introduces All Flash Lustre Storage Solution Targeting HPC

June 19, 2018

Citing the rise of IOPS-intensive workflows and more affordable flash technology, Cray today introduced the L300F, a scalable all-flash storage solution whose p Read more…

By John Russell

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

The Machine Learning Hype Cycle and HPC

June 14, 2018

Like many other HPC professionals I’m following the hype cycle around machine learning/deep learning with interest. I subscribe to the view that we’re probably approaching the ‘peak of inflated expectation’ but not quite yet starting the descent into the ‘trough of disillusionment. This still raises the probability that... Read more…

By Dairsie Latimer

Xiaoxiang Zhu Receives the 2018 PRACE Ada Lovelace Award for HPC

June 13, 2018

Xiaoxiang Zhu, who works for the German Aerospace Center (DLR) and Technical University of Munich (TUM), was awarded the 2018 PRACE Ada Lovelace Award for HPC for her outstanding contributions in the field of high performance computing (HPC) in Europe. Read more…

By Elizabeth Leake

U.S Considering Launch of National Quantum Initiative

June 11, 2018

Sometime this month the U.S. House Science Committee will introduce legislation to launch a 10-year National Quantum Initiative, according to a recent report by Read more…

By John Russell

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

Exascale USA – Continuing to Move Forward

June 6, 2018

The end of May 2018, saw several important events that continue to advance the Department of Energy’s (DOE) Exascale Computing Initiative (ECI) for the United Read more…

By Alex R. Larzelere

Exascale for the Rest of Us: Exaflops Systems Capable for Industry

June 6, 2018

Enterprise advanced scale computing – or HPC in the enterprise – is an entity unto itself, situated between (and with characteristics of) conventional enter Read more…

By Doug Black

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Hennessy & Patterson: A New Golden Age for Computer Architecture

April 17, 2018

On Monday June 4, 2018, 2017 A.M. Turing Award Winners John L. Hennessy and David A. Patterson will deliver the Turing Lecture at the 45th International Sympo Read more…

By Staff

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17


AMD @ SC17


ASRock Rack @ SC17

ASRock Rack



DDN Storage @ SC17

DDN Storage

Huawei @ SC17


IBM @ SC17


IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17


Lenovo @ SC17


Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17


Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17


Tyan @ SC17


Univa @ SC17


Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

Google I/O 2018: AI Everywhere; TPU 3.0 Delivers 100+ Petaflops but Requires Liquid Cooling

May 9, 2018

All things AI dominated discussion at yesterday’s opening of Google’s I/O 2018 developers meeting covering much of Google's near-term product roadmap. The e Read more…

By John Russell

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

HPE Wins $57 Million DoD Supercomputing Contract

February 20, 2018

Hewlett Packard Enterprise (HPE) today revealed details of its massive $57 million HPC contract with the U.S. Department of Defense (DoD). The deal calls for HP Read more…

By Tiffany Trader

Part One: Deep Dive into 2018 Trends in Life Sciences HPC

March 1, 2018

Life sciences is an interesting lens through which to see HPC. It is perhaps not an obvious choice, given life sciences’ relative newness as a heavy user of H Read more…

By John Russell

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Google Charts Two-Dimensional Quantum Course

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. At a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world's foremost experts in quantum computing, emphasized... Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This