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.

Results

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

Results

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.

Benefits

  • 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.

Obstacles

  • 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.)

Conclusion

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!

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurr Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Just how close real-wo Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the cam Read more…

By John Russell

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a ne Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which w Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling Read more…

By Steve Campbell

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu's Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural networ Read more…

By Tiffany Trader

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of "quantum supremacy," researchers are stretching the limits of today's most advance Read more…

By Tiffany Trader

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" process Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This