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RC25281 (AUS1204-004) April 23, 2012Computer Science                        IBM Research Report       Understanding System...
Understanding Systems and Architectures for Big Data      William M. Buros, Guan Cheng Chen, Mei-Mei Fu, Anne E. Gattiker,...
2.   BENCHMARKING METHODOLOGY                                     3. WORKLOAD OPTIMIZED SYSTEMS   Massive Scale Analytics ...
• Preliminary intermediate control of map and reduce            Table 1: Evaluation Environment                           ...
Table 2: Sample BigInsights (Hadoop) Job Configuration    Parameters                               Values    mapred.compres...
[4] IBM Netezza Data Warehouse Appliances. [5] IBM PowerLinux 7R2 Server...
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Understanding System and Architecture for Big Data


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Understanding System and Architecture for Big Data

  1. 1. RC25281 (AUS1204-004) April 23, 2012Computer Science IBM Research Report Understanding System and Architecture for Big Data Anne E. Gattiker, Fadi H. Gebara, Ahmed Gheith, H. Peter Hofstee, Damir A. Jamsek, Jian Li, Evan Speight IBM Research Division Austin Research Laboratory 11501 Burnet Road Austin, TX 78758 USA Ju Wei Shi, Guan Cheng Chen IBM Research Division China Research Laboratory Building 19, Zhouguancun Software Park 8 Dongbeiwang West Road, Haidian District Beijing, 100193 P.R.China Peter W. Wong IBM Linux Technology Center 11501 Burnet Road Austin TX 78758 USA Research Division Almaden - Austin - Beijing - Cambridge - Haifa - India - T. J. Watson - Tokyo - Zurich
  2. 2. Understanding Systems and Architectures for Big Data William M. Buros, Guan Cheng Chen, Mei-Mei Fu, Anne E. Gattiker, Fadi H. Gebara, Ahmed Gheith, H. Peter Hofstee, Damir A. Jamsek, Thomas G. Lendacky, Jian Li, Yan Li, John S. Poelman, Steven Pratt, Ju Wei Shi, Evan Speight, Peter W. Wong International Business Machines Corp. {wmb,chengc,mfu,gattiker,fhgebara,ahmedg,hofstee,jamsek,toml, jianli,liyancrl,poelman,slpratt,jwshi,speight,wpeter} refers to the quality of the information in the face of data un-The use of Big Data underpins critical activities in all sec- certainty from many different places: the data itself, sensortors of our society. Achieving the full transformative po- precision, model approximation, or inherent process uncer-tential of Big Data in this increasingly digital world re- tainty. Emerging social media also brings in new types ofquires both new data analysis algorithms and a new class data uncertainty such as rumors, lies, falsehoods and wishfulof systems to handle the dramatic data growth, the de- thinking. We believe the 4 Vs presently capture the mainmand to integrate structured and unstructured data ana- characteristics of Big Data.lytics, and the increasing computing needs of massive-scale The importance of Big Data in today’s society can notanalytics. In this paper, we discuss several Big Data re- be underestimated, and proper understanding and use ofsearch activities at IBM Research: (1) Big Data benchmark- this important resource will require both new data analysising and methodology; (2) workload optimized systems for algorithms and new systems to handle the dramatic dataBig Data; and (3) a case study of Big Data workloads on growth, the demand to integrate structured and unstruc-IBM Power systems. Our case study shows that prelimi- tured data analytics, and the increasing computing needs ofnary infrastructure tuning results in sorting 1TB data in massive-scale analytics. As a result, there exists much re- TM search in critical aspects of emerging analytics systems for9 minutes1 on 10 PowerLinux 7R2 systems [5] running TM Big Data. Examples of current research in this area include:IBM InfoSphere BigInsights . This translates to sorting processor, memory, and system architectures for data analy-11.4GB/node/minute for the IO intensive sort benchmark. sis; benchmarks, metrics, and workload characterization forWe also show that 4 PowerLinux 7R2 nodes can sort 1TB analytics and data-intensive computing; debugging and per-input with around 22 minutes. Further improvements are formance analysis tools for analytics and data-intensive com-expected as we continue full-stack optimizations on both puting; accelerators for analytics and data-intensive com-IBM software and hardware. puting; implications of data analytics to mobile and em- bedded systems; energy efficiency and energy-efficient de-1. INTRODUCTION signs for analytics; availability, fault tolerance and recovery The term “Big Data” refers to the continuing massive ex- issues; scalable system and network designs for high con-pansion in the data volume and variety as well as the velocity currency or high bandwidth data streaming; data manage-and veracity of data processing [12]. Volume refers to the ment and analytics for vast amounts of unstructured data;scale of the data and processing needs. Whether for data evaluation tools, methodologies and workload synthesis; OS,at rest or in motion, i.e., being a repository of information distributed systems and system management support; andor a stream, the desire for high speed is constant, hence MapReduce and other processing paradigms and algorithmsthe notion of velocity. Variety indicates that Big Data may for structured in many different ways, and techniques are This short paper briefly discusses three topics that IBMneeded to understand and process such variation. Veracity researchers and colleagues from other IBM divisions have been working on:1 All performance data contained in this publication was ob-tained in the specific operating environment and under the • Big Data benchmarking and methodologyconditions described below and is presented as an illustra-tion. Performance obtained in other operating environments • Workload optimized systems for Big Datamay vary and customers should conduct their own testing. • A case study of a Big Data workload on IBM Power systems We highlight the research directions that we are pursuing in this paper. More technical details and progress updates will be covered in several papers in an upcoming issue of the IBM Journal of Research and Development.Contact Author: Jian Li, IBM Research in Austin, 11501 Burnet Road,Austin, TX 78758; Phone: (512)286-7228; Email: Research Technical Report. July 10, 2012.Copyright 2012 .
  3. 3. 2. BENCHMARKING METHODOLOGY 3. WORKLOAD OPTIMIZED SYSTEMS Massive Scale Analytics is representative of a new class of While industry has made substantial investments in ex-workloads that justifies a re-thinking of how computing sys- tending its software capabilities in analytics and Big Data,tems should be optimized. We start by tackling the problem thus far these new workloads are being executed on systemsof the absence of a set of benchmarks that system hardware that were designed and configured in response to more tra-designers can use to measure the quality of their designs ditional workload demands.and that customers can use to evaluate competing hardware In this paper we present two key improvements to tradi-offerings in this fast-growing and still rapidly-changing mar- tional system design. The first is the addition of reconfig-ket. Existing benchmarks, such as HiBench [11], fall short in urable acceleration. While reconfigurable acceleration hasterms of both scale and relevance. We conceive a method- been used successfully in commercial systems and appliancesology for peta-scale data-size benchmark creation that in- before (e.g., DataPower R [6] and Netezza R [4]), we havecludes representative Big Data workloads and can be used demonstrated via a prototype system that such technologyas a driver of total system performance, with demands bal- can benefit the processing of unstructured data.anced across storage, network and computation. Creating The second innovation we discuss is a new modular andsuch a benchmark requires meeting unique challenges asso- dense design that also leverages acceleration. Achieving theciated with the data size and its unstructured nature. To computational and storage densities that this design pro-be useful, the benchmark also needs to be generic enough vides requires an increase in processing efficiency that isto be accepted by the community at large. We also ob- achieved by a combination of power-efficient processing coresserve unique challenges associated with massive scale ana- and offloading of performance-intensive functions to the re-lytics benchmark creation, along with a specific benchmark configurable logic.we have created to meet them. With an eye towards how analytics workloads are likely to The first consequence of the massive scale of the data is evolve and executing such workloads efficiently, we conceivethat the benchmark must be descriptive, rather than pre- of a system that leverages the accelerated dense scale-outscriptive. In other words, our proposed benchmark is pro- design in combination with powerful global server nodes thatvided as instructions for acquiring the required data and pro- orchestrate and manage computation. This system can alsocessing it, rather than providing benchmark code to run on be used to perform deeper in-memory analytics on selectedsupplied data. We propose the use of existing large datasets, data.such as the 25TB ClueWeb09 dataset [9] and the over 200TBStanford WebBase repository [10]. Challenges of using suchreal-world large datasets include physical data delivery (e.g., 4. BIG DATA ON POWER SYSTEMSvia shipped disk drives), and data formatting/“cleaning” of Apache Hadoop [1] has been widely deployed on clustersthe data to allow robust processing. of relatively large numbers of moderately sized, commodity We propose compute- and data-intensive processing tasks servers. However, it has not been widely used on large,that are representative of key massive-scale analytics work- multi-core, heavily threaded machines even though smallerloads to be applied to this unstructured data. These tasks systems have increasingly large core and hardware threadinclude major Big Data application areas, text analytics, counts. We describe an initial performance evaluation andgraph analytics and machine-learning. Specifically our bench- tuning of Hadoop on a large multi-core cluster with only amark efforts focused on document categorization based on handful of machines. The evaluation environment comprisesdictionary-matching, document and page ranking, and topic IBM InfoSphere BigInsights [3] on a 10-machine cluster of TMdetermination via non-negative matrix factorization. The IBM PowerLinux 7R2 systems.first of the three, in particular, required innovation in bench-mark creation, as there is no “golden reference” to establish 4.1 Evaluation Environmentcorrect document categorization. Existing datasets typically Table 1 shows the evaluation environment, which com-used as references for text-categorization assessments, such prises IBM InfoSphere BigInsights [3] on a 10-machine clus-as the Enron corpus [2], are orders of magnitude smaller than ter of IBM PowerLinux 7R2 servers. IBM InfoSphere BigIn-what we required. Our approach for overcoming this chal- sights provides the power of Apache Hadoop in an enterprise-lenge included utilizing publicly-accessible documents coded ready package. BigInsights enhances Hadoop by addingby subject, such as US Patent Office patents and applica- robust administration, workflow orchestration, provisioningtions, to create subject-specific dictionaries against which and security, along with best-in-class analytics tools fromto match documents. Unique challenges of ensuring “real- IBM Research. This version of BigInsights, version 1.3, usesworld” relevance includes covering non-word terms of impor- Hadoop 0.20.2 and its built-in HDFS file system.tance, such as band names that include regular expression Each PowerLinux 7R2 includes 16 POWER7 R cores @characters, and a “wisdom of crowds” approach that helps 3.55GHz that can scale to 3.86GHz in a “Dynamic Powerus meet those challenges. Saving - Favor Performance” mode, up to 64 hardware threads, It is our intention to make our benchmark public. The 128 GB of memory, 6 × 600GB internal SAS drives, andbenchmark is complementary to existing prescriptive bench- 24 × 600GB SAS drives in an external Direct Attached Stor-marks, such as Terasort and its variations that have been age (DAS) drawer. We used software RAID0 over LVM forwidely exercised in the Big Data community. In this paper, the 29 drives to each machine. One internal SAS drive iswe use Terasort as a case study in Section 4. dedicated as the boot disk. The machines are connected by 10Gb Ethernet network. Each machine has two 10Gbe connections to the top of the rack switch. We used RedHat Linux (RHEL6.2). All 10 PowerLinux 7R2 machines func- tion as Hadoop data nodes and task trackers. Note that we
  4. 4. • Preliminary intermediate control of map and reduce Table 1: Evaluation Environment stages to better utilize available memory capacity; Hardware • Reconfiguration of storage subsystem to remove fail- Cluster 10 PowerLinux 7R2 Servers over support of storage adapters for effective band- CPU 16 processor cores per server (160 total) width improvement since Hadoop handles storage fail- Memory 128GB per server (1280GB total) ures by replication at software level; Internal 6 600GB internal SAS drives Storage per server (36TB total) • JVM, Jitting, and GC tuning that better fit the POWER Storage 24 600GB SAS drives in IBM EXP24S SFF architecture. Expansion Gen2-bay Drawer, per server (144TB total) Network 2 10Gbe connections per server Currently, we are able to achieve an execution time of less Switch BNT BLADE RackSwitch G8264 than 9 minutes to sort 1TB input data from disk and writ- ing back 1TB output data to disk storage. This translates Software to sorting 11.4GB/node/minute for the IO intensive sort OS Red Hat Enterprise Linux 6.2 benchmark. Table 2 lists some of the BigInsights (Hadoop) Java IBM Java Version 7 SR1 job configuration parameters used in this Terasort run that BigInsights Version 1.3 (Hadoop v0.20.2) completed in 8 minutes and 44 seconds. MapReduce One Power 730 as NameNode/JobTracker Figure 1 shows the CPU utilization of one of the 10 nodes and 10 7R2s as DataNodes/TaskTrackers in the cluster during the Terasort run. All nodes have similar CPU utilization. Figure 1 shows that CPU utilization is very high during the map and map-reduce overlapping stages. As expected, it is only when all mappers finish and onlyuse one extra Power 730 Express Server as the master node reducers (380 as shown in Table 2 out of the 640 hardwarethat serves as the Name Node and Job Tracker2 . threads supported by the 10 PowerLinux 7R2 servers) are4.2 Results and Analysis writing output back to disks that CPU utilization drops. While we have large quantities of other system profile We have some early experiences with measuring and tun- data to help us understand the potential for further perfor-ing standard Hadoop programs, including some of the ones mance improvement, one thing is clear: high CPU utiliza-used in the HiBench [11] benchmark suite, and some from tion does not necessarily translate into high performance. Inreal-world customer engagements. In this paper, we use other words, the CPU may be busy doing inefficient comput-Terasort, a widely used sort program included in the Hadoop ing (during the Hadoop and Java framework, for instance),distribution, as a case study. While Terasort in Hadoop can or performing inefficient algorithms that artificially inflatebe configured to sort differing amounts of input data, we CPU utilization without improving performance. This leadsonly present results for sorting 1TB of input data. In addi- us to examine full-stack optimization during the next phasetion we compress neither input nor output data. of our research. Our initial trial is done with the default Hadoop map-reduce configuration, e.g., limited map and reduce task slots,which does not utilize the 16 processor cores in a single Pow-erLinux 7R2 system. As expected, the test takes hours tofinish. After initial adjustment of the number of mappersand reducers to fit to the parallel processing power of 16cores in a PowerLinux 7R2 system, the execution time dras-tically decreases. We then apply the following public-domain tuning meth-ods and reference the best practices from the PowerLinuxCommunity [8] to gradually improve the sort performance: • Four-way Simultaneous Multithreading (SMT4) to fur- ther increase computation parallelism and stress data Figure 1: CPU utilization on one PowerLinux 7R2 node parallelism via large L3 caches and high memory band- from a Terasort run of 8 minutes 44 seconds. width on POWER7 R ; • Aggressive read ahead setting and deadline disk IO As indicated above, we have only applied infrastructure scheduling at OS level; tuning in this stage of the study. In the next stage, we plan to incorporate the performance enhancement features • Large block size and buffer sizing in Hadoop; in IBM InfoSphere BigInsights for further improvement. We • Publicly available LZO compression [7] for intermedi- are also working on patches for better intermediate control ate data compression; in MapReduce. Newer Hadoop versions than 0.20.2 are ex-2 pected to continue to deliver performance improvements. Given the small size cluster in this study, our experiments Furthermore, we plan to apply reconfigurable accelerationshow negligible performance overhead of using one server technology as indicated in Section 3. In the meantime, scal-as both a master node (Name Node and Job Tracker) anda slave node (Data Node and Task Tracker). We separate ability studies are also important to understand the optimalthe P730 master node from the 10 7R2 slave nodes for the approach to (A) strong scaling of the BigInsights cluster inconvenience of cluster management. terms of scaling the number of nodes with constant input
  5. 5. Table 2: Sample BigInsights (Hadoop) Job Configuration Parameters Values true com.hadoop.compression.lzo.LzoCodec mapred.reduce.slowstart.completed.maps 0.01 mapred.reduce.parallel.copies 1 640 mapred.reduce.tasks 380 true io.sort.factor 120 mapred.jobtracker.taskScheduler org.apache.hadoop.mapred.JobQueueTaskScheduler flex.priority 0 false io.sort.mb 650 mapred.job.reduce.input.buffer.percent 0.96 mapred.job.shuffle.merge.percent 0.96 mapred.job.shuffle.input.buffer.percent 0.7 io.file.buffer.size 524288 io.sort.record.percent 0.138 io.sort.spill.percent 1.0 ’-server -Xlp -Xnoclassgc -Xgcpolicy:gencon -Xms890m -Xmx890m -Xjit:optLevel=hot -Xjit:disableProfiling -Xgcthreads4 -XlockReservation’ 64 mapred.tasktracker.reduce.tasks.maximum 49 dfs.replication 1 mapred.max.tracker.blacklists 20 dfs.block.size 536870912 mapred.job.reuse.jvm.num.tasks -1and (B) weak scaling of the BigInsights cluster that scales initial experience of sorting 1TB data on a 10-node Pow-up the number of nodes with corresponding increases in in- erLinux 7R2 cluster. As of this writing, it takes less thanput size3 . 9 minutes to complete the sort, which translates to sorting As a preliminary proof of concept, our experiment shows 11.4GB/node/minute for the IO intensive sort benchmark.that BigInsights v1.3 with only 4 PowerLinux 7R2 nodes can We expect additional improvements in the near future. Wesort 1TB input in slightly over 22 minutes. Thus, the Tera- also show that 4 PowerLinux 7R2 nodes can sort 1TB inputsort benchmark exhibits nearly linear scaling with strong with around 22 minutes. Further improvement is expectedscaling from 4 up through the 10 nodes in our cluster, lead- as we continue full-stack optimization on both software anding to a lower system cost and footprint for situations where hardware.a 22 minutes Terasort time is acceptable. While we have not observed that the network is a bot- 6. ACKNOWLEDGMENTStleneck in our 10-node cluster, this may change when thesystem scales up and the workload changes. Similar obser- We would like to thank the IBM InfoSphere BigInsightsvations apply to our disk subsystem, which currently has 30 team for their support. Particularly, we owe a debt of grat-disks in total per PowerLinux 7R2 server. itude to Berni Schiefer, Stewart Tate and Hui Liao for their Finally, we expect people to utilize performance analysis guidance and insights. Susan Proietti Conti, Gina King, An-similarly and judiciously size their systems for their partic- gela S Perez, Raguraman Tumaati-Krishnan, Anitra Powell,ular workloads and needs. This can be useful either when Demetrice Browder and Chuck Bryan have been supportingthey make purchasing decisions or when they reconfigure us to streamline the process along the way. Last but nottheir systems in production. least, we could not have reached this stage without the gen- erous support and advice from Dan P. Dumarot, Richard W. Bishop, Pat Buckland, Clark Anderson, Ken Blake, Geoffrey5. CONCLUSIONS Cagle and John Williams, among others. In this paper, we have presented our initial study on BigData benchmarking and methodology as well as workload 7. REFERENCESoptimized systems for Big Data. We have also discussed our [1] Apache hadoop. http://hadoop.apache.hadoop.3 [2] Enron Email Dataset. We borrow the two common notions of scalability, strongvs. weak scaling, from the High Performance Computing enron/.community, which we think fit well to Big Data analytics [3] IBM InfoSphere BigInsights. http://www-and data-intensive computing.
  6. 6. [4] IBM Netezza Data Warehouse Appliances. [5] IBM PowerLinux 7R2 Server. http://www- powerlinux/7r2/index.html. [6] IBM WebSphere DataPower SOA Appliances. http://www- [7] LZO: A Portable Lossless Data Compression Library. [8] PowerLinux Community. [9] The ClueWeb09 Dataset.[10] The Stanford WebBase Project. testbed/doc2/WebBase/.[11] S. Huang, J. Huang, J. Dai, T. Xie, and B. Huang. The HiBench benchmark suite: Characterization of the MapReduce-based data analysis. In IEEE International Conference on Data Engineering Workshops (ICDEW), Long Beach, CA, USA, 2010.[12] T. Morgan. IBM Global Technology Outlook 2012. In Technology Innovation Exchange, IBM Warwick, 2012.