Near-Real-Time Big Data: Hadoop 效 能 最 佳 化 調 校 分 析 美 商 英 特 爾 亞 太 科 技 有 限 公 司 台 灣 分 公 司 鄭 智 成

Transcription

1 Near-Real-Time Big Data: Hadoop 效 能 最 佳 化 調 校 分 析 美 商 英 特 爾 亞 太 科 技 有 限 公 司 台 灣 分 公 司 鄭 智 成

2 Legal Disclaimer INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS PROVIDED IN INTEL'S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. A "Mission Critical Application" is any application in which failure of the Intel Product could result, directly or indirectly, in personal injury or death. SHOULD YOU PURCHASE OR USE INTEL'S PRODUCTS FOR ANY SUCH MISSION CRITICAL APPLICATION, YOU SHALL INDEMNIFY AND HOLD INTEL AND ITS SUBSIDIARIES, SUBCONTRACTORS AND AFFILIATES, AND THE DIRECTORS, OFFICERS, AND EMPLOYEES OF EACH, HARMLESS AGAINST ALL CLAIMS COSTS, DAMAGES, AND EXPENSES AND REASONABLE ATTORNEYS' FEES ARISING OUT OF, DIRECTLY OR INDIRECTLY, ANY CLAIM OF PRODUCT LIABILITY, PERSONAL INJURY, OR DEATH ARISING IN ANY WAY OUT OF SUCH MISSION CRITICAL APPLICATION, WHETHER OR NOT INTEL OR ITS SUBCONTRACTOR WAS NEGLIGENT IN THE DESIGN, MANUFACTURE, OR WARNING OF THE INTEL PRODUCT OR ANY OF ITS PARTS. Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the absence or characteristics of any features or instructions marked "reserved" or "undefined". Intel reserves these for future definition and shall have no responsibility whatsoever for conflicts or incompatibilities arising from future changes to them. The information here is subject to change without notice. Do not finalize a design with this information. The products described in this document may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. Contact your local Intel sales office or your distributor to obtain the latest specifications and before placing your product order. Copies of documents which have an order number and are referenced in this document, or other Intel literature, may be obtained by calling , or go to: Copyright 2012 Intel Corporation.

3 Big Data A Foundation For Delivering Big Value Agenda Big Data as Insights that provides ROI Why work with Intel on Big Data 3

4 Big Data A Foundation For Delivering Big Value Virtuous Cycle of Data-Driven Innovation 40 Zettabytes of data will be generated WW in Clients Cloud Richer user experiences Intelligent Systems Richer data to analyze 2.8 Zettabytes of data will be generated WW in (1) IDC Digital Universe 2020, (2) IDC Richer data from devices 4

5 Big Data A Foundation For Delivering Big Value Forces Driving Big Data Advancement Intel TrueScale Infiniband HPC Enabling exascale computing on massive data sets Cloud Helping enterprises build open interoperable clouds Open Source Contributing code and fostering ecosystem * Other names and brands may be claimed as the property of others. 5

6 Big Data A Foundation For Delivering Big Value Intel Leverages the Power of Big Data Chip Design Validation: Cut Product Time to Market by 25% Faster analysis process for validating results Streamlined debug process through analysis of large volumes of historical test data Reseller Channel Management: Increased sales by $5M per Qtr. Decreased cost by $6M per Qtr. Smarter reseller engagement prioritization by leveraging advanced customer profile algorithms Cost efficient detection of non-complaint claims MALWARE Malware Detection: Proof of Concept (POC) Collecting and analyzing large amounts of server security data at the system, network, and application levels lead to discovery of new malware threats before they arise. MILLION new malware samples per quarter 1 CYBER ATTACKS 1 McAfee Threats Report: Second Quarter 2012, McAfee, (PDF) 2 Koebler, Jason, U.S. Nukes Face Up to 10 Million Cyber Attacks Daily, U.S. News & World Report (2012), MILLION U.S. cyber attacks per day 2 6

7 Big Data A Foundation For Delivering Big Value Unleash the power of platform TeraSort for 1TB sort: >4 hour process time Nearly 50x increase in your ability to discover insights Upgrade processor Intel Xeon 5600 HDD 1GbE ~50% reduction Upgrade to SSD ~80% reduction Upgrade to 10GbE ~50% reduction Hadoop processing time: <10 minutes with complete Intel-based solution Intel distribution ~40% reduction 7 Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. Source: Intel Internal testing For more information go to : intel.com/performance ` *Other brands and names are the property of their respective owners Whitepaper

8 Choice High Availability Secure Energy Efficient Responsive Big Data A Foundation For Delivering Big Value Big Data Building Blocks Compute Network Storage Software & Technologies Intel Xeon Product Family E3- E5-E7 Intel Atom Intel Ethernet Controllers Intel Ethernet Adapters Intelligent Storage 1 Scale-out Storage 1 Scale-up Storage 1 Intel Distribution for Apache Hadoop Intel Data Center Manager Intel Xeon Phi TM Intel Ethernet Switch Silicon Intel True Scale Fabric Intel SSD 710 series, DC S3700 (SATA) Intel SSD 910 series (PCIe) Intel Node Manager Intel Expressway Service Gateway Intel Cache Acceleration Software Intel s Lustre Intel VT and Intel TXT Intel AES-NI Intel s Foundational Technologies Offer Advanced Solutions for Big data Analytics Xeon-based storage systems are available in a wide range of configuration options from the industry s leading storage vendors

9 Big Data Compute Platform Optimizations Intel Xeon E5 Family Intel Xeon E7 Family RAM QPI 1 Xeon E QPI 2 CORE 1 CORE 2 QPI 3 CORE 3 CORE 4 Integrated PCI Express* 3.0 Up to 40 lanes per socket Up to 4 channels DDR MHz memory Up to 8 cores Up to 20 MB cache QPI 4 4 QPI 1.0 Lanes for robust scalability CORE 5 CORE 6 CORE 7 CORE 8 CORE 9 CORE 10 CACHE Up to 8 channels DDR MHz memory Up to 10 cores Up to 30 MB cache SCALE-OUT with Hadoop and analytic/dw engines SCALE-UP in-memory analytic engines and databases: Oracle*, SAS*, SAP Hana* Proof point: E5 Analytics 25X Improvement Hadoop on E5 Proof point: SAP HANA 9

10 Big Data A Foundation For Delivering Big Value Intel Ethernet Reduces Time to Process Large Data Sets Trends and Challenges Big data is hitting the enterprise with unprecedented volume, velocity, variety, complexity, and OPPORTUNITY 1GbE Network Connections VM VM VM Hypervisor VM VM VM Hypervisor Intel Ethernet Solution 10 Ports 1GbE 2 Ports 10GbE Up to 20x performance boost over legacy infrastructure with optimizations on Intel Xeon processors, Intel SSD storage, and 10Gb Intel Ethernet networking 10 Gigabit Ethernet allows quicker import and export of large data sets for processing Moving the Data with 10GbE Up to 80% Reduction in Cables & Switch ports Up to 15% Reduction in Infrastructure Costs Up to 2x Improved Bandwidth per Server

11 Big Data A Foundation For Delivering Big Value Intel CAS with Intel SSD Solution Added as cache layer accelerates Big Data workloads 50X IOPS 3X TPC-C 20X TPC-H Performance near equal to replacing all hard drives with SSDs at significantly lower cost throughput performance

12 Big Data A Foundation For Delivering Big Value Data Methods for the Right Data Structure Unstructured Data Emerging Technologies Analytical Paradigms MapReduce /Hive Structured Data Relational Database E X A L Y T I C S * Other names and brands may be claimed as the property of others. 12

13 Big Data A Foundation For Delivering Big Value Intel Distribution for Apache Hadoop* & Tools HiTune (URL) MapReduce File-based Encryption in HDFS Up to 20x faster decryption with AES-NI* Role-based access control for Hadoop services Instrument Aggregation Engine Report Engine Up to 8.5X faster Hive queries using HBase co-processor Optimized for SSD with Cache Acceleration Software Adaptive replication in HDFS and HBase Integrated text search with Lucene HiTune Controller HiBench (URL) 1 2 Simplified deployment & comprehensive monitoring Deployment of HBase across multiple datacenters Automated configuration with Intel Active Tuner Detailed profiling of Hadoop jobs Simplified design of HBase schemas (+ in 2.4) REST APIs for deployment and management (+ in 2.4) Micro Benchmarks Sort WordCount TeraSort 3 Machine Learning Bayesian Classification K-Means Clustering HiBench Web Search Nutch Indexing Page Rank Result = many Hadoop optimization tips (IDF2012 presentation Big Data Analytics on a Performance-optimized Hadoop Infrastructure ) 4 HDFS Enhanced DFSIO 13

14 Big Data A Foundation For Delivering Big Value Summary: Intel in Big Data The pervasiveness of Intel Architecture democratizes the implementation and performance of Big Data everywhere Optimized ISV software stacks and services Accelerate analytics: CPU, storage, and network Foster the growth of market partners Solution research and academia engagement Distribute analytics to the edge 14

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16 This slide MUST be used with any slides removed from this presentation Big Data A Foundation For Delivering Big Value Legal Disclaimers All products, computer systems, dates, and figures specified are preliminary based on current expectations, and are subject to change without notice. Intel processor numbers are not a measure of performance. Processor numbers differentiate features within each processor family, not across different processor families. Go to: Intel, processors, chipsets, and desktop boards may contain design defects or errors known as errata, which may cause the product to deviate from published specifications. Current characterized errata are available on request. Intel Virtualization Technology requires a computer system with an enabled Intel processor, BIOS, virtual machine monitor (VMM). Functionality, performance or other benefits will vary depending on hardware and software configurations. Software applications may not be compatible with all operating systems. Consult your PC manufacturer. For more information, visit No computer system can provide absolute security under all conditions. Intel Trusted Execution Technology (Intel TXT) requires a computer system with Intel Virtualization Technology, an Intel TXT-enabled processor, chipset, BIOS, Authenticated Code Modules and an Intel TXT-compatible measured launched environment (MLE). Intel TXT also requires the system to contain a TPM v1.s. For more information, visit Requires a system with Intel Turbo Boost Technology capability. Consult your PC manufacturer. Performance varies depending on hardware, software and system configuration. For more information, visit Intel AES-NI requires a computer system with an AES-NI enabled processor, as well as non-intel software to execute the instructions in the correct sequence. AES-NI is available on select Intel processors. For availability, consult your reseller or system manufacturer. For more information, see Intel product is manufactured on a lead-free process. Lead is below 1000 PPM per EU RoHS directive (2002/95/EC, Annex A). No exemptions required Halogen-free: Applies only to halogenated flame retardants and PVC in components. Halogens are below 900ppm bromine and 900ppm chlorine. Intel, Intel Xeon, Intel Core microarchitecture, the Intel Xeon logo and the Intel logo are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries. Copyright 2011, Intel Corporation. All rights reserved. 16

17 This slide MUST be used with any slides removed from this presentation Big Data A Foundation For Delivering Big Value Legal Disclaimers: Performance Performance tests and ratings are measured using specific computer systems and/or components and reflect the approximate performance of Intel products as measured by those tests. Any difference in system hardware or software design or configuration may affect actual performance. Buyers should consult other sources of information to evaluate the performance of systems or components they are considering purchasing. For more information on performance tests and on the performance of Intel products, Go to: Intel does not control or audit the design or implementation of third party benchmarks or Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmarks are reported and confirm whether the referenced benchmarks are accurate and reflect performance of systems available for purchase. Relative performance is calculated by assigning a baseline value of 1.0 to one benchmark result, and then dividing the actual benchmark result for the baseline platform into each of the specific benchmark results of each of the other platforms, and assigning them a relative performance number that correlates with the performance improvements reported. SPEC, SPECint, SPECfp, SPECrate. SPECpower, SPECjAppServer, SPECjEnterprise, SPECjbb, SPECompM, SPECompL, and SPEC MPI are trademarks of the Standard Performance Evaluation Corporation. See for more information. TPC Benchmark is a trademark of the Transaction Processing Council. See for more information. SAP and SAP NetWeaver are the registered trademarks of SAP AG in Germany and in several other countries. See for more information. INFORMATION IN THIS DOCUMENT IS PROVIDED AS IS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO THIS INFORMATION INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. Performance tests and ratings are measured using specific computer systems and/or components and reflect the approximate performance of Intel products as measured by those tests. Any difference in system hardware or software design or configuration may affect actual performance. Buyers should consult other sources of information to evaluate the performance of systems or components they are considering purchasing. For more information on performance tests and on the performance of Intel products, reference 17

18 This slide MUST be used with any slides removed from this presentation Big Data A Foundation For Delivering Big Value Optimization Notice Intel's compilers may or may not optimize to the same degree for non-intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice. Notice revision #

19 Big Data A Foundation For Delivering Big Value Characteristics of Big Data Categorize and Handle: Unstructured & Structured Data Marketing Automation CMS Data Velocity Real Time Near Real Time Need for Faster: Time to Insights SMS Audio Periodic Social Tech Automated Demand Generation Data Variety Video Reports Web Data Base Photo Table Batch KB MB GB TB PB Data Volume Scale to Comprehend: Massive Existing & Incoming Data 19

20 Bandwidth (MB/sec) Big Data A Foundation For Delivering Big Value Intel Xeon Processor E Product Family Integrated PCIe* Bandwidth PCIe* Bandwidth on Intel Xeon 5600 and Xeon E Higher is better Intel Xeon E5 Up to 3X more PCIe read / write bandwidth Intel Xeon lanes PCIe* 2.0 Xeon lanes PCIe* 2.0 Xeon E5 32 lanes PCIe* 3.0 Xeon E5 64 lanes PCIe* 3.0 Xeon E5 rd wr rdwr Up to 3X more R/W Bandwidth with Integrated PCIe* 3.0 Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. Source Intel internal measurements as of 6 March Intel Xeon processor E based platform with 64 lanes of PCIe* 3.0 (66 GB/s) vs. Intel Xeon processor X5670 based platform with 32 lanes of PCIe* 2.0 (18 GB/s. Please reference slide 47 for configuration details. For more information go to * Other names and brands may be claimed as the property of others 20

21 座 標 軸 標 題 Big Data A Foundation For Delivering Big Value Exponential Increase in I/O & Application Performance x 136K 86.7K K 0 HDD S CAS + HDD CAS + HDD 100% Random Reads IOPS 50X Average Completion Time Comparison HDD 31% 42% 53% Intel CAS SSD (write-through) Intel CAS (write-back) Hadoop* Mixed Workloads 1.5 SSD 1.2x Significant throughput performance increase Boosts performance across the Hadoop* Cluster 2 for I/O bound, read-intensive workloads 1 Solves the primary challenge I/O bottleneck 21 1Intel Server model 2600CO (Copper Pass); Intel E processor (2.7GHz), 32GB DDR2/1333 memory; Microsoft* Windows 2008R2 SP1, Intel CAS 2.0 release candidate 1; IOMeter ; 4K Random Read test; 32-queue depth; 800Gig Intel SSD 910 series, Intel RAID RS25AB080 with MR54p1 firmware; 8 x 10K SAS HDD in a RAID0 array 2One NameNode + 9 DataNodes, Intel Xeon processor E GHz (8-core/dual-processor)/Node; HDD: 2x1TB/Node (Seagate Constellation TB - ST NS); SSD: 2x480 GB/Node (520 Series GB SSDs - SSDSC2CW480A3); Intel CAS: 2SSDs + 2HDDs; 10GbE; RedHat EL 6.3; Intel Distro for Hadoop (IDH 2.1); Intel CAS v1.2.4 for Linux; Hadoop config: DFS block size 256MB; max map/reduce tasks per node=48; shuffle file compression; optimized network configuration Return

22 Big Data A Foundation For Delivering Big Value Lustre for Big Data Technical Computing Evaluating Hadoop + Lustre configurations for technical computing and other high-performance, parallel opportunities Intel Manager for Lustre Simple provisioning, monitoring and management Browser, CLI and REST interfaces, fully scriptable Lowers complexity and costs, highly extensible Priced per number of attached storage devices Lustre File System Proven sustained performance at massive scale Flexible, scale-up and scale-out solutions Fee-paid development of new features and enhancements Intel Support Services for Lustre Unrivaled Lustre support expertise Professional services offerings ensures success Priced per storage server 22

23 Intel IT Resource Center it center

24 Telco- China Mobile Group Guangdong Hadoop & Xeon optimized Big Data storage & analytics Challenge: Deliver real time access to Call Data Records (CDR) for billing self service Solution: Chose Hadoop + Xeon over RDMS to remove data access bottlenecks, increase storage, and scale system Benefits: Lower TCO, 30x performance increase, stable operation, analytics on subscriber usage for targeted promotions Data Characteristics: 30TB billing data/month Real-time retrieval of 30 days CDRs 300k records/second, 800k insert speed/sec 15 analytics queries 133 server nodes Analytics

25 BioScience: Genomics for Translational Medicine Hadoop for Data Correlation & Discovery Insights Challenge: Derive new value added patient discovery services while bringing down genome processing costs Solution: Dynamically partition/scale Hbase for correlation of patient data to all public data Benefits: Contributes to 800x reduction in cost to process 4 M genome variants Data Characteristics: 10 Node Hbase Cluster Billions of pre-computed correlations Data Ingest New Biomed Info- Products Billions of Precomputed Correlations 1 Genome 10 Million rows 100 Genomes 1Billion rows 1M Genomes 10 Trillion rows 100M Genomes 1 Quadrillion 1,000,000,000,000,000 rows 25

26 Public Sector- Smart Traffic Intelligent Transport System Regional Data Hadoop for Predictive Analytics Collection Challenge: Analyze city traffic to derive statistics for crime prevention, info sharing, and predictive traffic analysis Solution: Embed HBase client in camera for real-time inserts of structured/unstructured data Benefits: Automated queries for traffic violation Data mining of fake licenses <1 minute for all data captured for a week Predictive traffic forecasting Data Characteristics: camera data collection points Petabytes of traffic data & terabytes of images 2 billion HBase records App Servers Distributed Processing Across District Nodes Derived Services Crime Prevention Citizen Traffic Services Analytics 26

27 Telco- China Unicom Hadoop for Behavioral Analysis Challenge: Analyze subscriber web usage and billing to derive new information products Solution: Scale out storage based on Hbase with network optimization based on web traffic, log analysis for daily reporting Benefits: New customer segmentation Data Characteristics: 188 nodes, 14TB/server 2.5PB raw disk capacity High speed data loading Real-time query (latency <1s) Daily statistics & reports (sum, count, join, etc) Log Analysis Daily Reports Subscriber Usage & Billing ETL Storage, Analytics MapReduce/Hive Hbase HDFS New Customer Segmentation & Insights

28 Public Sector: Secure Data Movement for Big Data Intel Expressway Service Gateway API Security for Hadoop Challenge: Secure data movement from device to storage Solution: Service Gateway AuthN of cameras & expose analytics information to mobile & other clients Benefits: Secure, compliant data movement Security Characteristics: Java API connector to Hbase SSL for transport & OAuth for identity REST façade API proxy to expose SOAP services security camera across the city Facial recognition

29 Hadoop*: China Public Sector Intelligent Transportation System Camera to capture realtime images of cars entering city Real time traffic analysis, traffic statistics and crime prevention Apache Hadoop HBase client in endpoint software (camera) for real time inserts of structured & unstructured data 29