Big Data Cloud Storage Technology Comparison. Tony Pearson IBM Master Inventor and Senior Managing Consultant. June 26, IBM Corporation

Transcription

1 Big Data Cloud Storage Technology Comparison Tony Pearson IBM Master Inventor and Senior Managing Consultant June 26, IBM Corporation

2 Agenda What is Big Data? InfoSphere BigInsights Infrastructure and Storage Considerations Concluding Thoughts 2

3 An Explosion of Data 1.3 Billion RFID tags in Billion RFID today 2 Billion Internet users by Billon Mobile Phones World Wide Capital market data volumes grew 1,750%, Twitter process 7 terabytes of data every day World Data Centre for Climate 220 Terabytes of Web data 9 Petabytes of additional data Facebook processes 10 terabytes of data every day 3

4 Information Overload But Lacking Insight 44x as much Data and Content Over Coming Decade Zettabytes Business leaders frequently 1in3 make decisions based on information they don t trust, or don t have Business leaders say they don t have access to the information 1in2 they need to do their jobs ,000 Petabytes 80% Of world s data is unstructured 83% of CIOs cited Business intelligence and analytics as part of their visionary plans to enhance competitiveness of CEOs need to do a better job capturing and understanding information rapidly in order to 60% make swift business decisions 4

5 The Big Data Opportunity Extracting insight from an immense volume, variety and velocity of data, in context, beyond what was previously possible. Variety: Velocity: Volume: Manage the complexity of data in many different structures, ranging from relational, to logs, to raw text Streaming data and large volume data movement Scale from Terabytes to Zettabytes 5

6 Where did this begin Apache Hadoop Open source framework for harnessing large volumes of unstructured-data - Inspired by Google technologies (MapReduce, GFS) - Originally built to address scalability problems of web search and analytics Processing Storage Enables applications to run on thousands of nodes and leverage Petabytes of data in a highly parallel, cost effective manner - CPU + Disks = Hadoop Node - Nodes can be combined into clusters - New nodes can be added dynamically - Provides simple scalable growth 6

7 How IBM BigInsights extends Hadoop capabiltity Delivering enterprise-ready software Risk Exposure Failure Analysis Text Processing Advanced Analytics Log Analytics Performance & Availability Extreme storage capacity Security Hardened Architecture Climate modelling Scientific Research Management Disciplines Developer Value InfoSphere BigInsights (Internet Scale Analytics) Traditional / Non-traditional data sources 7

8 Infrastructure for the range of BigInsights deployments Value Enterprise Performance Characteristics Optimized for cost effective scale-out Classic Hadoop architecture Redundancy provided by Hadoop Typical customer use cases Customer sentiment analysis Internet behavior and buying pattern analysis Characteristics Enterprise class features Options to support business critical workloads Typical customer use cases Financial Fraud Detection Risk analysis Data warehouse offload for cold data Characteristics Highest performance Compute and I/O intensive workload options Typical customer use cases compliance analysis Credit card fraud detection Media analytics 8

9 Technology Comparison Internal Storage in System x Servers - Block-level access - Use GPFS-Shared Nothing Cluster (SNC) - Typical for most Hadoop installations External Storage DCS Block-level access - 60 drives in 4U drawer - Designed for Sequential workloads - Use GPFS-Shared Nothing Cluster Based on the IBM System x3630 M3: Ultra-dense, storage-rich server for Big Data SONAS - File-level access - Designed for unstructured data content used in Big Data analytics 9

10 BigInsights Hardware Foundation Rack-Level Features Up to 20 System x3630 M3 nodes Up to 840TB storage Up to 240 cores Up to 3,840GB memory Up to two 10Gb Ethernet or 40Gb InfiniBand switches Scalable to multi-rack configurations Available Enterprise and Performance Features Redundant storage Redundant networking High performance cores Increased memory High performance networking 10

11 BigInsights Value Node Features Value Data Node IBM System x3630 M3 Two Intel Xeon E5620 CPUs Data: 12 x 2TB NL SAS HDDs OS: 1 x 2TB NL SAS HDD 48GB DDR3 RDIMMs Value Management Node (JobTracker, NameNode, Console) IBM System x3630 M3 Two Intel Xeon E5620 CPUs Data: 4 x 2TB NL SAS HDDs OS: 2 x 2TB NL SAS HDD, RAID1 96GB DDR3 RDIMMs 11

12 IBM Storage Product Positioning Primary Data Enterprise Midrange SSD XIV SSD DS5000 SVC DS8000 Flash & Stash SSD SSD SSD Storwize V7000 N7000 SSD SSD N6000 SONAS Storwize V7000 Unified Mainframe Optimized NAS for all servers Distributed High Performance Computing, Big Data DCS3700 Entry Level DS3500 Unified Storage N3000 Random Sequential 12 12

13 Query languages like Pig and JAQL need good random I/O performance Sort requires better sequential throughput GPFS is twice HDFS for both of the above For document index lookups, client side caching is a big win 17x throughput speedup " & '( Proven data integrity Replicated metadata services *"# # %# %"! +,-.%# /01#% +2-! "#$% # %# $)%$ #! +,-#%$3 4 $ %8 $8 9$.%: 13

14 !" File System GPFS HDFS Robust No single point of failure NameNode vulnerability Data Integrity High Evidence of data loss Scale Thousands of nodes Thousands of nodes POSIX Compliance Full supports a wide range of applications Limited Data Management Security, Backup, Replication Limited MapReduce Performance Good Good Workload Isolation Supports disk isolation No support Traditional Application Performance Good Poor performance with random reads and writes 14

15 Evolution of the global namespace: GPFS Active File Management (AFM) GPFS GPFS GPFS GPFS GPFS GPFS GPFS introduced concurrent file system access from multiple nodes. Multi-cluster expands the global namespace by connecting multiple sites AFM takes global namespace truly global by automatically managing asynchronous replication of data

16 IBM NWA High level view of Scale-Out NAS Storage (SONAS) Benchmark Performance: 403,326 IOPS single file system (SPECsfs2008.nfs) SONAS Release 1.2 Single File System over 900TB usable 10 Interface Nodes; each with: - Maximum 144 GB of memory - One active 10GbE port 8 Storage Pods; each with: - 2 Storage nodes and 240 drives - Drive type: 15K RPM SAS hard drives - Data Protection: the drives were configured in RAID ranks 16 16

17 IBM Scale Out Network Attached Storage (SONAS) Enterprise Class Solution for IP-based File System Storage One global repository for application and user files - One huge file system, or up to 256 file systems per SONAS Enterprise solution for all applications, departments and users - Provision and monitor usage by application, file, department or whatever makes sense to the business - Includes ability to report usage and access patterns for chargeback - Capacity managed centrally - Extremely high utilization rates Simplified management of petabytes of storage Independently scalable performance and capacity eliminates trade-offs 17 IBM SONAS Cloud-ready

18 Concluding Thought: IBM s Value A complete stack for Big Data - Others require multi-vendor solutions Embracing the open source community - Product support and additional offerings - In-field expertise to ensure client success Enterprise-class focus - Performance tested - Administrative and development tooling - Deep integration with information management - software inside and outside IBM - Security and governance - High availability and backup System x and System Storage - Industry leading innovation and technology - Best in class reliability and availability - #1 in customer satisfaction 18

19 Thank You! June 26, IBM Corporation

20 About the Speaker Mr. Tony Pearson Master Inventor, Senior Managing Consultant IBM System Storage Tony Pearson Master Inventor, Senior Managing Consultant IBM System Storage 9000 S. Rita Road Bldg 9070 Mail 9070 Tucson, AZ (Office) Tony Pearson is a Master Inventor and Senior managing consultant for the IBM System Storage product line. Tony joined IBM Corporation in 1986 in Tucson, Arizona, USA, and has lived there ever since. In his current role, Tony presents briefings on storage topics covering the entire System Storage product line, Tivoli storage software products, and topics related to Cloud Computing. He interacts with clients, speaks at conferences and events, and leads client workshops to help clients with strategic planning for IBM s integrated set of storage management software, hardware, and virtualization products. Tony writes the Inside System Storage blog, which is read by hundreds of clients, IBM sales reps and IBM Business Partners every week. This blog was rated one of the top 10 blogs for the IT storage industry by Networking World magazine, and #1 most read IBM blog on IBM s developerworks. The blog has been published in series of books, Inside System Storage: Volume I through IV. Over the past years, Tony has worked in development, marketing and customer care positions for various storage hardware and software products. Tony has a Bachelor of Science degree in Software Engineering, and a Master of Science degree in Electrical Engineering, both from the University of Arizona. Tony holds 19 IBM patents for inventions on storage hardware and software products. 20

21 Additional Resources Twitter: Blog: Books: IBM Expert Network:

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