taming the big data tsunami using intel architecture
DESCRIPTION
Intel Developer Forum: Taming the Big Data Tsunami using Intel® Architecture by Clive D’Souza, Solutions Architect, Intel Corporation and Dhruv Bansal, Chief Science Officer, InfochimpsTRANSCRIPT
Taming the Big Data Tsunami using Intel® Architecture
DATS004
Clive D’Souza, Solutions Architect, Intel Corporation Dhruv Bansal, Chief Science Officer, Infochimps
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Agenda
• What is Big Data? • Why does Big Data matter? • How can Intel®
Architecture help • Summary
3
Big Data Tsunami Between the birth of the world and 2003, there were five Exabyte of information created. We now create five Exabyte every two days
Eric Schmidt
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Exp
onen
tial
Gro
wth Content Depots – Massive/
Unstructured
Traditional Unstructured
Traditional Structured Data
Enterprise Hosting Services
Lin
ear
Gro
wth
Source: IDC, 2011 Worldwide Enterprise Storage Systems 2011–2015 Forecast Update. Worldwide Enterprise Storage Consumption Capacity Shipped by Model, 2006–2015 (PB)
2.7 Zetabytes of data in 2012, 15 billion connected devices by 2015 !!!
Over 24 Petabytes Data processed by Google* every day in 2011
Four billion Pieces of content shared on Facebook* every day by July 2011
250 Million Tweets per day in October 2011
5.5 million Legitimate emails sent every second in 2011
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Big Data — Traits
Unstructured datasets whose Volume, Variety and Velocity is beyond the ability of typical database
software tools to capture, store, manage and analyze†
Volume
Variety Velocity
Value
Big Data Decrees • Speed is everything! • Use diverse data • Data never gets stale • Data growth will be
exponential • Big Data is real • Transformational to business Core Tenants
†Big data: The next frontier for innovation, competition, and productivity”, McKinsey Global Institute
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Big Data — Flow
Data Ingestion
Data Aggregation
Data Curation
Data - Query Enabled
Data Analytics
Compute/Network/IO/Storage-Intensive
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Agenda
• What is Big Data? • Why does Big Data matter? • How can Intel®
Architecture help • Summary
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Our Problem – Which 5K?
Image used with permission from Author
• Don’t know the future value of today’s data
• We cannot connect the dots we do not yet have
• The old collect, winnow, dissemble model fails spectacularly in the Big Data world
The “5K” is different for everybody!
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Intelligent 5K = Big Money!
Source: Mckinsey, 2011
Manufacturing • Up to 50 percent decrease in product development, assembly costs
• Up to seven percent reduction in working capital Europe public sector
administration • €250 billion value per year
• ~0.5 percent annual productivity growth
US health care • $300 billion value per year
• ~0.7 percent annual productivity growth
Global personal location data • $100 billion+ revenue for service providers
• Up to $700 billion value to end users
US retail • 60+ percent increase in net margin possible
• 0.5-1.0 percent annual productivity growth
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Big Data in play by
Infochimps*
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Growing Pains… • IT growth outpaced by Big
Data growth • Unparalleled data complexity • Need for speed – race to the
bottom! • Workload management • Data access, data silos, data
quality, data security • Shortage of data scientists • New domain – not easy to
implement
Big Data solutions will transform IT
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Big Data Means More Than Hadoop*
About Infochimps* www.infochimps.com
[Many people’s] understanding of “Hadoop” is like my understanding of “tango”: I know the word, I know one when I see one, but I can’t dance for ***.
Jeffrey Eisenberg
• Multi-language • Databases • Web • Realtime
• Java* • I/O-bound • Batch, map/reduce,
historical
Ecosystem Hadoop*
Big Data hardware needs more than I/O
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Sensors
Rich Media
System Logs
Mobile Web Logs
CRM
POS
ERM
Public Data
Internal DBs & Appliances
Documents
Full Data Stack Overview
Hadoop* (Batch/Historical
Analytics)
Data Storage (Analytic DBs and
filesystem)
Stream Processing (Real-Time Analytics)
Data Integration
(ETL and streaming)
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Hadoop*
Bulk/Large-Scale Processing Engine
Full Data Stack
Hbase* or
HDFS* Primary Analytics Datastore
Mongo* or MySQL*
Reporting Aggregates
Streaming Sources
Database Sources Sqoop*
Bulk Load Structured Datastores like SQL
Elasticsearch* Search Engine and API
Flume* Collect and Process Streaming or Fast-Changing Data
DataViz*
Tableau* LogiXML* Custom App etc.
Web Servers Local Disk Statistical
Packages
SAS*, R*, Stata*, etc.
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Demonstration When are two time series correlated?
AAPL
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Demonstration
Source Data: • Web traffic logs (Wikipedia, 3 mos.) • S&P 500* Stock Prices
Q: Traffic to which Wikipedia* articles is correlated with the price of AAPL?
AAPL
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Demonstration
AAPL
Tentative Answer: Traffic to articles about music, television, and video games are directly correlated with AAPL’s stock price.
Bonus: Also Jack Dorsey, CEO of Square!
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Full Data Stack
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Agenda
• What is Big Data? • Why does Big Data matter? • How can Intel®
Architecture help • Summary
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Intelligent Data Center
Compute WWW
VPN or LAN
Virtualized Servers
Dedicated Servers
“Centralized” Storage
Premium Storage
High-Capacity Storage
Low-Latency, Proximity Storage
HDD
Edge/M2M
HPC & Decision Support
IT/Web/Application Development
Infrastructures
IOPS/TB Optimized
$/TB Optimized
Unified Network
NVM
SSD
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Intel® Xeon® Processor = Heart of the Intelligent Data Center • Integrated PCI Express* Gen 3.0 • Intel® Hyper-Threading
Technology, two Threads/Core • Shared Last Level Cache, 2.5 MB/
Core • Higher memory bandwidth with
DDR3 • Integrated Memory Controller • PCIe Non-Transparent Bridge • Asynchronous DRAM self-refresh
(ADR) • Intel® QuickData Technology
Direct Memory Access
Intel® Xeon® powers Big Data compute
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Intelligent Storage Optimizations
De-duplication
Intelligent Tiering Thin Provisioning
Real Time Compression
BEFORE AFTER DE-DUPLICATION
APPLI 1
APPLI 2
APPLI 3
TRADITIONAL ALLOCATION THIN PROVISIONING ALLOCATED BUT FREE
USED
ALLOCATED BUT FREE
USED
USED
ALLOCATED BUT FREE
APPLI 1 APPLI 2 APPLI 3
SYSTEM-WIDE CAPACITY RESERVED
Up to 80% data reduction2 95% smaller backup1
Up to 80% reduction in disk expenses3
1 IBM storage simulcast, November 9, 2011 2 BM storage simulcast, November 9, 2011 3 Dell “Fluid Data Storage: Driving Flexibility in the Data Center”, February 2011 4 Intel IT study “Solving Intel IT’s Data Storage Growth Challenges
Up to 25% reduction in annual storage CapEx
growth4
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New Memory Hierarchies — Non-Volatile Memory
CPU Processing
Tim
elin
e
CPU Processing
SW 10µs
NVM 65µs IO
Processing
Time spent by application in CPU vs. IO
Application
• Enhanced Performance - Sequential R/W: 2.0/1.0 GB/s - Random R/W: 180/75 KIOPS - Latency R/W: 65/65µs
• High Endurance 25nm HET MLC - 10x drive writes/day for five years - 30x endurance over standard MLC
due to improved write amplitude and NAND management
Intel® Solid-State Drive 910 Series
Reduction of software latency dramatically increases
application IOPS as NVM latency decreases
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Intel Ethernet Controller
Intel QPI 1
Intel QPI 2 CORE 1 CORE 2
CORE 3 CORE 4
CORE 5 CORE 6
CORE 7 CORE 8
Intel Xeon® Processor E5-2600
CACHE IOC
PCI Express*
DMA Write directly To “IO allocated” LLC
Rx Packet
No Memory
Transactions
Core Reads Data 2
1
LLC Data to Core 3
Intel® Integrated IO Technology
Intel QPI - Intel QuickPath* Interconnect
Intel Ethernet Controller
Intel QPI 1
Intel QPI 2
CORE 1 CORE 2
CORE 3 CORE 4
CORE 5 CORE 6
CORE 7 CORE 8
Intel Xeon Processor E5-2600
CACHE
PCI Express*
Outbound Flow (Tx)
Tx P
acke
t
Core creates buffer for I/O device to read, putting data in cache (cache line allocated)
1
I/O request read of I/O data
2
Data to I/O 3
No Memory
Transactions
Intel Data Direct I/O Technology (Intel DDIO)
Inbound Flow (Rx)
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10GbE Completes the Job Faster
Economies of scale realized with 10GbE
4X Improvement
Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012.
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. For more information go to http://www.intel.com/performance. Configuration: Source: Intel internal measurements of average time for an I/O device read to local system memory under idle conditions comparing Intel® Xeon® processor E5-2600 product family (230ns) vs. Intel Xeon processor 5500 series (340ns). See notes in backup for configuration details.
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Up to four channels DDR3 1600 MHz memory
Up to eight cores Up to 20 MB cache
Integrated PCI Express* 3.0 Up to 40 lanes per socket
Platform and Software Optimizations
1 Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. 2 Source: Intel internal measurements of average time for an I/O device read to local system memory under idle conditions comparing Intel® Xeon® processor E5-2600 product family (230ns) vs.. Intel Xeon processor 5500 series (340ns). See notes in backup for configuration details
• Up to 80% Performance Boost vs. Prior Generation1
– Intel® Advanced Vector Extensions (Intel® AVX) - Reduce Compute Time
– Intel® Turbo Boost Technology — Increased Performance2 • Hadoop* Optimizations from Intel
– Built on Open Source Releases – Custom Tuning for Data Types and Scaling Approaches
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Intel® Intelligent Storage Acceleration Library (Intel® ISA-L)
Intel ISA-L enables OEMs to obtain more performance from Intel® CPUs
0
1
2
3
4
Normalized to Existing Open Source Solutions
AVX Multi-buffer Hashing Functions (Baseline case is without Intel ISA-L)
Note: For more information go to http://www.intel.com/performance
Algorithmic Library to address key Storage market segment needs • Optimized for Intel® Architecture • Enhances efficiency, data integrity,
security/encryption Benefits of using Intel® ISA-L • Allows maximum utilization of additional
cores • Faster time to market (TTM)/less
resources than developing in-house • Allows Intel to develop optimizations
using new architectural enhancements that promote faster TTM
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A Fresh Look at Intel® Virtualization Technology (Intel® VT) Intel® Virtualization Technology
Intel VT for IA-32 and Intel 64 (Intel VT-x) HW support for
isolated execution
Intel VT for Directed I/O
(Intel VT-d) HW support for
isolated I/O
VMM
VM2 VM1
Traditional Server VMM • Isolate development and
production environment • Technology demonstrators New Cloud Security Model • Isolation of workloads in
multi-tenant cloud • Memory monitoring for
malware detection • Device Isolation for protection
against DMA attacks
Hardware Provides Stronger Isolation of VMs
Intel® Virtualization Technology for IA-32, Intel® 64 and Intel® Architecture (Intel® VT-x)
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Intel® Trusted Execution Technology (Intel® TXT)
Internet
Compliance Hardware support for compliance reporting enhances auditability of cloud environment
Trusted Launch Verified platform integrity reduces malware threat
Trusted Pools Control VMs based on platform trust to better protect data
Intel® TXT • Enables isolation and tamper
detection in boot process • Complements runtime
protections • Hardware based trust provides
verification useful in compliance
• Trust status usable by security and policy applications to control workloads
Hardens and Helps Control the Platform
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Data Protection with Intel® AES-NI
Internet Intranet
Data in Motion Secure transactions used pervasively in ecommerce, banking, etc.
Data in Process Most enterprise and cloud applications offer encryption options to secure information and protect confidentiality
Intel® AES-NI • Special math functions built
in the processor accelerate processing of crypto algorithms like AES
• Includes 7 new instructions • Makes enabled encryption
software faster and stronger
Efficient Ways to Use Encryption for Data Protection
Intel® AES New Instructions (Intel® AES-NI)
Data at Rest Full disk encryption software protects data while saving to disk
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Agenda
• What is Big Data? • Why does Big Data matter? • How can Intel®
Architecture help • Summary
31
Big Data rEvolution
Collecting Connecting
Predicting
From To
Analyzing
From To
From To Structured Unstructured
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Summary
Intel® Architecture is foundational to finding “Your 5K”
• Big Data Phenomenon is Real
• Analytics based on Hadoop* will be the norm
• Compute, Network & Storage will converge for Big Data Solutions
Compute Intel® Xeon® Processor
Storage NVM, Tiered, JBOD 10GbE Network
Network
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Rev. 5/4/12