Transcript Big Decision

Big Decision
HPS Performance CoE
Jimmy ZHAO
June 10, 2013
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
HP’s Big Data
Benchmark Strategy
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
HP’s Big Data Community Engagement
HP has lead in BI performance for a long
time, and we are interested in working with
the WDBD to leverage that leadership to
Big Data
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HP is the only company who ever held #1 nonclustered results across 100GB, 300GB, 1TB,
3TB, 10TB, and 30TB in TPC-H (see attached
slide)
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Today HP continues to lead in non-clustered high-end
TPC-H: #1 x86 3TB, #1 10TB, and #1 30TB
HP has more TPC-H publication than others
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Business Intelligence (BI) Performance
Leadership
HP ProLiant
DL380 G7
DL585 G7
TPC-H non-clustered results*
Superdome 2 DL980 G7
HP Integrity
Superdome
Superdome
• Sustained leadership in BI performance over several years
• Multi-OS proof points: HP-UX, Windows, and Linux
• Multi-DB proof points: Oracle, SQL Server, and Sybase
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© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
*Results as of July 15, 2010. Cannot be shared externally without additional TPC data.
HAVEn – Big Data Analytics Platform
HAVEn
Hadoop/
Autonom
HDFS
y
IDOL
Process and
Catalog
massive
volumes of
distributed data
Social media
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Video
Analyze at
extreme scale
in real-time
index all
information
Audio
Email
Enterpris
Vertica
Texts
Mobile
Transactional
data
e
Security
Collect & unify
machine data
Documents
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IT/OT
nApps
Powering
HP Software
+ your apps
Search engine
Images
Big Data Benchmarking
Problem State
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New Business Model
Eco-system Analytics
Data Driven Business Model
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Know more from business, partners and
customers
• More business, customer, behavior,
effect and efficiency data from existing
systems
SoMolized Business – Social & Mobile
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Social marketing, advertisement and promotion
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Business process tracking and reengineering
Effectiveness data
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Marketing effectiveness
Customer understanding
Customer satisfaction
Popularity ranking – Like/Unlike
Mobile Internet
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Efficiency data
Anytime and anywhere: Time + Location data
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Requirements for Big Data Benchmarking
Modeling the real-world applications
Low cost
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Handling huge data volume
Data is variable
Multiple types of analysis
Model the real world infrastructure and
technologies
Demonstrating the new business
model
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Changes in the business systems
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Cloud & Big Data
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Reuse the same infrastructure for
variable analysis works
Simple framework
High Velocity
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Different kinds of queries
• Interactive/ad-hoc queries
Support business growth
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Number of analysis jobs
Size of data
SoMo model
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Decision Support System
Definition
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Traditional Data Mining Process
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…………
……
ETL
SemiStructure
Data mart
Structure
Data mart
Data
Warehouse
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Analytic
CRISP-DM vs. NG-DM
Modeling
Business
Understanding
Data
Preparation
Evaluation
BDPMED
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NG-DM
• Larger data volume
• More complicated
• Faster deploy
• Faster analytics
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Machine
Learning
BUPME-(ML)
DSS System - Scope
Sources
DSS System
Query Data In System
Transform
Extract
Machine Learning in System
Load
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Parallel
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B
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12
M
DU
Mix
BU
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ML
DU
P
DU
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DSS System Scope
Larger
Database
Larger &
Hybrid DW
In-database Analytic
Keep
growing
more
Visual &
Interactive
AI
Un-structured
Continuall
y
Integratio
n
Semi-Structured
Slowly
Structured
shrink
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Analytic
Benchmark Design
- Big Decision
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Why & How?
Benchmark for A DSS/Data Mining solutions
Big Decision – Big TPC-DS!
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Everything running in the same system
TPC-DS
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Engine of Analytics
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Mature and proved workload for BI
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Reflecting the real business model
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Mix workloads
Huge data volume
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Well defined scale factors
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Data from Social
SoMoized TPC-DS
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Data from Web log
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Additional data and dimension from new data
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Data from Comments
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Semi-structured and unstructured data
Broader Data support
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TB to PB or event Zeta Byte support
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Semi-structured data
NEW TPC-DS generator – Agile ETL
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Un-structured data
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Continuously data generation and injection
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Consider as part of the workloads
Continuous Data Integration
• ETL just a normal job of the system
• Data Integration whenever there’s data
Big Data Analytics
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New massive parallel processing technologies
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Convert queries to SQL liked queries
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Include interactive & regular Queries
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Include Machine Learning jobs
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Big Decision Block Diagram
TPC-DS
Marketing
Social
Message
SNS Marketing
Mobile log
Social
Feedbacks
Sales
Item
Web page
Customer
Web log
Reviews
Mobile log
Search & Social Advertise
Search
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Social Web
pages
Social
Advertise
Agile ETL
Extractio
n
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Transfor
m
Load
SoMolized Retails Data Model Design
• Almost the same data model
• Inject more data
– Networking data
– Behavior data
– Tracking data
– Preference data
• More complicated data
dimension
– Time + Location
SM_Sites
Date_Dim
Item
Time_Dim
SM_Promotio
n
SM_Web_Sales
Ship_Mode
Web_Page
Customer_Dem
ographics
Customer_A
ddress
SM_Custome
r
Household_
Demographi
cs
Income_Ban
d
Web & Mobile
Log
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Warehouse
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DI: Data Injectors
MR: Map Reduce Jobs
Workload Design
Sources
ML: Machine Learning Algorithms
DSS System
Query Data In System
Transform
Extract
20%
30%
Machine Learning in System
Load
B
U
Structured DI
Semi-structured
DI
DU
D
E
SQL
9 SQL
P
M
M
P
50%
SQL
Liked/MR
U
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Unstructured DI
90 SQL Liked/MR Job
M
P
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M
L
ML
Huge
Agile ETL
Mix and Parallel Analytics
Volume
to change without notice.
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Workloads
9 ML Jobs
Architecture - Deployment
Driver
Big Decision System
Batch Controller
Injectors
Flume
Second
ary NN
Name
Node
MR Driver
Query Driver
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Data
Node
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…
Data
Node N
Architecture
Agile ETL + MR/Query
Batch Controller
Query
MR
Query
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Semi-structured DI
MR
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Flume
Query
Drivers
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Unstructured DI
MR
Drivers
Structured DI
Injector Domain
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Quer
y
…
Modified
TPC-DS
Generator
Mining Domain
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Scale
Factor
based
DI: Data Injectors
MR: Map Reduce Jobs
Benchmarking Metrics
Batch Controller
Peak
SQL
SQL
SQL
SQL
Liked/
MR
SQL
Liked/
MR
SQL
Liked/
MR
SQL
Liked/
MR
SQL
ML
SQL
ML
SQL
ML
SQL
ML
SQL
SQL
Liked/
MR
SQL
Liked/
MR
SQL
Liked/
MR
SQL
Liked/
MR
SQL
Liked/
MR
DI
SQL
Liked/
MR
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DI
DI
DI
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ML
SQL
DI
SQL
Liked/
MR
DI
ML
DI
ML
SQL
SQL
Liked/
MR
DI
ML
DI
ML
SQL
SQL
Liked/
MR
DI
ML
DI
ML
SQL
SQL
Liked/
MR
DI
ML
DI
ML
SQL
SQL
Consistency
SQL
Scaling
DI
ML: Machine Learning Algorithms
SQL
Liked/
MR
DI
ML
ML
Thank you
?
© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.