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Towards Modeling Approach
Enabling Efficient Platform for
Heterogeneous Big Data Analysis
Andrey.Sadovykh@softeam.fr
1
Outlines
Introduction
Model-driven
development
Big Data
Juniper
Case-study
results
Conclusions
www.modeliosoft.com 2
20 ME
2006
17,5 ME
2005
70 ME
2013
Paris
Rennes
Nantes
Sophia
SOFTEAM – a French IT services / Software vendor
•SOFTEAM, a growing
company
 25 years’ experience
 850 experts
 Regular growth
• Specialist in OO technologies,
new architectures,
methodologies
• Banking, Defense, Telecom, …
www.modeliosoft.com 3
23 ME
2008
Modelio for Software
and System Engineering
• UML editor with 20 years’ history
o CloudML
o SysML
o MARTE
o Code generation
o Documentation
o Teamwork
www.modeliosoft.com 4
• Available under open
source at Modelio.org
MODEL-DRIVEN DEVELOPMENT
www.modeliosoft.com 5
It is all about models … Starting with UML
www.modeliosoft.com 6
Requirements
UML Use
Cases
Architecture
UML
Components
and Classes
Design
Refined
Classes
or Domain
Specific
Language
Implementation
Code
generation
Java, C++,
Frameworks
Model = Code
www.modeliosoft.com 7
Typical example: Control system for a frigate
• 800+ components
• Developed by 100+ engineers
• 1M+ LOC
• MDD fosters Productivity and Quality with
o Code generation
o Components reuse
o Tracing
o Automation
www.modeliosoft.com 8
Curious DSL example: Ruby on Rails
Haml HTML
%br{:clear => left’} <br clear=”left”/>
%p.foo Hello <p class=”foo”>Hello</p>
%p#foo Hello <p id=”foo”>Hello</p>
.foo <div class=”foo”>...</div>
#foo.bar <div id=”foo” class=”bar”>...</div>
www.modeliosoft.com 9
Feature: User can manually add movie
Scenario: Add a movie
Given I am on the RottenPotatoes home page
When I follow "Add new movie"
Then I should be on the Create New Movie page
When I fill in "Title" with "Men In Black"
And I should see "Men In Black"
Cucumber
and Capybara
HAML
What do we get from MDD?
Pros
• Design once, deploy
everywhere!
• Write your
transformation once,
transform anything!
Cons
• Transformations are
hard to write…
• How to make sure they
are CORRECT? i.e.
– Is there any
data/semantic loss?
www.modeliosoft.com 10
BIG DATA
www.modeliosoft.com 11
Volume, variety, velocity
1. @-mails sent
every second : 2,9
million
2. Video uploaded to
YouTube every
minute: 25 hours
3. Data processed by
Google every day:
24 petabytes
4. Tweets per day:
50 million
5. Products ordered
on Amazon per
second: 73 items
www.modeliosoft.com 12
Only 0,5 % of data is analyzed
• In 2012, 2 837EB generated
- just 0,5% actually
analyzed.
That still amounts to 14EB
(or 14.185 million
terabytes)
Source: IDC & EMC
www.modeliosoft.com 13
SQL or Hadoop
www.modeliosoft.com 14
Do you have some data and a
problem to solve?
yes
Data fits in memory?
*Inspired by: Aaron Cordova
Data fits on single RAID array?
Tons of options. Don’t
need database or
Hadoop
yes
no
no
yes
Solvable with SQL?
Use
MySQL
yes
Can you program?
Write a
prog.
yes
no
no
Dead
end
SQL or Hadoop (continued)
www.modeliosoft.com
15
* Inspired by: Aaron Cordova
Data fits on single RAID array?
no
Have lots of money?
Solvable with Oracle SQL?
no
Buy a SAN,
Use Oracle
yes
Do you have a PhD
in parallel prog. ?
no Roll your
own MPI
solution
yes
Solvable using
MapReduce?
no
yes
Can you program
MapReduce jobs?
Write
MapReduce on
Hadoop?
yes
no
Dead
end
no
Challenges
Hadoop MapReduce is the major trend
Success relies on personnel programming skills
Many problems are not solvable with Hadoop. Real-time?
MPI for high performance computing is an option when you have
a lots of money and a PhD
www.modeliosoft.com 16
www.modeliosoft.com 17
JUNIPER integrates Big Data technologies over MPI
www.modeliosoft.com 18
DOCs
StreamsDBs
Data Processing
Stage 1 Stage N
Business
Intelligence
Analytical
DBs
Visualization
dbdb
DOCsDOCs
Data Processing in JUNIPER
S1
S3
S2
Analytical
DBs
mpi
mpi
mpi
mpi
FPGA-enabled
nodes
Hadoop
HPC
Modelling in Juniper
www.modeliosoft.com 19
Models
High level
Architecture
(Nodes,Programs,
Streams…)
Real-time
constraints
Java
Code Code
Generation (+MPI initialization, communication, etc)
Reverse
Engineering
Schedulability
Analysis
Tool
(in progress)
Scheduling
Advisor
(in progress)
Measurements &
Advice
Deployment
Scripts
(in progress)
ConfigurationModel
Export
Code
Generation
Mapping Programming Model, UML and MARTE
www.modeliosoft.com 20
JUNIPER
Program
Channel
Cloud Node
Programming
Model
UML MARTE
Modelling the application and real-time constraints
www.modeliosoft.com 21
Real-time constrains
- response time
- bandwidth
Big Data flow
JUNIPER
Programs
Modelling the hardware infrastructure at a high level
www.modeliosoft.com 22
Cloud Node
CPU with 4 cores Hard drive
MPI code generation
www.modeliosoft.com 23
Code
Generation
JUNIPER
Application
Model
PETAFUEL CASE STUDY
www.modeliosoft.com 24
Risk: $45 million in half day
www.modeliosoft.com 25
Master Card debit card approval within 4 sec
www.modeliosoft.com 26
petaFuel -transactionapprovalwithin4 seconds
Transactions
Events
Stream
HistoricalData
30GB
Basic
Checks
FraudPatterns
Detection
Transaction
Approval
Decision
Juniper application model
www.modeliosoft.com 27
Deployment model
www.modeliosoft.com 28
MPI code generation
www.modeliosoft.com 29
public class EventProcessor {
public static final int RANK = 1;
public static IEventProcessor iEventProcessorImpl = new IEventProcessor() {
@Override
public void process(Event event) {
String key = getKeyFromTimestamp(event.getTimestamp());
String value = keyValueStoreIKeyValueStore.find(key);
if (value == null) {
keyValueStoreIKeyValueStore.put(key, "1");
} else {
int count = Integer.parseInt(value);
keyValueStoreIKeyValueStore.put(key, ""+(count+1);
}
}
…
};
…
public static void main(final String[] args) {
MPI.Init(args);
…
MPI.Finalize();
}
…
}
CONCLUSIONS
www.modeliosoft.com 30
Juniper trade-offs
www.modeliosoft.com 31
JUNIPER
Criteria Hadoop MPI
Communication HDFS - file system (httpd) HPC cluster interconnect (Infiniband)
Data flow Map Reduce Modeling + MPI comms
Parallelization Automatic Manually based on domain decomposition
Response time guaranties None Real-time for single node
Stages in multi-format No Any (incl. Hadoop + FPGA)
Hardware Commodity cluster HP cluster
Price € €€€
Skills + ++++
Customers General audience Critical systems
Prospects – more work
Work in progress
UML based language
• MPI Communication
• Timing properties
• Deployment
petaFuel case study
Future work
Modelling payload
Integrating schedulability
Running final evaluations
Final release
www.modeliosoft.com 32
Questions?
Andrey Sadovykh
Marcos Almeida
SOFTEAM | ModelioSoft
{name.surname}@softeam.fr
SOFTEAM R&D Web Site:
http://rd.softeam.com
Modelio Web Site :
http://www.modelio.org
JUNIPER Web Site :
http://www.juniper-project.org
www.modeliosoft.com 33
*
*for your questions

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JUNIPER: Towards Modeling Approach Enabling Efficient Platform for Heterogeneous Big Data Analysis

  • 1. www.modeliosoft.com Towards Modeling Approach Enabling Efficient Platform for Heterogeneous Big Data Analysis Andrey.Sadovykh@softeam.fr 1
  • 3. 20 ME 2006 17,5 ME 2005 70 ME 2013 Paris Rennes Nantes Sophia SOFTEAM – a French IT services / Software vendor •SOFTEAM, a growing company  25 years’ experience  850 experts  Regular growth • Specialist in OO technologies, new architectures, methodologies • Banking, Defense, Telecom, … www.modeliosoft.com 3 23 ME 2008
  • 4. Modelio for Software and System Engineering • UML editor with 20 years’ history o CloudML o SysML o MARTE o Code generation o Documentation o Teamwork www.modeliosoft.com 4 • Available under open source at Modelio.org
  • 6. It is all about models … Starting with UML www.modeliosoft.com 6 Requirements UML Use Cases Architecture UML Components and Classes Design Refined Classes or Domain Specific Language Implementation Code generation Java, C++, Frameworks
  • 8. Typical example: Control system for a frigate • 800+ components • Developed by 100+ engineers • 1M+ LOC • MDD fosters Productivity and Quality with o Code generation o Components reuse o Tracing o Automation www.modeliosoft.com 8
  • 9. Curious DSL example: Ruby on Rails Haml HTML %br{:clear => left’} <br clear=”left”/> %p.foo Hello <p class=”foo”>Hello</p> %p#foo Hello <p id=”foo”>Hello</p> .foo <div class=”foo”>...</div> #foo.bar <div id=”foo” class=”bar”>...</div> www.modeliosoft.com 9 Feature: User can manually add movie Scenario: Add a movie Given I am on the RottenPotatoes home page When I follow "Add new movie" Then I should be on the Create New Movie page When I fill in "Title" with "Men In Black" And I should see "Men In Black" Cucumber and Capybara HAML
  • 10. What do we get from MDD? Pros • Design once, deploy everywhere! • Write your transformation once, transform anything! Cons • Transformations are hard to write… • How to make sure they are CORRECT? i.e. – Is there any data/semantic loss? www.modeliosoft.com 10
  • 12. Volume, variety, velocity 1. @-mails sent every second : 2,9 million 2. Video uploaded to YouTube every minute: 25 hours 3. Data processed by Google every day: 24 petabytes 4. Tweets per day: 50 million 5. Products ordered on Amazon per second: 73 items www.modeliosoft.com 12
  • 13. Only 0,5 % of data is analyzed • In 2012, 2 837EB generated - just 0,5% actually analyzed. That still amounts to 14EB (or 14.185 million terabytes) Source: IDC & EMC www.modeliosoft.com 13
  • 14. SQL or Hadoop www.modeliosoft.com 14 Do you have some data and a problem to solve? yes Data fits in memory? *Inspired by: Aaron Cordova Data fits on single RAID array? Tons of options. Don’t need database or Hadoop yes no no yes Solvable with SQL? Use MySQL yes Can you program? Write a prog. yes no no Dead end
  • 15. SQL or Hadoop (continued) www.modeliosoft.com 15 * Inspired by: Aaron Cordova Data fits on single RAID array? no Have lots of money? Solvable with Oracle SQL? no Buy a SAN, Use Oracle yes Do you have a PhD in parallel prog. ? no Roll your own MPI solution yes Solvable using MapReduce? no yes Can you program MapReduce jobs? Write MapReduce on Hadoop? yes no Dead end no
  • 16. Challenges Hadoop MapReduce is the major trend Success relies on personnel programming skills Many problems are not solvable with Hadoop. Real-time? MPI for high performance computing is an option when you have a lots of money and a PhD www.modeliosoft.com 16
  • 18. JUNIPER integrates Big Data technologies over MPI www.modeliosoft.com 18 DOCs StreamsDBs Data Processing Stage 1 Stage N Business Intelligence Analytical DBs Visualization dbdb DOCsDOCs Data Processing in JUNIPER S1 S3 S2 Analytical DBs mpi mpi mpi mpi FPGA-enabled nodes Hadoop HPC
  • 19. Modelling in Juniper www.modeliosoft.com 19 Models High level Architecture (Nodes,Programs, Streams…) Real-time constraints Java Code Code Generation (+MPI initialization, communication, etc) Reverse Engineering Schedulability Analysis Tool (in progress) Scheduling Advisor (in progress) Measurements & Advice Deployment Scripts (in progress) ConfigurationModel Export Code Generation
  • 20. Mapping Programming Model, UML and MARTE www.modeliosoft.com 20 JUNIPER Program Channel Cloud Node Programming Model UML MARTE
  • 21. Modelling the application and real-time constraints www.modeliosoft.com 21 Real-time constrains - response time - bandwidth Big Data flow JUNIPER Programs
  • 22. Modelling the hardware infrastructure at a high level www.modeliosoft.com 22 Cloud Node CPU with 4 cores Hard drive
  • 23. MPI code generation www.modeliosoft.com 23 Code Generation JUNIPER Application Model
  • 25. Risk: $45 million in half day www.modeliosoft.com 25
  • 26. Master Card debit card approval within 4 sec www.modeliosoft.com 26 petaFuel -transactionapprovalwithin4 seconds Transactions Events Stream HistoricalData 30GB Basic Checks FraudPatterns Detection Transaction Approval Decision
  • 29. MPI code generation www.modeliosoft.com 29 public class EventProcessor { public static final int RANK = 1; public static IEventProcessor iEventProcessorImpl = new IEventProcessor() { @Override public void process(Event event) { String key = getKeyFromTimestamp(event.getTimestamp()); String value = keyValueStoreIKeyValueStore.find(key); if (value == null) { keyValueStoreIKeyValueStore.put(key, "1"); } else { int count = Integer.parseInt(value); keyValueStoreIKeyValueStore.put(key, ""+(count+1); } } … }; … public static void main(final String[] args) { MPI.Init(args); … MPI.Finalize(); } … }
  • 31. Juniper trade-offs www.modeliosoft.com 31 JUNIPER Criteria Hadoop MPI Communication HDFS - file system (httpd) HPC cluster interconnect (Infiniband) Data flow Map Reduce Modeling + MPI comms Parallelization Automatic Manually based on domain decomposition Response time guaranties None Real-time for single node Stages in multi-format No Any (incl. Hadoop + FPGA) Hardware Commodity cluster HP cluster Price € €€€ Skills + ++++ Customers General audience Critical systems
  • 32. Prospects – more work Work in progress UML based language • MPI Communication • Timing properties • Deployment petaFuel case study Future work Modelling payload Integrating schedulability Running final evaluations Final release www.modeliosoft.com 32
  • 33. Questions? Andrey Sadovykh Marcos Almeida SOFTEAM | ModelioSoft {name.surname}@softeam.fr SOFTEAM R&D Web Site: http://rd.softeam.com Modelio Web Site : http://www.modelio.org JUNIPER Web Site : http://www.juniper-project.org www.modeliosoft.com 33 * *for your questions