Cédric Fauvet
Cedric.fauvet@neotechnology.com
Twitter Francophone: @Neo4jFr
1
Confidential - Neo Technology, Inc.
New oppo...
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’...
The graph theory
An 840 : The horeseman’s problem
The Arab mathematician and chess master
al-Adli ar-Rumi solved the probl...
The graph theory
An 1735 The Königsberg’s 7 bridges
problem
How to pass through the bridges only once ?
Leonhard Euler
Swi...
The graph theory
2013: Today’s questions
• Collaboration
• Configuration management
• Geo mapping
• Molecule’s Interaction...
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’...
Neo Technology (Neo4j) Corporate Overview
• Neo4j founded 2000
• Headquartered in Palo Alto, California
• Engineering head...
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’...
Société
- Worldwide company
- 45 millions users, + 30 000 each days.
- Owner of the social networks
ApnaCircle (Inde) and ...
10
Company
- Worldwide leader in networking for the Internet
Solution
- Clustered Neo4j Enterprise architecture
- Part of ...
11
Company
- Leading telco provider in the Nordics
Solution
- Neo4j Enterprise solution
- Embedded + HA
- Replacing 10 yr-...
12
Company
-World wide leader in network infrastructure
-Large sales organization
Solution
-2x Highly Available Neo4j clus...
Use case – What’s in common ?
Alice
ACME
ACME
EMEA
Bob
Retail Co.
FooBar Inc.
Sales Rep
Sales Rep
Worked For
Worked For
So...
Use case – What’s a best path ?
Retail Co.
Bob
ACME
Steve
Jane
Liza
Pauline
William
Sales Rep
VP
CMO
Sales Rep
VP
Use case : Pattern matching
Fraud detection
Correspondance
Fraud detection
Pas de correspondance
Fraud detection
Graph navigation
Impact analysis
Start node
Impact analysis
Follow the relationships
Impact analysis
Evaluate each node
Impact analysis
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’...
Trend 1:Exponential
growth of data
0
250
500
750
1000
2007 2008 2009 2010
Exabytes of new unique digital information
size * connectivity = complexity
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’...
Neo4j
Tackles complex data:
– Large
– Densely-connected
– Semi-structured
Neo4j characteristics
• Fully ACID
– Including XA-compliant distributed two-phase commits
• High Availability / Read Scali...
Caractéristiques de Neo4j
• Transactions Full ACID
– XA-compliant distributed two-phase commits
• Haute disponibilité / Sc...
Agenda
• The gaph theory
• About Neo Technology
• Uses cases
• Vision du marché
• The Neo4j Technology
• Cypher the Neo4j’...
() --> ()
Cypher the Neo4j’s « SQL »
Based on ACSII-Art
(A) --> (B)
A B
Cypher the Neo4j’s « SQL »
Each node have a identifier
A -[:LOVES]-> B
LOVES
A B
Cypher the Neo4j’s « SQL »
Relationship
A --> B --> C
A B C
Cypher the Neo4j’s « SQL »
You can traverse the graph
A -[*]-> B
A B
A B
A B
Cypher the Neo4j’s « SQL »
You can dynamically traverse the graph
Cypher the Neo4j’s « SQL »
The friend of friend query
START john=node:node_auto_index(name = 'John')
MATCH john-[:friend]-...
Thank you
Let’s move forward together !
Cédric Fauvet Your contact in France and switzerland
E-mail : Cedric.fauvet@neotec...
New opportunities for connected data : Neo4j the graph database
New opportunities for connected data : Neo4j the graph database
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  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • Social networksRecommendations enginesBusiness intelligenceGeospatial applicationsMDMNetwork and systems managementProduct catalogueWeb analyticsIndexing your slow RDBMS
  • New opportunities for connected data : Neo4j the graph database

    1. 1. Cédric Fauvet Cedric.fauvet@neotechnology.com Twitter Francophone: @Neo4jFr 1 Confidential - Neo Technology, Inc. New opportunities for connected data : Neo4j the graph database
    2. 2. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
    3. 3. The graph theory An 840 : The horeseman’s problem The Arab mathematician and chess master al-Adli ar-Rumi solved the problem.
    4. 4. The graph theory An 1735 The Königsberg’s 7 bridges problem How to pass through the bridges only once ? Leonhard Euler Swiss mathematician
    5. 5. The graph theory 2013: Today’s questions • Collaboration • Configuration management • Geo mapping • Molecule’s Interaction (Biology) • Impact analysis • Master Data Management • Product management • Recommendation • Social
    6. 6. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
    7. 7. Neo Technology (Neo4j) Corporate Overview • Neo4j founded 2000 • Headquartered in Palo Alto, California • Engineering headquarter in Malmö, Sweden • Employees based in France, Germany, UK, Sweden, US, and Malaysia • 24/7 support on global basis • 100,000+ users • F500 customers such as Adobe, Cisco, Deutsche Telecom, Telenor, Deutsche Post, SFR, Lockheed Martin, and others • SI partners such as Accenture and dozens of local SI boutiques • Technology partners such as VMware, Informatica and Microsoft • Leader in the Graph Database arena • Mission: Help the world to make sense of data
    8. 8. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
    9. 9. Société - Worldwide company - 45 millions users, + 30 000 each days. - Owner of the social networks ApnaCircle (Inde) and Tianji (Chine) Problème Viadeo, integrated Neo4j as their backend database, to store all of their users and relationships. When their network expanded to a level that their traditional MySQL database couldn’t handle, Viadeo experienced performance and storage issues that would not perform at the rate the company was growing. Etude de cas: Réseau social Bénéfices & time frame - Real time recomendation with Neo4j. - Project timeframe = 8 weeks Solution Integrating Neo4j, Viadeo has highly accelerated their system in two ways. Neo4j increased Viadeo’s performance by requiring less storage space andless time to restructure the graph.
    10. 10. 10 Company - Worldwide leader in networking for the Internet Solution - Clustered Neo4j Enterprise architecture - Part of a larger infrastructure solution - Multi-region AWS deployment - Neo4j selected in competition with custom solution and Oracle Benefits & time frame - Highly flexible data analysis - Sub-second results for large, densely-connected datasets - User experience - competitive advantage - 12 month project Problem definition - Massive amounts of data tied to members, user groups, member content, etc. all interconnected - Need to infer collaborative relationships based on user-generated content Case study: Web/ISV - social collaboration Adobe
    11. 11. 11 Company - Leading telco provider in the Nordics Solution - Neo4j Enterprise solution - Embedded + HA - Replacing 10 yr-old Oracle, Berkeley DB and a mainframe environment Problem definition - Need: Reliable access control administration system for 5mio customers, subscriptions and agreements - Complex dependencies between groups, companies, individuals, accounts, products, subscriptions, services and agreements - Broad and deep graphs (master customers with 1000s of customers, subscriptions & agreements) Case study: Telco Telenor Benefits & time frame - Flexible and dynamic architecture - Exceptional performance -Low cost compared to alternatives -Extensible data model supports new applications and features
    12. 12. 12 Company -World wide leader in network infrastructure -Large sales organization Solution -2x Highly Available Neo4j clusters -One live cluster and one backup / hot spare cluster at a different datacenter -Total: 6 Embedded Enterprise Neo4j DBs Benefits & timeframe -Real time overview of sales accounts and owners -The ability to model complex rules for account ownership -Direct commissioning computation through the entire sales organization ->12 month development and rollout Problem definition -Intricate rules governing ownership of sales accounts -Complex rules for sales commissions -Queries complicated to structure with RDBMS -Oracle performance not good enough for online account management Case study: Sales account management Cisco
    13. 13. Use case – What’s in common ? Alice ACME ACME EMEA Bob Retail Co. FooBar Inc. Sales Rep Sales Rep Worked For Worked For Sold To
    14. 14. Use case – What’s a best path ? Retail Co. Bob ACME Steve Jane Liza Pauline William Sales Rep VP CMO Sales Rep VP
    15. 15. Use case : Pattern matching Fraud detection
    16. 16. Correspondance Fraud detection
    17. 17. Pas de correspondance Fraud detection
    18. 18. Graph navigation Impact analysis
    19. 19. Start node Impact analysis
    20. 20. Follow the relationships Impact analysis
    21. 21. Evaluate each node Impact analysis
    22. 22. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
    23. 23. Trend 1:Exponential growth of data 0 250 500 750 1000 2007 2008 2009 2010 Exabytes of new unique digital information
    24. 24. size * connectivity = complexity
    25. 25. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
    26. 26. Neo4j Tackles complex data: – Large – Densely-connected – Semi-structured
    27. 27. Neo4j characteristics • Fully ACID – Including XA-compliant distributed two-phase commits • High Availability / Read Scaling through master-slave replication with master failover • In-memory speeds with warm caches while maintaining full ACID • Cypher query language and Java APIs
    28. 28. Caractéristiques de Neo4j • Transactions Full ACID – XA-compliant distributed two-phase commits • Haute disponibilité / Scalabilité* – master-slave réplication avec master Fail-over – * Lecture • Hautes performance en mémoire – Caches évolués full ACID • Langage des requêtes – Cypher – Java APIs – JDBC – Rest API – Ruby
    29. 29. Agenda • The gaph theory • About Neo Technology • Uses cases • Vision du marché • The Neo4j Technology • Cypher the Neo4j’s « SQL »
    30. 30. () --> () Cypher the Neo4j’s « SQL » Based on ACSII-Art
    31. 31. (A) --> (B) A B Cypher the Neo4j’s « SQL » Each node have a identifier
    32. 32. A -[:LOVES]-> B LOVES A B Cypher the Neo4j’s « SQL » Relationship
    33. 33. A --> B --> C A B C Cypher the Neo4j’s « SQL » You can traverse the graph
    34. 34. A -[*]-> B A B A B A B Cypher the Neo4j’s « SQL » You can dynamically traverse the graph
    35. 35. Cypher the Neo4j’s « SQL » The friend of friend query START john=node:node_auto_index(name = 'John') MATCH john-[:friend]->()-[:friend]->fof RETURN john, fof
    36. 36. Thank you Let’s move forward together ! Cédric Fauvet Your contact in France and switzerland E-mail : Cedric.fauvet@neotechnology.com French speaking Twitter : @Neo4jFr French speaking community : meetup.com/graphdb-france
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