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Spring Data Neo4j Intro SpringOne 2012

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The document discusses Spring Data Neo4j, focusing on its integration with graph databases like Neo4j, and highlights its capabilities for managing connected data through a schema-free property graph. It covers features such as annotation-based programming, repository support, and the Cypher query language, offering insights into performance comparisons with relational databases. Additionally, the document showcases example applications and various use cases, such as social media data and online job search evolution.

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Spring Data Neo4j Michael Hunger - @mesirii Emil Eifrem - @emileifrem Neo Technology #neo4j © 2012 SpringOne 2GX. All rights reserved. Do not distribute without permission.
Twitter example To become part of todays Twitter-Graph
Twitter example To become part of todays Twitter-Graph please tweet with #s2gx #neo4j
Twitter example To become part of todays Twitter-Graph please tweet with #s2gx #neo4j ✓your attendance ✓questions ✓remarks ✓....
Agenda 1. Connected Data, Graph Databases and Neo4j 2. Spring Data Neo4j 3. Twitter-Graph - Sample App 4. Summary 5. Q & A
The Evolution of Web Search 4
The Evolution of Web Search Pre-1999 WWW Indexing 4
The Evolution of Web Search Pre-1999 1999 - 2012 WWW Indexing Google Invents PageRank 4
The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph 4
The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph Atomic Data 4
The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph Simple Atomic Data Connected Data 4
The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph Simple Rich Atomic Data Connected Data Connected Data 4
The Evolution of Online Job Search 5
The Evolution of Online Job Search 2010-11 Resume Scoring Atomic Data 5
The Evolution of Online Job Search 2010-11 2011-12 Resume Scoring Social Job Search Atomic Data Connected Data 5
The Evolution of Web Browsing 6
The Evolution of Web Browsing Pre-2012 Explicit Browsing Atomic Data 6
The Evolution of Web Browsing Pre-2012 2012+ Explicit Browsing Connected Browsing ? Atomic Data Connected Data 6
Other Markets In Evolution Core Industries Finance & Datacom / Web / ISV & Use Cases: Insurance Telecom Network /Cloud Mgmt MDM Social Geo 7
Other Markets In Evolution Core Industries Finance & Datacom / Life Media & Education, Government, Web / ISV Logistics Aerospace, & Use Cases: Insurance Telecom Sciences Publishing Not-for- Gaming, ... Profit Network / Cloud Mgmt MDM Social Geo Resource Auth & Access Control Content Management Recommend- ations Data Center Management Fraud Detection, ... 7
Everyone talks about graphs 4
Everyone talks about graphs Facebook Open Graph 4
Everyone talks about graphs Facebook Open Graph 4
Graph Databases
Connected data in a relational database 10
Connected data in a relational database 10
Connected data in a relational database foo 10
Connected data in a relational database foo bar 10
Connected data in a relational database foo foo_bar bar 10
Connected data in a relational database foo foo_bar bar 10
Connected data in a relational database foo foo_bar bar 10
Connected data in a relational database 10
Connected data in a relational database 10
Connected data in a relational database 10
Connected data in a relational database 10
Connected data in a relational database 10
Connected data in a relational database 10
The Property Graph model 11
The Property Graph model Em Joh il a n knows knows Alli Tob Lar son ias knows s knows And And knows knows rea rés s knows knows knows Pet Miic Mc knows Ian er knows a a knows knows De Mic lia h ael 11
The Property Graph model Em Joh il a n knows knows Alli Tob Lar son ias knows s Nodes And knows And knows knows rea rés s knows knows knows Pet Miic Mc knows Ian er knows a a knows knows De Mic lia h ael 11
The Property Graph model Em Joh il a n knows knows Alli Tob Lar son ias knows s Nodes And knows And knows knows rea rés s knows knows knows Pet Miic Mc knows Ian er knows a a knows knows De Mic lia h ael Relationships 11
The Property Graph model E Joh Em mil Joh a a n il n knows knows knows knows AllAlliso iso n Tob Tobia knows Lar Lar s Nodes n ias s knows s knows knows An And And And knows knows knows knows dre re rés rés as as knows knows knows knows knowsknows Pet e Pe t Miic M M c Mi knows Ian Ian r er knows knows a ica a ca knows knows knows knows knows De D Mi c M lia el hae ich ia l ae Relationships l Properties (each a key+value) + Indexes (for easy look-ups) 11
Whiteboard friendliness 4
Whiteboard friendliness Peter Andreas Allison Emil 4
Whiteboard friendliness Peter Andreas knows knows knows Allison knows Emil 4
Whiteboard friendliness Peter Andreas knows knows knows Allison knows Emil 4
Great! But what about performance? 13
Great! But what about performance? 13
Great! But what about performance? • a sample social graph 13
Great! But what about performance? • a sample social graph –with ~1,000 persons 13
Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person 13
Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 13
Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O 13
Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 13
Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 13
Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 13
Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 14
How do I query this graph? // lookup starting point in an index START user=node:User(name start n=node:People(name ==‘Andreas’) ‘Andreas’) MATCH (user)-[:HAS_SENT]->(tweet)-[:TAGGED_BY]->(tag) RETURN tag And rea s
How do I query this graph? // lookup starting point in an index START user=node:User(name start n=node:People(name ==‘Andreas’) ‘Andreas’) MATCH (user)-[:HAS_SENT]->(tweet)-[:TAGGED_BY]->(tag) RETURN tag And rea s
4
(Neo4j) -[:IS_A]-> (Graph Database) 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions - scalable: 34 billions nodes and relationships 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions - scalable: 34 billions nodes and relationships - fast with millons traversals / second 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions - scalable: 34 billions nodes and relationships - fast with millons traversals / second - Server with REST API, or Embeddable on the JVM 4
(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions - scalable: 34 billions nodes and relationships - fast with millons traversals / second - Server with REST API, or Embeddable on the JVM - higher-performance with High-Availability (read scaling) 4
(Cypher) -[:IS_A]-> (Graph Query Language) ๏ Declarative query language • Describe what you want, not how • Based on pattern matching ๏ Example: START tag=node:Tag(tag=”s12gx”), # index lookup MATCH sender-[:SENDER]->tweet-[:TAG]->tag WHERE new_friends.age > 18 RETURN sender.name, count(*) ORDER BY count(*) DESC LIMIT 10 4
(Neo4j) -[:LOVES]-> (Our Customers) 19
(Neo4j) -[:LOVES]-> (Our Customers) 19
Spring Data Neo4j
Spring Data Neo4j ๏ Spring Data: VMWare/SpringSource initiative to give Spring developers easy access to the emerging world of NOSQL ๏ Spring Data Neo4j is the integration library for Neo4j ๏ Simple programming model: annotation-based programming for applications with rich domains ๏ Always possible to drop down to core Neo4j-API ๏ simple and powerful repository abstraction ๏ two mapping modes ๏ Neo4j-Server support
Spring Data Neo4j features • Annotation-based programming model • Spring Data Commons Repository support • Neo4j Query and Traversal support – on dynamic fields and via repository methods • Neo4j indexing support (includes fulltext and numeric range queries) • Entity types stored in the graph database as well • Dynamic type projection (duck typing) • Cross-store support for partial JPA / graph entities • Support for JSR-303 (bean validation) • Support for the Neo4j Server (remote server and in server extension) • Neo4jTemplate with exception translation, optional transaction management and more 22
Classic Neo4j domain class public class Tag { private final Node underlyingNode; Tag( final Node node ) { underlyingNode = node; } public Node getUnderlyingNode() { return underlyingNode; } public final String getName() { return (String) underlyingNode.getProperty( “name” ); } public void setName( final String name ) { underlyingNode.setProperty( “name”, name ); } }
Spring Data Neo4j domain class @NodeEntity public class Tag { @GraphId private Long id; private String name; }
Spring Data Neo4j domain class @NodeEntity public class Tag { @GraphId private Long id; @Indexed(unique = true) private String name; }
Defining entity classes
Defining entity classes • @NodeEntity • Represents a node in the graph • Fields saved as properties on node • References stored as relationships between nodes • Instantiated using Java ‘new’ keyword, like any POJO • Also returned by lookup mechanisms • Type information stored in the graph
Defining entity classes
Defining entity classes • @RelationshipEntity • Represents a relationship in the graph • Fields saved as properties on relationship • Special fields for start- and end-nodes • Only returned by lookup methods
Tweet domain class @NodeEntity public class Tweet { @GraphId private Long id; @Indexed(unique=true) Long tweetId; String text; @Fetch User sender; // eager loading @RelatedTo(type="TAGGED") Collection<Tag> tags; @RelatedTo(type="MENTIONS") Set<User> mentions; @RelatedTo(type="SOURCE") Tweet source; }
Interface based Repositories • based on Repository infrastructure in Spring Data Commons • just define the interface and the namespace configuration • provide out-of-the-box support for –CRUD-Operations –Index-Lookups –Traversal-Execution –Annotated Graph-Queries (Cypher, Gremlin) –Derived Queries –Spatial Queries • extensible via custom methods with provided implementations 28
Repositories interface TweetRepository extends GraphRepository<Tweet> { Tweet findByTweetId(String id); Collection<Tweet> findByTagsName(String name); @Query("start user=node({0}) match user<-[:SENDER]-tweet-[:TAGGED]->tag return distinct tag.name") Page<String> findUserTags(User user); } <neo4j:repositories base-package=“com.example.repositories„/> @Controller public class TwitterController { @Autowired TweetRepository tweetRepository; @RequestMapping(value = "/tweet/{id}",...) public String show(Model model, @PathVariable String id) { Tweet tweet = tweetRepository.findByTweetId(id); model.addAttribute("tweet", tweet); return "/tweet/show"; }} 29
Neo4j-Template (I) • well known Spring Template Pattern • Resource / Transaction Management • Convenience Methods • Nodes and Entities handling & conversion • Fluent Query Result Handling • Works also via REST with Neo4j-Server • Exception Translation 30
Neo4j-Server-Support <bean id="restGraphDatabaseService" class="org.sf.data.neo4j.rest.SpringRestGraphDatabase"> <constructor-arg value="http://localhost:7473/db/data" /> </bean> <neo4j:config graphDatabaseService="restGraphDatabaseService"/> • drop-in replacement for the embedded GraphDatabase • works transparently with POJO-Entity-Mapping and Neo4j-Template 31
Sample App -uses Spring Social (minimally) -Simple Domain: Tweets, Users, Tags -connected Entities -Repositories, Service -standalone App with -embedded Neo4j-Database -Neo4j-Server
Whiteboard friendly - Abstract :Tweet :Tweet
Whiteboard friendly - Abstract :User PO ST ED :Tweet :User :Tweet POSTED
Whiteboard friendly - Abstract :User PO ST ED :Tweet S ION NT ME :User :Tweet POSTED
Whiteboard friendly - Abstract :User PO ST ED TAGGED :Tag :Tweet TAGG ED :Tag S ION ED NT G ME G :User :Tweet TA POSTED
Whiteboard friendly - Example Attending the #S2GX #SDN session by @mesirii Looking forward to my #SDN session
Whiteboard friendly - Example @peterneubauer PO ST ED Attending the #S2GX #SDN session by @mesirii Looking forward to @mesirii POSTED my #SDN session
Whiteboard friendly - Example @peterneubauer PO ST ED Attending the #S2GX #SDN session by @mesirii S ION NT ME Looking forward to @mesirii POSTED my #SDN session
Whiteboard friendly - Example @peterneubauer PO ST ED Attending the TAGGED #S2GX #S2GX #SDN TAGG session by ED @mesirii #SDN S ION ED NT G ME Looking forward to TA G @mesirii POSTED my #SDN session
Twitter - Graph Data Model 4
Spring Data Neo4j - Sample Domain Class & Service 4
Neo4j - Sample Graph Query START tag1=node:Tag("tag:*obama*") > MATCH tag1<-[:TAG]-tweet-[:sender]->user, user<-[:sender]-tweet2-[:TAG]->tag2 > WHERE tag2.tag =~ "(?i).*romney.*" > RETURN count(*) as cnt, tag1.tag,tag2.tag +--------------------------------------+ > ORDER BY count(*) DESC | cnt | tag1.tag | tag2.tag | > LIMIT 10; +--------------------------------------+ | 440 | "obama" | "voteromney" | | 148 | "obama" | "romneyryan2012" | | 65 | "obama" | "romney" | | 56 | "voteobama" | "romneyryan" | | 42 | "voteobama" | "romney" | +--------------------------------------+ 5 rows 194 ms 4
Neo4j - Twitter Sample App Code
Hubway Data Challenge - Import • Boston Bike Sharing Network • Import using Spring Data Neo4j • Visualization with Cypher & d3.js (Chord) http://bit.ly/neo_hubway 39
Cineasts.net - a social movie database Check Out: http://spring.neo4j.org/tutorial
Spring Data Neo4j Guidebook “Good Relationships” “I’m excited about Spring Data Neo4j.... makes working with Neo4j amazingly easy, and therefore has the potential to make you more successful as a developer.” Rod Johnson, founder of Spring - Spring Data Neo4j comes with a great Guide Book, featuring: - Forewords by Rod Johnson and Emil Eifrem - An easy to read, narrative tutorial walkthrough for cineasts.net - A comprehensive reference for all the details - Check it out: http://bit.ly/sd-book - Free e-book and printed
Spring Data Neo4j Guidebook “Good Relationships” “I’m excited about Spring Data Neo4j.... makes working with Neo4j amazingly easy, and therefore has the potential to make you more successful as a developer.” Rod Johnson, founder of Spring - Spring Data Neo4j comes with a great Guide Book, featuring: - Forewords by Rod Johnson and Emil Eifrem - An easy to read, narrative tutorial walkthrough for cineasts.net - A comprehensive reference for all the details - Check it out: http://bit.ly/sd-book - Free e-book and printed
O‘Reilly Spring Data Book „Modern Data Access for Enterprise Java“ • book by the Spring Data project leads • introduction to Spring Data & SD-repositories • covers all subprojects • available in O‘Reilly OFPS and O‘Reilly store (e-book) • http://bit.ly/sd-book
How do I get started? 4
How do I get started? ๏ Documentation 4
How do I get started? ๏ Documentation • http://spring.neo4j.org 4
How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes 4
How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book 4
How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships 4
How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j 4
How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j • http://neo4j.org/download 4
How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j • http://neo4j.org/download ๏ Participate • http://neo4j.org/forums • http://spring.neo4j.org/discussions 4
How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j • http://neo4j.org/download ๏ Participate • http://neo4j.org/forums • http://spring.neo4j.org/discussions • http://neo4j.meetup.com 4
How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j • http://neo4j.org/download ๏ Participate • http://neo4j.org/forums • http://spring.neo4j.org/discussions • http://neo4j.meetup.com • a session like this one ;) 4
GraphConnect Nov 6th 2012 San Francisco 50% dis CONNECT ABOUT | UPCOMING EVENTS | cou REGISTRATION | CONTACT nt a t our boo th WORLD’S LEADING GRAPH CONFERENCE SAN FRANCISCO 2012 4
Thank you ! Questions ? 4

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Spring Data Neo4j Intro SpringOne 2012

  • 1. Spring Data Neo4j Michael Hunger - @mesirii Emil Eifrem - @emileifrem Neo Technology #neo4j © 2012 SpringOne 2GX. All rights reserved. Do not distribute without permission.
  • 2. Twitter example To become part of todays Twitter-Graph
  • 3. Twitter example To become part of todays Twitter-Graph please tweet with #s2gx #neo4j
  • 4. Twitter example To become part of todays Twitter-Graph please tweet with #s2gx #neo4j ✓your attendance ✓questions ✓remarks ✓....
  • 5. Agenda 1. Connected Data, Graph Databases and Neo4j 2. Spring Data Neo4j 3. Twitter-Graph - Sample App 4. Summary 5. Q & A
  • 6. The Evolution of Web Search 4
  • 7. The Evolution of Web Search Pre-1999 WWW Indexing 4
  • 8. The Evolution of Web Search Pre-1999 1999 - 2012 WWW Indexing Google Invents PageRank 4
  • 9. The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph 4
  • 10. The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph Atomic Data 4
  • 11. The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph Simple Atomic Data Connected Data 4
  • 12. The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph Simple Rich Atomic Data Connected Data Connected Data 4
  • 13. The Evolution of Online Job Search 5
  • 14. The Evolution of Online Job Search 2010-11 Resume Scoring Atomic Data 5
  • 15. The Evolution of Online Job Search 2010-11 2011-12 Resume Scoring Social Job Search Atomic Data Connected Data 5
  • 16. The Evolution of Web Browsing 6
  • 17. The Evolution of Web Browsing Pre-2012 Explicit Browsing Atomic Data 6
  • 18. The Evolution of Web Browsing Pre-2012 2012+ Explicit Browsing Connected Browsing ? Atomic Data Connected Data 6
  • 19. Other Markets In Evolution Core Industries Finance & Datacom / Web / ISV & Use Cases: Insurance Telecom Network /Cloud Mgmt MDM Social Geo 7
  • 20. Other Markets In Evolution Core Industries Finance & Datacom / Life Media & Education, Government, Web / ISV Logistics Aerospace, & Use Cases: Insurance Telecom Sciences Publishing Not-for- Gaming, ... Profit Network / Cloud Mgmt MDM Social Geo Resource Auth & Access Control Content Management Recommend- ations Data Center Management Fraud Detection, ... 7
  • 22. Everyone talks about graphs Facebook Open Graph 4
  • 23. Everyone talks about graphs Facebook Open Graph 4
  • 25. Connected data in a relational database 10
  • 26. Connected data in a relational database 10
  • 27. Connected data in a relational database foo 10
  • 28. Connected data in a relational database foo bar 10
  • 29. Connected data in a relational database foo foo_bar bar 10
  • 30. Connected data in a relational database foo foo_bar bar 10
  • 31. Connected data in a relational database foo foo_bar bar 10
  • 32. Connected data in a relational database 10
  • 33. Connected data in a relational database 10
  • 34. Connected data in a relational database 10
  • 35. Connected data in a relational database 10
  • 36. Connected data in a relational database 10
  • 37. Connected data in a relational database 10
  • 38. The Property Graph model 11
  • 39. The Property Graph model Em Joh il a n knows knows Alli Tob Lar son ias knows s knows And And knows knows rea rés s knows knows knows Pet Miic Mc knows Ian er knows a a knows knows De Mic lia h ael 11
  • 40. The Property Graph model Em Joh il a n knows knows Alli Tob Lar son ias knows s Nodes And knows And knows knows rea rés s knows knows knows Pet Miic Mc knows Ian er knows a a knows knows De Mic lia h ael 11
  • 41. The Property Graph model Em Joh il a n knows knows Alli Tob Lar son ias knows s Nodes And knows And knows knows rea rés s knows knows knows Pet Miic Mc knows Ian er knows a a knows knows De Mic lia h ael Relationships 11
  • 42. The Property Graph model E Joh Em mil Joh a a n il n knows knows knows knows AllAlliso iso n Tob Tobia knows Lar Lar s Nodes n ias s knows s knows knows An And And And knows knows knows knows dre re rés rés as as knows knows knows knows knowsknows Pet e Pe t Miic M M c Mi knows Ian Ian r er knows knows a ica a ca knows knows knows knows knows De D Mi c M lia el hae ich ia l ae Relationships l Properties (each a key+value) + Indexes (for easy look-ups) 11
  • 44. Whiteboard friendliness Peter Andreas Allison Emil 4
  • 45. Whiteboard friendliness Peter Andreas knows knows knows Allison knows Emil 4
  • 46. Whiteboard friendliness Peter Andreas knows knows knows Allison knows Emil 4
  • 47. Great! But what about performance? 13
  • 48. Great! But what about performance? 13
  • 49. Great! But what about performance? • a sample social graph 13
  • 50. Great! But what about performance? • a sample social graph –with ~1,000 persons 13
  • 51. Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person 13
  • 52. Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 13
  • 53. Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O 13
  • 54. Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 13
  • 55. Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 13
  • 56. Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 13
  • 57. Great! But what about performance? • a sample social graph –with ~1,000 persons • average 50 friends per person • pathExists(a,b) limited to depth 4 • caches warmed up to eliminate disk I/O # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 14
  • 58. How do I query this graph? // lookup starting point in an index START user=node:User(name start n=node:People(name ==‘Andreas’) ‘Andreas’) MATCH (user)-[:HAS_SENT]->(tweet)-[:TAGGED_BY]->(tag) RETURN tag And rea s
  • 59. How do I query this graph? // lookup starting point in an index START user=node:User(name start n=node:People(name ==‘Andreas’) ‘Andreas’) MATCH (user)-[:HAS_SENT]->(tweet)-[:TAGGED_BY]->(tag) RETURN tag And rea s
  • 60. 4
  • 62. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: 4
  • 63. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph 4
  • 64. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data 4
  • 65. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: 4
  • 66. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions 4
  • 67. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions - scalable: 34 billions nodes and relationships 4
  • 68. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions - scalable: 34 billions nodes and relationships - fast with millons traversals / second 4
  • 69. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions - scalable: 34 billions nodes and relationships - fast with millons traversals / second - Server with REST API, or Embeddable on the JVM 4
  • 70. (Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database: - reliable with real ACID Transactions - scalable: 34 billions nodes and relationships - fast with millons traversals / second - Server with REST API, or Embeddable on the JVM - higher-performance with High-Availability (read scaling) 4
  • 71. (Cypher) -[:IS_A]-> (Graph Query Language) ๏ Declarative query language • Describe what you want, not how • Based on pattern matching ๏ Example: START tag=node:Tag(tag=”s12gx”), # index lookup MATCH sender-[:SENDER]->tweet-[:TAG]->tag WHERE new_friends.age > 18 RETURN sender.name, count(*) ORDER BY count(*) DESC LIMIT 10 4
  • 72. (Neo4j) -[:LOVES]-> (Our Customers) 19
  • 73. (Neo4j) -[:LOVES]-> (Our Customers) 19
  • 75. Spring Data Neo4j ๏ Spring Data: VMWare/SpringSource initiative to give Spring developers easy access to the emerging world of NOSQL ๏ Spring Data Neo4j is the integration library for Neo4j ๏ Simple programming model: annotation-based programming for applications with rich domains ๏ Always possible to drop down to core Neo4j-API ๏ simple and powerful repository abstraction ๏ two mapping modes ๏ Neo4j-Server support
  • 76. Spring Data Neo4j features • Annotation-based programming model • Spring Data Commons Repository support • Neo4j Query and Traversal support – on dynamic fields and via repository methods • Neo4j indexing support (includes fulltext and numeric range queries) • Entity types stored in the graph database as well • Dynamic type projection (duck typing) • Cross-store support for partial JPA / graph entities • Support for JSR-303 (bean validation) • Support for the Neo4j Server (remote server and in server extension) • Neo4jTemplate with exception translation, optional transaction management and more 22
  • 77. Classic Neo4j domain class public class Tag { private final Node underlyingNode; Tag( final Node node ) { underlyingNode = node; } public Node getUnderlyingNode() { return underlyingNode; } public final String getName() { return (String) underlyingNode.getProperty( “name” ); } public void setName( final String name ) { underlyingNode.setProperty( “name”, name ); } }
  • 78. Spring Data Neo4j domain class @NodeEntity public class Tag { @GraphId private Long id; private String name; }
  • 79. Spring Data Neo4j domain class @NodeEntity public class Tag { @GraphId private Long id; @Indexed(unique = true) private String name; }
  • 81. Defining entity classes • @NodeEntity • Represents a node in the graph • Fields saved as properties on node • References stored as relationships between nodes • Instantiated using Java ‘new’ keyword, like any POJO • Also returned by lookup mechanisms • Type information stored in the graph
  • 83. Defining entity classes • @RelationshipEntity • Represents a relationship in the graph • Fields saved as properties on relationship • Special fields for start- and end-nodes • Only returned by lookup methods
  • 84. Tweet domain class @NodeEntity public class Tweet { @GraphId private Long id; @Indexed(unique=true) Long tweetId; String text; @Fetch User sender; // eager loading @RelatedTo(type="TAGGED") Collection<Tag> tags; @RelatedTo(type="MENTIONS") Set<User> mentions; @RelatedTo(type="SOURCE") Tweet source; }
  • 85. Interface based Repositories • based on Repository infrastructure in Spring Data Commons • just define the interface and the namespace configuration • provide out-of-the-box support for –CRUD-Operations –Index-Lookups –Traversal-Execution –Annotated Graph-Queries (Cypher, Gremlin) –Derived Queries –Spatial Queries • extensible via custom methods with provided implementations 28
  • 86. Repositories interface TweetRepository extends GraphRepository<Tweet> { Tweet findByTweetId(String id); Collection<Tweet> findByTagsName(String name); @Query("start user=node({0}) match user<-[:SENDER]-tweet-[:TAGGED]->tag return distinct tag.name") Page<String> findUserTags(User user); } <neo4j:repositories base-package=“com.example.repositories„/> @Controller public class TwitterController { @Autowired TweetRepository tweetRepository; @RequestMapping(value = "/tweet/{id}",...) public String show(Model model, @PathVariable String id) { Tweet tweet = tweetRepository.findByTweetId(id); model.addAttribute("tweet", tweet); return "/tweet/show"; }} 29
  • 87. Neo4j-Template (I) • well known Spring Template Pattern • Resource / Transaction Management • Convenience Methods • Nodes and Entities handling & conversion • Fluent Query Result Handling • Works also via REST with Neo4j-Server • Exception Translation 30
  • 88. Neo4j-Server-Support <bean id="restGraphDatabaseService" class="org.sf.data.neo4j.rest.SpringRestGraphDatabase"> <constructor-arg value="http://localhost:7473/db/data" /> </bean> <neo4j:config graphDatabaseService="restGraphDatabaseService"/> • drop-in replacement for the embedded GraphDatabase • works transparently with POJO-Entity-Mapping and Neo4j-Template 31
  • 89. Sample App -uses Spring Social (minimally) -Simple Domain: Tweets, Users, Tags -connected Entities -Repositories, Service -standalone App with -embedded Neo4j-Database -Neo4j-Server
  • 90. Whiteboard friendly - Abstract :Tweet :Tweet
  • 91. Whiteboard friendly - Abstract :User PO ST ED :Tweet :User :Tweet POSTED
  • 92. Whiteboard friendly - Abstract :User PO ST ED :Tweet S ION NT ME :User :Tweet POSTED
  • 93. Whiteboard friendly - Abstract :User PO ST ED TAGGED :Tag :Tweet TAGG ED :Tag S ION ED NT G ME G :User :Tweet TA POSTED
  • 94. Whiteboard friendly - Example Attending the #S2GX #SDN session by @mesirii Looking forward to my #SDN session
  • 95. Whiteboard friendly - Example @peterneubauer PO ST ED Attending the #S2GX #SDN session by @mesirii Looking forward to @mesirii POSTED my #SDN session
  • 96. Whiteboard friendly - Example @peterneubauer PO ST ED Attending the #S2GX #SDN session by @mesirii S ION NT ME Looking forward to @mesirii POSTED my #SDN session
  • 97. Whiteboard friendly - Example @peterneubauer PO ST ED Attending the TAGGED #S2GX #S2GX #SDN TAGG session by ED @mesirii #SDN S ION ED NT G ME Looking forward to TA G @mesirii POSTED my #SDN session
  • 98. Twitter - Graph Data Model 4
  • 99. Spring Data Neo4j - Sample Domain Class & Service 4
  • 100. Neo4j - Sample Graph Query START tag1=node:Tag("tag:*obama*") > MATCH tag1<-[:TAG]-tweet-[:sender]->user, user<-[:sender]-tweet2-[:TAG]->tag2 > WHERE tag2.tag =~ "(?i).*romney.*" > RETURN count(*) as cnt, tag1.tag,tag2.tag +--------------------------------------+ > ORDER BY count(*) DESC | cnt | tag1.tag | tag2.tag | > LIMIT 10; +--------------------------------------+ | 440 | "obama" | "voteromney" | | 148 | "obama" | "romneyryan2012" | | 65 | "obama" | "romney" | | 56 | "voteobama" | "romneyryan" | | 42 | "voteobama" | "romney" | +--------------------------------------+ 5 rows 194 ms 4
  • 101. Neo4j - Twitter Sample App Code
  • 102. Hubway Data Challenge - Import • Boston Bike Sharing Network • Import using Spring Data Neo4j • Visualization with Cypher & d3.js (Chord) http://bit.ly/neo_hubway 39
  • 103. Cineasts.net - a social movie database Check Out: http://spring.neo4j.org/tutorial
  • 104. Spring Data Neo4j Guidebook “Good Relationships” “I’m excited about Spring Data Neo4j.... makes working with Neo4j amazingly easy, and therefore has the potential to make you more successful as a developer.” Rod Johnson, founder of Spring - Spring Data Neo4j comes with a great Guide Book, featuring: - Forewords by Rod Johnson and Emil Eifrem - An easy to read, narrative tutorial walkthrough for cineasts.net - A comprehensive reference for all the details - Check it out: http://bit.ly/sd-book - Free e-book and printed
  • 105. Spring Data Neo4j Guidebook “Good Relationships” “I’m excited about Spring Data Neo4j.... makes working with Neo4j amazingly easy, and therefore has the potential to make you more successful as a developer.” Rod Johnson, founder of Spring - Spring Data Neo4j comes with a great Guide Book, featuring: - Forewords by Rod Johnson and Emil Eifrem - An easy to read, narrative tutorial walkthrough for cineasts.net - A comprehensive reference for all the details - Check it out: http://bit.ly/sd-book - Free e-book and printed
  • 106. O‘Reilly Spring Data Book „Modern Data Access for Enterprise Java“ • book by the Spring Data project leads • introduction to Spring Data & SD-repositories • covers all subprojects • available in O‘Reilly OFPS and O‘Reilly store (e-book) • http://bit.ly/sd-book
  • 107. How do I get started? 4
  • 108. How do I get started? ๏ Documentation 4
  • 109. How do I get started? ๏ Documentation • http://spring.neo4j.org 4
  • 110. How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes 4
  • 111. How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book 4
  • 112. How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships 4
  • 113. How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j 4
  • 114. How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j • http://neo4j.org/download 4
  • 115. How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j • http://neo4j.org/download ๏ Participate • http://neo4j.org/forums • http://spring.neo4j.org/discussions 4
  • 116. How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j • http://neo4j.org/download ๏ Participate • http://neo4j.org/forums • http://spring.neo4j.org/discussions • http://neo4j.meetup.com 4
  • 117. How do I get started? ๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships ๏ Get Neo4j • http://neo4j.org/download ๏ Participate • http://neo4j.org/forums • http://spring.neo4j.org/discussions • http://neo4j.meetup.com • a session like this one ;) 4
  • 118. GraphConnect Nov 6th 2012 San Francisco 50% dis CONNECT ABOUT | UPCOMING EVENTS | cou REGISTRATION | CONTACT nt a t our boo th WORLD’S LEADING GRAPH CONFERENCE SAN FRANCISCO 2012 4
  • 119. Thank you ! Questions ? 4

Editor's Notes