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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.

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Twitter example To become part of todays Twitter-Graph

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Twitter example To become part of todays Twitter-Graph please tweet with #s2gx #neo4j

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Twitter example To become part of todays Twitter-Graph please tweet with #s2gx #neo4j ✓your attendance ✓questions ✓remarks ✓....

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Agenda 1. Connected Data, Graph Databases and Neo4j 2. Spring Data Neo4j 3. Twitter-Graph - Sample App 4. Summary 5. Q & A

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The Evolution of Web Search 4

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The Evolution of Web Search Pre-1999 WWW Indexing 4

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The Evolution of Web Search Pre-1999 1999 - 2012 WWW Indexing Google Invents PageRank 4

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The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph 4

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The Evolution of Web Search Pre-1999 1999 - 2012 2012-? WWW Indexing Google Invents Google Launches the PageRank Knowledge Graph Atomic Data 4

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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

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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

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The Evolution of Online Job Search 5

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The Evolution of Online Job Search 2010-11 Resume Scoring Atomic Data 5

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The Evolution of Online Job Search 2010-11 2011-12 Resume Scoring Social Job Search Atomic Data Connected Data 5

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The Evolution of Web Browsing 6

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The Evolution of Web Browsing Pre-2012 Explicit Browsing Atomic Data 6

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The Evolution of Web Browsing Pre-2012 2012+ Explicit Browsing Connected Browsing ? Atomic Data Connected Data 6

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Other Markets In Evolution Core Industries Finance & Datacom / Web / ISV & Use Cases: Insurance Telecom Network /Cloud Mgmt MDM Social Geo 7

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Other Markets In EvolutionCore 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 GeoResource Auth & Access Control ContentManagementRecommend- ationsData CenterManagement FraudDetection, ... 7

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Everyone talks about graphs 4

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Everyone talks about graphs Facebook Open Graph 4

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Everyone talks about graphs Facebook Open Graph 4

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Graph Databases

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Connected data in a relational database 10

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Connected data in a relational database 10

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Connected data in a relational database foo 10

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Connected data in a relational database foo bar 10

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Connected data in a relational database foo foo_bar bar 10

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Connected data in a relational database foo foo_bar bar 10

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Connected data in a relational database foo foo_bar bar 10

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Connected data in a relational database 10

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Connected data in a relational database 10

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Connected data in a relational database 10

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Connected data in a relational database 10

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Connected data in a relational database 10

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Connected data in a relational database 10

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The Property Graph model 11

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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

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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

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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

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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

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Whiteboard friendliness4

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Whiteboard friendliness Peter Andreas Allison Emil4

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Whiteboard friendliness Peter Andreas knows knows knows Allison knows Emil4

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Whiteboard friendliness Peter Andreas knows knows knows Allison knows Emil4

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Great! But what about performance? 13

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Great! But what about performance? 13

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Great! But what about performance?• a sample social graph 13

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Great! But what about performance?• a sample social graph –with ~1,000 persons 13

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Great! But what about performance?• a sample social graph –with ~1,000 persons• average 50 friends per person 13

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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

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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

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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

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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

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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

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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

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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

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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

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4

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(Neo4j) -[:IS_A]-> (Graph Database)4

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(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database:4

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(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph4

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(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data4

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(Neo4j) -[:IS_A]-> (Graph Database) - A Graph Database: - a schema-free Property Graph - perfect for complex, highly connected data - A Graph Database:4

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(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 Transactions4

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(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 relationships4

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(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 / second4

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(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 JVM4

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(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

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(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 104

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(Neo4j) -[:LOVES]-> (Our Customers) 19

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(Neo4j) -[:LOVES]-> (Our Customers) 19

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Spring Data Neo4j

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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

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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

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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 ); } }

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Spring Data Neo4j domain class @NodeEntity public class Tag { @GraphId private Long id; private String name; }

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Spring Data Neo4j domain class @NodeEntity public class Tag { @GraphId private Long id; @Indexed(unique = true) private String name; }

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Defining entity classes

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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

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Defining entity classes

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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

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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; }

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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

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Repositoriesinterface 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„/>@Controllerpublic 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

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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 Translation30

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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

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Sample App -uses Spring Social (minimally) -Simple Domain: Tweets, Users, Tags -connected Entities -Repositories, Service -standalone App with -embedded Neo4j-Database -Neo4j-Server

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Whiteboard friendly - Abstract :Tweet :Tweet

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Whiteboard friendly - Abstract :User PO ST ED :Tweet :User :Tweet POSTED

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Whiteboard friendly - Abstract :User PO ST ED :Tweet S ION NT ME :User :Tweet POSTED

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Whiteboard friendly - Abstract :User PO ST ED TAGGED :Tag :Tweet TAGG ED :Tag S ION ED NT G ME G :User :Tweet TA POSTED

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Whiteboard friendly - Example Attending the #S2GX #SDN session by @mesirii Looking forward to my #SDN session

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Whiteboard friendly - Example @peterneubauer PO ST ED Attending the #S2GX #SDN session by @mesirii Looking forward to @mesirii POSTED my #SDN session

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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

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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

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Twitter - Graph Data Model4

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Spring Data Neo4j - Sample Domain Class & Service4

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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 ms4

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Neo4j - Twitter Sample App Code

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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

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Cineasts.net - a social movie databaseCheck Out: http://spring.neo4j.org/tutorial

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Spring Data Neo4j Guidebook “Good Relationships”“I’m excited about Spring Data Neo4j.... makes working with Neo4jamazingly easy, and therefore has the potential to make you more successfulas 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

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Spring Data Neo4j Guidebook “Good Relationships”“I’m excited about Spring Data Neo4j.... makes working with Neo4jamazingly easy, and therefore has the potential to make you more successfulas 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

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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

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How do I get started?4

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How do I get started?๏ Documentation4

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How do I get started?๏ Documentation • http://spring.neo4j.org4

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How do I get started?๏ Documentation • http://spring.neo4j.org • Dev-Notes4

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How do I get started?๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book4

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How do I get started?๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships4

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How do I get started?๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships๏ Get Neo4j4

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How do I get started?๏ Documentation • http://spring.neo4j.org • Dev-Notes • Spring Data Book • Good Relationships๏ Get Neo4j • http://neo4j.org/download4

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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/discussions4

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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.com4

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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

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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 20124

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Thank you ! Questions ?4