![1010
Cypher
(A) -[:LOVES]-> (B)
LOVES
AA BB
Graph Patterns
START A=node:person(name=“A”)
MATCH
RETURN B as lover
ASCII art](https://image.slidesharecdn.com/neo4j-130425185336-phpapp02/85/raph-Databases-with-Neo4j-Emil-Eifrem-10-320.jpg)
raph Databases with Neo4j – Emil Eifrem
- 1. 1 Neo4j is Teh Awesome Graph Database 101 Graph Database 101 1 @emileifrem #neo4j
- 2. 2 “So what’s a graph database?” 2
- 3. 33 “A traditional relational database may tell you the average age of everyone in this room...”
- 4. 44 “... but a graph database will tell you who is most likely to buy you a beer!”
- 5. 5 No. Srsly. ๏ Nodes ๏ Relationships ๏ Properties 5
- 6. 6 How fast is it? 6
- 7. 7 How fast is it? ๏ 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
- 8. 8 How fast is it? ๏ 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
- 9. 9 So how do you query it? 9
- 10. 1010 Cypher (A) -[:LOVES]-> (B) LOVES AA BB Graph Patterns START A=node:person(name=“A”) MATCH RETURN B as lover ASCII art
- 11. 11 // step 1: find starting point START andreas=node:persons(name = ‘Andreas’) // step 2: describe pattern and results START andreas=node:persons(name = ‘Andreas’) MATCH (andreas)-->()-->(foaf) RETURN foaf Example: Finding Friends of Friends 11 (andreas)
- 12. 12 Neo4j is a Graph Database ๏ A Graph Database: •a Property Graph with Nodes, Relationships •and Properties on both •perfect for complex, highly connected data ๏ A Graph Database: •reliable with real ACID Transactions •scalable: high availability clustering in Neo4j Enterprise •server with HTTP API, or embeddable on the JVM •high-performance with High-Availability (read scaling) 12
- 13. 13 Who’s using graphs today? 13 Accenture
- 14. 14 So how do you get your hands on Neo4j? 14 ๏ Option A: Download and install locally... •go to http://neo4j.org •click the shiny “Download Neo4j Now” button •expand the archive, read the readmes ๏ Or B...
- 15. 15 Graphs In The Cloud (BETA) // new to heroku? get help $ heroku help // create a new application $ heroku create intro-to-neo4j // add Neo4j heroku addons:add neo4j --app intro-to-neo4j // find out about the application heroku info --app intro-to-neo4j // find the Neo4j Webadmin heroku config --app intro-to-neo4j // done trying it out? remove the application heroku destroy --app intro-to-neo4j 15
- 16. 16 I needs thy help ๏ Cloud Devops Engineer (San Mateo, London or Malmö, SE) •Come help us build a world class graph database cloud platform! ๏ Director of Community North America (San Mateo) •Head up our developer outreach and evangelism in NA ๏ Developer Evangelist (San Mateo) •Preach graphs to silicon valley and the world 16
- 18. Core IndustriesCore Industries & Use Cases:& Use Cases: Web / ISVWeb / ISV Finance &Finance & InsuranceInsurance Datacom /Datacom / TelecomTelecom Network /Cloud MgmtNetwork /Cloud Mgmt MDMMDM SocialSocial GeoGeo Early Adopter Graph Database Segments
- 19. Core IndustriesCore Industries & Use Cases:& Use Cases: Web / ISVWeb / ISV Finance &Finance & InsuranceInsurance Datacom /Datacom / TelecomTelecom Network /Cloud MgmtNetwork /Cloud Mgmt MDMMDM SocialSocial GeoGeo Core IndustriesCore Industries & Use Cases:& Use Cases: Web / ISVWeb / ISV Finance &Finance & InsuranceInsurance Datacom /Datacom / TelecomTelecom LogisticsLogistics LifeLife SciencesSciences Media &Media & PublishingPublishing Education,Education, Not-for-Not-for- ProfitProfit Government,Government, Aerospace,Aerospace, Gaming, ...Gaming, ... Network /CloudNetwork /Cloud MgmtMgmt MDMMDM SocialSocial GeoGeo Resource Auth &Resource Auth & Access ControlAccess Control ContentContent ManagementManagement Recommend-Recommend- ationsations Data CenterData Center ManagementManagement FraudFraud Detection, ...Detection, ... Early Adopter Graph Database SegmentsEarly Adopters Going Mainstream
- 20. 21 Telenor Resource authorization & Access Control in Telecommunications Resource authorization & Access Control in Telecommunications 21 Background • 10th largest Telco provider in the world, leading in the Nordics • Online self-serve system where large business customers manage employee subscriptions and plans • 24/7 availability critical to customer satisfaction Business problem Solution & Benefits
- 21. 22 Telenor Resource authorization & Access Control in Telecommunications Resource authorization & Access Control in Telecommunications 22
- 22. 23 Telenor Resource authorization & Access Control in Telecommunications Resource authorization & Access Control in Telecommunications 23 Background • 10th largest Telco provider in the world, leading in the Nordics • Online self-serve system where large business customers manage employee subscriptions and plans • 24/7 availability critical to customer satisfaction Business problem Solution & Benefits • Resource authorization and access control across millions of plans, customers, administrators and groups, all interconnected, becomes a challenge • Used Sybase RDBMS for pre-computing access rights daily • Pre-computation time projected to reach 9 hours in 2014 • Users cannot log in until their rights are computed • Resource graph easily modeled and queried in Neo4j • 1500 lines of stored procedures => 10s of lines of Neo4j code • All requests computed in real time in milliseconds • Changes to customer resources reflected immediately • Customer retention risks mitigated
- 23. 24 SFR Network Management in Telecommunications Network Management in Telecommunications 24 Background • Second largest Telco in France • Part of Vivendi Group, partnering with Vodaphone Business problem Solution & Benefits RouterRouter ServiceService DEPENDS_O N SwitchSwitch SwitchSwitch RouterRouter Fiber Link Fiber Link Fiber Link Fiber Link Fiber Link Fiber Link Oceanflo or Cable Oceanflo or Cable DEPENDS_ON DEPENDS_ON DEPEN DS_O N DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON LINKED LINKED LIN KED DEPENDS_ON
- 24. 25 SFR Network Management in Telecommunications Network Management in Telecommunications 25 RouterRouter ServiceService DEPENDS_O N SwitchSwitch SwitchSwitch RouterRouter Fiber LinkFiber Link Fiber LinkFiber Link Fiber LinkFiber Link Oceanfloor Cable Oceanfloor Cable DEPENDS_ON DEPENDS_ON DEPEN DS_O N DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON LINKED LINKED LIN KED DEPENDS_ON
- 25. 26 SFR Network Management in Telecommunications Network Management in Telecommunications 26 Background • Second largest Telco in France • Part of Vivendi Group, partnering with Vodaphone Business problem Solution & Benefits • Need for flexible network inventory management, aggregation, and troubleshooting • Impact analysis of planned and unplanned network outages, so that affected services can be notified or receive increased redundancy • Highly volatile network structure changing daily, with business requirements changing as well • Neo4j Enterprise with a highly available cluster • Dynamic system allowing for new applications to tie into network structure data • Near 1:1 mapping of real world to graph, greatly reducing modeling work • High adaptability to changing business requirements RouterRouter ServiceService DEPENDS_O N SwitchSwitch SwitchSwitch RouterRouter Fiber Link Fiber Link Fiber Link Fiber Link Fiber Link Fiber Link Oceanflo or Cable Oceanflo or Cable DEPENDS_ON DEPENDS_ON DEPEN DS_O N DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON DEPENDS_ON LINKED LINKED LIN KED DEPENDS_ON
- 26. 27 Adobe Content Management, Access Control & Collaboration Content Management, Access Control & Collaboration 27 Background • Creative Cloud, announced 2011, is a cloud-based offering for professional users of Adobe’s creative suiteCollaborative Cloud is the social element of the Creative Cloud, connecting professional users around the world Business problem Solution & Benefits
- 27. 2828 Domain GraphDeployment Architecture AdobeContent Management, Access Control & Collaboration Content Management, Access Control & Collaboration
- 28. 29 Adobe Content Management, Access Control & Collaboration Content Management, Access Control & Collaboration 29 Background • Creative Cloud, announced 2011, is a cloud-based offering for professional users of Adobe’s creative suiteCollaborative Cloud is the social element of the Creative Cloud, connecting professional users around the world Business problem Solution & Benefits • Identifies which collections a user has access toFinds third-party assets that are like a user’s assetsInfers professional relations based on user-generated content • Fit: • Graph model is a natural fit for social network • Collaborative user experience adds competitive advantage to Adobe offering • Flexibility: Data model can be easily evolved to support permissions and more sophisticated recommendation strategies • Performance: Sub-second results for large, densely-connected datasets
- 29. 30 Viadeo Recommendations in Social Recommendations in Social๏ Customer: a professional social network • 35 millions users, adding 30,000+ each day ๏ Goal: up-to-date recommendations • Scalable solution with real-time end-user experience • Low maintenance and reliable architecture • 8-week implementation 30 ๏ Problem: • Real-time recommendation imperative to attract new users and maintain positive user retentionClustered MySQL solution not scalable or fast enough to support real-time requirementsUpgrade from running a batch job ๏ initial hour-long batch job • but then success happened, and it became a day • then two days • With Neo4j, real time recommendations ๏ With Neo4j, real time recommendations
- 30. 32 First off: the name ๏ WE ALL HATES IT, M’KAY? 32
- 31. NOSQL is NOT... ๏ NO to SQL ๏ NEVER SQL
- 32. Not Only SQL NOSQL is simply
- 33. But why now?
- 34. 36 Trends in BigData & NOSQL 36 ๏ 1. increasing data size (big data) •“Every 2 days we create as much information as we did up to 2003” - Eric Schmidt ๏ 2. increasingly connected data (graph data) •for example, text documents to html ๏ 3. semi-structured data •individualization of data, with common sub-set ๏ 4. architecture - a facade over multiple services •from monolithic to modular, distributed applications
- 35. 37 4 Categories of NOSQL 37
- 36. 38 Key-Value Category ๏ “Dynamo:Amazon’s Highly Available Key-Value Store” (2007) ๏ Data model: •Global key-value mapping •Big scalable HashMap •Highly fault tolerant (typically) ๏ Examples: •Riak, Redis,Voldemort 38
- 37. 39 Key-Value: Pros & Cons ๏ Strengths •Simple data model •Great at scaling out horizontally •Scalable •Available ๏ Weaknesses: •Simplistic data model •Poor for complex data 39
- 38. 40 Column-Family Category ๏ Google’s “Bigtable: A Distributed Storage System for Structured Data” (2006) •Column-Family are essentially Big Table clones ๏ Data model: •A big table, with column families •Map-reduce for querying/processing ๏ Examples: •HBase, HyperTable, Cassandra 40
- 39. 41 Column-Family: Pros & Cons ๏ Strengths •Data model supports semi-structured data •Naturally indexed (columns) •Good at scaling out horizontally ๏ Weaknesses: •Unsuited for connected data 41
- 40. 42 Document Database Category ๏ Data model •Collections of documents •A document is a key-value collection •Index-centric, lots of map-reduce ๏ Examples •CouchDB, MongoDB 42
- 41. 43 Document Database: Pros & Cons ๏ Strengths •Simple, powerful data model •Good scaling (especially if sharding supported) ๏ Weaknesses: •Unsuited for connected data •Query model limited to keys (and indexes) 43
- 42. 44 Graph Database Category ๏ Data model: •Nodes & Relationships •Hypergraph, sometimes (edges with multiple endpoints) ๏ Examples: •Neo4j (of course), OrientDB, InfiniteGraph,AllegroGraph 44
- 43. 45 Graph Database: Pros & Cons ๏ Strengths •Powerful data model, as general as RDBMS •Fast, for connected data •Easy to query ๏ Weaknesses: •Requires conceptual shift ‣though graph-like thinking becomes addictive 45
- 44. 46 Scaling to Size Scaling to Complexity Key/Value stores ColumnFamily stores Document databases Graph databases My subjective view: > 90% of use cases 100+ billion of nodes and relationships The NOSQL Space