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[Webinar] Introduction to Cypher
- 2. Information Developer IT Project Manager Oracle DBA About Me maria.scharin@neo4j.com
- 3. • A brief history of graph databases • Graph thinking • Graph data modelling • Importing data • How to get started Agenda
- 4. A brief history of graph databases
- 5. Everything and Everyone is Connected • people, places, events • companies, markets • countries, history, politics • sciences, art, teaching • technology, networks, machines, applications, users • software, code, dependencies, architecture, deployments • criminals, fraudsters and their behavior
- 6. Seven Bridges of Königsberg
- 7. C 34,3%B 38,4%A 3,3% D 3,8% 1,8% 1,8% 1,8% 1,8% 1,8% E 8,1% F 3,9% Static world Connected World Native Graph Platform Neo4j is an internet-scale, native graph database which executes connected workloads faster than any other database management system. Neo4j
- 8. Ecosystem Neo4j Professional Services 300+ partners 47,000 group members 61,000 trained engineers 3.5M downloads Mindset “Graph Thinking” is all about considering connections in data as important as the data itself. Native Graph Platform Neo4j is an internet-scale, native graph database which executes connected workloads faster than any other database management system. Neo4j
- 10. Graph thinking: A modern Swedish family Icons designed by Freepik www.freepik.com/free-vector/people-icons-collection_1024244.htm Maria OscarThomas Ann FRIEND OF MOTHER OFMOTHER OF FRIEND OF Cody EX OF FATHER OF
- 11. Graph thinking: A modern Swedish family Icons designed by Freepik www.freepik.com/free-vector/people-icons-collection_1024244.htm FRIEND OF MOTHER OF FRIEND OF Thomas Ann Cody EX OFMARRIED TO Dan
- 12. Graph thinking: A modern Swedish family Icons designed by Freepik www.freepik.com/free-vector/people-icons-collection_1024244.htm Karl Ben Chris Carrie Elizabeth EX OF FRIEND OF MOTHER OF FRIEND OF Thomas Ann Cody EX OFMARRIED TO Dan
- 13. Graph thinking
- 14. AnnDan LOVES The property graph model
- 15. since: Jan 10, 2011 brand: “Volvo” model: “V70” D R IVES O W NS Car LIVES_WITH LOVES name:”Dan” born: May 29, 1970 twitter:”@dan” name:”Ann” born: Dec 5, 1975 LOVES Person Person The property graph model
- 16. Cypher: The Graph Query Language • The “SQL for graphs”: An open language (http://www.opencypher.org/) • The formal semantics are in the process of being defined: collaboration with the University of Edinburgh, UK • Declarative • Focus on pattern matching • Intuitive and easy to learn (ASCII-Art)
- 17. Cypher: The Graph Query Language CREATE LABEL PROPERTYLABEL PROPERTY RELATIONSHIP TYPE (:Person { name:“Dan”} ) - [:LOVES]-> (:Person { name:“Ann”} ) NODE NODERELATIONSHIP name:”Dan” name:”Ann” LOVES Person Person MERGE
- 18. Index-free adjacency Credit: Dan McCreary https://medium.com/@dmccreary/how-to-explain-index-free-adjacency-to-your-manager-1a8e68ec664a Maria’s house Ann’s house
- 19. Index-free adjacency Credit: Dan McCreary https://medium.com/@dmccreary/how-to-explain-index-free-adjacency-to-your-manager-1a8e68ec664a
- 20. Relational and Other NoSQL Databases ResponseTime Connectedness and Size of Data Set 0 to 2 hops 0 to 3 degrees Few connections 5+ hops 3+ degrees Thousands of connections Neo4j Real-Time Query Performance
- 22. Graph data modeling • Relational to graph • Different ways of modelling • Refactoring • Inference • A social recommendation example
- 23. Relational to Graph select restaurant.name from person join review on (...) join restaurant on(…) where person.name = “Maria” reviewperson business Maria Sushi Lovers The Burger Joint Pedro’s Pizza
- 24. reviewperson business Relational model Rows Joins Table names Columns Graph model Nodes Relationships Labels Properties Maria Sushi Lovers Pedro’s Pizza The Burger Joint Relational to Graph
- 25. Arrow tool: http://www.apcjones.com/arrows/# Graph data modelling
- 26. Graph data modelling: refactoring Arrow tool: http://www.apcjones.com/arrows/#
- 27. Graph data modelling: inference Arrow tool: http://www.apcjones.com/arrows/#
- 28. A Social Recommendation Maria Ann Diana FRIEND OF FRIEND OF LIKES LIKES iSushi :Restaurant New York :Location Sushi :Cuisine SERVES SERVES LOCATED_IN LOCATED_IN Sushi Lovers :Restaurant Icons designed by Freepik www.freepik.com/free-vector/people-icons-collection_1024244.htm
- 29. A Social Recommendation Some icons designed by Freepik www.freepik.com/free-vector/people-icons-collection_1024244.htm Maria Ann Diana FRIEND OF FRIEND OF LIKES LIKES iSushi New York Sushi SERVES SERVES LOCATED_IN LOCATED_IN :Restaurant :Location :Cuisine :Restaurant Sushi Lovers
- 30. A Social Recommendation Some icons designed by Freepik www.freepik.com/free-vector/people-icons-collection_1024244.htm Maria Ann Diana FRIEND OF FRIEND OF LIKES LIKES iSushi New York Sushi SERVES SERVES LOCATED_IN LOCATED_IN :Restaurant :Location :Cuisine :Restaurant Sushi Lovers https://neo4j.com/docs/developer-manual/current/cypher/query-tuning/
- 31. A Social Recommendation Some icons designed by Freepik www.freepik.com/free-vector/people-icons-collection_1024244.htm Maria Ann Diana FRIEND OF FRIEND OF LIKES LIKES iSushi New York Sushi SERVES SERVES LOCATED_IN LOCATED_IN :Restaurant :Location :Cuisine Sushi Lovers :Restaurant
- 32. Import data
- 33. Download and install Neo4j Desktop https://neo4j.com/download/
- 34. Download and install Neo4j Desktop
- 36. The Yelp dataset https://www.yelp.com/dataset json format Convert json files to csv format: https://github.com/johnymontana/neo4j-datasets/tree/master/yelp https://www.yelp.com/dataset
- 37. The Yelp dataset (Also available in sql) https://www.yelp.com/dataset
- 38. The Yelp dataset
- 39. Import data: LOAD CSV Cypher-Based LOAD CSV Capability • Transactional (ACID) writes • Initial and incremental loads of up to 10 million nodes and relationships • From HTTP and Files • Power of Cypher: • Create and update graph structures • Data conversion, filtering, aggregation • De-structuring of input data • Transaction size control • Via Neo4j Browser or cypher-shell • https://neo4j.com/docs/cypher-refcard/current/
- 40. Import data: LOAD CSV line ["yelping_since", "useful", "compliment_photos", "compliment_list", "compliment_funny", "funny", "review_count", "friends", "fans", "compliment_note", "compliment_plain", "compliment_writer", "compliment_cute", "average_stars", "user_id", "compliment_more", "elite", "compliment_hot", "cool", "name", "compliment_profile", "compliment_cool"] ["2014-11-03", "0", "0", "0", "0", "0", "8", "cvVMmlU1ouS3I5fhutaryQ;nj6UZ8tdGo8YJ9lUMTVWNw;RTtdEVhAmeWqCSp0IgJ99w;t3UKA1sl4e6LY_xsjuvI0A;s057_BvOfnKNvQquJf7VNg;VYrdepCgdzJ4WaxP7dBGpg;XXLSk6s QQDyr3dZ4zE-O0g;Py8ThfExQaXF2Woqr7kWUw;233YNvzVtZ1ObkaNkUzNIw;L6iE9NpmHHJQTk0JQlRlSA;Y7XTMgZ_q5Bj5f9KhK1R4Q", "[u'cvVMmlU1ouS3I5fhutaryQ', u'nj6UZ8tdGo8YJ9lUMTVWNw', u'RTtdEVhAmeWqCSp0IgJ99w', u't3UKA1sl4e6LY_xsjuvI0A', u's057_BvOfnKNvQquJf7VNg', u'VYrdepCgdzJ4WaxP7dBGpg', u'XXLSk6sQQDyr3dZ4zE-O0g', u'Py8ThfExQaXF2Woqr7kWUw', u'233YNvzVtZ1ObkaNkUzNIw', u'L6iE9NpmHHJQTk0JQlRlSA', u'Y7XTMgZ_q5Bj5f9KhK1R4Q']", "0", "0", "1", "0", "0", "4.67", "oMy_rEb0UBEmMlu-zcxnoQ", "0", "", "[]", "0", "0", "Johnny", "0", "0"] https://neo4j.com/docs/cypher-refcard/current/
- 41. Import data: LOAD CSV https://neo4j.com/docs/cypher-refcard/current/
- 42. Import data: LOAD CSV http://markhneedham.com/blog/2014/07/10/neo4j-load-csv-processing-hidden-arrays-in-your-csv-documents/ https://neo4j.com/docs/cypher-refcard/current/
- 43. Import data: LOAD CSV https://neo4j.com/docs/cypher-refcard/current/
- 44. Import data: LOAD CSV https://neo4j.com/docs/cypher-refcard/current/
- 45. Use JDBC with your sql database
- 46. Use JDBC with your sql database apoc.load.jdbc Access any database that provides a JDBC driver Execute queries whose results are turned into streams of rows Use the rows to update or create graph structures https://neo4j-contrib.github.io/neo4j-apoc-procedures/index30.html#_load_jdbc
- 47. Use JDBC with your sql database CALL apoc.load.jdbc(“jdbc:mysql://localhost:3306/yelp_db? user=user&password=pwd", “business") YIELD row RETURN row https://neo4j-contrib.github.io/neo4j-apoc-procedures/index30.html#_load_jdbc
- 48. Use JDBC with your sql database CALL apoc.load.jdbc("jdbc:mysql://localhost:3306/yelp_db? user=user&password=pwd", “business") YIELD row RETURN row.id, row.name, row.address, row.state https://neo4j-contrib.github.io/neo4j-apoc-procedures/index30.html#_load_jdbc
- 49. Use JDBC with your sql database CALL apoc.load.jdbc("jdbc:mysql://localhost:3306/yelp_db? user=user&password=pwd", “business") YIELD row MERGE (:Business {id: row.id, name:row.name, address:row.address, state:row.state}) https://neo4j-contrib.github.io/neo4j-apoc-procedures/index30.html#_load_jdbc
- 50. Import data: neo4j-admin import Command line bulk loader neo4j-admin import • For initial database population • Scale across CPUs and disk performance • Efficient RAM usage • Split- and compressed file support • For loads up to 10B+ records • Up to 1M records per second https://neo4j.com/docs/operations-manual/3.3/tutorial/import-tool/
- 51. • Neo4j Desktop - Free for download • Neo4j Browser (included in Neo4j Desktop) • Sandbox: neo4j.com/sandbox • Drivers - Official Drivers and Community Supported Drivers • Webinars and Tutorials • Community and Meetups Get started with Neo4j