Downloaded 17 times
![(Michael)-[:HELPS]->(People)-[:WORK_WITH]->(Neo4j)
• Coding
• Writing
• Speaking
• Helping
• Connecting
• Organizing](https://image.slidesharecdn.com/cxsyybarw2k2xmxlzz06-signature-1cedeb6a3d303e6737d0367c0debf8290c1f55972166275cd907f745cfebacc3-poli-150729171800-lva1-app6891/85/Graphs-fun-vjug2-2-320.jpg)
![MATCH (boss)-[:MANAGES*0..3]->(mgr),
(mgr)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN mgr.name AS Subordinate,
count(report) AS Total
Express Complex Queries Easily with Cypher
Find all direct reports and how
many people they manage,
each up to 3 levels down
Cypher Query
SQL Query](https://image.slidesharecdn.com/cxsyybarw2k2xmxlzz06-signature-1cedeb6a3d303e6737d0367c0debf8290c1f55972166275cd907f745cfebacc3-poli-150729171800-lva1-app6891/85/Graphs-fun-vjug2-30-320.jpg)
![Cypher: Powerful and Expressive Query Language
MATCH (:Person { name:“Dan”} ) -[:LOVES]-> (:Person { name:“Ann”} )
LOVES
Dan Ann
LABEL PROPERTY
NODE NODE
LABEL PROPERTY](https://image.slidesharecdn.com/cxsyybarw2k2xmxlzz06-signature-1cedeb6a3d303e6737d0367c0debf8290c1f55972166275cd907f745cfebacc3-poli-150729171800-lva1-app6891/85/Graphs-fun-vjug2-35-320.jpg)
![Basic Pattern: Tom Hanks Movies?
MATCH (:Person {name:”Tom Hanks"} ) -[:ACTED_IN]-> (:Movie {title:”Forrest Gump"} )
ACTED_IN
Tom Hanks
Forrest
Gump
LABEL PROPERTY
NODE NODE
Forrest
Gump
LABEL PROPERTY](https://image.slidesharecdn.com/cxsyybarw2k2xmxlzz06-signature-1cedeb6a3d303e6737d0367c0debf8290c1f55972166275cd907f745cfebacc3-poli-150729171800-lva1-app6891/85/Graphs-fun-vjug2-38-320.jpg)
![Basic Query: Tom Hanks‘ Movies?
MATCH
(actor:Person)-[:ACTED_IN]->(m:Movie)
WHERE actor.name = "Tom Hanks"
RETURN *](https://image.slidesharecdn.com/cxsyybarw2k2xmxlzz06-signature-1cedeb6a3d303e6737d0367c0debf8290c1f55972166275cd907f745cfebacc3-poli-150729171800-lva1-app6891/85/Graphs-fun-vjug2-39-320.jpg)
![Query Comparison: Colleagues of Tom Hanks?
SELECT *
FROM Person as actor
JOIN ActorMovie AS am1 ON (actor.id = am1.actor_id)
JOIN ActorMovie AS am2 ON (am1.movie_id = am2.movie_id)
JOIN Person AS coll ON (coll.id = am2.actor_id)
WHERE actor.name = "Tom Hanks“
MATCH
(actor:Person)-[:ACTED_IN]->()<-[:ACTED_IN]-(coll:Person)
WHERE actor.name = "Tom Hanks"
RETURN *](https://image.slidesharecdn.com/cxsyybarw2k2xmxlzz06-signature-1cedeb6a3d303e6737d0367c0debf8290c1f55972166275cd907f745cfebacc3-poli-150729171800-lva1-app6891/85/Graphs-fun-vjug2-41-320.jpg)
![Most prolific actors and their filmography?
MATCH (p:Person)-[:ACTED_IN]->(m:Movie)
RETURN p.name, count(*), collect(m.title) as movies
ORDER BY count(*) desc, p.name asc
LIMIT 10;](https://image.slidesharecdn.com/cxsyybarw2k2xmxlzz06-signature-1cedeb6a3d303e6737d0367c0debf8290c1f55972166275cd907f745cfebacc3-poli-150729171800-lva1-app6891/85/Graphs-fun-vjug2-43-320.jpg)
![Event: Watch a Repository
MATCH (w:WatchEvent)
WITH w LIMIT 1
MATCH p = (w)-[:EVENT_TIME]->(:Minute)<-[:CHILD*]-(:Year),
(w)-[:EVENT_ACTOR]->(u:User)-->(r:Repository)
<-[:WATCHED_REPOSITORY]-(w)
RETURN *;
https://twitter.com/ikwattro/status/618431227100532737](https://image.slidesharecdn.com/cxsyybarw2k2xmxlzz06-signature-1cedeb6a3d303e6737d0367c0debf8290c1f55972166275cd907f745cfebacc3-poli-150729171800-lva1-app6891/85/Graphs-fun-vjug2-55-320.jpg)
Uploaded byNeo4j
Graphs fun vjug2
This document summarizes Michael Hunger's presentation on how graphs make databases fun again. Some key points: - Traditional relational databases have issues modeling connected data and performing complex queries over relationships. Graph databases like Neo4j can more naturally represent connected data as nodes and relationships. - Neo4j was originally created to solve issues modeling connected data for a digital asset management system. It uses a graph data model and allows complex relationship queries through its Cypher query language. - The document demonstrates importing meetup data into Neo4j and running queries to find connections between users, groups, and topics. It also shows examples of querying actor relationships and movie data. - Tools are presented
More Related Content
Graphs fun vjug2
- 1. How Graphs makeDatabases Fun again Value in Relationships Michael Hunger (@mesirii) vJUG July 2015
- 2. (Michael)-[:HELPS]->(People)-[:WORK_WITH]->(Neo4j) • Coding • Writing •Speaking • Helping • Connecting • Organizing
- 3. Topics • Databases areNo Fun • Relational Pain -> Graph Fun • The world is a Graph • Neo4j • Model, Query, Import • Having Fun in the Developer Zone • GitHub Events • Software Analytics • Neo4j from Java
- 4. Databases are NoFun We have all been there
- 5. What pained me •Object vs. Database Model = Pain • Hard to Model • Object relational impedance mismatch (and ORMs) • Schema evolution = DBA Fights • Slow queries = JOIN Pain • Complex Queries = Pages of SQL • Query Optimization = Denormalization • Roundtrips (n+1 select, complex operations) = Cumbersome
- 6. What saved me?– Meeting Emil Geek Cruise in 2008
- 7. Neo4j: A Storyof Pain With a Happy Ending
- 8. History of Neo4j- Problem • Digital Asset Management System in 2000 • SaaS many users in many countries • Two hard use-cases • Multi language keyword search • Including synonyms / word hierarchies • Access Management to Assets for SaaS Scale • Groups, Hierarchies, Permissions, Realtime
- 9. History of Neo4j– Relational Attempt • Tried with many relational DBs • JOIN Performance Problems • Hierarchies, Networks, Graphs • Modeling Problems • Data Model evolution • No Success, even … • With expensive database consultants!
- 10. History of Neo4j– First working Implementation • Graph Model & API sketched on a napkin • Nodes connected by Relationships • Just like your conceptual model • Implemented network-database in memory • Java API, fast Traversals • Worked well, but … • No persistence, No Transactions • Long import / export time from relational storage
- 11. History of Neo4j- Solution • Evolved to full fledged database in Java • With persistence using files + memory mapping • Transactions with Transaction Log (WAL) • Lucene for fast entity lookup • Founded Company in 2007 • Neo4j (REST)-Server • Neo4j Clustering & HA • Cypher Query Language • Today …
- 12. Neo Technology Overview Product •Neo4j - World’s leading graph database • 1M+ downloads, adding 70k+ per month • 150+ enterprise subscription customers including over 50 of the Global 2000 Company • Neo Technology, Creator of Neo4j • 100+ employees with HQ in Silicon Valley, London, Munich, Paris and Malmö • $45M in funding from Fidelity, Sunstone, Conor, Creandum, Dawn Capital
- 13. What, Who, Where,How? Financial Services Communications Health & Life Sciences HR & Recruiting Media & Publishing Social Web Industry & Logistics Entertainment Consumer Retail Information ServicesBusiness Services http://neo4j.com/use-cases http://neo4j.com/customers
- 14. Why should Icare? Because Relationships Matter
- 15. What is itwith Relationships? • World is full of connected people, events, things • There is “Value in Relationships” ! • What about Data Relationships? • How do you store your object model? • How do you explain JOIN tables to your boss?
- 16. Neo4j – allowsyou to connect the dots • Was built to efficiently • store, • query and • manage highly connected data • Transactional, ACID • Real-time OLTP • Open source • Highly scalable already on few machines
- 17. Value from DataRelationships Common Use Cases Internal Applications Master Data Management Network and IT Operations Fraud Detection Customer-Facing Applications Real-Time Recommendations Graph-Based Search Identity and Access Management
- 18. Neo4j Browser –Built-in Learning
- 19. RDBMS to Graph– Familiar Examples
- 20. Neo4j Browser –Visualization
- 21.
- 22. Teaser: Meetup.com Import •For a Meetup Event • Import Attendees • For each Attendee • Import Interests / Topics • Import other Meetup Memberships • Other groups our members are in • Top 10 topics • Topics & Groups of active Member https://github.com/ikwattro/meetup2neo http://markhneedham.com/blog?s=meetup
- 23. From RDBMS toNeo4j Relational Pains = Graph Pleasure
- 24. Relational DBs Can’tHandle Relationships Well • Data Model built for tabular forms not JOINS managing connections was bolted on both in schema and query • Strict schema not suitable for variable structured data which is generated and used by todays applications • Data volume and JOIN number affect cost of query operation exponentially • Variable hierarchies and networks are hard to store and query so many “patterns” were developed … often only denormalization makes complex relational queries fast but destroys the good normalized data-model Built for Forms Joins are expensive Denormalize #FTW
- 25. Unlocking Value fromYour Data Relationships • Model your data naturally as a graph of data and relationships • Drive graph model from domain and use-cases • Use relationship information in real- time to transform your business • Add new relationships on the fly to adapt to your changing requirements
- 26. High Query Performancewith a Native Graph DB • Relationships are first class citizen • No need for joins, just follow pre- materialized relationships of nodes • Query & Data-locality – navigate out from your starting points • Only load what’s needed • Aggregate and project results as you go • Optimized disk and memory model for graphs
- 27. Relational Versus GraphModels Relational Model Graph Model KNOWS ANDREAS TOBIAS MICA DELIA Person FriendPerson-Friend ANDREAS DELIA TOBIAS MICA
- 28.
- 29. … Is Actually Order Product CustomerEmployee SOLD ORDERS Category Employee REPORTS_TO PART_OF PURCHASED Supplier SUPPLIES
- 30. MATCH (boss)-[:MANAGES*0..3]->(mgr), (mgr)-[:MANAGES*1..3]->(report) WHERE boss.name= “John Doe” RETURN mgr.name AS Subordinate, count(report) AS Total Express Complex Queries Easily with Cypher Find all direct reports and how many people they manage, each up to 3 levels down Cypher Query SQL Query
- 31. High Query Performance:Some Numbers • Traverse 2-4M+ relationships per second and core • Cost based query optimizer – complex queries return in milliseconds • Import 100K-1M records per second transactionally • Bulk import tens of billions of records in a few hours
- 32. Working with aGraph Model, Import, Query
- 33. The Whiteboard ModelIs the Physical Model Eliminates Graph-to- Relational Mapping In your data model Bridge the gap between business and IT models In your application Greatly reduce need for application code
- 34. CAR name: “Dan” born: May29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Property Graph Model Components Nodes • The objects in the graph • Can have name-value properties • Can be labeled Relationships • Relate nodes by type and direction • Can have name-value properties LOVES LOVES LIVES WITH PERSON PERSON
- 35. Cypher: Powerful andExpressive Query Language MATCH (:Person { name:“Dan”} ) -[:LOVES]-> (:Person { name:“Ann”} ) LOVES Dan Ann LABEL PROPERTY NODE NODE LABEL PROPERTY
- 36. Getting Data intoNeo4j Cypher-Based “LOAD CSV” Capability • Transactional (ACID) writes • Initial and incremental loads of up to 10 million nodes and relationships Command-Line Bulk Loader neo4j-import • For initial database population • For loads up to 10B+ records • Up to 1M records per second 4.58 million things and their relationships… Loads in 100 seconds! CSV
- 37.
- 38. Basic Pattern: TomHanks Movies? MATCH (:Person {name:”Tom Hanks"} ) -[:ACTED_IN]-> (:Movie {title:”Forrest Gump"} ) ACTED_IN Tom Hanks Forrest Gump LABEL PROPERTY NODE NODE Forrest Gump LABEL PROPERTY
- 39. Basic Query: TomHanks‘ Movies? MATCH (actor:Person)-[:ACTED_IN]->(m:Movie) WHERE actor.name = "Tom Hanks" RETURN *
- 40. Basic Query: TomHanks‘ Movies?
- 41. Query Comparison: Colleaguesof Tom Hanks? SELECT * FROM Person as actor JOIN ActorMovie AS am1 ON (actor.id = am1.actor_id) JOIN ActorMovie AS am2 ON (am1.movie_id = am2.movie_id) JOIN Person AS coll ON (coll.id = am2.actor_id) WHERE actor.name = "Tom Hanks“ MATCH (actor:Person)-[:ACTED_IN]->()<-[:ACTED_IN]-(coll:Person) WHERE actor.name = "Tom Hanks" RETURN *
- 42. Basic Query Comparison:Colleagues of Tom Hanks?
- 43. Most prolific actorsand their filmography? MATCH (p:Person)-[:ACTED_IN]->(m:Movie) RETURN p.name, count(*), collect(m.title) as movies ORDER BY count(*) desc, p.name asc LIMIT 10;
- 44. Most prolific actorsand their filmography?
- 45. Neo4j Query Planner Costbased Query Planner since Neo4j 2.2 • Uses database stats to select best plan • Currently for Read Operations • Query Plan Visualizer, finds • Non optimal queries • Cartesian Product • Missing Indexes, Global Scans • Typos • Massive Fan-Out
- 46. Query Planner Slight change,add an :Person label -> more stats available -> new plan with fewer database-hits
- 47.
- 48. Software Analytics Software isconnected information, aka a graph • Source -> AST • Inheritance, Composition, Delegation • Call Trees • Runtime Memory • Dependencies • Modules, Libraries • Tests • ... https://jqassistant.org
- 49. jQAssistant • GeekCruise: Myfirst Neo4j project • Software deteriorates • Develop rules and enforce them • Commercial Tools too inflexible • Open Source Software ... • Scanner -> Enhancer -> Analyzer • Enrichment, Concepts and Rulez in Cypher • Scanner Plugins • Integrate in Build Process • Fail, Generate Reports, ... https://jqassistant.org
- 51.
- 52.
- 53. Demo: GitHub Events •GitHub: Social Coding • GH-Events: • Fork, Comment, PR, ... • Archive + API • Application for Import: • GitHub ->PHP -> RabbitMQ -> Neo4j • Data: • 1M Users, 1.4M Repos, 62k Orgs • 13M Events https://github.com/ikwattro/github2cypher https://github.com/ikwattro/github-event
- 54. Model: GitHub Events(partial)
- 55. Event: Watch aRepository MATCH (w:WatchEvent) WITH w LIMIT 1 MATCH p = (w)-[:EVENT_TIME]->(:Minute)<-[:CHILD*]-(:Year), (w)-[:EVENT_ACTOR]->(u:User)-->(r:Repository) <-[:WATCHED_REPOSITORY]-(w) RETURN *; https://twitter.com/ikwattro/status/618431227100532737
- 56. Using Neo4j fromJava Choices Galore! Neo4j.com/developer/java
- 57. Neo4j: OSS, Madein Java • Java 7 / 8, Scala • High Performance IO, Memory Mapping, • Collections, Caches, Cursors, • Remoting, • Paxos, Raft, ... • Libraries • Jetty, Lucene, Netty, Parboiled, ... https://github.com/neo4j/neo4j
- 58. Using: Neo4j fromJava • Neo4j Java APIs • Server Extensions • Embedded • (parallel) Batch-Import APIs • REST / HTTP • JDBC Driver • Neo4j-OGM • Spring Data Neo4j • Upcoming: Binary Protocol Driver https://neo4j.com/developer/java
- 59. Get up tospeed with Neo4j Quickly and Easily
- 60. There Are Lotsof Ways to Easily Learn Neo4j neo4j.com/developer
- 61. Thank You! Ask Questions,or Tweet @neo4j | http://neo4j.com @mesirii | Michael Hunger
Editor's Notes
- #22 In the near future, many of your apps will be driven by data relationships and not transactions You can unlock value from business relationships with Neo4j
- #25 Presenter Notes - Challenges with current technologies? Database options are not suited to model or store data as a network of relationships Performance degrades with number and levels of relationships making it harder to use for real-time applications Not flexible to add or change relationships in realtime
- #26 Presenter Notes - Challenges with current technologies? Database options are not suited to model or store data as a network of relationships Performance degrades with number and levels of relationships making it harder to use for real-time applications Not flexible to add or change relationships in realtime
- #27 Presenter Notes - How does one take advantage of data relationships for real-time applications? To take advantage of relationships Data needs to be available as a network of connections (or as a graph) Real-time access to relationship information should be available regardless of the size of data set or number and complexity of relationships The graph should be able to accommodate new relationships or modify existing ones
- #28 Presenter Notes - How does one take advantage of data relationships for real-time applications? To take advantage of relationships Data needs to be available as a network of connections (or as a graph) Real-time access to relationship information should be available regardless of the size of data set or number and complexity of relationships The graph should be able to accommodate new relationships or modify existing ones
- #30 680/605
- #33 Presenter Notes - How does one take advantage of data relationships for real-time applications? To take advantage of relationships Data needs to be available as a network of connections (or as a graph) Real-time access to relationship information should be available regardless of the size of data set or number and complexity of relationships The graph should be able to accommodate new relationships or modify existing ones
- #49 In the near future, many of your apps will be driven by data relationships and not transactions You can unlock value from business relationships with Neo4j
- #54 In the near future, many of your apps will be driven by data relationships and not transactions You can unlock value from business relationships with Neo4j
- #58 In the near future, many of your apps will be driven by data relationships and not transactions You can unlock value from business relationships with Neo4j
- #65 In the near future, many of your apps will be driven by data relationships and not transactions You can unlock value from business relationships with Neo4j