![a
ACTED IN
m
(a)–[:ACTED_IN]->(m)
Adding in a relationship type](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-41-320.jpg)
![MATCH (a)-[:ACTED_IN]->(m)
RETURN a.name, m.name
a
ACTED IN
m
Adding in a relationship type](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-42-320.jpg)
![MATCH (team:Team)<-[:away_team]-(game)
WHERE team.name = "Arsenal"
RETURN game
Find Arsenal’s away matches](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-47-320.jpg)
![MATCH (team:Team)<-[:away_team]-(game)
WHERE team.name = "Arsenal"
RETURN game.name
Graph Pattern](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-48-320.jpg)
![MATCH (team:Team)<-[:away_team]-(game)
WHERE team.name = "Arsenal"
RETURN game.name
Anchor pattern in graph](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-49-320.jpg)
![MATCH (team:Team)<-[:away_team]-(game)
WHERE team.name = "Arsenal"
RETURN game.name
Create projection of results](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-50-320.jpg)
![MATCH (team)<-[:away_team]-(game:Game),
(game)<-[:contains_match]-(season:Season),
(team)<-[:for]-(stats)<-[:played]-(player),
(stats)-[:in]->(game)
WHERE season.name = "2012-2013"
RETURN player.name,
COLLECT(DISTINCT team.name),
SUM(stats.goals) as goals
ORDER BY goals DESC
LIMIT 10
Find the top away goal scorers](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-54-320.jpg)
![MATCH (team)<-[:away_team]-(game:Game),
(game)<-[:contains_match]-(season:Season),
(team)<-[:for]-(stats)<-[:played]-(player),
(stats)-[:in]->(game)
WHERE season.name = "2012-2013"
RETURN player.name,
COLLECT(DISTINCT team.name),
SUM(stats.goals) as goals
ORDER BY goals DESC
LIMIT 10
Multiple graph patterns](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-55-320.jpg)
![MATCH (team)<-[:away_team]-(game:Game),
(game)<-[:contains_match]-(season:Season),
(team)<-[:for]-(stats)<-[:played]-(player),
(stats)-[:in]->(game)
WHERE season.name = "2012-2013"
RETURN player.name,
COLLECT(DISTINCT team.name),
SUM(stats.goals) as goals
ORDER BY goals DESC
LIMIT 10
Anchor pattern in the graph](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-56-320.jpg)
![MATCH (team)<-[:away_team]-(game:Game),
(game)<-[:contains_match]-(season:Season),
(team)<-[:for]-(stats)<-[:played]-(player),
(stats)-[:in]->(game)
WHERE season.name = "2012-2013"
RETURN player.name,
COLLECT(DISTINCT team.name),
SUM(stats.goals) as goals
ORDER BY goals DESC
LIMIT 10
Group by player](https://image.slidesharecdn.com/neo4j-sqlrelay-20131129-131129101004-phpapp01/85/Football-graph-Neo4j-and-the-Premier-League-57-320.jpg)
Football graph - Neo4j and the Premier League
- 1. In a League of their Own: Neo4j and Premiership Football Mark Needham @markhneedham
- 2. • • • • • • Intro to graphs When do we need a graph? Property graph model Neo4j’s query language The football graph Using Neo4j from .NET Outline
- 4. Dancing With Michael Jackson Eating Brains You mean these?
- 5. Dancing With Michael Jackson Eating Brains Nope!
- 6. Node Relationship Ok so what’s a graph then?
- 7. The tube
- 8. The social network (graph)
- 9. Complexity What are graphs good for?
- 10. complexity = f(size, semi-structure, connectedness) Data Complexity
- 11. Size
- 12. complexity = f(size , semi-structure, connectedness) The Real Complexity
- 13. Semi-Structure
- 14. USER_ID FIRST_NAME LAST_NAME EMAIL_1 EMAIL_2 FACEBOOK 315 Mark Needham mark.needham@neotech nology.com m.h.needham@gmail.com NULL Email: mark.needham@neotechnology.com Email: m.h.needham@gmail.com Twitter: @markhneedham Skype: mk_jnr1984 TWITTER @markhneedham CONTACT USER Semi-Structure CONTACT_TYPE SKYPE mk_jnr1984
- 15. complexity = f(size , semi-structure, connectedness) The Real Complexity
- 16. Connectedness
- 17. Connectedness
- 18. Connectedness
- 19. Densely Connected Semi Structured When do we need a graph?
- 20. Lots of join tables Densely connected?
- 22. • Millions of ‘joins’ per second • Consistent query times as dataset grows • Join Complexity and Performance • Easy to evolve data model • Easy to ‘layer’ different types of data together Properties of graph databases
- 23. Property Graph Data Model
- 24. Nodes
- 25. • Used to represent entity attributes and/or metadata (e.g. timestamps, version) • Key-value pairs • Java primitives • Arrays • null is not a valid value • Every node can have different properties Nodes can have properties
- 26. What’s a node?
- 27. Relationships
- 28. • Relationships are first class citizens • Every relationship has a name and a direction – Add structure to the graph – Provide semantic context for nodes • Properties used to represent quality or weight of relationship, or metadata • Every relationship must have a start node and end node Relationships
- 29. Nodes can be connected by more than one relationship Nodes can have more than one relationship Self relationships are allowed Relationships
- 30. Labels
- 32. • Nodes – Entities • Relationships – Connect entities and structure domain • Properties – Entity attributes, relationship qualities, and metadata • Labels – Group nodes by role Four Building Blocks
- 33. Purposeful abstraction of a domain designed to satisfy particular application/end-user goals Models
- 37. • Declarative Pattern-Matching language • SQL-like syntax • Designed for graphs Introducing Cypher
- 39. a b (a) --> (b) It’s all about the ASCII art!
- 40. MATCH (a)-->(b) RETURN a, b a b The most basic query
- 41. a ACTED IN m (a)–[:ACTED_IN]->(m) Adding in a relationship type
- 42. MATCH (a)-[:ACTED_IN]->(m) RETURN a.name, m.name a ACTED IN m Adding in a relationship type
- 45. Find Arsenal’s away matches
- 46. Find Arsenal’s away matches
- 47. MATCH (team:Team)<-[:away_team]-(game) WHERE team.name = "Arsenal" RETURN game Find Arsenal’s away matches
- 48. MATCH (team:Team)<-[:away_team]-(game) WHERE team.name = "Arsenal" RETURN game.name Graph Pattern
- 49. MATCH (team:Team)<-[:away_team]-(game) WHERE team.name = "Arsenal" RETURN game.name Anchor pattern in graph
- 50. MATCH (team:Team)<-[:away_team]-(game) WHERE team.name = "Arsenal" RETURN game.name Create projection of results
- 51. Find Arsenal’s away matches
- 52. Evolving the football graph
- 53. Find the top away goal scorers
- 54. MATCH (team)<-[:away_team]-(game:Game), (game)<-[:contains_match]-(season:Season), (team)<-[:for]-(stats)<-[:played]-(player), (stats)-[:in]->(game) WHERE season.name = "2012-2013" RETURN player.name, COLLECT(DISTINCT team.name), SUM(stats.goals) as goals ORDER BY goals DESC LIMIT 10 Find the top away goal scorers
- 55. MATCH (team)<-[:away_team]-(game:Game), (game)<-[:contains_match]-(season:Season), (team)<-[:for]-(stats)<-[:played]-(player), (stats)-[:in]->(game) WHERE season.name = "2012-2013" RETURN player.name, COLLECT(DISTINCT team.name), SUM(stats.goals) as goals ORDER BY goals DESC LIMIT 10 Multiple graph patterns
- 56. MATCH (team)<-[:away_team]-(game:Game), (game)<-[:contains_match]-(season:Season), (team)<-[:for]-(stats)<-[:played]-(player), (stats)-[:in]->(game) WHERE season.name = "2012-2013" RETURN player.name, COLLECT(DISTINCT team.name), SUM(stats.goals) as goals ORDER BY goals DESC LIMIT 10 Anchor pattern in the graph
- 57. MATCH (team)<-[:away_team]-(game:Game), (game)<-[:contains_match]-(season:Season), (team)<-[:for]-(stats)<-[:played]-(player), (stats)-[:in]->(game) WHERE season.name = "2012-2013" RETURN player.name, COLLECT(DISTINCT team.name), SUM(stats.goals) as goals ORDER BY goals DESC LIMIT 10 Group by player
- 58. Find the top away goal scorers
- 59. • Goals scored in each month by Michu • Tottenham results when Gareth Bale scores • What did Wayne Rooney do in April? • Which players only score when a game is televised? Other football queries
- 62. Relational Graphs Tables Nodes - no need to set a property if it - assume records all have the same structure doesn’t exist Foreign keys between tables Relationships - joins calculated at run time - stored as a ‘Pre-computed - the more tables you join to a query the slower the query gets index’ at write time - very easy to do lots of ‘hops’ between relationships Graph vs Relational
- 63. Neo4j Server Application H T T P REST Client .NET and Neo4j
- 65. .NET and Neo4j
- 66. .NET and Neo4j
- 67. .NET and Neo4j
- 68. .NET and Neo4j
- 69. .NET and Neo4j
- 71. Graphs should be fun!
- 72. Last Wednesday of the month Ask for help if you get stuck
- 73. www.graphdatabases.com Come take a copy, it’s free!
Editor's Notes
- #2: In this talk, we'll look at how graph data and Neo4j can be used to model the English Premier League. We'll see how the graph model and Cypher query language makes it natural and fun to query multidimensional semi-structured data. We'll also see how graphs encourage discoverability so that we can spot interesting correlations and become king of the arcane football facts (e.g. how many goals have been scored at grounds in the North West of England by players originating from South America) at your local pub quiz. We'll also see what the graph model would look like if modeled in a relational way and show where the approach reaches its limits and the graph addresses and resolves those challenges.
- #4: Let’s get started and talk about graphs. Now in this context we’re thinking more of what are sometimes known as networks and…
- #5: …many people when they hear the word graph think of this.
- #6: Which isn’t what we’re going to be talking about today!
- #7: It’s not a new thing, you’ll already be familiar with lots of things that are graphs but perhaps you don’t know it yet. The London tube is perhaps the most famous example that Londoners at least use every day
- #8: It’s not a new thing, you’ll already be familiar with lots of things that are graphs but perhaps you don’t know it yet. The London tube is perhaps the most famous example that Londoners at least use every day
- #9: Or if not then you’ve certainly heard of the social network (graph)
- #17: An organisational hierarchy is a common model
- #18: An organisational hierarchy is a common model
- #19: Or of course as we mentioned earlier, a social network of friends of friends and so on is a popular graph
- #22: Null values all over the place
- #24: Now, as I say, graph databases allow you to store, manage and query your data as a graph. Neo4j adopts a very particular graph model, which we call the property graph model.So I’m going to spend the next few minutes talking about the important aspects of this model in more detail.In fact, I’m going to talk about the enhanced property graph model, which will be available in Neo4j 2.0 sometime later this year.
- #29: Pointer in memory and ultimately on disk
- #31: Analogy: Gmail labels. Every mail can have zero or more labels attached. Allow you to associate filters with groups of emails.
- #34: Always motivated by needs, problems, goals: not transparent window onto realityC18: Seven Bridges of KönigsbergGoal: Find path through the city that crosses each bridge once and once only
- #38: Which leads us perfectly into neo4j’s query language
- #44: Football is quite a nice domain for
- #45: Football is quite a nice domain for modelling in graphs because the data has a lot of dimensions to it
- #46: Football is quite a nice domain for
- #47: Football is quite a nice domain for
- #48: Football is quite a nice domain for
- #49: Football is quite a nice domain for
- #50: Football is quite a nice domain for
- #51: Football is quite a nice domain for
- #52: Football is quite a nice domain for
- #53: Football is quite a nice domain for
- #54: Football is quite a nice domain for
- #55: Football is quite a nice domain for
- #56: Football is quite a nice domain for
- #57: Football is quite a nice domain for
- #58: Football is quite a nice domain for
- #59: Football is quite a nice domain for
- #60: Football is quite a nice domain for
- #61: -> SQL - define your tables and relationships and generally don’t change that.Might denormalise or add indexes to speed up queries-> Graphs – define your initial nodes and relationships. May then add ‘layers’ to the graph to make implicit relationships explicit
- #62: Football is quite a nice domain for
- #63: How is this different to a relational database? We have tables (nodes) and foreign keys between tables (relationships)Those are calculated at run time – in a graph a relationship is a first Class citizen. Effectively a pre-computed indexYou can also traverse lots of ‘hops’ which becomes quite expensive when You do
- #72: If it’s not fun and It seems cumbersome then perhaps it’s the wrong tool for that particular data problem or it’s modeled in the wrong way. Might be worth asking
- #73: Might be worth asking for help if that isn’t happening or you’re stuck. We have a good community on Stack Overflow and a mailing list as well. You’ll get answers to any questions you have pretty quickly.
- #74: Please take a copy of t
- #75: Might be worth asking for help if that isn’t happening or you’re stuck. We have a good community on Stack Overflow and a mailing list as well. You’ll get answers to any questions you have pretty quickly.