As the only NoSQL database category that prioritizes relationships, graph databases provide all the flexibility of a NoSQL database with optimized performance for connected data. This webinar will walk you through how to model your data as a graph. We will demonstrate how to avoid pitfalls early on and how to optimize your model for answering questions as Cypher queries.

1. Graph Modeling
Tips and Tricks
Mark Needham @markhneedham

2. Introducing our data set...

3. meetup.com’s recommendations

4. Recommendation queries
‣ Several different types
• groups to join
• topics to follow
• events to attend
‣ As a user of meetup.com trying to find
groups to join and events to attend

5. How will this talk be structured?

6. Find similar groups to Neo4j
As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups

7. What makes groups similar?

8. As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups
Find similar groups to Neo4j

9. As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups
Nodes

10. As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups
Relationships

11. As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups
Labels

12. As a member of the Neo4j London group
I want to find other similar meetup groups
So that I can join those groups
Properties

13. Find similar groups to Neo4j
MATCH (group:Group {name: "Neo4j - London User Group"})
-[:HAS_TOPIC]->(topic)<-[:HAS_TOPIC]-(otherGroup)
RETURN otherGroup.name,
COUNT(topic) AS topicsInCommon,
COLLECT(topic.name) AS topics
ORDER BY topicsInCommon DESC, otherGroup.name
LIMIT 10

14. Find similar groups to Neo4j

15. Tip: Model incrementally
‣ Build the model for the question we need
to answer now then move onto the next
question

16. I’m already a member of these!

17. What other data can we get?

18. Exclude groups I’m a member of
As a member of the Neo4j London group
I want to find other similar meetup groups
that I’m not already a member of
So that I can join those groups

19. Exclude groups I’m a member of
As a member of the Neo4j London group
I want to find other similar meetup groups
that I’m not already a member of
So that I can join those groups

20. Exclude groups I’m a member of
MATCH (group:Group {name: "Neo4j - London User Group"})
-[:HAS_TOPIC]->(topic)<-[:HAS_TOPIC]-(otherGroup:Group)
RETURN otherGroup.name,
COUNT(topic) AS topicsInCommon,
EXISTS((:Member {name: "Mark Needham"})
-[:MEMBER_OF]->(otherGroup)) AS alreadyMember,
COLLECT(topic.name) AS topics
ORDER BY topicsInCommon DESC
LIMIT 10

21. Exclude groups I’m a member of

22. Exclude groups I’m a member of
MATCH (group:Group {name: "Neo4j - London User Group"})
-[:HAS_TOPIC]->(topic)<-[:HAS_TOPIC]-(otherGroup:Group)
WHERE NOT( (:Member {name: "Mark Needham"})
-[:MEMBER_OF]->(otherGroup) )
RETURN otherGroup.name,
COUNT(topic) AS topicsInCommon,
COLLECT(topic.name) AS topics
ORDER BY topicsInCommon DESC
LIMIT 10

23. Exclude groups I’m a member of

24. Find my similar groups
As a member of several meetup groups
I want to find other similar meetup groups
that I’m not already a member of
So that I can join those groups

25. Find my similar groups
As a member of several meetup groups
I want to find other similar meetup groups
that I’m not already a member of
So that I can join those groups

26. Find my similar groups
MATCH (member:Member {name: "Mark Needham"})
-[:INTERESTED_IN]->(topic),
(member)-[:MEMBER_OF]->(group)-[:HAS_TOPIC]->(topic)
WITH member, topic, COUNT(*) AS score
MATCH (topic)<-[:HAS_TOPIC]-(otherGroup)
WHERE NOT (member)-[:MEMBER_OF]->(otherGroup)
RETURN otherGroup.name,
COLLECT(topic.name),
SUM(score) as score
ORDER BY score DESC

27. Find my similar groups

28. Find Jonny’s similar groups

29. Oops...Jonny has no interests!

30. What is Jonny interested in?
As a member of several meetup groups
I want to find other similar meetup groups
that I’m not already a member of
So that I can join those groups
INTERESTED_IN?

31. What is Jonny interested in?
There’s an implicit INTERESTED_IN relationship
between the topics of groups I belong to but
don’t express an interest in. Let’s make it explicit
P
G
T
MEMBER_OF
HAS_TOPIC
P
G
T
MEMBER_OF
HAS_TOPIC
INTERESTED_IN

32. What is Jonny interested in?
MATCH (m:Member)-[:MEMBER_OF]->(group)-[:HAS_TOPIC]->(topic)
WITH m, topic, COUNT(*) AS times
WHERE times > 3
MERGE (m)-[:INTERESTED_IN]->(topic)

33. What is Jonny interested in?

34. Tip: Make the implicit explicit
‣ Fill in the missing links in the graph
‣ You could run this type of query once a day
during a quiet period
‣ On bigger graphs we’d run it in
batches to avoid loading the
whole database into memory

35. Find next group people join
As a member of a meetup group
I want to find out which meetup groups
other people join after this one
So that I can join those groups

36. Find next group people join
MATCH (group:Group {name: "Neo4j - London User Group"})
<-[membership:MEMBER_OF]-(member),
(member)-[otherMembership:MEMBER_OF]->(otherGroup)
WHERE membership.joined < otherMembership.joined
WITH member, otherGroup
ORDER BY otherMembership.joined
WITH member, COLLECT(otherGroup)[0] AS nextGroup
RETURN nextGroup.name, COUNT(*) AS times
ORDER BY times DESC

37. Find next group people join

38. It feels a bit clunky...
MATCH (group:Group {name: "Neo4j - London User Group"})
<-[membership:MEMBER_OF]-(member),
(member)-[otherMembership:MEMBER_OF]->(otherGroup)
WHERE membership.joined < otherMembership.joined
WITH member, otherGroup
ORDER BY otherMembership.joined
WITH member, COLLECT(otherGroup)[0] AS nextGroup
RETURN nextGroup.name, COUNT(*) AS times
ORDER BY times DESC
‣ We have to scan through all the MEMBER_OF
relationships to find the one we want
‣ It might make our lives easier if we made
membership a first class citizen of the domain

39. Facts can become nodes

40. Facts can become nodes

41. Refactor to facts
MATCH (member:Member)-[rel:MEMBER_OF]->(group)
MERGE (membership:Membership {id: member.id + "_" + group.id})
SET membership.joined = rel.joined
MERGE (member)-[:HAS_MEMBERSHIP]->(membership)
MERGE (membership)-[:OF_GROUP]->(group)

42. Refactor to facts
MATCH (member:Member)-[:HAS_MEMBERSHIP]->(membership)
WITH member, membership ORDER BY member.id, membership.joined
WITH member, COLLECT(membership) AS memberships
UNWIND RANGE(0,SIZE(memberships) - 2) as idx
WITH memberships[idx] AS m1, memberships[idx+1] AS m2
MERGE (m1)-[:NEXT]->(m2)

43. Find next group people join
MATCH (group:Group {name: "Neo4j - London User Group"})
<-[:OF_GROUP]-(membership)-[:NEXT]->(nextMembership),
(membership)<-[:HAS_MEMBERSHIP]-(member:Member)
-[:HAS_MEMBERSHIP]->(nextMembership),
(nextMembership)-[:OF_GROUP]->(nextGroup)
RETURN nextGroup.name, COUNT(*) AS times
ORDER BY times DESC

44. Comparing the approaches
vs
MATCH (group:Group {name: "Neo4j - London User Group"})
<-[membership:MEMBER_OF]-(member),
(member)-[otherMembership:MEMBER_OF]->(otherGroup)
WITH member, membership, otherMembership, otherGroup
ORDER BY member.id, otherMembership.joined
WHERE membership.joined < otherMembership.joined
WITH member, membership, COLLECT(otherGroup)[0] AS nextGroup
RETURN nextGroup.name, COUNT(*) AS times
ORDER BY times DESC
MATCH (group:Group {name: "Neo4j - London User Group"})
<-[:OF_GROUP]-(membership)-[:NEXT]->(nextMembership),
(membership)<-[:HAS_MEMBERSHIP]-(member:Member)
-[:HAS_MEMBERSHIP]->(nextMembership),
(nextMembership)-[:OF_GROUP]->(nextGroup)
RETURN nextGroup.name, COUNT(*) AS times
ORDER BY times DESC

45. How do I profile a query?
‣ EXPLAIN
• shows the execution plan without actually
executing it or returning any results.
‣ PROFILE
• executes the statement and returns the results
along with profiling information.
45

46. Neo4j’s longest plan (so far…)
46

47. Neo4j’s longest plan (so far…)
47

48. Neo4j’s longest plan (so far…)
48

49. What is our goal?
At a high level, the goal is
simple: get the number of
db hits down.
49

50. an abstract unit of storage
engine work.
What is a database hit?
“
”
50

51. Comparing the approaches
Cypher version: CYPHER 2.3,
planner: COST.
111656 total db hits in 330 ms.
vs
Cypher version: CYPHER 2.3,
planner: COST.
23650 total db hits in 60 ms.

52. Tip: Profile your queries
‣ Spike the different models and see which
one performs the best

53. Should we keep both models?
We could but when we add, edit or remove a membership
we’d have to keep both graph structures in sync.

54. Adding a group membership
WITH "Mark Needham" AS memberName,
"Neo4j - London User Group" AS groupName,
timestamp() AS now
MATCH (group:Group {name: groupName})
MATCH (member:Member {name: memberName})
MERGE (member)-[memberOfRel:MEMBER_OF]->(group)
ON CREATE SET memberOfRel.time = now
MERGE (membership:Membership {id: member.id + "_" + group.id})
ON CREATE SET membership.joined = now
MERGE (member)-[:HAS_MEMBERSHIP]->(membership)
MERGE (membership)-[:OF_GROUP]->(group)

55. Removing a group membership
WITH "Mark Needham" AS memberName,
"Neo4j - London User Group" AS groupName,
timestamp() AS now
MATCH (group:Group {name: groupName})
MATCH (member:Member {name: memberName})
MATCH (member)-[memberOfRel:MEMBER_OF]->(group)
MATCH (membership:Membership {id: member.id + "_" + group.id})
MATCH (member)-[hasMembershipRel:HAS_MEMBERSHIP]->(membership)
MATCH (membership)-[ofGroupRel:OF_GROUP]->(group)
DELETE memberOfRel, hasMembershipRel, ofGroupRel, membership

56. Let’s delete MEMBER_OF then...

57. ...not so fast!
As a member of several meetup groups
I want to find other similar meetup groups
that I’m not already a member of
So that I can join those groups

58. Why not delete MEMBER_OF?
MATCH (member:Member {name: "Mark Needham"})
-[:MEMBER_OF]->(group)-[:HAS_TOPIC]->(topic)
WITH member, topic, COUNT(*) AS score
MATCH (topic)<-[:HAS_TOPIC]-(otherGroup)
WHERE NOT (member)-[:MEMBER_OF]->(otherGroup)
RETURN otherGroup.name, COLLECT(topic.name), SUM(score) as score
ORDER BY score DESC
MATCH (member:Member {name: "Mark Needham"})
-[:HAS_MEMBERSHIP]->()-[:OF_GROUP]->(group:Group)-[:HAS_TOPIC]->(topic)
WITH member, topic, COUNT(*) AS score
MATCH (topic)<-[:HAS_TOPIC]-(otherGroup)
WHERE NOT (member)-[:HAS_MEMBERSHIP]->(:Membership)-[:OF_GROUP]->(otherGroup:Group)
RETURN otherGroup.name, COLLECT(topic.name), SUM(score) as score
ORDER BY score DESC

59. Why not delete MEMBER_OF?
MATCH (member:Member {name: "Mark Needham"})
-[:MEMBER_OF]->(group)-[:HAS_TOPIC]->(topic)
WITH member, topic, COUNT(*) AS score
MATCH (topic)<-[:HAS_TOPIC]-(otherGroup)
WHERE NOT (member)-[:MEMBER_OF]->(otherGroup)
RETURN otherGroup.name, COLLECT(topic.name), SUM(score) as score
ORDER BY score DESC
MATCH (member:Member {name: "Mark Needham"})
-[:HAS_MEMBERSHIP]->()-[:OF_GROUP]->(group:Group)-[:HAS_TOPIC]->(topic)
WITH member, topic, COUNT(*) AS score
MATCH (topic)<-[:HAS_TOPIC]-(otherGroup)
WHERE NOT (member)-[:HAS_MEMBERSHIP]->(:Membership)-[:OF_GROUP]->(otherGroup:Group)
RETURN otherGroup.name, COLLECT(topic.name), SUM(score) as score
ORDER BY score DESC
433318 total db hits in 485 ms.
83268 total db hits in 117 ms.

60. Tip: Maintaining multiple models
‣ Different models perform better for
different queries but worse for others
‣ Optimising for reads may mean we pay a
write and maintenance penalty

61. What about events?

62. Events in my groups
As a member of several meetup groups who
has previously attended events
I want to find other events hosted by those
groups
So that I can attend those events

63. Events in my groups
As a member of several meetup
groups who has previously
attended events
I want to find other events
hosted by those groups
So that I can attend those events

64. WITH 24.0*60*60*1000 AS oneDay
MATCH (member:Member {name: "Mark Needham"}),
(member)-[:MEMBER_OF]->(group),
(group)-[:HOSTED_EVENT]->(futureEvent)
WHERE futureEvent.time >= timestamp()
AND NOT (member)-[:RSVPD]->(futureEvent)
RETURN group.name, futureEvent.name,
round((futureEvent.time - timestamp()) / oneDay) AS days
ORDER BY days
LIMIT 10
Events in my groups

65. Events in my groups

66. + previous events attended
WITH 24.0*60*60*1000 AS oneDay
MATCH (member:Member {name: "Mark Needham"})
MATCH (futureEvent:Event)
WHERE futureEvent.time >= timestamp() AND NOT (member)-[:RSVPD]->(futureEvent)
MATCH (futureEvent)<-[:HOSTED_EVENT]-(group)
WITH oneDay, group, futureEvent, member, EXISTS((group)<-[:MEMBER_OF]-(member)) AS isMember
OPTIONAL MATCH (member)-[rsvp:RSVPD {response: "yes"}]->(pastEvent)<-[:HOSTED_EVENT]-(group)
WHERE pastEvent.time < timestamp()
RETURN group.name,
futureEvent.name,
isMember,
COUNT(rsvp) AS previousEvents,
round((futureEvent.time - timestamp()) / oneDay) AS days
ORDER BY days, previousEvents DESC

67. + previous events attended

68. RSVPD_YES vs RSVPD
I was curious whether refactoring
RSVPD {response: "yes"} to RSVPD_YES would have
any impact as Neo4j is optimised for querying
by unique relationship types.

69. Refactor to specific relationships
MATCH (m:Member)-[rsvp:RSVPD {response:"yes"}]->(event)
MERGE (m)-[rsvpYes:RSVPD_YES {id: rsvp.id}]->(event)
ON CREATE SET rsvpYes.created = rsvp.created,
rsvpYes.lastModified = rsvp.lastModified;
MATCH (m:Member)-[rsvp:RSVPD {response:"no"}]->(event)
MERGE (m)-[rsvpYes:RSVPD_NO {id: rsvp.id}]->(event)
ON CREATE SET rsvpYes.created = rsvp.created,
rsvpYes.lastModified = rsvp.lastModified;

70. RSVPD_YES vs RSVPD
RSVPD {response: "yes"}
vs
RSVPD_YES
Cypher version: CYPHER 2.3,
planner: COST.
688635 total db hits in 232 ms.
Cypher version: CYPHER 2.3,
planner: COST.
559866 total db hits in 207 ms.

71. Why would we keep RSVPD?
MATCH (m:Member)-[r:RSVPD]->(event)<-[:HOSTED_EVENT]-(group)
WHERE m.name = "Mark Needham"
RETURN event, group, r
MATCH (m:Member)-[r:RSVPD_YES|:RSVPD_NO|:RSVPD_WAITLIST]->(event),
(event)<-[:HOSTED_EVENT]-(group)
WHERE m.name = "Mark Needham"
RETURN event, group, r

72. Tip: Specific relationships
‣ Neo4j is optimised for querying by unique
relationship types…
‣ ...but sometimes we pay a query
maintenance cost to achieve this

73. + events my friends are attending
There’s an implicit FRIENDS relationship
between people who attended the same events.
Let’s make it explicit.
M
E
M
RSVPD
RSVPD
FRIENDS
M
E
M
RSVPD
RSVPD

74. + events my friends are attending
MATCH (m1:Member)
WHERE NOT m1:Processed
WITH m1 LIMIT {limit}
MATCH (m1)-[:RSVP_YES]->(event:Event)<-[:RSVP_YES]-(m2:Member)
WITH m1, m2, COLLECT(event) AS events, COUNT(*) AS times
WHERE times >= 5
WITH m1, m2, times, [event IN events | SIZE((event)<-[:RSVP_YES]-())] AS attendances
WITH m1, m2, REDUCE(score = 0.0, a IN attendances | score + (1.0 / a)) AS score
MERGE (m1)-[friendsRel:FRIENDS]-(m2)
SET friendsRel.score = row.score

75. Bidirectional relationships
‣ You may have noticed that we didn’t specify a
direction when creating the relationship
MERGE (m1)-[:FRIENDS]-(m2)
‣ FRIENDS is a bidirectional relationship. We only
need to create it once between two people.
‣ We ignore the direction when querying

76. + events my friends are attending
WITH 24.0*60*60*1000 AS oneDay
MATCH (member:Member {name: "Mark Needham"})
MATCH (futureEvent:Event)
WHERE futureEvent.time >= timestamp() AND NOT (member)-[:RSVPD]->(futureEvent)
MATCH (futureEvent)<-[:HOSTED_EVENT]-(group)
WITH oneDay, group, futureEvent, member, EXISTS((group)<-[:MEMBER_OF]-(member)) AS isMember
OPTIONAL MATCH (member)-[rsvp:RSVPD {response: "yes"}]->(pastEvent)<-[:HOSTED_EVENT]-(group)
WHERE pastEvent.time < timestamp()
WITH oneDay, group, futureEvent, member, isMember, COUNT(rsvp) AS previousEvents
OPTIONAL MATCH (futureEvent)<-[:HOSTED_EVENT]-()-[:HAS_TOPIC]->(topic)<-[:INTERESTED_IN]-(member)
WITH oneDay, group, futureEvent, member, isMember, previousEvents, COUNT(topic) AS topics
OPTIONAL MATCH (member)-[:FRIENDS]-(:Member)-[rsvpYes:RSVP_YES]->(futureEvent)
RETURN group.name, futureEvent.name, isMember, round((futureEvent.time - timestamp()) / oneDay) AS days,
previousEvents, topics, COUNT(rsvpYes) AS friendsGoing
ORDER BY days, friendsGoing DESC, previousEvents DESC
LIMIT 15

77. + events my friends are attending

78. Tip: Bidirectional relationships
‣ Some relationships are bidirectional in nature
‣ Neo4j always stores relationships with a
direction but we can choose to ignore that
when we query

79. tl;dr
‣ Model incrementally
‣ Always profile your queries
‣ Consider making the implicit explicit…
• ...but beware the maintenance cost
‣ Be specific with relationship types
‣ Ignore direction for bidirectional relationships

80. That’s all for today!
Questions? :-)
Mark Needham @markhneedham
https://github.com/neo4j-meetups/modeling-worked-example