In this show-and-tell session, we’ll find out how Neo4j can make us a professional League of Legends player. First, we’ll import game data into a Neo4j graph. Then we will push the limits of Cypher to uncover hidden secrets that lie in the depth of LoL. • Which champions perform best as allies? • What is the most effective strategy to advance your champion? • What enemy team setup is the most dangerous? This session covers a rarely seen graph domain (gaming) and will provide you will solid, factual advice on how to play LoL.

1. Name Surname
email@domain.com
Presentation TitleLeague of
Graphs

2. Dmitry Vrublevsky
@Me
Software engineer @
ƀ dmitry@vrublevsky.me
@FylmTM
į vrublevsky.me

5. Neueda4j / awesome-neo4j
{75 } A curated list of Neo4j resources.
Neueda4j / jetbrains-plugin-cypher
{24 } Cypher plugin for Jetbrains IDE’s.
? / ?
{? } Next awesome project!

6. Agenda
1. League of Legends
2. Neo4j
3. Data import
4. Queries

8. Winner?
League of Legends
“Poros are the mysterious, magical and most-loved
inhabitants originating from the Howling Abyss.”

9. League of Legends
is multiplayer online battle arena,
real-time strategy video game
developed and published by
Riot Games

11. Me
Unranked Bronze Silver Gold
Platinum Diamond Master Challenger

13. League of Legends?
1. I play this game a lot
2. Rich API
3. There is library for API
https://developer.riotgames.com

15. meraki-analytics/Orianna
A Java adaptation of the Riot Games LoL API
Summoner summoner =
RiotAPI.getSummonerByName(“FylmTM");
println(summoner.getName()
+ " is a level “ + summoner.getLevel());

16. Neo4j
Highly scalable native graph
database that leverages data
relationships as
first-class entities.
by Neo Technology, Inc.

17. http://db-engines.com/en/ranking

18. GRAPH

19. NOT GRAPH

20. Features
Native Processing &
Storage
ACID
Cypher - Graph Query
Language
REST & Native API
Lock Manager
High-performance cache
Clustering
Backups
Monitoring
Community Enterprise

21. First-class
details: ♥:LIKES
:DMITRY
:JUG
works_with: Neo4j
day: 28.05.2016
Properties
Labels
Type

22. Native
1. “Native” storage
2. “Native” processing

23. “Native” storage
Specifically designed to
store and manage graphs.

24. http://neo4j.com/developer/graph-db-vs-rdbms/

25. http://neo4j.com/developer/graph-db-vs-rdbms/

26. http://neo4j.com/developer/graph-db-vs-rdbms/

27. “Native” processing
Efficient way of processing
graph data since connected
nodes physically “point” to
each other
a.k.a. “index-free adjacency”

28. Storage layout
Node (15 bytes)
in_use
next_rel_id
next_prop_id
labels
extra
Relationship (34 bytes)
directed | in_use
first_node
second_node
rel_type
first_prev_rel_id
first_next_rel_id
second_prev_rel_id
second_next_rel_id
next_prop_id
first_in_chain_markers

29. Storage math
Node_Offset = Node_Size * Node_ID
Rel_Offset = Rel_Size * Rel_ID

30. Traversal (Node -> Relationship)
{5} Node (15 bytes)
next_rel_id {2}
{<int>} - entity id
<int>B - offset
Relationships (34 bytes)
{0}
2 * 34 = 68
0B
{1}34B
{2}68B
{3}102B
{4}136B
{5}170B

31. Traversal (Relationship -> Node)
{2} Relationship (34 bytes)
{<int>} - entity id
<int>B - offset
Nodes (15 bytes)
{0}0B
{1}15B
{2}30B
{3}45B
{4}60B
{5}75B
first_node {1}
second_node {4}

32. “Native” summary
O(1) traversal hops
Avoid super nodes!

33. Cypher
Cypher is a declarative
graph query language that
allows for expressive and
efficient querying.
https://github.com/opencypher/openCypher

34. Cypher
( ) - node
--> - relationship
Keywords:
- MATCH
- CREATE
- WHERE
- RETURN
MATCH (jug) RETURN jug
MATCH (attendees)-->(jug)
RETURN *
MATCH (attendees)-->(jug)
WHERE jug.city = “Kaunas”
RETURN *

35. Cypher
MATCH (you:Person {name: "Dmitry"})
CREATE (you)-[like:LIKE]->(neo:Database {name: "Neo4j"})
RETURN you, like, neo

36. Whiteboard friendly
data modelling

39. CREATE (alice:Person {name: "Alice"})
CREATE (bob:Person {name: "Bob"})
CREATE (carol:Person {name: "Carol"})
CREATE (iphone:Device {name: "iPhone"})
CREATE (alice)-[:HAS]->(iphone)
CREATE (bob)-[:WANTS]->(iphone)
CREATE (alice)<-[:FOLLOWS]-(bob)
CREATE (alice)-[:FOLLOWS]->(bob)
CREATE (carol)-[:FOLLOWS]->(bob)
CREATE (bob)<-[:AUTHOR]-(:Comment {text: "Thoughts?"})
<-[:COMMENT]-(iphone)
CREATE (carol)<-[:AUTHOR]-(:Comment {text: "<3 it"})
<-[:COMMENT]-(iphone)

40. Data import
1. Retrieve data using Orianna
2. Load data as-is in Neo4j

41. Dependencies
dependencies {
compile 'com.robrua:orianna:2.4.3-SNAPSHOT'
compile 'org.neo4j:neo4j:2.3.3'
}

42. Create database
File dbDir = new File(databasePath);
GraphDatabaseService db = new GraphDatabaseFactory()
.newEmbeddedDatabaseBuilder(dbDir)
.newGraphDatabase();

43. Transaction
try (Transaction tx = db.beginTx()) {
// Do cool stuff
tx.success();
}

44. Load data
Summoner summoner =
RiotAPI.getSummonerByName(“FylmTM");
Node node = db.createNode();
node.addLabel(Labels.Summoner);
node.setProperty(KEY_ID, summoner.getID());
node.setProperty(KEY_NAME, summoner.getName());
node.setProperty(KEY_LEVEL, summoner.getLevel());

45. In the end

47. neo4j-contrib/neo4j-apoc-procedures
Awesome procedures for Neo4j 3.0 - codenamed "apoc"
// examines the full graph to create the meta-graph
CALL apoc.meta.graph();
Apoc was the technician and driver on board of the
Nebuchadnezzar in the Matrix movie.
He was killed by Cypher.

48. Import result
• Season 6, EUNE region
• Players: me & 7 friends
MATCH (n) RETURN count(n) as nodeCount
nodeCount
336729

49. Queries

50. Query #1 How many games I have played?
MATCH (s:Summoner {name: "FylmTM"})
MATCH (s)-[:PARTICIPATED|:PARTICIPATED_IN_MATCH]->(sMatch)
RETURN count(sMatch); // 59 ms
Count
110

51. Query #2 Who are my friends?
MATCH (s:Summoner {name: "FylmTM"})
MATCH (s)-[:PARTICIPATED]-()-[:PARTICIPATED_IN_MATCH]
->(sMatch)<-
[:PARTICIPATED_IN_MATCH]-()-[:PARTICIPATED]-(f)
WITH f.name as friendName, count(f) as gamesTogether
WHERE gamesTogether > 2
RETURN friendName, gamesTogether
ORDER BY gamesTogether DESC // 55 ms
friendName gamesTogether
Cryptael 107
Henua 28
Iger 11
XXpoMMou 10
yesCold 8
eskiuzi 3

52. Query #3 Most dangerous champions?
name total won lost %
Alistar 7 1 6 86%
Jax 12 3 9 75%
LeBlanc 8 2 6 75%
Dr. Mundo 7 2 5 71%
Miss Fortune 10 3 7 70%
MATCH (s:Summoner {name: "FylmTM"})
MATCH (s)-[:PARTICIPATED]->()-[:PLAYED_FOR_TEAM]->
(team {winner: true})<-[:HAS_TEAM]-()-[:HAS_TEAM]->(o)
<-[:PLAYED_FOR_TEAM]-()-[:PARTICIPATED_WITH_CHAMPION]->(c)
RETURN c.name as championName, count(c) as winCount
ORDER BY winCount DESC

53. Alistar Jax

55. Query #4 Any statistics?
MATCH (s:Summoner) WHERE s.name IN ["MrMgr", "Cryptael"]
MATCH (s)-[:PARTICIPATED|:PARTICIPANT_TIMELINE*2]->(pt)-[r]->(ptd)
WHERE pt.role = "SOLO" AND pt.lane = "MIDDLE"
AND r.name IN ["getCreepsPerMinDeltas", "getGoldPerMinDeltas"]
WITH s.name as name, r.name as stat,
sum(ptd.zero_to_ten) as `sum_0-10`,
size(filter(x IN collect(ptd.zero_to_ten) WHERE x <> 0))
as `size_0-10`,
sum(ptd.ten_to_twenty) as `sum_10-20`,
size(filter(x IN collect(ptd.ten_to_twenty) WHERE x <> 0))
as `size_10-20`,
sum(ptd.twenty_to_thirty) as `sum_20-30`,
size(filter(x IN collect(ptd.twenty_to_thirty) WHERE x <> 0))
as `size_20-30`,
sum(ptd.thirty_to_end) as `sum_30+`,
size(filter(x IN collect(ptd.thirty_to_end) WHERE x <> 0))
as `size_30+`
RETURN name, stat,
`sum_0-10` / `size_0-10` as `0-10`,
`sum_10-20` / `size_10-20` as `10-20`,
`sum_20-30` / `size_20-30` as `20-30`,
`sum_30+` / `size_30+` as `30+`
ORDER BY name, stat

56. creeps/min
0
2
4
6
8
0-10 10-20 20-30 30+
Cryptael
MrMgr
gold/min
0
150
300
450
600
0-10 10-20 20-30 30+
Cryptael
MrMgr
Cryptael
MrMgr (#27 in EUNE)

57. Takeaways
• Neo4j is easy to use
• Build schema on the fly
• Cypher is a handy tool for exploration
• Find value in your data