SlideShare a Scribd company logo

「GraphDB徹底入門」〜構造や仕組み理解から使いどころ・種々のGraphDBの比較まで幅広く〜

Takahiro Inoue, profile picture
Takahiro Inoue

The document discusses graph databases and their properties. Graph databases are structured to store graph-based data by using nodes and edges to represent entities and their relationships. They are well-suited for applications with complex relationships between entities that can be modeled as graphs, such as social networks. Key graph database technologies mentioned include Neo4j, OrientDB, and TinkerPop which provides graph traversal capabilities.

Technology
1 of 91
Downloaded 1,193 times
1
2
Most read
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Most read
51
52
53
54
55
56
57
58
59
60
61
62
63
Most read
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
Graph DB GraphDB doryokujin +WEB ( Tokyo.Webmining #9-2)
[Me] doryokujin 2 2 33 [Company] 1
[ ] MongoDB JP TokyoWebMining MongoDB [ ] MongoDB MongoDB GraphDB
#1 [MongoTokyo] Mongo DB Congerence in Japan 2011 03 01 10gen 3 … http://www.10gen.com/conferences/ mongotokyo2011
#2 [gihyo ] gihyo.jp 2 DocumentDB GraphDB NoSQL
Graph Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
Graph Graph Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
Graph: Graph Graph DB Graph
Graph [Graph] Dots Lines vertices edges 1 (relationship) Dots Lines Graph
Undirected Graph [ (Undirected) Graph] Vertices: Edges: (relationship) (symmetric)
Directed Graph [ (Directed) Graph] Vertices: Edges: (relationship) (asymmetric)
Directed / Underected Graph friend follow friend follow follow friend follow [Facebook] [Twitter] ”Undirected Graph” Follow ”Directed Graph” ” ” ” ” ”friends” ”follow”
Single-Relational Graph Single-Relational Structures → Undirected / Directed Graph Single-Relatinal 1 Graph
Single-Relational Graph friend follow friend follow follow friend follow [Facebook] [Twitter] ”Undirected Graph” Follow ”Directed Graph” ”Facebook ” ”Twitter ” ”friends” ”follow”
Single-Relational Reply num:5 Reply Block num:5 Reply DM num:5 num:1 RT RT Reply DM num:2 num:2 num:2 num:1 [Twitter] Graph ”Directed Graph” ”Twitter ” ”Reply”,”RT”,”DM”,”Block”
*Facebook Flickr lives_in is is is follow lives_in friend is share * friend share follow follow is [ ] lives_in Undirected Directed is is is lives_in
Multi-Relational Graph Multi-Relational Structures lives_in: User → Country Share: Facebook → Flikcr
Multi-Relational Reply Reply Block DM Reply RT RT Reply DM [Twitter] ”Twitter ” ”Reply”,”RT”,”DM”,”Block”
Multi-Relational *Facebook Flickr lives_in has has has follow lives_in friend has share * has friend share follow lives_in [Multi-Relatinal Graph] has has has lives_in
Property Graph Property Graph Multi-Relational Graph (Property) Graph DB Graph 1 key/value id id_A follow id id_B follow 100 follow 500 follower 200 date 2011/01/23 follower 1000
Property Graph Reply num:5 Reply Block num:5 Reply DM num:5 num:1 RT RT Reply DM num:2 num:2 num:2 num:1 Graph ”Property Graph” ”Twitter ” ”Reply”,”RT”,”DM”,”Block” ”num”
Property Graph name doryokujin sex man lives_in birth 1985/05/14 has has id id_B follow follow 1000 follower 2000 lives_in date 2011/01/23 friend has friend date 2011/01/23 has friend follow follow date 2010/03/23 date 2011/01/23 name full name mail xxx@yyy address zzz lives_in id id_A follow 100 follower 200 has has date 2010/03/23 lives_in
Graph The Graph Traversal Pattern
Property Graph Property Graph Graph Property Graph Graph DB Tinker Pop Hyper Graph
Graph DB Gragh Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
Graph DB: Property Graph DB “Graph DB”
Graph DB [ DB ≠ Graph DB] Graph DB DB Graph DB
RDB Graph [Relatinal Database] A outV inV A B B C A C C D D D A
Document DB Graph [Document Database] A { A : { out : [B, C], in : [D] } B : { in : [A] B C } C : { out : [D], in : [A] } D : { out : [A], in : [C] D } }
XML DB Graph [XML Database] A <graphml> <graph> <node id=A /> <node id=B /> B C <node id=C /> <edge source=A target=B /> <edge source=A target=C /> <edge source=C target=D /> <edge source=D target=A /> </graph> D </graphml>
Graph DB [ ] “A graph database is any storage system that provides index-free adjacency” The Graph Traversal Programming Pattern (“adjacent”) ( “index-free” )
Non-Graph DB and Index-Based Adjacency B E 1. A 3. (B,C) A A B C B, C E D, E D E 2. C D log_2(n) (B,C) time cost
Graph DB and Index-Free Adjacency ‣ ”Mini - Index” B E ‣ 1. 1 A (B,C) ‣ C D id id_B follow 1000 follower 2000
Property (key/value) The Graph Traversal Programming Pattern
GraphDB: Graph Traversal Graph DB Graph DB Query
Graph DB Query Graph Query = Graph Traversal Traversal = Root Graph Graph Traversal (Root) Index-Free Adjacency
private  void  printFriends(  Node  person  ) {        Traverser  traverser  =  person.traverse(                Order.BREADTH_FIRST,    //                  StopEvaluator.END_OF_GRAPH,  //  Graph                ReturnableEvaluator.ALL_BUT_START_NODE,  //  Root  Node                MyRelationshipTypes.KNOWS,  //  ”KNOWS”                Direction.OUTGOING  );  //          for  (  Node  friend  :  traverser  )        {      //   Node ”name”                System.out.println(  friend.getProperty(  "name"  )  );        } Neo4j Wiki }
1 3 1 2 Trinity Morpheus Cypher Agent  Smith Neo4j Wiki
private  void  findHackers(  Node  startNode  ) Neo4j Wiki {        Traverser  traverser  =  startNode.traverse(                Order.BREADTH_FIRST,  //                  StopEvaluator.END_OF_GRAPH,  //  Graph                new  ReturnableEvaluator()  //                  {                        public  boolean  isReturnableNode(  TraversalPosition  currentPosition  )                        {                                Relationship  rel  =  currentPosition.lastRelationshipTraversed();                                if  (  rel  !=  null  &&  rel.isType(  MyRelationshipTypes.CODED_BY  )  )                                {                                        return  true;  //  “CODED_BY”                                  }                                return  false;  //                          }                },  //   2                MyRelationshipTypes.CODED_BY,  Direction.OUTGOING,  //                  MyRelationshipTypes.KNOWS,  Direction.OUTGOING  );  //          for  (  Node  hacker  :  traverser  )        {                TraversalPosition  position  =  traverser.currentPosition();                System.out.println(  "At  depth  "  +  position.depth()  +  "  =>  "                        +  hacker.getProperty(  "name"  )  );        } ∴  At  depth  4  =>  The  Architect
Graph DB [Data Locality] [Local Search, Social Network] 2 [Transition] Web [Recommendation]
[Graph Problems] [Shortest Path] 2 GraphDB Traversal Neo4jrb
Graph DB ” ” 10 ”Knows” Tables, Documents, Key/Value Model GraphDB Union, Intersection, Join
Graph DB [ ] Property Graph Index-Free Adjacency Graph Query = Graph Traversal Data Locality
Graph DB Graph Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
Neo4j
Neo4j [ ] HP Java AGPLv3 2003 24 8 2009 VC ACID Propety Graph Model / Gremlin Lucene
Neo4j [Language Binding - Framework] Python - Django Ruby - Ruby on Rails Clojure Scala Groovy - Griffin / Grails Java - Spring Framework Ruby Ruby Java
Neo4j [Tools] Shell Shell Graph Traverse Indexing neo4j-server Neo4j REST API Admin tools Online BackUp Neoclipse Neo4j ↑ Batch Insert
Neo4j [ver. 1.2] 1.2 Neo4j Server REST API Admin Interface High Availability Kernel
sones
sones [ ] HP C# AGPLv3 2011 VC ACID REST Interface Property Graph Model / Gremlin : Property Hyper Graph Graph Query Language(GQL)
sones [GQL] SQL Traversal Cheat Sheet Query • FROM User SELECT User.Friends.Friends.Name // aggregation • SELECT COUNT(User.Friends) • SELECT User.Friends.Random(2) • SELECT User.Friends.Name.Substring(2,5)
Orient DB
Orient DB [ ] HP Java Apache2.0 1997 C++ → Java Document-Graph DB ACID Shell / REST Interface Propety Graph Model / Gremlin
Orient DB [Document-Graph DB] [ ] Orient DB Object DB Key/Value Server Document DB // DATABASE OPEN ODatabaseDocumentTx db = new ODatabaseDocumentTx("remote:localhost/petshop").open ("admin", "admin"); // Document ODocument doc = new ODocument(db, "Person"); doc.field( "name", "Luke" ); doc.field( "surname", "Skywalker" ); doc.field( "city", new ODocument(db, "City").field("name","Rome").field("country", "Italy") );               // Transaction doc.save(); db.close();
Orient DB [Document-Graph DB] OGraphVertex OGraphEdge OGraphElement ODocumentWrapper Document SQL SELECT FROM OGraphVertex WHERE outEdges CONTAINS ( label = 'knows' ) //7 ”knows” SELECT FROM OGraphVertex WHERE outEdges TRAVERSE(0,7,'out,outEdges') ( @class = 'OGraphEdge' and label = 'knows' )
Orient DB [Language Binding Using Binary Protocol] Java C PHP JRuby (Ruby: soon) [Language Binding Using REST Protocol] Python Java Script
InfoGrid
InfoGrid [ ] HP JAVA AGPLv3 ACID REST Interface MeshObject Graph MeshBase _GDB = StoreMeshBase.create(_MySQLStore); MeshObject _xkcd = _GDB.getMeshObjectLifecycleManager ().createMeshObject(); _xkcd.setProperty("Name", "xkcd"); _xkcd.setProperty("Url", "http://www.xkcd.com"); _xkcd.relate(_good)
Infinite Graph
Infinite Graph [ ] HP C++ Academic and Start Up 2010 6 Distributed Graph DB ↑Objectivity/DB: distributed database server
「GraphDB徹底入門」〜構造や仕組み理解から使いどころ・種々のGraphDBの比較まで幅広く〜
Graph DB: Data SQL Like GraphDB License Language Protocol Gremlin Binding Model Query REST/ Property Ruby, Python, Neo4j AGPLv3 Java Yes Scala,... - JSON Graph REST/ Property sones AGPLv3 C# JSON Graph Yes - Yes (XML) (+Extend) REST/ Property PHP, Jruby, OrientDB Apache2.0 Java Yes Python, JS,... Yes JSON Graph Property REST/ Info Grid AGPLv3 Java Graph? - - - JSON (MeshObject) Infinite Property Product C++ - - - - Graph Graph
Graph DB [ ] Graph DB Neo4j Open Source Social Graph Software Not Ready Yet Graph DB Hypergtaph: PropertyGraph HyperGraph Pregel: bulk synchronous parallel model Distributed DB Google FlockDB: Distributed DB for storing adjancency lists Twitter
Tinker Pop Graph Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
Tinker Pop
Tinker Pop [Tinker Pop] HP Property Graph Model GraphDB Blueprints: A Property Graph Model Interface Gremlin: A Graph Traversal Language Pipes: A Data Flow Framework using Process Graphs Rexster: A RESTful Graph Shell Mutant: A Poly-ScriptEngine ScriptEngine
Tinker Pop
Tinker Pop: BluePrints
BluePrints [ ] HP GraphDB ”JDBC” Property Graph Model GraphDB [Now] Tinker Graph: in-memory property graph model Sail: Open RDF Neo4j, Orient DB, sones, ... [Future] Redis Infinite Graph, Dex
BluePrints GraphDB Graph graph = new Neo4jGraph("/tmp/graph/neo4j"); // Graph graph = new OrientGraph("/tmp/graph/orientdb"); Vertex a = graph.addVertex(null); Vertex b = graph.addVertex(null); a.setProperty("name","marko"); b.setProperty("name","aaron"); Edge e = graph.addEdge(null,a,b,"knows"); e.setProperty("since",2010); graph.shutdown();
BluePrints Transaction graph.startTransaction(); try{   Vertex luca = graph.addVertex(null);   luca.setProperty( "name", "Luca" );   Vertex marko = graph.addVertex(null);   marko.setProperty( "name", "Marko" );   Edge lucaKnowsMarko = graph.addEdge(null, luca, marko,"knows");   graph.stopTransaction(Conclusion.SUCCESS); } catch( Exception e ) {   graph.stopTransaction(Conclusion.FAILURE); }
Tinker Pop: Gremlin
Gremlin [ ] HP Gremlin = Graph Programing Language Blueprints GraphDB Shell GraphDB Query Java + Groovy
Gremlin Property Graph Basic Graph Traversals
doryokujin$ ./gremlin.sh ,,,/ (o o) -----oOOo-(_)-oOOo----- gremlin>  g  =  TinkerGraphFactory.createTinkerGraph() ==>tinkergraph[vertices:6  edges:6]  // 6 6 gremlin>  g.class ==>class   com.tinkerpop.blueprints.pgm.impls.tg.TinkerGraph gremlin>  //   gremlin>  g.V ==>v[3] ==>v[2] ... gremlin>  //   gremlin>  g.E ==>e[10][4-­‐created-­‐>5] ==>e[7][1-­‐knows-­‐>2] ==>e[9][1-­‐created-­‐>3] ... Getting Srarted
gremlin>  v  =  g.v(1)  //  id=1   ==>v[1] gremlin>  v.keys()  //   ==>age ==>name gremlin>  v.values()  //   ==>29 ==>marko gremlin>  v.name  +  '  is  '  +  v.age  +  '  years  old.' ==>marko  is  29  years  old. gremlin>  //  id=1,  name=marko   gremlin>  v.outE ==>e[7][1-­‐knows-­‐>2] ==>e[9][1-­‐created-­‐>3] ==>e[8][1-­‐knows-­‐>4] gremlin>  //   gremlin>  v.outE.weight ==>0.5 ==>0.4 ==>1.0 Getting Srarted
gremlin>  //  id=1 1.0 gremlin>  v.outE{it.weight  <  1.0}.inV ==>v[2] ==>v[3] gremlin>  //   gremlin>  list  =  []                                                           gremlin>  v.outE{it.weight  <  1.0}.inV  >>  list ==>v[2] ==>v[3] gremlin>  //  list property  maps gremlin>  list.collect{  it.map()  } ==>{name=vadas,  age=27} ==>{name=lop,  lang=java} gremlin>  //  list gremlin>  list.inE()               ==>e[7][1-­‐knows-­‐>2] ==>e[9][1-­‐created-­‐>3] ... Getting Srarted
gremlin>  list.inE{it.label=='knows'}    //   'knows' ==>e[7][1-­‐knows-­‐>2] gremlin>  list.inE()[[label:'knows']]  //   ==>e[7][1-­‐knows-­‐>2] gremlin>  list.inE()[[label:'knows']].outV.name  // :name   ==>marko Getting Srarted ~20000ms:  g.V.outE{it['label']=='followed_by'}.inV.outE{it['label']=='followed_by'}.inV.outE                      {it['label']=='followed_by'}.inV  >>-­‐1 ~9000ms:    g.V.outE{it.label=='followed_by'}.inV.outE{it.label=='followed_by'}.inV.outE                    {it.label=='followed_by'}.inV  >>-­‐1 ~8500ms:    g.V.outE{it.getLabel()=='followed_by'}.inV.outE{it.getLabel()=='followed_by'}.inV.outE                            {it.getLabel()=='followed_by'}.inV  >>-­‐1 ~6000ms:    g.V.outE[[label:'followed_by']].inV.outE[[label:'followed_by']].inV.outE                      [[label:'followed_by']].inV  >>-­‐1 ClosureFilterPipe vs. PropertyFIlterPipe
Tinker Pop: Pipes
Pipes [ ] HP Pipes = Data Flow Framework Pipes Graph Traversal 1 1 Pipes filtering, splitting, merging, traversing,...
Gremlin g:id-v('a')/outE[@label='knows']/inV/outE[@label='develops']/inV/@name Pipe pipe1 = new VertexEdgePipe(Step.OUT_EDGES); Pipe pipe2 = new LabelFilterPipe("knows", Filter.NOT_EQUALS); Pipe pipe3 = new EdgeVertexPipe(Step.IN_VERTEX); Pipe pipe4 = new VertexEdgePipe(Step.OUT_EDGES); Pipe pipe5 = new LabelFilterPipe("develops", Filter.NOT_EQUALS); Pipe pipe6 = new EdgeVertexPipe(Step.IN_VERTEX); Pipe pipe7 = new PropertyPipe("name"); Pipe pipeline = new Pipeline (pipe1,pipe2,pipe3,pipe4,pipe5,pipe6,pipe7); pipeline.setStarts(new SingleIterator(graph.getVertex("a")); for(String name : pipeline) {   System.out.println(name); } A Graph Processing Stack
Pipes Pipes public  class  NumCharsPipe  extends  AbstractPipe<String,Integer>  {    public  Integer  processNextStart()  {        String  word  =  this.starts.next();        return  word.length();    } } A Graph Processing Stack
Tinker Pop: Rexster
Rexster [ ] HP Rexster = A RESTful Graph Shell Blueprints GraphDB RESTful API (JSON) Gremlin
> http://localhost:8182/examplegraph/vertices/b {   "version":"0.1",   "results": {     "_type":"vertex",     "_id":"b",     "name":"aaron",     "type":"person"   },   "query_time":0.1537 } A Graph Processing Stack // g:key-v('name','DARK STAR')[0]: Usin gGremlin Code > http://localhost:8182/gratefulgraph/traversals/gremlin? script=g:key-v%28%27name%27,%27DARK%20STAR%27%29[0] {    "results":  [{        "_type":"vertex",        "_id":"89",        "name":"DARK  STAR",        "song_type":"original",        "performances":219,        "type":"song"}    ],    "query_time":6.753024,    "success":true,    "version" } Using Gremilin
Tinker Pop: Mutant
Mutant [ ] HP Mutant = A Poly-ScriptEngine ScriptEngine JVM Script Engine
Mutant Console marko:~/software/mutant$  ./mutant.sh              //          oO  ~~-­‐_ ___m(___m___~.___    MuTanT  0.1-­‐SNAPSHOT _|__|__|__|__|__|          [  ?h  =  help  ] [gremlin]  gremlin  0.6-­‐SNAPSHOT [Groovy]  Groovy  Scripting  Engine  2.0 [ruby]  JSR  223  JRuby  Engine  1.5.5 [ECMAScript]  Mozilla  Rhino  1.6  release  2 [AppleScript]  AppleScriptEngine  1.0 mutant[gremlin]>  $x  :=  12 [12] mutant[gremlin]>  ?x mutant[AppleScript]>  ?x mutant[Groovy]>  $x 12 mutant[Groovy]>  ?x mutant[ruby]>  $x 12 mutant[ruby]>  ?x mutant[ECMAScript]>  $x 12 Basic Examples
[ ] Graph DB Graph DB Graph Partitioning Pregel Neo4j
… ※ Graph DB http://snap.stanford.edu/data/index.html

More Related Content

PDF
イミュータブルデータモデル(入門編)
Yoshitaka Kawashima
 
PPTX
世界一わかりやすいClean Architecture
Atsushi Nakamura
 
PPTX
Redisの特徴と活用方法について
Yuji Otani
 
PDF
イミュータブルデータモデルの極意
Yoshitaka Kawashima
 
PDF
機械学習モデルのサービングとは？
Sho Tanaka
 
PPTX
MongoDBが遅いときの切り分け方法
Tetsutaro Watanabe
 
PPTX
テストコードの DRY と DAMP
Yusuke Kagata
 
PDF
DockerとPodmanの比較
Akihiro Suda
 
イミュータブルデータモデル(入門編)
Yoshitaka Kawashima
 
世界一わかりやすいClean Architecture
Atsushi Nakamura
 
Redisの特徴と活用方法について
Yuji Otani
 
イミュータブルデータモデルの極意
Yoshitaka Kawashima
 
機械学習モデルのサービングとは？
Sho Tanaka
 
MongoDBが遅いときの切り分け方法
Tetsutaro Watanabe
 
テストコードの DRY と DAMP
Yusuke Kagata
 
DockerとPodmanの比較
Akihiro Suda
 

What's hot (20)

PDF
マイクロにしすぎた結果がこれだよ！
mosa siru
 
PDF
コンテナの作り方「Dockerは裏方で何をしているのか？」
Masahito Zembutsu
 
PDF
マルチテナントのアプリケーション実装〜実践編〜
Yoshiki Nakagawa
 
PDF
Kinesis + Elasticsearchでつくるさいきょうのログ分析基盤
Amazon Web Services Japan
 
PDF
Serf / Consul 入門 ～仕事を楽しくしよう～
Masahito Zembutsu
 
PPTX
初心者向けMongoDBのキホン！
Tetsutaro Watanabe
 
PDF
ソーシャルゲームのためのデータベース設計
Yoshinori Matsunobu
 
PDF
それはYAGNIか? それとも思考停止か?
Yoshitaka Kawashima
 
PDF
AWSのログ管理ベストプラクティス
Akihiro Kuwano
 
PDF
Dockerfileを改善するためのBest Practice 2019年版
Masahito Zembutsu
 
PDF
PlaySQLAlchemy: SQLAlchemy入門
泰 増田
 
PDF
新入社員のための大規模ゲーム開発入門 サーバサイド編
infinite_loop
 
PPTX
AWSで作る分析基盤
Yu Otsubo
 
PDF
ビジネスパーソンのためのDX入門講座エッセンス版
Tokoroten Nakayama
 
PDF
テスト文字列に「うんこ」と入れるな
Kentaro Matsui
 
PPTX
データ履歴管理のためのテンポラルデータモデルとReladomoの紹介 #jjug_ccc #ccc_g3
Hiroshi Ito
 
PDF
開発速度が速い #とは（LayerX社内資料）
mosa siru
 
PDF
ヤフー社内でやってるMySQLチューニングセミナー大公開
Yahoo!デベロッパーネットワーク
 
PDF
Kubernetesのしくみ やさしく学ぶ 内部構造とアーキテクチャー
Toru Makabe
 
PDF
MLOps に基づく AI/ML 実運用最前線 ～画像、動画データにおける MLOps 事例のご紹介～（映像情報メディア学会2021年冬季大会企画セッショ...
NTT DATA Technology & Innovation
 
マイクロにしすぎた結果がこれだよ！
mosa siru
 
コンテナの作り方「Dockerは裏方で何をしているのか？」
Masahito Zembutsu
 
マルチテナントのアプリケーション実装〜実践編〜
Yoshiki Nakagawa
 
Kinesis + Elasticsearchでつくるさいきょうのログ分析基盤
Amazon Web Services Japan
 
Serf / Consul 入門 ～仕事を楽しくしよう～
Masahito Zembutsu
 
初心者向けMongoDBのキホン！
Tetsutaro Watanabe
 
ソーシャルゲームのためのデータベース設計
Yoshinori Matsunobu
 
それはYAGNIか? それとも思考停止か?
Yoshitaka Kawashima
 
AWSのログ管理ベストプラクティス
Akihiro Kuwano
 
Dockerfileを改善するためのBest Practice 2019年版
Masahito Zembutsu
 
PlaySQLAlchemy: SQLAlchemy入門
泰 増田
 
新入社員のための大規模ゲーム開発入門 サーバサイド編
infinite_loop
 
AWSで作る分析基盤
Yu Otsubo
 
ビジネスパーソンのためのDX入門講座エッセンス版
Tokoroten Nakayama
 
テスト文字列に「うんこ」と入れるな
Kentaro Matsui
 
データ履歴管理のためのテンポラルデータモデルとReladomoの紹介 #jjug_ccc #ccc_g3
Hiroshi Ito
 
開発速度が速い #とは（LayerX社内資料）
mosa siru
 
ヤフー社内でやってるMySQLチューニングセミナー大公開
Yahoo!デベロッパーネットワーク
 
Kubernetesのしくみ やさしく学ぶ 内部構造とアーキテクチャー
Toru Makabe
 
MLOps に基づく AI/ML 実運用最前線 ～画像、動画データにおける MLOps 事例のご紹介～（映像情報メディア学会2021年冬季大会企画セッショ...
NTT DATA Technology & Innovation
 
Ad

Similar to 「GraphDB徹底入門」〜構造や仕組み理解から使いどころ・種々のGraphDBの比較まで幅広く〜 (20)

PDF
The Graph Traversal Programming Pattern
Marko Rodriguez
 
PDF
The Definition of GraphDB
Takahiro Inoue
 
PDF
Undirected graphs
Nick Lee
 
PDF
Graphs - Chris Dixon & Matt Gattis
Razin Mustafiz
 
PDF
NOSQL overview and intro to graph databases with Neo4j (Geeknight May 2010)
Emil Eifrem
 
PDF
Neo4j - The Benefits of Graph Databases (OSCON 2009)
Emil Eifrem
 
PDF
NOSQL Overview, Neo4j Intro And Production Example (QCon London 2010)
Emil Eifrem
 
PDF
A NOSQL Overview And The Benefits Of Graph Databases (nosql east 2009)
Emil Eifrem
 
PDF
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...
Marko Rodriguez
 
PDF
Neo4j -- or why graph dbs kick ass
Emil Eifrem
 
PDF
Kickoff research project TU Ilmenau
Henning Rauch
 
PDF
Leveraging social data with semantics
Việt Long Plaza
 
PDF
Eifrem neo4j
Shridhar Joshi
 
PPTX
CSC 8101 Non Relational Databases
sjwoodman
 
PDF
Graph Databases: Trends in the Web of Data
Marko Rodriguez
 
PPTX
Socialmediaviz short
Tony Hirst
 
PPTX
An Introduction to NOSQL, Graph Databases and Neo4j
Debanjan Mahata
 
PDF
Graph Theory and Databases
Pere Urbón-Bayes
 
PDF
Gephi short introduction
Sébastien
 
KEY
Graphs in the Database: Rdbms In The Social Networks Age
Lorenzo Alberton
 
The Graph Traversal Programming Pattern
Marko Rodriguez
 
The Definition of GraphDB
Takahiro Inoue
 
Undirected graphs
Nick Lee
 
Graphs - Chris Dixon & Matt Gattis
Razin Mustafiz
 
NOSQL overview and intro to graph databases with Neo4j (Geeknight May 2010)
Emil Eifrem
 
Neo4j - The Benefits of Graph Databases (OSCON 2009)
Emil Eifrem
 
NOSQL Overview, Neo4j Intro And Production Example (QCon London 2010)
Emil Eifrem
 
A NOSQL Overview And The Benefits Of Graph Databases (nosql east 2009)
Emil Eifrem
 
Problem-Solving using Graph Traversals: Searching, Scoring, Ranking, and Reco...
Marko Rodriguez
 
Neo4j -- or why graph dbs kick ass
Emil Eifrem
 
Kickoff research project TU Ilmenau
Henning Rauch
 
Leveraging social data with semantics
Việt Long Plaza
 
Eifrem neo4j
Shridhar Joshi
 
CSC 8101 Non Relational Databases
sjwoodman
 
Graph Databases: Trends in the Web of Data
Marko Rodriguez
 
Socialmediaviz short
Tony Hirst
 
An Introduction to NOSQL, Graph Databases and Neo4j
Debanjan Mahata
 
Graph Theory and Databases
Pere Urbón-Bayes
 
Gephi short introduction
Sébastien
 
Graphs in the Database: Rdbms In The Social Networks Age
Lorenzo Alberton
 
Ad

More from Takahiro Inoue (20)

PDF
Treasure Data × Wave Analytics EC Demo
Takahiro Inoue
 
PDF
トレジャーデータとtableau実現する自動レポーティング
Takahiro Inoue
 
PDF
Tableauが魅せる Data Visualization の世界
Takahiro Inoue
 
PDF
トレジャーデータのバッチクエリとアドホッククエリを理解する
Takahiro Inoue
 
PDF
20140708 オンラインゲームソリューション
Takahiro Inoue
 
PDF
トレジャーデータ流，データ分析の始め方
Takahiro Inoue
 
PDF
オンラインゲームソリューション@トレジャーデータ
Takahiro Inoue
 
PDF
事例で学ぶトレジャーデータ 20140612
Takahiro Inoue
 
PDF
トレジャーデータ株式会社について（for all Data_Enthusiast!!）
Takahiro Inoue
 
PDF
この Visualization がすごい2014 〜データ世界を彩るツール6選〜
Takahiro Inoue
 
PDF
Treasure Data Intro for Data Enthusiast!!
Takahiro Inoue
 
PDF
Hadoop and the Data Scientist
Takahiro Inoue
 
PDF
MongoDB: Intro & Application for Big Data
Takahiro Inoue
 
PDF
An Introduction to Fluent & MongoDB Plugins
Takahiro Inoue
 
PDF
An Introduction to Tinkerpop
Takahiro Inoue
 
PDF
An Introduction to Neo4j
Takahiro Inoue
 
PDF
Large-Scale Graph Processing〜Introduction〜(完全版)
Takahiro Inoue
 
PDF
Large-Scale Graph Processing〜Introduction〜(LT版)
Takahiro Inoue
 
PDF
Advanced MongoDB #1
Takahiro Inoue
 
PDF
はじめてのGlusterFS
Takahiro Inoue
 
Treasure Data × Wave Analytics EC Demo
Takahiro Inoue
 
トレジャーデータとtableau実現する自動レポーティング
Takahiro Inoue
 
Tableauが魅せる Data Visualization の世界
Takahiro Inoue
 
トレジャーデータのバッチクエリとアドホッククエリを理解する
Takahiro Inoue
 
20140708 オンラインゲームソリューション
Takahiro Inoue
 
トレジャーデータ流，データ分析の始め方
Takahiro Inoue
 
オンラインゲームソリューション@トレジャーデータ
Takahiro Inoue
 
事例で学ぶトレジャーデータ 20140612
Takahiro Inoue
 
トレジャーデータ株式会社について（for all Data_Enthusiast!!）
Takahiro Inoue
 
この Visualization がすごい2014 〜データ世界を彩るツール6選〜
Takahiro Inoue
 
Treasure Data Intro for Data Enthusiast!!
Takahiro Inoue
 
Hadoop and the Data Scientist
Takahiro Inoue
 
MongoDB: Intro & Application for Big Data
Takahiro Inoue
 
An Introduction to Fluent & MongoDB Plugins
Takahiro Inoue
 
An Introduction to Tinkerpop
Takahiro Inoue
 
An Introduction to Neo4j
Takahiro Inoue
 
Large-Scale Graph Processing〜Introduction〜(完全版)
Takahiro Inoue
 
Large-Scale Graph Processing〜Introduction〜(LT版)
Takahiro Inoue
 
Advanced MongoDB #1
Takahiro Inoue
 
はじめてのGlusterFS
Takahiro Inoue
 

Recently uploaded (20)

PPT
Teaching material agriculture food technology
LiaRayya
 
PPTX
Digital-Transformation-Roadmap-for-Companies.pptx
David Malick Dieng
 
PDF
Dropbox Q2 2025 Financial Results & Investor Presentation
Dropbox
 
PDF
Blue Purple Modern Animated Computer Science Presentation.pdf.pdf
Akar16
 
DOCX
The AUB Centre for AI in Media Proposal.docx
GAONE9
 
PDF
Modernizing your data center with Dell and AMD
Principled Technologies
 
PDF
Unlocking AI with Model Context Protocol (MCP)
Brian McKeiver
 
PDF
CIFDAQ's Market Insight: SEC Turns Pro Crypto
CIFDAQ
 
PDF
NewMind AI Weekly Chronicles - August'25 Week I
NewMind AI
 
PPTX
Effective Security Operations Center (SOC) A Modern, Strategic, and Threat-In...
ReZa AdineH
 
PDF
Agricultural_Statistics_at_a_Glance_2022_0.pdf
SandeepSingh286037
 
PPTX
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
muhammadmuneebnaeem7
 
PDF
Encapsulation_ Review paper, used for researhc scholars
gurumoop
 
PDF
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
IAESIJAI
 
PDF
Empathic Computing: Creating Shared Understanding
Mark Billinghurst
 
PPTX
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
AVTRON Technologies LLC
 
PDF
The Rise and Fall of 3GPP – Time for a Sabbatical?
3G4G
 
PPTX
A Presentation on Artificial Intelligence
memembruh336
 
PDF
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
Peter Tan
 
PDF
Encapsulation theory and applications.pdf
gurumoop
 
Teaching material agriculture food technology
LiaRayya
 
Digital-Transformation-Roadmap-for-Companies.pptx
David Malick Dieng
 
Dropbox Q2 2025 Financial Results & Investor Presentation
Dropbox
 
Blue Purple Modern Animated Computer Science Presentation.pdf.pdf
Akar16
 
The AUB Centre for AI in Media Proposal.docx
GAONE9
 
Modernizing your data center with Dell and AMD
Principled Technologies
 
Unlocking AI with Model Context Protocol (MCP)
Brian McKeiver
 
CIFDAQ's Market Insight: SEC Turns Pro Crypto
CIFDAQ
 
NewMind AI Weekly Chronicles - August'25 Week I
NewMind AI
 
Effective Security Operations Center (SOC) A Modern, Strategic, and Threat-In...
ReZa AdineH
 
Agricultural_Statistics_at_a_Glance_2022_0.pdf
SandeepSingh286037
 
KOM of Painting work and Equipment Insulation REV00 update 25-dec.pptx
muhammadmuneebnaeem7
 
Encapsulation_ Review paper, used for researhc scholars
gurumoop
 
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
IAESIJAI
 
Empathic Computing: Creating Shared Understanding
Mark Billinghurst
 
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
AVTRON Technologies LLC
 
The Rise and Fall of 3GPP – Time for a Sabbatical?
3G4G
 
A Presentation on Artificial Intelligence
memembruh336
 
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
Peter Tan
 
Encapsulation theory and applications.pdf
gurumoop
 

「GraphDB徹底入門」〜構造や仕組み理解から使いどころ・種々のGraphDBの比較まで幅広く〜

  • 1. Graph DB GraphDB doryokujin +WEB ( Tokyo.Webmining #9-2)
  • 2. [Me] doryokujin 2 2 33 [Company] 1
  • 3. [ ] MongoDB JP TokyoWebMining MongoDB [ ] MongoDB MongoDB GraphDB
  • 4. #1 [MongoTokyo] Mongo DB Congerence in Japan 2011 03 01 10gen 3 … http://www.10gen.com/conferences/ mongotokyo2011
  • 5. #2 [gihyo ] gihyo.jp 2 DocumentDB GraphDB NoSQL
  • 6. Graph Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
  • 7. Graph Graph Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
  • 8. Graph: Graph Graph DB Graph
  • 9. Graph [Graph] Dots Lines vertices edges 1 (relationship) Dots Lines Graph
  • 10. Undirected Graph [ (Undirected) Graph] Vertices: Edges: (relationship) (symmetric)
  • 11. Directed Graph [ (Directed) Graph] Vertices: Edges: (relationship) (asymmetric)
  • 12. Directed / Underected Graph friend follow friend follow follow friend follow [Facebook] [Twitter] ”Undirected Graph” Follow ”Directed Graph” ” ” ” ” ”friends” ”follow”
  • 13. Single-Relational Graph Single-Relational Structures → Undirected / Directed Graph Single-Relatinal 1 Graph
  • 14. Single-Relational Graph friend follow friend follow follow friend follow [Facebook] [Twitter] ”Undirected Graph” Follow ”Directed Graph” ”Facebook ” ”Twitter ” ”friends” ”follow”
  • 15. Single-Relational Reply num:5 Reply Block num:5 Reply DM num:5 num:1 RT RT Reply DM num:2 num:2 num:2 num:1 [Twitter] Graph ”Directed Graph” ”Twitter ” ”Reply”,”RT”,”DM”,”Block”
  • 16. *Facebook Flickr lives_in is is is follow lives_in friend is share * friend share follow follow is [ ] lives_in Undirected Directed is is is lives_in
  • 17. Multi-Relational Graph Multi-Relational Structures lives_in: User → Country Share: Facebook → Flikcr
  • 18. Multi-Relational Reply Reply Block DM Reply RT RT Reply DM [Twitter] ”Twitter ” ”Reply”,”RT”,”DM”,”Block”
  • 19. Multi-Relational *Facebook Flickr lives_in has has has follow lives_in friend has share * has friend share follow lives_in [Multi-Relatinal Graph] has has has lives_in
  • 20. Property Graph Property Graph Multi-Relational Graph (Property) Graph DB Graph 1 key/value id id_A follow id id_B follow 100 follow 500 follower 200 date 2011/01/23 follower 1000
  • 21. Property Graph Reply num:5 Reply Block num:5 Reply DM num:5 num:1 RT RT Reply DM num:2 num:2 num:2 num:1 Graph ”Property Graph” ”Twitter ” ”Reply”,”RT”,”DM”,”Block” ”num”
  • 22. Property Graph name doryokujin sex man lives_in birth 1985/05/14 has has id id_B follow follow 1000 follower 2000 lives_in date 2011/01/23 friend has friend date 2011/01/23 has friend follow follow date 2010/03/23 date 2011/01/23 name full name mail xxx@yyy address zzz lives_in id id_A follow 100 follower 200 has has date 2010/03/23 lives_in
  • 23. Graph The Graph Traversal Pattern
  • 24. Property Graph Property Graph Graph Property Graph Graph DB Tinker Pop Hyper Graph
  • 25. Graph DB Gragh Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
  • 26. Graph DB: Property Graph DB “Graph DB”
  • 27. Graph DB [ DB ≠ Graph DB] Graph DB DB Graph DB
  • 28. RDB Graph [Relatinal Database] A outV inV A B B C A C C D D D A
  • 29. Document DB Graph [Document Database] A { A : { out : [B, C], in : [D] } B : { in : [A] B C } C : { out : [D], in : [A] } D : { out : [A], in : [C] D } }
  • 30. XML DB Graph [XML Database] A <graphml> <graph> <node id=A /> <node id=B /> B C <node id=C /> <edge source=A target=B /> <edge source=A target=C /> <edge source=C target=D /> <edge source=D target=A /> </graph> D </graphml>
  • 31. Graph DB [ ] “A graph database is any storage system that provides index-free adjacency” The Graph Traversal Programming Pattern (“adjacent”) ( “index-free” )
  • 32. Non-Graph DB and Index-Based Adjacency B E 1. A 3. (B,C) A A B C B, C E D, E D E 2. C D log_2(n) (B,C) time cost
  • 33. Graph DB and Index-Free Adjacency ‣ ”Mini - Index” B E ‣ 1. 1 A (B,C) ‣ C D id id_B follow 1000 follower 2000
  • 34. Property (key/value) The Graph Traversal Programming Pattern
  • 35. GraphDB: Graph Traversal Graph DB Graph DB Query
  • 36. Graph DB Query Graph Query = Graph Traversal Traversal = Root Graph Graph Traversal (Root) Index-Free Adjacency
  • 37. private  void  printFriends(  Node  person  ) {        Traverser  traverser  =  person.traverse(                Order.BREADTH_FIRST,    //                  StopEvaluator.END_OF_GRAPH,  //  Graph                ReturnableEvaluator.ALL_BUT_START_NODE,  //  Root  Node                MyRelationshipTypes.KNOWS,  //  ”KNOWS”                Direction.OUTGOING  );  //          for  (  Node  friend  :  traverser  )        {      //   Node ”name”                System.out.println(  friend.getProperty(  "name"  )  );        } Neo4j Wiki }
  • 38. 1 3 1 2 Trinity Morpheus Cypher Agent  Smith Neo4j Wiki
  • 39. private  void  findHackers(  Node  startNode  ) Neo4j Wiki {        Traverser  traverser  =  startNode.traverse(                Order.BREADTH_FIRST,  //                  StopEvaluator.END_OF_GRAPH,  //  Graph                new  ReturnableEvaluator()  //                  {                        public  boolean  isReturnableNode(  TraversalPosition  currentPosition  )                        {                                Relationship  rel  =  currentPosition.lastRelationshipTraversed();                                if  (  rel  !=  null  &&  rel.isType(  MyRelationshipTypes.CODED_BY  )  )                                {                                        return  true;  //  “CODED_BY”                                  }                                return  false;  //                          }                },  //   2                MyRelationshipTypes.CODED_BY,  Direction.OUTGOING,  //                  MyRelationshipTypes.KNOWS,  Direction.OUTGOING  );  //          for  (  Node  hacker  :  traverser  )        {                TraversalPosition  position  =  traverser.currentPosition();                System.out.println(  "At  depth  "  +  position.depth()  +  "  =>  "                        +  hacker.getProperty(  "name"  )  );        } ∴  At  depth  4  =>  The  Architect
  • 40. Graph DB [Data Locality] [Local Search, Social Network] 2 [Transition] Web [Recommendation]
  • 41. [Graph Problems] [Shortest Path] 2 GraphDB Traversal Neo4jrb
  • 42. Graph DB ” ” 10 ”Knows” Tables, Documents, Key/Value Model GraphDB Union, Intersection, Join
  • 43. Graph DB [ ] Property Graph Index-Free Adjacency Graph Query = Graph Traversal Data Locality
  • 44. Graph DB Graph Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
  • 45. Neo4j
  • 46. Neo4j [ ] HP Java AGPLv3 2003 24 8 2009 VC ACID Propety Graph Model / Gremlin Lucene
  • 47. Neo4j [Language Binding - Framework] Python - Django Ruby - Ruby on Rails Clojure Scala Groovy - Griffin / Grails Java - Spring Framework Ruby Ruby Java
  • 48. Neo4j [Tools] Shell Shell Graph Traverse Indexing neo4j-server Neo4j REST API Admin tools Online BackUp Neoclipse Neo4j ↑ Batch Insert
  • 49. Neo4j [ver. 1.2] 1.2 Neo4j Server REST API Admin Interface High Availability Kernel
  • 50. sones
  • 51. sones [ ] HP C# AGPLv3 2011 VC ACID REST Interface Property Graph Model / Gremlin : Property Hyper Graph Graph Query Language(GQL)
  • 52. sones [GQL] SQL Traversal Cheat Sheet Query • FROM User SELECT User.Friends.Friends.Name // aggregation • SELECT COUNT(User.Friends) • SELECT User.Friends.Random(2) • SELECT User.Friends.Name.Substring(2,5)
  • 54. Orient DB [ ] HP Java Apache2.0 1997 C++ → Java Document-Graph DB ACID Shell / REST Interface Propety Graph Model / Gremlin
  • 55. Orient DB [Document-Graph DB] [ ] Orient DB Object DB Key/Value Server Document DB // DATABASE OPEN ODatabaseDocumentTx db = new ODatabaseDocumentTx("remote:localhost/petshop").open ("admin", "admin"); // Document ODocument doc = new ODocument(db, "Person"); doc.field( "name", "Luke" ); doc.field( "surname", "Skywalker" ); doc.field( "city", new ODocument(db, "City").field("name","Rome").field("country", "Italy") );               // Transaction doc.save(); db.close();
  • 56. Orient DB [Document-Graph DB] OGraphVertex OGraphEdge OGraphElement ODocumentWrapper Document SQL SELECT FROM OGraphVertex WHERE outEdges CONTAINS ( label = 'knows' ) //7 ”knows” SELECT FROM OGraphVertex WHERE outEdges TRAVERSE(0,7,'out,outEdges') ( @class = 'OGraphEdge' and label = 'knows' )
  • 57. Orient DB [Language Binding Using Binary Protocol] Java C PHP JRuby (Ruby: soon) [Language Binding Using REST Protocol] Python Java Script
  • 59. InfoGrid [ ] HP JAVA AGPLv3 ACID REST Interface MeshObject Graph MeshBase _GDB = StoreMeshBase.create(_MySQLStore); MeshObject _xkcd = _GDB.getMeshObjectLifecycleManager ().createMeshObject(); _xkcd.setProperty("Name", "xkcd"); _xkcd.setProperty("Url", "http://www.xkcd.com"); _xkcd.relate(_good)
  • 61. Infinite Graph [ ] HP C++ Academic and Start Up 2010 6 Distributed Graph DB ↑Objectivity/DB: distributed database server
  • 63. Graph DB: Data SQL Like GraphDB License Language Protocol Gremlin Binding Model Query REST/ Property Ruby, Python, Neo4j AGPLv3 Java Yes Scala,... - JSON Graph REST/ Property sones AGPLv3 C# JSON Graph Yes - Yes (XML) (+Extend) REST/ Property PHP, Jruby, OrientDB Apache2.0 Java Yes Python, JS,... Yes JSON Graph Property REST/ Info Grid AGPLv3 Java Graph? - - - JSON (MeshObject) Infinite Property Product C++ - - - - Graph Graph
  • 64. Graph DB [ ] Graph DB Neo4j Open Source Social Graph Software Not Ready Yet Graph DB Hypergtaph: PropertyGraph HyperGraph Pregel: bulk synchronous parallel model Distributed DB Google FlockDB: Distributed DB for storing adjancency lists Twitter
  • 65. Tinker Pop Graph Graph Graph DB Graph Traversal Graph DB Neo4j, Sones, InfoGrid, OrientDB, InfiniteGraph Tinker Pop Gremlin, Blueprints, Pipes, Rexster, Mutant
  • 67. Tinker Pop [Tinker Pop] HP Property Graph Model GraphDB Blueprints: A Property Graph Model Interface Gremlin: A Graph Traversal Language Pipes: A Data Flow Framework using Process Graphs Rexster: A RESTful Graph Shell Mutant: A Poly-ScriptEngine ScriptEngine
  • 70. BluePrints [ ] HP GraphDB ”JDBC” Property Graph Model GraphDB [Now] Tinker Graph: in-memory property graph model Sail: Open RDF Neo4j, Orient DB, sones, ... [Future] Redis Infinite Graph, Dex
  • 71. BluePrints GraphDB Graph graph = new Neo4jGraph("/tmp/graph/neo4j"); // Graph graph = new OrientGraph("/tmp/graph/orientdb"); Vertex a = graph.addVertex(null); Vertex b = graph.addVertex(null); a.setProperty("name","marko"); b.setProperty("name","aaron"); Edge e = graph.addEdge(null,a,b,"knows"); e.setProperty("since",2010); graph.shutdown();
  • 72. BluePrints Transaction graph.startTransaction(); try{   Vertex luca = graph.addVertex(null);   luca.setProperty( "name", "Luca" );   Vertex marko = graph.addVertex(null);   marko.setProperty( "name", "Marko" );   Edge lucaKnowsMarko = graph.addEdge(null, luca, marko,"knows");   graph.stopTransaction(Conclusion.SUCCESS); } catch( Exception e ) {   graph.stopTransaction(Conclusion.FAILURE); }
  • 74. Gremlin [ ] HP Gremlin = Graph Programing Language Blueprints GraphDB Shell GraphDB Query Java + Groovy
  • 75. Gremlin Property Graph Basic Graph Traversals
  • 76. doryokujin$ ./gremlin.sh ,,,/ (o o) -----oOOo-(_)-oOOo----- gremlin>  g  =  TinkerGraphFactory.createTinkerGraph() ==>tinkergraph[vertices:6  edges:6]  // 6 6 gremlin>  g.class ==>class   com.tinkerpop.blueprints.pgm.impls.tg.TinkerGraph gremlin>  //   gremlin>  g.V ==>v[3] ==>v[2] ... gremlin>  //   gremlin>  g.E ==>e[10][4-­‐created-­‐>5] ==>e[7][1-­‐knows-­‐>2] ==>e[9][1-­‐created-­‐>3] ... Getting Srarted
  • 77. gremlin>  v  =  g.v(1)  //  id=1   ==>v[1] gremlin>  v.keys()  //   ==>age ==>name gremlin>  v.values()  //   ==>29 ==>marko gremlin>  v.name  +  '  is  '  +  v.age  +  '  years  old.' ==>marko  is  29  years  old. gremlin>  //  id=1,  name=marko   gremlin>  v.outE ==>e[7][1-­‐knows-­‐>2] ==>e[9][1-­‐created-­‐>3] ==>e[8][1-­‐knows-­‐>4] gremlin>  //   gremlin>  v.outE.weight ==>0.5 ==>0.4 ==>1.0 Getting Srarted
  • 78. gremlin>  //  id=1 1.0 gremlin>  v.outE{it.weight  <  1.0}.inV ==>v[2] ==>v[3] gremlin>  //   gremlin>  list  =  []                                                           gremlin>  v.outE{it.weight  <  1.0}.inV  >>  list ==>v[2] ==>v[3] gremlin>  //  list property  maps gremlin>  list.collect{  it.map()  } ==>{name=vadas,  age=27} ==>{name=lop,  lang=java} gremlin>  //  list gremlin>  list.inE()               ==>e[7][1-­‐knows-­‐>2] ==>e[9][1-­‐created-­‐>3] ... Getting Srarted
  • 79. gremlin>  list.inE{it.label=='knows'}    //   'knows' ==>e[7][1-­‐knows-­‐>2] gremlin>  list.inE()[[label:'knows']]  //   ==>e[7][1-­‐knows-­‐>2] gremlin>  list.inE()[[label:'knows']].outV.name  // :name   ==>marko Getting Srarted ~20000ms:  g.V.outE{it['label']=='followed_by'}.inV.outE{it['label']=='followed_by'}.inV.outE                      {it['label']=='followed_by'}.inV  >>-­‐1 ~9000ms:    g.V.outE{it.label=='followed_by'}.inV.outE{it.label=='followed_by'}.inV.outE                    {it.label=='followed_by'}.inV  >>-­‐1 ~8500ms:    g.V.outE{it.getLabel()=='followed_by'}.inV.outE{it.getLabel()=='followed_by'}.inV.outE                            {it.getLabel()=='followed_by'}.inV  >>-­‐1 ~6000ms:    g.V.outE[[label:'followed_by']].inV.outE[[label:'followed_by']].inV.outE                      [[label:'followed_by']].inV  >>-­‐1 ClosureFilterPipe vs. PropertyFIlterPipe
  • 81. Pipes [ ] HP Pipes = Data Flow Framework Pipes Graph Traversal 1 1 Pipes filtering, splitting, merging, traversing,...
  • 82. Gremlin g:id-v('a')/outE[@label='knows']/inV/outE[@label='develops']/inV/@name Pipe pipe1 = new VertexEdgePipe(Step.OUT_EDGES); Pipe pipe2 = new LabelFilterPipe("knows", Filter.NOT_EQUALS); Pipe pipe3 = new EdgeVertexPipe(Step.IN_VERTEX); Pipe pipe4 = new VertexEdgePipe(Step.OUT_EDGES); Pipe pipe5 = new LabelFilterPipe("develops", Filter.NOT_EQUALS); Pipe pipe6 = new EdgeVertexPipe(Step.IN_VERTEX); Pipe pipe7 = new PropertyPipe("name"); Pipe pipeline = new Pipeline (pipe1,pipe2,pipe3,pipe4,pipe5,pipe6,pipe7); pipeline.setStarts(new SingleIterator(graph.getVertex("a")); for(String name : pipeline) {   System.out.println(name); } A Graph Processing Stack
  • 83. Pipes Pipes public  class  NumCharsPipe  extends  AbstractPipe<String,Integer>  {    public  Integer  processNextStart()  {        String  word  =  this.starts.next();        return  word.length();    } } A Graph Processing Stack
  • 85. Rexster [ ] HP Rexster = A RESTful Graph Shell Blueprints GraphDB RESTful API (JSON) Gremlin
  • 86. > http://localhost:8182/examplegraph/vertices/b {   "version":"0.1",   "results": {     "_type":"vertex",     "_id":"b",     "name":"aaron",     "type":"person"   },   "query_time":0.1537 } A Graph Processing Stack // g:key-v('name','DARK STAR')[0]: Usin gGremlin Code > http://localhost:8182/gratefulgraph/traversals/gremlin? script=g:key-v%28%27name%27,%27DARK%20STAR%27%29[0] {    "results":  [{        "_type":"vertex",        "_id":"89",        "name":"DARK  STAR",        "song_type":"original",        "performances":219,        "type":"song"}    ],    "query_time":6.753024,    "success":true,    "version" } Using Gremilin
  • 88. Mutant [ ] HP Mutant = A Poly-ScriptEngine ScriptEngine JVM Script Engine
  • 89. Mutant Console marko:~/software/mutant$  ./mutant.sh              //          oO  ~~-­‐_ ___m(___m___~.___    MuTanT  0.1-­‐SNAPSHOT _|__|__|__|__|__|          [  ?h  =  help  ] [gremlin]  gremlin  0.6-­‐SNAPSHOT [Groovy]  Groovy  Scripting  Engine  2.0 [ruby]  JSR  223  JRuby  Engine  1.5.5 [ECMAScript]  Mozilla  Rhino  1.6  release  2 [AppleScript]  AppleScriptEngine  1.0 mutant[gremlin]>  $x  :=  12 [12] mutant[gremlin]>  ?x mutant[AppleScript]>  ?x mutant[Groovy]>  $x 12 mutant[Groovy]>  ?x mutant[ruby]>  $x 12 mutant[ruby]>  ?x mutant[ECMAScript]>  $x 12 Basic Examples
  • 90. [ ] Graph DB Graph DB Graph Partitioning Pregel Neo4j
  • 91. … ※ Graph DB http://snap.stanford.edu/data/index.html