Titan: The Rise of Big Graph Data

A graph is a data structure composed of vertices/dots and edges/lines. A graph database is a software system used to persist and process graphs. The common conception in today's database community is that there is a tradeoff between the scale of data and the complexity/interlinking of data. To challenge this understanding, Aurelius has developed Titan under the liberal Apache 2 license. Titan supports both the size of modern data and the modeling power of graphs to usher in the era of Big Graph Data. Novel techniques in edge compression, data layout, and vertex-centric indices that exploit significant orders are used to facilitate the representation and processing of a single atomic graph structure across a multi-machine cluster. To ensure ease of adoption by the graph community, Titan natively implements the TinkerPop 2 Blueprints API. This presentation will review the graph landscape, Titan's techniques for scale by distribution, and a collection of satellite graph technologies to be released by Aurelius in the coming summer months of 2012.

1. TITAN MARKO A. RODRIGUEZ MATTHIAS BROECHELER http://THINKAURELIUS.COM THE RISE OF BIG GRAPH DATA
2. A graph is a data structure composed of vertices/dots and edges/lines. A graph database is a software system used to persist and process graphs. The common conception in today's database community is that there is a tradeoff between the scale of data and the complexity/interlinking of data. To challenge this understanding, Aurelius has developed Titan under the liberal Apache 2 license. Titan supports both the size of modern data and the modeling power of graphs to usher in the era of Big Graph Data. Novel techniques in edge compression, data layout, and vertex-centric indices that exploit significant orders are used to facilitate the representation and processing of a single atomic graph structure across a multi-machine cluster. To ensure ease of adoption by the graph community, Titan natively implements the TinkerPop 2 Blueprints API. This presentation will review the graph landscape, Titan's techniques for scale by distribution, and a collection of satellite graph technologies to be released by Aurelius in the coming summer months of 2012. ABSTRACT
3. Dr. Marko A. Rodriguez is the founder of the graph consulting firm Aurelius. He has focused his academic and commercial career on the theoretical and applied aspects of graphs. Marko is a cofounder of TinkerPop and the primary developer of the Gremlin graph traversal language. Dr. Matthias Broecheler has been researching and developing large-scale graph database systems for many years in both academia and in his role as a cofounder of the Aurelius graph consulting firm. He is the primary developer of the distributed graph database Titan. Matthias focuses most of his time and effort on novel OLTP and OLAP graph processing solutions. SPEAKER BIOGRAPHIES
4. SPONSORS As the leading education services company, Pearson is serious about evolving how the world learns. We apply our deep education experience and research, invest in innovative technologies, and promote collaboration throughout the education ecosystem. Real change is our commitment and its results are delivered through connecting capabilities to create actionable, scalable solutions that improve access, affordability, and achievement. Aurelius is a team of software engineers and scientists committed to applying graph theory and network science to problems in numerous domains. Aurelius develops the theory and technology whereby graphs can be used to model, understand, predict, and influence the behavior of complex, interrelated social, economic, and physical networks. Jive is the pioneer and world's leading provider of social business solutions. Our products apply powerful technology that helps people connect, communicate and collaborate to get more work done and solve their biggest business challenges. Millions of users and many of the worldʼs most successful companies rely on Jive day in and day out to get work done, serve their customers and stay ahead of their competitors.
5. 1. ThE GRAPH LANDSCAPE OUTLINE 2. INTRODUCTION TO TITAN 3. THE FUTURE OF AURELIUS An introduction to graph computing. Graph technologies on the market today. Getting up and running with Titan. Titan's techniques for scalability. Satellite technologies and the OLAP story. The graph landscape reprise.
6. PART 1: ThE GRAPH LANDSCAPE MARKO A. RODRIGUEZ
7. GRAPH
8. VERTEX EDGE GRAPH
9. VERTEX EDGE GRAPH G = (V, E) Graph Vertices Edges
10. G = (V, E) Classic Textbook Graph Structure
11. V A homogenous set of vertices...
12. E ...connected by a homogenous set of edges.
13. RESTRICTED MODELING People and follows relationships...
14. RESTRICTED MODELING People and follows relationships... ...xor webpages and citations.
15. AN INTEGRATED MODEL IS TYPICALLY DESIRED mentions follows references references createdBy references follows
16. AN INTEGRATED MODEL IS USEFUL mentions follows references references createdBy references follows Allows for more interesting/novel algorithms. Allows for a universal model of things and their relationships. (beyond "textbook" graph algorithms) (a single, unified model of a domain of interest)
17. THE PROPERTY GRAPH Current Popular Graph Structure G = (V, E, λ) * Directed, attributed, edge-labeled graph * Multi-relational graph with key/value pairs on the elements
18. VERTEX
19. VERTEX name:hercules PROPERTIES
20. VERTEX name:hercules PROPERTIES KEY VALUE
21. name:hercules
22. name:hercules mother name:alcmene type:human
23. name:hercules mother name:alcmene type:human EDGE LABEL
24. name:hercules mother name:alcmene type:human
25. name:hercules mother name:alcmene type:human name:jupiter type:god father
26. name:hercules mother name:alcmene type:human name:jupiter type:god father IS HERCULES A DEMIGOD? DEMIGOD = HALF HUMAN + HALF GOD
27. name:hercules mother name:alcmene type:human name:jupiter type:god father gremlin> hercules ==>v[0]
28. name:hercules mother name:alcmene type:human name:jupiter type:god father gremlin> hercules.out('mother','father') ==>v[1] ==>v[2]
29. name:hercules mother name:alcmene type:human name:jupiter type:god father gremlin> hercules.out('mother','father').type ==>human ==>god DEMIGOD = HALF HUMAN + HALF GOD
30. name:hercules type:demigod mother name:alcmene type:human name:jupiter type:god father gremlin> hercules.type = 'demigod' ==>demigod DEMIGOD = HALF HUMAN + HALF GOD
31. STRUCTURE PROCESS COMPUTING
32. GRAPH TRAVERSAL STRUCTURE PROCESS COMPUTING
33. GRAPH TRAVERSAL STRUCTURE PROCESS COMPUTING GRAPH-BASED COMPUTING
34. WhY GRAPH-BASED COMPUTING?
35. INTUITIVE MODELING WhY GRAPH-BASED COMPUTING?
36. INTUITIVE MODELING EXPRESSIVE QUERYING WhY GRAPH-BASED COMPUTING?
37. INTUITIVE MODELING EXPRESSIVE QUERYING NUMEROUS ANALYSES Centrality Mixing Patterns Geodesics Path Expressions Ranking Inference Motifs Scoring WhY GRAPH-BASED COMPUTING?
38. f( )ˠ ANALYSES ARE THE EPIPHENOMENA OF TRAVERSAL
39. WHAT IS THE SIGNIFICANCE OF GRAPH ANALYSIS?
40. ANALYSES YIELD INSIGHTS ABOUT THE MODEL = DATA PRODUCTS DATA-DRIVEN DECISION SUPPORT
41. RECOMMENDATION People you may know. Products you might like. Movies you should watch and the friends you should watch them with. SOCIAL GRAPH RATINGS GRAPH SOCIAL+RATINGS GRAPH
42. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows WHO ELSE MIGHT HERCULES KNOW?
43. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows gremlin> hercules ==>v[0]
44. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows gremlin> hercules.out('knows') ==>v[1] ==>v[2] ==>v[3]
45. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows gremlin> hercules.out('knows').out('knows') ==>v[4] ==>v[5] ==>v[5] ==>v[6] ==>v[5]
46. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows gremlin> hercules.out('knows').out('knows').groupCount.cap ==>v[4]=1 ==>v[5]=3 ==>v[6]=1
47. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows knows HERCULES PROBABLY KNOWS NEPTUNE
48. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows knows HERCULES PROBABLY KNOWS NEPTUNE THIS IS A "TEXTBOOK STYLE" GRAPH
49. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother ...PROBABLY MORE SO WHEN OTHER TYPES OF EDGES ARE ANALYZED HERCULES PROBABLY KNOWS NEPTUNE
50. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother
51. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother likes
52. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother likes SOCIAL GRAPH
53. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother 7 human flesh likes SOCIAL GRAPH
54. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother 7 human flesh likes likes likes SOCIAL GRAPH
55. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother 7 human flesh likes likes likes 8 tartarus SOCIAL GRAPH
56. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother 7 human flesh likes likes likes 8 likes likes tartarus dislikes SOCIAL GRAPH
57. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother 7 human flesh likes likes likes 8 likes likes tartarus dislikes SOCIAL GRAPH RATINGS GRAPH
58. 0 2 hercules 1 3 5 4 6 cerberus nemean hydra knows knows knows pluto neptune jupiter knows knows knows knows knows father brother 7 human flesh likes likes likes composedOf 8 likes likes tartarus dislikes NEMEAN MIGHT LIKE TARTARUS smellsOf SOCIAL GRAPH RATINGS GRAPH PRODUCT GRAPH * Collaborative Filtering + Content-Based Recommendation
59. PATH FINDING How is this person related to this film? Which authors of this book also wrote a New York Times bestseller? Which movies are based on a book by a New York Times bestseller? MOVIE GRAPH BOOK GRAPH MOVIE+BOOK GRAPH
60. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn WHO PLAYED HERCULES IN WHAT MOVIE?
61. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules ==>v[0] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn WHO PLAYED HERCULES IN WHAT MOVIE?
62. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn') ==>v[7] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn WHO PLAYED HERCULES IN WHAT MOVIE?
63. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie') ==>v[7] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie WHO PLAYED HERCULES IN WHAT MOVIE?
64. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie').out('hasActor') ==>v[8] ==>v[10] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie WHO PLAYED HERCULES IN WHAT MOVIE?
65. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie').out('hasActor') .out('role') ==>v[0] ==>v[6] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie WHO PLAYED HERCULES IN WHAT MOVIE?
66. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie').out('hasActor') .out('role').retain(hercules) ==>v[0] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie WHO PLAYED HERCULES IN WHAT MOVIE?
67. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie').out('hasActor') .out('role').retain(hercules).back(2) ==>v[8] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie WHO PLAYED HERCULES IN WHAT MOVIE?
68. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie').out('hasActor') .out('role').retain(hercules).back(2).out('actor') ==>v[9] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie WHO PLAYED HERCULES IN WHAT MOVIE?
69. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie').out('hasActor') .out('role').retain(hercules).back(2).out('actor') .as('star') ==>v[9] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie star WHO PLAYED HERCULES IN WHAT MOVIE?
70. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie').out('hasActor') .out('role').retain(hercules).back(2).out('actor') .as('star').select ==>[movie:v[7], star:v[9]] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie star WHO PLAYED HERCULES IN WHAT MOVIE?
71. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn gremlin> hercules.out('depictedIn').as('movie').out('hasActor') .out('role').retain(hercules).back(2).out('actor') .as('star').select{it.name} ==>[movie:hercules in new york, star:arnold schwarzenegger] 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn movie star WHO PLAYED HERCULES IN WHAT MOVIE?
72. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn
73. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn
74. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn 12 the arms of hercules depictedIn
75. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn 12 the arms of hercules fred saberhagen 13 depictedIn writtenBy
76. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn 12 the arms of hercules fred saberhagen 13 depictedIn writtenBy 14 albuquerque livesIn
77. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn 12 the arms of hercules fred saberhagen 13 depictedIn writtenBy 14 albuquerque livesIn 15 25-North santa fe
78. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn 12 the arms of hercules fred saberhagen 13 depictedIn writtenBy 14 albuquerque livesIn 15 25-North santa fe 16 marko rodriguez livesIn
79. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn 12 the arms of hercules fred saberhagen 13 depictedIn writtenBy 14 albuquerque livesIn 15 25-North santa fe 16 marko rodriguez livesIn thinksHeIs
80. 0 hercules arnold schwarzenegger hasActor 7 hercules in new york depictedIn 10 8 actor role 9 hasActor 6 role jupiter ernest graves actor 11 depictedIn 12 the arms of hercules fred saberhagen 13 depictedIn writtenBy 14 albuquerque livesIn 15 25-North santa fe 16 marko rodriguez livesIn thinksHeIs MOVIE GRAPH BOOK GRAPH TRANSPORTATION GRAPH PROFILE GRAPH
81. SOCIAL INFLUENCE Who are the most influential people in java, mathematics, art, surreal art, politics, ...? Which region of the social graph will propagate this advertisement this furthest? Which 3 experts should review this submitted article? Which people should I talk to at the upcoming conference and what topics should I talk to them about? SOCIAL + COMMUNICATION + EXPERTISE + EVENT GRAPH
82. PATTERN IDENTIFICATION This connectivity pattern is a sign of financial fraud. When this motif is found, a red flag will be raised. Healthy discourse is typified by a discussion board with a branch factor in this range and a concept clique score in this range. TRANSACTION GRAPH DISCUSSION GRAPH
83. KNOWLEDGE DISCOVERY The terms "ice", "fans", "stanley cup," are classified as "sports" Given that all identified birds fly, it can be deduced that all birds fly. If contrary evidence is provided, then this "fact" can be retracted. WIKIPEDIA GRAPH EVIDENTIAL LOGIC GRAPH
84. WORLD MODEL
85. WORLD MODEL WORLD PROCESSES
86. WORLD MODEL WORLD PROCESSES A single world model and various types of traversers moving through that model to solve problems.
87. GRAPH TRAVERSAL STRUCTURE PROCESS COMPUTING GRAPH-BASED COMPUTING
88. GRAPH COMPUTING ENGINES
89. MEMORY-BASED GRAPHS Application iGraph http://igraph.sourceforge.net/ NetworkX http://networkx.lanl.gov/ JUNG http://jung.sourceforge.net/ Graph Framework
90. Application Application DISK-BASED GRAPHS Application Neo4j http://neo4j.org/ OrientDB http://orientdb.org Graph Database InfiniteGraph http://objectivity.com DEX http://www.sparsity-technologies.com/dex
91. CLUSTER-BASED GRAPHS Hama http://incubator.apache.org/hama/ Giraph http://incubator.apache.org/giraph/ GoldenOrb http://goldenorbos.org/ Application 3 Application 2 Application 1 Bulk Synchronous Parallel Processing * In the same spirit as Google's Pregel
92. MEMORY-bASED GRAPHS Graph size is constrained by local machine's RAM. Rich graph algorithm and visualization packages. Oriented towards "textbook-style" graphs. * Based on typical behavior
93. MEMORY-bASED GRAPHS DISK-BASED GRAPHS Graph size is constrained by local machine's RAM. Rich graph algorithm and visualization packages. Oriented towards "textbook-style" graphs. Graph size is constrained by local disk. Optimized for local graph algorithms. Oriented towards property graphs. * Based on typical behavior
94. MEMORY-bASED GRAPHS DISK-BASED GRAPHS CLUSTER-BASED GRAPHS Graph size is constrained by local machine's RAM. Rich graph algorithm and visualization packages. Oriented towards "textbook-style" graphs. Graph size is constrained by local disk. Optimized for local graph algorithms. Oriented towards property graphs. Graph size is constrained to cluster's total RAM. Optimized for global graph algorithms. Oriented towards "textbook-style" graphs. * Based on typical behavior
95. TINKERPOP Open source graph product group Support for various graph vendors Provides a vendor-agnostic graph framework * Encompassing the various graph computing styles Simple, well-defined products * Based on future directions http://tinkerpop.com
96. TINKERPOP Generic Graph API Dataflow Processing Traversal Language Object-Graph Mapper Graph Algorithms Graph Server http://tinkerpop.com http://${project.name}.tinkerpop.com
97. TINKERPOP INTEGRATION http://tinkerpop.com
98. AND NOW THERE IS ANOTHER...
104. TITAN
105. PART 2: INTRODUCTION TO TITAN MATTHIAS BROECHELER
106. ...need to represent and process graphs at the 100+ billion edge scale w/ thousands of concurrent transactions. ...desire a free, open source distributed graph database. ...need both local graph traversals (OLTP) and batch graph processing (OLAP). WhY CREATE TITAN? A number of Aurelius' clients...
107. ..."infinite size" graphs and "unlimited" users by means of a distributed storage engine. ...distribution via the liberal, free, open source Apache2 license. ...real-time local traversals (OLTP) and support for global batch processing via Hadoop (OLAP). TITAN's KEY FEATURES Titan provides...
108. matthias$
109. matthias$ wget http://thinkaurelius/titan.zip % Total % Received % Xferd Average Speed Time Time 100 99999 0 99999 0 0 11078 0 --:--:-- 0:01:01 matthias$
110. matthias$ wget http://thinkaurelius/titan.zip % Total % Received % Xferd Average Speed Time Time 100 99999 0 99999 0 0 11078 0 --:--:-- 0:01:01 matthias$ unzip titan.zip Archive: titan.zip creating: titan/ ... matthias$
111. matthias$ wget http://thinkaurelius/titan.zip % Total % Received % Xferd Average Speed Time Time 100 99999 0 99999 0 0 11078 0 --:--:-- 0:01:01 matthias$ unzip titan.zip Archive: titan.zip creating: titan/ ... matthias$ cd titan titan$
112. matthias$ wget http://thinkaurelius/titan.zip % Total % Received % Xferd Average Speed Time Time 100 99999 0 99999 0 0 11078 0 --:--:-- 0:01:01 matthias$ unzip titan.zip Archive: titan.zip creating: titan/ ... matthias$ cd titan titan$ bin/gremlin.sh \,,,/ (o o) -----oOOo-(_)-oOOo----- gremlin>
113. gremlin> g = TitanFactory.open('/tmp/local-titan') ==>titangraph[local:/tmp/local-titan]
114. gremlin> g = TitanFactory.open('/tmp/local-titan') ==>titangraph[local:/tmp/local-titan] LOCAL MACHINE MODE
115. gremlin> g.createKeyIndex('name',Vertex.class) ==>null gremlin> g.stopTransaction(SUCCESS) ==>null
116. gremlin> g.loadGraphML('data/graph-of-the-gods.xml') ==>null name:tartarus type:location name:pluto type:god lives brother name:jupiter type:god brother name:neptune type:god pet name:cerberus type:monster lives father name:saturn type:titan brother name:sea type:location lives name:sky type:location lives father battled name:hercules type:demigod name:hydra type:monster battled name:nemean type:monster battled name:alcmene type:human mother time:1 time:2 time:12 * The Graph of the Gods is a toy dataset distributed with Titan
117. gremlin> hercules = g.V('name','hercules').next() ==>v[24] name:tartarus type:location name:pluto type:god lives brother name:jupiter type:god brother name:neptune type:god pet name:cerberus type:monster lives father name:saturn type:titan brother name:sea type:location lives name:sky type:location lives father battled name:hercules type:demigod name:hydra type:monster battled name:nemean type:monster battled name:alcmene type:human mother time:1 time:2 time:12
118. gremlin> hercules.out('mother','father') ==>v[44] ==>v[16] name:tartarus type:location name:pluto type:god lives brother name:jupiter type:god brother name:neptune type:god pet name:cerberus type:monster lives father name:saturn type:titan brother name:sea type:location lives name:sky type:location lives father battled name:hercules type:demigod name:hydra type:monster battled name:nemean type:monster battled name:alcmene type:human mother time:1 time:2 time:12
119. gremlin> hercules.out('mother','father').name ==>alcmene ==>jupiter name:tartarus type:location name:pluto type:god lives brother name:jupiter type:god brother name:neptune type:god pet name:cerberus type:monster lives father name:saturn type:titan brother name:sea type:location lives name:sky type:location lives father battled name:hercules type:demigod name:hydra type:monster battled name:nemean type:monster battled name:alcmene type:human mother time:1 time:2 time:12
120. THAT WAS TITAN LOCAL. NEXT IS TITAN DISTRIBUTED. Broecheler, M., Pugliese, A., Subrahmanian, V.S., "COSI: Cloud Oriented Subgraph Identification in Massive Social Networks," Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining, pp. 248-255, 2010. http://www.knowledgefrominformation.com/2010/08/01/cosi-cloud-oriented-subgraph-identification-in-massive-social-networks/
121. -OR- BACKEND AGNOSTIC
122. titan$ bin/gremlin.sh \,,,/ (o o) -----oOOo-(_)-oOOo----- gremlin> conf = new BaseConfiguration(); ==>org.apache.commons.configuration.BaseConfiguration@763861e6 gremlin> conf.setProperty("storage.backend","cassandra"); gremlin> conf.setProperty("storage.hostname","77.77.77.77"); gremlin> g = TitanFactory.open(conf); ==>titangraph[cassandra:77.77.77.77] gremlin> TITAN DISTRIBUTED VIA CASSANDRA * There are numerous graph configurations: https://github.com/thinkaurelius/titan/wiki/Graph-Configuration
123. INHERITED FEATURES Continuously available with no single point of failure. Cassandra available at http://cassandra.apache.org/ No write bottlenecks to the graph as there is no master/slave architecture. Elastic scalability allows for the introduction and removal of machines. Caching layer ensures that continuously accessed data is available in memory. Built-in replication ensures data is available during machine failure.
124. titan$ bin/gremlin.sh \,,,/ (o o) -----oOOo-(_)-oOOo----- gremlin> conf = new BaseConfiguration(); ==>org.apache.commons.configuration.BaseConfiguration@763861e6 gremlin> conf.setProperty("storage.backend","hbase"); gremlin> conf.setProperty("storage.hostname","77.77.77.77"); gremlin> g = TitanFactory.open(conf); ==>titangraph[hbase:77.77.77.77] gremlin> TITAN DISTRIBUTED VIA HBASE * There are numerous graph configurations: https://github.com/thinkaurelius/titan/wiki/Graph-Configuration
125. INHERITED FEATURES Linear scalability with the addition of machines. HBase available at http://hbase.apache.org/ Strictly consistent reads and writes. HDFS-based data replication. Base classes for backing Hadoop MapReduce jobs with HBase tables. Generally good integration with the tools in the Hadoop ecosystem.
126. TITAN AND THE CAP THEOREM Consistency Partitionability Availability
127. Titan is all about ...
128. Titan is all about numerous concurrent users...
129. Titan is all about numerous concurrent users... high availability....
130. Titan is all about numerous concurrent users... high availability.... dynamic scalability...
131. EDGE COMPRESSION VERTEX-CENTRIC INDICES DATA MANAGEMENT THE HOW OF TITAN
132. DATA MANAGEMENT THE HOW OF TITAN
133. DATA MANAGEMENT MAIN DESIGN PRINCIPLES Optimistic Concurrency Control Fined-Grained Locking Control Immutable, Atomic Edges battled hercules cerberus battled hercules time:12 cerberus battled hercules time:12 successful:true cerberus 1 2 3 + + + + + -
134. DATA MANAGEMENT hercules jupiter father father mars Functional Declarations Datatype Constraints TYPE DEFINITION TitanKey timeKey = g.makeType().name("time") .dataType(Integer.class) time:12 TitanLabel father = g.makeType().name("father") .functional() Edge Label Signatures TitanLabel battled = g.makeType().name("battled") .signature(timeKey) battled hercules time:12 cerberus time:"twelve" Data management configurations allow Titan to optimize how information is stored/retrieved from disk.
135. DATA MANAGEMENT Unique Property Key/Value Pairs TYPE DEFINITION Endogenous Indices g.createKeyIndex("name",Vertex.class) name:hercules name:hermes name:jupiter name:jupiter status:king of the gods name:neptune status:king of the gods TitanKey status = g.makeType().name("status") .unique() Data management configurations allow Titan to optimize how information is stored/retrieved from disk.
136. DATA MANAGEMENT Ensures consistency over non-consistent storage backends. LOCKING SYSTEM hercules neptune father father jupiter hercules write write father jupiter hercules 1. Acquire lock at the end of the transaction. - locking mechanism depends on storage layer consistency guarantees. 2. Verify original read. 3. Fail transaction if any precondition is violated.
137. DATA MANAGEMENT ID MANAGEMENT Global ID Pool Maintained by Storage Engine [0,1,2,3,4,5,6,7,8,9,10,11]
138. DATA MANAGEMENT ID MANAGEMENT Pool Subsets Assigned to Individual Instances Global ID Pool Maintained by Storage Engine [0,1,2] [3,4,5] [6,7,8] [9,10,11] [0,1,2,3,4,5,6,7,8,9,10,11]
139. EDGE COMPRESSION THE HOW OF TITAN
140. EDGE COMPRESSION Natural graphs have a small world, community/cluster property. Watts, D. J., Strogatz, S. H., "Collective Dynamics of 'Small-World' Networks," Nature 393 (6684), pp. 440–442, 1998. Community 1 Community 2 High intra-connectivity within a community and low inter-connectivity between communities.
141. EDGE COMPRESSION
142. EDGE COMPRESSION 12345678 12345683 knows
143. EDGE COMPRESSION 12345678 12345683 knows
144. EDGE COMPRESSION 12345678 12345683 knows 12345678 9 12345683 24 bytes
145. EDGE COMPRESSION 12345678 12345683 knows 12345678 9 12345683 24 bytes 12345678 9 +5
146. EDGE COMPRESSION 12345678 12345683 knows 12345678 9 12345683 24 bytes 12345678 9 +5 12345678 9 + 5 7 bytes
147. VERTEX-CENTRIC INDICES THE HOW OF TITAN
148. VERTEX-CENTRIC INDICES Natural, real-world graphs contain vertices of high degree. Even if rare, their degree ensures that they exist on many paths. Traversing a high degree vertex means touching numerous incident edges and potentially touching most of the graph in only a few steps. THE SUPER NODE PROBLEM
149. VERTEX-CENTRIC INDICES A "super node" only exists from the vantage point of classic "textbook style" graphs. In the world of property graphs, intelligent disk-level filtering can interpret a "super node" as a more manageable low-degree vertex. Vertex-centric querying utilizes B-Trees and sort orders for speedy lookup of incident edges with particular qualities. A SUPER NODE SOLUTION
150. VERTEX-CENTRIC INDICES PUSHDOWN PREDICATES knows knows knows likes likes likes likes likes stars:5 stars:3 stars:3 stars:2 stars:2 vertex.query() 8 edges
151. VERTEX-CENTRIC INDICES PUSHDOWN PREDICATES knows knows likes likes likes likes likes stars:5 stars:3 stars:3 stars:2 stars:2 vertex.query().direction(OUT) 7 edges
152. VERTEX-CENTRIC INDICES PUSHDOWN PREDICATES likes likes likes likes likes stars:5 stars:3 stars:3 stars:2 stars:2 vertex.query().direction(OUT) .labels("likes") 5 edges
153. VERTEX-CENTRIC INDICES PUSHDOWN PREDICATES likes stars:5 1 edge vertex.query().direction(OUT) .labels("likes").has("stars",5)
154. VERTEX-CENTRIC INDICES PUSHDOWN PREDICATES Query Query.direction(Direction) Query Query.labels(String... labels) Query Query.has(String, Object, Compare) Query Query.has(String, Object) Query Query.range(String, Object, Object) Iterable<Vertex> Query.vertices() Iterable<Edge> Query.edges() PREDICATES GETTERS
155. VERTEX-CENTRIC INDICES battled battled battled knows knows time:1 time:2 time:12 DISK-LEVEL SORTING/INDEXING
156. VERTEX-CENTRIC INDICES battled battled battled knows knows battled knows time:1 time:2 time:12 DISK-LEVEL SORTING/INDEXING
157. VERTEX-CENTRIC INDICES battled battled battled knows knows battled w/ time 1-5 knows TitanLabel battled = g.makeType().name("battled") .primaryKey(time) time:1 time:2 time:12 battled w/ time 5-10 DISK-LEVEL SORTING/INDEXING
158. VERTEX-CENTRIC INDICES DISK-LEVEL SORTING/INDEXING father battled knows brother mother
159. VERTEX-CENTRIC INDICES DISK-LEVEL SORTING/INDEXING father battled knows brother mother
160. VERTEX-CENTRIC INDICES DISK-LEVEL SORTING/INDEXING father battled knows brother mother family TypeGroup family = TypeGroup.of(2,"family"); TitanLabel father = g.makeType().name("father") .group(family).makeEdgeLabel(); TitanLabel mother = g.makeType().name("mother") .group(family).makeEdgeLabel(); TitanLabel brother = g.makeType().name("brother") .group(family).makeEdgeLabel();
161. VERTEX-CENTRIC INDICES DISK-LEVEL SORTING/INDEXING father battled knows brother mother family vertex.query().group("family")...
162. EDGE COMPRESSION VERTEX-CENTRIC INDICES DATA MANAGEMENT THAT IS HOW TITAN WORKS
163. WHAT IF YOU WANTED TO CREATE TWITTER FROM SCRATCH? SIMULATING TWITTER
164. 3 BILLION EDGES 100 MILLION VERTICES 10000 CONCURRENT USERS 50 MACHINES 1 GRAPH DATABASE COMING JULY 2012
165. PART 3: THE FUTURE OF AURELIUS MATTHIAS BROECHELER MARKO A. RODRIGUEZ
166. AURELIUS' GRAPH COMPUTING STORY Titan as the highly scalable, distributed graph database solution. OLTP
167. AURELIUS' GRAPH COMPUTING STORY Titan as the highly scalable, distributed graph database solution. Titan as the source (and potential sink) for other graph processing solutions. OLTP OLAP
168. FAUNUS GOD OF HERDS
169. FAUNUS PATH ALGEBRA FOR HADOOP hercules battled battled theseus cretan bull theseus hercules ally Derived graphs are single-relational and are typically much smaller than their multi-relational source. Therefore, derived graphs can be subjected to "textbook-style" graph algorithms in both a meaningful and efficient manner. WHO IS THE MOST CENTRAL ALLY? A · A￿ ◦ n(I)
170. FAUNUS PATH ALGEBRA FOR HADOOP ally ally ally ally ally ally ally ally ally ally ally ally ally B · B ◦ n(I) "My allies' allies are my allies." B = A · A￿ ◦ n(I) (A · A￿)2 ◦ n(I)
171. FAUNUS PATH ALGEBRA FOR HADOOP Implements the multi-relational path algebra as a collection of Map/Reduce operations Rodriguez M.A., Shinavier, J., “Exposing Multi-Relational Networks to Single-Relational Network Analysis Algorithms,” Journal of Informetrics, 4(1), pp. 29-41, 2009. http://arxiv.org/abs/0806.2274 Support for "HadoopGraph" and HDFS file formats Project codename: TinkerPoop Reduce a massive property graph into a smaller semantically-rich single-relational graph. Used for global graph operations.
172. FULGORA GODDESS OF LIGHTNING
173. FULGORA AN EFFICIENt IN-MEMORY GRAPH ENGINE Non-transactional, in-memory graph engine. It is not a "database." Process ~90 billion edges in 68-Gigs of RAM assuming a small world topology. Perform complex graph algorithms in-memory. global graph analysis multi-relational graph analysis Similar in spirit to Twitter's Cassovary: https://github.com/twitter/cassovary
174. Stores a massive-scale property graph Generates a large-scale single-relational graph Analyzes compressed, large-scale single or multi-relational graphs in memory Map/Reduce Load into RAM on a single-machine Update element properties with algorithm results THE AURELIUS OLAP FLOW to a stats package Update graph with derived edges
175. Stores a massive-scale property graph Generates a large-scale single-relational graph Analyzes compressed, large-scale single or multi-relational graphs in memory Map/Reduce Load into RAM on a single-machine THE AURELIUS OLAP FLOW to a stats package theseus hercules ally hercules ally_centrality:0.0123
176. Stores a massive-scale property graph Generates a large-scale single-relational graph Analyzes compressed, large-scale single or multi-relational graphs in memory THE AURELIUS OLAP FLOW to a stats package
177. AURELIUS' USE OF BLUEPRINTS Aurelius products use the Blueprints API so any graph product can communicate with any other graph product. The code for graph databases, frameworks, algorithms, and batch-processing are written in terms of the Blueprints API. Aurelius encourages developers to use Blueprints/ TinkerPop in order to grow a rich ecosystem of interoperable graph technologies.
178. THE GRAPH LANDSCAPE REPRISE Speed of Traversal/Process Size of Graph/Structure * Not to scale. Did not want to overlap logos.
179. NEXT STEPS http://thinkaurelius.com http://thinkaurelius.github.com/titan/ Learn about applying graph theory and network science. Make use of and/or contribute to the free, open source Titan product.
180. THANK YOU
181. CREDITS PRESENTERS MARKO A. RODRIGUEZ MATTHIAS BROCHELER FINANCIAL SUPPORT PEARSON EDUCATION AURELIUS LOCATION PROVISIONS JIVE SOFTWARE MANY THANKS TO DAN LAROCQUE TINKERPOP COMMUNITY STEPHEN MALLETTE BOBBY NORTON KETRINA YIM