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Case Study: Real-time Recommendations with a Graph Database

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Whether you want to be Facebook or are selling shoelaces online, if you have users then you have a social graph. Reveal the hidden graph in your data by storing key elements in a graph database, focusing on the relationships between records rather than the aggregation of records. We will look at how a large European social network added real-time recommendations to their service with a hybrid of MySQL and Neo4j, covering: Graph concepts refresher: whiteboard friendly modeling Polyglot persistence: storing the right data in the right place Graph algorithms: recommendations for any domain

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Realtime Recommendations with a Graph Database Andreas Kollegger #neo4j @akollegger Wednesday, August 24, 11
What’s the plan? ๏Graph Database 101 ๏Case Study: Viadeo ๏Finding new Friends ๏Recommending new Sites 2 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
3 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Obey! 3 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Obey! ๏tweet using #neo4j 3 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Obey! ๏tweet using #neo4j ๏I am @akollegger 3 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
NOSQL Categories Size Data Complexity 4 Wednesday, August 24, 11
NOSQL Categories Key-Value Store Size Data Complexity 4 Wednesday, August 24, 11
NOSQL Categories Key-Value Store Size BigTable Clones Data Complexity 4 Wednesday, August 24, 11
NOSQL Categories Key-Value Store Size BigTable Clones Document Databases Data Complexity 4 Wednesday, August 24, 11
NOSQL Categories Key-Value Store Size BigTable Clones Document Databases Graph Databases Data Complexity 4 Wednesday, August 24, 11
NOSQL Categories Key-Value Store Size BigTable Clones Document Databases Graph Databases Data Complexity 4 Wednesday, August 24, 11
NOSQL Categories Key-Value Store (this is still billions Size BigTable of nodes & Clones relationships) Document Databases Graph 90% Databases of use cases Data Complexity 4 Wednesday, August 24, 11
Graph DB 101 (Reading a graph) 5 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
A graph database? 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
A graph database? ๏ no: not for storing charts & graphs, or vector artwork 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph • remember linked-list, tree? 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph • remember linked-list, tree? • graphs are the generalized connected data structure 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph • remember linked-list, tree? • graphs are the generalized connected data structure ๏ whiteboard friendly - no data mangling required 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph • remember linked-list, tree? • graphs are the generalized connected data structure ๏ whiteboard friendly - no data mangling required ๏ “A traditional relational database may tell you the average salary of everyone in this conference, but a graph database will tell you who is most likely to buy you a beer.” 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Q: What are graphs good for? 7 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Q: What are graphs good for? A: highly-connected data ๏ Recommendations ๏ Time series data ๏ Business intelligence ๏ Product catalogue ๏ Social computing ๏ Web analytics ๏ Geospatial ๏ Scientific computing (especially bioinformatics) ๏ MDM ๏ Systems management ๏ Indexing your slow RDBMS ๏ Genealogy ๏ And much more! 7 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
G=(V,E) ๏ A (Graph) -records data in- (Nodes) Graph ๏ A (Graph) -records data in- (Relationships) records data in records data in organized by ๏ (Nodes) are -organized by- (Relationships) Nodes Relationships have have ๏ (Nodes & Relationships) -have- (Properties) Properties ๏ Instead of a Data Model, you have a Data Graph ๏ Structure is defined by the Relationships you create 8 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query a graph with a traversal ๏ A (Traversal) -navigates- a (Graph) Traversal identifies navigates ๏ A (Traversal) -identifies- (Paths) Paths Graph ๏ (Paths) -order- (Nodes) records data in records data in order organized by Nodes Relationships ๏ Traversals reveal localized data ๏ Find data that is relevant to other data 9 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Indexes look-up Nodes or Relationships ๏ An (Index) -maps to either- Index special Traversal (Nodes or Relationships) maps to either maps to either ๏ An (Index) is a -special- (Traversal) organized by Nodes Relationships maps from have have ๏ Indexes find starting points for a traversal Properties ๏ Can be queried with simple constraints 10 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Neo4j is a Graph Database ๏ A (Graph Database) -manages- Graph Database a (Graph) and related (Indexes) manages manages ๏ A Graph Database: lookup Graph Indexes • Full ACID Transactions • High-Availability (read scaling) • 32 Billion Nodes, 32 Billion Relationships, 64 Billion Properties • Server with REST API • Or, Embeddable as a Java Library 11 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Neo4j Features ๏ Neo4j is stable • In 24/7 operation since 2003 ๏ Neo4j is under active development • open source, with a vibrant community ๏ High performance graph operations • high performance? like what? ... 12 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Example: Social Network “path exists” ๏ ~1,000 persons Madeline ๏ average 50 friends per person Andreas Emil Joanne ๏ pathExists(a,b) limited to depth 4 Gabriella Marcus Peter ๏ caches warmed up to eliminate disk I/O Jacob # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 13 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Example: Social Network “path exists” ๏ ~1,000 persons Madeline ๏ average 50 friends per person Andreas Emil Joanne ๏ pathExists(a,b) limited to depth 4 Gabriella Marcus Peter ๏ caches warmed up to eliminate disk I/O Jacob # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 13 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Example: Social Network “path exists” ๏ ~1,000 persons Madeline ๏ average 50 friends per person Andreas Emil Joanne ๏ pathExists(a,b) limited to depth 4 Gabriella Marcus Peter ๏ caches warmed up to eliminate disk I/O Jacob # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 13 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Example: Social Network “path exists” ๏ ~1,000 persons Madeline ๏ average 50 friends per person Andreas Emil Joanne ๏ pathExists(a,b) limited to depth 4 Gabriella Marcus Peter ๏ caches warmed up to eliminate disk I/O Jacob # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 13 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j • recommendations run in real-time 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j • recommendations run in real-time • what does a recommendation look like? 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j • recommendations run in real-time • what does a recommendation look like? 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j • recommendations run in real-time • what does a recommendation look like? ๏ Check out: http://www.youtube.com/watch?v=sLlbtoCfciE 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
A closer look at recommendations 15 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Recommendations 2 ways... 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Recommendations 2 ways... ๏Gremlin - graph scripting 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach ๏Cypher - declarative querying 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach ๏Cypher - declarative querying • a “pattern matching” approach 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach ๏Cypher - declarative querying • a “pattern matching” approach ๏Stig - functional graph query 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach ๏Cypher - declarative querying • a “pattern matching” approach ๏Stig - functional graph query • ok, not yet... Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j 16 Wednesday, August 24, 11
Gremlin 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 • g.v(0).in - nodes connected to Node 0 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 • g.v(0).in - nodes connected to Node 0 • g.v(1).out(“KNOWS”) - nodes connected by “KNOWS” 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 • g.v(0).in - nodes connected to Node 0 • g.v(1).out(“KNOWS”) - nodes connected by “KNOWS” ๏ details about Gremlin: https://github.com/tinkerpop/gremlin 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 • g.v(0).in - nodes connected to Node 0 • g.v(1).out(“KNOWS”) - nodes connected by “KNOWS” ๏ details about Gremlin: https://github.com/tinkerpop/gremlin ๏ props to @twarko for creating it 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher ๏ a pattern-matching query language (SQL-ish) 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 • start a=(1) match (a)-->(b) return b - traverse from node 1 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 • start a=(1) match (a)-->(b) return b - traverse from node 1 • start a=(1) match (a)--()--(c) return c - friends of friends 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 • start a=(1) match (a)-->(b) return b - traverse from node 1 • start a=(1) match (a)--()--(c) return c - friends of friends ๏ details about Cypher: 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 • start a=(1) match (a)-->(b) return b - traverse from node 1 • start a=(1) match (a)--()--(c) return c - friends of friends ๏ details about Cypher: • http://docs.neo4j.org/chunked/stable/cypher-query-lang.html 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
The Social Graph as a Data Graph 19 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
The Usual Social Graph, is also data status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 20 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
The Usual Social Graph, is also data status body: presenting at NoSQL name:Andreas Now! like right now! status me knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 20 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
with the Usual “status tweets” status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 21 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
with the Usual “status tweets” status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 21 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
and the Usual Suspects status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 22 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
and the Usual Suspects status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 22 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
and the Usual Business status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 23 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
and the Usual Business status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 23 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
and the Usual Likes status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 24 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
and the Usual Likes status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 24 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Making Friends 25 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
My Expanded Ego Network name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 26 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Me & My Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 27 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Me & My Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 27 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Friends of my Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 28 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Friends of my Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 28 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Friends of my Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 28 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Friend of a Friend of a Friend? name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 29 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Friend of a Friend of a Friend? name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 29 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Friend of a Friend of a Friend? name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows Whatever. 29 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) -[:KNOWS]->(foafs) return foafs 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) -[:KNOWS]->(foafs) return foafs 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) -[:KNOWS]->(foafs) return foafs // result: Emil, Stephen, Allison (& Peter?) 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) -[:KNOWS]->(foafs) return foafs // result: Emil, Stephen, Allison (& Peter?) Is that good enough? 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Ranking my Friends’ Friends (intuitively) name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Ranking my Friends’ Friends (intuitively) name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows 1 knows knows name: Emil knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Ranking my Friends’ Friends (intuitively) name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows 1 knows knows name: Emil 2 knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Ranking my Friends’ Friends (intuitively) 3 name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows 1 knows knows name: Emil 2 knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Ranking my Friends’ Friends (intuitively) 3 name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter ? name: Delia name: Tiberius knows 1 knows knows name: Emil 2 knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} ==>Emil=2.0 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} ==>Emil=2.0 ==>Allison=2.0 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} ==>Emil=2.0 ==>Allison=2.0 ==>Stephen=1.0 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} ==>Emil=2.0 ==>Allison=2.0 ==>Stephen=1.0 ==>Tiberius=0.5 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Like, like this 33 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
What else might I like? url: http://theoatmeal.com/comics/ name:Andreas state_web_summer/ likes:+1 knows name: Marko likes:+2 url: http://www.imdb.com/title/tt0133093/ knows likes:+1 name: Peter likes:+1 url: http://browsertoolkit.com/fault- tolerance.png likes:+1 likes:+2 url: http://www.imdb.com/title/tt0436992/ likes:+1 likes:+3 url: http://lensbaby.com/optics-pinhole.php url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 34 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
What else might I like? url: http://theoatmeal.com/comics/ name:Andreas state_web_summer/ likes:+1 knows name: Marko likes:+2 url: http://www.imdb.com/title/tt0133093/ knows likes:+1 name: Peter likes:+1 url: http://browsertoolkit.com/fault- tolerance.png likes:+1 likes:+2 url: http://www.imdb.com/title/tt0436992/ likes:+1 likes:+3 url: http://lensbaby.com/optics-pinhole.php url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 34 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Things to Like 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Things to Like // Cypher - websites my friends like 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url +------------------------------------------------+ 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url +------------------------------------------------+ | website.url | 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url +------------------------------------------------+ | website.url | +------------------------------------------------+ 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database
Case Study: Real-time Recommendations with a Graph Database

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Case Study: Real-time Recommendations with a Graph Database

  • 1. Realtime Recommendations with a Graph Database Andreas Kollegger #neo4j @akollegger Wednesday, August 24, 11
  • 2. What’s the plan? ๏Graph Database 101 ๏Case Study: Viadeo ๏Finding new Friends ๏Recommending new Sites 2 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 3. 3 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 4. Obey! 3 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 5. Obey! ๏tweet using #neo4j 3 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 6. Obey! ๏tweet using #neo4j ๏I am @akollegger 3 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 7. NOSQL Categories Size Data Complexity 4 Wednesday, August 24, 11
  • 8. NOSQL Categories Key-Value Store Size Data Complexity 4 Wednesday, August 24, 11
  • 9. NOSQL Categories Key-Value Store Size BigTable Clones Data Complexity 4 Wednesday, August 24, 11
  • 10. NOSQL Categories Key-Value Store Size BigTable Clones Document Databases Data Complexity 4 Wednesday, August 24, 11
  • 11. NOSQL Categories Key-Value Store Size BigTable Clones Document Databases Graph Databases Data Complexity 4 Wednesday, August 24, 11
  • 12. NOSQL Categories Key-Value Store Size BigTable Clones Document Databases Graph Databases Data Complexity 4 Wednesday, August 24, 11
  • 13. NOSQL Categories Key-Value Store (this is still billions Size BigTable of nodes & Clones relationships) Document Databases Graph 90% Databases of use cases Data Complexity 4 Wednesday, August 24, 11
  • 14. Graph DB 101 (Reading a graph) 5 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 15. A graph database? 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 16. A graph database? ๏ no: not for storing charts & graphs, or vector artwork 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 17. A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 18. A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph • remember linked-list, tree? 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 19. A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph • remember linked-list, tree? • graphs are the generalized connected data structure 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 20. A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph • remember linked-list, tree? • graphs are the generalized connected data structure ๏ whiteboard friendly - no data mangling required 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 21. A graph database? ๏ no: not for storing charts & graphs, or vector artwork ๏ yes: for storing data that is structured as a graph • remember linked-list, tree? • graphs are the generalized connected data structure ๏ whiteboard friendly - no data mangling required ๏ “A traditional relational database may tell you the average salary of everyone in this conference, but a graph database will tell you who is most likely to buy you a beer.” 6 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 22. Q: What are graphs good for? 7 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 23. Q: What are graphs good for? A: highly-connected data ๏ Recommendations ๏ Time series data ๏ Business intelligence ๏ Product catalogue ๏ Social computing ๏ Web analytics ๏ Geospatial ๏ Scientific computing (especially bioinformatics) ๏ MDM ๏ Systems management ๏ Indexing your slow RDBMS ๏ Genealogy ๏ And much more! 7 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 24. G=(V,E) ๏ A (Graph) -records data in- (Nodes) Graph ๏ A (Graph) -records data in- (Relationships) records data in records data in organized by ๏ (Nodes) are -organized by- (Relationships) Nodes Relationships have have ๏ (Nodes & Relationships) -have- (Properties) Properties ๏ Instead of a Data Model, you have a Data Graph ๏ Structure is defined by the Relationships you create 8 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 25. Query a graph with a traversal ๏ A (Traversal) -navigates- a (Graph) Traversal identifies navigates ๏ A (Traversal) -identifies- (Paths) Paths Graph ๏ (Paths) -order- (Nodes) records data in records data in order organized by Nodes Relationships ๏ Traversals reveal localized data ๏ Find data that is relevant to other data 9 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 26. Indexes look-up Nodes or Relationships ๏ An (Index) -maps to either- Index special Traversal (Nodes or Relationships) maps to either maps to either ๏ An (Index) is a -special- (Traversal) organized by Nodes Relationships maps from have have ๏ Indexes find starting points for a traversal Properties ๏ Can be queried with simple constraints 10 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 27. Neo4j is a Graph Database ๏ A (Graph Database) -manages- Graph Database a (Graph) and related (Indexes) manages manages ๏ A Graph Database: lookup Graph Indexes • Full ACID Transactions • High-Availability (read scaling) • 32 Billion Nodes, 32 Billion Relationships, 64 Billion Properties • Server with REST API • Or, Embeddable as a Java Library 11 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 28. Neo4j Features ๏ Neo4j is stable • In 24/7 operation since 2003 ๏ Neo4j is under active development • open source, with a vibrant community ๏ High performance graph operations • high performance? like what? ... 12 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 29. Example: Social Network “path exists” ๏ ~1,000 persons Madeline ๏ average 50 friends per person Andreas Emil Joanne ๏ pathExists(a,b) limited to depth 4 Gabriella Marcus Peter ๏ caches warmed up to eliminate disk I/O Jacob # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 13 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 30. Example: Social Network “path exists” ๏ ~1,000 persons Madeline ๏ average 50 friends per person Andreas Emil Joanne ๏ pathExists(a,b) limited to depth 4 Gabriella Marcus Peter ๏ caches warmed up to eliminate disk I/O Jacob # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 13 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 31. Example: Social Network “path exists” ๏ ~1,000 persons Madeline ๏ average 50 friends per person Andreas Emil Joanne ๏ pathExists(a,b) limited to depth 4 Gabriella Marcus Peter ๏ caches warmed up to eliminate disk I/O Jacob # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 13 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 32. Example: Social Network “path exists” ๏ ~1,000 persons Madeline ๏ average 50 friends per person Andreas Emil Joanne ๏ pathExists(a,b) limited to depth 4 Gabriella Marcus Peter ๏ caches warmed up to eliminate disk I/O Jacob # persons query time Relational database 1,000 2000ms Neo4j 1,000 2ms Neo4j 1,000,000 2ms 13 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 33. Case Study: Viadeo 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 34. Case Study: Viadeo ๏ Viadeo - 35 million user professional network 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 35. Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 36. Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 37. Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 38. Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 39. Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j • recommendations run in real-time 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 40. Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j • recommendations run in real-time • what does a recommendation look like? 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 41. Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j • recommendations run in real-time • what does a recommendation look like? 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 42. Case Study: Viadeo ๏ Viadeo - 35 million user professional network ๏ the past tech • recommendations running on MySQL as a batch job • took an hour, then success happened.. and it took two days ๏ now using Neo4j • recommendations run in real-time • what does a recommendation look like? ๏ Check out: http://www.youtube.com/watch?v=sLlbtoCfciE 14 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 43. A closer look at recommendations 15 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 44. Recommendations 2 ways... 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 45. Recommendations 2 ways... ๏Gremlin - graph scripting 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 46. Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 47. Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach ๏Cypher - declarative querying 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 48. Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach ๏Cypher - declarative querying • a “pattern matching” approach 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 49. Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach ๏Cypher - declarative querying • a “pattern matching” approach ๏Stig - functional graph query 16 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 50. Recommendations 2 ways... ๏Gremlin - graph scripting • an algorithmic approach ๏Cypher - declarative querying • a “pattern matching” approach ๏Stig - functional graph query • ok, not yet... Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j 16 Wednesday, August 24, 11
  • 51. Gremlin 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 52. Gremlin ๏ a graph scripting DSL 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 53. Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 54. Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 55. Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 56. Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 57. Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 • g.v(0).in - nodes connected to Node 0 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 58. Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 • g.v(0).in - nodes connected to Node 0 • g.v(1).out(“KNOWS”) - nodes connected by “KNOWS” 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 59. Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 • g.v(0).in - nodes connected to Node 0 • g.v(1).out(“KNOWS”) - nodes connected by “KNOWS” ๏ details about Gremlin: https://github.com/tinkerpop/gremlin 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 60. Gremlin ๏ a graph scripting DSL ๏ part of the TinkerPop stack ๏ try it out: • g - the graph itself • g.v(0) - node 0 • g.v(0).in - nodes connected to Node 0 • g.v(1).out(“KNOWS”) - nodes connected by “KNOWS” ๏ details about Gremlin: https://github.com/tinkerpop/gremlin ๏ props to @twarko for creating it 17 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 61. Cypher 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 62. Cypher ๏ a pattern-matching query language (SQL-ish) 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 63. Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 64. Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 65. Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 66. Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 • start a=(1) match (a)-->(b) return b - traverse from node 1 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 67. Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 • start a=(1) match (a)-->(b) return b - traverse from node 1 • start a=(1) match (a)--()--(c) return c - friends of friends 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 68. Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 • start a=(1) match (a)-->(b) return b - traverse from node 1 • start a=(1) match (a)--()--(c) return c - friends of friends ๏ details about Cypher: 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 69. Cypher ๏ a pattern-matching query language (SQL-ish) ๏ developed in-house ๏ try it out: • start a=(0) return a - get node 0 • start a=(1) match (a)-->(b) return b - traverse from node 1 • start a=(1) match (a)--()--(c) return c - friends of friends ๏ details about Cypher: • http://docs.neo4j.org/chunked/stable/cypher-query-lang.html 18 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 70. The Social Graph as a Data Graph 19 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 71. The Usual Social Graph, is also data status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 20 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 72. The Usual Social Graph, is also data status body: presenting at NoSQL name:Andreas Now! like right now! status me knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 20 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 73. with the Usual “status tweets” status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 21 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 74. with the Usual “status tweets” status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 21 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 75. and the Usual Suspects status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 22 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 76. and the Usual Suspects status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 22 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 77. and the Usual Business status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 23 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 78. and the Usual Business status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 23 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 79. and the Usual Likes status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 24 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 80. and the Usual Likes status body: presenting at NoSQL name:Andreas Now! like right now! status knows name: Marko knows body: mocking up a simplified social graph worked_at name: Peter likes employer:Neo Technology employer:Xilinx likes worked_at url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 24 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 81. Making Friends 25 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 82. My Expanded Ego Network name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 26 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 83. Me & My Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 27 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 84. Me & My Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 27 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 85. Friends of my Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 28 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 86. Friends of my Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 28 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 87. Friends of my Friends name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 28 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 88. Friend of a Friend of a Friend? name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 29 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 89. Friend of a Friend of a Friend? name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 29 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 90. Friend of a Friend of a Friend? name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows Whatever. 29 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 91. Query for Friends of Friends 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 92. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 93. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 94. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 95. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 96. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 97. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) -[:KNOWS]->(foafs) return foafs 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 98. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) -[:KNOWS]->(foafs) return foafs 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 99. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) -[:KNOWS]->(foafs) return foafs // result: Emil, Stephen, Allison (& Peter?) 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 100. Query for Friends of Friends // Gremlin - andreas to friends to their friends, counted andreas.out('KNOWS').out('KNOWS').name.groupCount(m) // Cypher - match from andreas to friends to their friends start andreas=(1) match (andreas)-[:KNOWS]->(friends) -[:KNOWS]->(foafs) return foafs // result: Emil, Stephen, Allison (& Peter?) Is that good enough? 30 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 101. Ranking my Friends’ Friends (intuitively) name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows knows knows name: Emil knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 102. Ranking my Friends’ Friends (intuitively) name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows 1 knows knows name: Emil knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 103. Ranking my Friends’ Friends (intuitively) name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows 1 knows knows name: Emil 2 knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 104. Ranking my Friends’ Friends (intuitively) 3 name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter name: Delia name: Tiberius knows 1 knows knows name: Emil 2 knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 105. Ranking my Friends’ Friends (intuitively) 3 name:Andreas knows knows name: Marko knows name: Stephen knows knows knows name: Peter ? name: Delia name: Tiberius knows 1 knows knows name: Emil 2 knows name: Allison knows 31 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 106. Query for Ranked extended FoaF 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 107. Query for Ranked extended FoaF // Gremlin - a bit more complex 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 108. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 109. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 110. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 111. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 112. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 113. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 114. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 115. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} ==>Emil=2.0 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 116. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} ==>Emil=2.0 ==>Allison=2.0 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 117. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} ==>Emil=2.0 ==>Allison=2.0 ==>Stephen=1.0 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 118. Query for Ranked extended FoaF // Gremlin - a bit more complex // - requiring “memory” and weights // - weights decrease with each “hop” outward // - extended hop, to include Stephen andreas.out('KNOWS').aggregate(x).out('KNOWS').except(x).name .groupCount(m){it}{it+1.0}.back(2) .out('KNOWS').except(x).name.groupCount(m){it}{it+0.5} ==>Emil=2.0 ==>Allison=2.0 ==>Stephen=1.0 ==>Tiberius=0.5 32 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 119. Like, like this 33 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 120. What else might I like? url: http://theoatmeal.com/comics/ name:Andreas state_web_summer/ likes:+1 knows name: Marko likes:+2 url: http://www.imdb.com/title/tt0133093/ knows likes:+1 name: Peter likes:+1 url: http://browsertoolkit.com/fault- tolerance.png likes:+1 likes:+2 url: http://www.imdb.com/title/tt0436992/ likes:+1 likes:+3 url: http://lensbaby.com/optics-pinhole.php url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 34 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 121. What else might I like? url: http://theoatmeal.com/comics/ name:Andreas state_web_summer/ likes:+1 knows name: Marko likes:+2 url: http://www.imdb.com/title/tt0133093/ knows likes:+1 name: Peter likes:+1 url: http://browsertoolkit.com/fault- tolerance.png likes:+1 likes:+2 url: http://www.imdb.com/title/tt0436992/ likes:+1 likes:+3 url: http://lensbaby.com/optics-pinhole.php url: http://www.amazon.com/Nikon-14-24mm-2-8G-Nikkor-Angle/dp/B000VDCTCI/ url: http://www.imdb.com/title/tt1475582/ 34 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 122. Query for Things to Like 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 123. Query for Things to Like // Cypher - websites my friends like 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 124. Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 125. Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 126. Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 127. Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url +------------------------------------------------+ 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 128. Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url +------------------------------------------------+ | website.url | 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11
  • 129. Query for Things to Like // Cypher - websites my friends like start andreas=(1) match (andreas)-[:KNOWS]->(friend) -[:LIKES]->(website) return website.url +------------------------------------------------+ | website.url | +------------------------------------------------+ 35 Andreas Kollegger --[known_as]--> @akollegger --[talks_about]--> #neo4j Wednesday, August 24, 11