Introduction to Graph Databases
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This document introduces graph databases as an alternative to relational databases for modeling data with complex relationships. It discusses how graph databases can be used to efficiently find connections between entities, such as finding the shortest path between two actors in a movie database. Examples are given of domains where graph databases are commonly used, like social networks, mapping, and bioinformatics. The document also provides an overview of Neo4j, a popular graph database technology.
![Cypher "What to find" vs. "How to find" // Find all the directors who have directed a movie scored by John Williams // that starred Kevin Bacon START actor=(actors, 'Kevin Bacon'), composer=(compsers, 'John Williams') MATCH (actor)-[:IN]->(movie)<-[:DIRECTED]-(director), ? ? ? (movie)<-[:SCORED]-(composer) RETURN director](https://image.slidesharecdn.com/8789799/85/Introduction-to-Graph-Databases-15-320.jpg)
![Resources http://neo4j.org http://docs.neo4j.org http://www.youtube.com/watch?v=UodTzseLh04 Emil Eifrem (Neo Tech. CEO) webinar Check out around the 54 minute mark http://github.com/jadell/Neo4jPHP http://joshadell.com [email_address] @josh_adell Google+, Facebook, LinkedIn](https://image.slidesharecdn.com/8789799/85/Introduction-to-Graph-Databases-19-320.jpg)
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Introduction to Graph Databases
- 1. Introduction to Graph Databases Josh Adell <josh.adell@gmail.com> 20110806
- 2. Who am I? Software developer:?PHP, Javascript, SQL http://www.dunnwell.com Fan of using the right tool for the job
- 3. The Problem
- 4. The Solution? > -- Given "Keanu Reeves" find a connection to "Kevin Bacon" > SELECT ??? FROM cast WHERE ??? +---------------------------------------------------------------------+ | actor_name ? ? ? ? ? ? ? ? | movie_title ? ? ? ? ? ? ? ? ? ? ? ? ? ?| +============================+========================================+ | Jennifer Connelley ? ? ? ? | Higher Learning ? ? ? ? ? ? ? ? ? ? ? ?| +----------------------------+----------------------------------------+ | Laurence Fishburne ? ? ? ? | Mystic River ? ? ? ? ? ? ? ? ? ? ? ? ? | +----------------------------+----------------------------------------+ | Laurence Fishburne ? ? ? ? | Higher Learning ? ? ? ? ? ? ? ? ? ? ? ?| +----------------------------+----------------------------------------+ | Kevin Bacon ? ? ? ? ? ? ? ?| Mystic River ? ? ? ? ? ? ? ? ? ? ? ? ? | +----------------------------+----------------------------------------+ | Keanu Reeves ? ? ? ? ? ? ? | The Matrix ? ? ? ? ? ? ? ? ? ? ? ? ? ? | +----------------------------+----------------------------------------+ | Laurence Fishburne ? ? ? ? | The Matrix ? ? ? ? ? ? ? ? ? ? ? ? ? ? | +----------------------------+----------------------------------------+
- 5. Find Every Actor at Each Degree > -- First degree > SELECT actor_name FROM cast WHERE movie_title IN (SELECT DISTINCT movie_title FROM cast WHERE actor_name='Kevin Bacon') > -- Second degree > SELECT actor_name FROM cast WHERE movie_title IN (SELECT DISTINCT movie_title FROM cast WHERE actor_name IN (SELECT actor_name?FROM cast WHERE movie_title IN (SELECT DISTINCT movie_title FROM cast WHERE actor_name='Kevin Bacon'))) > -- Third degree > SELECT actor_name FROM cast WHERE movie_title IN(SELECT DISTINCT movie_title FROM cast WHERE actor_name IN (SELECT actor_name FROM cast WHERE movie_title IN (SELECT DISTINCT movie_title FROM cast WHERE actor_name IN (SELECT actor_name?FROM cast WHERE movie_title IN (SELECT DISTINCT movie_title FROM cast WHERE actor_name='Kevin Bacon'))))
- 6. The Truth Relational databases aren't very good with relationsh ips Data RDBMs
- 7. The Real Problem Finding relationships across multiple degrees of separation ? ? ...and across multiple data types ? ? ...and where you don't even know there is a relationship
- 10. Relational Databases are Graphs!
- 11. Some Graph Use Cases Social networking Manufacturing Mapping and Geolocation Bioinformatics Fraud detection Multi-tenancy
- 12. Modelling a Domain with Graphs Graphs are "whiteboard-friendly" Nouns become nodes Verbs become relationships Properties are adjectives and adverbs
- 13. Graph Mining Paths Traversals Ad-hoc Queries
- 14. New Solution to the Bacon Problem $keanu = $actorIndex->find('name', 'Keanu Reeves'); $kevin =?$actorIndex->find('name', 'Kevin Bacon'); $path = $keanu->findPathTo($kevin);
- 15. Cypher "What to find" vs. "How to find" // Find all the directors who have directed a movie scored by John Williams // that starred Kevin Bacon START actor=(actors, 'Kevin Bacon'), composer=(compsers, 'John Williams') MATCH (actor)-[:IN]->(movie)<-[:DIRECTED]-(director), ? ? ? (movie)<-[:SCORED]-(composer) RETURN director
- 16. Are RDBs Useful At All? Aggregation Ordered data Truly tabular data Few or clearly defined relationships
- 17. Neo Technologies http://neo4j.org Embedded in Java applications Standalone server via REST Plugins: spatial, lucene, rdf Others: Tinkerpop OrientDB
- 18. Questions?
- 19. Resources http://neo4j.org http://docs.neo4j.org http://www.youtube.com/watch?v=UodTzseLh04 Emil Eifrem (Neo Tech. CEO) webinar Check out around the 54 minute mark http://github.com/jadell/Neo4jPHP http://joshadell.com [email_address] @josh_adell Google+, Facebook, LinkedIn
Editor's Notes
- #3: * graph db usage poll
- #4: * Six degrees game * Relational databases can't easily answer certain types of questions
- #5: * first pass using a relational database * cast table: actor_name, movie_title * hard to visualize the solution * In order to do this, you need to do multiple passes or joins
- #6: * Each degree adds a join * Increases complexity * Decreases performance * Stop when the actor you're looking for is in the list
- #7: * this problem highlights the ugly truth about RDBs * they weren't designed to handle these types of problems. * arbitrary path query * RDB relationships join data, but are not data in themselves * Set math * Gather everything in the set that matches these criteria, then tell me if this thing is in the set * 1 set, no problem * 2nd set no problem * 3rd set not related to 1st * 4th not related to 2nd * 5th related to 1st and 4th * etc. * Relationships are only available between overlapping sets
- #8: * disjoint sets
- #9: * Graphs * Not X-Y * Computer Science definition of graphs * A graph is an ordered pair? G = (V, E) ?where V is a set of? vertices ?and E is a set of? edges , which are?pairs of?vertices. * Node : vertex * Relationship : edge * Property : meta-datum attached to a node or relationship * Nodes can have arbitrary properties * Relationships are first-class citizens Have a type Have properties Have a direction Domain semantics Traversable in any direction * This is how graph dbs solve the problems that RDBs can't * Path : an ordered list of nodes and relationships * Paths are found using traversal algorithms
- #10: * Tree data-structures * Networks * Maps * vehicles on streets == packets through network * Relational databases are graphs!
- #11: * Make each record a node * Make every foreign key a relationship * RDB indexes are usually stored in a tree structure * Trees are graphs * Why not use RDBs? * The trouble with RDBs is how they are stored in memory and queried ? * Require a translation step from memory blocks to graph structure * Relationships not first-class citizens * Many problem domains map poorly to rows/tables
- #12: * Big Data ** billions of nodes and relationships in a single instance * &quot;Internet of Things&quot; buzzword * Social networking - friends of friends of friends of friends * Assembly/Manufacturing - 1 widget contains 3 gadgets each contain 2 gizmos * Map directions - starting at my house find a route to the office that goes past the pub * Multi-tenancy - root node per tenant * all queries start at root * No overlap between graphs = no accidental data spillage * Fraud: track transactions back to origination * Pretty much anything that can be drawn on a whiteboard
- #13: * Example: retail system * Customer makes Order * Store sells Order * Order contains Items * Supplier supplied Items * Customer rates Items * Did this customer rank supplier X highly? * Which suppliers sell the highest rated items? * Does item A get rated higher when ordered with Item B? * All can be answered with RDBs as well * Not as elegant * Not as performant
- #14: * This is where the power of graph dbs comes from * Paths - find any relationship chain between A and B * Kevin Bacon example, known start and end * Traversal - filter out paths that don't meet criteria * Complex path finding, base next decision on existing path from start to current position * Define path-finding (prune) and result filtering functions * Queries - Here is what I want, find it however you can * SPARQL, Gremlin, Cypher
- #15: * Actors are nodes * Movies are nodes * Relationship: Actor is IN a movie * pseudo-code shortened for brevity * Compare to degree selection join queries
- #16: * Cypher is &quot;what to find&quot; * describe the &quot;shape&quot; of the thing you're looking for * Very white-board friendly * Pros: easy to understand, query looks like domain model * Cons: not as powerful, not fully featured (YET) * result set is an array of arrays?
- #17: * RDBs are really good at data aggregation * Set math, duh * Have to traverse the whole graph in order to do aggregation * Truly tabular means not a lot of relationships between the data types
- #18: * billions of nodes and relationships in a single instance * cluster replication * transactions * native bindings for Ruby, Python, and language that can run in JVM * Licensing