![Six Degrees of Bacon
“…any individual involved in the Hollywood, California film industry
can be linked through his or her film roles to actor Kevin Bacon
within six steps”
[http://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon]
Gina Menza
Images Courtesy of IMDB (www.imdb.com)
A Tale of Two Kevins](https://image.slidesharecdn.com/ig-jaxconf2013-sixdegrees-130610035845-phpapp01/85/Revisiting-the-Six-Degrees-Problem-with-a-Graph-Database-Nick-Quinn-3-320.jpg)
Revisiting the Six Degrees Problem with a Graph Database - Nick Quinn
- 1. www.Objectivity.com Nick Quinn Lead Developer- InfiniteGraph
- 2. What are wetalking about today? Not that BaconThis Bacon! • Intro to the Six Degrees Problem • What is a Graph Database? • Why Bacon in Graph Database? • How we solved the problem Images Courtesy of IMDB (www.imdb.com)
- 3. Six Degrees ofBacon “…any individual involved in the Hollywood, California film industry can be linked through his or her film roles to actor Kevin Bacon within six steps” [http://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon] Gina Menza Images Courtesy of IMDB (www.imdb.com) A Tale of Two Kevins
- 4. Why Six Degreesof Bacon? Actor Age # of Projects Kevin Bacon 54 76 Harrison Ford 70 70 Tom Cruise 50 40 Julia Roberts 45 50 Tom Hanks 56 73 Denzel Washington 58 53 Michael Caine 80 157 Kiefer Sutherland 46 82 Kevin Bacon Images Courtesy of IMDB (www.imdb.com)
- 5. Bacon Numbers inGoogle In the summer of 2012, Google started to allow users to find the bacon number of any actor simply by following his or her name with “bacon number”. Morgan Freeman The Dark Night Rises appeared in Gary Oldman appeared in Kevin Bacon Criminal Law appeared in appeared in www.google.com Graphical Representation
- 6. What is aGraph Database ?
- 7. The Physical DataModel • Difference between relational & graph databases Meetings P1 Place TimeP2 Alice Denver 5-27-10Bob Calls From Time DurationTo Bob 13:20 25Carlos Bob 17:10 15Charlie Payments From Date AmountTo Carlos 5-12-10 100000Charlie Met 5-27-10 Alice Called 13:20 Bob Paid 100000 Carlos Charlie Called 17:10 Rows/Columns/Tables Relationship/Graph Optimized
- 8. Connecting Data Person Building ? WorkLive RR Visit Eat Shop
- 9. Who is GinaMenza? • How do we get meaning from highly connected data? Gina Menza Jury Forewoman Miss Jeffries Images Courtesy of IMDB (www.imdb.com)
- 10. Strength of ConnectionsMatter! • Why 6 degrees of separation and not 3.74? • We need analysis tools in order to – identify and filter out “unimportant” data and – infer what needs to be filtered as we investigate it. “When considering another person in the world, a friend of your friend knows a friend of their friend” - facebook
- 11. Why Bacon ina Graph Database ?
- 12. Graph Analysis • Whyuse Graph Databases for graph analysis? – Dynamic on Live Data – Feedback/Inference – Optimized for concurrent user access – Handles big data problems – Native Graph Traversal API – Manage memory efficiently
- 13. Paths to Bacon BaconNumber (Degree of Separation / 2) # of People 1 2823 2 323677 3 1088560 4 272905 5 22533 6 2300 Using the IMDB (www.imdb.com) data set, we can study how many paths can be found by degrees of separation from Kevin Bacon. Out of 5,067,124 nodes and 11,505,797 edges, we get the following: 0 200000 400000 600000 800000 1000000 1200000 1 2 3 4 5 6 # of People
- 14. Big Data +Graph = Big Graph Data 4 Degrees of Kevin Bacon (Breadth First up to 20K connections) Images generated using the IG Visualizer
- 15. Analyzing Bacon • Tobe able to perform meaningful analysis, these are things that you will need: – Ingest IMDB Dataset – About 50 Formatted compressed files (Largest > 200 MB) – Custom algorithm support to perform meaningful analysis • Optimize queries to get results back in reasonable time – Visualization tool to test and view the results of the navigation (optional)
- 16. How IG SizzlesYour Bacon Ingest Update Navigate Massive graph data require efficient and intelligent tools to analyze and understand it.
- 17. Super Simple JavaAPI Actor bacon = new Actor(“Kevin Bacon”); imdbGraphDB.addVertex( bacon ); Movie apollo= new Movie(“Apollo 13”, 1995); imdbGraphDB.addVertex( apollo ); ActedIn bacon2apollo = new ActedIn(“Jack Swigert”); imdbGraphDB.addEdge(bacon2apollo, bacon, apollo, Ed geKind.BIDIRECTIONAL, 1 /**weight**/); Ingest
- 18. Scaling Writes • Big/Fastdata demands write performance • Most NoSQL solutions allow you to scale writes by… – Partitioning the data – Understanding your consistency requirements – Allowing you to defer conflicts Ingest
- 19. App-2 (Ingest V2) App-2 (E23{ V2V3}) ScalingGraph Writes ACID Transactions InfiniteGraph Objectivity/DB Persistence Layer App-1 (Ingest V1) App-3 (Ingest V3) V1 V2 V3 App-1 (E1 2{ V1V2}) App-3 E12 E23 Ingest
- 20. High Performance EdgeIngest IG Core/API C1 C2 C3 E12 E23 TargetContainers PipelineContainers E(1->2) E(3->1) E(2->3) E(2->1) E(2->3) E(3->1) E(1->2) E(3->2) E(1->2) E(2->3) E(3->1) E(2->1) E(2->3) E(3->1) E(3->2) E(1->2) Pipeline Agent Ingest
- 21. Trade offs • Excellentfor efficient use of page cache • Able to maintain full database consistency • Achieves highest ingest rate in distributed environments • Almost always has highest “perceived” rate • Trading Off : • Eventual consistency in graph (connections) • Updates are still atomic, isolated and durable but phased • External agent performs graph building Ingest
- 22. Result… 1 client 2 clients 4clients 8 clients 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 500000 1 2 4 NodesandEdgespersecond 1 client 2 clients 4 clients 8 clients Ingest
- 23. Scaling Reads andQuery Distributed API Application(s) Partition 1 Partition 3Partition 2 Partition ...n Processor Processor Processor Processor Partitioning and Read Replicas… easy right !
- 24. Why are GraphsDifferent ? Distributed API Application(s) Partition 1 Partition 3Partition 2 Partition ...n Processor Processor Processor Processor Navigate
- 25. Distributed Navigation • Detectlocal hops and perform in memory traversal • Send the partial path to the distributed processing to continue the navigation. • Intelligently cache remote data when accessed frequently • Route tasks to other hosts when it is optimal Navigate
- 26. Distributed Navigation Server Processor DistributedAPI Partition 1 Partition 2 Processor Application A X Y B C D E P(A,B,C,D) F G Navigate
- 27. GraphViews Leveraging Schema inthe Graph Patient Prescription Drug Ingredient Outcome Complaint Visit Allergy Physician Navigate
- 28. Schema Enables Views •GraphViews are extremely powerful • Allow Big Data to appear small ! • Connection inference can lead to exponential gains in query performance • Views are reusable between queries • Views can be persisted • Built into the native kernel Navigate
- 29. Problem of Supernodes InGraph Theory, a “supernode” is a vertex with a disproportionally high number of connected edges. Supernodes make it difficult to do a navigational query in real-time due to the amount of effort it may be to pursue paths through it that may be unfruitful. Navigate Images generated using the IG Visualizer
- 30. Supernodes in Bacon Navigate Inthe IMDB data set, some examples of supernodes may be talk shows, awards shows, compilations or variety shows. Images generated using the IG Visualizer
- 31. How to avoidsupernodes 1. Setting policies on the navigation like the NoRevisitPolicy , MaximumResultCountPolicy and MaximumPathDepthPolicy can be used to customize the overall behavior of the navigation. PolicyChain policies = new PolicyChain(); // Only traverse the same vertex once policies.addPolicy(new NoRevisitPolicy()); // limits the number of paths that will be returned to 10K policies.addPolicy(new MaximumResultCountPolicy(10000)); // limits the path depth to 6 policies.addPolicy(new MaximumPathDepthPolicy(6)); Navigate
- 32. How to avoidsupernodes 2. Graph View to exclude or limit types GraphView view = new GraphView(); //Excludes all instances of TvShow from navigation view.excludeClass(myDb.getTypeId(TvShow.class.getName())); //Excludes all movies made for TV/Video view.excludeClass(myDb.getTypeId(Movie.class.getName()), “de tails.madeForTv || details.madeForVideo”); //Include ActedIn w/ characterName not containing “Himself” view.excludeClass(myDb.getTypeId(WorkedOn.class.getName())); view.includeClass(myDb.getTypeId(ActedIn.class.getName()), “!CONTAINS(characterName, “Himself”)”); Navigate Kevin Bacon Actor The Following TV Show Behind the Scenes Movie Apollo 13 Movie HimselfRyan Hardy Jack Swigert
- 33. How to avoidsupernodes 3. Using these policies and graph view, we can filter the size of the result set in our navigation: Navigator navigator = bacon.navigate(view, Guide.SIMPLE_BREADTH_FI RST, Qualifier.ANY, new VertexPredicate(Person.class, ""), policies, myResultHandler); navigator.start(); Navigate
- 34. Filtered Views inBacon The results of this navigation would look something like this… Navigate Images generated using the IG Visualizer
- 35. Why InfiniteGraph™? • Objectivity/DBis a proven foundation – Building distributed databases since 1993 – A complete database management system • Concurrency, transactions, cache, schema, query, indexing • It’s a Graph Specialist ! – Simple but powerful API tailored for data navigation. – Easy to configure distribution model
- 36. Advanced Configured Placement •Physically co-locate “closely related” data • Driven through a declarative placement model • Dramatically speeds “local” reads Facility Data Page(s)Patient Data Page(s) Mr Citizen Visit Visit Dr Jones San Jose Facility Dr Smith Primary Physician HasHas With At Located Located Facility Data Page(s) Dr Blake Sunny- vale Dr Quinn Located Located With At
- 37. Fully Distributed DataModel Zone 2Zone 1 HostA IG Core/API Distributed Object and Relationship Persistence Layer Customizable Placement HostB HostC HostX AddVertex()
- 38. Polyglot NoSQL Architectures DistributedData Processing Platform Document Graph Database RDBMS Partitioned Distributed DB (often Document / KV) Users Applications External/LegacyData TransformationMDM Business
- 39. What else! • Distributedupdate. Update … we are working on it.
- 40. Conclusion I hope thatyou enjoyed the bacon. My apologies to my kosher friends for any offense. Look out for new features coming soon!
- 41.
Editor's Notes
- #3 Kevin Norwood Bacon, 1958 in Pennsylvania
- #4 The “six degrees of kevin bacon” game was created in early 1994 by three Albright College students after Kevin Bacon made a comment in Premiere magazine that he had worked with everyone in Hollywood while promoting “The River Wild”.Initially, he disliked the game because he thought he was being ridiculed, but since, he used it in commercials and has started his own social charity website sixdegrees.org due to its popularity.
- #5 First Project: Animal House as “Chip Diller” 1978 According to IMDB, he has acted in 76 projects (Movies, TV shows). More than typical at his age because of his willingness to take small parts…
- #6 Bacon Number: How many movies (degrees) separate any hollywood film actor from Kevin Bacon?
- #8 A Graph Database is not just a graph style interface on top of a relational database. We store the data in such a way that optimizes around graph use cases. For any element, you can find the adjacent object without having to look it up in an index, instead the element has a direct pointer. Offer some kind of traversal query API and the traversal of relationships is optimized because there are no lookups. Explain the slide: As the number of people goes up, the number of meetings, calls and payments will go up and the size of these tables will grow. If you are interested in graph type questions like how many calls did Bob make or maybe how many times did Bob pay Charlie, it is much easier and much more suited to your needs to store the data in such a way that it is easier to answer those.
- #9 So what is a Graph Database? It is a database that represents and stores data in its connected form, as a graph structure. So instead of rows and columns, you have vertex and edge objects. Data structure for representing complicated relationships between entities. Some obvious use cases lie in health care, bioinformatics, social networks, network management, crime detection, fraud analysis, etc.
- #10 Gina Menza had a non-speaking part in both Sleepers and Outbreak. She was in “Wag the Dog”, but never really had any future parts. She is not a strong connector between the Kevins.In November of 2011, Facebook and the University of Milan released an annoucement that “there are on average 3.74 degrees of separation between any one Facebook user and another within the US”. Does this kill the six degrees problem? No, because we are not asking for any “Gina Menza” connection, we are looking for “Gary Oldman” connections. In other words, we are looking for meaningful connections between people. For example, the facebook pronouncement breaks down if you are not happy with just two people that liked “Pepsi”, but only with two people that are connected by people that they are tagged in photos with. The strength of the connections matters!
- #11 Gina Menza had a non-speaking part in both Sleepers and Outbreak. She was in “Wag the Dog”, but never really had any future parts. She is not a strong connector between the Kevins.In November of 2011, Facebook and the University of Milan released an annoucement that “there are on average 3.74 degrees of separation between any one Facebook user and another within the US”. Does this kill the six degrees problem? No, because we are not asking for any “Gina Menza” connection, we are looking for “Gary Oldman” connections. In other words, we are looking for meaningful connections between people. For example, the facebook pronouncement breaks down if you are not happy with just two people that liked “Pepsi”, but only with two people that are connected by people that they are tagged in photos with. The strength of the connections matters!
- #13 Alternate ways to handle graph analysisBatch Processing – MapReduce with Hadoop with some layer on top (Hama, GraphLab, Faunus)Used for global analytics, not real-timeAll in memory - Jung, NetworkX, iGraphRich ecosystem of visualization and algorithmic packagesThe data set size would be limited by the amount of memory that can be used. If ~ millions of edges, the amount of memory is usually too small.
- #15 Kevin Bacon – 4 Degrees up to 20,000 connectionsEdge Line Color = Black (which is why it seems blacked out)Green Background = MoviesPink Background = TV ShowsPurple Background = Actors/ActressesWhite Background = Distribution Companies
- #17 Some data is only actionable momentarilyIntelligenceIT SecuritySite/page visitFinancial / trading behaviorPresents a different type of challengeLatency of batch data processing becomes problematic
- #20 3 nodes, 2 edges in the middle. If both the edges come in at the same time, you will have to wait for the lock on the vertex in the middle. If this type of ingest is happening a much larger level, you will constantly be waiting for locks on shared vertex objects.
- #21 This is our answer to how to scale the ingest. Updates to the vertex objects are staged in target containers and then sorted and moved to pipeline containers where they are picked up and processed by pipeline agents which apply the updates asynchronously.
- #22 This an eventually consistent solution. We still support fully ACID transactions, but you have the choice. As you are doing the navigation, you may not necessarily see the update right away. You can set the consistency preference on a transaction basis, so some can be fully ACID and some eventually consistent.
- #23 Ingest rates on arrays of machines with pretty modest hardware on racks
- #24 In a lot of cases, especially KV stores and even RDBMS’s, it is relatively simple to scale the reads in especially if you have partitioned the data.
- #25 In a graph, it looks like more of a mess. It is not as easy to do this where the data is connected and we are performing a navigational query.
- #27 Pregel-like: wraps up a message and sends it over to the next hostDistributed Cache: Pieces of graph stored in memory, caches remote data stored frequentlyInfiniteGraph makes smart decisions about when to send a message vs doing it by bringing in remote cache in memory.
- #28 InfiniteGraph supports schema, but does not restrict connection types between verticesWe take advantage of schema when applying filters to graph using graph viewsWorking on hybrid schema support
- #31 1: All movies and shows that are associated with Kevin Bacon2: All projects with the word “Show” are highlighted3: All projects with the word “Award” are highlighted4: Just shows the actors associated with the Jay Leno show which is clearly a supernode
- #35 Also filtered on billing position on the edge types to only get the top 5 actors/actresses for each project.Path to Kevin Spacey through “Tremors”, Fred Ward and “Henry and June”Path to Morgan Freeman through “White Water Summer”, Sean Astin, and “The Long Way Home”
- #39 Mixing multiple databases together and using the strengths of each oneUse caseWeb Scale Application on the Frontend with relevant database to store user profiles, read and writeHadoop/MapReduce – on backend to crunch user data to target relevant advertising to themStore the output of that data in such a way to get good query performanceUse the graph database when the connected data can be seen such a way that it makes sense to retrieve it using graph traversals.