Making sense of the Graph Revolution

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In 2013:
- 1.4 Trillion digital interactions happen per month.
- 2.9 million emails are sent every second.
- 72.9 products are ordered on Amazon per second.

That is a lot of connected data, graphs are truly everywhere. Companies are finding that graph database technology is helping them make sense of their big data.

Objectivity’s Nick Quinn, Chief Architect of InfiniteGraph, shows us just how popular graph databases have become and where they are being used, as well as showing us the ins and outs.

Do you want to build technology that does great things with big data? You might want to find out what your colleagues are Tweeting about, make recommendations for apps, music or other retail that result in higher purchase rates, discover hidden connections between new and recorded medical research data, or maybe even leverage intel across government agencies to catch the bad guys.

All this is possible with a graph database.

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Making sense of the Graph Revolution

  1. 1. www.Objectivity.com Making Sense of the Graph Revolution Nick Quinn, Principal Engineer, InfiniteGraph 11/21/13 1
  2. 2. Why Call it a Revolution? • “a forcible overthrow of the current order in favor of a new system.” • NoSQL (Not Only SQL) – Driven by Choice + Big Data Needs • • • • Scalable Performing Distributed Highly Available
  3. 3. Big Data + Graph = Big Graph Data • Social Scale – 1 billion vertices, 100 billion edges • Web Scale – 50 billion vertices, 1 trillion edges • Brain Scale – 100 billion vertices, 100 trillion edges AND GROWING!
  4. 4. Why Call it a Graph Revolution? • After 2011, NoSQL and Graph database begin to follow same trend line and forecast.
  5. 5. The Growing Graph Database Landscape
  6. 6. What is a Graph Database? • A graph database is a native storage engine that enables efficient storage and retrieval of graph structured data. • Graph databases are typically used: – When the data source is highly connected, – Where the connections are important (add value to the data), and – When the user access pattern requires traversals of those connections.
  7. 7. What is a Graph Database • Graph Databases have a unique data model (Vertices and Edges). VERTEX 2 N EDGE • They are optimized around concurrent access of persisted data, so users can navigate the data as it is being added or updated.
  8. 8. Why Use a Graph Database? Relational Database Think about the SQL query for finding all links between the two “blue” rows... Good luck! Table_A Table_B Table_C Table_D Table_E Table_F Relational databases aren’t good at handling complex relationships! Table_G
  9. 9. Why use a Graph Database? Relational Database Table_A Table_B Table_C Table_D Table_E Table_F Table_G Objectivity/DB or InfiniteGraph - The solution can be found with a few lines of code A3 G4
  10. 10. Why use a Graph Database?
  11. 11. Specialized Graph Use Cases • Cyber Security – Identifying potential cyber threats and their targets • Network Management – Offer answers to very complex navigational queries on a social network that needs near real-time answers • Targeted Advertising – Customize marketing to the consumer by compiling a large knowledge graph with an integrated recommendation engine
  12. 12. Example 1 - Ad Placement Networks Smartphone Ad placement - based on the the user’s profile and location data captured by opt-in applications. • The location data can be stored and distilled in a key-value and column store hybrid database, such as Cassandra • The locations are matched with geospatial data to deduce user interests. • As Ad placement orders arrive, an application built on a graph database such as InfiniteGraph, matches groups of users with Ads: • Maximizes relevance for the user. • Yields maximum value for the advertiser and the placer.
  13. 13. Example 2 - Market Analysis The 10 companies that control a majority of U.S. consumer goods brands
  14. 14. Example 3 - Seed To Consumer Tracking ?
  15. 15. Supply Chain Management Use Case • Identify the optimal route for a fleet of trucks at a particular time of the year is quite complex. – number of drivers to pay and their salaries – gas, weather patterns, timing requirements, container sizes, distances, roads, hazards, repairs • Find the most optimal route during the winter in which certain highways will tend to become hazardous around the Great Lakes.
  16. 16. Supply Chain Management Use Case • Find the most cost-effective route in December with weather conditions X and highway conditions Y, and stay below Z latitude while optimizing costs to achieve a rush delivery GraphView myView = new GraphView(); myView.excludeClass(myGraphDb.getTypeId(Highway.class. getName()),“(weather.precipitation > precipitationX && weather.temperature < temperatureX) || traffic.speed < speedY || traffic.accidents > accidentsY ”); myView.excludeClass(myGraphDb.getTypeId(City.class.get Name()), “latitude >= Z”);
  17. 17. Supply Chain Management Use Case City origin,target = …; // Use query or index to lookup “origin” & “target” city VertexIdentifier resultQualifier = new VertexIdentifier(target); // Set policies PolicyChain myPolicies = new PolicyChain(); myPolicies.addPolicy(new MaximumPathDepthPolicy(MAXIMUM_STEPS)); myPolicies.addPolicy(new NoRevisitPolicy()); // Don’t revisit the cities more than once // Define logic on how to process results NavigationResultHandler myNavHandler = new NavigationResultHandler() { @Override public void handleResultPath(Path result) { // The first path returned is the shortest path, but may not be the cheapest float cost = calculateCost(result); float time = calculateTime(result); // Minimize cost … } @Override public void handleNavigatorFinished(Navigator navigator){} }; Navigator navigator = origin.navigate(myView, Guide.DEPTH_FIRST_SEARCH, Qualifier.ANY /** Path Qualifier **/, resultQualifier, myPolicies, myNavHandler); navigator.start();
  18. 18. Graph Database Challenge #1: Reading Distributed Data • If your graph data is distributed, traversing a desired path across partitions can be extremely difficult and slow.
  19. 19. Graph Database Challenge #1: Reading Distributed Data • Mitigate bottlenecks and optimize performance by using the following strategies: – Custom Placement: data isolation/localization of logically related information (to achieve close to subgraph partitioning) in order to minimize the number of network calls – Distributed Navigation Engine: Distributes the load on the partitions where the data is located.
  20. 20. Reading Distributed Data: Custom Placement • Consider the case where you are placing medical data for hospitals and patients. Using a custom placement model you can achieve fairly high isolation of the subgraphs. – Doctor ↔ Hospitals, Patients ↔ Visits.
  21. 21. Reading Distributed Data: Distributed Navigation Engine • Google Pregel (2010) – Batch algorithms on large graphs – Avoids passing graph state instead sends messages – Apache Giraph, Jpregel, Hama while any vertex is active or max iterations not reached: for each vertex:  this loop is run in parallel process messages from neighbors (update internal state) send messages to neighbors possibly synchronize results set active flag (unless no messages or state doesn’t change)
  22. 22. Reading Distributed Data: Distributed Navigation Engine • Pregel is optimized for large distributed graph analytics • Limitation on Pregel logic: When the traversal is occurring locally, the logic is to still execute by sending messages from vertex to vertex • Ideally, when local, the traversal should be executed in memory and when remote, pregel logic should be used. – InfiniteGraph’s Distributed Navigation Engine uses the QueryServer (oqs) to achieve this optimized behavior.
  23. 23. Graph Database Challenge #2: Supernodes • A supernode is a vertex with a disproportionally high number of outgoing edges. – Inefficient to traverse through these vertices
  24. 24. Supernodes (Avoid the Tonight Show!) In the IMDB data set, some examples of supernodes may be talk shows, awards shows, compilations or variety shows.
  25. 25. Supernodes: GraphViews and Policies • With InfiniteGraph, we offer two strategies to addressing the supernode problem within the navigation context. – Use GraphViews to filter out vertex or edge types – Globally limit the number of edges traversed using the FanoutLimitPolicy
  26. 26. Supernodes: GraphViews and Policies • Consider calculating number of links to interesting companies on LinkedIn. – If you are connected to recruiters, the navigation result set can be slowed down and possibly polluted if traversing through these recruiters. GraphView myView = new GraphView(); myView.excludeClass(myGraphDb.getTypeId(Person.class .getName()), “CONTAINS(profession, ‘recruiter’)”; PolicyChain chain = new PolicyChain(); // Limits # of edges traversed to 10 chain.addPolicy(new FanoutLimitPolicy(10));
  27. 27. Supernodes: Edge Discovery Methods • If walking the graph, edge discovery methods are available on the vertex API allows for easy lookup. Vertex start = …; // lookup by query or index // Get all ‘Facebook’ connections EdgePredicate edgeQualifier = new EdgePredicate(Knows.class, “how == ‘Facebook’”); Iterable edgeHandles = start.getEdges(edgeQualifier); • More edge discovery methods and optimizations are coming!
  28. 28. Graph Database Challenge #3: Writing Distributed Data App-1 (E1 2{ V1V21) (Ingest V }) App-2 (E23{ V2V32) (Ingest V}) App-3 (Ingest V3) InfiniteGraph Objectivity/DB Persistence Layer VV1 1 EE12 12 VV2 2 EE23 23 VV3 3
  29. 29. Graph Database Challenge #3: Writing Distributed Data • Concurrent writes (multithreaded, multiprocess and/or multiuser access) to a database that holds highly connected data highly contentious locking behavior poor write performance retrying transactions • NoSQL databases with relaxed consistency modes typically offer higher write performance – System maintains data integrity (ACID), handles lock conflicts, optimizes batch processing
  30. 30. Writing Distributed Data: Accelerated Ingest (Pipelining) • InfiniteGraph offers relaxed consistency ingest mode, Accelerated Ingest. – Vertex, Edge objects are placed immediately – Edge updates are “pipelined” (no lock contention) and updates are batch processed (optimized) – Graph is built up in background – Achieves highest rate of ingest in distributed environments
  31. 31. Writing Distributed Data: Accelerated Ingest (Pipelining) IG Core/API EE23 23 Target Containers EE12 12 E(2->3) E(1->2) E(3->1) E(2->1) E(1->2) E(2->3) E(2->3) E(3->1) E(1->2) E(3->2) E(2->1) E(2->3) E(3->1) Pipeline CC1 1 Pipeline Containers E(1->2) CC2 2 E(3->1) E(3->2) Agent CC3 3
  32. 32. Acclerated Ingest Performance Results
  33. 33. Graph Database Challenge #4: Tools • Typically, when databases don’t offer tools for analysis or visualization, the tools that are used are general purpose. • Tools offered by databases are generally integrated well with native features. – Sometimes exposing “hidden” features – These tools can generally be useful for debugging and development of applications built on top of the database.
  34. 34. Tools: The IG Visualizer • Excellent for development and debugging of application built on top of IG database.
  35. 35. Why InfiniteGraph ? ™ • Objectivity/DB is 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. 36. QUESTIONS?

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