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Graph Databases : Connecting the Dots in Big Data Darren Wood Chief Architect, InfiniteGraph
Relationships are  everywhere
Graph Databases ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © InfiniteGraph
Graph Databases ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © InfiniteGraph
Physical Storage Comparison Copyright © InfiniteGraph Meetings P1 Place Time P2 Alice Denver 5-27-10 Bob Calls From Time Duration To Bob 13:20 25 Carlos Bob 17:10 15 Charlie Payments From Date Amount To Carlos 5-12-10 100000 Charlie Met 5-27-10 Alice Called 13:20 Bob Payed 100000 Carlos Charlie Called 17:10 Rows/Columns/Tables Relationship/Graph Optimized
Simple API Copyright © InfiniteGraph Vertex alice = myGraph.addVertex(new Person(“Alice”));  Vertex bob = myGraph.addVertex(new Person(“Bob”));  Vertex carlos = myGraph.addVertex(new Person(“Carlos”));  Vertex charlie = myGraph.addVertex(new Person(“Charlie”)); alice.addEdge(new Meeting(“Denver”, “5-27-10”), bob); bob.addEdge(new Call(timestamp), carlos); carlos.addEdge(new Payment(100000.00), charlie); bob.addEdge(new Call(timestamp), charlie); Alice Carlos Charlie Bob Meets Calls Pays Calls
Query and Navigation ,[object Object],[object Object],[object Object],Copyright © InfiniteGraph Alice Carlos Charlie Bob Meets Calls Pays Calls “ Find all paths between Alice and Charlie” “ Find all paths between Alice and Charlie – within 2 degrees” “ Find all paths between Alice and Charlie – events in May 2010”
Navigation Example Copyright © InfiniteGraph // Create a qualifier that describes the target vertex Qualifier findCharliePredicate =  new  VertexPredicate(personType,  "name == ’Charlie'" ); // Construct a navigator which starts with Alice and uses a result qualifier // to find all paths in the graph to Charlie Navigator charlieFinder = alice.navigate( Guide.SIMPLE_BREADTH_FIRST, // default guide  Qualifier.ANY,  // no path constraints findCharliePredicate , // find paths ending with Charlie  myResultHandler); // fire results to supplied handler // Start the navigator charlieFinder.start();
Navigational Query Performance
Scaling Graphs – Getting Data In Copyright © InfiniteGraph IG Core/API Configuration Navigation Execution Management Extensions Session / TX Management Placement Standard Blocking Ingest/Placement (MDP Plugin) Objectivity/DB App-1 (Ingest V 1 ) App-2 (Ingest V 2 ) App-3 (Ingest V 3 ) V 1 V 2 V 3 App-1 (E 1 2 { V 1 V 2 }) App-2 (E 23 { V 2 V 3 }) App-3 E 12 E 23
Accelerated Ingest Copyright © InfiniteGraph IG Core/API Configuration Navigation Execution Management Extensions Session / TX Management Placement (Standard) Placement (Accelerated) V 1 V 2 V 3 E 12 E 23 Distributed Pipelines Staging Containers Pipeline Containers 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)
Choose Your Own Consistency… Copyright © InfiniteGraph // Describe your requested model using policies PolicyChain  myPolicies =  new PolicyChain(new EdgePipeliningPolicy( true )); // Start a transaction with the policies you want Transaction tx =  myGraph.beginTransaction( AccessMode.READ_WRITE, myPolicies); // This code doesn’t change, can be used with any policies alice.addEdge(new Meeting(“Denver”, “5-27-10”), bob); bob.addEdge(new Call(timestamp), carlos); tx.commit();
Indexing Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © InfiniteGraph
InfiniteGraph Visualizer Copyright © InfiniteGraph
Scaling Graphs – Distributed Navigation ,[object Object],[object Object],Copyright © InfiniteGraph Alice Carlos Charlie Bob Meets Calls Pays Dave Eve Chuck Calls Lives With Meets
Big Distributed Data (Traditional - Huge Generalization) Copyright © InfiniteGraph Distributed API Application(s) Partition 1 Partition 3 Partition 2 Partition ... n Processor Processor Processor Processor
Big Distributed Data (Graph) Copyright © InfiniteGraph Distributed API Application(s) Partition 1 Partition 3 Partition 2 Partition ... n Processor Processor Processor Processor
Some customers and partners
Thankyou ! Copyright © InfiniteGraph [email_address]

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Graph Databases: Connecting the Dots in Big Data

  • 1. Graph Databases : Connecting the Dots in Big Data Darren Wood Chief Architect, InfiniteGraph
  • 2. Relationships are everywhere
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  • 5. Physical Storage Comparison Copyright © InfiniteGraph Meetings P1 Place Time P2 Alice Denver 5-27-10 Bob Calls From Time Duration To Bob 13:20 25 Carlos Bob 17:10 15 Charlie Payments From Date Amount To Carlos 5-12-10 100000 Charlie Met 5-27-10 Alice Called 13:20 Bob Payed 100000 Carlos Charlie Called 17:10 Rows/Columns/Tables Relationship/Graph Optimized
  • 6. Simple API Copyright © InfiniteGraph Vertex alice = myGraph.addVertex(new Person(“Alice”)); Vertex bob = myGraph.addVertex(new Person(“Bob”)); Vertex carlos = myGraph.addVertex(new Person(“Carlos”)); Vertex charlie = myGraph.addVertex(new Person(“Charlie”)); alice.addEdge(new Meeting(“Denver”, “5-27-10”), bob); bob.addEdge(new Call(timestamp), carlos); carlos.addEdge(new Payment(100000.00), charlie); bob.addEdge(new Call(timestamp), charlie); Alice Carlos Charlie Bob Meets Calls Pays Calls
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  • 8. Navigation Example Copyright © InfiniteGraph // Create a qualifier that describes the target vertex Qualifier findCharliePredicate = new VertexPredicate(personType, "name == ’Charlie'" ); // Construct a navigator which starts with Alice and uses a result qualifier // to find all paths in the graph to Charlie Navigator charlieFinder = alice.navigate( Guide.SIMPLE_BREADTH_FIRST, // default guide Qualifier.ANY, // no path constraints findCharliePredicate , // find paths ending with Charlie myResultHandler); // fire results to supplied handler // Start the navigator charlieFinder.start();
  • 10. Scaling Graphs – Getting Data In Copyright © InfiniteGraph IG Core/API Configuration Navigation Execution Management Extensions Session / TX Management Placement Standard Blocking Ingest/Placement (MDP Plugin) Objectivity/DB App-1 (Ingest V 1 ) App-2 (Ingest V 2 ) App-3 (Ingest V 3 ) V 1 V 2 V 3 App-1 (E 1 2 { V 1 V 2 }) App-2 (E 23 { V 2 V 3 }) App-3 E 12 E 23
  • 11. Accelerated Ingest Copyright © InfiniteGraph IG Core/API Configuration Navigation Execution Management Extensions Session / TX Management Placement (Standard) Placement (Accelerated) V 1 V 2 V 3 E 12 E 23 Distributed Pipelines Staging Containers Pipeline Containers 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)
  • 12. Choose Your Own Consistency… Copyright © InfiniteGraph // Describe your requested model using policies PolicyChain myPolicies = new PolicyChain(new EdgePipeliningPolicy( true )); // Start a transaction with the policies you want Transaction tx = myGraph.beginTransaction( AccessMode.READ_WRITE, myPolicies); // This code doesn’t change, can be used with any policies alice.addEdge(new Meeting(“Denver”, “5-27-10”), bob); bob.addEdge(new Call(timestamp), carlos); tx.commit();
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  • 16. Big Distributed Data (Traditional - Huge Generalization) Copyright © InfiniteGraph Distributed API Application(s) Partition 1 Partition 3 Partition 2 Partition ... n Processor Processor Processor Processor
  • 17. Big Distributed Data (Graph) Copyright © InfiniteGraph Distributed API Application(s) Partition 1 Partition 3 Partition 2 Partition ... n Processor Processor Processor Processor
  • 18. Some customers and partners
  • 19. Thankyou ! Copyright © InfiniteGraph [email_address]

Hinweis der Redaktion

  1. Relationships and connections are EVERYWHERE. Examples include CRM, Telecom, Intelligence, Research, Healthcare, Finance and yes, social networks too. But notice, it’s absolutely not just about social networks, in the Facebook sense. ANY application that needs to find connections and relationships separated by more than 2 degrees, is a good candidate for InfiniteGraph.
  2. InfiniteGraph  (built on Objectivity/DB) is optimized for high speed traversal of complex relationships. Compared to traditional technologies, we return results faster by several orders of magnitude, and performance will not degrade or suddenly drop-off.