Titan: Scaling Graphs and TinkerPop3
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Titan is a scalable graph database that can distribute and query graph data across multiple machines. This presentation provides a general introduction to graph computing and Titan in particular. It also focuses on some recent development for Titan 0.9 and TinkerPop 3.
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Titan: Scaling Graphs and TinkerPop3
- 1. AURELIUS THINKAURELIUS.COM Titan:db Graphs at Scale #TitanDB Matthias Broecheler @mbroecheler December 2nd, MMXIV
- 2. software is eating the world
- 3. >;N; >LCP?H software is eating the world
- 5. Tabular Relational Flexibility Handling NoSQL
- 6. Tabular Relational Flexibility Handling Data Model Query Language NoSQL
- 7. Tabular Relational NoSQL Graph Flexibility Handling Data Model Query Language Simplicity Flexibility Handling Data Model Query Language
- 8. productid: 52235 name: cup price: 12.55 userid: matt email: matt@ password: 12345 User Product
- 9. User Product userid email password ma. ma.@ 12345 john john@ qwerty billy billy@ abcde produc=d name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32
- 10. productid: 52235 name: cup price: 12.55 userid: matt email: matt@ password: 12345 User Product buy time: 9/5/14
- 11. User Product userid email password ma. ma.@ 12345 john john@ qwerty billy billy@ abcde produc=d name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32 userid produc=d =me ma. 52235 9/5/14 billy 42215 8/7/14 billy 42215 8/7/14 Buy
- 13. 7B;N >C> -;NN <OSg g.V.has(‘email’,’matt@’).out(‘buy’).valueMap!
- 14. 7B;N >C> -;NN <OSg SELECT Product.* ! FROM User! INNER JOIN Buy ! !ON User.userid = Buy.userid! INNER JOIN Product ! !ON Buy.productid = Product.productid! WHERE User.email = ‘matt@’ !
- 15. SELECT TOP (5) [t14].[ProductName] FROM (SELECT COUNT(*) AS [value], [t13].[ProductName] FROM [customers] AS [t0] CROSS APPLY (SELECT [t9].[ProductName] FROM [orders] AS [t1] CROSS JOIN [order details] AS [t2] INNER JOIN [products] AS [t3] ON [t3].[ProductID] = [t2].[ProductID] CROSS JOIN [order details] AS [t4] INNER JOIN [orders] AS [t5] ON [t5].[OrderID] = [t4].[OrderID] LEFT JOIN [customers] AS [t6] ON [t6].[CustomerID] = [t5].[CustomerID] CROSS JOIN ([orders] AS [t7] CROSS JOIN [order details] AS [t8] INNER JOIN [products] AS [t9] ON [t9].[ProductID] = [t8].[ProductID]) WHERE NOT EXISTS(SELECT NULL AS [EMPTY] FROM [orders] AS [t10] CROSS JOIN [order details] AS [t11] INNER JOIN [products] AS [t12] ON [t12].[ProductID] = [t11].[ProductID] WHERE [t9].[ProductID] = [t12].[ProductID] AND [t10].[CustomerID] = [t0].[CustomerID] AND [t11].[OrderID] = [t10].[OrderID]) AND [t6].[CustomerID] <> [t0].[CustomerID] AND [t1].[CustomerID] = [t0].[CustomerID] AND [t2].[OrderID] = [t1].[OrderID] AND [t4].[ProductID] = [t3].[ProductID] AND [t7].[CustomerID] = [t6].[CustomerID] AND [t8].[OrderID] = [t7].[OrderID]) AS [t13] WHERE [t0].[CustomerID] = N'ALFKI' GROUP BY [t13].[ProductName]) AS [t14] ORDER BY [t14].[value] DESC
- 16. g.V.has('customerId’,cid).as('customer') .out('orderedProduct’).as('products') .in('orderedProduct').except('customer') .out('orderedProduct’).except('products') .productName.groupCount.cap. .map{it.sort{-it.value}}.next()[0..<5] h.p://sql2gremlin.com/
- 17. TinkerPop 3 coming soon Graph Server Graph Algorithms Object-Graph Mapper Traversal Language Dataflow Processing Generic Graph API http://www.tinkerpop.com/docs/3.0.0.M6/
- 18. label: demigod name: Hercules label: god name: Neptune age: 4500 brothermother label: god name: Jupiter label: monster name: Cerberus father father brother battled pet time:12 label: god name: Pluto age: 4000 label: human name: Alcmene age: 45 label: titan name: Saturn age: 10000 label: monster name: Hydra battled time: 2
- 19. AURELIUS THINKAURELIUS.COM Titan:db Architecture & Internals titandb.io
- 21. Flexible Persistence Partitionability ConsistencyAvailability
- 22. Graph Schema ! Labels & Keys ! Vertex Property Cardinality ! Single, Set, List ! Edge Multiplicity ! Multi, Simple, Many2One, One2Many, One2One ! Constraints & Consistency
- 25. Vertex Representation 5 Property Property Out-Edge In-Edge Out-Edge In-Edge In-Edge LIQ CH>C=?M @IL @;MN P?LN?R =?HNLC= KO?LC?M <SN? IL>?L MILNCHA =?FF t =IFOGH q P;FO? LIQ E?S
- 26. Vertex-Centric Index ! Create a vertex-centric index per edge label or property key to optimize for retrieval patterns ! Through Titan’s management API ! Supports multiple indexes name: Hercules surname: Mighty label: user time:24 bought title: “Muscle building for beginners” label: product [bought, time, DESC]
- 27. battled battled battled time: 3 time: 5 v time: 1 fatherfought fought mother battled time: 9 v!
- 28. battled battled battled time: 3 time: 5 v time: 1 mother father battled time: 9 v.outE()!
- 29. battled battled battled time: 3 time: 5 v time: 1 battled time: 9 v.outE(‘battled’)!
- 30. v battled battled time: 1 time: 3 v.outE(‘battled’)! .has(‘time’,lt,5)! .inV!
- 31. Faster Querying ! Multi-Query ! Bulk querying reduces latency ! Currently manual ! Order in vertex-centric query ! Adjacency-constraint ! Faster edge retrievals " Titan Query Optimizer
- 32. Graph Index ! Composite internal index ! Can be unique ! Label specific index ! Multiple external indexes label: user name: Hercules surname: Mighty time:24 bought label: product title: “Muscle building for beginners”
- 33. g.E.has(‘location’,WITHIN, Geoshape.circle(38,24,50)! Full text & Geo Search
- 34. Token Ring (BOP) Graph Partitioning - assigns ids to map vertices into “optimal” token range - Maintains virtual partitions Vertical Partitioning = divide communities
- 35. Vertex Partitioning Each partition contains one “part” of a partitioned vertex Horizontal Partitioning = split super vertices
- 37. label: demigod name: Hercules label: god name: Neptune age: 4500 brothermother label: god name: Jupiter label: monster name: Cerberus father father brother battled pet time:12 label: god name: Pluto age: 4000 label: human name: Alcmene age: 45 label: titan name: Saturn age: 10000 label: monster name: Hydra battled time: 2 OLTP
- 38. label: demigod name: Hercules label: god name: Neptune age: 4500 brothermother label: god name: Jupiter label: monster name: Cerberus father father brother battled pet time:12 label: god name: Pluto age: 4000 label: human name: Alcmene age: 45 label: titan name: Saturn age: 10000 label: monster name: Hydra battled time: 2 OLAP
- 39. Faunus Work Flow g.V.out .out .count() hdfs://user/ubuntu/ output/job-0/ output/job-1/ output/job-2/ { graph* sideeffect* Compressed HDFS Graphs ! stored in sequence files ! variable length encoding ! prefix compression
- 40. Degree Distribution GitHub Network g.V.sideEffect{ it.degree = it.out(‘follows’).count() }.degree.groupCount
- 41. Degree Distribution P(k) ~ k-γ γ = 2.2
- 42. Complete Architecture ! Users & Apps Processing Event OLTP OLAP Q Continuous Computing Interactive Query Batch Query (ETL)
- 43. Use Cases
- 46. http://arli.us/edu-planet-scale © Sean York @ Pearson Education
- 47. http://arli.us/musicgraphintro Music Graph Knowledge Graph
- 49. 7B?H NI OM? 'L;JB $;N;<;M?Mg Map+ NoSQL K V V V V Persistence Tabular Data-driven Graph
- 50. 7B?H NI OM? 'L;JB $;N;<;M?Mg Map+ NoSQL K V V V V Persistence Tabular Data-driven Graph Complexity
- 51. AURELIUS THINKAURELIUS.COM @AURELIUSGRAPHS aureliusgraphs@googlegroups.com