Cassandra Summit 2014: TitanDB - Scaling Relationship Data and Analysis with Cassandra
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Presenter: Matthias Broecheler, Managing Partner at Aurelius LLC Storing relationship data in Cassandra entails data denormalization or pointer chasing inside the application which reduces developer productivity, is error prone, and slow due to lack of optimization. Titan:db exposes a property graph data model directly atop Cassandra which makes storing and querying relationship data fast, easy, and scalable to huge graphs. This talk demonstrates how Titan's features enable complex, multi-relational databases in Cassandra and discusses customer use cases for recommendation and personalization engines.
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Cassandra Summit 2014: TitanDB - Scaling Relationship Data and Analysis with Cassandra
- 1. AURELIUS THINKAURELIUS.COM Titan:db Scaling Relationship Data with C* Matthias Broecheler @mbroecheler September XI, MMXIV #CassandraSummit
- 2. Storing relationship data in Cassandra entails data denormalization or pointer chasing inside the application which reduces developer productivity, is error prone, and slow due to lack of optimization. Titan:db exposes a property graph data model directly atop Cassandra which makes storing and querying relationship data fast, easy, and scalable to huge graphs. This talk demonstrates how Titan's features enable complex, multi-relational databases in Cassandra and discusses customer use cases for recommendation and personalization engines.
- 4. Titan = Cassandra + Graph
- 5. Titan 0.5
- 6. Cassandra ,CH?;L M=;F;<CFCNS @;OFN NIF?L;H=? IJ?H MIOL=? GOFNC >;N;=?HN?L BCAB J?L@ILG;H=?
- 7. Key ColumnA ColumnB ColumnC ColumnD ColumnE ColumnF
- 9. username email password ma7 ma7@ 12345 john john@ qwerty billy billy@ abcde producDd name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32 User Product CREATE INDEX ON User.username, User.email, Product.productid
- 10. CREATE INDEX ON username(User), email(User), productid(Product) User Product productid: 52235 name: cup price: 12.55 username: matt email: matt@ password: 12345
- 11. username producDd Dme ma7 52235 9/5/14 billy 42215 8/7/14 billy 42215 8/7/14 Buy username email password ma7 ma7@ 12345 john john@ qwerty billy billy@ abcde producDd name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32
- 12. User Product productid: 52235 name: cup price: 12.55 username: matt email: matt@ password: 12345 buy time: 9/5/14
- 13. What did ‘matt’ buy? Application level join username producDd Dme ma7 52235 9/5/14 billy 42215 8/7/14 billy 42215 8/7/14 Buy username email password ma7 ma7@ 12345 john john@ qwerty billy billy@ abcde producDd name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32
- 14. What did ‘matt’ buy? g.V.has(‘username’,’matt’) .out(‘buy’) User Product productid: 52235 name: cup price: 12.55 username: matt email: matt@ password: 12345 buy time: 9/5/14
- 15. What did ‘matt’ recently buy? Application level join username producDd Dme ma7 52235 9/5/14 billy 42215 8/7/14 billy 42215 8/7/14 Buy username email password ma7 ma7@ 12345 john john@ qwerty billy billy@ abcde producDd name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32
- 16. g.V.has(‘username’,’matt’) .outE(‘buy’).orderBy(‘time’,DESC) [0..9].inV What did ‘matt’ recently buy? User Product productid: 52235 name: cup price: 12.55 username: matt email: matt@ password: 12345 buy time: 9/5/14
- 17. What did ‘matt’ recently buy? slow User Product productid: 52235 name: cup price: 12.55 username: matt email: matt@ password: 12345 buy time: 9/5/14 g.V.has(‘username’,’matt’) .outE(‘buy’).orderBy(‘time’,DESC) [0..9].inV
- 18. username producDd Dme ma7 52235 9/5/14 billy 42215 8/7/14 billy 42215 8/7/14 What did ‘matt’ recently buy? Rewrite join logic username Dme producDd ma7 9/5/14 52235 billy 8/7/14 42215 billy 8/7/14 42215 username email password ma7 ma7@ 12345 john john@ qwerty billy billy@ abcde producDd name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32
- 19. User Product productid: 52235 name: cup price: 12.55 username: matt email: matt@ password: 12345 buy time: 9/5/14 What did ‘matt’ recently buy? CREATE INDEX ON buy edges by time OUT direction g.V.has(‘username’,’matt’) .outE(‘buy’).orderBy(‘time’,DESC) [0..9].inV
- 20. producDd username Dme 52235 ma7 9/5/14 42215 billy 8/7/14 42215 billy 8/7/14 Who bought ‘52235’? More application joins producDd Dme producDd 52235 9/5/14 ma7 42215 8/7/14 billy 42215 8/7/14 billy username email password ma7 ma7@ 12345 john john@ qwerty billy billy@ abcde producDd name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32
- 21. User Product productid: 52235 name: cup price: 12.55 username: matt email: matt@ password: 12345 buy time: 9/5/14 g.V.has(‘productid’,52235) .in(‘buy’) CREATE INDEX ON buy edges by time IN direction Who bought ‘52235’?
- 22. Product join tables won’t scale username producDd Dme username Dme producDd username email password ma7 ma7@ 12345 john john@ qwerty billy billy@ abcde producDd name price 52235 cup 12.55 42215 spoon 7.22 24529 knife 5.32 producDd username Dme 52235 ma7 9/5/14 42215 billy 8/7/14 42215 billy 8/7/14 producDd Dme producDd 52235 9/5/14 ma7 42215 8/7/14 billy 42215 8/7/14 billy
- 23. User Product productid: 52235 name: cup price: 12.55 username: matt email: matt@ password: 12345 buy time: 9/5/14 PARTITION Product Vertices
- 24. Token Ring (BOP) Edge Cut - assigns ids to map vertices into “optimal” token range - Maintains virtual partitions
- 25. Vertex Cut
- 28. Transactions v = g.V.has(‘username’,’matt’) .has(‘password’,’12345’) p = g.V.has(‘productid’,52235) e = v.addEdge(‘buy’,p) e.setProperty(‘time’,’9/11/2014’) o = g.addVertex([orderid:242343]) o.addEdge(‘buyer’,v) o.addEdge(‘product’,p) g.commit() unit of work Atomicity Consistency Isolation Durability
- 29. Transaction Consistency u = g.addVertex([username:’matt’]) p = g.V.has(‘username’,’senior’) u.addEdge(‘father’,p) p.setProperty(‘surname’,’Jones’) g.commit() Locks acquired to ensure consistency constraints are enforced • Index Uniqueness • Multiplicity Constraints • Cardinality Constraints
- 30. Polyglot Data Architecture © Jay Kreps @ LinkedIn
- 32. Use Cases
- 35. © Sean York @ Pearson Education