Graph analysis over relational database
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The document discusses the transition from relational databases to graph analysis, highlighting the use cases and methodologies for performing graph analysis on relational data. It emphasizes the benefits of using a graph model, including simplified relationships and improved performance, along with strategies for mapping relational data to a graph model. Additionally, it presents potential challenges such as data migration costs and encourages avoiding migration through live queries.
Graph analysis over relational database
- 2. Graph Analysis over Relational Database Gabriele Ponzi, Senior Developer @Gabriele_Ponzi
- 3. G.Ponzi> whoami Senior developer at Arcade Analytics MSc in Computer Science Academic and work experience in Databases, Big Data and NoSQL world, distributed data handling, information extraction and visualisation Passionate developer always been in love with cutting edge technologies
- 4. Agenda Goal: From relational to graph Why and when perform analysis over a graph Use cases good for graph analysis Model Translation From ER Model to Graph Model: mapping strategies Demo
- 5. Goal: RDBMS as a graph turn upside down your plain old RDBMS explore relations as a graph
- 6. Why? Joins are hard to write Joins are for developers Graph model is very close to the plain old normalized ER-model Visual navigation is for human beings Can perform Graph Algorithms on partial loaded data!
- 8. Costs of importing into a graph database Write custom process to export from RDBMS and import into a graph Hard to keep in sync work on OLD data Time consuming should wait for sync or import Cost $$$ on maintenance
- 9. ...then let’s avoid migration please!
- 10. ...then let’s avoid migration please! live queries are probably better!
- 12. Graph G = (V,E)
- 13. Graph: reference model Property Graph Model ● Vertices ○ set of outgoing edges ○ set of incoming edges ○ collection of properties (key-value map) ● Edges ○ outgoing tail vertex ○ incoming head vertex ○ label that denotes the type of relationship between its two vertices ○ collection of properties (key-value map)
- 14. Graph model crash course Model pairwise relations between objects Objects: “Nodes”, “Vertices” Relations: “Edges”, “Arcs”, “Links”
- 15. Nodes (Vertices) Objects in the graph, related by edges Can contain information
- 16. Edges Can be directed or undirected Can store information, usually in the form of an edge “weight”
- 17. Use cases
- 20. Recommendation
- 21. Algorithms Shortest Path Dijkstra Page rank Centrality Modularity Clustering, then max degree, min degree, average degree
- 23. RDBMS Connector How to perform a Graph Analysis over a data source with a different data model?
- 24. RDBMS Connector How to perform a Graph Analysis over a data source with a different data model?
- 25. RDBMS Connector: General Workflow
- 26. RDBMS Connector: General Workflow
- 27. RDBMS Connector: General Workflow
- 28. RDBMS Connector: General Workflow
- 29. RDBMS Connector: General Workflow
- 30. Mapping strategies Graph Model allows us to perform several optimisations through the aggregation of some info.
- 31. Mapping strategies Arcade provides two different strategies, each with a specific impact on the mapping between the two domains: naive strategy naive-aggregate strategy
- 32. Mapping: naive strategy Basic approach: E-R Model directly translated into a coherent Graph Model as follows: ● Entity Vertex Type ● Relationship Edge Type Pros: ● Fast ● Few mapping rules needed Cons: ● Model overheads
- 35. Mapping: naive aggregate strategy Basic approach + aggregation: N-N Relationships are aggregated ● Entity Vertex Type ● Relationship Edge Type ● Join Table Aggregator Edge Pros: ● Overhead decreasing ● Resulting graph is less complex, then analysis in simpler! ● Better performances Cons: ● More mapping rules needed
- 36. Mapping: naive aggregate strategy
- 38. Strategies comparison Naive strategy output Naive aggregate strategy output
- 39. Other useful model alterations Aggregation of 1 or more tables into a single Vertex Type
- 40. Other useful model alterations Aggregation of 1 or more tables into a single Vertex Type Single table splitting into multiple Vertex TypesOR
- 41. Other useful model alterations Aggregation of 1 or more tables into a single Vertex Type Single table splitting into multiple Vertex TypesOR Aggregation of 1 or more Relationships into a single Edge Type Single Relationship splitting into multiple Edge TypesOR
- 42. Other useful model alterations Aggregation of 1 or more tables into a single Vertex Type Single table splitting into multiple Vertex TypesOR Aggregation of 1 or more Relationships into a single Edge Type Single Relationship splitting into multiple Edge TypesOR New Vertex or Edge Type definition could be useful too
- 43. Demo use case: DVD Rental E-R Model
- 44. Demo use case: DVD Rental Graph Model (without aggregation)
- 45. Demo use case: DVD Rental Graph Model (without aggregation)
- 46. Demo time
- 47. Thanks for your attention!