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Graph analysis over relational database
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.
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Graph analysis over relational database
- 2. Graph Analysis over RelationalDatabase Gabriele Ponzi, Senior Developer @Gabriele_Ponzi
- 3. G.Ponzi> whoami Senior developerat 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 relationalto 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 asa graph turn upside down your plain old RDBMS explore relations as a graph
- 6. Why? Joins are hardto 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!
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- 8. Costs of importinginto 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 avoidmigration please!
- 10. ...then let’s avoidmigration please! live queries are probably better!
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- 13. Graph: reference model PropertyGraph 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 crashcourse Model pairwise relations between objects Objects: “Nodes”, “Vertices” Relations: “Edges”, “Arcs”, “Links”
- 15. Nodes (Vertices) Objects inthe graph, related by edges Can contain information
- 16. Edges Can be directedor undirected Can store information, usually in the form of an edge “weight”
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- 21. Algorithms Shortest Path Dijkstra Page rank Centrality Modularity Clustering,then max degree, min degree, average degree
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- 23. RDBMS Connector How toperform a Graph Analysis over a data source with a different data model?
- 24. RDBMS Connector How toperform a Graph Analysis over a data source with a different data model?
- 25. RDBMS Connector: GeneralWorkflow
- 26. RDBMS Connector: GeneralWorkflow
- 27. RDBMS Connector: GeneralWorkflow
- 28. RDBMS Connector: GeneralWorkflow
- 29. RDBMS Connector: GeneralWorkflow
- 30. Mapping strategies Graph Modelallows us to perform several optimisations through the aggregation of some info.
- 31. Mapping strategies Arcade providestwo different strategies, each with a specific impact on the mapping between the two domains: naive strategy naive-aggregate strategy
- 32. Mapping: naive strategy Basicapproach: 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
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- 35. Mapping: naive aggregatestrategy 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 aggregatestrategy
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- 38. Strategies comparison Naive strategyoutput Naive aggregate strategy output
- 39. Other useful modelalterations Aggregation of 1 or more tables into a single Vertex Type
- 40. Other useful modelalterations Aggregation of 1 or more tables into a single Vertex Type Single table splitting into multiple Vertex TypesOR
- 41. Other useful modelalterations 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 modelalterations 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)
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- 47. Thanks for yourattention!
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