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Uploaded byGagan Agrawal
Graph db
The document discusses using graph databases for insights into connected data. It provides an overview of graph databases, comparing them to relational databases and NoSQL stores. It discusses how graph databases are better suited than other models for richly connected data due to their native support of relationships. The document also covers graph data modeling, the Cypher query language, examples of graph databases in real world domains, and aspects of graph database internals like scalability.
Graph db
- 1. Using Graph DatabasesFor Insights Into Connected Data Gagan Agrawal Xebia
- 2. 2 Agenda High level viewof Graph Space Comparison with RDBMS and other NoSQL stores Data Modeling Cypher : Graph Query Language Graphs in Real World Graph Database Internals
- 3. 3 What is aGraph?
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- 5. 5 What is aGraph? A collection of vertices and edges. Set of nodes and the relationships that connect them. Graph Represents - Entities as NODES The way those entities relate to the world as RELATIONSHIP Allows to model all kind of scenarios System of road Medical history Supply chain management Data Center
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- 8. 8 High Level viewof Graph Space Graph Databases - Technologies used primarily for transactional online graph persistence – OLTP. Graph Compute Engines - Tecnologies used primarily for offline graph analytics - OLAP.
- 9. 9 Graph Databases Online databasemanagement system with - Create, Read, Update, Delete methods that expose a graph data model. Built for use with transactional (OLTP) systems. Used for richly connected data. Querying is performed through traversals. Can perform millions of traversal steps per second. Traversal step resembles a join in a RDBMS
- 10. 10 Graph Database Properties TheUnderlying Storage : Native / Non-Native The Processing Engine : Native / Non-Native
- 11. 11 Graph DB –The Underlying Storage Native Graph Storage – Optimized and designed for storing and managing graphs. Non-Native Graph Storage – Serialize the graph data into a relational database, an object oriented database, or some other general purpose data store.
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- 13. 13 Graph DB –The processing Engine Index free adjacency – Connected Nodes physically point to each other in the database
- 14. 14 Non-Native : IndexLook-Up
- 15. 15 Native : IndexFree Adjacency
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- 17. 17 Power of GraphDatabases Performance Flexibility Agility
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- 19. 19 Relational Databases LackRelationships Initially designed to codify paper forms and tabular structures. Deal poorly with relationships. The rise in connectedness translates into increased joins. Lower performance. Difficult to cater for changing business needs.
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- 21. 21 RDBMS What products dida customer buy? Which customers bought this product? Which customers bought this product who also bought that product?
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- 23. 23 Query to findfriends-of-friends
- 24. 24 NoSQL Databases alsolack Relationships NOSQL Databases e.g key-value, document or column oriented store sets of disconnected values/documents/columns. Makes it difficult to use them for connected data and graphs. One of the solution is to embed an aggregate's identifier inside the field belonging to another aggregate. Effectively introducing foreign keys Requires joining aggregates at the application level.
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- 26. 26 NoSQL DB Relationships betweenaggregates aren't first class citizens in the data model. Foreign aggregate "links" are not reflexive. Asking the database "Who has bought a particular product" is an expensive operation. Need to use some external compute infrastructure e.g Hadoop for such processing. Do not maintain consistency of connected data. Do not support index-free adjacency.
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- 28. 28 Graph DB EmbracesRelationships
- 29. 29 Graph DB Find friends-of-friendsin a social network, to a maximum depth of 5. Total records : 1,000,000 Each with approximately 50 friends
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- 33. 33 Data Modeling “Whiteboard” friendly Thetypical whiteboard view of a problem is a GRAPH. Sketch in our creative and analytical modes, maps closely to the data model inside the database.
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- 35. 35 Cypher : GraphQuery Language Pattern-Matching Query Language Humane language Expressive Declarative : Say what you want, now how Borrows from well know query languages Aggregation, Ordering, Limit Update the Graph
- 36. 36 Cypher Cypher Representation : (c)-[:KNOWS]->(b)-[:KNOWS]->(a),(c)-[:KNOWS]- >(a) (c)-[:KNOWS]->(b)-[:KNOWS]->(a)<-[:KNOWS]-(c)
- 37. 37 Cypher START c=node:user(name='Michael') MATCH (c)-[:KNOWS]->(b)-[:KNOWS]->(a),(c)- [:KNOWS]->(a) RETURN a, b
- 38. 38 Other Cypher Clauses WHERE Providescriteria for filtering pattern matching results. CREATE and CREATE UNIQUE Create nodes and relationships DELETE Removes nodes, relationships and properties SET Sets property values
- 39. 39 Other Cypher Clauses FOREACH Performsan updating action for graph element in a list. UNION Merge results from two or more queries. WITH Chains subsequent query parts and forward results from one to the next. Similar to piping commands in UNIX.
- 40. 40 Comparison of Relationaland Graph Modeling
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- 45. 45 Query to findfaulty Equipment
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- 47. 47 Fine Grained vsGeneric Relationships DELIVERY_ADDRESS VS ADDRESS{type : 'delivery'}
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- 50. 50 Graphs in theReal World
- 51. 51 Common Use Cases Social Recommendations Geo LogisticsNetworks : for package routing, finding shortest Path Financial Transaction Graphs : for fraud detection Master Data Management Bioinformatics : Era7 to relate complex web of information that includes genes, proteins and enzymes Authorization and Access Control : Adobe Creative Cloud, Telenor
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- 53. 53 Non Functional Characteristics Transactions FullyACID Recoverability Availability Scalability
- 54. 54 Scalability Capacity (Graph Size) Latency(Response Time) Read and Write Throughput
- 55. 55 Capacity 1.9 Release ofNeo4j can support single graphs having 10s of billions of nodes, relationships and properties. The Neo4j team has publicly expressed the intention to support 100B+ nodes/relationships/properties in a single graph as part of its 2013 roadmap.
- 56. 56 Latency RDBMS – moredata in tables/indexes result in longer join operations. Graph DB doesn't suffer the same latency problem. Index is used to find starting node. Traversal uses a combination of pointer chasing and pattern matching to search the data. Performance does not depend on total size of the dataset. Depends only on the data being queried.
- 57. 57 Throughput Constant performance irrespectiveof graph size.
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