Graph Databases 101
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This document provides an overview of graph databases, comparing them to relational databases. Graph databases use nodes, edges, and properties to represent data, with edges representing relationships between nodes. This structure maps more directly to object models than relational databases. Graph databases can also scale to large datasets more easily than relational databases due to not requiring expensive join operations. The document then discusses the Neo4j graph database, including its features like using a property graph model, ACID transactions, and massive scalability. It also covers the Cypher query language and REST API for interacting with Neo4j.
![What to find
vs
How to find
// Find all the directors who have directed a movie scored by John Williams
// that starred Kevin Bacon
!
START actor=(actors, 'Kevin Bacon'), composer=(compsers, 'John Williams')
MATCH (actor)-[:IN]->(movie)<-[:DIRECTED]-(director),
   (movie)<-[:SCORED]-(composer)
RETURN director](https://image.slidesharecdn.com/graph-databases-101-131110114233-phpapp02/85/Graph-Databases-101-12-320.jpg)
Graph Databases 101
- 1. Graph DB 101 with Neo4j and friends ! Paulo Traça [email protected]
- 2. The Problem
- 3. RDBMS are good at Aggregation Ordered data Truly tabular data Few or clearly defined relationships
- 4. Relational databases arenât very good with relationships.
- 5. Graph DBâs
- 6. A graph database uses graph structure with nodes, edges, and properties to represent and store data. ! By definition, a graph database is any storage system that provides index-free adjacency. This means that every element contains a direct pointer to its adjacent element and no index lookups are necessary.
- 7. NODES Nodes represent Entities such as people, businesses, accounts, or any other item you might want to keep track of.
- 8. Properties Properties are pertinent information that relate to nodes.
- 9. EDGES Edges are the lines that connect nodes to nodes or nodes to properties and they represent the relationship between the two. Most of the important information is really stored in the edges. Meaningful patterns emerge when one examines the connections and interconnections of nodes, properties, and edges.
- 10. Graph databases focus on the interconnection between Entities
- 11. Compared with relational databases Graph databases are often faster for associative data sets ! map more directly to the structure of object-oriented applications ! Scale more naturally to large data sets as they do not typically require expensive join operations. ! As they depend less on a rigid schema, they are more suitable to manage ad-hoc and changing data with evolving schemas.
- 12. What to find vs How to find // Find all the directors who have directed a movie scored by John Williams // that starred Kevin Bacon ! START actor=(actors, 'Kevin Bacon'), composer=(compsers, 'John Williams') MATCH (actor)-[:IN]->(movie)<-[:DIRECTED]-(director), Â Â Â (movie)<-[:SCORED]-(composer) RETURN director
- 13. Modelling a Domain with Graphs Graphs are "whiteboard-friendly" Nouns become nodes Verbs become relationships Properties are adjectives and adverbs
- 14. NEO4J
- 15. Key Features Java Based Property Graph Model Uses Lucene for indexing of graph/relationship properties ACID Transactions Massive scalability Extensible High performance on deep transversal
- 16. Key Features Property Graph Model ! . Nodes . Nodes have key / value properties . Relationships between nodes . Relationships have labels . Relationships have key / value properties . Relationships are directed but transversal at equals speed in both directions . Semantics of the directions is up to the applications
- 17. Key Features ACID Transactions Custom JTA/JTS compliant transaction manager distributed transactions two-phase commit (2PC) Transaction recovery deadlock detection ! Massive Scalability Supporting billions of nodes/relationships props on a single machine
- 18. Key Features .Can run embedded or Standalone via REST . Plugins : Spatial data, Lucene, RDF, SOLR . Runs on major platforms : Mac | Windows | Unix . Extensive documentation . Active community . Open Source
- 20. Finding Stuff
- 21. TRANSVERSAL
- 22. CYPHER Cypher is a declarative graph query language that allows for expressive and efficient querying and updating of the graph store without having to write traversals through the graph structure in code
- 23. CYPHER START: Starting points in the graph, obtained via index lookups or by element IDs. MATCH: The graph pattern to match, bound to the starting points in START. WHERE: Filtering criteria. RETURN: What to return. CREATE: Creates nodes and relationships. DELETE: Removes nodes, relationships and properties. SET: Set values to properties. FOREACH: Performs updating actions once per element in a list. WITH: Divides a query into multiple, distinct parts.
- 24. Graph Algorithms Shortest paths, all paths, all simple paths, Dijkstra and A*.
- 25. REST API
- 26. DEMO TIME
- 27. Thatâs All Folks Thank you! ! ! @ptraca