Download to read offline
![Contrast
with
Rela.onal
Graphs
are
o]en
referred
to
as
“Whiteboard
Friendly”.
The
data
model
reflects
the
way
a
domain
expert
would
naturally
draw
their
data
on
a
whiteboard
“The
schema
is
the
data”.
Schema
flexibility
allows
the
system
to
change
in
response
to
a
changing
environment](https://image.slidesharecdn.com/20150518fedictpresentation-150519172026-lva1-app6891/75/Intro-to-Graphs-for-Fedict-16-2048.jpg)
Uploaded byRik Van Bruggen
Intro to Graphs for Fedict
This document provides an introduction and overview of graph databases. It begins with an introduction to graphs and their history, then discusses what graph databases are and how they complement relational databases. It introduces Neo4j as an example graph database and describes its key aspects like the labeled property graph data model and Cypher query language. The document then discusses when graph databases are applicable and provides examples. It demonstrates graph querying and concludes with case studies and next steps.
More Related Content
Similar to Intro to Graphs for Fedict
Intro to Graphs for Fedict
- 1. Intro to Graph Databases in a NOSQL world 19th of May 2015
- 2. Agenda • About Graphs • About Graph Databases – About Neo4j • Graph Querying – Short demonstra:on • Case Studies • Q&A
- 3.
- 5. Meet Leonhard Euler (again?) • Swiss mathema:cian • Inventor of Graph Theory (1736)
- 6.
- 7. A B D C
- 8. A B D C 1 2 3 4 7 6 5
- 9.
- 10. Complemen.ng RelationalDatabases VOLUME COMPLEXITY
- 11.
- 12. RDBMS Living in a NOSQL World Complexity Column Family Size Key-‐Value Store Document Databases Graph Databases 90% of Use cases Rela:onal Databases Naviga:onal Databases
- 13. So what is a graph database? • OLTP database • “end-‐user” transac:ons • Model, store, manage data as a graph
- 14. What is a graph? Vertex Edge
- 15. What is a graph? Node Rela:onship
- 16. Contrast with Rela.onal Graphs are o]en referred to as “Whiteboard Friendly”. The data model reflects the way a domain expert would naturally draw their data on a whiteboard “The schema is the data”. Schema flexibility allows the system to change in response to a changing environment
- 17. Neo4j is a Graph Database • JVM based • ACID transac:ons • Rich Java APIs • Query language • Using the Labeled Property Graph model
- 18. Cypher: THE graph query language • Learning from RDBMS’ evolu:on • Introduc:on of SQL! • Key characteris:cs • Declara:ve: tell it what you want, not how to get it • Expressive: Op:mize for reading • Pagern matching: easy on your brain! • Idempotent: state change expressed idempotently
- 19. Labeled Property Graph Model Author Book Reader Reader Author Book Author
- 20. Labeled Property Graph Summary • Nodes • Containers for proper:es • Grouped together in subgraphs by “Labels” • Proper:es • Key-‐value pairs • Primi:ve and array values • Rela:onships • Name • Direc:on • May also contain proper:es • Rela:onships (ctd.) • Must have a start node and an end node (no dangling rela:onships) • Start node and end node can be the same (e.g. ‘self’ rela:onships) • Nodes can be connected by more than one rela:onship
- 21. What are graphs good for? Complexity
- 22. Data Complexity complexity= f(size, semi-structure, connectedness)
- 23. complexity = f(size,semi-structure, connectedness) The Real Complexity
- 24.
- 25. Semi-‐Structure Email: rik@neotechnology.com Email: rik@vanbruggen.be Twiger: @rvanbruggen Skype: rvanbruggen USER CONTACT CONTACT_TYPE FIRST_NAME LAST_NAME USER_ID EMAIL_1 EMAIL_2 TWITTER FACEBOOK SKYPE Rik Van Bruggen 315 rik@neotechnology.com rik@vanbruggen.be @rvanbruggen NULL rvanbruggen
- 26. complexity = f(size,semi-structure, connectedness) The Real Complexity
- 27.
- 28. When Should I Use Graph Databases?? • Densely-‐connected, semi-‐structured domains • Lots of join tables? Connectedness • Lots of sparse tables? Semi-‐structure • Data Model Vola:lity • Easy to evolve • “Graphy” Query pagerns • Deeps Join Complexity and Performance • Pathfinding opera:ons • Millions of ‘joins’ per second • Consistent query :mes as dataset grows
- 29.
- 30. Querying a Graph • “Graph local” vs “Graph global” • Contextualized “ego-‐centric” queries • “Parachute” into graph • Start node(s) • Found through Index lookups • Crawl the surrounding graph • 2 million+ joins per second • No more Index lookups: Index-‐free adjacency
- 31. Queries: Paern Matching Pagern
- 32.
- 33.
- 34. www.neo4j.com www.meetup.com/graphdb-‐belgium rik@neotechnology.com or +32 478 686800 Q&A, Conclusion, Next Steps