The 2nd graph database in sv meetup
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The document introduces the Cypher query language for graph databases, emphasizing its benefits and features such as human-readability and graph pattern matching. It discusses the property graph model, compares Cypher to SQL and SPARQL, and details the syntax for querying graph data effectively. Additionally, the document highlights key concepts like match clauses, variable length relationships, and the evolution of Cypher, making it a valuable resource for understanding this query language.
![Cypher is Human-Readable
Finding all ancestor-descendant pairs in the graph
with recursive
as (
select
parent, child as descendant,
1 as level from source
union all
select
d.parent, s.child, d.level + 1
from descendants as d
join source s on d.descendant = s.parent
)
select * from descendants
order by parent, level, descendant ;
SQL
MATCH
p=(descendant)-[:Parent*]->(ancestor)
RETURN
(ancestor), (descendant), length(p)
ORDER BY (ancestor), (descendant), length(p)
Cypher
descendant ancestor](https://image.slidesharecdn.com/the2ndgraphdatabaseinsvmeetup-161007205222/85/The-2nd-graph-database-in-sv-meetup-8-320.jpg)
![Graph Pattern
● How can we represent graph patterns in a query?
○ Use ASCII art to represent the graph pattern easily
○ Like a diagram
● Node : ( )
● Relationship : --> or <-- (with direction),
-- (without direction)
● Node label : ( :LABEL_NAME )
● Relationship type : -[ :TYPE_NAME ]->
:Actor :Movie
ACTS_IN
(:Actor)-[:ACTS_IN]->(:Movie)](https://image.slidesharecdn.com/the2ndgraphdatabaseinsvmeetup-161007205222/85/The-2nd-graph-database-in-sv-meetup-11-320.jpg)
![Graph Pattern
● Property
○ Node property: ( { field : value, … } )
○ Relationship property: -[ { field : value, … } ]->
● Assign nodes and relations to variables
:Actor
name=’TOM’
:Movie (:Actor {name: “TOM”})-[:ACTS_IN]->(:Movie)
ACTS_IN
(a:Actor {name: “TOM”})-[ r:ACTS_IN]->(b:Movie)](https://image.slidesharecdn.com/the2ndgraphdatabaseinsvmeetup-161007205222/85/The-2nd-graph-database-in-sv-meetup-12-320.jpg)
![Graph Pattern
Represent more complicated patterns
:Person :Movie
RATED
:Person
RATED
FRIEND
(a:Person)-[:RATED]->(m:Movie)<-[:RATED]-( c:Person),
(a)-[:FRIEND]-(c)
Path1
Path2
Path1
Path2](https://image.slidesharecdn.com/the2ndgraphdatabaseinsvmeetup-161007205222/85/The-2nd-graph-database-in-sv-meetup-13-320.jpg)
![MATCH Clause
● Find the specified patterns
● Return matched variables to the next clause
MATCH
(a:Person)-[:RATED]->( m:Movie)<-[:RATED]-( c:Person),(a)-[:FRIEND]-(c
)
a (node) m (node) c (node)
Results of MATCH clause](https://image.slidesharecdn.com/the2ndgraphdatabaseinsvmeetup-161007205222/85/The-2nd-graph-database-in-sv-meetup-14-320.jpg)
![Uniqueness in Pattern Matching
● Cypher defines that pattern matching does not match a relationship to be
matched to several relationships in a pattern
● If we want multiple matching, then separate into multiple MATCH clauses
b
a
c
Graph Data Query Pattern
Friend r1:Friend r2:Friend
MATCH (a)-[r1:Friend]->(b), (a)-[r2:Friend]->(c) MATCH (a)-[r1:Friend]->(b)
MATCH (a)-[r2:Friend]->(c)](https://image.slidesharecdn.com/the2ndgraphdatabaseinsvmeetup-161007205222/85/The-2nd-graph-database-in-sv-meetup-17-320.jpg)
![Variable Length Relationship Matching
● One of the important features of the Cypher
● Finding all people links between two specified persons
Kisung Kim Joshua
MATCH
(:person {name: “Kisung Kim”})- [:friends*]->(:person {name: “Joshua”})
MATCH
(:person {name: “Kisung Kim”})- [:friends*3..5]->(:person {name:
“Joshua”})
Specify the path length 3 to 5
?](https://image.slidesharecdn.com/the2ndgraphdatabaseinsvmeetup-161007205222/85/The-2nd-graph-database-in-sv-meetup-18-320.jpg)
The 2nd graph database in sv meetup
- 1. Introduction to Cypher Graph Query Language October 6, 2016 Graph Databases in Silicon Valley Meetup
- 2. Overview Goal Understand the benefits of Cypher query language Graph Data Models and Query Languages Basics of Cypher Query Language Features and Execution Structure
- 3. Who am I Ph.D Kisung Kim - Chief Technology Officer of Bitnine Global Inc. Developed a distributed relational database engine in TmaxSoft Lead the development of a new graph database, Agens Graph in Bitnine Global Graph Enthusiast !
- 4. Graph Data Model and Query Languages ● Property graph model ○ Cypher query language from Cypher ○ AQL from ArangoDB ○ OrientDB’s SQL dialect ○ Tinkerpop API, a domain specific language, from Titan ● RDF graph model ○ W3C standard recommendation for the Semantic Web ○ SPARQL query language ● Facebook’s GraphQL (?)
- 5. Property Graph Model Terminology: Node(vertex) - Entity Relationships(Edge) Property - Attribute Label(type) - Group nodes and relationships person company works_for Name: Kisung Kim Email: [email protected] Name: Bitnine Global Homepage: http://bitnine.net title: CTO Team: agens graph Property Node Relationship Very intuitive and easy to model E-R diagram to property graphs
- 6. Cypher ● Declarative query language for the property graph model ○ Inspired by SQL and SPARQL ○ Designed to be human-readable query language ● Developed by Neo technology Inc. since 2011 ● Cypher is now evolving ○ Current version is 3.0 ● OpenCypher.org ○ Participate in developing the query language
- 8. Cypher is Human-Readable Finding all ancestor-descendant pairs in the graph with recursive as ( select parent, child as descendant, 1 as level from source union all select d.parent, s.child, d.level + 1 from descendants as d join source s on d.descendant = s.parent ) select * from descendants order by parent, level, descendant ; SQL MATCH p=(descendant)-[:Parent*]->(ancestor) RETURN (ancestor), (descendant), length(p) ORDER BY (ancestor), (descendant), length(p) Cypher descendant ancestor
- 9. Cypher Query Example Graph Pattern Results
- 10. Graph Pattern Matching ● Graph pattern matching is at the heart of Cypher ● Find subgraphs which are matched to the specified graph pattern ○ Subgraph isomorphism Query Pattern Graph Data
- 11. Graph Pattern ● How can we represent graph patterns in a query? ○ Use ASCII art to represent the graph pattern easily ○ Like a diagram ● Node : ( ) ● Relationship : --> or <-- (with direction), -- (without direction) ● Node label : ( :LABEL_NAME ) ● Relationship type : -[ :TYPE_NAME ]-> :Actor :Movie ACTS_IN (:Actor)-[:ACTS_IN]->(:Movie)
- 12. Graph Pattern ● Property ○ Node property: ( { field : value, … } ) ○ Relationship property: -[ { field : value, … } ]-> ● Assign nodes and relations to variables :Actor name=’TOM’ :Movie (:Actor {name: “TOM”})-[:ACTS_IN]->(:Movie) ACTS_IN (a:Actor {name: “TOM”})-[ r:ACTS_IN]->(b:Movie)
- 13. Graph Pattern Represent more complicated patterns :Person :Movie RATED :Person RATED FRIEND (a:Person)-[:RATED]->(m:Movie)<-[:RATED]-( c:Person), (a)-[:FRIEND]-(c) Path1 Path2 Path1 Path2
- 14. MATCH Clause ● Find the specified patterns ● Return matched variables to the next clause MATCH (a:Person)-[:RATED]->( m:Movie)<-[:RATED]-( c:Person),(a)-[:FRIEND]-(c ) a (node) m (node) c (node) Results of MATCH clause
- 15. Cypher Clause ● For reading ○ MATCH / OPTIONAL MATCH ● For updating ○ CREATE ○ MERGE ○ SET ● For filtering ○ WHERE ● For handling results ○ WITH, RETURN ○ And ORDER BY, LIMIT, SKIP https://s3.amazonaws.com/artifacts.opencypher.org/M02/railroad/Cypher.html
- 16. Cypher Query Structure Pipelined Execution Clauses are provided results from the former clause MATCH MATCH RETURN tom movie tom movie nicole tom.name movie.title nicole.name Clause Chain Result Format
- 17. Uniqueness in Pattern Matching ● Cypher defines that pattern matching does not match a relationship to be matched to several relationships in a pattern ● If we want multiple matching, then separate into multiple MATCH clauses b a c Graph Data Query Pattern Friend r1:Friend r2:Friend MATCH (a)-[r1:Friend]->(b), (a)-[r2:Friend]->(c) MATCH (a)-[r1:Friend]->(b) MATCH (a)-[r2:Friend]->(c)
- 18. Variable Length Relationship Matching ● One of the important features of the Cypher ● Finding all people links between two specified persons Kisung Kim Joshua MATCH (:person {name: “Kisung Kim”})- [:friends*]->(:person {name: “Joshua”}) MATCH (:person {name: “Kisung Kim”})- [:friends*3..5]->(:person {name: “Joshua”}) Specify the path length 3 to 5 ?
- 19. Summary Query database using graph patterns using Cypher Cyper features ● Graph pattern syntax ● Uniqueness restriction of relationship matching ● Pipelined execution structure ● Variable length path matching Graph query is much easier than SQL query
- 20. Thank you How do you feel about Graph Database?