Understanding Graph Databases with Neo4j and Cypher

Understanding Graph Databases with Neo4j and Cypher

Group Members

S.S. Niranga MS-14901836

Nipuna Pannala MS-14902208

Ruhaim Izmeth MS-14901218

Trends in Data

Data is getting bigger:“Every 2 days we create as much information as we did up to 2003”– Eric Schmidt, Google

The History of Graph Theory

● 1736: Leonard Euler writes a paper on the “Seven Bridges of Konisberg”

● 1845: Gustav Kirchoff publishes his electrical circuit laws

● 1852: Francis Guthrie poses the “Four Color Problem”

● 1878: Sylvester publishes an article in Nature magazine that describes graphs

● 1936: Dénes Kőnig publishes a textbook on Graph Theory

● 1941: Ramsey and Turán define Extremal Graph Theory

● 1959: De Bruijn publishes a paper summarizing Enumerative Graph Theory

● 1959: Erdos, Renyi and Gilbert define Random Graph Theory

● 1969: Heinrich Heesch solves the “Four Color” problem

● 2003: Commercial Graph Database products start appearing on the market

What is Graph database?

“A traditional relational database may tell you the average age of everyone in this room..

..but a graph database will tell you who is most likely to buy you a beer!”

What does a Graph database look like?

What is a Graph Database?

● A database with an explicit graph structure

● Each node knows its adjacent nodes

● As the number of nodes increases, the cost of a local

step (or hop) remains the same

● Plus an Index for lookups

Compared to Relational Databases

Optimized for aggregation Optimized for connections

Complexity Vs Size

What to Choose?

http://db-engines.com/en/ranking/graph+dbms

What is Neo4j?● Neo4j is an open-source graph database, implemented in Java.

● Neo4j version 1.0 was released in February, 2010.

● Neo4j version 2.0 was released in December, 2013

● Neo4j was developed by Neo Technology, Inc.

● Neo Technology board of directors consists of Rod Johnson, (founder of the Spring Framework), Magnus Christerson (Vice President of Intentional Software Corp), Nikolaj Nyholm (CEO of Polar Rose), Sami Ahvenniemi (Partner at Conor Venture Partners) and Johan Svensson (CTO of Neo Technology).

Entities in Graph DBs (Neo4j)

● Nodes

● Relationships

● Properties

● Labels

● Paths

● Traversal

● Schema (index and constraints)

Neo4j Properties

Ex.

Ex.

Neo4j Labels

Ex.

Neo4j Nodes

Neo4j Relationships

Ex.

Neo4j PathsEx.

Introducing - CypherQuery Language for Neo4j

Relational SchemaPerson

p_namep_id

Book

b_titleb_id

p_type

Wrote

b_idp_id

Purchased

b_id pur_datep_id

Cypher - Few KeywordsGeneral Clauses● Return● Order by● Limit

Writing Clauses● Create● Merge● Set● Delete● Remove

Reading Clauses● Match● Optional Match● Where● Aggregation

See Full list at Cypher RefCardhttp://neo4j.com/docs/stable/cypher-refcard/

Functions● Predicates● Scalar functions● Collection functions● Mathematical functions● String functions

Cypher Demo

http://console.neo4j.org/

or

if Neo4j is locally installed

http://localhost:7474

Cypher

Creating nodes

CREATE (:Person)

CREATE (:Person { name:"John Le Carre" })

CREATE ({ name:"John Le Carre" })

CREATE (:Person:Author { name:"John Le Carre" })

CREATE (:Person:Author { name:"Graham Greene" }),

(:Book { title:"Tinker, Tailor, Soldier, Spy" }),

(:Book { title:"Our Man in Havana" }),

(:Person { name:"Ian" }),

(:Person { name:"Alan" })

Cypher

Modifying nodes

MATCH (p:Person { namme:"Alan" })

SET p += {name2 : "Alan2"}

MATCH (p:Person { namme:"Alan" })

SET p.name = "Alan"

MATCH (p:Person { namme:"Alan" })

SET p = {name : "Alan"}

CREATE (:Person { namme:"Alan" })

MATCH (p:Person { name2:"Alan2" })DELETE p

MATCH (p:Person { namme:"Alan" })REMOVE p.namme

Cypher Relationships

Cypher - Creating Relationships

CREATE (john:Person:Author { name:"John Le Carre" }),(b:Book { title:"Tinker, Tailor, Soldier, Spy" }),(john)-[:WROTE]->(b)

MATCH (p:Person { name:"Ian" }),(b:Book { title:"Our Man in Havana" })MERGE (p)-[:PURCHASED { date:"09-09-2011" }]->(b)

MATCH(graham:Person:Author { name:"Graham Greene" }),(b:Book { title:"Our Man in Havana" })

MERGE (graham)-[:WROTE]->(b)

MATCH (t:Book { title:"Tinker, Tailor, Soldier, Spy" }),(i:Person { name:"Ian" }),(a:Person { name:"Alan" })MERGE (i)-[:PURCHASED { date:"03-02-2011" }]->(t)<-[:PURCHASED { date:"05-07-2011" }]-(a)

Cypher - Modifying Relationships

MATCH

(graham:Person {name:"Graham Greene"})-[r]->(b:Book {title:"Our Man in Havana" })DELETE r

MATCH (p:Person { name:"Ian" })-[r]->(b:Book { title:"Our Man in Havana" })SET r.date = "09-09-2012"

MATCH (graham:Person:Author { name:"Graham Greene" }),(b:Book { title:"Our Man in Havana" })MERGE (graham)-[:WORTE]->(b)

Cypher - Querying DBs Find All Books

SQL

SELECT * FROM Books

Cypher Query

MATCH (b:Book)RETURN b

Person (p_id, p_name, p_type)Wrote (p_id, b_id)Book (b_id, b_title )Purchased (p_id, b_id, pur_date)

Cypher Result+-----------------------------------------------+| b |+-----------------------------------------------+| Node[2]{title:"Tinker, Tailor, Soldier, Spy"} || Node[3]{title:"Our Man in Havana"} |+-----------------------------------------------+2 rows2 ms

Cypher - Querying DBs Find All Authors

SQL

SELECT * FROM Person where p_type=”Author”

Cypher Query

MATCH (a:Author)RETURN a

Person (p_id, p_name, p_type)Wrote (p_id, b_id)Book (b_id, b_title )Purchased (p_id, b_id, pur_date)

Cypher Result+-------------------------------+| a |+-------------------------------+| Node[0]{name:"John Le Carre"} || Node[1]{name:"Graham Greene"} |+-------------------------------+2 rows8 ms

Cypher - Querying DBs Find All Authors and the Books written by them

SQL

SELECT p.p_name, b.b_title FROM Person p, Wrote w, Book b where p.p_type=”Author” and w.p_id = p.p_id andw.b_id = b.b_id

Cypher Query

MATCH (a:Author)-[:WROTE]->(b:Book)RETURN a,b

Person (p_id, p_name, p_type)Wrote (p_id, b_id)Book (b_id, b_title )Purchased (p_id, b_id, pur_date)

Cypher Result+-------------------------------------------------------------------------------+| a | b |+-------------------------------------------------------------------------------+| Node[0]{name:"John Le Carre"} | Node[2]{title:"Tinker, Tailor, Soldier, Spy"} || Node[1]{name:"Graham Greene"} | Node[3]{title:"Our Man in Havana"} |+-------------------------------------------------------------------------------+2 rows12 ms

Cypher - Querying DBs Find Books written by Graham Greene

SQL

SELECT b.b_title FROM Person p, Wrote w, Book b where p.p_type=”Author” and w.p_id = p.p_id andw.b_id = b.b_id andp.name = “Graham Greene”

Cypher Query

MATCH (a:Author)-[:WROTE]->(b:Book)WHERE a.name = 'Graham Greene'RETURN b

Person (p_id, p_name, p_type)Wrote (p_id, b_id)Book (b_id, b_title )Purchased (p_id, b_id, pur_date)

Cypher Result+------------------------------------+| b |+------------------------------------+| Node[3]{title:"Our Man in Havana"} |+------------------------------------+1 row13 ms

Cypher - Querying DBs Find names of all persons, the books they purchased and the date the purchase was made

SQL

SELECT p.p_name, pur.pur_date, b.b_titleFROM Person p, Book b, Purchased pur WHERE pur.p_id=p.p_id and b.b_id = pur.b_id

Cypher Query

MATCH (a)-[r:PURCHASED]->(b)RETURN a,r.date,b

Person (p_id, p_name, p_type)Wrote (p_id, b_id)Book (b_id, b_title )Purchased (p_id, b_id, pur_date)

Cypher Result+-------------------------------------------------------------------------------------+| a | r.date | b |+-------------------------------------------------------------------------------------+| Node[4]{name:"Ian"} | "09-09-2011" | Node[3]{title:"Our Man in Havana"} || Node[4]{name:"Ian"} | "03-02-2011" | Node[2]{title:"Tinker, Tailor, Soldier, Spy"} || Node[5]{name:"Alan"} | "05-07-2011" | Node[2]{title:"Tinker, Tailor, Soldier, Spy"} |+-------------------------------------------------------------------------------------+3 rows

Cypher - Querying DBs Find how Graham Greene is related to Ian

SQL

I won’t attempt!!!

Cypher Query

MATCH (a:Author)-[r*]-(p:Person { name:'Ian' })WHERE a.name = 'Graham Greene'RETURN a,r,p

Person (p_id, p_name, p_type)Wrote (p_id, b_id)Book (b_id, b_title )Purchased (p_id, b_id, pur_date)

Cypher Result+--------------------------------------------------------------------------------------------------------+| a | r | p |+--------------------------------------------------------------------------------------------------------+| Node[1]{name:"Graham Greene"} | [:WROTE[1] {},:PURCHASED[0] {date:"09-09-2011"}] | Node[4]{name:"Ian"} |+--------------------------------------------------------------------------------------------------------+1 row38 ms

Support for Graph Algorithms● shortestPath● allSimplePaths● allPaths● dijkstra (optionally with

cost_property and default_cost parameters)

Neo4j - Default locking behavior for Concurrency

● When adding, changing or removing a property on a node or relationship a write lock will be taken on the specific node or relationship.

● When creating or deleting a node a write lock will be taken for the specific node.

● When creating or deleting a relationship a write lock will be taken on the specific relationship and both its nodes.

Neo4j - Performance● As JVM runs on a shared environment, the way the

JVM is configured greatly related to Performance.

● More optimized for querying than CRUD operations, Batch updates are recommended

● Indexes can be set on nodes, relationships and their properties. Can boost query response times

● Mixed reports on querytimes and performance, upcoming releases are optimizing this.

Neo4j Capacity - Data size

In Neo4j, data size is mainly limited by the address space of the primary keys for Nodes, Relationships, Properties and Relationship types. Currently, the address space is as follows:

nodes 2^35 (∼ 34 billion)relationships 2^35 (∼ 34 billion)properties 2^36 to 2^38 depending on property types (maximum ∼

274 billion, always at least ∼ 68 billion)relationship types

2^15 (∼ 32 000)

Calling Neo4j JVM Server

Neo4j DB

Java Application

Web Application Web REST API

Java APIOfficially supported languages● Java● .NET● JavaScript● Python● Ruby● PHP

Neo4j EditionsEnterpriseEnterprise Lock Manager

High Performance Cache

Clustering

Hot Backups

Advanced Monitoring

NOT FREE

CommunityFREE

OPEN SOURCE

If you’ve ever● Joined more than 7 tables together

● Modeled a graph in a table

● Written a recursive CTE (Common Table Expression)

● Tried to write some crazy stored procedure with multiple

recursive self and inner joins

You should use Neo4j

Disadvantages● JVM should configured properly to get the

optimal performance.

● Neo4j DB cannot be distributed. They should replicated.

● Inappropriate for transactional information like accounting and banking.

Who use Neo4j?

Thank you !!!