The document introduces Morpheus, a property graph query language and framework integrated with Apache Spark, allowing the processing of graphs alongside traditional tabular data. It explains the structure of a property graph, the syntax and capabilities of the Cypher query language, and provides examples of various queries and operations. Morpheus enables users to create and manipulate property graphs using Spark's DataFrame API, enhancing analytical capabilities within the Spark ecosystem.

1. Presented By:
Swantika Gupta
Software Consultant
Morpheus - Cypher
for Apache Spark

2. Agenda
Why are we discussing Morpheus?
The Property Graph Model
Introduction to Cypher
Cypher Syntax
Hello Morpheus
Demo

3. Why are we discussing Morpheus?
● Graphs are coming to Spark !
● Neo4j’s OpenSource project - Morpheus, previously called as Cypher for Apache Spark
(CAPS), brings Property Graph and it’s querying language Cypher as an external plugin to
Spark
● Apache Spark accepted the proposal to add Morpheus as a core spark component, under the
name SparkGraph to Spark 3.0
● Interoperability between SparkSQL for tabular use cases and Cypher querying for graph use
cases.

4. The Property Graph Model
● Property Graphs are an extension to the elementary Graphs.
● Along with Nodes and relationships, Property Graphs have labels and key-value property pairs
on the entities - nodes and edges.
● Nodes : Basic Entity in graph
○ Holds any number of key-value pairs as node properties
○ Tagged with Labels, representing their roles in our domain
● Relationships : Directed, named connections between two node entities.
○ Has a start node, an end node, a direction and a type
○ Can have properties as key-value pairs.
● Nodes and Relationships together form a powerful piece - Pattern

5. The Property Graph Model
Relational Data
Model
Property Graph Data
Model

6. The Property Graph Model
● “Whiteboard Friendly”, Whiteboard model is the physical model
Node and Relationship FormatWhiteboard Model

7. The Property Graph Model
Final Model in Neo4jAdd labels & properties

8. Introduction to Cypher
● Neo4j’s Graph Query Language
● Declarative, SQL-inspired
● Allows use of ASCII-Art Syntax to write queries for the graph
● Describes visual patterns in graph
● Heavily based on patterns and is designed to recognize various versions of patterns of nodes
and relationships.
● Key Inspiration for ISO Project creating a Standard GQL

9. ● Cypher uses ASCII-Art to create patterns
( p:Person {name:'Gloria Foster'} ) - [ a:ACTED_IN ] -> (m:Movie{name:’The Matrix
Reloaded’})
● For Nodes: Represented using Parentheses
○ ( ) or ( p ), where p is a variable
○ (p: Person), p is a variable representing a Person node
● For Relationships: Represented using -->, <--, --
○ -[:Acted_in]->, specifies the type of relationship to search for
○ -[rel:Acted_in]->, rel is the variable name to the relationship
● For Node or Relationship Properties:
○ (p:Person {name:”Jennifer”}), a person node ‘p’ with name “Jennifer”
○ -[rel:Acted_in {year:”2012”}]->, an Acted_in relationship ‘rel’ between 2 nodes with year 2012
Cypher Syntax

10. Reserved Keywords to depict actions in a pattern
● MATCH : Searches for a node, relationship, property, label or pattern in a graph database
○ Similar to SQL’s “SELECT”
● RETURN : Specifies what values or results should be returned by the cypher query
○ Return nodes, relationships, properties, patterns
● MATCH and RETURN together form the most important property of Cypher and Property
Graphs, Pattern Matching
Cypher Syntax

11. ● Find all nodes with label ‘Person’:
MATCH (p:Person) RETURN p
● Find a node with label ‘Person’ and name ‘Hugh Jackman’
MATCH (p:Person{name:’Hugh Jackman’}) RETURN p
● Find the movie node in which a ‘Person’ node with name ‘Hugh Jackman’ worked
MATCH (p:Person{name:’Hugh Jackman’}) -[:ACTED_IN]-> (m:Movie) RETURN m
● Find the name of the movie in which the ‘Person’ node with name ‘Hugh Jackman’ worked
MATCH (p:Person{name:’Hugh Jackman’}) -[:ACTED_IN]-> (m:Movie) RETURN m.name
● Find the name of the movies in which ‘Hugh Jackman’ acted in year 2010
MATCH (p:Person{name:’Hugh Jackman’}) -[:ACTED_IN{year:2008}]-> (m:Movie)
RETURN m.name
Cypher Syntax

12. ● CREATE : Inserts a node, relationship or pattern inside the graph DB
MATCH (p:Person {name:’Jack Nicholson’})
MATCH (m:Movie{name:’A few good men’})
CREATE p -[rel:ACTED_IN]-> m
● SET : Modifies an existing property of a node or a relationship
MATCH (p:Person{name:’Jack Nicholson’}) SET p.birthdate = date(‘1937-04-22’)
● DELETE : Deletes a node or a relationship
MATCH (p:Person{name:’Jack Nicholson’}) DELETE p
● DETACH DELETE : Deletes a node and its relationship with any other node
MATCH (p:Person{name:’Jack Nicholson’}) DETACH DELETE p
Cypher Syntax

13. ● Originally called as Cypher for Apache Spark (CAPS)
● Brings the world of Graph processing and querying to Apache Spark, though as an external
plug-in right now
● Allows integration with Hive, Neo4j, Relational DB, and files stored in HDFS or S3
● Use Spark cluster to run Analytical Graph Queries
● Builds on Spark SQL Dataframe API, offers integration with Standard Spark SQL processing
● Uses Spark features such as Catalyst Optimizer
● Provides conversion from SQL Dataframes to Graphs and back to Dataframes too.
Hello Morpheus

14. ● Initiate Morpheus Session from Spark Session
Hello Morpheus

15. ● Create Nodes from Dataframe and it’s Mapping
Hello Morpheus

16. ● Initiate Morpheus Session from Spark Session
● Finally, create Property Graph from the nodes and relationship
Hello Morpheus

17. Demo

18. Thank You !
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