Fuse graph, document and relational data from transactional and analytic data sources, into a property graph “bird’s eye view”. The property graph data model is Chen’s “entity relationship” model, without clutter. Use “ASCII Art” visual property graph schemas to define “graph data lifts”, mapping from data lake, RDBMS, RDF or graph data cloud services into Spark. Graphs in Spark draw on multiple data sources. Leverage the Cypher query language to combine, split, and project graphs in Spark memory. Graph data is “woven” in Spark without altering or copying the original source. The results of graph workloads can be written back into HDFS or other file systems. Graphs can be read from, stored and merged into a Neo4j transactional database. And tabular datasets can be extracted from graphs. Data scientists and engineers load, wrangle and analyze mixed model data through Morpheus transformations. Enterprises use graphs to catalogue their disparate data assets and processes. They store graph datasets in the data lake. In a world of concern about data protection, see how graph data lifts allow tailored, canonical data views to be realized, in Spark, without remodeling and moving data. Morpheus combines SparkSQL and Cypher queries, and table/graph functions.Choose the right language for the job: eliminate cumbersome multi-joins for connected-data traversals by using super-concise Cypher patterns for sub-graph detection and graph projection; use the power of table projection, grouping, aggregation in SparkSQL, all in one application. Feel free to “dismantle your graph”: expose your graph nodes or relationships as dataframes, or as Hive tables. Key Takeaways Graph technology meets Big Data and Spark Analytics Property graphs: the superset data model Graph, relational and document data, interwoven Lift, split, combine, and create new graphs, from any data source Get your data fit to exploit graph compute, without losing any of your existing tools undefined undefined undefined undefined undefined

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Neo4j Morpheus: Interweaving Documents,
Tables and Graph Data in Spark
Alastair Green, Mats Rydberg
Neo4j
#SAISDD9
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Introduction
Mats Rydberg Engineering Lead for Cypher for Apache Spark and Neo4j
Morpheus, Cypher Language Group
Alastair Green Lead, Neo4j Query Languages team, PM for Cypher for Apache
Spark/Neo4j Morpheus and Cypher for Gremlin
Neo4j Morpheus
A product in gestation, based on Cypher for Apache Spark
Enriching Spark’s graph capability
Combining Spark SQL with Spark graph querying
Interweaving graph, table and nested/document data
Integrating Spark analytics and Neo4j operational data
Advancing graph query language (GQL) features

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Property Graphs meet Big Data
The Property Graph data model is becoming increasingly mainstream
Cloud graph data services like Azure CosmosDB or Amazon Neptune
Simple graph features in SQLServer 2017, multiple new graph DB products
Read-only graph queries coming in the SQL:2020 standard
Neo4j becoming common operational store in retail, finance, telcos … and more
Increasing interest in graph algorithms over graph data as a basis for AI
Apache® Spark is the leading scale-out clustered memory solution for Big Data
Spark 2: Data viewed as tables (DataFrames), processed by SQL,
in function chains, using queries and user functions,
transforming immutable tabular data sets

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Property Graphs Nodes (Entities)
Node
● Represents an entity within the graph
● Can have labels
4

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Property Graphs Relationships
5
Node
● Represents an entity within the graph
● Can have labels
Relationship
● Connects a start node with an end node
● Has one type

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Property Graphs
Properties
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Node
● Represents an entity within the graph
● Can have labels
Relationship
● Connects a start node with an end node
● Has one type
Property
● Describes a node/relationship: e.g. name, age, weight etc
● Key-value pair: String key; typed value (string, number, bool, list, ...)

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Graph patterns
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Searching For Graph Patterns
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Operational Tables and Graphs → Analytics

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Spark: SQL and Cypher
Apache® Spark is the leading scale-out clustered memory solution for Big Data
Spark2: Data viewed as tables (Dataframes)
With StructType schema to describe the type
Processed by SQL and custom functions in function chains
transforming input immutable Dataframes into output tables
Cypher for Apache Spark (CAPS) mirrors the capabilities of Spark SQL
Data viewed as graphs (made up of Dataframes)
With PropertyGraph.schema to describe the graph type
Processed by Cypher and user functions in function chains ,
transforming input immutable PropertyGraphs into output graphs

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Cypher
for Apache Spark
Neo4j Graph DB + Cypher for Apache Spark
CypherSession
PropertyGraph
PropertyGraphCatalog
Cypher Queries
for CRUD
Graph Algorithms
Neo4j
Neo4j
Database
Cypher Queries
for CR[no UD]
Property Graph
Data Sources
as Plugins
Graphs of
Dataframes
Spark Cypher
Engine

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Cypher
for Apache Spark
Cypher Property Graph Data Sources
CypherSession
PropertyGraph
PropertyGraphCatalog
Cypher Property
Graph Data Sources
Cypher Queries
for CRUD
Graph Algorithms
Neo4j
Neo4j
Database Spark Cypher
Engine
Cypher Queries
for CR[no UD]
Property Graph
Data Sources
as Plugins
Graphs of
Dataframes
Neo4j
Morpheus

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Cypher
for Apache Spark
SQL Property Graph Data Source
CypherSession
PropertyGraph
PropertyGraphCatalog
Cypher Property
Graph Data Sources
Cypher Queries
for CRUD
Graph Algorithms
Neo4j
Neo4j
Database Spark Cypher
Engine
Cypher Queries
for CR[no UD]
Property Graph
Data Sources
as Plugins
Graphs of
Dataframes
Neo4j
Morpheus
SQL
Property Graph
Data Sources
Hive,
Oracle, MySQL,
SQLServer
Cypher Schema
SQL Graph DDL

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Cypher
for Apache Spark
FS Property Graph Data Source
CypherSession
PropertyGraph
PropertyGraphCatalog
Cypher Property
Graph Data Sources
Cypher Queries
for CRUD
Graph Algorithms
Neo4j
Neo4j
Database
Filesystem
Property Graph
Data Sources
LFS, HDFS,
S3, GFS, DBFS
Text, ORC, Parquet
Spark Cypher
Engine
Cypher Queries
for CR[no UD]
Property Graph
Data Sources
as Plugins
Graphs of
Dataframes
Neo4j
Morpheus
SQL
Property Graph
Data Sources
Hive,
Oracle, MySQL,
SQLServer
Cypher Schema
SQL Graph DDL

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Cypher
for Apache Spark
Future Property Graph Data Sources
CypherSession
PropertyGraph
PropertyGraphCatalog
Cypher Property
Graph Data Sources
Cypher Queries
for CRUD
Graph Algorithms
Neo4j
Gremlin
Databases
Neo4j
Database
Filesystem
Property Graph
Data Sources
LFS, HDFS,
S3, GFS, DBFS
Text, ORC, Parquet
Spark Cypher
Engine
Cypher Queries
for CR[no UD]
Property Graph
Data Sources
as Plugins
Graphs of
Dataframes
Neo4j
Morpheus
SQL
Property Graph
Data Sources
Hive,
Oracle, MySQL,
SQLServer
Cypher Schema
SQL Graph DDL

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Graphs and
Views in the
Catalog
// named graph session.people
// view function session.peopleByCountry()

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Querying (multiple) named graphs
// Session
implicit val session: CAPSSession = CAPSSession.local()
...
// Query
val result = session.cypher(
"""|FROM GRAPH socialNetwork
|MATCH (p:Person)
|FROM GRAPH products
|MATCH (c:Customer)-[:ORDERED]->(i:Item) WHERE p.name = c.name
|RETURN p.name, c.id, count(i.price) AS amount
""".stripMargin)
result.show()

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Cypher DB
ODS
SQL DB
ODSFILE SYSTEM
SPARK Analytics Platform
HIVE
Turning Tables into Graphs
Graph
DDL for
schema
and table
mappings
Cypher
Graph
Queries
Bolt JDBCSparkSQL/Hive

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“Tables for Labels”
In Cypher for Apache Spark graphs have a schema (graph type)
The schema defines
The properties that belong to a label
The node types and relationship types that occur in the graph
Node type is a label set (one or more labels → node type)
Relationship triplets that permit node and relationship type combinations
A graph instance is made up of tables, one per node type/relationship type

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Mapping SQL tables into a Property Graph

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Cypher DB
ODS
SQL DB
ODS
SPARK Analytics Platform
Delta
views
Synchronizing SQL data source with Neo4j
SQL
PGDS
map delta
graph into
Spark
Neo4j
Graph
Merge
delta
graph
Bolt
FILE SYSTEM
HIVE
JDBC
SparkSQL/Hive

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Demo: Customer360 incremental merge
22
Seed
2017-01-01
Delta
2017-01-02
Delta
2017-01-03
Delta
...
SQL (Hive)
Neo4j
Operational
Store
SQL PGDS +
Neo4j Merge

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Customer360 graph data model
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Query
composition

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Query composition: graphs in, graphs out
val graph = session.cypher("
// Let's query two graphs...
FROM GRAPH socialNetwork MATCH (p:Person)
FROM GRAPH products MATCH (c:Customer) WHERE p.email = c.email
// ...and construct a new graph from the result
CONSTRUCT ON socialNetwork, products
CREATE (p)-[:IS]->(c)
RETURN GRAPH
").graph

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Graph construction

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Query
composition

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Cypher DDL to define schema objects
CATALOG CREATE GRAPH session.peopleUS {
FROM session.people
MATCH (us:Person)-[R*]-(n)
WHERE us.nationality = ‘USA’
RETURN GRAPH OF *
}
CATALOG CREATE VIEW session.peopleByCountry($countryCode, $peopleGraph) {
FROM $peopleGraph
MATCH (us:Person)-[:R*]-(n)
WHERE us.nationality = $countryCode
RETURN GRAPH OF *
}

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// named graph
FROM session.peopleUS
MATCH (people:Person)
RETURN people.lastName
// view over graph
FROM session.peopleByCountry(‘USA’, teradata.europe.people)
MATCH (people:Person)
RETURN people.lastName
Using named graphs and view functions

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Query composition: get data from new graph
// Let's query the graph we just constructed
val result = graph.cypher("
MATCH (person:Person)-[:FRIEND_OF]-(friend:Person),
(friend)-[:IS]->(customer:Customer),
(customer)-[:BOUGHT]->(product:Product)
RETURN DISTINCT product.title AS recommendation, person.name
ORDER BY recommendation
")
result.show()

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SQL and Cypher working together
// Query with Cypher
val results = socialNetwork.cypher("
MATCH (a:Person)-[r:FRIEND_OF]->(b)
RETURN a.name AS friend1, b.name AS friend2, r.since AS since
")
// Extract DataFrame representing the query result and register
results.records.asDataFrame.createOrReplaceTempView("friends")
// Query with SQL
spark.sql("
SELECT friend1, friend2, since FROM friends ORDER BY since
").show()

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Demo: Database snapshots for analytics

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Cypher Graph Schema Labels and Properties
CREATE GRAPH snb WITH GRAPH SCHEMA (
LABEL (Company),
LABEL (Message {
creationDate : TIMESTAMP?,
locationIP : STRING?,
content : STRING?,
length : INTEGER?
}),
LABEL (LIKES {
creationDate : TIMESTAMP?
}),
...

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Cypher Graph Schema Node/Edge Types
// allowed node label sets (node types)
(Message, Post),
(Company),
(Country),
(Person),
...
// allowed relationship (edge) types
[LIKES],
[KNOWS],
[IS_LOCATED_IN],
...

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Cypher Graph Schema Relationship Triplets
// relationship triplets
(Message) <0 .. *> -[IS_LOCATED_IN]-> <1> (Country),
(Person) <1> -[LIKES]-> <0 .. *> (Message),
(Company) <0 .. *> -[IS_LOCATED_IN]-> <1> (Country),
(Person) <0 .. *> -[KNOWS]-> <0 .. *> (Person),
// end of pure graph schema

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SQL Graph DDL Mapping “Tables to Labels”
RELATIONSHIP LABEL SETS (
(HAS_CREATOR)
FROM "postHasCreator" edge
START NODES
LABEL SET (Message, Post)
FROM "Post" start_nodes
JOIN ON start_nodes.ID = edge."post"
END NODES
LABEL SET (Person)
FROM "Person" end_nodes
JOIN ON end_nodes.ID = edge."creator"
...
))
Relationship Label Set → Relationship Type
SQL Relationship Source Table
Node Label Set → Node Type
SQL Node Source Table

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SQL PGQ and GQL
Morpheus SQL Property Graph Data Source SQL Graph DDL
A prototype of SQL Property Graph Query (SQL/PGQ)
Will be part of ISO SQL:202x
Morpheus SQL PGDS Graph Schema and Composable Graph Queries
“Prequel to GQL”
GQL is an initiative to create an ISO international GQL standard alongside SQL
SQL PGQ planned as a (major) subset of a wider “native” full CRUD GQL

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From Cypher GQL

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Spark SQL and Cypher → Spark SQL and GQL
Cypher for Apache Spark developed by Neo4j
● Source code under Apache 2.0 license
● github.com/openCypher/cypher-for-apache-spark
Considering SPIP to bring enhanced graph support to Spark
● Ongoing discussion with Databricks Apache Spark/GraphFrames developers
● Concept: DataFrame-based graphs and Cypher graph queries
Directions
● Cypher → ISO GQL
● Cypher for Spark → Spark GQL + Spark SQL
spark.sql
spark.gql

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Neo4j Morpheus planned timeline
Neo4j Morpheus a new product, complementing the Neo4j transactional DB
● Commercially supported Cypher for Apache Spark
● Certified for Spark distributions and for SQL data sources
Limited access Early Adopter release scheduled for end October
Sign up to join EA programme https://neo4j.com/morpheus/
Documentation: https://neo4j.com/docs/morpheus-user-guide/preview/
To come
● Cypher language feature expansion
● Performance and scale testing in Q4/Q1
H1 2019 1.0 release as an extension to Neo4j Enterprise

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Questions?