Trivadis TechEvent 2016 Introduction to DataStax Enterprise (DSE) Graph by Guido Schmutz

BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENF
HAMBURG KOPENHAGEN LAUSANNE MÜNCHEN STUTTGART WIEN ZÜRICH
Introduction to DataStax Enterprise
(DSE) Graph
Guido Schmutz
@gschmutz guidoschmutz.wordpress.com

Agenda
1. Why a Graph Database?
2. DataStax Enterprise Edition – DSE Graph
3. DSE Graph in Action
• Working with Graph Schemas
• Inserting Graph Data
• Traversing Graph Data
• Indexing Graph Data
4. Summary
Introduction to DataStax Enterprise (DSE) Graph2 9/9/2016

Why a Graph Database?

Know your domain
Connectedness of Datalow high
Document
Data
Store
Key-Value
Stores
Wide-
Column
Store
Graph
Databases
Relational
Databases

RDBMS vs. Graph Database
RDBMS Graph Database
Process to query data elements
(joins) is inefficient on large data set
or many relationships
Better performance for relationship queries
due to specialized index structures
Expressing JOIN-intensive queries in
SQL is time-consuming and error
prone
Intuitive query language enabling faster
application development

RDBMS vs. Graph Database – Data Modelling
VS. !"
!"
!"
#" #"
$"
$"
$"
partner!
worksFor 
!
develops!
develops!
uses!
uses!
reportsTo!
reportsTo!
manages!
worksFor 
!
worksFor 
!
Source: DataStax

Graph Use Cases
Master Data Management
Customer 360
Recommendation
Personalization
Security
Fraud Detection
Internet of Things (IoT)

Introduction to the Graph Model – Property Graph
Node / Vertex
• Represent Entities
• Can contain properties
(key-value pairs)
Relationship / Edge
• Lines between nodes
• may be directed or
undirected
Properties
• Values about node or
relationship
• Allow to add semantic to
relationships
User 1 Movie
rated
knows rated
User 2
userId: 16134540
name: Peter
location: Palo Alto
userId: 18898576
name: Guido
location: Berne
rating: 8
key: value
rating: 6
movieId: m1000
title: Sully
year: 2016
duration: 95
country: United States
production: [FilmNation,
Flashlight Films]

DataStax Enterprise Edition – DSE
Graph

DataStax Enterprise Edition – DSE Graph
Introduction to DataStax Enterprise (DSE) Graph14
• A scale-out graph database purposely built
for cloud applications that need to act on
complex and highly connected relationships.
• Supports a property graph model natively
inside the DataStax product, engineered
specifically for DataStax Enterprise
(Cassandra, Search, Analytics).
• Store & find relationships in data fast and
easy in large graphs.
• Part of DSE’s multi-model platform.
9/9/2016
Image: DataStax

DataStax Enterprise Graph
Real-time graph database management system
• Adopts Apache TinkerPop standards
for data and traversal
• Uses Apache Cassandra for scalable
storage and retrieval
• Leverages Apache Solr for full-text
search and indexing
• Integrates Apache Spark for fast
analytic traversal
• Supports comprehensive data security
for the enterprise
Image: DataStax

Why Cassandra?
• All nodes participate in a cluster
• Shared nothing
• Add or remove as needed
• More capacity? Add more servers
• Each node owns a range of partitions
• Consistent Hashing

DSE Graph - Architecture
Image: DataStax

Today’s Needs and the solution in DSE Graph
Need DSE Graph Feature
Store and Access Data Quickly Graph Data Model + Gremlin
Flexible, Fast Application Builds DSE Graph + DSE Studio & DSE Drivers
Analyze Information Graph Analytics with DSE Analytics
Search and Find Quickly Graph Search with DSE Search
Ingest and Export Data DSE Graph Loader
Secure Information DSE Security
Manage and Monitor Opscenter
Availability, Scale, Operational Ease,
Performance…
DataStax Enterprise

• Web-based developer solution which helps developers visually explore, query, and
trouble-shoot DSE Graph in one intuitive UI.
• Auto-completion, result set visualization, execution management, and much more.
Developer support with DataStax Studio

Data Loading Support with DSE Graph Loader
• Simplifies loading large amounts of enterprise data from various sources into DSE
Graph efficiently and robustly.
• Inspects incoming data for schema compliance.
• Uses declarative data mappings and custom transformations to handle diverse types
of data.
Graph
Loader

Data Mappings
Batch Loading
Stream Ingestion
RDBMSJSON
DSE Graph

Operational support with DataStax OpsCenter
• Web-based operations solution which can launch, manage, monitor and trouble-
shoot DSE clusters and deployments.
• Launch wizard, failure alerts, monitoring dashboards, and much more.

DSE Graph in Action

Sample Graph: Movies Database
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
User Movie belongsTo
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

DSE Graph in Action – Working
with Graph Schemas

Graph Schema API
Schema management basics
• Accessible via schema object
• Defined for each graph in the DSE Graph system
• Provides methods propertyKey, vertexLabel, edgeLabel, etc.
• Supports fluent interface
Four important elements of a graph schema
• Property keys
• Vertex labels
• Edge labels
• Indexes

Graph Schema API – Property Keys
Define types of properties to be used with Vertices and Edges
• Creating single-cardinality property keys (single() is assumed by default)
• Creating multi-property keys (can only be associated with vertices)
• Creating meta-property keys (can only be associated with vertices)
schema.propertyKey("title").Text().single().create();
schema.propertyKey("year").Int().create();
schema.propertyKey("production").Text().multiple().create();
schema.propertyKey("source").Text().create();
schema.propertyKey("date").Timestamp().create();
schema.propertyKey("budget").Text().multiple().
properties("source","date").create();
Movie
movieId: Text
title: Text
year: Int
duration: Int
country: Text
production: Text*

Graph Schema API – Vertex Labels
Define types of vertices to be used in a graph
• Creating vertex labels
• Different labels can use same property key
schema.vertexLabel("movie").
properties("movieId","title",”year",”duration",”country","production").
create();
schema.vertexLabel("genre").properties("genreId","name").create();
schema.vertexLabel("person").properties("personId","name").create();
Movie
movieId: Text
title: Text
year: Int
duration: Int
country: Text
production: Text*
Genre
genreId: Text
name: Text
Person
personId: Text
name: Text

Graph Schema API – Edge Labels
Define types of edges to be used in a graph
• Creating single-cardinality edge label definition
• Creating multi-cardinality edge label definition (multiple() is assumed by default)
schema.edgeLabel("rated").
single().
properties("rating").
connection("user","movie").
create();
schema.edgeLabel("actor").
multiple().
properties("rating").
connection("movie","person").
create();
User Movie
rated (0..1)
rating: int
Person Movie
actor (0..*)

Graph Schema API – Management of Graph Schemas
• Listing all schema definitions
• Listing individual schema elements
• Dropping all schema definitions (will also delete all graph data !!!)
schema.describe()
schema.vertexLabel("user").describe()
schema.edgeLabel("rated").describe()
schema.clear()

DSE Graph in Action – Inserting
Graph Data

Creating Vertices and Vertex Properties
Adding a new User vertex
Adding a new Movie vertex
Vertex u = graph.addVertex("user");
u.property("userId","u2016");
u.property("age",36);
u.property("gender","M");
User
userId: u2016
age: 26
gender: M
Vertex m = graph.addVertex(label, "movie",
"movieId", "m1000",
"title", "Sully",
"year", 2016,
"duration", 95,
"country", "United States");
m.property("production", "FilmNation");
m.property("production", "Flashlight Films");
Movie
movieId: m1000
title: Sully
year: 2016
Duration: 95
Country: United States
Production: [FilmNation,
Flashlight Films]

Creating Vertices and Vertex Properties
Adding an edge between the user vertex and the movie vertex
Vertex u = graph.addVertex("user", ...);
Vertex m = graph.addVertex("movie", ...);
Edge e = u.addEdge("rated", m);
e.property("rating", 8);
User
userId: u2016
age: 26
gender: M
Movie
rated
rating: 8
movieId: m1000
title: Sully
year: 2016
Duration: 95
Country: United States
Production: [FilmNation,
Flashlight Films]

Gremlin I/O
Exporting and importing existing graphs and subgraphs
• Migration between Apache TinkerPop-enabled graph databases
• Serialization and interchange of graphs between databases and tools
• Creating backup copies of graph
• Different file formats for graph serialization
Format Description
GraphML Common XML vocabulary for graph representation. Supported by many graph-related tools,
applications and libraries. "Lossy" format as it lacks support for complex data types and graph
variables.
GraphSON Apache TinkerPop-originated JSON-based, lossless format for graph representation.
Gryo Fast, space-efficient, losseless, binary graph serialization format for use by JVM languages.
Gryo is enalbed by Kryo – a fast and efficient object graph serialization framework.

Gremlin I/O
Exporting to and importing from a GraphML file
Exporting to and importing from a GraphSON file
Exporting to and importing from a Kryo file
graph.io(IoCore.graphml()).writeGraph("KillrVideo.xml")
graph.io(IoCore.graphml()).readGraph("KillrVideo.xml")
graph.io(IoCore.graphson()).writeGraph("KillrVideo.json")
graph.io(IoCore.graphson()).readGraph("KillrVideo.json")
graph.io(IoCore.gryo()).writeGraph("KillrVideo.json")
graph.io(IoCore.gryo()).readGraph("KillrVideo.json")

DSE Graph in Action – Traversing
Graph Data

Gremlin Traversals in DSE Graph
• Standard Gremlin Language
• Gremlin is defined by Apache TinkerPop
• Expressive fluent language to define traversals (Groovy)
• Production vs. Development modes
• Production (default) requires an explicitly defined graph schema and proper graph indexes to
avoid expensive scans
• Enabling graph scans in production mode (acceptable if scanning small portions of data =>
caution is advised!)
g.V().has("movie","movieId","m366").values("title","year")
schema.config().option("graph.schema_mode").get()
schema.config().option("graph.schema_mode").set("Development")
schema.config().option("graph.allow_scan").set(true)

Gremlin Traversals in DSE Graph
OLTP vs. OLAP Traversals
OLTP traversals OLAP traversals
Generate instantaneous responses Take longer to execute
Resemble targeted database queries Involve broader-scope data analysis
Heavily rely on vertex ids or indexes Require expensive graph scans
Access small subgraphs Move to the outgoing edges
Traverse short paths with few branches Move to the incoming edges

Gremlin Traversal Ingredients
Traversal source
Traversal Steps
Traverser
g = grap.traversal()
g.V().has("title","Alice in Wonderland").has("year",2010).
out("director").values("name")

Defining Gremlin Traversals
Linear motif: Traversal is a sequence of steps
g.
V().
has("title","Alice in Wonderland").
has("year",2010).
out("director").
values("name")
// Sample Output:
// Tim Burton
Person
Movie
screenwriter
movieId: m267
title: Alice in Wonderland
year: 2010
…
Person
Person
actor
personId: p5206
Name: Linda Woolverton
director
personId: p8153
Name: Tim Burton
personId: p4361
Name: Johnny Depp

Defining Gremlin Traversals
Netsted motif: Traversal is a tree of steps
g.
V().
has("title","Alice in Wonderland").
has("year",2010).
union(__.out("director"),
out("screenwriter")).
values("name")
// Sample Output:
// Tim Burton
// Linda Woolverton
Person
Movie
screenwriter
movieId: m267
title: Alice in Wonderland
year: 2010
…
Person
Person
actor
personId: p5206
Name: Linda Woolverton
director
personId: p8153
Name: Tim Burton
personId: p4361
Name: Johnny Depp

Simple Traversal
Navigating from a Vertex:
Step Description
out([label], …) Move to the outgoing vertices
in([label], …) Move to the incoming vertices
both([label], …) Move to both the incoming and
outgoing vertices
outE([label], …) Move to the outgoing edges
inE([label], …) Move to the incoming edges
bothE([label], …) Move to both the incoming and
outgoing edges
MovieUser
Genre
Genre
rated
belongsTo
actor
both()
bothE()
in() inE() outE() out()

Simple Traversal
Navigating from an Edge:
Step Description
outV() Move to the outgoing vertices
inV() Move to the incoming vertices
bothV() Move to both the incoming and outgoing
vertices
otherV() Move to the vertex that was not the vertex
that was moved from
MovieUser
rated
outV() bothV() inV()

Simple Traversal with in
Find Jonny Depp’s movies release in 2010 or later:
g.V().
hasLabel("person").
has("name","Johnny Depp").
in("actor").
has("year",gte(2010)).
values("title")
"Into the Woods",
"Pirates of the Caribbean: On Stranger Tides",
"Alice in Wonderland"
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Simple Traversal with in and inE
Find user rating for Jonny Depp’s movies released in
2010 or later:
g.V().
hasLabel("person").
in("actor").
inE("rated").
values("rating")
3,
7,
7,
7,
5,
5,
8,
...
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Simple Traversal with in, inE and outV
Find ages of users who left 7 or 8 star rating for
Johnny Depp’s movies released in 2010 or later:
g.V().
hasLabel("person").
in("actor").
inE("rated").
has("rating", within(7,8)).
outV().
values("age")
60,
37,
63,
62,
57,
...
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Path Traversal with path
Find a path from a user to a friend
g.V().
has("user","userId","u1").
out("knows").out("knows").
path().by("userId").limit(1)
g.V().
has(“user“,"userId","u1").
out(“knows“).out("knows").
path().by("age").limit(1)
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Path Traversal with path
Find a shortest, simple path between to actors
g.V().has("person","name","Johnny Depp").
repeat(both("actor").simplePath().timeLimit(1000)).
until(has("person","name","Leonardo DiCaprio")).
path().by(choose(hasLabel("person"),
values("name"),values("title"))).limit(1)
// Sample Output
// "Johnny Depp", "Dead Man", "Gabriel Byrne",
// "The Man in the Iron Mask", "Leonardo DiCaprio"
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Projecting Traversal with select
Find Johnny Depp’s movie titles
as("actor").
in("actor").as("movie").
select("actor","movie").
by("name").
by("title").
sample(1)
// Sample Output
// "actor": "Johnny Depp", "movie": "Dead Man"
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Projecting Traversal with select
Find Johnny Depp’s movie titles, years, and genres
as("a").
in("actor").as("t","y").
out("belongsTo").as("g").
select("a","t","y","g").
by("name").by("title").by("year").by("name").
sample(3)
// Sample output:
// [a:Johnny Depp, t:Alice in Wonderland, y:2010, g:Fantasy]
// [a:Johnny Depp, t:Alice in Wonderland, y:2010, g:Animation]
// [a:Johnny Depp, t:Alice in Wonderland, y:2010, g:Adventure]
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Statistical Traversal with count
Find the number of vertices and edges in a graph
g.V().count()
// 10797
g.E().count()
// 69054
g.V().hasLabel("user").count()
// 1100
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Statistical Traversal with mean, min and max
Find the average, smallest and largest user ages
g.V().hasLabel("user").values("age").mean()
// 32.01282051282051
g.V().hasLabel("user").values("age").min()
// 12
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Statistical Traversal with group().by().by() pattern
Find the average ages of female and male users in the graph
g.V().hasLabel("user").
group().
by("gender").
by(values("age").mean())
// F : 30.561371841155236
// M : 32.01282051282051
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

Statistical Traversal with groupCount
Find the vertex and edge distribution by label and graph
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text
g.V().groupCount().by(label)
// [movie:920,
// person:8759,
// genre:18,
// user:1100]

Declarative Traversal with match
Find directors who appeared in their own movies
g.V().match(
__.as("m").out("actor").as("p"),
__.as("m").out("director").as("p")).
select("p").by("name").dedup().
sample(3)
// Sample Output.
// "Gene Kelly",
// "Sylvester Stallone",
// "Terry Jones"
Person
Genre
movieId : text
title : text
year : int
duration : int
country : text
production : text*
personId : text
name : text
actor
rated
director
composer
screen
writer
cinemato
grapher
knows
rating : int
userId : text
age : int
gender : text
genreId : int
name : text

DSE Graph in Action – Indexing
Graph Data

Graph Schema API – Vertex Indexes (I)
Efficiently retrieve all vertices with a known label and a given vertex value
• Creating and Using materialized view index on a high cardinality property
• Creating and Using secondary index on a low cardinality property
schema.vertexLabel("movie").index("moviedsById").materialized().by("movieId")
.add()
g.V().hasLabel("movie").has("movieId","m267")
schema.vertexLabel("movie").index("moviesByYear").secondary().by("year")
.add()
g.V().has("movie","year",2010)

Graph Schema API – Vertex Indexes (II)
Efficiently retrieve all vertices with a known label and a given vertex value
• Creating and Using full text search index on a text property
• Creating and Using string search index on a text property
schema.vertexLabel("movie").index("search").search().by("title").asText()
.add()
g.V().has("movie","title",Search.tokenRegex("Wonder.*"))
schema.vertexLabel("movie").index("search").search().by("country").asString()
.add()
g.V().has("movie","country",Search.prefix("U"))

Graph Schema API – Property Indexes
Efficiently retrieve properties of a known vertex that have associated meta-properties
whose values are known or fall into a known range
• Creating and Using a Property Index
schema.vertexLabel("movie").index("movieBudgetBySource")
.property("budget").by("source")
.add()
g.V().has("movie","movieId","m267").properties("budget").
.has("source","Los Angeles Times").value()
Movie
movieId: Text
title: Text
year: Int
duration: Int
country: Text
production: Text*
budget: Int* [
source: Text
date: Timestamp]
schema.vertexLabel("movie").index("movieBudgetByDate")
.property("budget").by("date")
.add()

Graph Schema API – Edge Indexes (I)
Efficiently traverse edges that are incident to a known vertex, have a known label, and
have properties whose values are known or fall into a known range
• Creating and Using an Edge Index
• Find how many users rated a particular movie with an
8-star rating
schema.vertexLabel("movie").index("toUsersByRating")
.inE("rated").by("rating")
.add()
g.V().has("movie", "movieId", "m267").
inE("rated").has("rating",8).count()
movieId: Text
…
User Movie
rated (0..1)
rating: int

Graph Schema API – Edge Indexes (II)
Efficiently traverse edges that are incident to a known vertex, have a known label, and
have properties whose values are known or fall into a known range
• Creating and Using an Edge Index
• Find movies rated with a greater-than-7 rating
by a particular user
schema.vertexLabel("user").index("toMoviesByRating")
.outE("rated").by("rating")
.add()
g.V().has("user", "userId", "u1").
outE("rated").has("rating",gt(7)).inV()
movieId: Text
…
User Movie
rated (0..1)
rating: int
userId: Text
…

Summary

Summary - A Complete Integrated Solution for Graph
Introduction to DataStax Enterprise (DSE) Graph63
Server Visual Management/Monitoring
Visual Development Integrated Drivers (CQL, Gremlin, etc.)
Java Python C++ More…
9/9/2016 Images: DataStax

Trivadis Enterprise Knowledge Graph
Presentation
Project
Employee
Event
Course
Term
Certiﬁcation Department
Locationskill (level, since)
teaches
located in (since)
related to
related to
author
participates in (since, role)
part of (since)owns (since)
related to
related to
created for
related to / child of
managed by
Customer for
Event Type isOf
managed by
located in
responsible unit
owned by
Project Type
isOf
Industry
forin
participates in
Publication
related to
for
Social Media
Proﬁle
uses
related to
Instant
Messaging
uses
located in
responsible (for)
previous year
main
teacher

Armasuisse W&T Social Network Graph
Twitter User Tweetpublishes (timestamp)
Term
mentions (timestamp)
Place
uses
followedBy
Youtube
Channel
id
name
language
timestamp
lastUpdateTime
Youtube
Videopublishes (timestamp)
name
type
lastUpdateTime
id
targetIds
timestamp
language
lastUpdateTime
uses
id
street
country
name
type
url
lastUpdateTime
Url
id
name
hasFavorites
hasLikes
hasDislikes
hasComments
lastUpdateTime
uses
Person
owns
Webpage
retweets
replies
id
shortUrl
url
lastUpdateTime
uses
uses
linksTo
owns
linksTo
located
id
name
lastUpdateTime
id
name
lastUpdateTime
Geo Point
id
location
altitude
lastUpdateTime
uses
uses
Entity
uses (time)
uses
uses

More Information
• DS330: DataStax Enterprise Graph – Self-paced course offered for free by
DataStax

Guido Schmutz
Technology Manager
guido.schmutz@trivadis.com
9/9/2016 Introduction to DataStax Enterprise (DSE) Graph68
@gschmutz guidoschmutz.wordpress.com

Trivadis TechEvent 2016 Introduction to DataStax Enterprise (DSE) Graph by Guido Schmutz

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