by Ganesh Shankaran, Sr. Solutions Architect, AWS Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.

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Using Graph Databases
Ganesh Shankaran
ganessh@amazon.com
Sr Solutions Architect

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HIGHLY CONNECTED DATA
Retail Fraud DetectionRestaurant RecommendationsSocial Networks

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U S E C A S E S FO R H I G H L Y C O N N E C T E D D A T A
Social Networking
Life Sciences Network & IT OperationsFraud Detection
Recommendations Knowledge Graphs

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A G R A P H D A T A B A S E IS O P T IM IZ E D F O R E F F IC IE N T
S T O R A G E A N D R E T R IE V A L O F H IG H L Y C O N N E C T E D D A T A .

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Open Source Apache TinkerPop™
Gremlin Traversal Language
W3C Standard
SPARQL Query Language
R E S O U R C E D E S C R I P T I O N
F R A M E W O R K ( R D F )
P R O P E R T Y G R A P H
LEADING GRAPH MODELS AND FRAMEWORKS

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AMAZON NEPTUNE
F u l l y m a n a g e d g r a p h d a t a b a s e
FAST RELIABLE OPEN
Query billions of
relationships with
millisecond latency
6 replicas of your data
across 3 AZs with full
backup and restore
Build powerful
queries easily with
Gremlin and SPARQL
Supports Apache
TinkerPop & W3C
RDF graph models
EASY

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AMAZON NEPTUNE HIGH LEVEL ARCHITECTURE

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SETTING UP NEPTUNE VIA CONSOLE
https://yukon.aws.amazon.com/neptune?region=us-east-1

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• Secure deployment in a VPC
• Increased availability through deployment
in two subnets in two different Availability
Zones (AZs)
• Cluster volume always spans three AZ to
provide durable storage
• See https://alpha-docs-
aws.amazon.com/neptune/latest/userguide
/get-started-prerequisites.html for VPC
setup details
NEPTUNE: SECURE VPC DEPLOYMENT

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AMAZON NEPTUNE PARTNERS AND ECOSYSTEM
. . . and more.
Clients
compatible with
the open
source Apache
TinkerPop
project.
Clients
compatible with
the W3C
Semantic Web
RDF and
SPARQL
standards.

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RDF GRAPHS

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URIs AS GLOBALLY UNIQUE IDENTIFIERS
URIs to identify nodes and edge labels
<https://permid.org/1-4295902158>
=> identifies the company “Netflix Inc”
organization:isIncorporatedIn1
=> identifies the relationship “is incorporated in”
<http://sws.geonames.org/6252001/>
=> identifies country “USA”
1 This is a shortcut for
<http://permid.org/ontology/organization/isIncorporatedIn>.
RDF uses XML prefix notation, where the prefix organization is a
shortcut for <http://permid.org/ontology/organization/>.

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RDF GRAPH: COLLECTION OF TRIPLES (1)
# Every edge in the RDF graph is represented as
# a (subject, predicate, object) triple
<https://permid.org/1-4295902158>
organization:isIncorporatedIn
<http://sws.geonames.org/6252001/> .
subject
predicate
object

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RDF GRAPH: COLLECTION OF TRIPLES (2)
# Every edge in the RDF graph is represented as
# a (subject, predicate, object) triple
<https://permid.org/1-4295902158>
organization:isIncorporatedIn
<http://sws.geonames.org/6252001/> .
<https://permid.org/1-4295902158>
vcard:organization-name
"Netflix Inc" .
subject
predicate
object
(URI)
subject
predicate
object
(literal)
Literals are “sinks” in the graph. They do not
have any outgoing edges.
RDF supports strings and other XML datatypes
(bool, integer, dates, floats, doubles, …)

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RDF GRAPH: COLLECTION OF TRIPLES (3)
# Every edge in the RDF graph is represented as
# a (subject, predicate, object) triple
<https://permid.org/1-4295902158>
organization:isIncorporatedIn
<http://sws.geonames.org/6252001/> .
<https://permid.org/1-4295902158>
vcard:organization-name
"Netflix Inc" .
<https://permid.org/1-4295902158>
organization:hasRegisteredPhoneNumber
"13026587581" .
<http://sws.geonames.org/6252001/>
iso:countryCode
”US” .
Same URI used as both subject and object, depending on
whether we represent outgoing vs. incoming RDF edges.

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THE BENEFIT OF URIs: LINKED DATA
Linking across datasets by referencing globally unique URIs
GeoNames
Wikidata
PermID
Example: PermID (re)uses <http://sws.geonames.org/6252001/>
as a global Identifier for the USA, which is an identifier rooted in GeoNames.

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THE LINKED OPEN DATA CLOUD
Linking Open Data cloud diagram 2017, by Andrejs Abele, John P. McCrae, Paul Buitelaar,
Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/ (CC-BY-SA)
Example: SNOMED CT (Systematized Nomenclature of Medicine –Clinical Terms)

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PROPERTY GRAPH
A property graph is a set of vertices and edges with respective properties (i.e. key/value pairs)
• Vertex represents entities/domains
• Edge represents directional relationship
between vertices.
• Each edge has a label that denotes the
type of relationship
• Each vertex & edge has a unique identifier
• Vertex and edges can have properties
• Properties express non-relational information about the vertices and edges

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PROPERTY GRAPH & APACHE TINKERPOP™
• Structure (graph)
Data model represented by a vertex/edge/property topology
• Process (traversal)
Means by which graph is analyzed
• Apache TinkerPop™
Open source graph computing framework
• Gremlin
Graph traversal language used to analyze the graph

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AMAZON NEPTUNE & APACHE TINKERPOP™
• Full compatibility with Tinkerpop Gremlin 3.3.0 (latest version released Aug 2017)
• Optimized query execution engine for Gremlin query language
• Enhanced Gremlin features
• Optional user supplied IDs
• Multiple labels for vertex

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GREMLIN: GRAPH TRAVERSAL LANGUAGE
• Domain specific language for graph
• Open source under Apache v2 license
• Open source clients for multiple languages like Java, Python, .Net, Groovy etc.
• Gremlin is a language composed of steps
• A step performs an atomic operation on the data stream
• Steps are chained together to form a traversal
• The output to a previous step is input to the next step that follows

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MUSIC BRAINZ DATASET
artist FROM_AREA area
work
PART_OF
MEMBER_
OF_BAND
COM
POSER
LYRICIST
W
RITER
labelPUBLISHING
FROM_AREA
https://musicbrainz.org/
RELEASED_IN

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QUERY EXPLAINED
Show the place where the writer of song “I Miss You” lives
g.V()

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QUERY EXPLAINED
Show the place where the writer of song “I Miss You” lives
hasLabel("work")

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QUERY EXPLAINED
Show the place where the writer of song “I Miss You” lives
has("type", "Song").has("name", "I Miss You")

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QUERY EXPLAINED
Show the place where the writer of song “I Miss You” lives
in("WRITER")

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QUERY EXPLAINED
Show the place where the writer of song “I Miss You” lives
out("FROM_AREA")

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QUERY EXPLAINED
Show the place where the writer of song “I Miss You” lives
Values("name")

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Source: https://github.com/apache/tinkerpop/blob/master/docs/static/images/gremlin-dashboard.png
LET’S TAKE GREMLIN FOR A SPIN!

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GREMLIN CONSOLE
• Interactive REPL groovy shell
• Uses WebSocket!

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GREMLIN: SIMPLE FILTERS
Filter on values steps has(),
hasNot(),
where(),
hasLabel()
Filter on elements
steps
dedup(),
limit(),
coin()
Boolean connectives
steps
and(),
or()

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GREMLIN: PROJECTION QUERY

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GREMLIN: MUTATION

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More Examples

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