What’s the big deal with Graph Databases?

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“What’s the big deal with Graph Databases”
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What’s the big deal with Graph
Databases?
Alex Barbosa Coqueiro – Head of Public
Sector Solutions Architecture for Latin
America, Canada and Caribbean at AWS
5
The Feature Presentation

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
What’s the big deal with Graph Databases?
Alex Coqueiro
Head of Solutions Architecture Team
AWS Public Sector for Latin America, Canada and Caribbean
@alexbcbr

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
What is this all about?
1

The origin by Leonhard Euler in 1736
Vertex/Nodes
Edges

Graphs are all around us
Social
networking
Recommendations
Knowledge
graphs
Fraud
detection
Protein
Research
Network & IT
operations
Customer
360

Knowledge Graph Application
What museums should Alice
visit while in Paris?
Who painted the Mona Lisa?
What artists have paintings
in The Louvre?

Highly Connected Data Use Cases
Retail Fraud DetectionRestaurant RecommendationsSocial Networks

… Architecture …
2

Building Your Solution
Datastore Query Visualization
?

Challenges Building Apps with Highly Connected DataRELATIONAL DATABASE CHALLENGES BUILDING APPS
WITH HIGHLY CONNECTED DATA
Unnatural for
querying graph
Inefficient
graph processing
Rigid schema inflexible
for changing data

DIFFERENT APPROACHES FOR HIGHLY CONNECTED DATA
Purpose-built for a business process Purpose-built to answer questions about relationships

Graph Database is optimized for efficient storage and
retrieval of HIGHLY CONNECTED DATA

Large variety of solutions
• Deploy software on demand (E.g. AgensGraph, Neo4J)
• 1,400+ ISVs
• Over 4,500 product listings
• 200,000 active customers
• Over 650 million hours of EC2 deployed monthly
• Deployed in 17 regions
• Offers 35 categories
• Flexible consumption and contract models
• Easy and secure deployment, almost instantly
• One consolidated bill
• Always evolving
• Deploy directly in your EC2 instance (E.g. JanusGraph)
• Code could be customized to your specific needs
• https://github.com/awslabs/dynamodb-janusgraph-
storage-backend

AMAZON NEPTUNE
Fully managed graph database
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

Fully Managed Service
Easily configurable via the console
Backup and restore, point-in-time recovery
Multi-AZ high availability
Support for up to 15 read replicas
Supports encryption at rest
Supports encryption in transit (TLS)
B E N E F I T S

AMAZON NEPTUNE HIGH LEVEL ARCHITECTURE
Bulk load
from
Amazon S3
Database
Mgmt.

Distributed storage architecture
• Performance, availability, durability
• Scale-out replica architecture
• Shared storage volume with 10-GB
segments striped across hundreds of
nodes
• Data are replicated 6 times across 3
AZs
• Hotspot rebalance, fast database
recovery
• Log applicator embedded in storage
layer
Delivered as a managed service
Master Replica Replica
Primary
Shared storage volume
Replica Replica
Gremlin/
SPARQL
Transactions
Caching
Gremlin/
SPARQL
Transactions
Caching
Gremlin/
SPARQL
Transactions
Caching
AZ
1
AZ
2
AZ
3

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential… Pandemic disease use case …
3

AWS
Marketplace
Amazon EC2
Amazon
Neptune
?

RESOURCE DESCRIPTION
FRAMEWORK (RDF)
PROPERTY GRAPH
GRAPH MODELS

PROPERTY GRAPH
A property graph is a set of vertices and edges with respective properties
• Vertex represents entities/domains (often referred to as nodes)
• 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 which express non-relational information
FRIEND
name:
Bill
name:
Sarah
UserUser
Since 11/29/16

PROPERTY GRAPH & APACHE TINKERPOP
• Apache TinkerPop
Open source graph computing framework for
Property Graph
• Gremlin
Graph traversal language used to analyze the graph
Amazon Neptune is fully compatibility with Tinkerpop Gremlin and provides
optimized query execution engine for Gremlin query language.

CREATING A TINKERPOP GRAPH
//Connect to Neptune and receive a remote graph, g.
user1 = g.addVertex (id, 1, label, "User", "name", ”Alex");
user2 = g.addVertex (id, 2, label, "User", "name", ”Sue");
...
user1.addEdge(”KNOWS", user2, id, 21);
KNOWS
name:
Alex
name:
Sue
User
User

W3C Standard
SPARQL Query Language
RESOURCE DESCRIPTION
FRAMEWORK (RDF)
PROPERTY GRAPH
GRAPH MODELS

RDF GRAPHS
• RDF Graphs are described as a collection of triples: subject, predicate, and object.
• Internationalized Resource Identifiers (IRIs) uniquely identify subjects.
• The Object can be an IRI or Literal.
• A Literal in RDF is like a property and RDF supports the XML data types.
• When the Object is an IRI, it forms an “Edge” in the graph.
<http://www.socialnetwork.com/person#1>
rdf:type contacts:User;
contact:name: ”Bill” .
subject
predicate
Object (literal)
name:
Bill
User
<http://www.socialnetwork.com/person#1>IRI
<http://www.socialnetwork.com/person#1>
contacts:friend
<http://www.socialnetwork.com/person#2> .
subject
predicate
Object (IRI)
FRIEND
#1 2#2

SQL RELATIONAL DATABASE QUERY
SELECT distinct c.CompanyName
FROM customers AS c
JOIN orders AS o ON /* Join the customer from the order */
(c.CustomerID = o.CustomerID)
JOIN order_details AS od /* Join the order details from the order */
ON (o.OrderID = od.OrderID)
JOIN products as p /* Join the products from the order details */
ON (od.ProductID = p.ProductID)
WHERE p.ProductName = ’Echo'; /* Find the product named ‘Echo’ */
Find the name of companies that purchased the ‘Echo’.

SPARQL DECLARATIVE GRAPH QUERY
PREFIX sales_db: <http://sales.widget.com/>
SELECT distinct ?comp_name WHERE {
?customer <sales_db:HAS_ORDER> ?order ; #customer graph pattern
<sales_db:CompanyName> ?comp_name . #orders graph pattern
?order <sales_db:HAS_DETAILS> ?order_d . #order details graph pattern
?order_d <sales_db:HAS_PRODUCT> ?product . #products graph pattern
?product <sales_db:ProductName> “Echo” .
}
* Source : http://www.playnexacro.com/index.html#show:article

GREMLIN IMPERATIVE GRAPH TRAVERSAL
/* All products named ”Echo” */
g.V().hasLabel(‘Product’).has('name',’Echo')
.in(’HAS_PRODUCT') /* Traverse to order details */
.in(‘HAS_DETAILS’) /* Traverse to order */
.in(’HAS_ORDER’) /* Traverse to Customer */
.values(’CompanyName’).dedup() /* Unique Company Name */

AWS
Marketplace
Amazon EC2
Amazon
Neptune
Graphexp

4
… Key Takeaways …

USE CASE

AWS
Marketplace
Amazon
Neptune

Learn Gremlin
http://kelvinlawrence.net/book/Gremlin-Graph-Guide.html
https://github.com/krlawrence/graph

Learn SPARQL
https://logd.tw.rpi.edu/tutorial/a_crash_course_on_sparql
https://www.w3.org/TR/sparql11-query/

Samples
https://github.com/aws-samples/amazon-neptune-samples

Explore use cases using Neptune
https://aws.amazon.com/blogs/database/analyze-amazon-neptune-graphs-using-amazon-sagemaker-jupyter-notebooks/

Reference architectures
https://github.com/aws-samples/aws-dbs-refarch-graph/

Use cases, videos, blog posts, and code
https://aws.amazon.com/neptune/developer-resources/

Customer Reference

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Thank YOU
Alex Coqueiro
Head of Solutions Architecture Team
AWS Public Sector for Latin America, Canada and Caribbean
@alexbcbr

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What’s the big deal with Graph Databases?

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