natc 2013 - using graph databases for insights into connected data

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Using Graph Databases For Insights Into Connected Data

Gagan Agrawal

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Agenda

High level view of Graph Space Comparison with RDBMS and other NoSQL

stores Data Modeling Cypher : Graph Query Language Graph Database Internals Graphs In Real World

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What is a Graph?

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Graph

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What is a Graph? A collection of vertices and edges. Set of nodes and the relationships that connect

them. Graph Represents -

Entities as NODES The way those entities relate to the world as

RELATIONSHIP Allows to model all kind of scenarios

System of road Medical history Supply chain management Data Center

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Example – Twitter's Data

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Example – Twitter's Data

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High Level view of Graph Space Graph Databases - Technologies used primarily

for transactional online graph persistence – OLTP.

Graph Compute Engines - Tecnologies used primarily for offline graph analytics - OLAP.

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Graph Databases Online database management system with -

Create, Read, Update, Delete

methods that expose a graph data model. Built for use with transactional (OLTP) systems. Used for richly connected data. Querying is performed through traversals. Can perform millions of traversal steps per

second. Traversal step resembles a join in a RDBMS

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Graph Database Properties

The Underlying Storage : Native / Non-Native

The Processing Engine : Native / Non-Native

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Graph DB – The Underlying Storage Native Graph Storage – Optimized and designed

for storing and managing graphs.

Non-Native Graph Storage – Serialize the graph data into a relational database, an object oriented database, or some other general purpose data store.

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Native Graph Storage

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Graph DB – The processing Engine

Index free adjacency – Connected Nodes physically point to each other in the database

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Non-Native : Index Look-Up

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Native : Index Free Adjacency

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Graph Databases

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Power of Graph Databases

Performance

Flexibility

Agility

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Comparison Relational Databases

NoSQL Databases

Graph Databases

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Relational Databases Lack Relationships Initially designed to codify paper forms and

tabular structures. Deal poorly with relationships. The rise in connectedness translates into

increased joins. Lower performance. Difficult to cater for changing business needs.

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RDBMS

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Query to find friends-of-friends

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NoSQL Databases also lack Relationships NOSQL Databases e.g key-value, document or

column oriented store sets of disconnected values/documents/columns.

Makes it difficult to use them for connected data and graphs.

One of the solution is to embed an aggregate's identifier inside the field belonging to another aggregate.

Effectively introducing foreign keys Requires joining aggregates at the application

level.

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NoSQL DB Relationships between aggregates aren't first

class citizens in the data model. Foreign aggregate "links" are not reflexive. Need to use some external compute infrastructure

e.g Hadoop for such processing. Do not maintain consistency of connected data. Do not support index-free adjacency.

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NoSQL DB

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Graph DB Embraces Relationships

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Graph DB Find friends-of-friends in a social network, to a

maximum depth of 5. Total records : 1,000,000 Each with approximately 50 friends

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NoSQL Comparison

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Data Modeling with Graph

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Data Modeling “Whiteboard” friendly

The typical whiteboard view of a problem is a GRAPH.

Sketch in our creative and analytical modes, maps closely to the data model inside the database.

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The Property Graph Model

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Cypher : Graph Query Language Pattern-Matching Query Language Humane language Expressive Declarative : Say what you want, now how Borrows from well know query languages Aggregation, Ordering, Limit Update the Graph

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Cypher Cypher Representation : (c)-[:KNOWS]->(b)-[:KNOWS]->(a), (c)-[:KNOWS]-

>(a)

(c)-[:KNOWS]->(b)-[:KNOWS]->(a)<-[:KNOWS]-(c)

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Cypher

START c=node:user(name='Michael')MATCH (c)-[:KNOWS]->(b)-[:KNOWS]->(a), (c)-

[:KNOWS]->(a)RETURN a, b

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Other Cypher Clauses WHERE

Provides criteria for filtering pattern matching results.

CREATE and CREATE UNIQUE Create nodes and relationships

DELETE Removes nodes, relationships and properties

SET Sets property values

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Other Cypher Clauses FOREACH

Performs an updating action for graph element in a list.

UNION Merge results from two or more queries.

WITH Chains subsequent query parts and forward

results from one to the next. Similar to piping commands in UNIX.

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Comparison of Relational and Graph Modeling

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Systems Management Domain

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Tables and Relationships

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Graph Representation

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Query to find faulty Equipment

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Matched Paths

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Graph Database Internals

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Non Functional Characteristics

Transactions Fully ACID

Recoverability Availability Scalability

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Scalability Capacity (Graph Size)

Latency (Response Time)

Read and Write Throughput

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Capacity 1.9 Release of Neo4j can support single graphs

having 10s of billions of nodes, relationships and properties.

The Neo4j team has publicly expressed the intention to support 100B+ nodes/relationships/properties in a single graph.

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Latency RDBMS – more data in tables/indexes result in

longer join operations. Graph DB doesn't suffer the same latency

problem. Index is used to find starting node. Traversal uses a combination of pointer chasing

and pattern matching to search the data. Performance does not depend on total size of the

dataset. Depends only on the data being queried.

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Throughput Constant performance irrespective of graph size.

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Graphs in the Real World

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Common Use Cases Social Recommendations Geo Logistics Networks : for package routing, finding shortest

Path Financial Transaction Graphs : for fraud detection

Master Data Management Bioinformatics : Era7 to relate complex web of information

that includes genes, proteins and enzymes Authorization and Access Control : Adobe Creative

Cloud, Telenor

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Who uses Neo4j ?

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Resources

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Thank You