graph databases

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Graph Databases

Vinoth Kannan

Introduction & Concepts

vinoth.kannan@widas.de

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Overview of NoSQL

What is a Graph Database

Concept

Some Use Cases

Conclusion

Agenda

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Overview of NoSQL

NoSQLNot Only SQL

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Types of NoSQL

Key Value Stores Column Family Document Databases Graph Databases

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Key-Value StoreTypes of NoSQL

Based on Amazon’s Dynamo platform: Highly Available Key-Value StoreData Model:

Global key-value mappingBig scalable HashMapHighly fault tolerant

Examples:Redis, Riak, Voldemort, Tokyo

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Column Family NoSQL Types

Based on BigTable: Google’s Distributed Storage System for Structured DataData Model:

A big table, with column familiesMap Reduce for querying/processingEvery row can have its own Schema

Examples:HBase, HyperTable, Cassandra

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Document DatabasesNoSQL Types

Based on Lotus NotesData Model:

A collection of documentsA document is a key value collectionIndex-centric, lots of map-reduce

Examples:CouchDB, MongoDB

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Graph DatabasesNoSQL Types

Based on Euler & Graph TheoryData Model:

Nodes and Relationships

Examples:Neo4j, OrientDB, InfiniteGraph, AllegroGraph, Titan

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NoSQL PerformaceComplexity vs Size

Data Size

Dat

a C

ompl

exity

RDBMS

K-V Store

CF Store

Document Store

GraphStore

………………..

What is a Graph?

An abstract representation of a set of objects where some pairs are connected by links.

Object (Vertex, Node)

Link (Edge, Arc, Relationship)

Name

Different Types of Graphs

Graph Type Diagram

Undirected Graph

Directed Graph

Pseudo Graph

Multi Graph

Hyper Graph

Different Types of Graphs

Graph Type Diagram

Weighted Graph

Labeled Graph

Property Graph

What is a Graph Database?

A database with an explicit graph structureEach node knows its adjacent nodes Even as the number of nodes increases, the cost of a local step (or hop) remains the samePlus an Index for lookupsTransactional based

Compared to Relational Databases

Optimized for aggregation Optimized for connections

Compared to Key Value Stores

Optimized for simple look-ups Optimized for traversing connected data

Compared to Key Value Stores

Optimized for “trees” of data Optimized for seeing the forest and the trees, and the branches, and the trunks

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Friends RecommendationWondered How ?

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Graph DatabasesBasic Concepts – Social Data

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FRIENDFRIEND

FRIEND

RELATED

FRIEND

Name= “Vinoth”City= “PF “

Name= “Thomas”

City= “Wimsheim”

Name= “Elena”

Name= “Joachim” Name= “Emanuel”

Name= “Y”

FRIEND

Since : 2012

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Graph Search Feature of FBWondered How ?

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Graph DatabasesBasic Concepts – Connection based

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WORKSFRIEND

FRIEND

Name= “Vinoth”

City= “PF”

Name= “Thomas”

City= “Wimsheim”

Name= “Elena”

FRIEND

Since : 2012

Name= “WIDAS”

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Graph DatabasesBasic Concepts – Spatial Data

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ROAD

ROAD

ROAD

ROAD

ROAD

Name= “WIDAS”Lat = 48.510Lon = 8.790

ROAD

Name= “Pforzheim Cafe”Lat = 48.530Lon = 8.420

Name= “Stuttgart Hbf”Lat = 48.460Lon = 9.1040

distance: 24 kmName= “…..”Lat = 41.000Lon = 9.840

distance: 51 km

distance: 12 km

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Power of Graph Database

Social Data

Spatial Data

+

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Graph DatabasesBasic Concepts – Social and Spatial Data

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LIKES

ROAD

FRIENDS

ROAD

Name= “WIDAS”Lat = 41.000Lon = 40.840

ROAD

distance: 12 km

Name= “Pforzheim”Lat = 41.000Lon = 40.840

Name= “Stuttgart”Lat = 41.000Lon = 40.840

distance: 24 km Name= ThomasTravel_rating = expert

distance: 51 km

Name= ElenaTravel_rating = novice

Some Use Cases

Highly connected data (social networks)Recommendations (e-commerce)Path Finding (how do I know you?)Anamoly Detection (Financial Services)

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FDS System with GraphDB

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Name= “Vinoth”

IBAN= “DE1234”

WITHDRAWS3

Name= “ATM@Romania”Lat = 41.000Lon = 40.840

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LIVES

Name= “Pforzheim”Lat = 41.000Lon = 40.840

6TRANSFERS

Name= “Xing Lee”Country = “China”IBAN = “XXXXXX”

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Name= “Blacklist”

amount: € 4500

MARKED

Thank you!