graph databases
DESCRIPTION
My presentation about introduction to graph database.TRANSCRIPT
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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
………………..
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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
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Different Types of Graphs
Graph Type Diagram
Undirected Graph
Directed Graph
Pseudo Graph
Multi Graph
Hyper Graph
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Different Types of Graphs
Graph Type Diagram
Weighted Graph
Labeled Graph
Property Graph
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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
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Compared to Relational Databases
Optimized for aggregation Optimized for connections
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Compared to Key Value Stores
Optimized for simple look-ups Optimized for traversing connected data
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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
1
3
2
6 12
9
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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3
2
6
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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3
2
6 12
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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
1
3
2
6 12
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
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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
1
Name= “Vinoth”
IBAN= “DE1234”
WITHDRAWS3
Name= “ATM@Romania”Lat = 41.000Lon = 40.840
2
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
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Thank you!