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Welcome 
School of Engineering, CUSAT 1
A SEMINAR ON NEO4J 
Presented by: Vishnu Sanker 
Project guide: Dr. Sudheep Elayidom
Contents 
•Trends in big data 
•NoSQL 
•Graphs 
•Neo4j 
•Brief introduction to Cypher 
•Pros and Cons of Neo4j 
School of Engineering, CUSAT 3
TRENDS IN BIG DATA 
1. Increasing data size (big data) 
•“Every 2 days we create as much information as we did up to 2003” 
-Eric Schmidt 
2. Increasingly connected data (graph data) 
•For example, text documents to html 
3. Semi-structured data 
•Individualization of data, with common sub-set 
4. Architecture 
•From monolithic to modular, distributed applications 
School of Engineering, CUSAT 4
NO SQL 
School of Engineering, CUSAT 5
NOSQL 
•Carlo Strozzi used the term NoSQL in 1998 to name his lightweight, open-source relational database that did not expose the standard SQL interface 
•Provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. 
School of Engineering, CUSAT 6
BENEFITS OF NOSQL 
•Large volumes of structured, semi-structured and unstructured data 
•Agile sprints, quick iteration, and frequent code pushes 
•Flexible, easy to use object-oriented programming 
•Efficient, scale-out architecture instead of expensive, monolithic architecture 
School of Engineering, CUSAT 7
TYPES OF NOSQL 
•Column 
-distributed data store is a NoSQL object of the lowest level in a keyspace. It is a tuple (a key-value pair) consisting of three elements 
Unique name: Used to reference the column 
Value: The content of the column. 
Timestamp: Used to determine the valid content 
•Document oriented 
-designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data 
•Key value pairs 
-collection of key value pairs 
•Graph 
-database that uses graph structures with nodes, edges, and properties to represent and store data 
School of Engineering, CUSAT 8
GRAPHS 
School of Engineering, CUSAT 9
GRAPHS 
A GRAPH DATABASE... 
NO: not for charts & diagrams, or vector artwork 
YES: for storing data that is structured as a graph 
School of Engineering, CUSAT 10
Graphs Everywhere 
๏Relationships in 
•Politics, Economics, History, Science, Transportation 
๏Biology, Chemistry, Physics, Sociology 
•Body, Ecosphere, Reaction, Interactions 
๏Internet 
•Hardware, Software, Interaction 
๏Social Networks 
•Family, Friends 
•Work, Communities 
•Neighbours, Cities, Society 
School of Engineering, CUSAT 11
School of Engineering, CUSAT 12
Good Relationships 
๏The world is rich, messy and related data 
๏Relationships are as least as important as the things they connect 
๏Complex interactions 
๏Always changing, change of structures as well 
๏Graph: Relationships are part of the data 
๏RDBMS: Relationships part of the fixed schema 
School of Engineering, CUSAT 13
HOW AN RDB IS REPRESENTED BY GRAPH 
RDB PROPERTY GRAPH 
School of Engineering, CUSAT 14
NEO4J -A GRAPH DATABASE 
NEO4j -A GRAPH DATABASE 
School of Engineering, CUSAT 15
GRAPHS 
School of Engineering, CUSAT 16
School of Engineering, CUSAT 17
Neo4j is a Graph Database 
๏A Graph Database: 
•a schema-free Property Graph 
•perfect for complex, highly connected data 
๏Why NEO4J: 
•reliable with real ACID Transactions 
•fast with more than 1M traversals / second 
•Server with REST API, or Embeddable on the JVM 
•scale out for higher-performance reads with High-Availability 
School of Engineering, CUSAT 18
DATA MODELING FOR NEO4J 
School of Engineering, CUSAT 19
School of Engineering, CUSAT 20
School of Engineering, CUSAT 21
School of Engineering, CUSAT 22
School of Engineering, CUSAT 23
School of Engineering, CUSAT 24
School of Engineering, CUSAT 25
School of Engineering, CUSAT 26
SAMPLE CODE 
School of Engineering, CUSAT 27
School of Engineering, CUSAT 28
CYPHER 
School of Engineering, CUSAT 29
CYPHER -QUERY LANGUAGE FOR NEO4J 
•Declarative query language 
•Describe what you want, not how 
•Based on pattern matching 
•declarative grammar with clauses (like SQL) 
•aggregation, ordering, limits 
•create, update, delete 
School of Engineering, CUSAT 30
Cypher: START +RETURN 
๏START <lookup> RETURN<expressions> 
๏START binds terms using simple look-up 
•directly using known ids 
•or based on indexed Property 
๏RETURN expressions specify result set 
School of Engineering, CUSAT 31
Cypher: MATCH 
๏START <lookup> MATCH <pattern> RETURN <expr> 
๏MATCH describes a pattern of nodes+relationships 
•node terms in optional parenthesis 
•lines with arrows for relationships 
School of Engineering, CUSAT 32
Cypher: WHERE 
๏START <lookup> [MATCH <pattern>] 
๏WHERE <condition> RETURN <expr> 
๏WHERE filters nodes or relationships 
•uses expressions to constrain elements 
School of Engineering, CUSAT 33
Cypher: SET 
๏SET [<node property>] [<relationship property>] 
•update a property on a node or relationship 
•must follow a START 
School of Engineering, CUSAT 34
Cypher: DELETE 
๏DELETE [<node>|<relationship>|<property>] 
•delete a node, relationship or property 
•must follow a START 
•to delete a node, all relationships must be deleted 
first 
School of Engineering, CUSAT 35
PROS AND CONS OF NEO4J 
PROS 
•Powerful data model -as generalized as rdbms 
•Connected data is locally indexed 
•Easy to query 
Cons 
•Sharding 
•Needs new way of thinking 
School of Engineering, CUSAT 36
Concluding... 
•Neo4j is property graph database 
•It is scalable, flexible, and is totally designed in java 
•Cypher is a query language for neo4j, which is highly declarative and flexible aswell 
School of Engineering, CUSAT 37
School of Engineering, CUSAT 38
School of Engineering, CUSAT 39

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A seminar on neo4 j

  • 1. Welcome School of Engineering, CUSAT 1
  • 2. A SEMINAR ON NEO4J Presented by: Vishnu Sanker Project guide: Dr. Sudheep Elayidom
  • 3. Contents •Trends in big data •NoSQL •Graphs •Neo4j •Brief introduction to Cypher •Pros and Cons of Neo4j School of Engineering, CUSAT 3
  • 4. TRENDS IN BIG DATA 1. Increasing data size (big data) •“Every 2 days we create as much information as we did up to 2003” -Eric Schmidt 2. Increasingly connected data (graph data) •For example, text documents to html 3. Semi-structured data •Individualization of data, with common sub-set 4. Architecture •From monolithic to modular, distributed applications School of Engineering, CUSAT 4
  • 5. NO SQL School of Engineering, CUSAT 5
  • 6. NOSQL •Carlo Strozzi used the term NoSQL in 1998 to name his lightweight, open-source relational database that did not expose the standard SQL interface •Provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. School of Engineering, CUSAT 6
  • 7. BENEFITS OF NOSQL •Large volumes of structured, semi-structured and unstructured data •Agile sprints, quick iteration, and frequent code pushes •Flexible, easy to use object-oriented programming •Efficient, scale-out architecture instead of expensive, monolithic architecture School of Engineering, CUSAT 7
  • 8. TYPES OF NOSQL •Column -distributed data store is a NoSQL object of the lowest level in a keyspace. It is a tuple (a key-value pair) consisting of three elements Unique name: Used to reference the column Value: The content of the column. Timestamp: Used to determine the valid content •Document oriented -designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data •Key value pairs -collection of key value pairs •Graph -database that uses graph structures with nodes, edges, and properties to represent and store data School of Engineering, CUSAT 8
  • 9. GRAPHS School of Engineering, CUSAT 9
  • 10. GRAPHS A GRAPH DATABASE... NO: not for charts & diagrams, or vector artwork YES: for storing data that is structured as a graph School of Engineering, CUSAT 10
  • 11. Graphs Everywhere ๏Relationships in •Politics, Economics, History, Science, Transportation ๏Biology, Chemistry, Physics, Sociology •Body, Ecosphere, Reaction, Interactions ๏Internet •Hardware, Software, Interaction ๏Social Networks •Family, Friends •Work, Communities •Neighbours, Cities, Society School of Engineering, CUSAT 11
  • 13. Good Relationships ๏The world is rich, messy and related data ๏Relationships are as least as important as the things they connect ๏Complex interactions ๏Always changing, change of structures as well ๏Graph: Relationships are part of the data ๏RDBMS: Relationships part of the fixed schema School of Engineering, CUSAT 13
  • 14. HOW AN RDB IS REPRESENTED BY GRAPH RDB PROPERTY GRAPH School of Engineering, CUSAT 14
  • 15. NEO4J -A GRAPH DATABASE NEO4j -A GRAPH DATABASE School of Engineering, CUSAT 15
  • 16. GRAPHS School of Engineering, CUSAT 16
  • 18. Neo4j is a Graph Database ๏A Graph Database: •a schema-free Property Graph •perfect for complex, highly connected data ๏Why NEO4J: •reliable with real ACID Transactions •fast with more than 1M traversals / second •Server with REST API, or Embeddable on the JVM •scale out for higher-performance reads with High-Availability School of Engineering, CUSAT 18
  • 19. DATA MODELING FOR NEO4J School of Engineering, CUSAT 19
  • 27. SAMPLE CODE School of Engineering, CUSAT 27
  • 29. CYPHER School of Engineering, CUSAT 29
  • 30. CYPHER -QUERY LANGUAGE FOR NEO4J •Declarative query language •Describe what you want, not how •Based on pattern matching •declarative grammar with clauses (like SQL) •aggregation, ordering, limits •create, update, delete School of Engineering, CUSAT 30
  • 31. Cypher: START +RETURN ๏START <lookup> RETURN<expressions> ๏START binds terms using simple look-up •directly using known ids •or based on indexed Property ๏RETURN expressions specify result set School of Engineering, CUSAT 31
  • 32. Cypher: MATCH ๏START <lookup> MATCH <pattern> RETURN <expr> ๏MATCH describes a pattern of nodes+relationships •node terms in optional parenthesis •lines with arrows for relationships School of Engineering, CUSAT 32
  • 33. Cypher: WHERE ๏START <lookup> [MATCH <pattern>] ๏WHERE <condition> RETURN <expr> ๏WHERE filters nodes or relationships •uses expressions to constrain elements School of Engineering, CUSAT 33
  • 34. Cypher: SET ๏SET [<node property>] [<relationship property>] •update a property on a node or relationship •must follow a START School of Engineering, CUSAT 34
  • 35. Cypher: DELETE ๏DELETE [<node>|<relationship>|<property>] •delete a node, relationship or property •must follow a START •to delete a node, all relationships must be deleted first School of Engineering, CUSAT 35
  • 36. PROS AND CONS OF NEO4J PROS •Powerful data model -as generalized as rdbms •Connected data is locally indexed •Easy to query Cons •Sharding •Needs new way of thinking School of Engineering, CUSAT 36
  • 37. Concluding... •Neo4j is property graph database •It is scalable, flexible, and is totally designed in java •Cypher is a query language for neo4j, which is highly declarative and flexible aswell School of Engineering, CUSAT 37