An Introduction to NOSQL, Graph Databases and Neo4j
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Neo4j is a graph database that stores data in nodes and relationships. It allows for efficient querying of connected data through graph traversals. Key aspects include nodes that can contain properties, relationships that connect nodes and also contain properties, and the ability to navigate the graph through traversals. Neo4j provides APIs for common graph operations like creating and removing nodes/relationships, running traversals, and managing transactions. It is well suited for domains that involve connected, semi-structured data like social networks.
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An Introduction to NOSQL, Graph Databases and Neo4j
- 1. NOSQL Databases and Neo4j
- 2. Database and DBMS • Database - Organized collection of data • The term database is correctly applied to the data and their supporting data structures. • DBMS - Database Management System: a software package with computer programs that controls the creation, maintenance and use of a database.
- 3. Types of Happening Databases • Relational database – nothing new but still in use and it seems it will always be a happening one. • Cloud databases – everything is cloudy. • Data warehouse – Huge! Huge! Huge! archives. • Embedded databases – you can’t see them :P • Document oriented database – the In thing. • Hypermedia database – WWW. • Graph database – facebook, twitter, social network.
- 4. NOSQL is simply… Not OnlySQL
- 5. Why NOSQL now? Driving trends
- 6. Trend 1: Data Size 3000 2011? 2500 2000 1500 2010 1000 500 2009 2007 2008 0
- 7. Trend 2: Connectedness GGG Onotologies RDFa Folksonomies Information connectivity Tagging Wikis UGC Blogs Feeds Hypertext Text Documents
- 8. Trend 3: Semi-structured information • Individualisationof content – 1970’s salary lists, all elements exactly one job – 2000’s salary lists, we need many job columns! • Store more data about each entity • Trend accelerated by the decentralization of content generation – Age of participation (“web 2.0”)
- 9. Trend 4: Architecture 1980’s: Single Application Application DB
- 10. Trend 4: Architecture 1990’s: Integration Database Antipattern Application Application Application DB
- 11. Trend 4: Architecture 2000’s: SOA RESTful, hypermedia, composite apps Application Application Application DB DB DB
- 12. Side note: RDBMS performance Salary list Most Web apps Social Network Location-based services
- 15. Key-Value Stores • “Dynamo: Amazon’s Highly Available Key- Value Store” (2007) • Data model: – Global key-value mapping – Highly fault tolerant (typically) • Examples: – Riak, Redis, Voldemort
- 16. Column Family (BigTable) • Google’s “Bigtable: A Distributed Storage System for Structured Data” (2006) • Data model: – A big table, with column families – Map-reduce for querying/processing • Examples: – HBase, HyperTable, Cassandra
- 17. Document Databases • Data model – Collections of documents – A document is a key-value collection – Index-centric, lots of map-reduce • Examples – CouchDB, MongoDB
- 18. Graph Databases • Data model: – Nodes with properties – Named relationships with properties – Hypergraph, sometimes • Examples: – Neo4j (of course), SonesGraphDB, OrientDB, InfiniteGraph, AllegroGraph
- 19. Why Graph Databases? • Schema Less and Efficient storage of Semi Structured Information • No O/R mismatch – very natural to map a graph to an Object Oriented language like Ruby. • Express Queries as Traversals. Fast deep traversal instead of slow SQL queries that span many table joins. • Very natural to express graph related problem with traversals (recommendation engine, find shortest parth etc..) • Seamless integration with various existing programming languages. • ACID Transaction with rollbacks support. • Whiteboard friendly – you use the language of node,properties and relationship to describe your domain (instead of e.g. UML) and there is no need to have a complicated O/R mapping tool to implement it in your database. You can say that Neo4j is “Whiteboard friendly” !(http://video.neo4j.org/JHU6F/live-graph- session-how-allison-knows-james/)
- 20. Social Network “path exists” Performance • Experiment: • ~1k persons # persons query time • Average 50 friends per Relational 1000 2000ms database person • pathExists(a,b) limited to depth 4
- 21. Social Network “path exists” Performance • Experiment: • ~1k persons # persons query time • Average 50 friends per Relational 1000 2000ms database person Neo4j 1000 2ms • pathExists(a,b) limited to depth 4
- 22. Social Network “path exists” Performance • Experiment: • ~1k persons # persons query time • Average 50 friends per Relational 1000 2000ms database person Neo4j 1000 2ms • pathExists(a,b) Neo4j 1000000 2ms limited to depth 4
- 23. What are graphs good for? • Recommendations • Business intelligence • Social computing • Geospatial • Systems management • Web of things • Genealogy • Time series data • Product catalogue • Web analytics • Scientific computing (especially bioinformatics) • Indexing your slow RDBMS • And much more!
- 24. Graphs
- 25. Directed Graphs
- 27. Depth First Search ?????????????????
- 28. Graph Databases • A graph database stores data in a graph, the most generic of data structures, capable of elegantly representing any kind of data in a highly accessible way.
- 29. Graphs • “A Graph —records data in→ Nodes —which have→ Properties”
- 30. Graphs • “Nodes —are organized by→ Relationships — which also have→ Properties”
- 31. Query a graph with Traversal • “A Traversal —navigates→ a Graph; it — identifies→ Paths —which order→ Nodes”
- 32. Indexes • “An Index —maps from→ Properties —to either→ Nodes or Relationships”
- 33. Neo4j is a Graph Database • “A Graph Database —manages a→ Graph and —also manages related→ Indexes”
- 34. Neo4j – Hey! This is why I am a Graph Database. • The fundamental units that form a graph are nodes and relationships. • In Neo4j, both nodes and relationships can contain properties. • Nodes are often used to represent entities, but depending on the domain relationships may be used for that purpose as well.
- 35. Node in Neo4j
- 36. Relationships in Neo4j • Relationships between nodes are a key part of Neo4j.
- 39. Properties • Both nodes and relationships can have properties. • Properties are key-value pairs where the key is a string. • Property values can be either a primitive or an array of one primitive type. For example String, int and int[] values are valid for properties.
- 40. Properties
- 41. Paths in Neo4j • A path is one or more nodes with connecting relationships, typically retrieved as a query or traversal result.
- 42. Traversals in Neo4j • Traversing a graph means visiting its nodes, following relationships according to some rules. • In most cases only a subgraph is visited, as you already know where in the graph the interesting nodes and relationships are found. • Traversal API • Depth first and Breadth first.
- 45. Wrap mutating operations in a transaction.
- 46. Creating a small graph
- 47. Print the data
- 48. Remove the data
- 49. The Matrix Graph Database
- 51. Resources & References • Neo4j website : http://neo4j.org/ • Neo4j learning resources: http://neo4j.org/resources/ • Videos about Neo4j: http://video.neo4j.org/ • Neo4j tutorial: http://docs.neo4j.org/chunked/snapshot/tuto rials.html • Neo4j Java API documentation: http://api.neo4j.org/current/
Editor's Notes
- Future stores will be mixed – right shape for the right jobPolyglot persistenceFrameworks (e.g. spring data) embracing this already
- UGC = User Generated ContentGGG = Giant Global Graph (what the web will become)
- This is strictly about connected data – joins kill performance there.No bashing of RDBMS performance for tabular transaction processingGreen line denotes “zone of SQL adequacy”
- Fowler points out that KV/Column/Document stores are all aggregates: they’re different from graphs because they enforce structure at design time – as an aggregate of data.Clump of data that can be co-located on a cluster instance and which is accessed together.“a fundamental unit of storage which is a rich structure of closely related data: for key-value stores it's the value, for document stores it's the document, and for column-family stores it's the column family. In DDD terms, this group of data is an aggregate.”
- History – Amazon decide that they always wanted the shopping basket to be available, but couldn’t take a chance on RDBMSSo they built their ownBig risk, but simple data model and well-known computing science underpinning it (e.g. consistent hashing, Bloom filters for sensible replication)+ Massive read/write scale- Simplistic data model moves heavy lifting into the app tier (e.g. map reduce)
- People talk about Codd’s relational model being mature because it was proposed in 1969 – 42 years old.Euler’s graph theory was proposed in 1736 – 275 years old.