An Introduction to NOSQL, Graph Databases and Neo4j
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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/
Notes de l'éditeur
- 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.