Agenda • Trends in Data • NOSQL • What is a Graph? • What is a Graph Database? • What is Neo4j?
Trends in Data
Data is getting bigger: “Every 2 days we create as much information as we did up to 2003” – Eric Schmidt, Google
Data is more connected: • Text (content) • HyperText (added pointers) • RSS (joined those pointers) • Blogs (added pingbacks) • Tagging (grouped related data) • RDF (described connected data) • GGG (content + pointers + relationships + descriptions)
Data is more Semi-Structured: • If you tried to collect al the data of every movie ever made, how would you model it? • Actors, Characters, Locations, Dates, Costs, Ratings, Showings, Ticket Sales, etc.
NOSQL Not Only SQL
Less than 10% of the NOSQL Vendors
Key Value Stores • Most Based on Dynamo: Amazon Highly Available Key-Value Store • Data Model: – Global key-value mapping – Big scalable HashMap – Highly fault tolerant (typically) • Examples: – Redis, Riak, Voldemort
Key Value Stores: Pros and Cons • Pros: – Simple data model – Scalable • Cons – Create your own “foreign keys” – Poor for complex data
Column Family • Most Based on BigTable: Google’s Distributed Storage System for Structured Data • Data Model: – A big table, with column families – Map Reduce for querying/processing • Examples: – HBase, HyperTable, Cassandra
Column Family: Pros and Cons • Pros: – Supports Simi-Structured Data – Naturally Indexed (columns) – Scalable • Cons – Poor for interconnected data
Document Databases • Data Model: – A collection of documents – A document is a key value collection – Index-centric, lots of map-reduce • Examples: – CouchDB, MongoDB
Document Databases: Pros and Cons • Pros: – Simple, powerful data model – Scalable • Cons – Poor for interconnected data – Query model limited to keys and indexes – Map reduce for larger queries
Graph Databases • Data Model: – Nodes and Relationships • Examples: – Neo4j, OrientDB, InfiniteGraph, AllegroGraph
Graph Databases: Pros and Cons • Pros: – Powerful data model, as general as RDBMS – Connected data locally indexed – Easy to query • Cons – Sharding ( lots of people working on this) • Scales UP reasonably well – Requires rewiring your brain
Living in a NOSQL World R Gr D a B p M h S Databases Document plexity Databases BigTable Com Clones Key-Value Store Relational Databases 90% of Size Use Cases
What is a Graph?
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)
Different Kinds of Graphs • Undirected Graph • Directed Graph • Pseudo Graph • Multi Graph • Hyper Graph
More Kinds of Graphs • Weighted Graph • Labeled Graph • Property Graph
What is a Graph Database? • A database with an explicit graph structure • Each node knows its adjacent nodes • As the number of nodes increases, the cost of a local step (or hop) remains the same • Plus an Index for lookups
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
What is Neo4j?
What is Neo4j? • A Graph Database + Lucene Index • Property Graph • Full ACID (atomicity, consistency, isolation, durability) • High Availability (with Enterprise Edition) • 32 Billion Nodes, 32 Billion Relationships, 64 Billion Properties • Embedded Server • REST API
Good For • Highly connected data (social networks) • Recommendations (e-commerce) • Path Finding (how do I know you?) • A* (Least Cost path) • Data First Schema (bottom-up, but you stil need to design)
// then traverse to find results start n=(people-index, name, “Andreas”) match (n)--()--(foaf) return foaf n
Cypher Pattern Matching Query Language (like SQL for graphs) // get node 0 start a=(0) return a // traverse from node 1 start a=(1) match (a)-->(b) return b // return friends of friends start a=(1) match (a)--()--(c) return c
Gremlin A Graph Scripting DSL (groovy-based) // get node 0 g.v(0) // nodes with incoming relationship g.v(0).in // outgoing “KNOWS” relationship g.v(0).out(“KNOWS”)
If you’ve ever • Joined more than 7 tables together • Modeled a graph in a table • Written a recursive CTE • Tried to write some crazy stored procedure with multiple recursive self and inner joins You should use Neo4j
Language LanguageCountry Country language_code language_code country_code language_name country_code country_name word_count primary flag_uri Language Country name name IS_SPOKEN_IN code code word_count as_primary flag_uri
Country name flag_uri language_name number_of_words yes_in_langauge no_in_language currency_code currency_name Country Language name name flag_uri SPEAKS number_of_words yes no Currency USES_C code URRENCY name
Neo4j Data Browser
console.neo4j.org Try it right now: start n=node(*) match n-[r:LOVES]->m return n, type(r), m Notice the two nodes in red, they are your result set.