Introduction to Graph Databases
Download as pptx, pdf163 likes82,256 views
Max De Marzi gave an introduction to graph databases using Neo4j as an example. He discussed trends in big, connected data and how NoSQL databases like key-value stores, column families, and document databases address these trends. However, graph databases are optimized for interconnected data by modeling it as nodes and relationships. Neo4j is a graph database that uses a property graph data model and allows querying and traversal through its Cypher query language and Gremlin scripting language. It is well-suited for domains involving highly connected data like social networks.
![name: “Canada”
languages_spoken: “[ „English‟, „French‟ ]”
language:“English” spoken_in
name: “USA”
name: “Canada”
language:“French” spoken_in
name: “France”](https://image.slidesharecdn.com/beginnerpresentation-120429104540-phpapp01/85/Introduction-to-Graph-Databases-36-320.jpg)
![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.](https://image.slidesharecdn.com/beginnerpresentation-120429104540-phpapp01/85/Introduction-to-Graph-Databases-40-320.jpg)
More Related Content
What's hot (20)
Viewers also liked (15)
Similar to Introduction to Graph Databases (20)
More from Max De Marzi (20)
Recently uploaded (20)
Introduction to Graph Databases
- 1. Introduction to Graph Databases Chicago Graph Database Meet-Up Max De Marzi
- 2. About Me Built the Neography Gem (Ruby Wrapper to the Neo4j REST API) Playing with Neo4j since 10/2009 • My Blog: http://maxdemarzi.com • Find me on Twitter: @maxdemarzi • Email me: [email protected] • GitHub: http://github.com/maxdemarzi
- 3. Agenda • Trends in Data • NOSQL • What is a Graph? • What is a Graph Database? • What is Neo4j?
- 5. Data is getting bigger: “Every 2 days we create as much information as we did up to 2003” – Eric Schmidt, Google
- 6. 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)
- 7. Data is more Semi-Structured: • If you tried to collect all the data of every movie ever made, how would you model it? • Actors, Characters, Locations, Dates, Costs, Ratings, Showings, Ticket Sales, etc.
- 9. Less than 10% of the NOSQL Vendors
- 10. 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
- 11. Key Value Stores: Pros and Cons • Pros: – Simple data model – Scalable • Cons – Create your own “foreign keys” – Poor for complex data
- 12. 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
- 13. Column Family: Pros and Cons • Pros: – Supports Simi-Structured Data – Naturally Indexed (columns) – Scalable • Cons – Poor for interconnected data
- 14. Document Databases • Data Model: – A collection of documents – A document is a key value collection – Index-centric, lots of map-reduce • Examples: – CouchDB, MongoDB
- 15. 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
- 16. Graph Databases • Data Model: – Nodes and Relationships • Examples: – Neo4j, OrientDB, InfiniteGraph, AllegroGraph
- 17. 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
- 18. Living in a NOSQL World RDBMS Graph Databases Complexity Document Databases BigTable Clones Key-Value Relational Store Databases 90% of Use Cases Size
- 19. What is a Graph?
- 20. 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)
- 21. Different Kinds of Graphs • Undirected Graph • Directed Graph • Pseudo Graph • Multi Graph • Hyper Graph
- 22. More Kinds of Graphs • Weighted Graph • Labeled Graph • Property Graph
- 23. 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
- 24. Compared to Relational Databases Optimized for aggregation Optimized for connections
- 25. Compared to Key Value Stores Optimized for simple look-ups Optimized for traversing connected data
- 26. Compared to Key Value Stores Optimized for “trees” of data Optimized for seeing the forest and the trees, and the branches, and the trunks
- 27. What is Neo4j?
- 28. 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
- 29. 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 still need to design)
- 30. Property Graph
- 31. // then traverse to find results start n=(people-index, name, “Andreas”) match (n)--()--(foaf) return foaf n
- 32. 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
- 33. 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”)
- 34. 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
- 35. 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
- 36. name: “Canada” languages_spoken: “[ „English‟, „French‟ ]” language:“English” spoken_in name: “USA” name: “Canada” language:“French” spoken_in name: “France”
- 37. 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 code name
- 39. Neo4j Console
- 40. 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.
- 41. What does a Graph look like?
- 42. Questions? ?
Editor's Notes
- #22: An undirected graph is one in which edges have no orientation. The edge (a, b) is identical to the edge (b, a).A directed graph or digraph is an ordered pair D = (V, A)A pseudo graph is a graph with loopsA multi graph allows for multiple edges between nodesA hyper graph allows an edge to join more than two nodes
- #23: A weighted graph has a number assigned to each edgeAlabeled graph has a label assigned to each node or edgeA property graph has keys and values for each node or edge
- #29: Atomic = all or nothing, consistent = stay consistent from one tx to another, isolation = no tx will mess with another tx, durability = once tx committed, it stays