Transcript
Graph Databases and
Neo4j
Data is getting bigger:
“Every 2 days we create as much information as we did up to 2003”
– Eric Schmidt, Google
NOSQL
Key Value Stores
Most Based on Dynamo: Amazon Highly Available Key-Value Store
Data Model:
Global key-value mapping
Big scalable Hash Map
Highly fault tolerant (typically)
Examples:
Redis, Riak, Voldemort
Pros & Cons
Pros:
Simple data model
Scalable
Cons:
Create your own “foreign keys”
Poor for complex data
Column Family
Most Based on Big Table: 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
Pros & 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
Pros & 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
Pros & Cons
Pros:
Powerful data model, as general as RDBMS
Connected data locally indexed
Easy to query
Cons:
Requires rewiring your brain
Com
plex
ity
Big TableClones
Size
Key-ValueStore
DocumentDatabases
GraphDatabases
90% ofUse Cases
RelationalDatabases
A Graph Database uses graph structure with nodes, edges and properties to represent and store data.
By definition, a graph database is any storage system that provides index-free adjacency. This means that every element contains a direct pointer to its adjacent element and no index lookups are necessary.
Graph databases focus on the interconnection between Entities.
Graph Database definition
Compared with RDBMSGraph databases are often faster for associative data sets
Map more directly to the structure of object-oriented applications
Scale more naturally to large data sets as they do not typically require expensive join operations.
As they depend less on a rigid schema, they are more suitable to manage ad-hoc and changing data with evolving schemas.
Finding Extended Friends
NodesNodes represent Entities such as people, businesses, accounts, or any other item you might want to keep track of.
PropertiesProperties are pertinent information that relate to nodes.
EdgesEdges are the lines that connect nodes to nodes or nodes to properties and they represent the Relationship between the two.
Most of the important information is really stored in the edges.
Meaningful patterns emerge when one examines the connections and interconnections of nodes, properties and edges.
What is Neo4j?• A Graph Database
• 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
Key Features•Runs on major platforms : Mac | Windows | Unix
•Extensive documentation
•Active community
•Open Source
CYPHERCypher is a declarative graph query language that allows for expressive and efficient querying and updating of the graph store without having to write traversal through the graph structure in code.
CYPHERSTART: Starting points in the graph, obtained via index lookups or by element IDs.
MATCH: The graph pattern to match, bound to the starting points in START.
WHERE: Filtering criteria.
RETURN: What to return.
CREATE: Creates nodes and relationships.
DELETE: Removes nodes, relationships and properties.
SET: Set values to properties.
FOREACH: Performs updating actions once per element in a list.
WITH: Divides a query into multiple, distinct parts.
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