Graph Database and Neo4j
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- 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
- 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: Googles 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
- Complexity Big Table Clones Size Key-Value Store Document Databases Graph Databases 90% of Use Cases Relational Databases
- 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 RDBMS Graph 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
- Nodes Nodes represent Entities such as people, businesses, accounts, or any other item you might want to keep track of.
- Properties Properties are pertinent information that relate to nodes.
- Edges Edges 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
- CYPHER Cypher 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.
- CYPHER START: 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.