What is the pros and cons of MongoDB (document-based), HBase (column-based) and Neo4j (objects graph)?
I'm particularly interested to know some of the typical use cases for each one.
What are good examples of problems that graphs can solve better than the alternative?
Maybe any Slideshare or Scribd worthy presentation?
MongoDB
Scalability: Highly available and consistent but sucks at relations and many distributed writes. It's primary benefit is storing and indexing schemaless documents. Document size is capped at 4mb and indexing only makes sense for limited depth. See http://www.paperplanes.de/2010/2/25/notes_on_mongodb.html
Best suited for: Tree structures with limited depth
Use Cases: Diverse Type Hierarchies, Biological Systematics, Library Catalogs
Neo4j
Scalability: Highly available but not distributed. Powerful traversal framework for high-speed traversals in the node space. Limited to graphs around several billion nodes/relationships. See http://highscalability.com/neo4j-graph-database-kicks-buttox
Best suited for: Deep graphs with unlimited depth and cyclical, weighted connections
Use Cases: Social Networks, Topological analysis, Semantic Web Data, Inferencing
HBase
Scalability: Reliable, consistent storage in the petabytes and beyond. Supports very large numbers of objects with a limited set of sparse attributes. Works in tandem with Hadoop for large data processing jobs. http://www.ibm.com/developerworks/opensource/library/os-hbase/index.html
Best suited for: directed, acyclic graphs
Use Cases: Log analysis, Semantic Web Data, Machine Learning
