问题描述
MongoDB(基于文档),HBase(基于列)和Neo4j(对象图)的优缺点是什么?
我特别感兴趣的是了解每种典型用例。
也许任何Slideshare或Scribd值得介绍吗?
MongoDB 可扩展性:高度可用并且一致,但会影响关系和许多分布式写入。它的主要好处是存储和索引无模式文档。文档大小限制在4mb,索引仅适用于有限的深度。请参阅
最适合:深度有限的树结构
用例:不同类型层次结构,生物系统学,图书馆目录
$ b Neo4j
可伸缩性:高度可用但不是分布式。强大的遍历节点空间中的高速遍历框架。仅限于数十亿个节点/关系的图表。请参阅
最适合:定向,非循环图
用例:日志分析,语义Web数据,机器学习
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
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