如何决定使用哪种NoSQL技术？ [英] How to decide which NoSQL technology to use?

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。

图表可以比其他解决方案更好地解决问题的好例子是什么？

也许任何Slideshare或Scribd值得介绍吗？

解决方案

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 http://www.paperplanes.de/2010/2/25/notes_on_mongodb。 html

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 http://highscalability.com/neo4j-graph-database-kicks-buttox

最适合于：深度无限的深度图和周期性加权连接

<用户案例：社交网络，拓扑分析，语义Web数据，推理

HBase

可伸缩性：可靠，一致的数据存储在PB级及以上。支持具有有限稀疏属性集的非常大量的对象。与Hadoop一起用于大型数据处理作业。 http://www.ibm.com/developerworks/opensource/library /os-hbase/index.html

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?

解决方案

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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