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
I know this might seem like an odd place to point to but, Heroku has recently gone nuts with their noSQL offerings and have an OK overview of many of the current projects. It is in no way a Slideshare press but it will help you start the comparison process:
MongoDB is document database unlike Relational database. The document stores semi structured data like JSON object ( schema free)
Key features:
Schema can change over evolution of application
Full indexing
Load balancing & Data sharding
Data replication
Consistency & Partitioning in CAP theory ( Consistency-Availability-Partitioning)
When to use:
Real time analytics
High speed logging
Semi structured data management
When not to use:
Highly transactional applications with strong ACID properties ( Atomicity, Consistency, Isolation & Durability). RDBMS is preferred in this use case.
Operating on data sets involving relations - foreign keys etc
HBASE:
HBase is an open source, non-relational, distributed column family database
Key features:
It provides a fault-tolerant way of storing large quantities of sparse data (small amounts of information caught within a large collection of empty or unimportant data, such as finding the 50 largest items in a group of 2 billion records, or finding the non-zero items representing less than 0.1% of a huge collection)
Supports variable schema where each row is different
Can serve as the input and output for MapReduce job
Compression, in-memory operation, and Bloom filters on a per-column (A data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set) 5.Achieve CP on CAP
When to use HBase:
If you’re loading data by key, searching data by key (or range), serving data by key, querying data by key
Storing data by row that doesn’t conform well to a schema (variable schema)
When not to use HBase:
For relational analytics
Full table scans
Data to be aggregated, analyzed by rows instead of columns
Neo4j:
Neo4j is graph database using Property Graph Data Model (Data is stored as a graph and nodes & relationships with properties)
Key features:
Supports full ACID(Atomicity, Consistency, Isolation and Durability) rules
Supports Indexes by using Apache Lucence
Schema free, bottom-up data model design
High scalability has been achieved due to compact storage and memory caching available for graphs
When to use:
Master data management
Network and IT Operations
Real time recommendations
Fraud detection
Social network (like facebook)
When not to use:
Bulk queries/Scans
If your application requires Partitioning & Sharding of data
Have a look at comparison of various NoSQL technologies in this article
You could also evaluate a Multi-Model DBMS, as the second generation of NoSQL product. With a Multi-Model you don't have all the compromises on choosing just one model, but rather more than one model.
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MongoDB
可扩展性： 高度可用且一致，但在关系和许多分布式写入方面表现不佳。它的主要好处是存储和索引无模式文档。文档大小上限为 4mb，索引仅对有限的深度有意义。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： 多样化类型层次结构、生物系统学、图书馆目录
Neo4j
可扩展性： 高度可用，但不是分布式的。强大的遍历框架，可实现节点空间的高速遍历。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： 具有无限深度和循环加权连接的深度图
用例： 社交网络、拓扑分析、语义 Web 数据、推理
HBase
可扩展性：可靠、一致的存储（PB 级及以上）。支持具有有限的稀疏属性集的大量对象。与 Hadoop 协同工作以执行大型数据处理作业。 http://www.ibm.com/developerworks/opensource/library /os-hbase/index.html
最适合：有向、非循环图
用例：日志分析、语义网络数据、机器学习
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
我知道这似乎是一个奇怪的地方，但是 Heroku 最近对他们的 noSQL 产品很着迷，并且对许多当前项目有一个很好的概述。它绝不是 Slideshare 印刷机，但它将帮助您开始比较过程：
http://blog.heroku.com/archives/2010/7/20/nosql/?utm_medium=email&utm_source= EmailBlast&utm_content=619506254&utm_campaign=HerokuSeptemberNewsletter-VersionB&utm_term=NoSQLHerokuandYou
I know this might seem like an odd place to point to but, Heroku has recently gone nuts with their noSQL offerings and have an OK overview of many of the current projects. It is in no way a Slideshare press but it will help you start the comparison process:
http://blog.heroku.com/archives/2010/7/20/nosql/?utm_medium=email&utm_source=EmailBlast&utm_content=619506254&utm_campaign=HerokuSeptemberNewsletter-VersionB&utm_term=NoSQLHerokuandYou
查看一下 NoSQL 数据库的概览比较：
http://kkovacs.eu /cassandra-vs-mongodb-vs-couchdb-vs-redis
Checkout this for at glance comparison of NoSQL dbs:
http://kkovacs.eu/cassandra-vs-mongodb-vs-couchdb-vs-redis
MongoDB：
MongoDB 是文档数据库，与关系数据库不同。该文档存储半结构化数据，如 JSON 对象（无架构）
主要特征：
何时使用：
何时不使用：
HBASE：
HBase 是一个开源、非关系型、分布式列族数据库
主要特性：
5.在CAP上实现CP
何时使用HBase：
何时不使用 HBase：
Neo4j：
Neo4j 是使用属性图数据模型的图形数据库（数据存储为图形和节点以及与属性的关系）
主要功能：
<强>何时使用：
何时不使用：
在这篇文章中查看各种 NoSQL 技术的比较
来源：
Wiki、幻灯片共享、Cloudera,教程点,Neo4j
MongoDB:
MongoDB is document database unlike Relational database. The document stores semi structured data like JSON object ( schema free)
Key features:
When to use:
When not to use:
HBASE:
HBase is an open source, non-relational, distributed column family database
Key features:
5.Achieve CP on CAP
When to use HBase:
When not to use HBase:
Neo4j:
Neo4j is graph database using Property Graph Data Model (Data is stored as a graph and nodes & relationships with properties)
Key features:
When to use:
When not to use:
Have a look at comparison of various NoSQL technologies in this article
Sources:
Wiki, Slide share, Cloudera,Tutorials Point,Neo4j
您还可以评估多模型 DBMS，作为第二代 NoSQL 产品。有了多型号，您就不必只选择一种型号，而是可以选择多种型号。
第一个多模型 NoSQL 是 OrientDB。
You could also evaluate a Multi-Model DBMS, as the second generation of NoSQL product. With a Multi-Model you don't have all the compromises on choosing just one model, but rather more than one model.
The first multi-model NoSQL is OrientDB.
关于 MongoDB 和 NoRM（MongoDB 的 .net 扩展）的相当不错的文章
http://lukencode.com/2010/07 /09/开始使用-mongodb-and-norm/
Pretty decent article here on MongoDB and NoRM (.net extensions for MongoDB)
http://lukencode.com/2010/07/09/getting-started-with-mongodb-and-norm/