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首先使用实体​​框架代码与nosql数据库

[英]Use Entity framework code first with nosql database

Can I use Entity Framework Code First Approach with NoSql Database? 我可以在NoSql数据库中使用实体框架代码优先方法吗? And how NoSql can be advantage over SQL Database for a application with large data. 对于具有大数据的应用程序,NoSql如何优于SQL数据库。

I have been using BrightstarDb ( http://www.brightstardb.com/ ) for the past few months and it offers code-first model generation based on Entity Framework. 过去几个月我一直在使用BrightstarDb( http://www.brightstardb.com/ ),它提供基于实体框架的代码优先模型生成。

BrightstarDb is a NoSql database that is also an RDF triplestore, so it's allows for more complicated relations between Entities than most traditional NoSql databases. BrightstarDb是一个NoSql数据库,也是一个RDF三元组,因此它允许实体之间比大多数传统的NoSql数据库更复杂的关系。

Can i use Entity Framework Code First Approach with NoSql Database ? 我可以在NoSql数据库中使用实体框架代码优先方法吗?

No, you can't. 不,你不能。

and how NoSql can be advantage over SQL Database for a application with large data. 以及对于具有大数据的应用程序,NoSql如何优于SQL数据库。

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NoSQL database systems are often highly optimized for retrieve and append operations and often offer little functionality beyond record storage (eg key-value stores). NoSQL数据库系统通常针对检索和追加操作进行了高度优化,并且通常在记录存储之外提供很少的功能(例如,键值存储)。 The reduced run time flexibility compared to full SQL systems is compensated by significant gains in scalability and performance for certain data models. 与完整SQL系统相比,运行时灵活性的降低得益于某些数据模型的可扩展性和性能的显着提高。

In short, NoSQL database management systems are useful when working with a huge quantity of data and the data's nature does not require a relational model for the data structure. 简而言之,NoSQL数据库管理系统在处理大量数据时非常有用,而且数据的性质不需要数据结构的关系模型。 The data could be structured, but it is of minimal importance and what really matters is the ability to store and retrieve great quantities of data, and not the relationships between the elements. 数据可以是结构化的,但它的重要性最小,真正重要的是存储和检索大量数据的能力,而不是元素之间的关系。 For example, to store millions of key-value pairs in one or a few associative arrays or to store millions of data records. 例如,将数百万个键值对存储在一个或几个关联数组中或存储数百万个数据记录。 This is particularly useful for statistical or real-time analyses for growing list of elements (such as Twitter posts or the Internet server logs from a big group of users). 这对于增加元素列表(例如Twitter帖子或来自大量用户的Internet服务器日志)的统计或实时分析特别有用。

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