什么时候应该使用SQL Azure和何时应使用表存储？ [英] When should I use Sql Azure and when should I use table Storage?

问题描述

当我应该使用SQL Azure和我时候应该使用表存储？
我在想，用表存储事务处理方案如借记信用账户那种情景并使用SQL Azure的时候数据将不会被用于交易目的，例如报告。
你有什么感想？

When should i use Sql Azure and when should I use table Storage? I was thinking , use table storage for transaction processing scenarios e.g. debit credit accounts kind of scenario and use Sql Azure when data will not be used for transactional purposes e.g reporting. What do you think?

推荐答案

这是一个非常好的问题和解决方案架构师设计有天青为当做出强硬，难以逆转的决定之一。

This is an excellent question and one of the tougher and harder to reverse decisions that solution architects have to make when designing for Azure.

有复式尺寸需要考虑：
在消极的一面，SQL Azure是对GB存储相对昂贵，没有规模超好，仅限于150gigs /数据库，
然而，这是非常重要的，也有针对SQL Azure和你的开发人员没有交易费用已经知道如何code反对。

There are mutliple dimensions to consider: On the negative side, SQL Azure is relatively expensive for gigabyte of storage, does not scale super well and is limited to 150gigs/database, however, and this is very important, there are no transaction fees against SQL Azure and your developers already know how to code against it.

ATS是不同的动物都在一起。能够巨型的可扩展性，它是便宜到店，但得到昂贵的频繁访问。这还需要从节点操纵CPU能力显著量。它基本上迫使你的计算节点的所有关系活动的代表团转交给他们成为小型数据库服务器。

ATS is a different animal all together. Capable of mega-scalability, it is dirt cheap to store, but gets expensive to frequently access. It also requires significant amount of CPU power from your nodes to manipulate. It basically forces your compute nodes to become mini-db servers as the delegation of all relational activity is turned over to them.

所以，在我看来，经常访问的，这并不需要巨大的可扩展性，而不是规模超大，应运往SQL Azure的，否则的Azure表服务。

So, in my opinion, frequently accessed data that does not need huge scalability and is not super large in size should be destined for SQL Azure, otherwise Azure Table Services.

您具体的例子，从金融交易的交易数据是ATS一个完美的地方，而元信息（账号资料，姓名，地址等），非常适合SQL Azure上。

Your specific example, transactional data from financial transactions is a perfect place for ATS, while meta information (account profiles, names, addresses, etc.) is perfect for SQL Azure.

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