分片和标准化是相互排斥的吗？
我遇到了一个评论，这让我想知道：如果你使用数据库可扩展性的分片方法，这是否意味着数据库是非规范化的？你能有一个规范化的分片数据库吗？
I ran across a comment that made me wonder: If you use a sharding approach to db scalability, does that mean the database is denormalized? Can you have a normalized, sharded database?
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它们并不相互排斥。在扩展大规模数据集时，两者经常被使用，但其中一个与另一个并没有太大关系。您绝对可以拥有分片的、规范化的数据库……或非规范化的、非分片的数据库。
在分片中，您只需采用给定的模式（标准化或非标准化）并将其分布在多个物理/逻辑数据存储中。例如，这允许您让具有特定特征（例如“AD”中的姓氏）的所有用户居住在给定的数据库实例上。请注意，如何对数据库进行分片是一个至关重要的决定，并且高度依赖于实现。
另一方面，非规范化可以在有或没有分片数据库的情况下完成，旨在通过减少回答特定问题所需的连接/子查询来简化查询。当然，那么您通常会以编程方式维护数据完整性。
一些不错的读物：
分片理论与分片实践
一些高度可扩展的数据库实现“在野外”
The are not mutually exclusive. Both are often used when scaling massive datasets, but one doesn't really have much to do with the other. You can absolutely have a sharded, normalized database...or a denormalized, nonsharded database.
In sharding, you're just taking a given schema (normalized or not) and distributing it across a number of physical/logical data stores. This allows, for example, you to have all your users with a particular characteristic (e.g., last name in 'A-D') to live on a given database instance. Note that HOW you shard your database is a crucial decision and will be highly implementation dependent.
Denormalization, on the other hand, can be done with or without a sharded database and is intended to simply queries by reducing the joins/subqueries needed to answer a particular question. Of course, then you would typically programmatically maintain data integrity.
Some good reading:
Sharding theory & practice
Some highly-scalable database implementations 'in the wild'