blocks|key|1726681|text|分布式数据库并不像Orion所暗示的那样幼稚；在优化分布式数据集上的完全关系查询方面已经做了大量工作。你可能想看看Teradata，Netezza，Greenplum，Vertica，AsterData等公司都在做些什么。(甲骨文最终也加入了这场游戏，他们最近宣布了这一消息；微软以过去名为DataAllegro的公司的名义收购了他们的solition+)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1726682|也就是说，当数据扩展到the级时，这些问题就变得非常重要。如果您不需要从RDBMs获得的严格的事务性和一致性保证，那么反规范化和不进行连接通常要容易得多。特别是如果你不需要太多的交叉引用的话。特别是如果您不进行即席分析，但需要通过任意转换进行编程访问。|1726683|反规范化被高估了。当您处理100Tera时就会发生这种情况，但这并不意味着每个开发人员都应该使用这个事实，这些开发人员从来没有费心学习数据库，并且由于糟糕的模式规划和查询优化而在查询一两百万行时遇到了困难。|1726684|但如果你在100Tera的范围内，请务必...|1726685|哦，这些技术受到热捧的另一个原因是，人们发现有些东西本来就不属于数据库，并意识到它们处理的不是特定字段中的关系，而是基本的键值对。对于本不应该出现在数据库中的内容，Map-Reduce框架或某种持久的、最终一致的存储系统是完全有可能的。|1726686|在较小的全球范围内，我强烈推荐BerkeleyDB来解决这类问题。|1726687|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|P|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|Q|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|R|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|S|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|M|$]]

Distributed databases aren't quite as naive as Orion implies; there has been quite a bit of work done on optimizing fully relational queries over distributed datasets. You may want to look at what companies like Teradata, Netezza, Greenplum, Vertica, AsterData, etc are doing. (Oracle got in the game, finally, as well, with their recent announcement; Microsoft bought their solition in the name of the company that used to be called DataAllegro).

That being said, when the data scales up into terabytes, these issues become very non-trivial. If you don't need the strict transactionality and consistency guarantees you can get from RDBMs, it is often far easier to denormalize and not do joins. Especially if you don't need to cross-reference much. Especially if you are not doing ad-hoc analysis, but require programmatic access with arbitrary transformations.

Denormalization is overrated. Just because that's what happens when you are dealing with a 100 Tera, doesn't mean this fact should be used by every developer who never bothered to learn about databases and has trouble querying a million or two rows due to poor schema planning and query optimization.

But if you are in the 100 Tera range, by all means...

Oh, the other reason these technologies are getting the buzz -- folks are discovering that some things never belonged in the database in the first place, and are realizing that they aren't dealing with relations in their particular fields, but with basic key-value pairs. For things that shouldn't have been in a DB, it's entirely possible that the Map-Reduce framework, or some persistent, eventually-consistent storage system, is just the thing.

On a less global scale, I highly recommend BerkeleyDB for those sorts of problems.

blocks|key|1726740|text|如果您谈论的数据实际上是只读的，则规则会发生变化。在数据更改的情况下，去正规化是最困难的，因为所需的工作量增加了，并且锁定存在更多问题。如果数据几乎没有变化，那么去正规化就不是太大的问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1726741|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

If you are talking about data that is virtually read-only, the rules change. Denormalisation is hardest in situations where data changes because the work required is increased and there are more problems with locking. If the data barely changes then denormalisation is not so much of a problem.

blocks|key|1726820|text|所以我得到的是，整个“去规范化，无连接”的哲学存在，并不是因为连接本身不能在大型系统中扩展，而是因为它们实际上不可能在分布式数据库中实现。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1726821|当您存储单一类型的大部分不变数据时(就像Google那样)，这似乎是非常合理的。我是不是走对了路？|1726822|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

So what I'm getting is that the whole "denormalize, no joins" philosophy exists, not because joins themselves don't scale in large systems, but because they're practically impossible to implement in distributed databases. 

This seems pretty reasonable when you're storing largely invariant data of a single type (Like Google does). Am I on the right track here?

blocks|key|1511798|text|现在，您需要为数据库找到更多的互操作环境。更常见的情况是，您不仅需要关系型数据库，如MySQL或MySQL，还需要大数据场，如Hadoop或非关系型数据库，如MongoDB。在某些情况下，所有这些DB都将在一个解决方案中使用，因此它们的性能必须在宏观范围内尽可能相等。这意味着，你将不能使用比方说Azure+SQL作为关系数据库和一个具有2个核心和3+3GB内存的VM用于MongoDB。您必须扩展您的解决方案，并在可能的情况下使用DB+as+a+Service+(如果不可能，则在云中构建自己的集群)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1511799|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Novaday You need to find more interoperational environment for databases. More frequently You don't need only an relational DBs, like MySQL or MS SQL but also Big Data farms as Hadoop or non-relational DBs like MongoDB. In some cases all those DBs will be used in one solution so their performance must be as equal as possible in macro scale. It means, that You will not be able to use let say Azure SQL as relational DB and one VM with 2 cores and 3GB of RAM for MongoDB. You must scale-up Your solution and use DB as a Service when it is possible (if it is not possible, then build Your own cluster in a cloud).

New school datastore paradigms like Google BigTable and Amazon SimpleDB are specifically designed for scalability, among other things. Basically, disallowing joins and denormalization are the ways this is being accomplished.

In <a href="https://stackoverflow.com/questions/173726/why-are-database-joins-expensive">this</a> topic, however, the consensus seems to be that joins on large tables don't necessarilly have to be too expensive and denormalization is "overrated" to some extent
Why, then, do these aforementioned systems disallow joins and force everything together in a single table to achieve scalability? Is it the sheer volumes of data that needs to be stored in these systems (many terabytes)? 
Do the general rules for databases simply not apply to these scales?
Is it because these database types are tailored specifically towards storing many similar objects? 
Or am I missing some bigger picture?

Pro's of databases like BigTable, SimpleDB

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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新的学校数据存储范例，如Google BigTable和Amazon SimpleDB，是专门为可伸缩性等而设计的。基本上，不允许连接和反规范化是实现这一点的方法。然而，在主题中，人们的共识似乎是，大型表上的连接不一定要太昂贵，在某种程度上，反规范化被“高估”了。那么，为什么前面提到的系统不允许连接，并将所有东西都强制...

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