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luotitan authored and medcl committed Nov 7, 2016
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[[modeling-your-data]]

= Modeling Your Data
= 数据建模

[partintro]
--

Elasticsearch is a different kind of beast, especially if you come from the
world of SQL.((("modeling your data"))) It comes with many benefits: performance, scale, near real-time
search, and analytics across massive amounts of data. And it is easy to get
going! Just download and start using it.
Elasticsearch 是如此与众不同,特别是如果你来自 SQL 的世界。((("modeling your data")))
Elasticsearch 有非常多的优点:高性能、可扩展、近实时搜索,并支持大数据量的数据分析。一切都很容易!
只需下载并开始使用它。

But it is not magic. To get the most out of Elasticsearch, you need to
understand how it works and how to make it work for your needs.
但它不是魔法。为了充分利用 Elasticsearch,你需要了解它的工作机制,以及如何让它如你所需的进行工作。

Handling relationships between entities is not as obvious as it is with a
dedicated relational store. The golden rule of a relational database--normalize your data--does not apply to Elasticsearch. In <<relations>>,
<<nested-objects>>, and <<parent-child>> we discuss the pros and cons of
the available approaches.
和专用的关系型数据存储有所不同,Elasticsearch 并没有对处理实体之间的关系给出直接的方法。
一个关系数据库的黄金法则是 --规范化你的数据(范式)-- 但这不适用于 Elasticsearch。
在 <<relations>> 、 <<nested-objects>> 和 <<parent-child>> 我们讨论了这些提供的方法的优点和缺点。

Then in <<scale>> we talk about the features that Elasticsearch offers
that enable you to scale out quickly and flexibly. Scale is not one-size-fits-all. You need to think about how data flows through your system, and
design your model accordingly. Time-based data like log events or social
network streams require a very different approach than more static collections
of documents.

And finally, we talk about the one thing in Elasticsearch that doesn't scale.
然后在 <<scale>> 我们谈论 Elasticsearch 提供的快速、灵活的扩容能力。
当然扩容并没有一个放之四海而皆准的方案。你需要考虑这些通过系统产生的数据流的具体特点,
据此设计你的模型。例如日志事件或者社交网络流这些时间序列数据类型,和静态文档集合在处理模型上有着很大的不同。

最后,我们聊一下 Elasticsearch 里面不能伸缩的一件事。
--

include::400_Relationships.asciidoc[]
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include::404_Parent_Child.asciidoc[]

include::410_Scaling.asciidoc[]


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