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Chapter/chapter1 part7 #441

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chapter1_part8:/010_Intro/45_Distributed.asciidoc
node Jan 6, 2017
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chapter1_part7:/010_Intro/35_Tutorial_Aggregations.asciidoc
node Jan 6, 2017
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29 changes: 11 additions & 18 deletions 010_Intro/35_Tutorial_Aggregations.asciidoc
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=== Analytics
[[_analytics]]
=== 分析

Finally, we come to our last business requirement: allow managers to run
analytics over the employee directory.((("analytics"))) Elasticsearch has functionality called
_aggregations_, which ((("aggregations")))allow you to generate sophisticated analytics over your
data. It is similar to `GROUP BY` in SQL, but much more powerful.
终于到了最后一个业务需求：支持管理者对雇员目录做分析。((("analytics"))) Elasticsearch 有一个功能叫聚合（aggregations），((("aggregations")))允许我们基于数据生成一些精细的分析结果。聚合与 SQL 中的 `GROUP BY` 类似但更强大。

For example, let's find the most popular interests enjoyed by our employees:
举个例子，挖掘出雇员中最受欢迎的兴趣爱好：

[source,js]
--------------------------------------------------
Expand All @@ -20,7 +18,7 @@ GET /megacorp/employee/_search
--------------------------------------------------
// SENSE: 010_Intro/35_Aggregations.json

Ignore the syntax for now and just look at the results:
暂时忽略掉语法，直接看看结果：

[source,js]
--------------------------------------------------
Expand Down Expand Up @@ -53,6 +51,7 @@ in sports. These aggregations are not precalculated; they are generated on
the fly from the documents that match the current query. If we want to know
the popular interests of people called Smith, we can just add the
appropriate query into the mix:
可以看到，两位员工对音乐感兴趣，一位对林地感兴趣，一位对运动感兴趣。这些聚合并非预先统计，而是从匹配当前查询的文档中即时生成。如果想知道叫 Smith 的雇员中最受欢迎的兴趣爱好，可以直接添加适当的查询来组合查询：

[source,js]
--------------------------------------------------
Expand All @@ -74,7 +73,7 @@ GET /megacorp/employee/_search
--------------------------------------------------
// SENSE: 010_Intro/35_Aggregations.json

The `all_interests` aggregation has changed to include only documents matching our query:
`all_interests` 聚合已经变为只包含匹配查询的文档：

[source,js]
--------------------------------------------------
Expand All @@ -93,8 +92,7 @@ The `all_interests` aggregation has changed to include only documents matching o
}
--------------------------------------------------

Aggregations allow hierarchical rollups too.((("aggregations", "hierarchical rollups in"))) For example, let's find the
average age of employees who share a particular interest:
聚合还支持分级汇总 ((("aggregations", "hierarchical rollups in"))) 。比如，查询特定兴趣爱好员工的平均年龄：

[source,js]
--------------------------------------------------
Expand All @@ -114,8 +112,7 @@ GET /megacorp/employee/_search
--------------------------------------------------
// SENSE: 010_Intro/35_Aggregations.json

The aggregations that we get back are a bit more complicated, but still fairly
easy to understand:
得到的聚合结果有点儿复杂，但理解起来还是很简单的：

[source,js]
--------------------------------------------------
Expand Down Expand Up @@ -147,10 +144,6 @@ easy to understand:
}
--------------------------------------------------

The output is basically an enriched version of the first aggregation we ran.
We still have a list of interests and their counts, but now each interest has
an additional `avg_age`, which shows the average age for all employees having
that interest.
输出基本是第一次聚合的加强版。依然有一个兴趣及数量的列表，只不过每个兴趣都有了一个附加的 `avg_age` 属性，代表有这个兴趣爱好的所有员工的平均年龄。

Even if you don't understand the syntax yet, you can easily see how complex aggregations and groupings can be accomplished using this feature.
The sky is the limit as to what kind of data you can extract!
即使现在不太理解这些语法也没有关系，依然很容易了解到复杂聚合及分组通过 Elasticsearch 特性实现得很完美。可提取的数据类型毫无限制。
47 changes: 12 additions & 35 deletions 010_Intro/45_Distributed.asciidoc
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=== Distributed Nature
[[_distributed_nature]]
=== 分布式特性

At the beginning of this chapter, we said that Elasticsearch((("distributed nature of Elasticsearch"))) can scale out to
hundreds (or even thousands) of servers and handle petabytes of data. While
our tutorial gave examples of how to use Elasticsearch, it didn't touch on the
mechanics at all. Elasticsearch is distributed by nature, and it is designed
to hide the complexity that comes with being distributed.
在本章开头，我们提到过 Elasticsearch((("distributed nature of Elasticsearch"))) 可以横向扩展至数百（甚至数千）的服务器节点，同时可以处理PB级数据。我们的教程给出了一些使用 Elasticsearch 的示例，但并不涉及任何内部机制。Elasticsearch 天生就是分布式的，并且在设计时屏蔽了分布式的复杂性。

The distributed aspect of Elasticsearch is largely transparent. Nothing in
the tutorial required you to know about distributed systems, sharding, cluster
discovery, or dozens of other distributed concepts. It happily ran the
tutorial on a single node living inside your laptop, but if you were to run
the tutorial on a cluster containing 100 nodes, everything would work in
exactly the same way.
Elasticsearch 在分布式方面几乎是透明的。教程中并不要求了解分布式系统、分片、集群发现或其他的各种分布式概念。可以使用笔记本上的单节点轻松地运行教程里的程序，但如果你想要在 100 个节点的集群上运行程序，一切依然顺畅。

Elasticsearch tries hard to hide the complexity of distributed systems. Here are some of
the operations happening automatically under the hood:
Elasticsearch 尽可能地屏蔽了分布式系统的复杂性。这里列举了一些在后台自动执行的操作：

* Partitioning your documents into different containers((("documents", "partitioning into shards")))((("shards"))) or _shards_, which
can be stored on a single node or on multiple nodes
* 分配文档到不同的容器((("documents", "partitioning into shards")))((("shards"))) 或 _分片_ 中，文档可以储存在一个或多个节点中

* Balancing these shards across the nodes in your cluster to spread the
indexing and search load
* 按集群节点来均衡分配这些分片，从而对索引和搜索过程进行负载均衡

* Duplicating each shard to provide redundant copies of your data, to
prevent data loss in case of hardware failure
* 复制每个分片以支持数据冗余，从而防止硬件故障导致的数据丢失

* Routing requests from any node in the cluster to the nodes that hold the
data you're interested in
* 将集群中任一节点的请求路由到存有相关数据的节点

* Seamlessly integrating new nodes as your cluster grows or redistributing
shards to recover from node loss
* 集群扩容时无缝整合新节点，重新分配分片以便从离群节点恢复

As you read through this book, you'll encounter supplemental chapters about the
distributed nature of Elasticsearch. These chapters will teach you about
how the cluster scales and deals with failover (<<distributed-cluster>>),
handles document storage (<<distributed-docs>>), executes distributed search
(<<distributed-search>>), and what a shard is and how it works
(<<inside-a-shard>>).

These chapters are not required reading--you can use Elasticsearch without
understanding these internals--but they will provide insight that will make
your knowledge of Elasticsearch more complete. Feel free to skim them and
revisit at a later point when you need a more complete understanding.
当阅读本书时，将会遇到有关 Elasticsearch 分布式特性的补充章节。这些章节将介绍有关集群扩容、故障转移(<<distributed-cluster>>) 、应对文档存储(<<distributed-docs>>) 、执行分布式搜索(<<distributed-search>>) ，以及分区（shard）及其工作原理(<<inside-a-shard>>) 。

这些章节并非必读，完全可以无需了解内部机制就使用 Elasticsearch，但是它们将从另一个角度帮助你了解更完整的 Elasticsearch 知识。可以根据需要跳过它们，或者想更完整地理解时再回头阅读也无妨。