From 9134516976d408f8b14bae78077762e67942710f Mon Sep 17 00:00:00 2001 From: node Date: Thu, 5 Jan 2017 17:44:43 +0800 Subject: [PATCH 1/3] chapter1_part5:/010_Intro/25_Tutorial_Indexing.asciidoc --- 010_Intro/25_Tutorial_Indexing.asciidoc | 106 +++++++++--------------- 1 file changed, 38 insertions(+), 68 deletions(-) diff --git a/010_Intro/25_Tutorial_Indexing.asciidoc b/010_Intro/25_Tutorial_Indexing.asciidoc index 4bfe58049..402714fc5 100644 --- a/010_Intro/25_Tutorial_Indexing.asciidoc +++ b/010_Intro/25_Tutorial_Indexing.asciidoc @@ -1,87 +1,63 @@ -=== Finding Your Feet +=== 适应新环境 -To give you a feel for what is possible in Elasticsearch and how easy -it is to use, let's start by walking through a simple tutorial that covers -basic concepts such as indexing, search, and aggregations. +为了对 Elasticsearch 能实现什么及其上手容易程度有一个基本印象，让我们从一个简单的教程开始并介绍索引、搜索及聚合等基础概念。 -We'll introduce some new terminology and basic concepts along the way, but it -is OK if you don't understand everything immediately. We'll cover all the -concepts introduced here in _much_ greater depth throughout the rest of the -book. +我们将一并介绍一些新的技术术语和基础概念，因此即使无法立即全盘理解也无妨。在本书后续内容中，我们将深入介绍这里提到的所有概念。 -So, sit back and enjoy a whirlwind tour of what Elasticsearch is capable of. +接下来尽情享受 Elasticsearch 探索之旅。 -==== Let's Build an Employee Directory +==== 创建一个雇员目录 -We happen((("employee directory, building (example)"))) to work for _Megacorp_, and as part of HR's new _"We love our -drones!"_ initiative, we have been tasked with creating an employee directory. -The directory is supposed to foster employer empathy and -real-time, synergistic, dynamic collaboration, so it has a few -business requirements: +我们受雇于 ((("employee directory, building (example)"))) _Megacorp_ 公司，作为 HR 部门新的 _“热爱无人机”_ （_"We love our +drones!"_）激励项目的一部分，我们的任务是为此创建一个雇员目录。该目录应当能培养雇员认同感及支持实时、高效、动态协作，因此有一些业务需求： -* Enable data to contain multi value tags, numbers, and full text. -* Retrieve the full details of any employee. -* Allow structured search, such as finding employees over the age of 30. -* Allow simple full-text search and more-complex _phrase_ searches. -* Return highlighted search _snippets_ from the text in the - matching documents. -* Enable management to build analytic dashboards over the data. +* 支持包含多值标签、数值、以及全文本的数据 +* 检索任一雇员的完整信息 +* 允许结构化搜索，比如查询 30 岁以上的员工 +* 允许简单的全文搜索以及较复杂的短语搜索 +* 支持在匹配文档内容中高亮显示搜索片段 +* 支持基于数据创建和管理分析仪表盘 -=== Indexing Employee Documents +=== 索引雇员文档 -The first order of business is storing employee data.((("documents", "indexing")))((("indexing"))) This will take the form -of an _employee document_: a single document represents a single -employee. The act of storing data in Elasticsearch is called _indexing_, but -before we can index a document, we need to decide _where_ to store it. +第一个业务需求就是存储雇员数据。((("documents", "indexing")))((("indexing"))) 这将会以 _雇员文档_ 的形式存储：一个文档代表一个雇员。存储数据到 Elasticsearch 的行为叫做 _索引_ ，但在索引一个文档之前，需要确定将文档存储在哪里。 -An Elasticsearch cluster can((("clusters", "indices in")))(((in clusters"))) contain multiple _indices_, which in -turn contain multiple _types_.((("tables"))) These types hold multiple _documents_, +An Elasticsearch cluster can((("clusters", "indices in")))(((in clusters"))) contain multiple _indices_, which in turn contain multiple _types_.((("tables"))) These types hold multiple _documents_, and each document has((("fields"))) multiple _fields_. +一个 Elasticsearch 集群可以 ((("clusters", "indices in")))(((in clusters"))) 包含多个 _索引_ ，相应的每个索引可以包含多个 _类型_ 。((("tables"))) 这些不同的类型存储着多个 _文档_ ，每个文档又有 ((("fields"))) 多个 _属性_ 。 .Index Versus Index Versus Index ************************************************** -You may already have noticed that the word _index_ is overloaded with -several meanings in the context of Elasticsearch.((("index, meanings in Elasticsearch"))) A little -clarification is necessary: +你也许已经注意到 _索引_ 这个词在 Elasticsearch 语境中包含多重意思， ((("index, meanings in Elasticsearch"))) 所以有必要做一点儿说明： -Index (noun):: +索引（名词）： -As explained previously, an _index_ is like a _database_ in a traditional -relational database. It is the place to store related documents. The plural of -_index_ is _indices_ or _indexes_. +如前所述，一个 _索引_ 类似于传统关系数据库中的一个 _数据库_ ，是一个存储关系型文档的地方。 _索引_ (_index_) 的复数词为 _indices_ 或 _indexes_ 。 -Index (verb):: +索引（动词）： -_To index a document_ is to store a document in an _index (noun)_ so -that it can be retrieved and queried. It is much like the `INSERT` keyword in -SQL except that, if the document already exists, the new document would -replace the old. +_索引一个文档_ 就是存储一个文档到一个 _索引_ （名词）中以便它可以被检索和查询到。这非常类似于 SQL 语句中的 `INSERT` 关键词，除了文档已存在时新文档会替换就文档情况之外。 -Inverted index:: +倒排索引： -Relational databases add an _index_, such as a B-tree index,((("relational databases", "indices"))) to specific -columns in order to improve the speed of data retrieval. Elasticsearch and -Lucene use a structure called((("inverted index"))) an _inverted index_ for exactly the same -purpose. +关系型数据库通过增加一个 _索引_ 比如一个 B树（B-tree）索引 ((("relational databases", "indices"))) 到指定的列上，以便提升数据检索速度。Elasticsearch 和 Lucene 使用了一个叫做 ((("inverted index"))) _倒排索引_ 的结构来达到相同的目的。 + -By default, every field in a document is _indexed_ (has an inverted index) -and thus is searchable. A field without an inverted index is not searchable. -We discuss inverted indexes in more detail in <>. +默认的，一个文档中的每一个属性都是 _被索引_ 的（有一个倒排索引）和可搜索的。一个没有倒排索引的属性是不能被搜索到的。我们将在 <> 讨论倒排索引的更多细节。 ************************************************** -So for our employee directory, we are going to do the following: +对于雇员目录，我们将做如下操作： -* Index a _document_ per employee, which contains all the details of a single - employee. -* Each document will be((("types", "in employee directory (example)"))) of _type_ `employee`. -* That type will live in the `megacorp` _index_. -* That index will reside within our Elasticsearch cluster. +* 每个雇员索引一个文档，包含该雇员的所有信息。 +* 每个文档都将是((("types", "in employee directory (example)"))) `employee` _类型_ 。 +* 该类型位于 _索引_ `megacorp` 内。 +* 该索引保存在我们的 Elasticsearch 集群中。 In practice, this is easy (even though it looks like a lot of steps). We can perform all of those actions in a single command: +实践中这非常简单（尽管看起来有很多步骤），我们可以通过一条命令完成所有这些动作： [source,js] -------------------------------------------------- @@ -96,28 +72,22 @@ PUT /megacorp/employee/1 -------------------------------------------------- // SENSE: 010_Intro/25_Index.json -Notice that the path `/megacorp/employee/1` contains three pieces of -information: +注意，路径 `/megacorp/employee/1` 包含了三部分的信息： +megacorp+:: - The index name + 索引名称 +employee+:: - The type name + 类型名称 +1+:: - The ID of this particular employee + 特定雇员的ID -The request body--the JSON document--contains all the information about -this employee. His name is John Smith, he's 25, and enjoys rock climbing. +请求体 —— JSON 文档 —— 包含了这位员工的所有详细信息，他的名字叫 John Smith ，今年 25 岁，喜欢攀岩。 -Simple! There was no need to perform any administrative tasks first, like -creating an index or specifying the type of data that each field contains. We -could just index a document directly. Elasticsearch ships with defaults for -everything, so all the necessary administration tasks were taken care of in -the background, using default values. +很简单！无需进行执行管理任务，如创建一个索引或指定每个属性的数据类型之类的，可以直接只索引一个文档。Elasticsearch 默认地完成其他一切，因此所有必需的管理任务都在后台使用默认设置完成。 -Before moving on, let's add a few more employees to the directory: +进行下一步前，让我们增加更多的员工信息到目录中： [source,js] -------------------------------------------------- From e823455e4e047dab48efc527770dbd639c2aff60 Mon Sep 17 00:00:00 2001 From: node Date: Thu, 5 Jan 2017 17:57:20 +0800 Subject: [PATCH 2/3] chapter1_part5:/010_Intro/25_Tutorial_Indexing.asciidoc --- 010_Intro/25_Tutorial_Indexing.asciidoc | 1 + 1 file changed, 1 insertion(+) diff --git a/010_Intro/25_Tutorial_Indexing.asciidoc b/010_Intro/25_Tutorial_Indexing.asciidoc index 402714fc5..ddbf76dcd 100644 --- a/010_Intro/25_Tutorial_Indexing.asciidoc +++ b/010_Intro/25_Tutorial_Indexing.asciidoc @@ -1,3 +1,4 @@ +[[tutorial-indexing]] === 适应新环境 为了对 Elasticsearch 能实现什么及其上手容易程度有一个基本印象，让我们从一个简单的教程开始并介绍索引、搜索及聚合等基础概念。 From fa768834bea706397394a219134ba65dc9342f0b Mon Sep 17 00:00:00 2001 From: node Date: Fri, 6 Jan 2017 09:36:05 +0800 Subject: [PATCH 3/3] fix review issues --- 010_Intro/25_Tutorial_Indexing.asciidoc | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/010_Intro/25_Tutorial_Indexing.asciidoc b/010_Intro/25_Tutorial_Indexing.asciidoc index ddbf76dcd..a4f6144ba 100644 --- a/010_Intro/25_Tutorial_Indexing.asciidoc +++ b/010_Intro/25_Tutorial_Indexing.asciidoc @@ -1,4 +1,4 @@ -[[tutorial-indexing]] +[[_finding_your_feet]] === 适应新环境 为了对 Elasticsearch 能实现什么及其上手容易程度有一个基本印象，让我们从一个简单的教程开始并介绍索引、搜索及聚合等基础概念。 @@ -23,9 +23,6 @@ drones!"_）激励项目的一部分，我们的任务是为此创建一个雇 第一个业务需求就是存储雇员数据。((("documents", "indexing")))((("indexing"))) 这将会以 _雇员文档_ 的形式存储：一个文档代表一个雇员。存储数据到 Elasticsearch 的行为叫做 _索引_ ，但在索引一个文档之前，需要确定将文档存储在哪里。 - -An Elasticsearch cluster can((("clusters", "indices in")))(((in clusters"))) contain multiple _indices_, which in turn contain multiple _types_.((("tables"))) These types hold multiple _documents_, -and each document has((("fields"))) multiple _fields_. 一个 Elasticsearch 集群可以 ((("clusters", "indices in")))(((in clusters"))) 包含多个 _索引_ ，相应的每个索引可以包含多个 _类型_ 。((("tables"))) 这些不同的类型存储着多个 _文档_ ，每个文档又有 ((("fields"))) 多个 _属性_ 。 .Index Versus Index Versus Index @@ -56,8 +53,6 @@ _索引一个文档_ 就是存储一个文档到一个 _索引_ （名词）中 * 该类型位于 _索引_ `megacorp` 内。 * 该索引保存在我们的 Elasticsearch 集群中。 -In practice, this is easy (even though it looks like a lot of steps). We -can perform all of those actions in a single command: 实践中这非常简单（尽管看起来有很多步骤），我们可以通过一条命令完成所有这些动作： [source,js]