ElasticSearch必备知识：从索引别名、分词器、文档管理、路由到搜索详解

妖狐艹你老母 2021-11-02 09:52 283阅读 0赞

原文：[https://www.enmotech.com/web/detail/1/839/1.html ][https_www.enmotech.com_web_detail_1_839_1.html]

ElasticSearch必备知识：从索引别名、分词器、文档管理、路由到搜索详解（上）

[https://www.enmotech.com/web/detail/1/840/1.html][https_www.enmotech.com_web_detail_1_840_1.html]

ElasticSearch必备知识：从索引别名、分词器、文档管理、路由到搜索详解（下）

导读：之前我们分享了[ElasticSearch最全详细使用教程：入门、索引管理、映射详解][ElasticSearch]，本文详细介绍ElasticSearch的索引别名、分词器、文档管理、路由、搜索详解。

**一、索引别名**

--------------------

![索引别名][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjQ3NDg0OTQwNjQ2NTUuanBn]

**1. 别名的用途**

如果希望一次查询可查询多个索引。  
如果希望通过索引的视图来操作索引，就像数据库库中的视图一样。  
索引的别名机制，就是让我们可以以视图的方式来操作集群中的索引，这个视图可是多个索引，也可是一个索引或索引的一部分。

**2. 新建索引时定义别名**

> PUT /logs\_20162801 \{ "mappings" : \{ "type" : \{ "properties" : \{ "year" : \{"type" : "integer"\} \} \} \},  "aliases" : \{ "current\_day" : \{\}, "2016" : \{ "filter" : \{ "term" : \{"year" : 2016 \} \} \} \} \}

**3. 创建别名 /\_aliases**

为索引test1创建别名alias1

> POST /\_aliases  
> \{  
> "actions" : \[  
> \{ "add" : \{ "index" : "test1", "alias" : "alias1" \} \}  
> \]  
> \}

**4. 删除别名**

> POST /\_aliases  
> \{  
> "actions" : \[  
> \{ "remove" : \{ "index" : "test1", "alias" : "alias1" \} \}  
> \]  
> \}

还可以这样写

> DELETE /\{index\}/\_alias/\{name\}

**5. 批量操作别名**

删除索引test1的别名alias1，同时为索引test2添加别名alias1

> POST /\_aliases  
> \{  
> "actions" : \[  
> \{ "remove" : \{ "index" : "test1", "alias" : "alias1" \} \},  
> \{ "add" : \{ "index" : "test2", "alias" : "alias1" \} \}  
> \]  
> \}

**6. 为多个索引定义一样的别名**

方式1：

> POST /\_aliases
> 
> \{  
> "actions" : \[  
> \{ "add" : \{ "index" : "test1", "alias" : "alias1" \} \},  
> \{ "add" : \{ "index" : "test2", "alias" : "alias1" \} \}  
> \]  
> \}

方式2：

> POST /\_aliases  
> \{  
> "actions" : \[  
> \{ "add" : \{ "indices" : \["test1", "test2"\], "alias" : "alias1" \} \}  
> \]  
> \}

注意：只可通过多索引别名进行搜索，不可进行文档索引和根据id获取文档。

方式3：通过统配符\*模式来指定要别名的索引

> POST /\_aliases  
> \{  
> "actions" : \[  
> \{ "add" : \{ "index" : "test\*", "alias" : "all\_test\_indices" \} \}  
> \]  
> \}

注意：在这种情况下，别名是一个点时间别名，它将对所有匹配的当前索引进行别名，当添加/删除与此模式匹配的新索引时，它不会自动更新。

**7. 带过滤器的别名**

索引中需要有字段

> PUT /test1  
> \{  
> "mappings": \{  
> "type1": \{  
> "properties": \{  
> "user" : \{  
> "type": "keyword"  
> \}  
> \}  
> \}  
> \}  
> \}

过滤器通过Query DSL来定义，将作用于通过该别名来进行的所有Search, Count,

Delete By Query and More Like This 操作。

> POST /\_aliases  
> \{  
> "actions" : \[  
> \{  
> "add" : \{  
> "index" : "test1",  
> "alias" : "alias2",  
> "filter" : \{ "term" : \{ "user" : "kimchy" \} \}  
> \}  
> \}  
> \]  
> \}

**8. 带routing的别名**

可在别名定义中指定路由值，可和filter一起使用，用来限定操作的分片，避免不需要的其他分片操作。

> POST /\_aliases
> 
> \{  
> "actions" : \[  
> \{  
> "add" : \{  
> "index" : "test",  
> "alias" : "alias1",  
> "routing" : "1"  
> \}  
> \}  
> \]  
> \}

为搜索、索引指定不同的路由值

> POST /\_aliases  
> \{  
> "actions" : \[  
> \{  
> "add" : \{  
> "index" : "test",  
> "alias" : "alias2",  
> "search\_routing" : "1,2",  
> "index\_routing" : "2"  
> \}  
> \}  
> \]  
> \}

**9. 以PUT方式来定义一个别名**

> PUT /\{index\}/\_alias/\{name\}  
> PUT /logs\_201305/\_alias/2013

带filter 和 routing

> PUT /users  
> \{  
> "mappings" : \{  
> "user" : \{  
> "properties" : \{  
> "user\_id" : \{"type" : "integer"\}  
> \}  
> \}  
> \}  
> \}  
> PUT /users/\_alias/user\_12  
> \{  
> "routing" : "12",  
> "filter" : \{  
> "term" : \{  
> "user\_id" : 12  
> \}  
> \}  
> \}

**10. 查看别名定义信息**

> GET /\{index\}/\_alias/\{alias\}  
> GET /logs\_20162801/\_alias/\*  
> GET /\_alias/2016  
> GET /\_alias/20\*

**二、分词器**

--------------------

![分词器][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjU0NjY2MTgwMjk1NjIuanBn]

**1. 认识分词器**

1.1 Analyzer分析器  
在ES中一个Analyzer 由下面三种组件组合而成：  
character filter ：字符过滤器，对文本进行字符过滤处理，如处理文本中的html标签字符。处理完后再交给tokenizer进行分词。一个analyzer中可包含0个或多个字符过滤器，多个按配置顺序依次进行处理。  
tokenizer：分词器，对文本进行分词。一个analyzer必需且只可包含一个tokenizer。  
token filter：词项过滤器，对tokenizer分出的词进行过滤处理。如转小写、停用词处理、同义词处理。一个analyzer可包含0个或多个词项过滤器，按配置顺序进行过滤。

1.2 如何测试分词器

> POST \_analyze  
> \{  
> "analyzer": "whitespace",  
> "text": "The quick brown fox."  
> \}  
>   
> POST \_analyze  
> \{  
> "tokenizer": "standard",  
> "filter": \[ "lowercase", "asciifolding" \],  
> "text": "Is this déja vu?"  
> \}

![测试][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjU1OTIyNjIwOTQ0MjQucG5n]

position：第几个词

offset：词的偏移位置

**2. 内建的character filter**

HTML Strip Character Filter  
　　html\_strip ：过滤html标签，解码HTML entities like &.  
Mapping Character Filter  
　　mapping ：用指定的字符串替换文本中的某字符串。  
Pattern Replace Character Filter  
　　pattern\_replace ：进行正则表达式替换。

2.1 HTML Strip Character Filter

> 
> POST \_analyze 
> \{ 
> "tokenizer": "keyword", 
> "char\_filter": \[ "html\_strip" \], 
> "text": "I&apos;m so happy!" 
> \}

在索引中配置：

> PUT my\_index  
> \{  
> "settings": \{  
> "analysis": \{  
> "analyzer": \{  
> "my\_analyzer": \{  
> "tokenizer": "keyword",  
> "char\_filter": \["my\_char\_filter"\]  
> \}  
> \},  
> "char\_filter": \{  
> "my\_char\_filter": \{  
> "type": "html\_strip",  
> "escaped\_tags": \["b"\]  
> \}  
> \}  
> \}  
> \}  
> \}

escaped\_tags 用来指定例外的标签。如果没有例外标签需配置，则不需要在此进行客户化定义，在上面的my\_analyzer中直接使用 html\_strip

测试：

> POST my\_index/\_analyze 
> \{ 
> "analyzer": "my\_analyzer", 
> "text": "I&apos;m so happy!" 
> \}

![测试分词器][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjU5NzAzMTIwNTI1NjcucG5n]

2.2 Mapping character filter

官网链接：[https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-mapping-charfilter.html ][https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-mapping-charfilter.html]

> PUT my\_index  
> \{  
> "settings": \{
> 
> "analysis": \{
> 
> "analyzer": \{  
> "my\_analyzer": \{  
> "tokenizer": "keyword",  
> "char\_filter": \[  
> "my\_char\_filter"  
> \]  
> \}  
> \},  
> "char\_filter": \{  
> "my\_char\_filter": \{  
> "type": "mapping",  
> "mappings": \[  
> "٠ => 0",  
> "١ => 1",  
> "٢ => 2",  
> "٣ => 3",  
> "٤ => 4",  
> "٥ => 5",  
> "٦ => 6",  
> "٧ => 7",  
> "٨ => 8",  
> "٩ => 9"  
> \]  
> \}  
> \}  
> \}  
> \}  
> \}

测试

> POST my\_index/\_analyze  
> \{  
> "analyzer": "my\_analyzer",  
> "text": "My license plate is ٢٥٠١٥"  
> \}

2.3 Pattern Replace Character Filter

官网链接：[https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-pattern-replace-charfilter.html ][https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-pattern-replace-charfilter.html]

> PUT my\_index
> 
> \{  
> "settings": \{  
> "analysis": \{  
> "analyzer": \{  
> "my\_analyzer": \{  
> "tokenizer": "standard",  
> "char\_filter": \[  
> "my\_char\_filter"  
> \]  
> \}  
> \},  
> "char\_filter": \{  
> "my\_char\_filter": \{  
> "type": "pattern\_replace",  
> "pattern": "(\\\\d+)-(?=\\\\d)",  
> "replacement": "$1\_"  
> \}  
> \}  
> \}  
> \}  
> \}

测试

> POST my\_index/\_analyze  
> \{  
> "analyzer": "my\_analyzer",  
> "text": "My credit card is 123-456-789"  
> \}

**3. 内建的Tokenizer**

官网链接：[https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-tokenizers.html][https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-tokenizers.html]

> Standard Tokenizer  
> Letter Tokenizer  
> Lowercase Tokenizer  
> Whitespace Tokenizer  
> UAX URL Email Tokenizer  
> Classic Tokenizer  
> Thai Tokenizer  
> NGram Tokenizer  
> Edge NGram Tokenizer  
> Keyword Tokenizer  
> Pattern Tokenizer  
> Simple Pattern Tokenizer  
> Simple Pattern Split Tokenizer  
> Path Hierarchy Tokenizer

前面集成的中文分词器Ikanalyzer中提供的tokenizer：ik\_smart 、 ik\_max\_word

测试tokenizer

> POST \_analyze  
> \{  
> "tokenizer": "standard",  
> "text": "张三说的确实在理"  
> \}  
>   
> POST \_analyze  
> \{  
> "tokenizer": "ik\_smart",  
> "text": "张三说的确实在理"  
> \}

**4. 内建的Token Filter**

ES中内建了很多Token filter ，详细了解：[https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-tokenizers.html][https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-tokenizers.html]

Lowercase Token Filter ：lowercase 转小写  
Stop Token Filter ：stop 停用词过滤器  
Synonym Token Filter：synonym 同义词过滤器

说明：中文分词器Ikanalyzer中自带有停用词过滤功能。

4.1 Synonym Token Filter 同义词过滤器

> PUT /test\_index 
> \{ 
> "settings": \{ 
> "index" : \{ 
> "analysis" : \{ 
> "analyzer" : \{ 
> "my\_ik\_synonym" : \{ 
> "tokenizer" : "ik\_smart", 
> "filter" : \["synonym"\] 
> \} 
> \}, 
> "filter" : \{ 
> "synonym" : \{ 
> "type" : "synonym", 
>  
> "synonyms\_path" : "analysis/synonym.txt" 
> \} 
> \} 
> \} 
> \} 
> \} 
> \}

同义词定义格式

ES同义词格式支持 solr、 WordNet 两种格式。

在analysis/synonym.txt中用solr格式定义如下同义词

张三,李四  
电饭煲,电饭锅 => 电饭煲  
电脑 => 计算机,computer

注意：

文件一定要UTF-8编码

一行一类同义词，=> 表示标准化为

测试：通过例子的结果了解同义词的处理行为

> POST test\_index/\_analyze  
> \{  
> "analyzer": "my\_ik\_synonym",  
> "text": "张三说的确实在理"  
> \}  
>   
> POST test\_index/\_analyze  
> \{  
> "analyzer": "my\_ik\_synonym",  
> "text": "我想买个电饭锅和一个电脑"  
> \}

**5. 内建的Analyzer**

官网链接：[https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-analyzers.html][https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-analyzers.html]

> Standard Analyzer
> 
> Simple Analyzer  
> Whitespace Analyzer  
> Stop Analyzer  
> Keyword Analyzer  
> Pattern Analyzer  
> Language Analyzers  
> Fingerprint Analyzer

集成的中文分词器Ikanalyzer中提供的Analyzer：ik\_smart 、 ik\_max\_word

内建的和集成的analyzer可以直接使用。如果它们不能满足我们的需要，则我们可自己组合字符过滤器、分词器、词项过滤器来定义自定义的analyzer

5.1 自定义 Analyzer

配置参数：

> PUT my\_index8  
> \{  
> "settings": \{  
> "analysis": \{  
> "analyzer": \{  
> "my\_ik\_analyzer": \{  
> "type": "custom",  
> "tokenizer": "ik\_smart",  
> "char\_filter": \[  
> "html\_strip"  
> \],  
> "filter": \[  
> "synonym"  
> \]  
> \}  
> \},  
> "filter": \{  
> "synonym": \{  
> "type": "synonym",  
> "synonyms\_path": "analysis/synonym.txt"  
> \}  
> \} \} \}\}

5.2 为字段指定分词器

> PUT my\_index8/\_mapping/\_doc  
> \{  
> "properties": \{  
> "title": \{  
> "type": "text",  
> "analyzer": "my\_ik\_analyzer"  
> \}  
> \}  
> \}

如果该字段的查询需要使用不同的analyzer

> PUT my\_index8/\_mapping/\_doc  
> \{  
> "properties": \{  
> "title": \{  
> "type": "text",  
> "analyzer": "my\_ik\_analyzer",  
> "search\_analyzer": "other\_analyzer"  
> \}  
> \}  
> \}

测试结果

> PUT my\_index8/\_doc/1  
> \{  
> "title": "张三说的确实在理"  
> \}  
>   
> GET /my\_index8/\_search  
> \{  
> "query": \{  
> "term": \{  
> "title": "张三"  
> \}  
> \}  
> \}

5.3 为索引定义个default分词器

> PUT /my\_index10  
> \{  
> "settings": \{  
> "analysis": \{  
> "analyzer": \{  
> "default": \{  
> "tokenizer": "ik\_smart",  
> "filter": \[  
> "synonym"  
> \]  
> \}  
> \},  
> "filter": \{  
> "synonym": \{  
> "type": "synonym",  
> "synonyms\_path": "analysis/synonym.txt"  
> \}  
> \}  
> \}  
> \},  
> "mappings": \{  
> "\_doc": \{  
> "properties": \{  
> "title": \{  
> "type": "text"  
> \}  
> \}  
> \}  
> \}  
> \}

测试结果：

> PUT my\_index10/\_doc/1  
> \{  
> "title": "张三说的确实在理"  
> \}  
>   
> GET /my\_index10/\_search  
> \{  
> "query": \{  
> "term": \{  
> "title": "张三"  
> \}  
> \}  
> \}

**6. Analyzer的使用顺序**

我们可以为每个查询、每个字段、每个索引指定分词器。

在索引阶段ES将按如下顺序来选用分词：

首先选用字段mapping定义中指定的analyzer  
字段定义中没有指定analyzer，则选用 index settings中定义的名字为default 的analyzer。  
如index setting中没有定义default分词器，则使用 standard analyzer.

查询阶段ES将按如下顺序来选用分词：  
The analyzer defined in a full-text query.  
The search\_analyzer defined in the field mapping.  
The analyzer defined in the field mapping.  
An analyzer named default\_search in the index settings.  
An analyzer named default in the index settings.  
The standard analyzer.

**三、文档管理**

--------------------

![文档管理][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2Njg4NjkyODAwOTU3NzYuanBn]

**1. 新建文档**

指定文档id，新增/修改

> PUT twitter/\_doc/1  
> \{  
> "id": 1,  
> "user" : "kimchy",  
> "post\_date" : "2009-11-15T14:12:12",  
> "message" : "trying out Elasticsearch"  
> \}

新增，自动生成文档id

> POST twitter/\_doc/  
> \{  
> "id": 1,  
> "user" : "kimchy",  
> "post\_date" : "2009-11-15T14:12:12",  
> "message" : "trying out Elasticsearch"  
> \}

返回结果说明：

![返回结果说明][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2Njg4ODQyOTkwMTE3MDkucG5n]

**2. 获取单个文档**

> HEAD twitter/\_doc/11  
> GET twitter/\_doc/1

不获取文档的source：

> GET twitter/\_doc/1?\_source=false

获取文档的source：

> GET twitter/\_doc/1/\_source  
> \{  
> "\_index": "twitter",  
> "\_type": "\_doc",  
> "\_id": "1",  
> "\_version": 2,  
> "found": true,  
> "\_source": \{  
> "id": 1,  
> "user": "kimchy",  
> "post\_date": "2009-11-15T14:12:12",  
> "message": "trying out Elasticsearch"  
> \}\}

获取存储字段

> PUT twitter11  
> \{  
> "mappings": \{  
> "\_doc": \{  
> "properties": \{  
> "counter": \{  
> "type": "integer",  
> "store": false  
> \},  
> "tags": \{  
> "type": "keyword",  
> "store": true  
> \} \} \} \}\}  
> PUT twitter11/\_doc/1  
> \{  
> "counter" : 1,  
> "tags" : \["red"\]  
> \}  
> GET twitter11/\_doc/1?stored\_fields=tags,counter

**3. 获取多个文档 \_mget**

方式1：

> GET /\_mget
> 
> \{  
> "docs" : \[  
> \{  
> "\_index" : "twitter",  
> "\_type" : "\_doc",  
> "\_id" : "1"  
> \},  
> \{  
> "\_index" : "twitter",  
> "\_type" : "\_doc",  
> "\_id" : "2"  
> "stored\_fields" : \["field3", "field4"\]  
> \}  
> \]  
> \}

方式2：

> GET /twitter/\_mget  
> \{  
> "docs" : \[  
> \{  
> "\_type" : "\_doc",  
> "\_id" : "1"  
> \},  
> \{  
> "\_type" : "\_doc",  
> "\_id" : "2"  
> \}  
> \]  
> \}

方式3：

> GET /twitter/\_doc/\_mget  
> \{  
> "docs" : \[  
> \{  
> "\_id" : "1"  
> \},  
> \{  
> "\_id" : "2"  
> \}  
> \]  
> \}

方式4：

> GET /twitter/\_doc/\_mget  
> \{  
> "ids" : \["1", "2"\]  
> \}

**4. 删除文档**

指定文档id进行删除  
DELETE twitter/\_doc/1

用版本来控制删除  
DELETE twitter/\_doc/1?version=1

返回结果：

> \{  
> "\_shards" : \{  
> "total" : 2,  
> "failed" : 0,  
> "successful" : 2  
> \},  
> "\_index" : "twitter",  
> "\_type" : "\_doc",  
> "\_id" : "1",  
> "\_version" : 2,  
> "\_primary\_term": 1,  
> "\_seq\_no": 5,  
> "result": "deleted"  
> \}

查询删除

> POST twitter/\_delete\_by\_query  
> \{  
> "query": \{  
> "match": \{  
> "message": "some message"  
> \}  
> \}  
> \}

当有文档有版本冲突时，不放弃删除操作（记录冲突的文档，继续删除其他复合查询的文档）

> POST twitter/\_doc/\_delete\_by\_query?conflicts=proceed  
> \{  
> "query": \{  
> "match\_all": \{\}  
> \}  
> \}

通过task api 来查看 查询删除任务  
GET \_tasks?detailed=true&actions=\*/delete/byquery

查询具体任务的状态  
GET /\_tasks/taskId:1

取消任务  
POST \_tasks/task\_id:1/\_cancel

**5. 更新文档**

指定文档id进行修改

> PUT twitter/\_doc/1  
> \{  
> "id": 1,  
> "user" : "kimchy",  
> "post\_date" : "2009-11-15T14:12:12",  
> "message" : "trying out Elasticsearch"  
> \}

乐观锁并发更新控制

> PUT twitter/\_doc/1?version=1  
> \{  
> "id": 1,  
> "user" : "kimchy",  
> "post\_date" : "2009-11-15T14:12:12",  
> "message" : "trying out Elasticsearch"  
> \}

返回结果

> \{  
> "\_index": "twitter",  
> "\_type": "\_doc",  
> "\_id": "1",  
> "\_version": 3,  
> "result": "updated",  
> "\_shards": \{  
> "total": 3,  
> "successful": 1,  
> "failed": 0  
> \},  
> "\_seq\_no": 2,  
> "\_primary\_term": 3  
> \}

**6. Scripted update 通过脚本来更新文档**

6.1 准备一个文档

> PUT uptest/\_doc/1  
> \{  
> "counter" : 1,  
> "tags" : \["red"\]  
> \}

6.2 对文档1的counter + 4

> POST uptest/\_doc/1/\_update  
> \{  
> "script" : \{  
> "source": "ctx.\_source.counter += params.count",  
> "lang": "painless",  
> "params" : \{  
> "count" : 4  
> \}  
> \}  
> \}

6.3 往数组中加入元素

> POST uptest/\_doc/1/\_update  
> \{  
> "script" : \{  
> "source": "ctx.\_source.tags.add(params.tag)",  
> "lang": "painless",  
> "params" : \{  
> "tag" : "blue"  
> \}  
> \}  
> \}

脚本说明：painless是es内置的一种脚本语言，ctx执行上下文对象（通过它还可访问\_index, \_type, \_id, \_version, \_routing and \_now (the current timestamp) ），params是参数集合

说明：脚本更新要求索引的\_source 字段是启用的。更新执行流程：

> a、获取到原文档  
> b、通过\_source字段的原始数据，执行脚本修改。  
> c、删除原索引文档
> 
> d、索引修改后的文档

它只是降低了一些网络往返，并减少了get和索引之间版本冲突的可能性。

6.4 添加一个字段

> POST uptest/\_doc/1/\_update  
> \{  
> "script" : "ctx.\_source.new\_field = 'value\_of\_new\_field'"  
> \}

6.5 移除一个字段

> POST uptest/\_doc/1/\_update  
> \{  
> "script" : "ctx.\_source.remove('new\_field')"  
> \}

6.6 判断删除或不做什么

> POST uptest/\_doc/1/\_update  
> \{  
> "script" : \{  
> "source": "if (ctx.\_source.tags.contains(params.tag)) \{ ctx.op = 'delete' \} else \{ ctx.op = 'none' \}",  
> "lang": "painless",  
> "params" : \{  
> "tag" : "green"  
> \}  
> \}  
> \}

6.7 合并传人的文档字段进行更新

> POST uptest/\_doc/1/\_update  
> \{  
> "doc" : \{  
> "name" : "new\_name"  
> \}
> 
> \}

6.8 再次执行7，更新内容相同，不需做什么

> \{  
> "\_index": "uptest",  
> "\_type": "\_doc",  
> "\_id": "1",  
> "\_version": 4,  
> "result": "noop",  
> "\_shards": \{  
> "total": 0,  
> "successful": 0,  
> "failed": 0  
> \}  
> \}

6.9 设置不做noop检测

> POST uptest/\_doc/1/\_update  
> \{  
> "doc" : \{  
> "name" : "new\_name"  
> \},  
> "detect\_noop": false  
> \}

什么是noop检测？

即已经执行过的脚本不再执行

6.10 upsert 操作：如果要更新的文档存在，则执行脚本进行更新，如不存在，则把 upsert中的内容作为一个新文档写入。

> POST uptest/\_doc/1/\_update
> 
> \{  
> "script" : \{  
> "source": "ctx.\_source.counter += params.count",  
> "lang": "painless",  
> "params" : \{  
> "count" : 4  
> \}  
> \},  
> "upsert" : \{  
> "counter" : 1  
> \}  
> \}

**7. 通过条件查询来更新文档**

满足查询条件的才更新

> POST twitter/\_update\_by\_query  
> \{  
> "script": \{  
> "source": "ctx.\_source.likes++",  
> "lang": "painless"  
> \},  
> "query": \{  
> "term": \{  
> "user": "kimchy"  
> \}  
> \}  
> \}

**8. 批量操作**

批量操作API /\_bulk 让我们可以在一次调用中执行多个索引、删除操作。这可以大大提高索引数据的速度。批量操作内容体需按如下以新行分割的json结构格式给出：

语法：

> action\_and\_meta\_data\\n  
> optional\_source\\n  
> action\_and\_meta\_data\\n  
> optional\_source\\n  
> ....  
> action\_and\_meta\_data\\n  
> optional\_source\\n

说明：

> action\_and\_meta\_data: action可以是 index, create, delete and update ，meta\_data 指: \_index ,\_type,\_id 请求端点可以是: /\_bulk, /\{index\}/\_bulk, \{index\}/\{type\}/\_bulk

示例：

> POST \_bulk
> 
> \{ "index" : \{ "\_index" : "test", "\_type" : "\_doc", "\_id" : "1" \} \}  
> \{ "field1" : "value1" \}  
> \{ "delete" : \{ "\_index" : "test", "\_type" : "\_doc", "\_id" : "2" \} \}  
> \{ "create" : \{ "\_index" : "test", "\_type" : "\_doc", "\_id" : "3" \} \}  
> \{ "field1" : "value3" \}  
> \{ "update" : \{"\_id" : "1", "\_type" : "\_doc", "\_index" : "test"\} \}  
> \{ "doc" : \{"field2" : "value2"\} \}

8.1 curl + json 文件 批量索引多个文档

注意：accounts.json要放在执行curl命令的同等级目录下，后续学习的测试数据基本都使用这份银行的数据了  
curl -H "Content-Type: application/json" -XPOST "localhost:9200/bank/\_doc/\_bulk?pretty&refresh" --data-binary "@accounts.json"

**9. reindex 重索引**

Reindex API /\_reindex 让我们可以将一个索引中的数据重索引到另一个索引中（拷贝），要求源索引的\_source 是开启的。目标索引的setting 、mapping 信息与源索引无关。

什么时候需要重索引？

即当需要做数据的拷贝的时候

> POST \_reindex  
> \{  
> "source": \{  
> "index": "twitter"  
> \},  
> "dest": \{  
> "index": "new\_twitter"  
> \}  
> \}

重索引要考虑的一个问题：目标索引中存在源索引中的数据，这些数据的version如何处理。

1. 如果没有指定version\_type 或指定为 internal，则会是采用目标索引中的版本，重索引过程中，执行的就是新增、更新操作。

> POST \_reindex
> 
> \{  
> "source": \{  
> "index": "twitter"  
> \},  
> "dest": \{  
> "index": "new\_twitter",  
> "version\_type": "internal"  
> \}

2. 如果想使用源索引中的版本来进行版本控制更新，则设置 version\_type 为extenal。重索引操作将写入不存在的，更新旧版本的数据。

> POST \_reindex
> 
> \{  
> "source": \{  
> "index": "twitter"  
> \},  
> "dest": \{  
> "index": "new\_twitter",  
> "version\_type": "external"  
> \}  
> \}

如果你只想从源索引中复制目标索引中不存在的文档数据，可以指定 op\_type 为 create 。此时存在的文档将触发 版本冲突（会导致放弃操作），可设置“conflicts”: “proceed“，跳过继续

> POST \_reindex
> 
> \{  
> "conflicts": "proceed",  
> "source": \{  
> "index": "twitter"  
> \},  
> "dest": \{  
> "index": "new\_twitter",  
> "op\_type": "create"  
> \}  
> \}

你也可以只索引源索引的一部分数据，通过 type 或 查询来指定你需要的数据

> POST \_reindex  
> \{  
> "source": \{  
> "index": "twitter",  
> "type": "\_doc",  
> "query": \{  
> "term": \{  
> "user": "kimchy"  
> \}  
> \}  
> \},  
> "dest": \{  
> "index": "new\_twitter"  
> \}  
> \}

可以从多个源获取数据

> POST \_reindex  
> \{  
> "source": \{  
> "index": \["twitter", "blog"\],  
> "type": \["\_doc", "post"\]  
> \},  
> "dest": \{  
> "index": "all\_together"  
> \}  
> \}

可以限定文档数量

> POST \_reindex  
> \{  
> "size": 10000,  
> "source": \{  
> "index": "twitter",  
> "sort": \{ "date": "desc" \}  
> \},  
> "dest": \{  
> "index": "new\_twitter"  
> \}  
> \}

可以选择复制源文档的哪些字段

> POST \_reindex  
> \{  
> "source": \{  
> "index": "twitter",  
> "\_source": \["user", "\_doc"\]  
> \},  
> "dest": \{  
> "index": "new\_twitter"  
> \}  
> \}

可以用script来改变文档

> POST \_reindex
> 
> \{  
> "source": \{  
> "index": "twitter"  
> \},  
> "dest": \{  
> "index": "new\_twitter",  
> "version\_type": "external"  
> \},  
> "script": \{  
> "source": "if (ctx.\_source.foo == 'bar') \{ctx.\_version++; ctx.\_source.remove('foo')\}",  
> "lang": "painless"  
> \}  
> \}

可以指定路由值把文档放到哪个分片上

> POST \_reindex  
> \{  
> "source": \{  
> "index": "source",  
> "query": \{  
> "match": \{  
> "company": "cat"  
> \}  
> \}  
> \},  
> "dest": \{  
> "index": "dest",  
> "routing": "=cat"  
> \}  
> \}

从远程源复制

> POST \_reindex  
> \{  
> "source": \{  
> "remote": \{  
> "host": "http://otherhost:9200",  
> "username": "user",  
> "password": "pass"  
> \},  
> "index": "source",  
> "query": \{  
> "match": \{  
> "test": "data"  
> \}  
> \}  
> \},  
> "dest": \{  
> "index": "dest"  
> \}  
> \}

通过\_task 来查询执行状态  
GET \_tasks?detailed=true&actions=\*reindex

**10. refresh**

对于索引、更新、删除操作如果想操作完后立马重刷新可见，可带上refresh参数

> PUT /test/\_doc/1?refresh  
> \{"test": "test"\}  
> PUT /test/\_doc/2?refresh=true  
> \{"test": "test"\}

refresh 可选值说明

未给值或=true，则立马会重刷新读索引。  
=false ，相当于没带refresh 参数，遵循内部的定时刷新。  
=wait\_for ，登记等待刷新，当登记的请求数达到index.max\_refresh\_listeners 参数设定的值时(defaults to 1000)，将触发重刷新。

**四、路由详解**

--------------------

![路由详解][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwMzUzNTUwNzgxODEucG5n]

**1. 集群组成**

第一个节点启动

![第一个节点启动][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwNDI0NjcwNTQ4NTYucG5n]

说明：首先启动的一定是主节点，主节点存储的是集群的元数据信息

Node2启动

![第二个节点][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwNTAxNDkwNzc5NjEucG5n]

说明：

Node2节点启动之前会配置集群的名称Cluster-name：ess，然后配置可以作为主节点的ip地址信息discovery.zen.ping.unicast.hosts: \[“10.0.1.11",“10.0.1.12"\]，配置自己的ip地址networ.host: 10.0.1.12；

Node2启动的过程中会去找到主节点Node1告诉Node1我要加入到集群里面了，主节点Node1接收到请求以后看Node2是否满足加入集群的条件，如果满足就把node2的ip地址加入的元信息里面，然后广播给集群中的其他节点有

新节点加入，并把最新的元信息发送给其他的节点去更新

Node3..NodeN加入

![第三个节点加入][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwNTc4MjkwNzA0NTQucG5n]

说明：集群中的所有节点的元信息都是和主节点一致的，因为一旦有新的节点加入进来，主节点会通知其他的节点同步元信息

**2. 在集群中创建索引的流程**

![在集群中创建索引的流程][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwNjUzMDIwNzg3ODIuanBn]

**3. 有索引的集群**

![有索引的集群][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwNzIzNTgwMzE0OTYucG5n]

**4. 集群有节点出现故障，如主节点挂了，会重新选择主节点**

![重新选择主节点][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwNzc5NDcwNTU5MTcucG5n]

**5. 在集群中索引文档**

![集群中索引文档][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwODI4MjkwNzU5NTEucG5n]

索引文档的步骤：  
1. node2计算文档的路由值得到文档存放的分片（假定路由选定的是分片0）。  
2. 将文档转发给分片0(P0)的主分片节点 node1。  
3. node1索引文档，同步给副本（R0）节点node3索引文档。  
4. node1向node2反馈结果  
5. node2作出响应  
6. 文档是如何路由的

文档该存到哪个分片上？  
决定文档存放到哪个分片上就是文档路由。ES中通过下面的计算得到每个文档的存放分片：  
shard = hash(routing) % number\_of\_primary\_shards

参数说明：

routing 是用来进行hash计算的路由值，默认是使用文档id值。我们可以在索引文档时通过routing参数指定别的路由值

number\_of\_primary\_shards：创建索引时指定的主分片数

> POST twitter/\_doc?routing=kimchy  
> \{  
> "user" : "kimchy",  
> "post\_date" : "2009-11-15T14:12:12",  
> "message" : "trying out Elasticsearch"  
> \}

在索引、删除、更新、查询中都可以使用routing参数（可多值）指定操作的分片。

创建索引时强制要求给定路由值：

> PUT my\_index2  
> \{  
> "mappings": \{  
> "\_doc": \{  
> "\_routing": \{  
> "required": true  
> \}  
> \}  
> \}  
> \}

**7. 在集群中进行搜索**

![在集群中进行搜索][aHR0cHM6Ly93d3cuZW5tb3RlY2guY29tL3B1YmxpY19yZXNvdXJjZXMvdWUvdXBsb2FkL2ltYWdlLzIwMTkwODEzLzE1NjU2NjkwODk5NjUwNjI0MDkucG5n]

搜索的步骤：如要搜索

索引 s0  
1. node2解析查询。  
2. node2将查询发给索引s0的分片/副本（R1,R2,R0）节点  
3. 各节点执行查询，将结果发给Node2

**想了解更多关于数据库、云技术的内容吗？**

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![ElasticSearch最全详细使用教程下篇（二）][ElasticSearch 1]

**数据和云小程序”DBASK“在线问答，随时解惑，欢迎了解和关注！**

![20190813140042717.jpg][]

[https_www.enmotech.com_web_detail_1_839_1.html]: https://www.enmotech.com/web/detail/1/839/1.html
[https_www.enmotech.com_web_detail_1_840_1.html]: https://www.enmotech.com/web/detail/1/840/1.html
[ElasticSearch]: https://www.enmotech.com/web/detail/1/835/1.html
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[https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-mapping-charfilter.html]: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-mapping-charfilter.html
[https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-pattern-replace-charfilter.html]: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-pattern-replace-charfilter.html
[https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-tokenizers.html]: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-tokenizers.html
[https_www.elastic.co_guide_en_elasticsearch_reference_current_analysis-analyzers.html]: https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-analyzers.html
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[ElasticSearch 1]: /images/20211101/7a2e1fd4905141ee9e6e5d71d3039152.png
[20190813140042717.jpg]: https://img-blog.csdnimg.cn/20190813140042717.jpg