elasticsearch系列三：索引详解（分词器、文档管理、路由详解（集群））
目录
一、分词器
1. 认识分词器
1.1 Analyzer 分析器
1.2 如何测试分词器
2. 内建的字符过滤器(character filter)
2.1 HTML过滤字符过滤器(HTML Strip Character Filter ）
2.2 mapping映射字符过滤器(Mapping character filter)
2.3 正则替换字符过滤器(Pattern Replace Character Filter)
3. 内建的分词器(Tokenizer)
4. 内建的Token Filter
4.1 同义词过滤器(Synonym Token Filter )
5. 内建的分析器Analyzer
5.1 自定义分析器Analyzer
5.2 为字段指定分词器
5.3 为索引定义default分词器
6. 分析器Analyzer的使用顺序
6.1 索引阶段Analyzer的使用顺序
6.2 查询阶段Analyzer的使用顺序
二、文档管理
1. 新建文档
2. 获取单个文档
2.1 HEAD请求根据ID获取单个文档
2.2 GET请求根据ID获取单个文档
2.3 根据ID不获取文档的source
2.4 根据ID获取文档的source
2.5 获取存储字段
3. 获取多个文档 _mget
4. 删除文档
4.1 指定文档id进行删除
4.2 用版本来控制删除
4.3 查询删除
4.4 当有文档有版本冲突时，不放弃删除操作
4.5 通过task api 来查看、查询删除任务
4.6 查询具体任务的状态
4.7 取消任务
5. 更新文档
5.1 指定文档id进行修改
5.2 乐观锁并发更新控制
6.Scripted update 通过脚本来更新文档
6.1、准备一个文档
6.2、对文档1的counter + 4
6.3、往数组中加入元素
6.4、添加一个字段
6.5、移除一个字段
6.6、根据判断条件执行删除或不做什么
6.7、合并传入的文档字段进行更新
6.8、再次执行6.7，更新内容相同，不需做什么
6.9、设置不做noop检测
6.10、upsert 操作
7. 通过条件查询来更新文档
8. 批量操作
8.1 curl + json 文件 批量向索引写入多个文档
9. reindex 重索引
10. refresh
三、路由详解
1. 集群组成
2. 在集群中创建索引的流程
3. 有索引的集群
4. 集群有节点出现故障，如主节点挂了，会重新选择主节点
5. 在集群中索引文档
6. 文档是如何路由的
7. 在集群中进行搜索
8. Master节点的工作是什么？
一、分词器
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?"
}
position：第几个词
offset：词的偏移位置
2. 内建的字符过滤器(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过滤字符过滤器(HTML Strip Character Filter ）
html_strip ：过滤html标签，解码HTML entities like "& "。
POST _analyze
{
"tokenizer": "keyword",
"char_filter": [ "html_strip" ],
"text": "I'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'm so happy!
"
}2.2 mapping映射字符过滤器(Mapping character filter)
官网链接：https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-mapping-charfilter.html
mapping ：用指定的字符串替换文本中的某字符串。
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"
]
}
}
}
}
}
测试示例：
POST my_index/_analyze
{
"analyzer": "my_analyzer",
"text": "My license plate is ٢٥٠١٥"
} 
2.3 正则替换字符过滤器(Pattern Replace Character Filter)
官网链接：https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-pattern-replace-charfilter.html
pattern_replace ：进行正则表达式替换。
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_"
}
}
}
}
}测试示例：
POST my_index/_analyze
{
"analyzer": "my_analyzer",
"text": "My credit card is 123-456-789"
} 
3. 内建的分词器(Tokenizer)
官网链接：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示例：
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. 内建的词项过滤器(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
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 同义词过滤器(Synonym Token Filter )
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. 内建的分析器Analyzer
官网链接：
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 自定义分析器Analyzer
配置参数：
| tokenizer | 分词器，必须包含 |
| char_filter | 字符过滤器数组 |
| filter | 词项过滤器数组 |
| position_increment_gap | 多值间的词项position增长间隔，默认是100。这是用来解决多值字段的短语查询不准确问题。 |
自定义 Analyzer示例：
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"
}
}
}测试示例：
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"
}
}
}
}
}测试示例：
PUT my_index10/_doc/1
{
"title": "张三说的确实在理"
}
GET /my_index10/_search
{
"query": {
"term": {
"title": "张三"
}
}
}6. 分析器Analyzer的使用顺序
我们可以为每个查询、每个字段、每个索引指定分析器。
6.1 索引阶段Analyzer的使用顺序
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；
若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。
若index setting中没有定义default分词器，则使用 standard analyzer。
6.2 查询阶段Analyzer的使用顺序
在查询阶段，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.
二、文档管理
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：
2. 获取单个文档
2.1 HEAD请求根据ID获取单个文档
HEAD twitter/_doc/11
2.2 GET请求根据ID获取单个文档
GET twitter/_doc/1
2.3 根据ID不获取文档的source
GET twitter/_doc/1?_source=false
2.4 根据ID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"
}}2.5 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
4.1 指定文档id进行删除
DELETE twitter/_doc/14.2 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"
}4.3 查询删除
POST twitter/_delete_by_query
{
"query": {
"match": {
"message": "some message"
}
}
}4.4 当有文档有版本冲突时，不放弃删除操作
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": {}
}
}4.5 通过task api 来查看、查询删除任务
GET _tasks?detailed=true&actions=*/delete/byquery4.6 查询具体任务的状态
GET /_tasks/taskId:14.7 取消任务
POST _tasks/task_id:1/_cancel5. 更新文档
5.1 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"
}5.2 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、根据判断条件执行删除或不做什么
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、合并传入的文档字段进行更新
POST uptest/_doc/1/_update
{
"doc" : {
"name" : "new_name"
}
}6.8、再次执行6.7，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 批量向索引写入多个文档
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"
accounts.json：
{"index":{"_id":"1"}}
{"account_number":1,"balance":39225,"firstname":"Amber","lastname":"Duke","age":32,"gender":"M","address":"880 Holmes Lane","employer":"Pyrami","email":"[email protected]","city":"Brogan","state":"IL"}
{"index":{"_id":"6"}}
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{"account_number":82,"balance":41412,"firstname":"Concetta","lastname":"Barnes","age":39,"gender":"F","address":"195 Bayview Place","employer":"Fitcore","email":"[email protected]","city":"Summerfield","state":"NC"}
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{"account_number":195,"balance":5025,"firstname":"Kaye","lastname":"Gibson","age":31,"gender":"M","address":"955 Hopkins Street","employer":"Zork","email":"[email protected]","city":"Ola","state":"WY"}
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{"account_number":203,"balance":21890,"firstname":"Eve","lastname":"Wyatt","age":33,"gender":"M","address":"435 Furman Street","employer":"Assitia","email":"[email protected]","city":"Jamestown","state":"MN"}
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{"account_number":208,"balance":40760,"firstname":"Garcia","lastname":"Hess","age":26,"gender":"F","address":"810 Nostrand Avenue","employer":"Quiltigen","email":"[email protected]","city":"Brooktrails","state":"GA"}
{"index":{"_id":"210"}}
{"account_number":210,"balance":33946,"firstname":"Cherry","lastname":"Carey","age":24,"gender":"M","address":"539 Tiffany Place","employer":"Martgo","email":"[email protected]","city":"Fairacres","state":"AK"}
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{"account_number":215,"balance":37427,"firstname":"Copeland","lastname":"Solomon","age":20,"gender":"M","address":"741 McDonald Avenue","employer":"Recognia","email":"[email protected]","city":"Edmund","state":"ME"}
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{"account_number":222,"balance":14764,"firstname":"Rachelle","lastname":"Rice","age":36,"gender":"M","address":"333 Narrows Avenue","employer":"Enaut","email":"[email protected]","city":"Wright","state":"AZ"}
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{"account_number":227,"balance":19780,"firstname":"Coleman","lastname":"Berg","age":22,"gender":"M","address":"776 Little Street","employer":"Exoteric","email":"[email protected]","city":"Eagleville","state":"WV"}
{"index":{"_id":"234"}}
{"account_number":234,"balance":44207,"firstname":"Betty","lastname":"Hall","age":37,"gender":"F","address":"709 Garfield Place","employer":"Miraclis","email":"[email protected]","city":"Bendon","state":"NY"}
{"index":{"_id":"239"}}
{"account_number":239,"balance":25719,"firstname":"Chang","lastname":"Boyer","age":36,"gender":"M","address":"895 Brigham Street","employer":"Qaboos","email":"[email protected]","city":"Belgreen","state":"NH"}
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{"account_number":241,"balance":25379,"firstname":"Schroeder","lastname":"Harrington","age":26,"gender":"M","address":"610 Tapscott Avenue","employer":"Otherway","email":"[email protected]","city":"Ebro","state":"TX"}
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{"account_number":246,"balance":28405,"firstname":"Katheryn","lastname":"Foster","age":21,"gender":"F","address":"259 Kane Street","employer":"Quantalia","email":"[email protected]","city":"Bath","state":"TX"}
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{"account_number":253,"balance":20240,"firstname":"Melissa","lastname":"Gould","age":31,"gender":"M","address":"440 Fuller Place","employer":"Buzzopia","email":"[email protected]","city":"Lumberton","state":"MD"}
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{"account_number":258,"balance":5712,"firstname":"Lindsey","lastname":"Hawkins","age":37,"gender":"M","address":"706 Frost Street","employer":"Enormo","email":"[email protected]","city":"Gardners","state":"AK"}
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{"account_number":330,"balance":41620,"firstname":"Yvette","lastname":"Browning","age":34,"gender":"F","address":"431 Beekman Place","employer":"Marketoid","email":"[email protected]","city":"Talpa","state":"CO"}
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{"account_number":359,"balance":29927,"firstname":"Vanessa","lastname":"Harvey","age":28,"gender":"F","address":"679 Rutledge Street","employer":"Zentime","email":"[email protected]","city":"Williston","state":"IL"}
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{"account_number":361,"balance":23659,"firstname":"Noreen","lastname":"Shelton","age":36,"gender":"M","address":"702 Tillary Street","employer":"Medmex","email":"[email protected]","city":"Derwood","state":"NH"}
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{"account_number":366,"balance":42368,"firstname":"Lydia","lastname":"Cooke","age":31,"gender":"M","address":"470 Coleman Street","employer":"Comstar","email":"[email protected]","city":"Datil","state":"TN"}
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{"account_number":373,"balance":9671,"firstname":"Simpson","lastname":"Carpenter","age":21,"gender":"M","address":"837 Horace Court","employer":"Snips","email":"[email protected]","city":"Tolu","state":"MA"}
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{"account_number":378,"balance":27100,"firstname":"Watson","lastname":"Simpson","age":36,"gender":"F","address":"644 Thomas Street","employer":"Wrapture","email":"[email protected]","city":"Keller","state":"TX"}
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{"account_number":380,"balance":35628,"firstname":"Fernandez","lastname":"Reid","age":33,"gender":"F","address":"154 Melba Court","employer":"Cosmosis","email":"[email protected]","city":"Boyd","state":"NE"}
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{"account_number":385,"balance":11022,"firstname":"Rosalinda","lastname":"Valencia","age":22,"gender":"M","address":"933 Lloyd Street","employer":"Zoarere","email":"[email protected]","city":"Waverly","state":"GA"}
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{"account_number":397,"balance":37418,"firstname":"Leonard","lastname":"Gray","age":36,"gender":"F","address":"840 Morgan Avenue","employer":"Recritube","email":"[email protected]","city":"Edenburg","state":"AL"}
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{"account_number":400,"balance":20685,"firstname":"Kane","lastname":"King","age":21,"gender":"F","address":"405 Cornelia Street","employer":"Tri@Tribalog","email":"kaneking@[email protected]","city":"Gulf","state":"VT"}
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{"account_number":405,"balance":5679,"firstname":"Strickland","lastname":"Fuller","age":26,"gender":"M","address":"990 Concord Street","employer":"Digique","email":"[email protected]","city":"Southmont","state":"NV"}
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{"account_number":412,"balance":27436,"firstname":"Ilene","lastname":"Abbott","age":26,"gender":"M","address":"846 Vine Street","employer":"Typhonica","email":"[email protected]","city":"Cedarville","state":"VT"}
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{"account_number":417,"balance":1788,"firstname":"Wheeler","lastname":"Ayers","age":35,"gender":"F","address":"677 Hope Street","employer":"Fortean","email":"[email protected]","city":"Ironton","state":"PA"}
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{"account_number":424,"balance":36818,"firstname":"Tracie","lastname":"Gregory","age":34,"gender":"M","address":"112 Hunterfly Place","employer":"Comstruct","email":"[email protected]","city":"Onton","state":"TN"}
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{"account_number":429,"balance":46970,"firstname":"Cantu","lastname":"Lindsey","age":31,"gender":"M","address":"404 Willoughby Avenue","employer":"Inquala","email":"[email protected]","city":"Cowiche","state":"IA"}
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{"account_number":431,"balance":13136,"firstname":"Laurie","lastname":"Shaw","age":26,"gender":"F","address":"263 Aviation Road","employer":"Zillanet","email":"[email protected]","city":"Harmon","state":"WV"}
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{"account_number":436,"balance":27585,"firstname":"Alexander","lastname":"Sargent","age":23,"gender":"M","address":"363 Albemarle Road","employer":"Fangold","email":"[email protected]","city":"Calpine","state":"OR"}
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{"account_number":443,"balance":7588,"firstname":"Huff","lastname":"Thomas","age":23,"gender":"M","address":"538 Erskine Loop","employer":"Accufarm","email":"[email protected]","city":"Corinne","state":"AL"}
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{"account_number":448,"balance":22776,"firstname":"Adriana","lastname":"Mcfadden","age":35,"gender":"F","address":"984 Woodside Avenue","employer":"Telequiet","email":"[email protected]","city":"Darrtown","state":"WI"}
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{"account_number":450,"balance":2643,"firstname":"Bradford","lastname":"Nielsen","age":25,"gender":"M","address":"487 Keen Court","employer":"Exovent","email":"[email protected]","city":"Hamilton","state":"DE"}
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{"account_number":455,"balance":39556,"firstname":"Lynn","lastname":"Tran","age":36,"gender":"M","address":"741 Richmond Street","employer":"Optyk","email":"[email protected]","city":"Clinton","state":"WV"}
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{"account_number":462,"balance":10871,"firstname":"Calderon","lastname":"Day","age":27,"gender":"M","address":"810 Milford Street","employer":"Cofine","email":"[email protected]","city":"Kula","state":"OK"}
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{"account_number":467,"balance":6312,"firstname":"Angelica","lastname":"May","age":32,"gender":"F","address":"384 Karweg Place","employer":"Keeg","email":"[email protected]","city":"Tetherow","state":"IA"}
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{"account_number":474,"balance":35896,"firstname":"Obrien","lastname":"Walton","age":40,"gender":"F","address":"192 Ide Court","employer":"Suremax","email":"[email protected]","city":"Crucible","state":"UT"}
{"index":{"_id":"479"}}
{"account_number":479,"balance":31865,"firstname":"Cameron","lastname":"Ross","age":40,"gender":"M","address":"904 Bouck Court","employer":"Telpod","email":"[email protected]","city":"Nord","state":"MO"}
{"index":{"_id":"481"}}
{"account_number":481,"balance":20024,"firstname":"Lina","lastname":"Stanley","age":33,"gender":"M","address":"361 Hanover Place","employer":"Strozen","email":"[email protected]","city":"Wyoming","state":"NC"}
{"index":{"_id":"486"}}
{"account_number":486,"balance":35902,"firstname":"Dixie","lastname":"Fuentes","age":22,"gender":"F","address":"991 Applegate Court","employer":"Portico","email":"[email protected]","city":"Salix","state":"VA"}
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{"account_number":493,"balance":5871,"firstname":"Campbell","lastname":"Best","age":24,"gender":"M","address":"297 Friel Place","employer":"Fanfare","email":"[email protected]","city":"Kidder","state":"GA"}
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{"account_number":498,"balance":10516,"firstname":"Stella","lastname":"Hinton","age":39,"gender":"F","address":"649 Columbia Place","employer":"Flyboyz","email":"[email protected]","city":"Crenshaw","state":"SC"}
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时。
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。
9.1 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"
}
}9.2 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"
}
}9.3 如果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"
}
}9.4 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"
}
}9.5 可以从多个源获取数据
POST _reindex
{
"source": {
"index": ["twitter", "blog"],
"type": ["_doc", "post"]
},
"dest": {
"index": "all_together"
}
}9.6 可以限定文档数量
POST _reindex
{
"size": 10000,
"source": {
"index": "twitter",
"sort": { "date": "desc" }
},
"dest": {
"index": "new_twitter"
}
}9.7 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"
}
}9.8 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"
}
}9.9 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"
}
}9.10 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"
}
}9.11 通过_task 来查询执行状态
GET _tasks?detailed=true&actions=*reindex10. 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。
三、路由详解
1. 集群组成
第一个节点启动：
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：
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在启动的过程中，会去找主节点Node1，告诉Node1“我要加入到集群里面了”。主节点Node1在接收到请求以后，查看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：
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
（0）向Node3请求创建索引。
（1）Node3转发请求给master(即Node1)。
（2）master选择节点存放分片、副本，记录元信息。
（3）master通知“参与存放索引分片、副本的节点”创建分片、副本。
（4）参与节点向master节点反馈结果。
（5）等待时间到了，master向node3反馈结果信息，node3响应请求。
（6）主节点将元信息广播给所有从节点。
3. 有索引的集群
4. 集群有节点出现故障，如主节点挂了，会重新选择主节点
5. 在集群中索引文档
索引文档的步骤：
1、node2计算文档的路由值，得到存放文档的分片（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。
在创建索引时，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
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。
4、Node2合并结果，作出响应。
8. Master节点的工作是什么？
1. 存储集群的元信息，如集群名称、集群中的节点。
2. 转发创建索引和索引文档的请求。
3. 和其他的节点进行通信，告诉其他节点有新的节点加入等。