Elasticsearch 7.4.0官方文档操作 - 数据库

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官方文档地址

https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html

1.0.0 `设置Elasticsearch`

1.1.0 `安装Elasticsearch`

1.1.1 `Linux安装Elasticsearch`

Linux下,非Docker启动Elasticsearch 6.3.0,安装ik分词器插件,以及使用Kibana测试Elasticsearch

1.1.2 `Docker安装Elasticsearch`

Linux使用Docker启动Elasticsearch并配合Kibana使用,安装ik分词器

2.0.0 `Elasticsearch入门`

2.1.0 `索引文档`

添加文档,请求体是JSON格式

PUT /customer/_doc/1 { "name": "John Doe" }

这里添加了索引customer 类型_doc 文档id1 添加文档内容{"name": "John Doe"}
索引不存在,则自动创建
这是新文档,所以文档版本是1

{ "_index" : "customer", "_type" : "_doc", "_id" : "1", "_version" : 1, "result" : "created", "_shards" : { "total" : 2, "successful" : 1, "failed" : 0 }, "_seq_no" : 0, "_primary_term" : 1 }

获取文档

GET /customer/_doc/1

结果

{ "_index" : "customer", "_type" : "_doc", "_id" : "1", "_version" : 1, "_seq_no" : 0, "_primary_term" : 1, "found" : true, "_source" : { "name" : "John Doe" } }

批量插入,使用关键字_bulk索引为bank

把下面三个点换成accounts.json

POST /bank/_bulk ...

查看插入的数据量

GET /_cat/indices?v

2.2.0 `开始搜索`

按照account_number进行升序,检索bank索引的全部文档

GET /bank/_search { "query": { "match_all": {} }, "sort": [ { "account_number": "asc" } ] }

默认显示前10个文档hits

{ "took" : 138, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1000, "relation" : "eq" }, "max_score" : null, "hits" : [ { "_index" : "bank", "_type" : "_doc", "_id" : "0", "_score" : null, "_source" : { "account_number" : 0, "balance" : 16623, "firstname" : "Bradshaw", "lastname" : "Mckenzie", "age" : 29, "gender" : "F", "address" : "244 Columbus Place", "employer" : "Euron", "email" : "bradshawmckenzie@euron.com", "city" : "Hobucken", "state" : "CO" }, "sort" : [ 0 ] }, { "_index" : "bank", "_type" : "_doc", "_id" : "1", "_score" : null, "_source" : { "account_number" : 1, "balance" : 39225, "firstname" : "Amber", "lastname" : "Duke", "age" : 32, "gender" : "M", "address" : "880 Holmes Lane", "employer" : "Pyrami", "email" : "amberduke@pyrami.com", "city" : "Brogan", "state" : "IL" }, "sort" : [ 1 ] },...

took搜索花费时间单位:毫秒ms
timed_out搜索是否超时
_shards搜索了多少分片,成功,失败,跳过的分片数
max_score找到的最相关的文档的分数
hits.total.value匹配多少文档
hits.sort文档的排序位置
hits._score文档的相关性分数(在使用时不适用match_all)

分页查询from size

跳过前5个文档,然后再往下查找十个文档

GET /bank/_search { "query": { "match_all": {} }, "sort": [ { "account_number": "asc" } ], "from": 5, "size": 10 }

条件查询match

默认进行分词查找有关mill或lane的词

匹配19个

GET /bank/_search { "query": { "match": { "address": "mill lane" } } }

短语搜索match_phrase

查找有关mill lane的短语

匹配1个

GET /bank/_search { "query": { "match_phrase": { "address": "mill lane" } } }

多条件查找bool

must都满足 must_not都不满足 should满足任何一个

默认按照相关性分数排序

在索引bank中查找age=40 and state!='ID'的文档

GET /bank/_search { "query": { "bool": { "must": [ { "match": { "age": "40" } } ], "must_not": [ { "match": { "state": "ID" } } ] } } }

过滤器filter

查找20000<=balance<=30000

GET /bank/_search { "query": { "bool": { "must": { "match_all": {} }, "filter": { "range": { "balance": { "gte": 20000, "lte": 30000 } } } } } }

2.3.0 `使用聚合分析结果`

terms分组,聚合名称group_by_state

对字段state进行分组,降序返回账户最多的10种

GET /bank/_search { "size": 0, "aggs": { "group_by_state": { "terms": { "field": "state.keyword" } } } }

结果

size=0所以hits不显示内容
聚合默认是前10条,默认按照分组文档数量降序

{ "took" : 2, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1000, "relation" : "eq" }, "max_score" : null, "hits" : [ ] }, "aggregations" : { "group_by_state" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 743, "buckets" : [ { "key" : "TX", "doc_count" : 30 }, { "key" : "MD", "doc_count" : 28 }, { "key" : "ID", "doc_count" : 27 }, { "key" : "AL", "doc_count" : 25 }, { "key" : "ME", "doc_count" : 25 }, { "key" : "TN", "doc_count" : 25 }, { "key" : "WY", "doc_count" : 25 }, { "key" : "DC", "doc_count" : 24 }, { "key" : "MA", "doc_count" : 24 }, { "key" : "ND", "doc_count" : 24 } ] } } }

avg计算平均数

对分组的每项数据计算balance平均值

GET /bank/_search { "size": 0, "aggs": { "group_by_state": { "terms": { "field": "state.keyword" }, "aggs": { "average_balance": { "avg": { "field": "balance" } } } } } }

结果,添加了一个我们自定义的字段average_balance用来存放平均值

... { "key" : "TX", "doc_count" : 30, "average_balance" : { "value" : 26073.3 } }, ...

order排序

对分组的balance计算平均值,并按照平均值进行降序

GET /bank/_search { "size": 0, "aggs": { "group_by_state": { "terms": { "field": "state.keyword", "order": { "average_balance": "desc" } }, "aggs": { "average_balance": { "avg": { "field": "balance" } } } } } }

结果

... "aggregations" : { "group_by_state" : { "doc_count_error_upper_bound" : -1, "sum_other_doc_count" : 827, "buckets" : [ { "key" : "CO", "doc_count" : 14, "average_balance" : { "value" : 32460.35714285714 } }, { "key" : "NE", "doc_count" : 16, "average_balance" : { "value" : 32041.5625 } }, { "key" : "AZ", "doc_count" : 14, "average_balance" : { "value" : 31634.785714285714 } }, ...

3.0.0 `映射`

映射类型,元字段 _index _type _id _source
字段类型
- 简单的 text keyword date long double boolean ip
- 层级关系的 object nested
- 特殊的 geo_point geo_shape completion

创建索引my-index

并创建字段age email name类型分别是integer keyword text

PUT /my-index { "mappings": { "properties": { "age": { "type": "integer" }, "email": { "type": "keyword" }, "name": { "type": "text" } } } }

添加字段到已存在的索引

添加字段employee-id到my-index索引并设置类型keyword

设置"index": false使字段不能被检索

PUT /my-index/_mapping { "properties": { "employee-id": { "type": "keyword", "index": false } } }

更新映射的字段
不能更新现有字段的映射,以下情况除外
- 添加新properties到object类型的字段
- 使用field映射参数已启用multi-fields
- 更改ignore_above映射参数
更新现有字段会使数据失效,如果想改字段的映射,可以创建一个正确映射的索引并重新导入数据
如果只选重命名字段的话,可以使用alias字段

查看映射

GET /my-index/_mapping

结果

{ "my-index" : { "mappings" : { "properties" : { "age" : { "type" : "integer" }, "email" : { "type" : "keyword" }, "employee-id" : { "type" : "keyword", "index" : false }, "name" : { "type" : "text" } } } } }

查看一个或多个字段的映射

查看多个可以使用GET /my-index/_mapping/field/employee-id,age

GET /my-index/_mapping/field/employee-id

结果

{ "my-index" : { "mappings" : { "employee-id" : { "full_name" : "employee-id", "mapping" : { "employee-id" : { "type" : "keyword", "index" : false } } } } } }

3.1.0 `删除映射类型`

什么是映射类型
- 一个索引可以有多个类型
- 每个类型可以有自动的字段
- 不同类型可以有相同字段
- 同索引不同类型可以是父子关系

下面表示在twitter索引的user tweet类型中查找字段user_name为kimchy的文档

GET twitter/user,tweet/_search { "query": { "match": { "user_name": "kimchy" } } }

为什么要删除映射类型
- 因为同索引不同类型同字段定义的映射需要相同
- 有可能不同类型同字段,但字段类型不同,会干扰Lucene的高效压缩文档的能力
替换映射类型
- 每个文档类型设置不同索引
  - 可以设置A索引,设置B索引,这样同字段类型就不会发生冲突
  - 将较少文档的索引设置主分片少,文档多的索引设置主分片多

7.0.0及其以后不建议使用指定类型的文档,将使用_doc作为类型

添加或定义映射时,数据类型默认为_doc

PUT toopo { "mappings": { "properties": { "distance": { "type": "long" }, "transit_mode": { "type": "keyword" } } } }

添加了映射vc 文档类型_doc 添加了_id为1 也可以不指定id随机生成

并且添加了字段c 创建了自动映射

POST vc/_doc/1 { "c":22 }

添加了索引pan并添加两个文档文档的_id随机

添加了字段foo 会自动创建字段类型

如果想执行_id可以使用{ "index" : {"_id":"1"} }

POST pan/_bulk { "index" : {} } { "foo" : "baz" } { "index" : {} } { "foo" : "qux" }

3.2.0 `映射参数`

以下参数对于某些或所有字段数据类型是通用的

analyzer
normalizer
boost
coerce
copy_to
doc_values
dynamic
enabled
fielddata
eager_global_ordinals
format
ignore_above
ignore_malformed
index_options
index_phrases
index_prefixes
index
fields
norms
null_value
position_increment_gap
properties
search_analyzer
similarity
store
term_vector

3.2.1 `analyzer`

设置分词器,仅限于text类型,默认使用standard

例如设置字段cx使用ik分词器

PUT nx { "mappings": { "properties": { "cx":{ "type": "text", "analyzer": "ik_max_word" } } } }

可以测试分词的情况

GET nx/_analyze { "field": "cx", "text": ["我的热情"] }

结果

{ "tokens" : [ { "token" : "我", "start_offset" : 0, "end_offset" : 1, "type" : "CN_CHAR", "position" : 0 }, { "token" : "的", "start_offset" : 1, "end_offset" : 2, "type" : "CN_CHAR", "position" : 1 }, { "token" : "热情", "start_offset" : 2, "end_offset" : 4, "type" : "CN_WORD", "position" : 2 } ] }

3.2.2 `coerce`

它用来设置是否支持字段类型自动转换,默认为true 表示可以

添加文档1则可以成功,文档2则不可以添加,因为"10"不是integer类型

PUT my_index { "mappings": { "properties": { "number_one": { "type": "integer" }, "number_two": { "type": "integer", "coerce": false } } } }PUT my_index/_doc/1 { "number_one": "10" }PUT my_index/_doc/2 { "number_two": "10" }

全局设置禁用"index.mapping.coerce": false

因为文档字段number_one设置了true所以文档1可以添加,文档2则不可以添加

PUT my_index { "settings": { "index.mapping.coerce": false }, "mappings": { "properties": { "number_one": { "type": "integer", "coerce": true }, "number_two": { "type": "integer" } } } }PUT my_index/_doc/1 { "number_one": "10" } PUT my_index/_doc/2 { "number_two": "10" }

3.2.3 `copy_to`

可以将一个字段的内容传递给另外一个字段

在实际文档1的_source中字段c还是不存在,只存在a b字段

但是这里查询字段c含有John和Smith单词可以查找到

PUT my_index { "mappings": { "properties": { "a": { "type": "text", "copy_to": "c" }, "b": { "type": "text", "copy_to": "c" }, "c": { "type": "text" } } } }PUT my_index/_doc/1 { "a": "John", "b": "Smith" }GET my_index/_search { "query": { "match": { "c": { "query": "John Smith", "operator": "and" } } } }

不会修改原始_source中的值,只会在检索分析中存在
可以支持一个字段到多个字段"copy_to": ["b","c"]
不支持继承特性,例如字段a设置了"copy_to":"b",字段b设置了"copy_to":"c",检索分析过程中c中无a值,只有b值

3.2.4 `doc_values`

如果不需要对字段排序 聚合 脚本就可以禁用它,节省空间

默认为true启用

这里虽然设置了false 但还可以查询

PUT my_index { "mappings": { "properties": { "a": { "type": "keyword" }, "b": { "type": "keyword", "doc_values": false } } } }

3.2.5 `dynamic`

动态添加了索引,字段,映射类型

PUT my_index/_doc/1 { "username": "johnsmith", "name": { "first": "John", "last": "Smith" } }PUT my_index/_doc/2 { "username": "marywhite", "email": "mary@white.com", "name": { "first": "Mary", "middle": "Alice", "last": "White" } }

dynamic的值
true默认,可以将新字段自动添加并字段设置映射类型
false可以将新字段添加到_source中,但这个字段不可用于检索,除非重新删除索引,重新定义映射
strict不可以添加新字段,除非重新删除索引,重新定义映射

这里文档1,2,4都可以添加成功,但是文档4的"b4"字段用来检索也检索不到,因为映射没有添加b4 当然更没有添加b33

PUT my_index { "mappings": { "dynamic": false, "properties": { "a": { "properties": { "b1": {"type": "text"}, "b2": { "dynamic": true, "properties": {} }, "b3": { "dynamic": "strict", "properties": {} } } } } } }POST my_index/_doc/1 { "a":{ "b1":"are you ok" } } POST my_index/_doc/2 { "a":{ "b2":{ "b22":"are you ok" } } } POST my_index/_doc/3 { "a":{ "b3":{ "b33":"are you ok" } } } POST my_index/_doc/4 { "a":{ "b4":"are you ok" } }

3.2.6 `enabled`

适用于类型object的字段,设置为false之后

可以以任何类型添加数据,数据都会被储存在_source中

PUT my_index { "mappings": { "properties": { "a": { "type": "object", "enabled": false }, "b":{"type": "integer"} } } }PUT my_index/_doc/1 { "a": { "arbitrary_object": { "some_array": [ "foo", "bar", { "baz": 2 } ] } }, "b":1 }PUT my_index/_doc/2 { "a": "none", "b":2 }PUT my_index/_doc/3 { "a": 3, "b":3 }

可以以查询不禁用字段来在_source中显示,或者以查询全部来查询出来或以_id值来查询

GET my_index/_search { "query": { "match": { "b": 1 } } }GET my_index/_searchGET my_index/_doc/2

查询映射可知,它不会储存在映射中

GET my_index/_mapping

结果

{ "my_index" : { "mappings" : { "properties" : { "a" : { "type" : "object", "enabled" : false }, "b" : { "type" : "integer" } } } } }

可以设置全部禁用

PUT my_index { "mappings": { "enabled": false } }

可以在全部禁用的索引里面添加任何字段,每个字段可以添加任何类型

PUT my_index/_doc/session_1 { "user_id": "kimchy", "session_data": { "arbitrary_object": { "some_array": [ "foo", "bar", { "baz": 2 } ] } }, "last_updated": "2015-12-06T18:20:22" }

只能以查找全部或者_id来查询出数据

GET my_index/_searchGET my_index/_doc/session_1

查看映射

GET my_index/_mapping

结果

{ "my_index" : { "mappings" : { "enabled" : false } } }

3.2.7 `fielddate`

用于字段类型text

因为text不可以用于排序 聚合操作

如果想用也可以,需要进行设置

设置"fielddata": true
直接使用my_field即可

PUT my_index/_mapping { "properties": { "my_field": { "type": "text", "fielddata": true } } }

设置"fields": {"keyword": {"type": "keyword"}}
使用my_field.keyword来替换my_field的使用

PUT my_index { "mappings": { "properties": { "my_field": { "type": "text", "fields": { "keyword": { "type": "keyword" } } } } } }

3.2.8 `format`

ELasticsearch会将传入的date类型解析为一个long值,是UTC的毫秒数

format自定义date数据格式也可以表示为 yyyy-MM-dd HH:mm:ss

PUT my_index { "mappings": { "properties": { "date": { "type": "date", "format": "yyyy-MM-dd" } } } }

3.2.9 `ignore_above`

用于字符串来设置限定长度,如果大于长度会储存在_source但不可以被检索,聚合

PUT my_index { "mappings": { "properties": { "a": { "type": "keyword", "ignore_above": 3 } } } }PUT my_index/_doc/1 { "a": "aaa" }PUT my_index/_doc/2 { "a": "aaaa" }

可以使用查找全部或指定_id找到

GET my_index/_searchGET my_index/_doc/2

查询,聚合,排序则不可以,测试聚合

GET my_index/_search { "aggs": { "a_name": { "terms": { "field": "a" } } } }

结果

{ "took" : 68, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 2, "relation" : "eq" }, "max_score" : 1.0, "hits" : [ { "_index" : "my_index", "_type" : "_doc", "_id" : "1", "_score" : 1.0, "_source" : { "a" : "aaa" } }, { "_index" : "my_index", "_type" : "_doc", "_id" : "2", "_score" : 1.0, "_source" : { "a" : "aaaa" } } ] }, "aggregations" : { "a_name" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "aaa", "doc_count" : 1 } ] } } }

3.2.10 `ignore_malformed`

忽略格式错误的数据传入,默认false

文档1可以执行,文档2不可以执行
在查询中不可以指定查询格式错误的数据

PUT my_index { "mappings": { "properties": { "a": { "type": "integer", "ignore_malformed": true }, "b": { "type": "integer" } } } }PUT my_index/_doc/1 { "a": "foo" }PUT my_index/_doc/2 { "b": "foo" }

全局设置,这里字段a可以插入错误的数据,b则不可以插入错误的数据

PUT my_index { "settings": { "index.mapping.ignore_malformed": true }, "mappings": { "properties": { "a": { "type": "byte" }, "b": { "type": "integer", "ignore_malformed": false } } } }

注意

ignore_malformed不可以用于nested object range数据类型

3.2.11 `index`

检索true 不检索false 默认为true
不检索的字段不可被查询

3.2.12 `fields`

可以把String类型的字段映射为text类型,也可以映射为keyword类型

添加字段city类型为text 内部字段raw类型keyword
可以使用city用于全文检索,也可以使用city.raw实现排序,聚合操作

PUT my_index { "mappings": { "properties": { "city": { "type": "text", "fields": { "raw": { "type": "keyword" } } } } } }PUT my_index/_doc/1 { "city": "New York" }PUT my_index/_doc/2 { "city": "York" }GET my_index/_search { "query": { "match": { "city": "york" } }, "sort": { "city.raw": "asc" }, "aggs": { "Cities": { "terms": { "field": "city.raw" } } } }

多字段

添加字段a是text类型,默认使用standard分词器,
在字段a里面嵌套了一个字段b,也是text类型,使用english分词器

PUT my_index { "mappings": { "properties": { "a": { "type": "text", "fields": { "b": { "type": "text", "analyzer": "english" } } } } } }PUT my_index/_doc/1 { "a": "quick brown fox" } PUT my_index/_doc/2 { "a": "quick brown foxes" }

查找在字段a 和字段a.b 中内容为quick brown foxes的文档

"type": "most_fields" 可以设置相关性得分相加

GET my_index/_search { "query": { "multi_match": { "query": "quick brown foxes", "fields": [ "a", "a.b" ], "type": "most_fields" } } }

3.2.13 `norms`

对于仅用于筛选或聚合的字段设置

norms设置为false后表示不对其评分

也可以使用PUT对现有字段进行设置norms为false

一旦设置为false后就不可再改为true

设置字段a不进行评分

PUT my_index/_mapping { "properties": { "a": { "type": "text", "norms": false } } }

3.2.14 `null_value`

一个null值不能被检索

当字段设置null时,或者设置为空数组,或者数组中的值都为null时,则当做该字段没有值

需要与字段的类型相同,例如:不可以使用long字段类型设置"null_value": "xxx"

它只可以影响检索却不能影响到元文档

下面设置了字段a如果为null的话,可以使用xxx代替检索该字段为null值的文档
检索结果为文档1,3,4 因为检索时会把为null的值看出xxx 空数组不包含任何,所以不会被检索到

PUT my_index { "mappings": { "properties": { "a": { "type":"keyword", "null_value": "xxx" } } } }PUT my_index/_doc/1 { "a": null }PUT my_index/_doc/2 { "a": [] }PUT my_index/_doc/3 { "a": [null] }PUT my_index/_doc/4 { "a": [null,1] }GET my_index/_search { "query": { "term": { "a": "xxx" } } }

3.2.15 `properties`

适用于类型object nested的字段,可以添加任何数据类型

同索引不同字段下可以有进行不同的设置,可以使用PUT来为字段添加新属性

创建索引时定义
使用PUT添加或更新映射类型时定义
添加新字段的文档进行动态定义

定义manager为object类型,定义employees为nested类型

PUT my_index { "mappings": { "properties": { "manager": { "properties": { "age": { "type": "integer" }, "name": { "type": "text" } } }, "employees": { "type": "nested", "properties": { "age": { "type": "integer" }, "name": { "type": "text" } } } } } }PUT my_index/_doc/1 { "region": "US", "manager": { "name": "Alice White", "age": 30 }, "employees": [ { "name": "John Smith", "age": 34 }, { "name": "Peter Brown", "age": 26 } ] }

点符号,可以用于检索和聚合等

必须知道内字段的完整路径

GET my_index/_search { "query": { "match": { "manager.name": "Alice White" } }, "aggs": { "Employees": { "nested": { "path": "employees" }, "aggs": { "Employee Ages": { "histogram": { "field": "employees.age", "interval": 5 } } } } } }

3.2.16 `store`

设置字段为true 默认false 可以在检索结果的_source中只显示这些字段

查询结果的文档只显示两个属性title date

PUT my_index { "mappings": { "properties": { "title": { "type": "text", "store": true }, "date": { "type": "date", "store": true }, "content": { "type": "text" } } } }PUT my_index/_doc/1 { "title": "Some short title", "date": "2015-01-01", "content": "A very long content field..." }GET my_index/_search { "stored_fields": [ "title", "date", "content" ] }

3.3.0 `动态映射`

创建了哪些东西
索引data
创建了一个_id为"1"文档
创建了字段类型为long的字段count 并添加了值为5

PUT data/_doc/1 { "count": 5 }

3.3.1 `动态字段映射`

默认情况下是支持动态映射的,因为dynamic默认为true

除非你设置了object的dynamic为false或者strict

默认映射的类型
null不会添加任何字段
true或false -> boolean
有小数的话 -> float
整数类型 -> long
对象 -> object
数组 -> 取决于第一个不是null的值
字符串 -> 通过日期检测date 通过数字检测double``long 其他的为text keyword

3.3.0 `元字段`

_index文档所属的索引
_type文档的映射类型
_id文档编号
_source文档正文的原始JSON
_size文档的_source提供的字段大小,单位:字节
_field_names文档中包含非空值的所有字段
_ignored由于导致索引时间被忽略的文档中的所有字段
_routing一个自定义的路由值,用于将文档路由到特定的分片
_meta特定于应用程序的元数据

3.3.1 `_id`

每个文档都有一个_id唯一标识它的索引

指定文档id添加了文档1,文档2
使用terms来根据字段元字段_id来批量匹配

PUT my_index/_doc/1 { "text": "Document with ID 1" }PUT my_index/_doc/2 { "text": "Document with ID 2" }GET my_index/_search { "query": { "terms": { "_id": [ "1", "2" ] } } }

3.3.2 `_index`

添加索引1index_1文档1,索引2index_2文档2
在索引1,索引2中查询元字段_index为index_1 index_2
并聚合按照_index进行分组,取前十条数据并按照_index进行升序

PUT index_1/_doc/1 { "text": "Document in index 1" }PUT index_2/_doc/2 { "text": "Document in index 2" }GET index_1,index_2/_search { "query": { "terms": { "_index": ["index_1", "index_2"] } }, "aggs": { "indices": { "terms": { "field": "_index", "size": 10 } } }, "sort": [ { "_index": { "order": "asc" } } ] }

3.3.3 `_meta`

Elasticsearch不会使用这些元数据,例如可以存文档所属的类

添加元数据

PUT my_index { "mappings": { "_meta": { "class": "MyApp::User", "version": { "min": "1.0", "max": "1.3" } } } }

查询元数据

GET my_index/_mapping

修改元数据

PUT my_index/_mapping { "_meta": { "class": "MyApp2::User3", "version": { "min": "1.3", "max": "1.5" } } }

3.3.4 `_routing`

创建_routing的文档

PUT my_index/_doc/1?routing=user1 { "title": "This is a document" }

查找具有_routing的文档,必须要知道_routing的值

GET my_index/_doc/1?routing=user1

使用_routing字段进行查询

GET my_index/_search { "query": { "terms": { "_routing": [ "user1" ] } } }

指定多个路由值查询

GET my_index/_search?routing=user1,user2 { "query": { "match": { "title": "document" } } }

如果设置了_routing为true时,在插入数据时必须指定路由值,否则异常

PUT my_index2 { "mappings": { "_routing": { "required": true } } }PUT my_index2/_doc/1 { "text": "No routing value provided" }

3.3.5 `_source`

包括原JSON文档,如果在_source中存在的字段在映射中不存在,则认为该字段不可被检索

3.3.6 `_type`

已经废除,现在使用_doc代表默认的文档类型

3.4.0 `字段数据类型`

核心数据类型

字符串
- text keyword
数值类型
- long integer short byte double float half_float scaled_float
日期类型
- date
日期纳秒
- date_nanos
布尔类型
- boolean
二进制
- binary
范围
- integer_range float_range long_range double_range date_range

复杂数据类型

单个json对象

object
数组JSON对象

nested

地理数据类型

地理位置
- geo_point纬度/经度积分
地理形状
- geo_shape用于多边形等复杂形状

专业数据类型

ip表示IPv4 IPv6地址
completion提供自动完成建议
token_count计算字符串中令牌的数量
murmur3在索引时计算值的哈希并将其存储在索引中
annotated-text索引包含特殊标记的文本(通常用于标识命名实体)
percolator接受来自查询 dsl 的查询
join定义同一索引内文档的父/子关系
rank_feature记录数字功能,以提高查询时的点击率
rank_features记录数字功能，以提高查询时的点击率。
dense_vector记录浮点值的密集矢量
sparse_vector记录浮点值的稀疏矢量
search_as_you_type针对查询优化的文本字段,以实现按类型完成
alias为现有字段定义别名
flattened允许将整个 JSON 对象索引为单个字段
shape用于任意笛卡尔几何

数组

在Elasticsearch中不需要定义专业的数组字段类型,任何字段都可以包含一个或多个值,数组中必须具有相同的值

多字段

一个String字段的text类型可以用于全文检索,keyword类型则用于排序,聚合,可以使用分词器进行检索

3.4.1 `Alias`

别名限制

目标需要是具体字段,而不是对象或者其他的别名
创建别名,目标字段需要存在
如果定义了嵌套对象,别名也有其功能
不能定义多个字段使用同一个别名

添加别名

添加了字段distance的别名route_length_miles

PUT trips { "mappings": { "properties": { "distance": { "type": "long" }, "route_length_miles": { "type": "alias", "path": "distance" }, "transit_mode": { "type": "keyword" } } } }

不可以使用别名进行POST添加数据,要使用原字段

POST trips/_doc { "distance":58 }POST trips/_bulk {"index":{}} {"distance":88}

使用别名查询

GET /trips/_search { "query": { "range" : { "route_length_miles" : { "gte" : 39 } } } }

不能用于哪些关键字

一般情况下别名可以用于很多地方,查询,聚合,排序,但是下列字段不允许

copy_to _source term geo_shape more_like_this

3.4.2 `Arrays`

在Elasticsearch中,没有专业的数组类型,默认任何字段都可以包含零个或多个值,但是数组中的所有值需要有相同的数据类型,例如

字符串数组 [ "one", "two" ]
整数数组 [ 1, 2 ]
数组的数组 [ 1, [ 2, 3 ]] 相同于 [ 1, 2, 3 ]
对象数组 [ { "name": "Mary", "age": 12 }, { "name": "John", "age": 10 }]

注意事项

对象数组无法正常工作,无法独立于数组中其他对象而被检索,需要使用字段类型nested而不是object
动态添加字段是,数组的第一个值确定后,后面的要与之对应,至少要保证可以强制转换为相同的数据类型
数组可以含有null值,这些null值也可以替换为已配置的null_value或跳过,空数组会认为缺失字段-没有值的字段

PUT my_index/_doc/1 { "message": "some arrays in this document...", "tags": [ "elasticsearch", "wow" ], "lists": [ { "name": "prog_list", "description": "programming list" }, { "name": "cool_list", "description": "cool stuff list" } ] }PUT my_index/_doc/2 { "message": "no arrays in this document...", "tags": "elasticsearch", "lists": { "name": "prog_list", "description": "programming list" } }GET my_index/_search { "query": { "match": { "tags": "elasticsearch" } } }

3.4.3 `Binary`

传入二进制的Base64编码,并且不能含有换行符\n,默认不储存,不可检索

PUT my_index { "mappings": { "properties": { "name": { "type": "text" }, "blob": { "type": "binary" } } } }PUT my_index/_doc/1 { "name": "Some binary blob", "blob": "U29tZSBiaW5hcnkgYmxvYg==" }

字段参数

doc_values默认true 设置false可以节省空间,但不可以用于排序聚合脚本,但可以用于查询
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

3.4.4 `Boolean`

布尔类型
假 false "false"
真 true "true"

在检索的时候使用true或"true"都是一样的结果

但是如果你添加了"false" 则在_source中显示也为"false"

在聚合terms的时候
- false
  - key为0
  - key_as_string为"false"
- true
  - key为1
  - key_as_string为"true"

POST my_index/_doc/1 { "is_published": true }POST my_index/_doc/2 { "is_published": false }GET my_index/_search { "aggs": { "publish_state": { "terms": { "field": "is_published" } } } }

参数

doc_values默认true 设置false可以节省空间,但不可以用于排序聚合脚本,但可以用于查询
index默认true 设置false使此字段不可被检索
null_value设置一个值在检索的时候来替换null
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

3.4.5 `Date`

日期类型,可以使用format参数来指定类型,还可以使用||符号来写多个日期格式

定义多个日期类型,插入数据时都不匹配就报错

PUT my_index { "mappings": { "properties": { "date": { "type": "date", "format": "yyyy-MM-dd HH:mm:ss SSS||yyyy-MM-dd HH:mm:ss||yyyy-MM-dd" } } } }

format也可以使用now表示系统时间,也可以使用日期数学

+1h加1小时
-1d减去一天
/d四舍五入到最近一天
如果now为2001-01-01 12:00:00

now+1h 表示为2001-01-01 13:00:00

参数

doc_values默认true 设置false可以节省空间,但不可以用于排序聚合脚本,但可以用于查询
format默认strict_date_optional_time||epoch_millis 也可以自定义格式yyyy-MM-dd HH:mm:ss||yyyy-MM-dd
idnex默认true 设置false使此字段不可被检索
null_value设置一个值在检索的时候来替换null
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

3.4.6 `Flattened`

拼合数据类型

应该不被全文检索,因为它的所有值都可作为关键字

在检索期间,所有值都作为字符串进行检索,不需要对数字类型,日期类型进行特殊处理

插入数据

PUT bug_reports { "mappings": { "properties": { "title": { "type": "text" }, "labels": { "type": "flattened" } } } }POST bug_reports/_doc/1 { "title": "Results are not sorted correctly.", "labels": { "priority": "urgent", "release": ["v1.2.5", "v1.3.0"], "timestamp": { "created": 1541458026, "closed": 1541457010 } } }

在整个对象的全部值中查找"urgent"

POST bug_reports/_search { "query": { "term": {"labels": "urgent"} } }

如果想查找特定的类型可以使用点符号

POST bug_reports/_search { "query": { "term": {"labels.release": "v1.3.0"} } }

支持的操作

term terms terms_set
prefix
range
match multi_match
query_string simple_query_string
exists

查询时无法使用通配符,例如"labels.time*"

注意,所有查询,包括range操作都将值看做字符串关键字

不支持高度显示

可以对设置flattened的字段进行排序,以及简单聚合,例如terms

与查询一样没有对数字的支持,所有值都为关键字,排序按照字典排序

因为它无法储存内部的映射,所以不可以设置store参数

支持的参数
doc_values默认true 设置false可以节省空间,但不可以用于排序聚合脚本,但可以用于查询
ignore_above设置内部字段的长度,用于字符串来设置限定长度,如果大于长度会储存在_source但不可以被检索,聚合
index默认true 设置false使此字段不可被检索
null_value设置一个值在检索的时候来替换null

3.4.7 `IP`

可以为 IPv4 IPv6地址

支持的参数

doc_values默认true 设置false可以节省空间,但不可以用于排序聚合脚本,但可以用于查询
index默认true 设置false使此字段不可被检索
null_value设置一个IPv4值在检索的时候来替换null
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

3.4.8 `Join`

添加映射,关系在relations中定义
可以定义单个,也可以定义多个,父只可以有一个,子可以多个
每个索引中只可以有一个join字段

创建映射 a父级 b子级

PUT my_index { "mappings": { "properties": { "my_join_field": { "type": "join", "relations": { "a": "b" } } } } }

添加两个父文档,使用name来指定父级名称

PUT my_index/_doc/1 { "text": "I'm a...", "my_join_field": { "name": "a" } }PUT my_index/_doc/2 { "text": "I'm a...", "my_join_field": { "name": "a" } }

也可以直接指定,简化版

PUT my_index/_doc/1 { "text": "I'm a...", "my_join_field": "a" }PUT my_index/_doc/2 { "text": "I'm a...", "my_join_field": "a" }

创建两个子文档,需要指定路由值,其中name指向子级名称,parent指向父级文档的_id

PUT my_index/_doc/3?routing=1 { "text": "I'm b...", "my_join_field": { "name": "b", "parent": "1" } }PUT my_index/_doc/4?routing=1 { "text": "I'm b...", "my_join_field": { "name": "b", "parent": "1" } }

join的限制

每个索引只允许有一个join字段映射
父子文档必须在同一分片,这就表示对子文档进行检索,删除,更新需要提供路由值
一个字段可以有多个子级,但只可以有一个父级
可以向join中添加新的字段
可以将子元素添加到现有的元素中,但该元素需要已经是父级

全部查找,根据_id排序,默认升序

GET my_index/_search { "query": { "match_all": {} }, "sort": ["_id"] }

父文档查询

查找父id为1并且子级名称为b的文档
根据父级名称为a的文档,显示前十条

GET my_index/_search { "query": { "parent_id": { "type": "b", "id": "1" } }, "aggs": { "parents": { "terms": { "field": "my_join_field#a", "size": 10 } } } }

全局顺序(global ordinals)

如果不经常使用join并经常插入数据,可以禁用它

PUT my_index { "mappings": { "properties": { "my_join_field": { "type": "join", "relations": { "a": "b" }, "eager_global_ordinals": false } } } }

指定多个子级

父级a
子级b c

PUT my_index { "mappings": { "properties": { "my_join_field": { "type": "join", "relations": { "a": ["b", "c"] } } } } }

多级别父级,这样设置性能会下降

父级a 子级b c
父级b 子级d

PUT my_index { "mappings": { "properties": { "my_join_field": { "type": "join", "relations": { "a": ["b", "c"], "b": "d" } } } } }

插入子文档

这里name指向子级名称 parent指向父级文档的_id 也就是父级名称b的_id值

PUT my_index/_doc/3?routing=1 { "text": "I'm d...", "my_join_field": { "name": "d", "parent": "2" } }

3.4.9 `Keyword`

它可以排序,聚合

它只能按准确的值检索,如果想全文检索可以设置为text

PUT my_index { "mappings": { "properties": { "tags": { "type": "keyword" } } } }

接收的参数

doc_values默认true 设置false可以节省空间,但不可以用于排序聚合脚本,但可以用于查询
eager_global_ordinals默认false 设置true可以在应用刷新时立即加载全局顺序,经常用于聚合的可以开启
fields多字段,出于不同目的为同一字符串进行设置,可以一个用于全文检索,一个用于排序,聚合
ignore_above设置内部字段的长度,用于字符串来设置限定长度,如果大于长度会储存在_source但不可以被检索,聚合
index默认true 设置false使此字段不可被检索
norms默认设置为false后表示不对其评分,也可以使用PUT对现有字段进行设置norms为false 一旦设置为false后就不可再改为true
null_value设置一个值在检索的时候来替换null
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

3.4.10 `Nested`

nested是object的专用版本,表示对象数组

插入数据,默认为object类型
在其内部会转化为

{ "group" : "fans", "user.first" : [ "alice", "john" ], "user.last" : [ "smith", "white" ] }

PUT my_index/_doc/1 { "group" : "fans", "user" : [ { "first" : "John", "last" : "Smith" }, { "first" : "Alice", "last" : "White" } ] }

所以同时搜索Alice and Smith也可以搜索到

GET my_index/_search { "query": { "bool": { "must": [ { "match": { "user.first": "Alice" }}, { "match": { "user.last": "Smith" }} ] } } }

设置nested映射,插入数据

PUT my_index { "mappings": { "properties": { "user": { "type": "nested" } } } }PUT my_index/_doc/1 { "group" : "fans", "user" : [ { "first" : "John", "last" : "Smith" }, { "first" : "Alice", "last" : "White" } ] }

这时如果同时检索Alice and Smith就匹配不到文档了,因为没有一个文档是user.first=Alice amd user.last=Smith
这里是path执行查询的nested类型的字段名称

GET my_index/_search { "query": { "nested": { "path": "user", "query": { "bool": { "must": [ { "match": { "user.first": "Alice" }}, { "match": { "user.last": "Smith" }} ] } } } } }

查询在类型nested的字段名称user,并且user.first=Alice amd user.last=White的文档
并且高亮显示匹配到的user.first

GET my_index/_search { "query": { "nested": { "path": "user", "query": { "bool": { "must": [ { "match": { "user.first": "Alice" }}, { "match": { "user.last": "White" }} ] } }, "inner_hits": { "highlight": { "fields": { "user.first": {} } } } } } }

字段参数

dynamic默认true 没有指定properties时是否支持动态映射,为false可以添加到_source但不会创建映射也不会被检索,为strict会插入新字段异常
properties嵌套对象可以是任何数据类型,可以将新属性添加到现有对象中

nested映射的上限值

index.mapping.nested_fields.limit默认值50
index.mapping.nested_objects.limit默认值10000

3.4.11 `Numeric`

类型的选取

如果没有小数根据自己的大小范围选择byte short integer long
如果有精度根据需求选择

注意

double float half_float类型会考虑+0.0与-0.0的区别
使用term查询-0.0不会匹配到+0.0 反之亦然
如果上限是-0.0 不会匹配+0.0
如果下限是+0.0 不会匹配-0.0

接受参数

coerce默认true将字符串转为数字,并截取整数部分(小数点前面部分)
doc_values默认true 设置false可以节省空间,但不可以用于排序聚合脚本,但可以用于查询
ignore_malformed默认false格式错误发生异常为true则插入数据在_source但不创建映射,不能用于检索
index默认true 设置false使此字段不可被检索
null_value设置一个值在检索的时候来替换null
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

3.4.12 `Object`

JSON文档可以嵌套对象,对象可以再嵌套对象

这里整个JSON文档是一个Object
JSON文档里面包含了一个manager的Object
manager里面再包含了一个name的Object

PUT my_index/_doc/1 { "region": "US", "manager": { "age": 30, "name": { "first": "John", "last": "Smith" } } }

其内部构造

{ "region": "US", "manager.age": 30, "manager.name.first": "John", "manager.name.last": "Smith" }

创建映射,不需要设置type 因为object是默认值

这里表示最外层的文档是一个Object
文档内部包含了一个manager的Object
manager里面再包含了一个name的Object

PUT my_index { "mappings": { "properties": { "region": { "type": "keyword" }, "manager": { "properties": { "age": { "type": "integer" }, "name": { "properties": { "first": { "type": "text" }, "last": { "type": "text" } } } } } } } }

接受参数

dynamic默认true 没有指定properties时是否支持动态映射,为false可以添加到_source但不会创建映射也不会被检索,为strict会插入新字段异常
enabled默认true 为false时可以以任何类型添加数据,数据都会被储存在_source中,但不会创建映射,也不能被检索
properties嵌套对象可以是任何数据类型,可以将新属性添加到现有对象中

3.4.13 `Range`

创建映射

PUT range_index { "mappings": { "properties": { "expected_attendees": { "type": "integer_range" }, "time_frame": { "type": "date_range", "format": "yyyy-MM-dd HH:mm:ss||yyyy-MM-dd" } } } }

添加文档,日期格式可以为format的一种

日期也可以使用now表示系统时间

也可以使用日期数学

+1h
-1d
/d

PUT range_index/_doc/1 { "expected_attendees" : { "gte" : 10, "lte" : 20 }, "time_frame" : { "gte" : "2015-10-31 12:00:00", "lte" : "2015-11-01" } }

数组范围查询文档

GET range_index/_search { "query" : { "term" : { "expected_attendees" : { "value": 12 } } } }

日期范围查询文档

WITHIN搜索范围包含文档范围,可以相等
CONTAINS文档范围包含搜索范围,可以相等
INTERSECTS默认搜索范围和文档范围有相交部分,包括相等

GET range_index/_search { "query" : { "range" : { "time_frame" : { "gte" : "2015-10-31", "lte" : "2015-11-01", "relation" : "WITHIN" } } } }

接受参数

coerce默认true将字符串转为数字,并截取整数部分(小数点前面部分)
index默认true 设置false使此字段不可被检索
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

3.4.14 `Text`

文本数据类型

同一字段最好包括text文本和keyword关键字这样可以text全文检索,而keyword用于排序,聚合

添加映射

PUT my_index { "mappings": { "properties": { "full_name": { "type": "text" } } } }

接受字段

analyzer默认standard 指定分词器,使用ik分词器ik_max_word
eager_global_ordinals默认false 设置true可以在应用刷新时立即加载全局顺序,经常用于聚合的可以开启
fielddata默认false 设置字段是否可用于排序,聚合,脚本
fields多字段,出于不同目的为同一字符串进行设置,可以一个用于全文检索,一个用于排序,聚合
index默认true 设置false使此字段不可被检索
norms默认设置为false后表示不对其评分,也可以使用PUT对现有字段进行设置norms为false 一旦设置为false后就不可再改为true
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

3.4.15 `Token count`

令牌计数

创建映射,插入文档

PUT my_index { "mappings": { "properties": { "name": { "type": "text", "fields": { "length": { "type": "token_count", "analyzer": "standard" } } } } } }PUT my_index/_doc/1 { "name": "John Smith" }PUT my_index/_doc/2 { "name": "Rachel Alice Williams" }

检索文档

"Rachel Alice Williams"会被当做Rachel Alice Williams三个令牌
查找令牌数为3的文档,仅匹配文档2,如果改为2 则仅匹配文档1

GET my_index/_search { "query": { "term": { "name.length": 3 } } }

接受参数

analyzer默认standard 指定分词器,使用ik分词器ik_max_word
doc_values默认true 设置false可以节省空间,但不可以用于排序聚合脚本,但可以用于查询
index默认true 设置false使此字段不可被检索
null_value设置一个值在检索的时候来替换null
store默认false 设置true可以检索只显示true的字段,和_source差不多用于过滤显示hits中_source字段

4.0.0 `查询DSL`

4.1.0 `复合查询`

bool
- must should与相关性分数有关 must_not filter与相关性分数无关,表示过滤
boosting
- positive表示匹配的文档减少相关性分数negative
constant_score
- 查询的文档_score都是常量
dis_max
- 接受多个查询,并返回满足任意一个的文档,当配合bool使用时,将使用匹配的最佳文档

4.1.1 `bool`

must都满足,相关性_score提高
must_not都不满足,相关性_score为0
should满足任何一个
filter都满足,但是相关性_score全部一致

GET _search { "query": { "bool" : { "must" : { "term" : { "user" : "kimchy" } }, "filter": { "term" : { "tag" : "tech" } }, "must_not" : { "range" : { "age" : { "gte" : 10, "lte" : 20 } } }, "should" : [ { "term" : { "tag" : "wow" } }, { "term" : { "tag" : "elasticsearch" } } ] } } }

4.1.2 `boosting`

positive必须,返回的文档需要与此匹配
negative必须,降低匹配文档相关性
negative_boost必须,值介于0,1.0之间浮点数,得分与之相乘

GET /_search { "query": { "boosting" : { "positive" : { "term" : { "text" : "apple" } }, "negative" : { "term" : { "text" : "pie tart fruit crumble tree" } }, "negative_boost" : 0.5 } } }

4.1.3 `constant_score`

filter必须,过滤文档,不考虑相关性分数

GET /_search { "query": { "constant_score" : { "filter" : { "term" : { "user" : "kimchy"} } } } }

4.1.4 `dis_max`

返回一条相关性分数最高的文档
queries必须,包含一个或多个条件,满足条件越多,相关性分数越高
tie_breaker表示[0,1.0]浮点数,与相关性分数相乘

GET /_search { "query": { "dis_max" : { "queries" : [ { "term" : { "title" : "Quick pets" }}, { "term" : { "body" : "Quick pets" }} ], "tie_breaker" : 0.7 } } }

4.2.0 `全文查询`

4.2.1 `intervals`

下面检索字段my_text
- 可以匹配my favorite food is cold porridge
- 不可以匹配when it's cold my favorite food is porridge

POST _search { "query": { "intervals" : { "my_text" : { "all_of" : { "ordered" : true, "intervals" : [ { "match" : { "query" : "my favorite food", "max_gaps" : 0, "ordered" : true } }, { "any_of" : { "intervals" : [ { "match" : { "query" : "hot water" } }, { "match" : { "query" : "cold porridge" } } ] } } ] } } } } }

4.2.2 `match`

可以全文查询也可以模糊查询

也可以使用analyzer指定分词器
简单查询

GET /_search { "query": { "match" : { "message" : "this is a test" } } }

operator and默认为or

GET /_search { "query": { "match" : { "message" : { "query" : "this is a test", "operator" : "and" } } } }

4.2.3 `match_bool_prefix`

下面两者相等,匹配前缀表示quick* or brown* or f*

GET /_search { "query": { "match_bool_prefix" : { "message" : "quick brown f" } } }GET /_search { "query": { "bool" : { "should": [ { "term": { "message": "quick" }}, { "term": { "message": "brown" }}, { "prefix": { "message": "f"}} ] } } }

4.2.4 `match_phrase`

短语匹配,可指定分词器

GET /_search { "query": { "match_phrase" : { "message" : { "query" : "this is a test", "analyzer" : "ik_max_word" } } } }

4.2.5 `match_phrase_prefix`

短语匹配前缀,也可以添加参数analyzer来指定分词器
只能匹配到前缀,例如
- "how"
  - 可以匹配how are you how old are you what how
  - 不可以匹配whow are you whathow you因为这些不是how开头
- h
  - 可以匹配how are what here
  - 不可以匹配elasticsearch match 因为这些不是h开头
下面可以匹配quick brown fox two quick brown ferrets
不可以匹配the fox is quick and brown

GET /_search { "query": { "match_phrase_prefix" : { "message" : { "query" : "quick brown f" } } } }

4.2.6 `multi_match`

可以匹配多字段查询

表示在subject or message中查询this is a test

GET /_search { "query": { "multi_match" : { "query": "this is a test", "fields": [ "subject", "message" ] } } }

使用通配符* 表示零个或多个
可以匹配title first_name last_name

GET /_search { "query": { "multi_match" : { "query": "Will Smith", "fields": [ "title", "*_name" ] } } }

里面可以有analyzer来指定分词器
type可以指定查询类型
best_fields

GET /_search { "query": { "multi_match" : { "query": "brown fox", "type": "best_fields", "fields": [ "subject", "message" ], "tie_breaker": 0.3 } } }GET /_search { "query": { "dis_max": { "queries": [ { "match": { "subject": "brown fox" }}, { "match": { "message": "brown fox" }} ], "tie_breaker": 0.3 } } }

operator and
所有术语都存在

GET /_search { "query": { "multi_match" : { "query": "Will Smith", "type": "best_fields", "fields": [ "first_name", "last_name" ], "operator": "and" } } }

most_fields

GET /_search { "query": { "multi_match" : { "query": "quick brown fox", "type": "most_fields", "fields": [ "title", "title.original", "title.shingles" ] } } }GET /_search { "query": { "bool": { "should": [ { "match": { "title": "quick brown fox" }}, { "match": { "title.original": "quick brown fox" }}, { "match": { "title.shingles": "quick brown fox" }} ] } } }

phrase_prefix

GET /_search { "query": { "multi_match" : { "query": "quick brown f", "type": "phrase_prefix", "fields": [ "subject", "message" ] } } }GET /_search { "query": { "dis_max": { "queries": [ { "match_phrase_prefix": { "subject": "quick brown f" }}, { "match_phrase_prefix": { "message": "quick brown f" }} ] } } }

minimum_should_match

可以指定分词的个数, 1 -> 匹配任意一个词 2 -> 匹配任意两个词 3 -> 因为超过了分词量,所以匹配不到 GET a1/_search { "query": { "match": { "name": { "query": "小米电视", "minimum_should_match": 1 } } } }3x0.66=1.98,因为1.98<2 所以匹配任意一个 GET a1/_search { "query": { "match": { "name": { "query": "小米智能电视", "minimum_should_match": "66%" } } } }3x0.67=2.01,因为2.01>2 所以匹配任意两个 GET a1/_search { "query": { "match": { "name": { "query": "小米智能电视", "minimum_should_match": "67%" } } } }

cross_fields
至少匹配一个Will or Smith

GET /_search { "query": { "multi_match" : { "query": "Will Smith", "type": "cross_fields", "fields": [ "first_name", "last_name" ], "operator": "and" } } }

bool_prefix
与match_bool_prefix相似

GET /_search { "query": { "multi_match" : { "query": "quick brown f", "type": "bool_prefix", "fields": [ "subject", "message" ] } } }

4.2.7 `query_string`

GET /_search { "query": { "query_string" : { "query" : "(new york city) OR (big apple)", "default_field" : "content" } } }

status:active
- status字段包含active
title:(quick OR brown)
- title字段包含quick或brown
author:"John Smith"
- 包含短语John Smith
book.\*:(quick OR brown)
- *需要使用反斜杠进行转义,可以匹配book.title book.content
_exists_:title
- title非空
通配符
- ?代表一个 *代表零个或多个
- 使用*可以匹配"" " " 但不可以匹配null
空格空查询
- 如果是"" 或" " 他将不返回文档
下面可以匹配必须含有a不能含有d的所有值,再此前提再多出b c会提高相关性得分
相当于((a AND b) OR (a AND c) OR a) AND NOT d

{ "bool": { "must": { "match": "a" }, "should": { "match": "b c" }, "must_not": { "match": "d" } } }

4.3.0 `连接查询`

4.3.1 `nested`

单个查询

添加字段a为nested类型

PUT my_index { "mappings" : { "properties" : { "a" : { "type" : "nested" } } } }

检索文档
path对应nested类型文档的名称
a.b表示a字段下的b属性
score_mode
- avg默认,匹配子对象的平均相关性得分
- min匹配子对象的最小相关性得分
- max匹配子对象的最大相关性得分
- none不使用匹配子对象的相关性分数,设置父文档相关性分数0
- sum匹配子对象的相关性得分相加
ignore_unmapped
- 默认false 为true表示指定path错误也不会报异常,结果为空

GET /my_index/_search { "query": { "nested" : { "path" : "a", "query" : { "bool" : { "must" : [ { "match" : {"a.b" : "blue"} }, { "range" : {"a.c" : {"gt" : 5}} } ] } }, "score_mode" : "avg" } } }

嵌套查询

创建映射,添加文档

PUT /drivers { "mappings" : { "properties" : { "driver" : { "type" : "nested", "properties" : { "last_name" : { "type" : "text" }, "vehicle" : { "type" : "nested", "properties" : { "make" : { "type" : "text" }, "model" : { "type" : "text" } } } } } } } }PUT /drivers/_doc/1 { "driver" : { "last_name" : "McQueen", "vehicle" : [ { "make" : "Powell Motors", "model" : "Canyonero" }, { "make" : "Miller-Meteor", "model" : "Ecto-1" } ] } }PUT /drivers/_doc/2 { "driver" : { "last_name" : "Hudson", "vehicle" : [ { "make" : "Mifune", "model" : "Mach Five" }, { "make" : "Miller-Meteor", "model" : "Ecto-1" } ] } }

嵌套nested检索

GET /drivers/_search { "query" : { "nested" : { "path" : "driver", "query" : { "nested" : { "path" : "driver.vehicle", "query" : { "bool" : { "must" : [ { "match" : { "driver.vehicle.make" : "Powell Motors" } }, { "match" : { "driver.vehicle.model" : "Canyonero" } } ] } } } } } } }

4.3.2 `has_child`

创建映射
a父级 b子级

PUT /my_index { "mappings": { "properties" : { "my-join-field" : { "type" : "join", "relations": { "a": "b" } } } } }

检索
type必须为子级文档的字段名称
query查询条件
ignore_unmapped默认false 为true表示指定type错误也不会报异常
max_children查询的父文档,子级最大数
min_children查询的父文档,子级最小数
score_mode
- none默认不使用匹配子文档的相关性分数,设置父文档相关性分数0
- avg匹配子文档的平均相关性得分
- min匹配子文档的最小相关性得分
- max匹配子文档对的最大相关性得分
- sum匹配子文档的相关性得分相加

GET my_index/_search { "query": { "has_child" : { "type" : "child", "query" : { "match_all" : {} }, "max_children": 10, "min_children": 2, "score_mode" : "min" } } }

4.3.3 `has_parent`

创建映射

PUT /my-index { "mappings": { "properties" : { "my-join-field" : { "type" : "join", "relations": { "a": "b" } }, "tag" : { "type" : "keyword" } } } }

检索文档

GET /my-index/_search { "query": { "has_parent" : { "parent_type" : "a", "query" : { "term" : { "tag" : { "value" : "Elasticsearch" } } } } } }

4.3.4 `parent_id`

创建映射
a父级 b子级

PUT my_index { "mappings": { "properties" : { "my-join-field" : { "type" : "join", "relations": { "a": "b" } } } } }

添加父文档

POST /my_index/_doc/1 { "text": "I'm a...", "my-join-field": "a" }

添加子文档
路由值也必须指定
name子文档字段名称
parent对应父文档的_id

POST /my_index/_doc/2?routing=1 { "text": "I'm b...", "my-join-field": { "name": "b", "parent": "1" } }

parent_id检索文档
type为子级文档字段名称
id为关联父级文档_id
ignore_unmapped默认false 为true表示指定type错误也不会报异常

GET my_index/_search { "query": { "parent_id": { "type": "b", "id": "1" } } }

4.4.0 `match_all`

查询所有文档,相关性分数1.0

GET mm/_search { "query": { "match_all": {} } }

设置相关性分数2.0

GET mm/_search { "query": { "match_all": { "boost": 2 } } }

简写版

GET mm/_searchGET mm/_search {}

全部不匹配

GET mm/_search { "query": { "match_none": {} } }

4.5.0 `词语标准查询`

4.5.1 `exists`

查找不到的原因

字段是null 或 []
字段设置不可被检索"index":false
字段长度超出ignore_above限制
字段格式错误,设置了"ignore_malformed":true

GET /_search { "query": { "exists": { "field": "user" } } }

可以匹配到

空字符串"" " "或"-"
数组中包含null和一个不为null的值,例如[null,"111"]
设置了null_value的字段,即使为null也可以被检索到

使用must_not查找相反

GET /_search { "query": { "bool": { "must_not": { "exists": { "field": "user" } } } } }

4.5.2 `fuzzy`

模糊查询

更改一个字符 box -> fox
删除一个字符 black -> lack
插入一个字符 sic -> sick
转换两个相邻字符位置 act -> cat

GET /_search { "query": { "fuzzy": { "user": { "value": "ki" } } } }

4.5.3 `ids`

按照文档的_id值返回满足的文档

GET /_search { "query": { "ids" : { "values" : ["1", "4", "100"] } } }

4.5.4 `prefix`

前缀查询

查找字段user以ki开头的词语

GET /_search { "query": { "prefix": { "user": { "value": "ki" } } } }

简化版

GET /_search { "query": { "prefix" : { "user" : "ki" } } }

4.5.5 `range`

范围查询,所用参数

lt>
lte>=
gt<
gte<=
format字段为date类型时,指定日期格式,检索时,覆盖映射格式
relation
- INTERSECTS默认搜索范围和文档范围有相交部分,包括相等
- CONTAINS文档范围包含搜索范围,可以相等
- WITHIN搜索范围包含文档范围,可以相等
time_zone不会转化now 但会转化日期数学now-1h
boost默认1.0 指定相关性分数

GET _search { "query": { "range" : { "age" : { "gte" : 10, "lte" : 20, "boost" : 2.0 } } } }GET _search { "query": { "range" : { "timestamp" : { "gte" : "now-1d/d", "lt" : "now/d" } } } }

4.5.6 `regexp`

正则查询,不适用中文

.表示任意一个字母,不能匹配符号例如@ # ^ . ※ 一个空格
?表示重复前面那个字符0次或1次
- 例如abc?可以匹配ab abc
+ *表示重复前面那个字符0次或多次
- 例如ab+可以匹配ab abb abbb 不可以匹配abc abbbc
{}表示匹配最小最大次数
- a{2}匹配aa
- a{2,4}匹配aa aaa aaaa
- a{2,}匹配至少2次或无限次
[]匹配括号中一个字符
- [abc]匹配a b c

GET /_search { "query": { "regexp": { "user": { "value": "k.*y" } } } }

4.5.7 `term`

精确查询,不应该使用对text字段使用,对于text应该用match

GET /_search { "query": { "term": { "user": { "value": "Kimchy", "boost": 1.0 } } } }

为什么不能使用term对text类型进行检索

例如:Quick Brown Foxes!会被解析为[quick, brown, fox]

这是在通过term精确检索Quick Brown Foxes!就会找不到...

4.5.8 `terms`

与term相同,只不过terms是查询多个值

GET /_search { "query" : { "terms" : { "user" : ["kimchy", "elasticsearch"], "boost" : 1.0 } } }

创建索引,插入文档

PUT my_index { "mappings" : { "properties" : { "color" : { "type" : "keyword" } } } }PUT my_index/_doc/1 { "color": ["blue", "green"] }PUT my_index/_doc/2 { "color": "blue" }

在索引my_index中检索与索引my_index且文档ID为2与字段color相同词语的文档
如果在创建索引时指定了路由值,则必须设置routing参数

GET my_index/_search { "query": { "terms": { "color" : { "index" : "my_index", "id" : "2", "path" : "color" } } } }

4.5.9 `wildcard`

通配符查询,不适用中文

?匹配任何单个字母
*匹配0个或多个字母
下面查询ki*y可以匹配kiy kity kimchy

GET /_search { "query": { "wildcard": { "user": { "value": "ki*y" } } } }

5.0.0 `聚合`

5.1.0 `度量聚合`

5.1.1 `avg`

平均值聚合

GET /exams/_search?size=0 { "aggs" : { "avg_grade" : { "avg" : { "field" : "grade" } } } }

结果

{ ... "aggregations": { "avg_grade": { "value": 75.0 } } }

5.1.2 `extended_stats`

扩展统计聚合

GET /exams/_search { "size": 0, "aggs" : { "grades_stats" : { "extended_stats" : { "field" : "grade" } } } }

结果

{ ... "aggregations": { "grades_stats": { "count": 2, "min": 50.0, "max": 100.0, "avg": 75.0, "sum": 150.0, "sum_of_squares": 12500.0, "variance": 625.0, "std_deviation": 25.0, "std_deviation_bounds": { "upper": 125.0, "lower": 25.0 } } } }

5.1.3 `max`

最大值聚合

POST /sales/_search?size=0 { "aggs" : { "max_price" : { "max" : { "field" : "price" } } } }

结果

{ ... "aggregations": { "max_price": { "value": 200.0 } } }

5.1.4 `min`

最小值聚合

POST /sales/_search?size=0 { "aggs" : { "min_price" : { "min" : { "field" : "price" } } } }

结果

{ ... "aggregations": { "min_price": { "value": 10.0 } } }

5.1.5 `stats`

统计聚合

POST /exams/_search?size=0 { "aggs" : { "grades_stats" : { "stats" : { "field" : "grade" } } } }

结果

{ ... "aggregations": { "grades_stats": { "count": 2, "min": 50.0, "max": 100.0, "avg": 75.0, "sum": 150.0 } } }

5.1.6 `sum`

POST /sales/_search?size=0 { "query" : { "constant_score" : { "filter" : { "match" : { "type" : "hat" } } } }, "aggs" : { "hat_prices" : { "sum" : { "field" : "price" } } } }

结果

{ ... "aggregations": { "hat_prices": { "value": 450.0 } } }

5.1.7 `value_count`

共多少个值

如果文档1{"a":"a"} 文档2{"a":["a","aa"," ","",null]}
共有5个值

例如:

POST /sales/_search?size=0 { "aggs" : { "types_count" : { "value_count" : { "field" : "type" } } } }

结果

{ ... "aggregations": { "types_count": { "value": 7 } } }

5.2.0 `桶聚合`

度量聚合是嵌套桶聚合里面的

5.2.1 `adjacency_matrix`

相邻矩阵聚合

PUT /emails/_bulk { "index" : { "_id" : 1 } } { "accounts" : ["hillary", "sidney"]} { "index" : { "_id" : 2 } } { "accounts" : ["hillary", "donald"]} { "index" : { "_id" : 3 } } { "accounts" : ["vladimir", "donald"]}GET emails/_search { "size": 0, "aggs" : { "interactions" : { "adjacency_matrix" : { "filters" : { "grpA" : { "terms" : { "accounts" : ["hillary", "sidney"] }}, "grpB" : { "terms" : { "accounts" : ["donald", "mitt"] }}, "grpC" : { "terms" : { "accounts" : ["vladimir", "nigel"] }} } } } } }

结果

按照filters的自定义名称grpA grpB grpC进行表示key

... "aggregations" : { "interactions" : { "buckets" : [ { "key" : "grpA", "doc_count" : 2 }, { "key" : "grpA&grpB", "doc_count" : 1 }, { "key" : "grpB", "doc_count" : 2 }, { "key" : "grpB&grpC", "doc_count" : 1 }, { "key" : "grpC", "doc_count" : 1 } ] } } }

5.2.2 `children`

子级聚合

创建映射a父级 b子级,添加文档

PUT child_example { "mappings": { "properties": { "my_join": { "type": "join", "relations": { "a": "b" } } } } }PUT child_example/_doc/1 { "my_join": "a", "tags": [ "windows-server-2003", "windows-server-2008", "file-transfer" ] } PUT child_example/_doc/2?routing=1 { "my_join": { "name": "b", "parent": "1" }, "owner": { "display_name": "Sam" } } PUT child_example/_doc/3?routing=1 { "my_join": { "name": "b", "parent": "1" }, "owner": { "display_name": "Troll" } }

聚合文档

GET child_example/_search?size=0 { "aggs": { "top-tags": { "terms": { "field": "tags.keyword", "size": 10 }, "aggs": { "to-answers": { "children": { "type" : "b" }, "aggs": { "top-names": { "terms": { "field": "owner.display_name.keyword", "size": 10 } } } } } } } }

结果

... "aggregations" : { "top-tags" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "file-transfer", "doc_count" : 1, "to-answers" : { "doc_count" : 2, "top-names" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "Sam", "doc_count" : 1 }, { "key" : "Troll", "doc_count" : 1 } ] } } }, ...

5.2.3 `composite`

复合聚合

POST xll/_bulk {"index":{}} { "keyword": "foo", "number": 23 } {"index":{}} { "keyword": "foo", "number": 65 } {"index":{}} { "keyword": "foo", "number": 76 } {"index":{}} { "keyword": "bar", "number": 23 } {"index":{}} { "keyword": "bar", "number": 65 } {"index":{}} { "keyword": "bar", "number": 76 }GET xll/_search { "size": 0, "aggs": { "xx": { "composite": { "sources": [ {"ccc": {"terms": {"field": "keyword.keyword"}}}, {"bbb":{"terms": {"field": "number"}}} ] } } } }

结果

... "aggregations" : { "xx" : { "after_key" : { "ccc" : "foo", "bbb" : 76 }, "buckets" : [ { "key" : { "ccc" : "bar", "bbb" : 23 }, "doc_count" : 1 }, ...

5.2.4 `date_histogram`

日期间隔聚合

calendar_interval日历间隔
- minute m 1m
- hour h 1h
- day d 1d
- week w 1w
- month M 1M
- quarter q 1q
- year y 1y
fixed_interval固定间隔,不能用小数1.5h可以用90m代替
- milliseconds ms,seconds s
- minutes m
- hours h
- days d
插入数据,聚合文档

PUT /cars/_bulk { "index": {}} { "price" : 10000, "color" : "red", "make" : "honda", "sold" : "2014-10-28" } { "index": {}} { "price" : 20000, "color" : "red", "make" : "honda", "sold" : "2014-11-05" } { "index": {}} { "price" : 30000, "color" : "green", "make" : "ford", "sold" : "2014-05-18" } { "index": {}} { "price" : 15000, "color" : "blue", "make" : "toyota", "sold" : "2014-07-02" } { "index": {}} { "price" : 12000, "color" : "green", "make" : "toyota", "sold" : "2014-08-19" } { "index": {}} { "price" : 20000, "color" : "red", "make" : "honda", "sold" : "2014-11-05" } { "index": {}} { "price" : 80000, "color" : "red", "make" : "bmw", "sold" : "2014-01-01" } { "index": {}} { "price" : 25000, "color" : "blue", "make" : "ford", "sold" : "2014-02-01" }GET cars/_search { "size": 0, "aggs": { "x": { "date_histogram": { "field": "sold", "calendar_interval": "month", "format": "yyyy-MM-dd", "min_doc_count": 1 } } } }

结果

"key_as_string" : "2014-01-01",包括[2014-01-01,2014-02-01)

... "aggregations" : { "x" : { "buckets" : [ { "key_as_string" : "2014-01-01", "key" : 1388534400000, "doc_count" : 1 }, { "key_as_string" : "2014-02-01", "key" : 1391212800000, "doc_count" : 1 }, ...

使用extended_bounds扩展日期,来计算全年的情况

GET cars/_search { "size": 0, "aggs": { "x": { "date_histogram": { "field": "sold", "calendar_interval": "month", "format": "yyyy-MM-dd", "extended_bounds": { "min": "2014-01-01", "max": "2014-12-31" } } } } }

结果

... "aggregations" : { "x" : { "buckets" : [ { "key_as_string" : "2014-01-01", "key" : 1388534400000, "doc_count" : 1 }, { "key_as_string" : "2014-02-01", "key" : 1391212800000, "doc_count" : 1 }, { "key_as_string" : "2014-03-01", "key" : 1393632000000, "doc_count" : 0 }, { "key_as_string" : "2014-04-01", "key" : 1396310400000, "doc_count" : 0 }, { "key_as_string" : "2014-05-01", "key" : 1398902400000, "doc_count" : 1 }, { "key_as_string" : "2014-06-01", "key" : 1401580800000, "doc_count" : 0 }, { "key_as_string" : "2014-07-01", "key" : 1404172800000, "doc_count" : 1 }, { "key_as_string" : "2014-08-01", "key" : 1406851200000, "doc_count" : 1 }, { "key_as_string" : "2014-09-01", "key" : 1409529600000, "doc_count" : 0 }, { "key_as_string" : "2014-10-01", "key" : 1412121600000, "doc_count" : 1 }, { "key_as_string" : "2014-11-01", "key" : 1414800000000, "doc_count" : 2 }, { "key_as_string" : "2014-12-01", "key" : 1417392000000, "doc_count" : 0 } ] } } }

间隔固定30天

GET cars/_search { "size": 0, "aggs": { "x": { "date_histogram": { "field": "sold", "fixed_interval": "30d", "format": "yyyy-MM-dd" } } } }

结果

... "aggregations" : { "x" : { "buckets" : [ { "key_as_string" : "2013-12-11", "key" : 1386720000000, "doc_count" : 1 }, { "key_as_string" : "2014-01-10", "key" : 1389312000000, "doc_count" : 1 }, { "key_as_string" : "2014-02-09", "key" : 1391904000000, "doc_count" : 0 }, { "key_as_string" : "2014-03-11", "key" : 1394496000000, "doc_count" : 0 }, ...

5.2.5 `filter`

过滤聚合,只影响聚合不影响检索

GET cars/_search { "size": 0, "aggs": { "x": { "filter": { "range": {"price": {"gte": 25000}} }, "aggs": { "x": {"terms": {"field": "price"}} } } } }

结果

... "aggregations" : { "x" : { "doc_count" : 3, "x" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : 25000, "doc_count" : 1 }, { "key" : 30000, "doc_count" : 1 }, { "key" : 80000, "doc_count" : 1 } ] } } } }

5.2.6 `filters`

过滤聚合

插入文档,聚合文档

PUT /logs/_bulk { "index" : { "_id" : 1 } } { "body" : "warning: page could not be rendered" } { "index" : { "_id" : 2 } } { "body" : "authentication error" } { "index" : { "_id" : 3 } } { "body" : "warning: connection timed out" } { "index" : { "_id" : 4 } } { "body": "info: user Bob logged out" }GET logs/_search { "size": 0, "aggs": { "x": { "filters": { "filters": { "error":{"match":{"body":"error"}}, "warning":{"match":{"body":"warning"}} } } } } }

结果

... "aggregations" : { "x" : { "buckets" : { "error" : { "doc_count" : 1 }, "warning" : { "doc_count" : 2 } } } } }

匿名filters聚合

GET logs/_search { "size": 0, "aggs": { "x": { "filters": { "filters": [ {"match":{"body":"error"}}, {"match":{"body":"warning"}} ] } } } }

结果

... "aggregations" : { "x" : { "buckets" : [ { "doc_count" : 1 }, { "doc_count" : 2 } ] } } }

其他桶

"other_bucket": true默认桶名称_other_
"other_bucket_key": "oooo"自定义桶名称,指定了这个可以省略other_bucket

GET logs/_search { "size": 0, "aggs": { "x": { "filters": { "filters": { "error":{"match":{"body":"error"}}, "warning":{"match":{"body":"warning"}} }, "other_bucket_key": "oooo" } } } }

结果

... "aggregations" : { "x" : { "buckets" : { "error" : { "doc_count" : 1 }, "warning" : { "doc_count" : 2 }, "oooo" : { "doc_count" : 1 } } } } }

5.2.7 `global`

全局聚合,对其他桶无关

avg_price计算所有产品的平均价格
t_shirts计算所有T恤价格

POST /sales/_search?size=0 { "query" : { "match" : { "type" : "t-shirt" } }, "aggs" : { "all_products" : { "global" : {}, "aggs" : { "avg_price" : { "avg" : { "field" : "price" } } } }, "t_shirts": { "avg" : { "field" : "price" } } } }

结果

{ ... "aggregations" : { "all_products" : { "doc_count" : 7, "avg_price" : { "value" : 140.71428571428572 } }, "t_shirts": { "value" : 128.33333333333334 } } }

5.2.8 `histogram`

数值间隔聚合

POST /sales/_search?size=0 { "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50 } } } }

结果

{ ... "aggregations": { "prices" : { "buckets": [ { "key": 0.0, "doc_count": 1 }, { "key": 50.0, "doc_count": 1 }, { "key": 100.0, "doc_count": 0 }, { "key": 150.0, "doc_count": 2 }, { "key": 200.0, "doc_count": 3 } ] } } }

最小文档数

POST /sales/_search?size=0 { "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "min_doc_count" : 1 } } } }

结果

{ ... "aggregations": { "prices" : { "buckets": [ { "key": 150.0, "doc_count": 2 }, { "key": 200.0, "doc_count": 3 } ] } } }

扩展范围

POST /sales/_search?size=0 { "query" : { "constant_score" : { "filter": { "range" : { "price" : { "to" : "500" } } } } }, "aggs" : { "prices" : { "histogram" : { "field" : "price", "interval" : 50, "extended_bounds" : { "min" : 0, "max" : 500 } } } } }

5.2.9 `missing`

缺失聚合

字段值为null
字段值为[]
字段长度超出ignore_above限制
字段格式错误,设置了"ignore_malformed":true

GET abv/_search { "size": 0, "aggs": { "x": { "missing": { "field": "a.keyword" } } } }

结果,可以再嵌套聚合查询桶内的_id

... "aggregations" : { "x" : { "doc_count" : 2 } } }

5.2.10 `nested`

嵌套聚合

创建映射,插入文档,聚合文档

PUT /products { "mappings": { "properties" : { "resellers" : { "type" : "nested", "properties" : { "reseller" : { "type" : "text" }, "price" : { "type" : "double" } } } } } }PUT /products/_doc/0 { "name": "LED TV", "resellers": [ { "reseller": "companyA", "price": 350 }, { "reseller": "companyB", "price": 500 } ] }GET /products/_search { "query" : { "match" : { "name" : "led tv" } }, "aggs" : { "x" : { "nested" : { "path" : "resellers" }, "aggs" : { "min_price" : { "min" : { "field" : "resellers.price" } } } } } }

结果

... "aggregations" : { "x" : { "doc_count" : 2, "min_price" : { "value" : 350.0 } } } }

5.2.11 `parent`

父级聚合

创建映射,插入文档,聚合文档

PUT parent_example { "mappings": { "properties": { "join": { "type": "join", "relations": { "a": "b" } } } } }PUT parent_example/_doc/1 { "join": { "name": "a" }, "tags": [ "windows-server-2003", "windows-server-2008", "file-transfer" ] }PUT parent_example/_doc/2?routing=1 { "join": { "name": "b", "parent": "1" }, "owner": { "display_name": "Sam" } }PUT parent_example/_doc/3?routing=1&refresh { "join": { "name": "b", "parent": "1" }, "owner": { "display_name": "Troll" } }POST parent_example/_search?size=0 { "aggs": { "top-names": { "terms": { "field": "owner.display_name.keyword", "size": 10 }, "aggs": { "to-questions": { "parent": { "type" : "b" }, "aggs": { "top-tags": { "terms": { "field": "tags.keyword", "size": 10 } } } } } } } }

结果

... "aggregations" : { "top-names" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "Sam", "doc_count" : 1, "to-questions" : { "doc_count" : 1, "top-tags" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "file-transfer", "doc_count" : 1 }, { "key" : "windows-server-2003", "doc_count" : 1 }, { "key" : "windows-server-2008", "doc_count" : 1 } ] } } }, { "key" : "Troll", "doc_count" : 1, ... } ] } } }

5.2.12 `range`

范围聚合

创建文档,聚合文档
{"to": 102}表示[最小值,102)
{"from": 102,"to":104}表示[102,104)
{"from": 104}表示[104,最大值]

PUT bnm/_bulk {"index":{"_id":1}} {"a":101} {"index":{"_id":2}} {"a":102} {"index":{"_id":3}} {"a":103} {"index":{"_id":4}} {"a":104} {"index":{"_id":5}} {"a":105} {"index":{"_id":6}} {"a":106}GET bnm/_search { "size": 0, "aggs": { "x": { "range": { "field": "a", "ranges": [ {"to": 102}, {"from": 102,"to":104}, {"from": 104} ] } } } }

结果

"aggregations" : { "x" : { "buckets" : [ { "key" : "*-102.0", "to" : 102.0, "doc_count" : 1 }, { "key" : "102.0-104.0", "from" : 102.0, "to" : 104.0, "doc_count" : 2 }, { "key" : "104.0-*", "from" : 104.0, "doc_count" : 3 } ] } } }

自定义每个范围名称名称

GET bnm/_search { "size": 0, "aggs": { "x": { "range": { "field": "a", "ranges": [ {"key": "one", "to": 102}, {"key": "two", "from": 102,"to":104}, {"key": "three", "from": 104} ] } } } }

结果

... "aggregations" : { "x" : { "buckets" : [ { "key" : "one", "to" : 102.0, "doc_count" : 1 }, { "key" : "two", "from" : 102.0, "to" : 104.0, "doc_count" : 2 }, { "key" : "three", "from" : 104.0, "doc_count" : 3 } ] } } }

5.2.13 `terms`

分组

field需要分组的字段"field":"a"
min_doc_count匹配最小文档数"min_doc_count":1
order排序,根据桶的key降序,也可以使用_count代表文档数 "order": {"_key": "desc"}
size要显示的记录数"size":3
exclude要排除的值,例如排除key为102的值"exclude": ["102"]
include只包含哪些值,例如只包含key为102的值"include": ["102"]

聚合文档

a2>a3.variance 表示"a2"中的"a3"的"variance"属性
按照价格两万一次分割,过滤了只取"red","green"一共6个文档,并且根据分割块进行价格计算扩展统计,
根据分割每一块的扩展统计的方差来升序排列,并且排除分割内至少数量为1
这里"a1"//单值桶 "a2"//多值桶 "a3"//度量指标

GET cars/_search { "size": 0, "aggs": { "a1": { "histogram": { "field": "price", "interval": 20000, "min_doc_count": 1, "order": {"a2>a3.variance": "asc"} }, "aggs": { "a2": { "filter": { "terms": {"color": ["red","green"]} }, "aggs": { "a3": { "extended_stats": {"field": "price"} } } } } } } }

结果

... "aggregations": { "a1": {//多值桶 "buckets": [ { "key": 80000,//[80000,100000)有1条 "doc_count": 1, "a2": {//单值桶 "doc_count": 1,//[80000,100000) 并且属于["red","green"]有1条 "a3": { "count": 1, "min": 80000, "max": 80000, "avg": 80000, "sum": 80000, "sum_of_squares": 6400000000, "variance": 0,//属于["red","green"]1条的方差 "std_deviation": 0, "std_deviation_bounds": { "upper": 80000, "lower": 80000 } } } },...

5.3.0 `管道聚合`

5.3.1 `avg_bucket`

桶平均值

插入文档

PUT gg/_bulk {"index":{"_id":1}} {"x":"x1","y":11} {"index":{"_id":2}} {"x":"x2","y":22} {"index":{"_id":3}} {"x":"x1","y":33} {"index":{"_id":4}} {"x":"x3","y":44} {"index":{"_id":5}} {"x":"x2","y":55}

聚合文档
计算分组的sum值的平均值

GET gg/_search { "size": 0, "aggs": { "f1": { "terms": { "field": "x.keyword" }, "aggs": { "f11": { "sum": { "field": "y" } } } }, "f2":{ "avg_bucket": { "buckets_path": "f1>f11" } } } }

结果

"aggregations" : { "f1" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "x1", "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key" : "x2", "doc_count" : 2, "f11" : { "value" : 77.0 } }, { "key" : "x3", "doc_count" : 1, "f11" : { "value" : 44.0 } } ] }, "f2" : { "value" : 55.0 } } }

5.3.2 `derivative`

桶衍生

插入文档

PUT gg/_bulk {"index":{"_id":1}} {"x":"2019-01-05","y":11} {"index":{"_id":2}} {"x":"2019-02-15","y":22} {"index":{"_id":3}} {"x":"2019-01-05","y":33} {"index":{"_id":4}} {"x":"2019-03-18","y":44} {"index":{"_id":5}} {"x":"2019-03-27","y":55}

一阶衍生
f12为当前f11减去上一个f11
第一个不会显示f12 因为它没有上一个

GET gg/_search { "size": 0, "aggs": { "f1": { "date_histogram": { "field": "x", "calendar_interval":"month", "min_doc_count": 1, "format": "yyyy-MM-dd" }, "aggs": { "f11": { "sum": {"field": "y"} }, "f12":{ "derivative": {"buckets_path": "f11"} } } } } }

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 }, "f12" : { "value" : -22.0 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 }, "f12" : { "value" : 77.0 } } ] } } }

二阶衍生
f12为当前f11减去上一个f11
f13为当前f12减去上一个f12
第一个不会显示f12 因为它没有上一个
第一个第二个都不会显示f13 因为它们都没有上一个

结果

给一阶衍生的f12加一个属性normalized_value
设置"unit": "day" -> 当前的normalized_value表示当前的f12除以当前的key_as_string减去上一个key_as_string的天数

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 }, "f12" : { "value" : -22.0, "normalized_value" : -0.7096774193548387 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 }, "f12" : { "value" : 77.0, "normalized_value" : 2.75 } } ] } } }

5.3.3 `max_bucket`

桶最大值

GET gg/_search { "size": 0, "aggs": { "f1": { "date_histogram": { "field": "x", "calendar_interval":"month", "min_doc_count": 1, "format": "yyyy-MM-dd" }, "aggs": { "f11": { "sum": {"field": "y"} } } }, "f12":{ "max_bucket": {"buckets_path": "f1>f11"} } } }

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 } } ] }, "f12" : { "value" : 99.0, "keys" : [ "2019-03-01" ] } } }

5.3.4 `min_bucket`

桶最小值

GET gg/_search { "size": 0, "aggs": { "f1": { "date_histogram": { "field": "x", "calendar_interval":"month", "min_doc_count": 1, "format": "yyyy-MM-dd" }, "aggs": { "f11": { "sum": {"field": "y"} } } }, "f12":{ "min_bucket": {"buckets_path": "f1>f11"} } } }

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 } } ] }, "f12" : { "value" : 22.0, "keys" : [ "2019-02-01" ] } } }

5.3.5 `sum_bucket`

桶求和

GET gg/_search { "size": 0, "aggs": { "f1": { "date_histogram": { "field": "x", "calendar_interval":"month", "min_doc_count": 1, "format": "yyyy-MM-dd" }, "aggs": { "f11": { "sum": {"field": "y"} } } }, "f12":{ "sum_bucket": {"buckets_path": "f1>f11"} } } }

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 } } ] }, "f12" : { "value" : 165.0 } } }

5.3.6 `stats_bucket`

桶统计

GET gg/_search { "size": 0, "aggs": { "f1": { "date_histogram": { "field": "x", "calendar_interval":"month", "min_doc_count": 1, "format": "yyyy-MM-dd" }, "aggs": { "f11": { "sum": {"field": "y"} } } }, "f12":{ "stats_bucket": {"buckets_path": "f1>f11"} } } }

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 } } ] }, "f12" : { "count" : 3, "min" : 22.0, "max" : 99.0, "avg" : 55.0, "sum" : 165.0 } } }

5.3.7 `extended_stats_bucket`

桶扩展统计

GET gg/_search { "size": 0, "aggs": { "f1": { "date_histogram": { "field": "x", "calendar_interval":"month", "min_doc_count": 1, "format": "yyyy-MM-dd" }, "aggs": { "f11": { "sum": {"field": "y"} } } }, "f12":{ "extended_stats_bucket": {"buckets_path": "f1>f11"} } } }

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 } } ] }, "f12" : { "count" : 3, "min" : 22.0, "max" : 99.0, "avg" : 55.0, "sum" : 165.0, "sum_of_squares" : 12221.0, "variance" : 1048.6666666666667, "std_deviation" : 32.38312317653544, "std_deviation_bounds" : { "upper" : 119.76624635307088, "lower" : -9.766246353070883 } } } }

5.3.8 `cumulative_sum`

桶累加

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 }, "f12" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 }, "f12" : { "value" : 66.0 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 }, "f12" : { "value" : 165.0 } } ] } } }

5.3.9 `cumulative_cardinality`

桶累加基数

GET gg/_search { "size": 0, "aggs": { "f1": { "date_histogram": { "field": "x", "calendar_interval":"month", "min_doc_count": 1, "format": "yyyy-MM-dd" }, "aggs": { "f11": { "cardinality": {"field": "y"} }, "f12":{ "cumulative_cardinality": {"buckets_path": "f11"} } } } } }

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 2 }, "f12" : { "value" : 2 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 1 }, "f12" : { "value" : 3 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 2 }, "f12" : { "value" : 5 } } ] } } }

5.3.10 `bucket_sort`

桶排序

对f11桶进行排序,排除第1个,显示前2条

结果

... "aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-01-01", "key" : 1546300800000, "doc_count" : 2, "f11" : { "value" : 44.0 } }, { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 } } ] } } }

不排序,只对数据进行截断

结果

"aggregations" : { "f1" : { "buckets" : [ { "key_as_string" : "2019-02-01", "key" : 1548979200000, "doc_count" : 1, "f11" : { "value" : 22.0 } }, { "key_as_string" : "2019-03-01", "key" : 1551398400000, "doc_count" : 2, "f11" : { "value" : 99.0 } } ] } } }

官方文档地址

1.0.0 设置Elasticsearch

1.1.0 安装Elasticsearch

1.1.1 Linux安装Elasticsearch

1.1.2 Docker安装Elasticsearch

2.0.0 Elasticsearch入门

2.1.0 索引文档

2.2.0 开始搜索

2.3.0 使用聚合分析结果

3.0.0 映射

3.1.0 删除映射类型

3.2.0 映射参数

3.2.1 analyzer

3.2.2 coerce

3.2.3 copy_to

3.2.4 doc_values

3.2.5 dynamic

3.2.6 enabled

3.2.7 fielddate

3.2.8 format

3.2.9 ignore_above

3.2.10 ignore_malformed

3.2.11 index

3.2.12 fields

3.2.13 norms

3.2.14 null_value

3.2.15 properties

3.2.16 store

3.3.0 动态映射

3.3.1 动态字段映射

3.3.0 元字段

3.3.1 _id

3.3.2 _index

3.3.3 _meta

3.3.4 _routing

3.3.5 _source

3.3.6 _type

3.4.0 字段数据类型

3.4.1 Alias

3.4.2 Arrays

3.4.3 Binary

3.4.4 Boolean

3.4.5 Date

3.4.6 Flattened

3.4.7 IP

3.4.8 Join

3.4.9 Keyword

3.4.10 Nested

3.4.11 Numeric

3.4.12 Object

3.4.13 Range

3.4.14 Text

3.4.15 Token count

4.0.0 查询DSL

4.1.0 复合查询

4.1.1 bool

4.1.2 boosting

4.1.3 constant_score

4.1.4 dis_max

4.2.0 全文查询

4.2.1 intervals

4.2.2 match

4.2.3 match_bool_prefix

4.2.4 match_phrase

4.2.5 match_phrase_prefix

4.2.6 multi_match

4.2.7 query_string

4.3.0 连接查询

4.3.1 nested

4.3.2 has_child

4.3.3 has_parent

4.3.4 parent_id

4.4.0 match_all

4.5.0 词语标准查询

4.5.1 exists

4.5.2 fuzzy

4.5.3 ids

4.5.4 prefix

4.5.5 range

4.5.6 regexp

1.0.0 `设置Elasticsearch`

1.1.0 `安装Elasticsearch`

1.1.1 `Linux安装Elasticsearch`

1.1.2 `Docker安装Elasticsearch`

2.0.0 `Elasticsearch入门`

2.1.0 `索引文档`

2.2.0 `开始搜索`

2.3.0 `使用聚合分析结果`

3.0.0 `映射`

3.1.0 `删除映射类型`

3.2.0 `映射参数`

3.2.1 `analyzer`

3.2.2 `coerce`

3.2.3 `copy_to`

3.2.4 `doc_values`

3.2.5 `dynamic`

3.2.6 `enabled`

3.2.7 `fielddate`

3.2.8 `format`

3.2.9 `ignore_above`

3.2.10 `ignore_malformed`

3.2.11 `index`

3.2.12 `fields`

3.2.13 `norms`

3.2.14 `null_value`

3.2.15 `properties`

3.2.16 `store`

3.3.0 `动态映射`

3.3.1 `动态字段映射`

3.3.0 `元字段`

3.3.1 `_id`

3.3.2 `_index`

3.3.3 `_meta`

3.3.4 `_routing`

3.3.5 `_source`

3.3.6 `_type`

3.4.0 `字段数据类型`

3.4.1 `Alias`

3.4.2 `Arrays`

3.4.3 `Binary`

3.4.4 `Boolean`

3.4.5 `Date`

3.4.6 `Flattened`

3.4.7 `IP`

3.4.8 `Join`

3.4.9 `Keyword`

3.4.10 `Nested`

3.4.11 `Numeric`

3.4.12 `Object`

3.4.13 `Range`

3.4.14 `Text`

3.4.15 `Token count`

4.0.0 `查询DSL`

4.1.0 `复合查询`

4.1.1 `bool`

4.1.2 `boosting`

4.1.3 `constant_score`

4.1.4 `dis_max`

4.2.0 `全文查询`

4.2.1 `intervals`

4.2.2 `match`

4.2.3 `match_bool_prefix`

4.2.4 `match_phrase`

4.2.5 `match_phrase_prefix`

4.2.6 `multi_match`

4.2.7 `query_string`

4.3.0 `连接查询`

4.3.1 `nested`

4.3.2 `has_child`

4.3.3 `has_parent`

4.3.4 `parent_id`

4.4.0 `match_all`

4.5.0 `词语标准查询`

4.5.1 `exists`

4.5.2 `fuzzy`

4.5.3 `ids`

4.5.4 `prefix`

4.5.5 `range`

4.5.6 `regexp`

4.5.7 `term`