Elasticsearch 7.4.0官方文档操作

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/_search

GET 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/_search

GET 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/_search

GET 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 ,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/_search

GET 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

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"}
        },
        "f13":{
          "derivative": {"buckets_path": "f12"}
        }
      }
    }
  }
}

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
          },
          "f13" : {
            "value" : 99.0
          }
        }
      ]
    }
  }
}

• 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

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",
            "unit": "day"
          }
        }
      }
    }
  }
}

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

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":{
          "cumulative_sum": {"buckets_path": "f11"}
        }
      }
    }
  }
}

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

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":{
          "bucket_sort": {
            "sort": [
              {"f11":{"order":"desc"}}
              ],
            "from": 1,
            "size": 2
          }
        }
      }
    }
  }
}

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

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":{
          "bucket_sort": {
            "from": 1,
            "size": 2
          }
        }
      }
    }
  }
}

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
          }
        }
      ]
    }
  }
}