elasticsearch(三) mapping之type,anlayzer
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1.造数据
POST my_index/my_type/1{ "group":"高富帅", "id": "1234", "sex": 1, "attribute": "eating,car,girls", "birthday": "1900-12-10", "lat_lng": "31.2427760000,121.4903420000"}
第1位高富帅,来自南京西路东路地铁站
POST my_index/my_type/2{ "group":"白富美", "id": "2234", "sex": 2, "attribute": "eat,dog,boys", "birthday": "1989-12-10", "lat_lng": "31.2433470000,121.5087220000"}
第2位白富美来自陆家嘴
PUT my_index/my_type/3{ "group":"小妹妹", "id": "3234", "sex": 2, "attribute": "eat dog boy flowers", "birthday": "2010-12-10", "lat_lng": "31.2257000000,121.5508340000"}
第3位小妹妹来自世纪大道
PUT my_index/my_type/4{ "group":"空姐", "id": "4234", "sex": 2, "attribute": "eat,dog,girl", "birthday": "1995-12-10", "lat_lng": "31.1573860000,121.8150200000"}
第4位空姐,来自浦东机场
2.mapping是啥?
看下刚刚我们建的索引的mapping长啥样
GET my_index/_mapping/my_type
response:
{ "my_index": { "mappings": { "my_type": { "properties": { "attribute": { "type": "string" }, "birthday": { "type": "date", "format": "strict_date_optional_time||epoch_millis" }, "group": { "type": "string" }, "id": { "type": "string" }, "lat_lng": { "type": "string" }, "sex": { "type": "long" } } } } }}
很明显,mapping 定义了每个field的数据类型(用途之一),es很聪明,能自动确定类型:
“1900-12-10” -> date
1 -> long
然而,有些field要让它完全猜对我们的心思还是有些强人所难,比如:
“1234” -> string
“31.2427760000,121.4903420000” -> striing
我其实希望
“1234” - > int
“31.2427760000,121.4903420000” -> (维度,经度)
后面会提到怎样修改type,不过在此之前,先了解下es有哪些type
3.Field Types
• String:string
• Whole number: byte, short, integer, long(默认)
• Floating-point:float,double
• Boolean:boolean
• Date:date
• lat/lon points:geo_point
所有的type见:Field datatypes
指定type
要改变field的类型,必须先删掉之前的索引!
DELETE /my_index
再重新建
PUT /my_index{ "mappings": { "my_type": { "properties": { "id": { "type": "string" }, "birthday": { "type": "date" }, "sex": { "type": "short" }, "attribute": { "type": "string" }, "lat_lng": { "type": "geo_point" } } } }}
检查下
GET my_index/_mapping/my_type
{ "my_index": { "mappings": { "my_type": { "properties": { "attribute": { "type": "string" }, "birthday": { "type": "date", "format": "strict_date_optional_time||epoch_millis" }, "id": { "type": "string" }, "lat_lng": { "type": "geo_point" }, "sex": { "type": "short" } } } } }}
再将之前的4个doc全塞进去!
很好,type就是我们想要的啦,有啥用,目前的app很多有附近搜索,我们也来小试牛刀下
位置搜索
GET my_index/my_type/_search{ "query": { "geo_distance": { "distance": "4km", "lat_lng": "31.2393950000,121.4837130000" } }}
lat_lng:搜索的中心,我这里用的是人民广场
distance:搜索半径,我这里设为4km,单位可以是m,有兴趣的可以了解下geohash,就大概知道为啥能这么快实现啦
当然,还可以指定区域搜索,更多精彩内容见:Geo Distance Query,Geo Location and Search
4.Analyzers
ref:Analysis and Analyzers
上一节已经稍稍讲过analyzer,总之,建索引的时候,es会按每个field配置的analyzer分析field值,用来建倒排索引(Inverted Index),搜索的时候,也会按搜索字段的analyzer分析查询值。
和type类似的思路,先了解下es有哪些analyzer,接着指定analyzer。
Analyzers
介绍2个简单的,自己运行理解下吧
- whitespace(空格)
GET /_analyze?analyzer=whitespace{ "text":"full-text books, tired sleeping"}
- english
GET /_analyze?analyzer=english{ "text":"full-text books, tired sleeping"}
分词,词干化后剩下:full,text,book,tire,sleep
其它Built-in Analyzers analyzer,自定义analyzer见:Analyzers
指定analyzer
同样要先删掉之前的索引
DELETE /my_index
PUT /my_index{ "mappings": { "my_type": { "properties": { "id": { "index": "no", "type": "string" }, "birthday": { "index": "not_analyzed", "type": "date" }, "sex": { "index": "not_analyzed", "type": "short" }, "attribute": { "index": "analyzed", "analyzer": "whitespace", "type": "string" }, "lat_lng": { "type": "geo_point" } } } }}
index:控制fiel是否被索引,是否要分析
no:指定field不参与建索引,当然也无法搜索该字段
analyzed:分析字段(缺省时,默认analyzed)
not_analyzed:不分析字段
analyzer:控制怎样被索引(缺省时,默认 standard )
_all field
补充一个可以搜索所有字段的内容,_all field,要去找房子啦o(╯□╰)o,自己看看吧
"_all": { "enabled": false }
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