# 3.2.1.字段的数据类型 ### 数据类型分类 **核心数据类型** | 分类 | 字段的数据类型 | | :---------: | --------------------------------------------------------------------- | | String 字符串型 | text和keyword | | Numeric 数字型 | long, integer, short, byte, double, float, half\_float, scaled\_float | | Date 日期型 | date | | Boolean 布尔型 | boolean | | Binary 二进制型 | binary | | Range 范围型 | integer\_range, float\_range, long\_range, double\_range, date\_range | **复合数据类型** | 分类 | 数据类型 | | :--------: | -------------------------------- | | Array 数组型 | 支持数组形式，不需要一个专有的字段数据类型 | | Object 对象型 | object数据类型：表现形式其实就是单一的JSON对象 | | Nested 嵌套型 | nested数据类型：表现形式是多个Object型组成的一个数组 | **Geo地理数据类型** | 分类 | 数据类型 | | :-------------: | ------------------------- | | Geo-point 地理坐标型 | geo\_point数据类型：描述纬度/经度坐标 | | Geo-Shape 地理图形型 | geo\_shape数据类型：描述多边形等复杂形状 | **特定数据类型** | 分类 | 数据类型 | | :---------------: | ----------------------------------------------------------------------------------------------------------------------------------------------------- | | IP型 | ip：描述IPv4 和 IPv6 地址 | | Completion补全型 | completion:提供自动完成的提示 | | Token count 令牌计数型 | token\_count:用于统计字符串中的词条数量 | | mapper-murmur3 型 | murmur3:计算哈希值在指数时间和并存储他们在索引中 | | Attachment 附件型 | 查看[mapper-attachments](https://www.elastic.co/guide/en/elasticsearch/plugins/5.2/mapper-attachments.html)插件来支持索引附件，如微软Office格式，开放文档格式，EPUB，HTML等附件类型。 | | Percolator 抽取型 | 接受特定领域查询语言（query-dsl）的查询 | ## 多字段 通常用于为不同目的用不同的方法索引同一个字段。例如，***string***字段可以映射为一个***text***字段用于全文检索，同样可以映射为一个***keyword***字段用于排序和聚合。另外，你可以使用（分析器） ***standard analyzer，english analyzer，french analyzer*** 来索引一个***text*** 字段 这就是 ***muti-fields*** 的目的。大多数的数据类型通过fields参数来支持***muti-fields***。 ### 多字段详解 > 解析一下上面的意思: 插入一条测试数据 ``` PUT my_index/my_type/1 { "name": "Some binary blob" } PUT my_index/my_type/2 { "name": "some apples" } PUT my_index/my_type/3 { "name": "Ha apples" } PUT my_index/my_type/4 { "name": "a man" } PUT my_index/my_type/5 { "name": "many apples" } ``` 查看自动创建的mapping ``` GET /my_index/my_type/_mapping ``` 返回结果: ``` { "my_index": { "mappings": { "my_type": { "properties": { "name": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } } } } } } } ``` 你会发现,5.x版本的Elasticsearch 会在每个字段name的mapping下多出来一个fields的对象,出现了一个已名字为keyword的类型为keyword的字段,这个字段默认是不分词了,所以就可以使用此字段来进行排序和不拆分查询. 1.name 字段,type类型是text,是分词的,所以"Some binary blob"会被分成,"Some","binary", "blob"三个词进行倒排索引 2.name.keyword字段,type类型是keyword,是不分词的正排索引 查看例子: #### **term查询(不分词查询,精确匹配的情况)** **当你通过term查询时,会以"Some binary blob" 整个词组进行查询,如果对name字段进行搜索,是没有值可以返回的.** ``` GET /my_index/my_type/_search { "query": { "term": { "name": "Some binary blob" } } } ``` 无返回结果 ``` { "took": 3, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 0, "max_score": null, "hits": [] } } ``` **使用name.keywords字段进行term查询** ``` GET /my_index/my_type/_search { "query": { "term": { "name.keyword": "Some binary blob" } } } ``` 查出结果: ``` { "took": 3, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 1, "max_score": 0.2876821, "hits": [ { "_index": "my_index", "_type": "my_type", "_id": "1", "_score": 0.2876821, "_source": { "name": "Some binary blob" } } ] } } ``` #### match**查询(分词查询,模糊匹配的情况)** match查询 会对查询词inxS分词,`ome binary blob`会被拆分成四种情况进行搜索 1. `ome词` 2. `binary词` 3. `blob词` 4. `ome binary blob 词` ``` GET /my_index/my_type/_search { "query": { "match": { "name": "Some binary blob" } } } ``` 返回两条数据 ``` { "took": 3, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 2, "max_score": 0.7594807, "hits": [ { "_index": "my_index", "_type": "my_type", "_id": "1", "_score": 0.7594807, "_source": { "name": "Some binary blob" } }, { "_index": "my_index", "_type": "my_type", "_id": "2", "_score": 0.62191015, "_source": { "name": "some apples" } } ] } } ``` 使用name.keyword进行match查询 ``` GET /my_index/my_type/_search { "query": { "match": { "name.keyword": "Some binary blob" } } } ``` 返回结果: ``` { "took": 20, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 1, "max_score": 0.2876821, "hits": [ { "_index": "my_index", "_type": "my_type", "_id": "1", "_score": 0.2876821, "_source": { "name": "Some binary blob" } } ] } } ``` #### 排序的情况 **由于name字段是text类型,分词后倒排索引.所以是无法进行排序的,因此下面会报错** ``` GET /my_index/my_type/_search { "query": { "match_all": {} }, "sort": { "name": { "order": "desc" } } }' ``` 报错结果: ``` { "error": { "root_cause": [ { "type": "illegal_argument_exception", "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [name] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory." } ], "type": "search_phase_execution_exception", "reason": "all shards failed", "phase": "query", "grouped": true, "failed_shards": [ { "shard": 0, "index": "my_index", "node": "7bJsCFK-QlalolMWGqOoxA", "reason": { "type": "illegal_argument_exception", "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [name] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory." } } ], "caused_by": { "type": "illegal_argument_exception", "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [name] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory." } }, "status": 400 } ``` 使用name.keyword字段进行,则ok. ``` GET /my_index/my_type/_search { "query": { "match_all": {} }, "sort": { "name.keyword": { "order": "asc" } } }' ``` 返回结果: ``` { "took": 1, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 5, "max_score": null, "hits": [ { "_index": "my_index", "_type": "my_type", "_id": "3", "_score": null, "_source": { "name": "Ha apples" }, "sort": [ "Ha apples" ] }, { "_index": "my_index", "_type": "my_type", "_id": "1", "_score": null, "_source": { "name": "Some binary blob" }, "sort": [ "Some binary blob" ] }, { "_index": "my_index", "_type": "my_type", "_id": "4", "_score": null, "_source": { "name": "a man" }, "sort": [ "a man" ] }, { "_index": "my_index", "_type": "my_type", "_id": "5", "_score": null, "_source": { "name": "many apples" }, "sort": [ "many apples" ] }, { "_index": "my_index", "_type": "my_type", "_id": "2", "_score": null, "_source": { "name": "some apples" }, "sort": [ "some apples" ] } ] } } ``` #### 总结: 1. 如果是模糊查询,一定要使用text类型的字段进行查询,倒排索引效率高 2. 如果你是一个精确的匹配,并且需要排序,聚合操作,则需要使用keyword类型的字段. 3. 在5.x之前的版本解决方案只能建立两个字段进行两种不通的分词器操作.一个字段分词,一个字段不分词来达到相同的效果.