Elasticsearch 查询描述语言
Elasticsearch 使用基于 JSON 数据格式的查询来执行搜索
查询由两个子句组成:
-
叶子查询短语
这种短语包括 匹配 ( matching ) 、词 ( term ) 或范围 ( range )，用于在特定的字段中查找特定的值
-
复合查询短语
这种查询短语由 叶子查询短语 和其它复合查询短语组成，用于提取所需要的数据
Elasticsearch 支持数量庞大的各种不同类型的查询
这些查询以 query 开始，然后使用 JSON 对象包含条件和过滤器
本章接下来的内容将学习和介绍各种不同类型的查询
匹配所有的查询
这是最基本的查询，它会返回所有的内容，其中的每个对象的得分都是 1.0
这种查询的请求正文一般为
{ "query":{ "match_all":{} } }
例如下面的请求用于查询 user* 索引中的所有数据
POST http://localhost:9200/user*/_search?pretty
请求正文
{ "query":{ "match_all":{} } }
响应内容
{ "took": 21, "timed_out": false, "_shards": { "total": 10, "successful": 10, "skipped": 0, "failed": 0 }, "hits": { "total": 5, "max_score": 1, "hits": [ { "_index": "user", "_type": "user", "_id": "2", "_score": 1, "_source": { "nickname": "枫晚", "description": "停车坐爰枫林晚", "street": "苏州大学", "city": "Suzhou", "state": "Jiangsu", "zip": "215006", "location": [ 120.65426, 31.30797 ], "money": 10235, "tags": [ "Java", "Android" ], "vitality": "3.5" } }, { "_index": "user_admin", "_type": "user", "_id": "2", "_score": 1, "_source": { "nickname": "雅少", "description": "虚怀若谷", "street": "四川大学", "city": "Chengdu", "state": "Sichuan", "zip": "610044", "location": [ 104.094537, 30.640174 ], "money": 68023, "tags": [ "Python", "HTML" ], "vitality": "7.8" } }, { "_index": "user", "_type": "user", "_id": "1", "_score": 1, "_source": { "nickname": "question", "description": "问题少年也是少年", "street": "张江高科技园区", "city": "Shanghai", "state": "Shanghai", "zip": "201204", "location": [ 121.60632, 31.199305 ], "money": 13648, "tags": [ "VUE", "HTML" ], "vitality": "8.8" } }, { "_index": "user_admin", "_type": "user", "_id": "1", "_score": 1, "_source": { "nickname": "语飞", "description": "简单教程，简单编程", "street": "东四十条", "city": "Beijing", "state": "Beijing", "zip": "100007", "location": [ 116.432727, 39.937732 ], "money": 5201814, "tags": [ "PHP", "Python" ], "vitality": "9.0" } }, { "_index": "user_admin", "_type": "user", "_id": "3", "_score": 1, "_source": { "nickname": "歌者", "description": "程序设计也是设计，研发新菜也是研发", "street": "五道口", "city": "Beijing", "state": "Beijing", "zip": "100083", "location": [ 116.346346, 39.999333 ], "money": 71128, "tags": [ "Java", "Scala" ], "vitality": "6.9" } } ] } }
全文检索查询
这些查询会搜索整个文本，如章节或新闻文章
这种查询会用到与指定的索引或文档相关联的分析器，由分析器对内容进行分析
接下来，我们就看看 Elasticsearch 支持哪些全文检索查询
匹配查询
这种查询将文本或短语与一个或多个字段的值进行匹配，返回匹配成功的文档
例如下面的查询请求用于查询所有城市为 pune 的学校
POST http://localhost:9200/user*/_search?pretty
请求正文
{ "query":{ "match" : { "city":"Chengdu" } } }
响应内容
{ "took" : 15, "timed_out" : false, "_shards" : { "total" : 10, "successful" : 10, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 0.2876821, "hits" : [ { "_index" : "user_admin", "_type" : "user", "_id" : "2", "_score" : 0.2876821, "_source" : { "nickname" : "雅少", "description" : "虚怀若谷", "street" : "四川大学", "city" : "Chengdu", "state" : "Sichuan", "zip" : "610044", "location" : [ 104.094537, 30.640174 ], "money" : 68023, "tags" : [ "Python", "HTML" ], "vitality" : "7.8" } } ] } }
多字段匹配查询
这种查询会将文本或短语与多个字段进行匹配
例如下面的请求，将在 city 和 state 字段上查找匹配 shanghai 的内容
POST http://localhost:9200/user*/_search?pretty
请求正文
{ "query":{ "multi_match" : { "query": "shanghai", "fields": [ "city", "state" ] } } }
返回响应
{ "took" : 30, "timed_out" : false, "_shards" : { "total" : 10, "successful" : 10, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 0.2876821, "hits" : [ { "_index" : "user", "_type" : "user", "_id" : "1", "_score" : 0.2876821, "_source" : { "nickname" : "question", "description" : "问题少年也是少年", "street" : "张江高科技园区", "city" : "Shanghai", "state" : "Shanghai", "zip" : "201204", "location" : [ 121.60632, 31.199305 ], "money" : 13648, "tags" : [ "VUE", "HTML" ], "vitality" : "8.8" } } ] } }
查询字符串查询
这种查询会使用 query_string 来指定要查询的关键字，然后用查询分析器来分析查询关键字
例如下面的查询请求将返回包含 语 和 枫 的查询结果
POST http://localhost:9200/user*/_search?pretty
请求正文
{ "query":{ "query_string":{ "query":"语 枫" } } }
返回响应
{ "took" : 111, "timed_out" : false, "_shards" : { "total" : 10, "successful" : 10, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 2, "max_score" : 0.2876821, "hits" : [ { "_index" : "user", "_type" : "user", "_id" : "2", "_score" : 0.2876821, "_source" : { "nickname" : "枫晚", "description" : "停车坐爰枫林晚", "street" : "苏州大学", "city" : "Suzhou", "state" : "Jiangsu", "zip" : "215006", "location" : [ 120.65426, 31.30797 ], "money" : 10235, "tags" : [ "Java", "Android" ], "vitality" : "3.5" } }, { "_index" : "user_admin", "_type" : "user", "_id" : "1", "_score" : 0.2876821, "_source" : { "nickname" : "语飞", "description" : "简单教程，简单编程", "street" : "东四十条", "city" : "Beijing", "state" : "Beijing", "zip" : "100007", "location" : [ 116.432727, 39.937732 ], "money" : 5201814, "tags" : [ "PHP", "Python" ], "vitality" : "9.0" } } ] } }
术语级别查询
这种查询主要用于查询结构化的数据，比如数字、日期和枚举 ( emuns )
例如下面的查询返回所有 zip 为 100007 的数据
POST http://localhost:9200/user*/_search?pretty
请求正文
{ "query":{ "term":{"zip":"100007"} } }
响应内容
{ "took" : 23, "timed_out" : false, "_shards" : { "total" : 10, "successful" : 10, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 0.2876821, "hits" : [ { "_index" : "user_admin", "_type" : "user", "_id" : "1", "_score" : 0.2876821, "_source" : { "nickname" : "语飞", "description" : "简单教程，简单编程", "street" : "东四十条", "city" : "Beijing", "state" : "Beijing", "zip" : "100007", "location" : [ 116.432727, 39.937732 ], "money" : 5201814, "tags" : [ "PHP", "Python" ], "vitality" : "9.0" } } ] } }
范围查询
这种查询主要用于查询那些值处于某个范围区间的对象
为此，我们还需要使用下面的关键字
| 关键字 | 说明 |
|---|---|
| gte | 大于等于 |
| gt | 大于 |
| lte | 小于等于 |
| lt | 小于 |
例如下面的查询用于返回那些 vitality 在 5.5 以上的文档
POST http://localhost:9200/user*/_search?pretty
请求正文
{ "query":{ "range":{ "vitality":{ "gte":5.5 } } } }
响应内容
{ "took" : 71, "timed_out" : false, "_shards" : { "total" : 10, "successful" : 10, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 4, "max_score" : 1.0, "hits" : [ { "_index" : "user_admin", "_type" : "user", "_id" : "2", "_score" : 1.0, "_source" : { "nickname" : "雅少", "description" : "虚怀若谷", "street" : "四川大学", "city" : "Chengdu", "state" : "Sichuan", "zip" : "610044", "location" : [ 104.094537, 30.640174 ], "money" : 68023, "tags" : [ "Python", "HTML" ], "vitality" : "7.8" } }, { "_index" : "user", "_type" : "user", "_id" : "1", "_score" : 1.0, "_source" : { "nickname" : "question", "description" : "问题少年也是少年", "street" : "张江高科技园区", "city" : "Shanghai", "state" : "Shanghai", "zip" : "201204", "location" : [ 121.60632, 31.199305 ], "money" : 13648, "tags" : [ "VUE", "HTML" ], "vitality" : "8.8" } }, { "_index" : "user_admin", "_type" : "user", "_id" : "1", "_score" : 1.0, "_source" : { "nickname" : "语飞", "description" : "简单教程，简单编程", "street" : "东四十条", "city" : "Beijing", "state" : "Beijing", "zip" : "100007", "location" : [ 116.432727, 39.937732 ], "money" : 5201814, "tags" : [ "PHP", "Python" ], "vitality" : "9.0" } }, { "_index" : "user_admin", "_type" : "user", "_id" : "3", "_score" : 1.0, "_source" : { "nickname" : "歌者", "description" : "程序设计也是设计，研发新菜也是研发", "street" : "五道口", "city" : "Beijing", "state" : "Beijing", "zip" : "100083", "location" : [ 116.346346, 39.999333 ], "money" : 71128, "tags" : [ "Java", "Scala" ], "vitality" : "6.9" } } ] } }
还存在一些其它类型的术语级别查询，我们就不一一介绍了，先罗列在此
| 查询 | 说明 |
|---|---|
| 存在查询 | 返回某个字段不为 null 的对象 |
| 缺失查询 | 与存在查询相反，这种查询用于搜索不存在某个字段或即使存在其值也为 null 的对象 |
| 通配符或正则查询 | 使用正则表达式来匹配某个字段的值是否符合某个模式的对象 |
类型查询
用于查询那些符合某个类型的文档
例如下面的查询请求用于查询符合 user 类型的文档
POST http://localhost:9200/user*/_search
请求正文
{
"query":{
"type" : {
"value" : "user"
}
}
}
响应内容
返回的响应一定是 user 索引索引中的全部 JSON 对象文档
复合查询
这种查询使用布尔运算符( 例如 and 、or 和 not ) 运算符来组合不同的索引或函数调用返回的不同的查询的结果
例如下面的复合查询
POST http://localhost:9200/user*/_search?pretty
请求正文
{ "query":{ "filtered":{ "query":{ "match":{ "state":"UP" } }, "filter":{ "range":{ "rating":{ "gte":4.0 } } } } } }
响应正文
{ "took":16, "timed_out":false, "_shards":{"total":10, "successful":10, "failed":0}, "hits":{"total":0, "max_score":null, "hits":[]} }
连接查询
这种查询用于包含多个映射或文档的情况
有两种类型的连接查询
-
嵌套查询
这种查询一般用于处理嵌套映射的情况，我们会在下一章节详细介绍
-
has_child 和 has_parent 查询
这种查询用于检索文档的子文档或父文档，以找出匹配查询的对象
例如下面这个查询请求
POST http://localhost:9200/tutorials/_search
请求正文
{ "query": { "has_child" : { "type" : "article", "query" : { "match" : { "Text" : "This is article 1 of chapter 1" } } } } }返回响应内容
{ "took":21, "timed_out":false, "_shards":{"total":5, "successful":5, "failed":0}, "hits":{ "total":1, "max_score":1.0, "hits":[{ "_index":"tutorials", "_type":"chapter", "_id":"1", "_score":1.0, "_source":{ "Text":"this is chapter one" } }] } }
地理信息查询
这种查询可以处理地理位置信息和地理点
一般用于找出学校或任何地点附近的任何其它地理对象
对于这种查询，我们需要使用地理点数据类型
例如下面的查询
POST http://localhost:9200/user*/_search?pretty
请求正文
{ "query":{ "bool": { "filter":{ "geo_distance":{ "distance":"100km", "location":[116.4448,39.943042] } } } } }
响应正文
{ "error" : { "root_cause" : [ { "type" : "query_shard_exception", "reason" : "field [location] is not a geo_point field", "index_uuid" : "VYLD0ybxRLeVB_KsJ8ZjDw", "index" : "user" }, { "type" : "query_shard_exception", "reason" : "field [location] is not a geo_point field", "index_uuid" : "_acBr-_YSCiaHImp1VObGg", "index" : "user_admin" } ], "type" : "search_phase_execution_exception", "reason" : "all shards failed", "phase" : "query", "grouped" : true, "failed_shards" : [ { "shard" : 0, "index" : "user", "node" : "4zwAMlTzRCaioBeOE9PaNw", "reason" : { "type" : "query_shard_exception", "reason" : "field [location] is not a geo_point field", "index_uuid" : "VYLD0ybxRLeVB_KsJ8ZjDw", "index" : "user" } }, { "shard" : 0, "index" : "user_admin", "node" : "4zwAMlTzRCaioBeOE9PaNw", "reason" : { "type" : "query_shard_exception", "reason" : "field [location] is not a geo_point field", "index_uuid" : "_acBr-_YSCiaHImp1VObGg", "index" : "user_admin" } } ] }, "status" : 400 }
如果你在运行上面的范例时抛出了异常，那么可以运行下面的请求为索引添加映射
{ "mappings":{ "user":{ "properties":{ "location":{ "type":"geo_point" } } } } }