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:
1、 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
2、 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": "站长",
"description": "pottercoding.cn 程序员波特，程序员编程资料站 ，教程 ",
"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" : "站长",
"description" : "pottercoding.cn 程序员波特，程序员编程资料站 ，教程 ",
"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" : "站长",
"description" : "pottercoding.cn 程序员波特，程序员编程资料站 ，教程 ",
"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" : "站长",
"description" : "pottercoding.cn 程序员波特，程序员编程资料站 ，教程 ",
"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
1、 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
2、 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"
}
}
}
}