ElasticSearch多种查询操作

• 前言
• 1 词条查询
• 1.1 等值查询-term1.2 多值查询-terms1.3 范围查询-range1.4 前缀查询-prefix1.5 通配符查询-wildcard
• 2 复合查询
• 2.1 布尔查询2.2 Filter查询
• 3 聚合查询
• 3.1 最值、平均值、求和3.2 去重查询3.3 分组聚合3.3.1 单条件分组3.3.2 多条件分组3.4 过滤聚合

前言

• ElasticSearch第一篇：ElasticSearch基础：从倒排索引说起，快速认知ES

这篇博文的主题是ES的查询，因此我整理了尽可能齐全的ES查询场景，形成下面的图：

本文基于elasticsearch 7.13.2版本，es从7.0以后，发生了很大的更新。7.3以后，已经不推荐使用TransportClient这个client，取而代之的是Java High Level REST Client。

测试使用的数据示例

首先是，Mysql中的部分测试数据：

Mysql中的一行数据在ES中以一个文档形式存在：

{  "_index" : "person",  "_type" : "_doc",  "_id" : "4",  "_score" : 1.0,  "_source" : {    "address" : "峨眉山",    "modifyTime" : "2021-06-29 19:46:25",    "createTime" : "2021-05-14 11:37:07",    "sect" : "峨嵋派",    "sex" : "男",    "skill" : "降龙十八掌",    "name" : "宋青书",    "id" : 4,    "power" : 50,    "age" : 21  }}

简单梳理了一下ES JavaAPI的相关体系，感兴趣的可以自己研读一下源码。

接下来，我们用十几个实例，迅速上手ES的查询操作，每个示例将提供SQL语句、ES语句和Java代码。

1 词条查询

所谓词条查询，也就是ES不会对查询条件进行分词处理，只有当词条和查询字符串完全匹配时，才会被查询到。

1.1 等值查询-term

等值查询，即筛选出一个字段等于特定值得所有记录。

SQL：

select * from person where name = '张无忌';

而使用ES查询语句却很不一样（注意查询字段带上keyword）：

GET /person/_search{"query": {"term": {"name.keyword": {"value": "张无忌","boost": 1.0}}}}

ElasticSearch 5.0以后，string类型有重大变更，移除了string类型，string字段被拆分成两种新的数据类型: text用于全文搜索的，而keyword用于关键词搜索的。

查询结果：

{  "took" : 0,  "timed_out" : false,  "_shards" : { // 分片信息    "total" : 1, // 总计分片数    "successful" : 1, // 查询成功的分片数    "skipped" : 0, // 跳过查询的分片数    "failed" : 0  // 查询失败的分片数  },  "hits" : { // 命中结果    "total" : {      "value" : 1, // 数量      "relation" : "eq"  // 关系：等于    },    "max_score" : 2.8526313,  // 最高分数    "hits" : [      {        "_index" : "person", // 索引        "_type" : "_doc", // 类型        "_id" : "1",        "_score" : 2.8526313,        "_source" : {          "address" : "光明顶",          "modifyTime" : "2021-06-29 16:48:56",          "createTime" : "2021-05-14 16:50:33",          "sect" : "明教",          "sex" : "男",          "skill" : "九阳神功",          "name" : "张无忌",          "id" : 1,          "power" : 99,          "age" : 18        }      }    ]  }}

Java中构造ES请求的方式：（后续例子中只保留SearchSourceBuilder的构建语句）

/** * term精确查询 * * @throws IOException */@Autowiredprivate RestHighLevelClient client;@Testpublic void queryTerm() throws IOException {// 根据索引创建查询请求    SearchRequest searchRequest = new SearchRequest("person");    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();    // 构建查询语句    searchSourceBuilder.query(QueryBuilders.termQuery("name.keyword", "张无忌"));    System.out.println("searchSourceBuilder=====================" + searchSourceBuilder);    searchRequest.source(searchSourceBuilder);    SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);    System.out.println(JSONObject.toJSON(response));}

仔细观察查询结果，会发现ES查询结果中会带有_score这一项，ES会根据结果匹配程度进行评分。打分是会耗费性能的，如果确认自己的查询不需要评分，就设置查询语句关闭评分：

GET /person/_search{"query": {"constant_score": {"filter": {"term": {"sect.keyword": {"value": "张无忌","boost": 1.0}}},"boost": 1.0}}}

Java构建查询语句：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 这样构造的查询条件，将不进行score计算，从而提高查询效率searchSourceBuilder.query(QueryBuilders.constantScoreQuery(QueryBuilders.termQuery("sect.keyword", "明教")));

1.2 多值查询-terms

多条件查询类似Mysql里的IN查询，例如：

select * from persons where sect in('明教','武当派');

ES查询语句：

GET /person/_search{"query": {"terms": {"sect.keyword": ["明教","武当派"],"boost": 1.0}}}

Java实现：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.termsQuery("sect.keyword", Arrays.asList("明教", "武当派")));}

1.3 范围查询-range

范围查询，即查询某字段在特定区间的记录。

SQL:

select * from pesons where age between 18 and 22;

ES查询语句：

GET /person/_search{"query": {"range": {"age": {"from": 10,"to": 20,"include_lower": true,"include_upper": true,"boost": 1.0}}}}

Java构建查询条件：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.rangeQuery("age").gte(10).lte(30));}

1.4 前缀查询-prefix

前缀查询类似于SQL中的模糊查询。

SQL：

select * from persons where sect like '武当%';

ES查询语句：

{"query": {"prefix": {"sect.keyword": {"value": "武当","boost": 1.0}}}}

Java构建查询条件：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.prefixQuery("sect.keyword","武当"));

1.5 通配符查询-wildcard

通配符查询，与前缀查询类似，都属于模糊查询的范畴，但通配符显然功能更强。

SQL：

select * from persons where name like '张%忌';

ES查询语句：

{"query": {"wildcard": {"sect.keyword": {"wildcard": "张*忌","boost": 1.0}}}}

Java构建查询条件：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.wildcardQuery("sect.keyword","张*忌"));

2 复合查询

前面的例子都是单个条件查询，在实际应用中，我们很有可能会过滤多个值或字段。先看一个简单的例子：

select * from persons where sex = '女' and sect = '明教';

这样的多条件等值查询，就要借用到组合过滤器了，其查询语句是：

{"query": {"bool": {"must": [{    "term": {"sex": {"value": "女","boost": 1.0}}},{"term": {"sect.keywords": {"value": "明教","boost": 1.0}}}],"adjust_pure_negative": true,"boost": 1.0}}}

Java构造查询语句：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .must(QueryBuilders.termQuery("sex", "女"))        .must(QueryBuilders.termQuery("sect.keyword", "明教")));

2.1 布尔查询

布尔过滤器（bool filter）属于复合过滤器（compound filter）的一种 ，可以接受多个其他过滤器作为参数，并将这些过滤器结合成各式各样的布尔（逻辑）组合。

bool 过滤器下可以有4种子条件，可以任选其中任意一个或多个。filter是比较特殊的，这里先不说。

{   "bool" : {      "must" :     [],      "should" :   [],      "must_not" : [],   }}

• must：所有的语句都必须匹配，与 ‘=’ 等价。
• must_not：所有的语句都不能匹配，与 ‘!=’ 或 not in 等价。
• should：至少有n个语句要匹配，n由参数控制。

精度控制：

所有 must 语句必须匹配，所有 must_not 语句都必须不匹配，但有多少 should 语句应该匹配呢？默认情况下，没有 should 语句是必须匹配的，只有一个例外：那就是当没有 must 语句的时候，至少有一个 should 语句必须匹配。

我们可以通过 minimum_should_match 参数控制需要匹配的 should 语句的数量，它既可以是一个绝对的数字，又可以是个百分比：

GET /person/_search{"query": {"bool": {"must": [{"term": {"sex": {"value": "女","boost": 1.0}}}],"should": [{"term": {"address.keyword": {"value": "峨眉山","boost": 1.0}}},{"term": {"sect.keyword": {"value": "明教","boost": 1.0}}}],"adjust_pure_negative": true,"minimum_should_match": "1","boost": 1.0}}}

Java构建查询语句：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .must(QueryBuilders.termQuery("sex", "女"))        .should(QueryBuilders.termQuery("address.word", "峨眉山"))        .should(QueryBuilders.termQuery("sect.keyword", "明教"))        .minimumShouldMatch(1));

最后，看一个复杂些的例子，将bool的各子句联合使用：

select *frompersonswhere sex = '女'andage between 30 and 40and sect != '明教'and (address = '峨眉山' OR skill = '暗器')

用 Elasticsearch 来表示上面的 SQL 例子：

GET /person/_search{"query": {"bool": {"must": [{"term": {"sex": {"value": "女","boost": 1.0}}},{"range": {"age": {"from": 30,"to": 40,"include_lower": true,"include_upper": true,"boost": 1.0}}}],"must_not": [{"term": {"sect.keyword": {"value": "明教","boost": 1.0}}}],"should": [{"term": {"address.keyword": {"value": "峨眉山","boost": 1.0}}},{"term": {"skill.keyword": {"value": "暗器","boost": 1.0}}}],"adjust_pure_negative": true,"minimum_should_match": "1","boost": 1.0}}}

用Java构建这个查询条件：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery()        .must(QueryBuilders.termQuery("sex", "女"))        .must(QueryBuilders.rangeQuery("age").gte(30).lte(40))        .mustNot(QueryBuilders.termQuery("sect.keyword", "明教"))        .should(QueryBuilders.termQuery("address.keyword", "峨眉山"))        .should(QueryBuilders.rangeQuery("power.keyword").gte(50).lte(80))        .minimumShouldMatch(1);  // 设置should至少需要满足几个条件// 将BoolQueryBuilder构建到SearchSourceBuilder中searchSourceBuilder.query(boolQueryBuilder);

2.2 Filter查询

query和filter的区别：query查询的时候，会先比较查询条件，然后计算分值，最后返回文档结果；而filter是先判断是否满足查询条件，如果不满足会缓存查询结果（记录该文档不满足结果），满足的话，就直接缓存结果，filter不会对结果进行评分，能够提高查询效率。

filter的使用方式比较多样，下面用几个例子演示一下。

方式一，单独使用：

{"query": {"bool": {"filter": [{"term": {"sex": {"value": "男","boost": 1.0}}}],"adjust_pure_negative": true,"boost": 1.0}}}

单独使用时，filter与must基本一样，不同的是filter不计算评分，效率更高。

Java构建查询语句：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .filter(QueryBuilders.termQuery("sex", "男")));

方式二，和must、must_not同级，相当于子查询：

select * from (select * from persons where sect = '明教')) a where sex = '女';

ES查询语句：

{"query": {"bool": {"must": [{"term": {"sect.keyword": {"value": "明教","boost": 1.0}}}],"filter": [{"term": {"sex": {"value": "女","boost": 1.0}}}],"adjust_pure_negative": true,"boost": 1.0}}}

Java：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .must(QueryBuilders.termQuery("sect.keyword", "明教"))        .filter(QueryBuilders.termQuery("sex", "女")));

方式三，将must、must_not置于filter下，这种方式是最常用的：

{"query": {"bool": {"filter": [{"bool": {"must": [{"term": {"sect.keyword": {"value": "明教","boost": 1.0}}},{"range": {"age": {"from": 20,"to": 35,"include_lower": true,"include_upper": true,"boost": 1.0}}}],"must_not": [{"term": {"sex.keyword": {"value": "女","boost": 1.0}}}],"adjust_pure_negative": true,"boost": 1.0}}],"adjust_pure_negative": true,"boost": 1.0}}}

Java:

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .filter(QueryBuilders.boolQuery()                .must(QueryBuilders.termQuery("sect.keyword", "明教"))                .must(QueryBuilders.rangeQuery("age").gte(20).lte(35))                .mustNot(QueryBuilders.termQuery("sex.keyword", "女"))));

3 聚合查询

接下来，我们将用一些案例演示ES聚合查询。

3.1 最值、平均值、求和

案例：查询最大年龄、最小年龄、平均年龄。

SQL：

select max(age) from persons;

ES：

GET /person/_search{"aggregations": {"max_age": {"max": {"field": "age"}}}}

Java：

@Autowiredprivate RestHighLevelClient client;@Testpublic void maxQueryTest() throws IOException {// 聚合查询条件    AggregationBuilder aggBuilder = AggregationBuilders.max("max_age").field("age");    SearchRequest searchRequest = new SearchRequest("person");    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();    // 将聚合查询条件构建到SearchSourceBuilder中    searchSourceBuilder.aggregation(aggBuilder);    System.out.println("searchSourceBuilder----->" + searchSourceBuilder);    searchRequest.source(searchSourceBuilder);    // 执行查询，获取SearchResponse    SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);    System.out.println(JSONObject.toJSON(response));}

使用聚合查询，结果中默认只会返回10条文档数据（当然我们关心的是聚合的结果，而非文档）。返回多少条数据可以自主控制：

GET /person/_search{"size": 20,"aggregations": {"max_age": {"max": {"field": "age"}}}}

而Java中只需增加下面一条语句即可：

searchSourceBuilder.size(20);

与max类似，其他统计查询也很简单：

AggregationBuilder minBuilder = AggregationBuilders.min("min_age").field("age");AggregationBuilder avgBuilder = AggregationBuilders.avg("min_age").field("age");AggregationBuilder sumBuilder = AggregationBuilders.sum("min_age").field("age");AggregationBuilder countBuilder = AggregationBuilders.count("min_age").field("age");

3.2 去重查询

案例：查询一共有多少个门派。

SQL：

select count(distinct sect) from persons;

ES：

{"aggregations": {"sect_count": {"cardinality": {"field": "sect.keyword"}}}}

Java：

@Testpublic void cardinalityQueryTest() throws IOException {// 创建某个索引的request    SearchRequest searchRequest = new SearchRequest("person");    // 查询条件    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();    // 聚合查询    AggregationBuilder aggBuilder = AggregationBuilders.cardinality("sect_count").field("sect.keyword");    searchSourceBuilder.size(0);    // 将聚合查询构建到查询条件中    searchSourceBuilder.aggregation(aggBuilder);    System.out.println("searchSourceBuilder----->" + searchSourceBuilder);    searchRequest.source(searchSourceBuilder);    // 执行查询，获取结果    SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);    System.out.println(JSONObject.toJSON(response));}

3.3 分组聚合

3.3.1 单条件分组

案例：查询每个门派的人数

SQL：

select sect,count(id) from mytest.persons group by sect;

ES：

{"size": 0,"aggregations": {"sect_count": {"terms": {"field": "sect.keyword","size": 10,"min_doc_count": 1,"shard_min_doc_count": 0,"show_term_doc_count_error": false,"order": [{"_count": "desc"},{"_key": "asc"}]}}}}

Java：

SearchRequest searchRequest = new SearchRequest("person");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.size(0);// 按sect分组AggregationBuilder aggBuilder = AggregationBuilders.terms("sect_count").field("sect.keyword");searchSourceBuilder.aggregation(aggBuilder);

3.3.2 多条件分组

案例：查询每个门派各有多少个男性和女性

SQL：

select sect,sex,count(id) from mytest.persons group by sect,sex;

ES：

{"aggregations": {"sect_count": {"terms": {"field": "sect.keyword","size": 10},"aggregations": {"sex_count": {"terms": {"field": "sex.keyword","size": 10}}}}}}

3.4 过滤聚合

前面所有聚合的例子请求都省略了 query ，整个请求只不过是一个聚合。这意味着我们对全部数据进行了聚合，但现实应用中，我们常常对特定范围的数据进行聚合，例如下例。

案例：查询明教中的最大年龄。 这涉及到聚合与条件查询一起使用。

SQL：

select max(age) from mytest.persons where sect = '明教';

ES：

GET /person/_search{"query": {"term": {"sect.keyword": {"value": "明教","boost": 1.0}}},"aggregations": {"max_age": {"max": {"field": "age"}}}}

Java：

SearchRequest searchRequest = new SearchRequest("person");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 聚合查询条件AggregationBuilder maxBuilder = AggregationBuilders.max("max_age").field("age");// 等值查询searchSourceBuilder.query(QueryBuilders.termQuery("sect.keyword", "明教"));searchSourceBuilder.aggregation(maxBuilder);

另外还有一些更复杂的查询例子。

案例：查询0-20，21-40，41-60，61以上的各有多少人。

SQL：

select sum(case when age<=20 then 1 else 0 end agegroup1sumcase when age>20 and age <=40 then 1 else 0 end agegroup2sumcase when age>40 and age <=60 then 1 else 0 end agegroup3sumcase when age>60 and age <=200 then 1 else 0 end) ageGroup4from mytest.persons;

ES：

{"size": 0,"aggregations": {"age_avg": {"range": {"field": "age","ranges": [{"from": 0.0,"to": 20.0},{"from": 21.0,"to": 40.0},{"from": 41.0,"to": 60.0},{"from": 61.0,"to": 200.0}],"keyed": false}}}}

Java：

查询结果：

"aggregations" : {  "age_avg" : {    "buckets" : [      {        "key" : "0.0-20.0",        "from" : 0.0,        "to" : 20.0,        "doc_count" : 3      },      {        "key" : "21.0-40.0",        "from" : 21.0,        "to" : 40.0,        "doc_count" : 13      },      {        "key" : "41.0-60.0",        "from" : 41.0,        "to" : 60.0,        "doc_count" : 4      },      {        "key" : "61.0-200.0",        "from" : 61.0,        "to" : 200.0,        "doc_count" : 1      }    ]  }}

以上是ElasticSearch查询的全部内容，丰富详实，堪比操作手册，强烈建议收藏！ElasticSearch多种查询操作

ElasticSearch多种查询操作

• 前言
• 1 词条查询
• 1.1 等值查询-term1.2 多值查询-terms1.3 范围查询-range1.4 前缀查询-prefix1.5 通配符查询-wildcard
• 2 复合查询
• 2.1 布尔查询2.2 Filter查询
• 3 聚合查询
• 3.1 最值、平均值、求和3.2 去重查询3.3 分组聚合3.3.1 单条件分组3.3.2 多条件分组3.4 过滤聚合

前言

• ElasticSearch第一篇：ElasticSearch基础：从倒排索引说起，快速认知ES

这篇博文的主题是ES的查询，因此我整理了尽可能齐全的ES查询场景，形成下面的图：

本文基于elasticsearch 7.13.2版本，es从7.0以后，发生了很大的更新。7.3以后，已经不推荐使用TransportClient这个client，取而代之的是Java High Level REST Client。

测试使用的数据示例

首先是，Mysql中的部分测试数据：

Mysql中的一行数据在ES中以一个文档形式存在：

{  "_index" : "person",  "_type" : "_doc",  "_id" : "4",  "_score" : 1.0,  "_source" : {    "address" : "峨眉山",    "modifyTime" : "2021-06-29 19:46:25",    "createTime" : "2021-05-14 11:37:07",    "sect" : "峨嵋派",    "sex" : "男",    "skill" : "降龙十八掌",    "name" : "宋青书",    "id" : 4,    "power" : 50,    "age" : 21  }}

简单梳理了一下ES JavaAPI的相关体系，感兴趣的可以自己研读一下源码。

接下来，我们用十几个实例，迅速上手ES的查询操作，每个示例将提供SQL语句、ES语句和Java代码。

1 词条查询

所谓词条查询，也就是ES不会对查询条件进行分词处理，只有当词条和查询字符串完全匹配时，才会被查询到。

1.1 等值查询-term

等值查询，即筛选出一个字段等于特定值的所有记录。

SQL：

select * from person where name = '张无忌';

而使用ES查询语句却很不一样（注意查询字段带上keyword）：

GET /person/_search{"query": {"term": {"name.keyword": {"value": "张无忌","boost": 1.0}}}}

ElasticSearch 5.0以后，string类型有重大变更，移除了string类型，string字段被拆分成两种新的数据类型: text用于全文搜索的，而keyword用于关键词搜索的。

查询结果：

{  "took" : 0,  "timed_out" : false,  "_shards" : { // 分片信息    "total" : 1, // 总计分片数    "successful" : 1, // 查询成功的分片数    "skipped" : 0, // 跳过查询的分片数    "failed" : 0  // 查询失败的分片数  },  "hits" : { // 命中结果    "total" : {      "value" : 1, // 数量      "relation" : "eq"  // 关系：等于    },    "max_score" : 2.8526313,  // 最高分数    "hits" : [      {        "_index" : "person", // 索引        "_type" : "_doc", // 类型        "_id" : "1",        "_score" : 2.8526313,        "_source" : {          "address" : "光明顶",          "modifyTime" : "2021-06-29 16:48:56",          "createTime" : "2021-05-14 16:50:33",          "sect" : "明教",          "sex" : "男",          "skill" : "九阳神功",          "name" : "张无忌",          "id" : 1,          "power" : 99,          "age" : 18        }      }    ]  }}

Java中构造ES请求的方式：（后续例子中只保留SearchSourceBuilder的构建语句）

/** * term精确查询 * * @throws IOException */@Autowiredprivate RestHighLevelClient client;@Testpublic void queryTerm() throws IOException {// 根据索引创建查询请求    SearchRequest searchRequest = new SearchRequest("person");    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();    // 构建查询语句    searchSourceBuilder.query(QueryBuilders.termQuery("name.keyword", "张无忌"));    System.out.println("searchSourceBuilder=====================" + searchSourceBuilder);    searchRequest.source(searchSourceBuilder);    SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);    System.out.println(JSONObject.toJSON(response));}

仔细观察查询结果，会发现ES查询结果中会带有_score这一项，ES会根据结果匹配程度进行评分。打分是会耗费性能的，如果确认自己的查询不需要评分，就设置查询语句关闭评分：

GET /person/_search{"query": {"constant_score": {"filter": {"term": {"sect.keyword": {"value": "张无忌","boost": 1.0}}},"boost": 1.0}}}

Java构建查询语句：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 这样构造的查询条件，将不进行score计算，从而提高查询效率searchSourceBuilder.query(QueryBuilders.constantScoreQuery(QueryBuilders.termQuery("sect.keyword", "明教")));

1.2 多值查询-terms

多条件查询类似Mysql里的IN查询，例如：

select * from persons where sect in('明教','武当派');

ES查询语句：

GET /person/_search{"query": {"terms": {"sect.keyword": ["明教","武当派"],"boost": 1.0}}}

Java实现：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.termsQuery("sect.keyword", Arrays.asList("明教", "武当派")));}

1.3 范围查询-range

范围查询，即查询某字段在特定区间的记录。

SQL:

select * from pesons where age between 18 and 22;

ES查询语句：

GET /person/_search{"query": {"range": {"age": {"from": 10,"to": 20,"include_lower": true,"include_upper": true,"boost": 1.0}}}}

Java构建查询条件：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.rangeQuery("age").gte(10).lte(30));}

1.4 前缀查询-prefix

前缀查询类似于SQL中的模糊查询。

SQL：

select * from persons where sect like '武当%';

ES查询语句：

{"query": {"prefix": {"sect.keyword": {"value": "武当","boost": 1.0}}}}

Java构建查询条件：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.prefixQuery("sect.keyword","武当"));

1.5 通配符查询-wildcard

通配符查询，与前缀查询类似，都属于模糊查询的范畴，但通配符显然功能更强。

SQL：

select * from persons where name like '张%忌';

ES查询语句：

{"query": {"wildcard": {"sect.keyword": {"wildcard": "张*忌","boost": 1.0}}}}

Java构建查询条件：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.wildcardQuery("sect.keyword","张*忌"));

2 复合查询

前面的例子都是单个条件查询，在实际应用中，我们很有可能会过滤多个值或字段。先看一个简单的例子：

select * from persons where sex = '女' and sect = '明教';

这样的多条件等值查询，就要借用到组合过滤器了，其查询语句是：

{"query": {"bool": {"must": [{    "term": {"sex": {"value": "女","boost": 1.0}}},{"term": {"sect.keywords": {"value": "明教","boost": 1.0}}}],"adjust_pure_negative": true,"boost": 1.0}}}

Java构造查询语句：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .must(QueryBuilders.termQuery("sex", "女"))        .must(QueryBuilders.termQuery("sect.keyword", "明教")));

2.1 布尔查询

布尔过滤器（bool filter）属于复合过滤器（compound filter）的一种 ，可以接受多个其他过滤器作为参数，并将这些过滤器结合成各式各样的布尔（逻辑）组合。

bool 过滤器下可以有4种子条件，可以任选其中任意一个或多个。filter是比较特殊的，这里先不说。

{   "bool" : {      "must" :     [],      "should" :   [],      "must_not" : [],   }}

• must：所有的语句都必须匹配，与 ‘=’ 等价。
• must_not：所有的语句都不能匹配，与 ‘!=’ 或 not in 等价。
• should：至少有n个语句要匹配，n由参数控制。

精度控制：

所有 must 语句必须匹配，所有 must_not 语句都必须不匹配，但有多少 should 语句应该匹配呢？默认情况下，没有 should 语句是必须匹配的，只有一个例外：那就是当没有 must 语句的时候，至少有一个 should 语句必须匹配。

我们可以通过 minimum_should_match 参数控制需要匹配的 should 语句的数量，它既可以是一个绝对的数字，又可以是个百分比：

GET /person/_search{"query": {"bool": {"must": [{"term": {"sex": {"value": "女","boost": 1.0}}}],"should": [{"term": {"address.keyword": {"value": "峨眉山","boost": 1.0}}},{"term": {"sect.keyword": {"value": "明教","boost": 1.0}}}],"adjust_pure_negative": true,"minimum_should_match": "1","boost": 1.0}}}

Java构建查询语句：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .must(QueryBuilders.termQuery("sex", "女"))        .should(QueryBuilders.termQuery("address.word", "峨眉山"))        .should(QueryBuilders.termQuery("sect.keyword", "明教"))        .minimumShouldMatch(1));

最后，看一个复杂些的例子，将bool的各子句联合使用：

select *frompersonswhere sex = '女'andage between 30 and 40and sect != '明教'and (address = '峨眉山' OR skill = '暗器')

用 Elasticsearch 来表示上面的 SQL 例子：

GET /person/_search{"query": {"bool": {"must": [{"term": {"sex": {"value": "女","boost": 1.0}}},{"range": {"age": {"from": 30,"to": 40,"include_lower": true,"include_upper": true,"boost": 1.0}}}],"must_not": [{"term": {"sect.keyword": {"value": "明教","boost": 1.0}}}],"should": [{"term": {"address.keyword": {"value": "峨眉山","boost": 1.0}}},{"term": {"skill.keyword": {"value": "暗器","boost": 1.0}}}],"adjust_pure_negative": true,"minimum_should_match": "1","boost": 1.0}}}

用Java构建这个查询条件：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery()        .must(QueryBuilders.termQuery("sex", "女"))        .must(QueryBuilders.rangeQuery("age").gte(30).lte(40))        .mustNot(QueryBuilders.termQuery("sect.keyword", "明教"))        .should(QueryBuilders.termQuery("address.keyword", "峨眉山"))        .should(QueryBuilders.rangeQuery("power.keyword").gte(50).lte(80))        .minimumShouldMatch(1);  // 设置should至少需要满足几个条件// 将BoolQueryBuilder构建到SearchSourceBuilder中searchSourceBuilder.query(boolQueryBuilder);

2.2 Filter查询

query和filter的区别：query查询的时候，会先比较查询条件，然后计算分值，最后返回文档结果；而filter是先判断是否满足查询条件，如果不满足会缓存查询结果（记录该文档不满足结果），满足的话，就直接缓存结果，filter不会对结果进行评分，能够提高查询效率。

filter的使用方式比较多样，下面用几个例子演示一下。

方式一，单独使用：

{"query": {"bool": {"filter": [{"term": {"sex": {"value": "男","boost": 1.0}}}],"adjust_pure_negative": true,"boost": 1.0}}}

单独使用时，filter与must基本一样，不同的是filter不计算评分，效率更高。

Java构建查询语句：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .filter(QueryBuilders.termQuery("sex", "男")));

方式二，和must、must_not同级，相当于子查询：

select * from (select * from persons where sect = '明教')) a where sex = '女';

ES查询语句：

{"query": {"bool": {"must": [{"term": {"sect.keyword": {"value": "明教","boost": 1.0}}}],"filter": [{"term": {"sex": {"value": "女","boost": 1.0}}}],"adjust_pure_negative": true,"boost": 1.0}}}

Java：

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .must(QueryBuilders.termQuery("sect.keyword", "明教"))        .filter(QueryBuilders.termQuery("sex", "女")));

方式三，将must、must_not置于filter下，这种方式是最常用的：

{"query": {"bool": {"filter": [{"bool": {"must": [{"term": {"sect.keyword": {"value": "明教","boost": 1.0}}},{"range": {"age": {"from": 20,"to": 35,"include_lower": true,"include_upper": true,"boost": 1.0}}}],"must_not": [{"term": {"sex.keyword": {"value": "女","boost": 1.0}}}],"adjust_pure_negative": true,"boost": 1.0}}],"adjust_pure_negative": true,"boost": 1.0}}}

Java:

SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 构建查询语句searchSourceBuilder.query(QueryBuilders.boolQuery()        .filter(QueryBuilders.boolQuery()                .must(QueryBuilders.termQuery("sect.keyword", "明教"))                .must(QueryBuilders.rangeQuery("age").gte(20).lte(35))                .mustNot(QueryBuilders.termQuery("sex.keyword", "女"))));

3 聚合查询

接下来，我们将用一些案例演示ES聚合查询。

3.1 最值、平均值、求和

案例：查询最大年龄、最小年龄、平均年龄。

SQL：

select max(age) from persons;

ES：

GET /person/_search{"aggregations": {"max_age": {"max": {"field": "age"}}}}

Java：

@Autowiredprivate RestHighLevelClient client;@Testpublic void maxQueryTest() throws IOException {// 聚合查询条件    AggregationBuilder aggBuilder = AggregationBuilders.max("max_age").field("age");    SearchRequest searchRequest = new SearchRequest("person");    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();    // 将聚合查询条件构建到SearchSourceBuilder中    searchSourceBuilder.aggregation(aggBuilder);    System.out.println("searchSourceBuilder----->" + searchSourceBuilder);    searchRequest.source(searchSourceBuilder);    // 执行查询，获取SearchResponse    SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);    System.out.println(JSONObject.toJSON(response));}

使用聚合查询，结果中默认只会返回10条文档数据（当然我们关心的是聚合的结果，而非文档）。返回多少条数据可以自主控制：

GET /person/_search{"size": 20,"aggregations": {"max_age": {"max": {"field": "age"}}}}

而Java中只需增加下面一条语句即可：

searchSourceBuilder.size(20);

与max类似，其他统计查询也很简单：

AggregationBuilder minBuilder = AggregationBuilders.min("min_age").field("age");AggregationBuilder avgBuilder = AggregationBuilders.avg("min_age").field("age");AggregationBuilder sumBuilder = AggregationBuilders.sum("min_age").field("age");AggregationBuilder countBuilder = AggregationBuilders.count("min_age").field("age");

3.2 去重查询

案例：查询一共有多少个门派。

SQL：

select count(distinct sect) from persons;

ES：

{"aggregations": {"sect_count": {"cardinality": {"field": "sect.keyword"}}}}

Java：

@Testpublic void cardinalityQueryTest() throws IOException {// 创建某个索引的request    SearchRequest searchRequest = new SearchRequest("person");    // 查询条件    SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();    // 聚合查询    AggregationBuilder aggBuilder = AggregationBuilders.cardinality("sect_count").field("sect.keyword");    searchSourceBuilder.size(0);    // 将聚合查询构建到查询条件中    searchSourceBuilder.aggregation(aggBuilder);    System.out.println("searchSourceBuilder----->" + searchSourceBuilder);    searchRequest.source(searchSourceBuilder);    // 执行查询，获取结果    SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);    System.out.println(JSONObject.toJSON(response));}

3.3 分组聚合

3.3.1 单条件分组

案例：查询每个门派的人数

SQL：

select sect,count(id) from mytest.persons group by sect;

ES：

{"size": 0,"aggregations": {"sect_count": {"terms": {"field": "sect.keyword","size": 10,"min_doc_count": 1,"shard_min_doc_count": 0,"show_term_doc_count_error": false,"order": [{"_count": "desc"},{"_key": "asc"}]}}}}

Java：

SearchRequest searchRequest = new SearchRequest("person");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();searchSourceBuilder.size(0);// 按sect分组AggregationBuilder aggBuilder = AggregationBuilders.terms("sect_count").field("sect.keyword");searchSourceBuilder.aggregation(aggBuilder);

3.3.2 多条件分组

案例：查询每个门派各有多少个男性和女性

SQL：

select sect,sex,count(id) from mytest.persons group by sect,sex;

ES：

{"aggregations": {"sect_count": {"terms": {"field": "sect.keyword","size": 10},"aggregations": {"sex_count": {"terms": {"field": "sex.keyword","size": 10}}}}}}

3.4 过滤聚合

前面所有聚合的例子请求都省略了 query ，整个请求只不过是一个聚合。这意味着我们对全部数据进行了聚合，但现实应用中，我们常常对特定范围的数据进行聚合，例如下例。

案例：查询明教中的最大年龄。 这涉及到聚合与条件查询一起使用。

SQL：

select max(age) from mytest.persons where sect = '明教';

ES：

GET /person/_search{"query": {"term": {"sect.keyword": {"value": "明教","boost": 1.0}}},"aggregations": {"max_age": {"max": {"field": "age"}}}}

Java：

SearchRequest searchRequest = new SearchRequest("person");SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();// 聚合查询条件AggregationBuilder maxBuilder = AggregationBuilders.max("max_age").field("age");// 等值查询searchSourceBuilder.query(QueryBuilders.termQuery("sect.keyword", "明教"));searchSourceBuilder.aggregation(maxBuilder);

另外还有一些更复杂的查询例子。

案例：查询0-20，21-40，41-60，61以上的各有多少人。

SQL：

select sum(case when age<=20 then 1 else 0 end agegroup1sumcase when age>20 and age <=40 then 1 else 0 end agegroup2sumcase when age>40 and age <=60 then 1 else 0 end agegroup3sumcase when age>60 and age <=200 then 1 else 0 end) ageGroup4from mytest.persons;

ES：

{"size": 0,"aggregations": {"age_avg": {"range": {"field": "age","ranges": [{"from": 0.0,"to": 20.0},{"from": 21.0,"to": 40.0},{"from": 41.0,"to": 60.0},{"from": 61.0,"to": 200.0}],"keyed": false}}}}

Java：

查询结果：

"aggregations" : {  "age_avg" : {    "buckets" : [      {        "key" : "0.0-20.0",        "from" : 0.0,        "to" : 20.0,        "doc_count" : 3      },      {        "key" : "21.0-40.0",        "from" : 21.0,        "to" : 40.0,        "doc_count" : 13      },      {        "key" : "41.0-60.0",        "from" : 41.0,        "to" : 60.0,        "doc_count" : 4      },      {        "key" : "61.0-200.0",        "from" : 61.0,        "to" : 200.0,        "doc_count" : 1      }    ]  }}

以上是ElasticSearch查询的全部内容，丰富详实，堪比操作手册，强烈建议收藏！ElasticSearch多种查询操作

来源：

https://blog.csdn.net/mu_wind/article/details/118267537