一文全览各种 ES 查询在 Java 中的实现

2 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。



2.1 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。



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

*/

@Autowired

private RestHighLevelClient client;

@Test

public 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")));

2.2 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")));

}

2.3 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));

}

2.4 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"));

}

2.5 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"));

03 负责查询

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"))

);

3.1 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 * from persons where sex = '女' and age between 30 and 40 and 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);

3.2 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")))

);

04 聚合查询

接下来，我们将用一些案例演示ES聚合查询。



4.1 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：



@Autowired

private RestHighLevelClient client;

@Test

public 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");

4.2 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：



@Test

public 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));

}



4.3 分组聚合

4.3.1 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);

4.3.2 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

}

}

}

}

}

}



4.4 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) ageGroup1,

sum(case when age >20 and age <=40 then 1 else 0 end) ageGroup2,

sum(case when age >40 and age <=60 then 1 else 0 end) ageGroup3,

sum(case when age >60 and age <=200 then 1 else 0 end) ageGroup4

from

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

}

}

}

}



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

}

]

}

}





原文链接：https://blog.csdn.net/wang20010104/article/details/130482294