一文详解各种ElasticSearch查询在Java中的实现
作者:雨会停rain
以下为摘录自用,非本人撰写
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。
01 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。
02 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 } ] } }
总结
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