ElasticSearch进阶：各种ES查询在Java中的实现

Java Elasticsearch

这里基于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，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。
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")));

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，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。
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);

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，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：

@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：

@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));
}

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。这意味着对全部数据进行了聚合，但现实应用中，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
      },
      {
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