网站制作流程有哪些步骤?/网站推广策划思路的内容

 ES JavaAPI的相关体系：

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。

     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';

 【ES】「注意查询字段带上keyword」

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

注意⚠️：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));
}

查看查询结果，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")));

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")));
}

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));
}

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"));

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"));

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"))
);

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。

{"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 *
frompersons
where sex = '女'
andage 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);

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语句构建：

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语句构建：

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")))
);

聚合查询

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SearchResponseSearchResponse 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");

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requestSearchRequest 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));
}

分组聚合

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);

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}}}}}
}

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}}}
}

【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}]}
}