前言:elasticsearch 查询有很多关键字，查询的条件有固定格式，返回结果提示不明确，让ES使用起来有点不方便的感觉，ES查询方式很多，简单介绍几种使用点的，实用的
此处简单梳理一下最常用的查询
模糊匹配查询
类似 mysql 语法中的 like ‘%value%’
http://localhost:9200/fileindex1/_search
{
"query": {
"match\_phrase\_prefix": {
"name": {
"query": "测试",
"max\_expansions": 50
}
}}
}
@Test
void testSearch2() throws IOException {
SearchRequest searchRequest = new SearchRequest("fileindex1");
//构建搜索条件
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
//查询条件,我们可以使用SearchSourceBuilder工具来实现
//精确匹配QueryBuilders.termQuery()
MatchPhrasePrefixQueryBuilder matchQueryBuilder = QueryBuilders.matchPhrasePrefixQuery("name","测试");
sourceBuilder.query(matchQueryBuilder);
sourceBuilder.timeout(new TimeValue(60, TimeUnit.SECONDS));
searchRequest.source(sourceBuilder);
SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
System.out.println(JSON.toJSONString(searchResponse));
System.out.println("+++++++++++++++++++++++++++++++++++++++++");
for (SearchHit hit : searchResponse.getHits()) {
System.out.println(hit.getSourceAsMap());
}
}
类似于百度的分词查询
将查询的内容分解，提取。例如 文档数据库 ，就会被分词器 分解为 文档 ，数据 ，数据库等 查询返回更加有广度
分词查询接口
http://localhost:9200/_analyze
{
"analyzer":"ik\_max\_word",
"text":"文档数据库"
}
分词解析
{
"111": [
{
"111": "文档",
"start\_offset": 0,
"end\_offset": 2,
"type": "CN\_WORD",
"position": 0
},
{
"111": "数据库",
"start\_offset": 2,
"end\_offset": 5,
"type": "CN\_WORD",
"position": 1
},
{
"111": "数据",
"start\_offset": 2,
"end\_offset": 4,
"type": "CN\_WORD",
"position": 2
},
{
"111": "库",
"start\_offset": 4,
"end\_offset": 5,
"type": "CN\_CHAR",
"position": 3
}
]
}
查询会将分词解析出的分词
ES查询条件 分词查询
http://localhost:9200/fileindex1/_search
{
"query": {
"match": {
"name": "文档数据库"
}
}
}
@Test
void testSearch1() throws IOException {
SearchRequest searchRequest = new SearchRequest("fileindex1");
//构建搜索条件
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
//查询条件,我们可以使用SearchSourceBuilder工具来实现
//精确匹配QueryBuilders.termQuery()
MatchQueryBuilder matchQueryBuilder = QueryBuilders.matchQuery("name","测试");
sourceBuilder.query(matchQueryBuilder);
sourceBuilder.timeout(new TimeValue(60, TimeUnit.SECONDS));
searchRequest.source(sourceBuilder);
SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
System.out.println(JSON.toJSONString(searchResponse));
System.out.println("+++++++++++++++++++++++++++++++++++++++++");
for (SearchHit hit : searchResponse.getHits()) {
System.out.println(hit.getSourceAsMap());
}
}
根据具体字段精确查询内容
{
"query": {
"fuzzy": {
"attachment.author.keyword": "测试"
}
}
}
// 根据字段精准查询
@Test
void testSearch3() throws IOException {
SearchRequest searchRequest = new SearchRequest("fileindex1");
//构建搜索条件
SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
//查询条件,我们可以使用SearchSourceBuilder工具来实现
//精确匹配QueryBuilders.termQuery()
FuzzyQueryBuilder matchQueryBuilder = QueryBuilders.fuzzyQuery("attachment.author.keyword","user
");
sourceBuilder.query(matchQueryBuilder);
sourceBuilder.timeout(new TimeValue(60, TimeUnit.SECONDS));
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