elasticsearch 基于ik分词器的分词查询和模糊匹配

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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));
        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());
        }
    }

ES 高亮显示查询

http://localhost:9200/fileindex1/_search
{
  "query": {
    "bool": {
      "should": [
        {
          "match": {
            "attachment.author": "NOOB"
          }
        },
        {
          "match": {
            "attachment.content": "省政府"
          }
        }
      ]
    }
  },
  "highlight": {
    "fields": {
      "attachment.author": {},
      "attachment.content": {}
    }
  }
}

java 实现方式

  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("attachment.content","皇帝");
        sourceBuilder.query(matchQueryBuilder);
        sourceBuilder.timeout(new TimeValue(60, TimeUnit.SECONDS));
        searchRequest.source(sourceBuilder);
        // 高亮显示
        HighlightBuilder highlightBuilder = new HighlightBuilder();
        highlightBuilder.field("attachment.author").field("attachment.content");
        highlightBuilder.preTags("<span style=\"color: red\">");
        highlightBuilder.postTags("</span>");
        sourceBuilder.highlighter(highlightBuilder);
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        //System.out.println(JSON.toJSONString(searchResponse));
        System.out.println("+++++++++++++++++++++++++++++++++++++++++");
        System.out.println(JSON.toJSONString(searchResponse.getHits()));
        for (SearchHit hit : searchResponse.getHits()) {
            System.out.println(hit.getHighlightFields());
        }
    }