springboot使用rest-high-level-client集成elasticsearch 7.5.1

2020-12-17 10:35

阅读：394

标签：percent img 建库 comm 排序 rds log order 创建索引

添加pom

 
        org.elasticsearch.client
            elasticsearch-rest-high-level-client
            7.5.1org.elasticsearch
                    elasticsearch
                org.elasticsearch.client
                    elasticsearch-rest-client
                org.elasticsearch.client
            elasticsearch-rest-client
            7.5.1org.elasticsearch
            elasticsearch
            7.5.1

yml添加配置

es:
  host: 192.168.1.107
  port: 9200
  scheme: http

初始化client

package com.zh.search.config;


import org.apache.http.HttpHost;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class ElasticConfig {

    @Value("${es.host}")
    public String host;
    @Value("${es.port}")
    public int port;
    @Value("${es.scheme}")
    public String scheme;

    @Bean
    public RestClientBuilder restClientBuilder() {
        return RestClient.builder(makeHttpHost());
    }

    @Bean
    public RestClient restClient(){
        return RestClient.builder(new HttpHost(host, port, scheme)).build();
    }

    private HttpHost makeHttpHost() {
        return new HttpHost(host, port, scheme);
    }

    @Bean
    public RestHighLevelClient restHighLevelClient(@Autowired RestClientBuilder restClientBuilder){
        return new RestHighLevelClient(restClientBuilder);
    }
}

在resource下创建索引配置json文件，

settings.json

{
  "number_of_shards": 5,
  "number_of_replicas": 1,
  "refresh_interval": "5s",
  "analysis": {
    "analyzer": {
//      ik细粒度
      "ikSearchAnalyzer": {
        "type": "custom",
        "tokenizer": "ik_max_word",
        "char_filter": [
          "tsconvert"
        ]
      },
//      ik粗粒度分词
      "ikSmartSearchAnalyzer": {
        "type": "custom",
        "tokenizer": "ik_smart",
        "char_filter": [
          "tsconvert"
        ]
      },
//      拼音分词
      "pinyinSimpleAnalyzer": {
        "tokenizer": "my_pinyin"
      },
//      拼音，大小写，短语分词
      "pinyinComplexAnalyzer": {
        "tokenizer": "ik_smart",
        "filter": [
          "lowercase",
          "pinyin_simple_filter",
          "edge_ngram_filter"
        ]
      },
//      大小写转换分词
      "lowercaseAnalyzer": {
        "type": "custom",
        "tokenizer": "keyword",
        "filter": "lowercase"
      }
    },
    "tokenizer" : {
      "my_pinyin" : {
        "type" : "pinyin",
        "keep_separate_first_letter" : false,
        "keep_full_pinyin" : true,
        "keep_original" : true,
        "limit_first_letter_length" : 16,
        "lowercase" : true,
        "remove_duplicated_term" : true
      }
    },
    "filter": {
//      短语过滤
      "edge_ngram_filter": {
        "type": "edge_ngram",
        "min_gram": 1,
        "max_gram": 50
      },
//      拼音过滤
      "pinyin_simple_filter": {
        "type": "pinyin",
        "first_letter": "prefix",
        "padding_char": " ",
        "limit_first_letter_length": 50, //设置first_letter结果的最大长度，默认值：16
//        "keep_separate_first_letter" : false, //启用该选项时，将保留第一个字母分开，例如：刘德华> l，d，h，默认：false，注意：查询结果也许是太模糊，由于长期过频
//        "keep_full_pinyin" : true,  //当启用该选项，例如：刘德华> [ liu，de，hua]，默认值：true
//        "keep_original" : true, //启用此选项时，也将保留原始输入，默认值：false
//        "remove_duplicated_term" : true,  //启用此选项后，将删除重复的术语以保存索引，例如：de的> de，default：false，注意：位置相关的查询可能会受到影响
        "lowercase": true //小写非中文字母，默认值：true
      }
    },
    "char_filter": {
//      简繁体过滤
      "tsconvert": {
        "type": "stconvert",
        "convert_type": "t2s"
      }
    }
  }
}

创建索引映射文件

commodity-mapping.json

{
  "properties": {
    "id": {
      "type": "integer"
    },
    "keyword": {
      //text和keyword的区别text：存储数据时候，会自动分词，并生成索引，keyword：存储数据时候，不会分词建立索引
      "type": "text",
      "analyzer": "ikSearchAnalyzer",
      "search_analyzer": "ikSmartSearchAnalyzer",
      "fields": {
        "pinyin": {
          "type": "text",
          "analyzer": "pinyinComplexAnalyzer",
          "search_analyzer": "pinyinComplexAnalyzer",
          "store": false,
          "term_vector": "with_offsets"
        }
      }
    },
    "ownerNature": {
      "type": "keyword"
    },
    "model": {
      "type": "keyword",
      //不能通过这个字段搜索
      "index": false
    },
    "weight": {
      "type": "integer"
    },
    "createTime": {
      "type": "date",
      "format": "yyyy-MM-dd HH:mm:ss"
    }
  }
}

采用json的方式，我觉得直观一点

创建索引（需要注意的是，7.x后，es删除了type，只允许存在一种type，不需要指定type的值，默认是_doc）

public void init() throws Exception {
        this.createIndex("commodity");
    }

/**
     * 创建索引
     * @param index
     * @throws IOException
     */
    public void createIndex(String index) throws IOException {
        //如果存在就不创建了
        if(this.existsIndex(index)) {
            System.out.println(index+"索引库已经存在!");
            return;
        }
        // 开始创建库
        CreateIndexRequest request = new CreateIndexRequest(index);
        //配置文件
        ClassPathResource seResource = new ClassPathResource("mapper/setting.json");
        InputStream seInputStream = seResource.getInputStream();
        String seJson = String.join("\n",IOUtils.readLines(seInputStream,"UTF-8"));
        seInputStream.close();
        //映射文件
        ClassPathResource mpResource = new ClassPathResource("mapper/"+index+"-mapping.json");
        InputStream mpInputStream = mpResource.getInputStream();
        String mpJson = String.join("\n",IOUtils.readLines(mpInputStream,"UTF-8"));
        mpInputStream.close();

        request.settings(seJson, XContentType.JSON);
        request.mapping(mpJson, XContentType.JSON);

        //设置别名
        request.alias(new Alias(index+"_alias"));
        CreateIndexResponse createIndexResponse = restHighLevelClient.indices().create(request, RequestOptions.DEFAULT);
        boolean falg = createIndexResponse.isAcknowledged();
        if(falg){
            System.out.println("创建索引库:"+index+"成功！" );
        }
    }

判断索引是否存在

    /**
     * 判断索引是否存在
     * @param index
     * @return
     * @throws IOException
     */
    public boolean existsIndex(String index) throws IOException {
        GetIndexRequest getRequest = new GetIndexRequest(index);
        getRequest.local(false);
        getRequest.humanReadable(true);
        return restHighLevelClient.indices().exists(getRequest, RequestOptions.DEFAULT);
    }

删除索引

    /**
     * 删除索引
     * @param index
     * @return
     * @throws IOException
     */
    public boolean delIndex(String index) throws IOException {
        DeleteIndexRequest request = new DeleteIndexRequest(index);
        AcknowledgedResponse deleteIndexResponse = restHighLevelClient.indices().delete(request, RequestOptions.DEFAULT);
        return deleteIndexResponse.isAcknowledged();
    }

添加索引数据

    /**
     * 保存文档
     * @param kv 对应json映射里面键值对，index是索引名称
     * @return
     * @throws IOException
     */
    public boolean save(Kv kv) throws IOException {
        IndexRequest request = new IndexRequest(kv.getStr("index"))
                .id(kv.getStr("id")).source(kv);
        IndexResponse response = restHighLevelClient.index(request,RequestOptions.DEFAULT);
        return response.isFragment();
    }

删除索引数据

    /**
     * 根据id删除文档
     * @param id
     * @return
     * @throws IOException
     */
    public boolean delById(String id) throws IOException {
        DeleteRequest request = new DeleteRequest(ModuleConstants.COMMODITY.toLowerCase(),id);
        DeleteResponse response = restHighLevelClient.delete(request,RequestOptions.DEFAULT);
        return response.isFragment();
    }

IK，拼音，短语分词分页搜索

 @Resource
    private RestHighLevelClient restHighLevelClient;
    @Resource
    private OutputChannel outputChannel;

    /**
     分页分词关键词查询
     * 使用QueryBuilder
     termQuery("key", obj) 完全匹配
     termsQuery("key", obj1, obj2..)   一次匹配多个值
     matchQuery("key", Obj) 单个匹配, field不支持通配符, 前缀具高级特性
     multiMatchQuery("text", "field1", "field2"..);  匹配多个字段, field有通配符忒行
     matchAllQuery();         匹配所有文件
     * 组合查询
     must(QueryBuilders) :   AND
     mustNot(QueryBuilders): NOT
     should:                  : OR
     percent_terms_to_match：匹配项（term）的百分比，默认是0.3
     min_term_freq：一篇文档中一个词语至少出现次数，小于这个值的词将被忽略，默认是2
     max_query_terms：一条查询语句中允许最多查询词语的个数，默认是25
     stop_words：设置停止词，匹配时会忽略停止词
     min_doc_freq：一个词语最少在多少篇文档中出现，小于这个值的词会将被忽略，默认是无限制
     max_doc_freq：一个词语最多在多少篇文档中出现，大于这个值的词会将被忽略，默认是无限制
     min_word_len：最小的词语长度，默认是0
     max_word_len：最多的词语长度，默认无限制
     boost_terms：设置词语权重，默认是1
     boost：设置查询权重，默认是1
     analyzer：设置使用的分词器，默认是使用该字段指定的分词器
     */
    @Override
    public Page page(SearchVo searchVo){
        Page page = new Page(searchVo.getCurrent(),searchVo.getSize(),0);
        // 页码
        try {
            // 构建查询
            SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
            // 索引查询
            BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
            //boost 设置权重
            //分词查询
            boolQueryBuilder.should(QueryBuilders.matchQuery("keyword", searchVo.getKeyword()).boost(2f));
            //拼音查询
            boolQueryBuilder.should(QueryBuilders.matchPhraseQuery("keyword.pinyin", searchVo.getKeyword()).boost(2f));
            //模糊查询，不区分大小写
//            boolQueryBuilder.should(QueryBuilders.wildcardQuery("keyword", "*"+searchVo.getKeyword().toLowerCase()+"*").boost(2f));
            //指定商家的性质
            if(StrKit.notBlank(searchVo.getKeyword1())){
                boolQueryBuilder.must(QueryBuilders.termQuery("ownerNature",searchVo.getKeyword1()));
            }
            //必须满足should其中一个条件
            boolQueryBuilder.minimumShouldMatch(1);
            //时间范围查询
//            boolQueryBuilder.must(QueryBuilders.rangeQuery("createTime")
//                    .from(DateKit.format(DateKit.getDayBegin(),"yyyy-MM-dd HH:mm:ss"))
//                    .to(DateKit.format(DateKit.getDayBegin(),"yyyy-MM-dd HH:mm:ss")));
            sourceBuilder.query(boolQueryBuilder);
            //设置返回的字段
//            String[] includeFields = new String[] {"keyword"};
//            sourceBuilder.fetchSource(includeFields,null);
            // 分页设置
            sourceBuilder.from(searchVo.getFrom());
            sourceBuilder.size(searchVo.getSize());
            //        sourceBuilder.sort("id", SortOrder.ASC); // 设置排序规则
            sourceBuilder.timeout(new TimeValue(60, TimeUnit.SECONDS));

            SearchRequest searchRequest = new SearchRequest(searchVo.getIndex());
            searchRequest.source(sourceBuilder);
            SearchResponse response = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
            SearchHits searchHits = response.getHits();
            page.setTotal(searchHits.getTotalHits().value);
            List list = new ArrayList();
            for (SearchHit hit : searchHits.getHits()) {
                SearchVo vo = new SearchVo();
                Kv kv = Kv.create().set(hit.getSourceAsMap());
                vo.setId(kv.getStr("id"));
                vo.setKeyword(kv.getStr("keyword"));
                vo.setKeyword1(kv.getStr("ownerNature"));
                vo.setModel(kv.getStr("model"));
                list.add(vo);
            }
            page.setRecords(list);
        } catch (Exception e) {
            e.printStackTrace();
        }
        //收集关键词搜索记录
        searchVo.setIndex(ModuleConstants.KEYWORD.toLowerCase());
        outputChannel.searchSaveOutput().send(MessageBuilder.withPayload(searchVo).build());

        return page;
    }

IK，拼音，短语分词分页并高亮关键词搜索

    @Resource
    private RestHighLevelClient restHighLevelClient;

    @Override
    public Page pageHigh(SearchVo searchVo){
        Page page = new Page(searchVo.getCurrent(),searchVo.getSize(),0);
        // 页码
        try {
            // 构建查询
            SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
            // 索引查询
            BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
            //boost 设置权重
            //分词查询
            boolQueryBuilder.should(QueryBuilders.matchQuery("keyword", searchVo.getKeyword()).boost(2f));
            //拼音查询
            boolQueryBuilder.should(QueryBuilders.matchPhraseQuery("keyword.pinyin", searchVo.getKeyword()).boost(2f));
            //模糊查询，不区分大小写
//            boolQueryBuilder.should(QueryBuilders.wildcardQuery("keyword", "*"+searchVo.getKeyword().toLowerCase()+"*").boost(2f));
            //必须满足should其中一个条件
            boolQueryBuilder.minimumShouldMatch(1);
            //时间范围查询
//            boolQueryBuilder.must(QueryBuilders.rangeQuery("createTime")
//                    .from(DateKit.format(DateKit.getDayBegin(),"yyyy-MM-dd HH:mm:ss"))
//                    .to(DateKit.format(DateKit.getDayBegin(),"yyyy-MM-dd HH:mm:ss")));
            sourceBuilder.query(boolQueryBuilder);
            //设置返回的字段
            String[] includeFields = new String[] {"keyword"};
            sourceBuilder.fetchSource(includeFields,null);
            // 高亮设置
            List highlightFieldList = new ArrayList();
            highlightFieldList.add("keyword");
            HighlightBuilder highlightBuilder = new HighlightBuilder();
            for (int x = 0; x ").postTags("");
                highlightBuilder.field(field);
            }
            sourceBuilder.highlighter(highlightBuilder);
            // 分页设置
            sourceBuilder.from(searchVo.getFrom());
            sourceBuilder.size(searchVo.getSize());
            //        sourceBuilder.sort("id", SortOrder.ASC); // 设置排序规则
            sourceBuilder.timeout(new TimeValue(60, TimeUnit.SECONDS));
            //不指定索引，则搜索所有的索引
            SearchRequest searchRequest = new SearchRequest(searchVo.getIndex());
            searchRequest.source(sourceBuilder);
            SearchResponse response = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
            SearchHits searchHits = response.getHits();
            page.setTotal(searchHits.getTotalHits().value);
            List list = new ArrayList();
            Pattern pattern = Pattern.compile("(?i)"+searchVo.getKeyword());
            for (SearchHit hit : searchHits.getHits()) {
                SearchVo vo = new SearchVo();
                Kv kv = Kv.create().set(hit.getSourceAsMap());
                vo.setKeyword(kv.getStr("keyword"));
                //高亮字段（拼音不做高亮，拼音的高亮有问题，会将整个字符串高亮）
                if (!StringUtils.isEmpty(hit.getHighlightFields().get("keyword"))) {
                    Text[] text = hit.getHighlightFields().get("keyword").getFragments();
                    vo.setKeyword(text[0].toString());
                }
                //ngram短语，模糊搜索高亮,不区分大小写直接字符串替换
                String keyword = vo.getKeyword();
                if(!keyword.contains("")){
                    Matcher matcher = pattern.matcher(keyword);
                    if(matcher.find()){
                        String s = matcher.group();
                        vo.setKeyword(keyword.replace(s,""+s+""));
                    }
                }
                list.add(vo);
            }
            page.setRecords(list);
        } catch (Exception e) {
            e.printStackTrace();
        }
        return page;
    }

springboot使用rest-high-level-client集成elasticsearch 7.5.1

标签：percent img 建库 comm 排序 rds log order 创建索引

原文地址：https://www.cnblogs.com/JasonKong/p/14103998.html

上一篇：中通消息平台 Kafka 顺序消费线程模型的实践与优化

下一篇：归并排序（Merge Sort）

文章来自：搜素材网的编程语言模块，转载请注明文章出处。
文章标题：springboot使用rest-high-level-client集成elasticsearch 7.5.1
文章链接：http://soscw.com/index.php/essay/36833.html

亲，登录后才可以留言！

springboot使用rest-high-level-client集成elasticsearch 7.5.1

添加pom

yml添加配置

初始化client

在resource下创建索引配置json文件，

settings.json

创建索引映射文件

commodity-mapping.json

创建索引（需要注意的是，7.x后，es删除了type，只允许存在一种type，不需要指定type的值，默认是_doc）

判断索引是否存在

删除索引

添加索引数据

删除索引数据

IK，拼音，短语分词分页搜索

IK，拼音，短语分词分页并高亮关键词搜索

评论

热门文章

推荐文章

最新文章

置顶文章