ElasticSearch入门看这篇就够了

ElasticSearch入门

什么是ES？

ES是一个开源的分布式搜索引擎，可以用来实现搜索、日志统计分析、系统监控等,es底层基于lucene实现

什么是elastic stack ？

以elasticsearch为核心的技术栈，包括beats、logstash、kibana

ES概念介绍

docker创建es

# 创建存放IK分词器的目录
mkdir -p /mnt/elasticsearch/plugins/ik

# 上传IK分词器

# 解压IK到指定路径
tar -xf ik-7.4.0.tar -C /mnt/elasticsearch/plugins/ik

# 创建单机版elasticsearch容器
docker run -id --name elasticsearch \
--net seckill_network --ip 172.36.0.13 \
-v /mnt/elasticsearch/plugins:/usr/share/elasticsearch/plugins \
--restart=always -p 9200:9200 -p 9300:9300 \
-e "discovery.type=single-node" elasticsearch:7.12.1

# 创建kibana
docker run -id --name kibana --net seckill_network \
-e ELASTICSEARCH_HOSTS=http://172.36.0.13:9200 \
--restart=always -p 5601:5601 kibana:7.12.1

# 安装head插件
docker pull mobz/elasticsearch-head:5

docker run --restart=always --name elasticsearch-head -di -p 9100:9100 docker.io/mobz/elasticsearch-head:5

IK分词器作用

创建倒排索引时对文档分词，用户搜索时对内容进行分词

分为粗粒度ik_smart 和ik_max_word 细粒度 两种模式

添加字典和禁用字典在IKAnalyzer.cfg配置中查看

DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
	<comment>IK Analyzer 扩展配置comment>
	
	<entry key="ext_dict">entry>
	 
	<entry key="ext_stopwords">entry>
	
	
	
	
properties>

DSL语句创建索引库

PUT /abcd
{
  "mappings": {
    "properties": {
      "info" :{
        "type": "text",
         "analyzer": "ik_smart"
      },
      "email": {
        "type": "keyword" ,
        "index": false
       }
    }
  }
}
// keyword：不进行分词 列如国家 人名

索引库一旦创建 不可修改原来的字段 但可以新增

DSL语句创建文档

POST /abcd/_doc/1
{
  "email": "[email protected]",
  "info": "abcd"
}

DSL 全量修改

PUT /abcd/_doc/1
{
  "email": "[email protected]",
  "info": "abcdedf"
}

DSL 增量修改

POST /abcd/_update/1
{
  "doc": {
    "email": "[email protected]"
  }
}

RestClient操作索引库和文档

       <dependency>
            <groupId>org.elasticsearch.clientgroupId>
            <artifactId>elasticsearch-rest-high-level-clientartifactId>
        dependency>

# 添加索引
    @Test
    void addIndex() throws IOException {
        CreateIndexRequest createRequest = new CreateIndexRequest("hotel");
        createRequest.mapping("", XContentType.JSON);
        client.indices().create(createRequest, RequestOptions.DEFAULT);
    }

  # 添加文档数据
    @Test
    void addDoc() throws IOException {
        // 1.根据id查询酒店数据
        Hotel hotel = hotelService.getById(36934L);
        HotelDoc hotelDoc = new HotelDoc(hotel);

        String jsonString = JSON.toJSONString(hotelDoc);
        IndexRequest indexRequest = new IndexRequest("hotel").id(hotelDoc.getId().toString());
        indexRequest.source(jsonString, XContentType.JSON);
        client.index(indexRequest, RequestOptions.DEFAULT);
    }

   # 修改文档数据
    @Test
    void updateDoc() throws IOException {
        UpdateRequest updateRequest = new UpdateRequest("hotel", "36934");
        updateRequest.doc(
                "city", "湘潭"
        );
        client.update(updateRequest, RequestOptions.DEFAULT);
    }

  #批量添加
   @Test
    void batchDoc() throws IOException {
        BulkRequest buikRequest = new BulkRequest();
        List<Hotel> list = hotelService.list();

        for (Hotel hotel : list) {
            HotelDoc hotelDoc = new HotelDoc(hotel);
            buikRequest.add(new IndexRequest("hotel").id(hotelDoc.getId().toString())
                    .source(JSON.toJSONString(hotelDoc), XContentType.JSON));
        }

        client.bulk(buikRequest, RequestOptions.DEFAULT);
    }

DSL query查询语法

# 查询所有
GET /hotel/_search
{
  "query": {
    "match_all": {}
  }
}

# 全文搜索
GET /hotel/_search
{
  "query": {
    "match": {
      "all": "如家"
    }
  }
}

GET /hotel/_search
{
  "query": {
    "multi_match": {
      "query": "如家",
      "fields": ["brand","name"]
    }
  }
}

# match根据一个字段查询 multi根据多个字段

# 精确查询
GET /hotel/_search
{
  "query": {
    "term": {
      "city": {
        "value": "上海"
      }
    }
  }
}

GET /hotel/_search
{
  "query": {
    "range": {
      "price": {
        "gte": 1000,
        "lte": 2688
      }
    }
  }
}
# term精确匹配 range范围查询

# 地理坐标查询
GET /hotel/_search
{
  "query": {
    "geo_distance": {
      "distance": "15km",
      "location": "31.21, 121.5"
    }
  }
}

GET /hotel/_search
{
  "query": {
    "geo_bounding_box":{
      "location": {
        "top_left": {
          "lat": 31.1,
          "lon": 121.5
        },
        "bottom_right": {
          "lat": 30.9,
          "lon": 121.7
        }
      }
    }
  }
}

# geo_distance 类似附近的人功能 以自已为中心条件为15km查找附近匹配的文档  # geo_bounding_box 查询geo_point落在某个矩形范围的所有文档

#复合查询  function_score 算分函数查询 可以控制文档相关性算分  ES5.0前使用TF+IDF算法 5.0以后使用BM25
GET /hotel/_search
{
  "query": {
    "function_score": {
      "query": {
        "match": {
          "all": "外滩"
        }
      },
      "functions": [
        {
          "filter": {
            "term": {
              "brand": "如家"
            }
          },
          "weight": 10
        }
      ],
      "boost_mode": "sum"
    }
  }
}

# 复合查询:布尔查询
GET /hotel/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "match": {
            "name": "如家"
          }
        }
      ],
      "must_not": [
        {
          "range": {
            "price": {
             "gte": 400
            }
          }
        }
      ],
      "filter": {
        "geo_distance": {
          "distance": "10km",
          "location": "31.21, 121.5"
        }
      }
    }
  }
}

# must: 必须匹配  should: 选择性匹配 或  must_not: 必须不匹配 不参与算分  取反 filter：必须匹配 不参与算分

# 排序 分页 高亮
GET /hotel/_search
{
  "query": {
    "match": {
      "all": "如家"
    }
  },
  "sort": [
    {
      "_geo_distance": {
        "location": {
          "lat": 31.21,
          "lon": 121.5
        },
        "order": "desc"
      }
    }
  ],
  "highlight": {
    "fields": {   
      "name": {      # 高亮的字段
        "pre_tags": "",  
        "post_tags": "", 
        "require_field_match": "false"   #是否需要匹配字段 因为上面all是3个字段组装的 如果为true name不会高亮
      }
    }
  }, 
  "from": 2,
  "size": 20
}

client实现布尔查询

    @Test
    void queryBool() throws IOException {
        SearchRequest searchRequest = new SearchRequest("hotel");
        // 构建条件
        BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();
        boolQuery.must(QueryBuilders.matchQuery("name", "如家"));
        boolQuery.mustNot(QueryBuilders.rangeQuery("price").gte(400));
        boolQuery.filter(QueryBuilders.geoDistanceQuery("location").point(31.21, 121.5).distance("10km"));
        searchRequest.source().query(boolQuery);
        // 布尔查询
        SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
        // 解析结果
        SearchHits hits = response.getHits();
        // 获取总数
        long value = hits.getTotalHits().value;
        System.out.println(value);
        // 获取数据
        for (SearchHit hit : hits.getHits()) {
            HotelDoc hotelDoc = JSON.parseObject(hit.getSourceAsString(), HotelDoc.class);
            System.out.println(hotelDoc);
        }
    }
}

client实现排序高亮

  @Test
    void queryHighlight() throws IOException {
        SearchRequest searchRequest = new SearchRequest("hotel");
        // 构建条件
        searchRequest.source().query(QueryBuilders.matchQuery("all", "如家"));
        // 排序
        searchRequest.source().sort(SortBuilders.geoDistanceSort("location", 31.21, 121.5).order(SortOrder.DESC).unit(DistanceUnit.KILOMETERS));
        // 高亮
        searchRequest.source().highlighter(new HighlightBuilder().field("name").preTags("").postTags("").requireFieldMatch(false));
        SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
        // 解析结果
        SearchHits hits = response.getHits();
        // 获取总数
        long value = hits.getTotalHits().value;
        System.out.println(value);
        // 获取数据
        for (SearchHit hit : hits.getHits()) {
            HotelDoc hotelDoc = JSON.parseObject(hit.getSourceAsString(), HotelDoc.class);
            // 解析高亮字段
            Map<String, HighlightField> map = hit.getHighlightFields();
            HighlightField name = map.get("name");
            // 获取高亮字段
            String highlightName = name.getFragments()[0].toString();
            hotelDoc.setName(highlightName);
            System.out.println(hotelDoc);
        }
    }
}

# 布尔 复合 练习
    public PageResult<HotelDoc> querylist(RequestParam param) {
        try {
            SearchRequest searchRequest = new SearchRequest("hotel");
            BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();
            // 数据查询
            if (StringUtils.isBlank(param.getKey())) {
                boolQuery.must((QueryBuilders.matchAllQuery()));
            } else {
                boolQuery.must(QueryBuilders.matchQuery("all", param.getKey()));
            }
            // 星级判断
            if (StringUtils.isNotBlank(param.getStarName())) {
                boolQuery.filter(QueryBuilders.termQuery("startName", param.getStarName()));
            }
            // 品牌过滤
            if (StringUtils.isNotBlank(param.getBrand())) {
                boolQuery.filter(QueryBuilders.termQuery("brand", param.getBrand()));
            }
            // 城市过滤
            if (StringUtils.isNotBlank(param.getCity())) {
                boolQuery.filter(QueryBuilders.termQuery("city", param.getCity()));
            }
            // 价格判断
            if (Objects.nonNull(param.getMinPrice()) && Objects.nonNull(param.getMaxPrice())) {
                boolQuery.filter(QueryBuilders.rangeQuery("price").gte(param.getMinPrice()).lte(param.getMaxPrice()));
            }
            // 复合查询 算法控制
            FunctionScoreQueryBuilder functionScoreQuery = QueryBuilders.functionScoreQuery(boolQuery, new FunctionScoreQueryBuilder.FilterFunctionBuilder[]{
                    new FunctionScoreQueryBuilder.FilterFunctionBuilder(
                            QueryBuilders.termQuery("isAD", true)
                            , ScoreFunctionBuilders.weightFactorFunction(5))  // isAD需要自已添加  功能：广告置顶
            });
            searchRequest.source().query(functionScoreQuery);
            // 分页
            int page = (param.getPage() - 1) * param.getSize();
            int size = param.getSize();
            searchRequest.source().from(page).size(size);
            // 附近功能 + 排序
            if (StringUtils.isNotBlank(param.getLocation())) {
                searchRequest.source().sort(SortBuilders.geoDistanceSort("location", new GeoPoint(param.getLocation()))
                        .order(SortOrder.ASC)
                        .unit(DistanceUnit.KILOMETERS));
            }

            SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);
            return buildResult(response);
        } catch (IOException e) {
            throw new RuntimeException();
        }
    }

什么是数据聚合？

对文档数据进行统计、分析、计算

常见种类有哪些？参与聚合的字段必须是什么类型的？

常见有桶：对文档数据分组，并统计每组数据 度量：对数据进行计算 avg sum 管道：对其它聚合再做操作

字段为keyword、数值、日期、布尔

# 桶聚合操作
GET /hotel/_search
{
  "size" : 0,   # 只查询统计的结果 不显示文档数据
  "aggs": {
    "brangAgg": {
      "terms": {   # 聚合字段的类型
        "field": "brand",   
        "size": 20,   # 获取多少条数据
        "order": {       
          "_count": "asc"  # 根据count值排序
        }
      }
    }
  }
}

api实现

    void testAggregation() throws IOException {
        SearchRequest searchRequest = new SearchRequest("hotel");

        searchRequest.source().size(0);
        searchRequest.source().aggregation(AggregationBuilders
                .terms("brandAgg")
                .field("brand").size(20)
                .order(BucketOrder.count(true)));


        SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);

        Aggregations aggregations = response.getAggregations();
        Terms brangAgg = aggregations.get("brandAgg");
        List<? extends Terms.Bucket> buckets = brangAgg.getBuckets();


        for (Terms.Bucket bucket : buckets) {
            String key = bucket.getKeyAsString();
            System.out.println(key);
        }
    }

# 实现度量聚合
GET /hotel/_search
{
  "size" : 0, 
  "aggs": {
    "brangAgg": {
      "terms": {
        "field": "brand",
        "size": 20,
        "order": {
          "scoreAgg.avg": "asc"
        }
      },
      "aggs": {     # brands的子聚合  分组后对每组分别计算
        "scoreAgg": {
          "stats": {  # 聚合类型 stats可以计算min max avg
            "field": "score"
          }
        }
      }
    }
  }
}

# 拼音分词器
# 上传到虚拟机中，elasticsearch的plugin目录
PUT /test
{
  "settings": {
    "analysis": {
      "analyzer": { // 自定义分词器
        "my_analyzer": {  // 分词器名称
          "tokenizer": "ik_max_word",
          "filter": "py"
        }
      },
      "filter": { // 自定义tokenizer filter
        "py": { // 过滤器名称
          "type": "pinyin", // 过滤器类型，这里是pinyin
		  "keep_full_pinyin": false,
          "keep_joined_full_pinyin": true,
          "keep_original": true,
          "limit_first_letter_length": 16,
          "remove_duplicated_term": true,
          "none_chinese_pinyin_tokenize": false
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "my_analyzer",
        "search_analyzer": "ik_smart"
      }
    }
  }
}

# 自动查询补全  要求文档类型必须是completion 内容是多个词条形成的数组
PUT test3
{
  "mappings": {
    "properties": {
      "title":{
        "type": "completion"
      }
    }
  }
}

POST /test/_search
{
  "suggest": {
    "title_suggest": {
      "text": "s", // 关键字
      "completion": {
        "field": "title", // 补全字段
        "skip_duplicates": true, // 跳过重复的
        "size": 10 // 获取前10条结果
      }
    }
  }
}

api实现

 void testSuggest() throws IOException {
        SearchRequest searchRequest = new SearchRequest("hotel");
        searchRequest.source().suggest(new SuggestBuilder().addSuggestion("title_suggest",
                SuggestBuilders.completionSuggestion("suggestion")
                        .prefix("hz")
                        .skipDuplicates(true).size(20)));

        SearchResponse response = client.search(searchRequest, RequestOptions.DEFAULT);

        Suggest suggest = response.getSuggest();
        CompletionSuggestion suggestion = suggest.getSuggestion("title_suggest");

        List<CompletionSuggestion.Entry.Option> options = suggestion.getOptions();
        for (CompletionSuggestion.Entry.Option option : options) {
            String string = option.getText().toString();
            System.out.println(string);
        }
    }
}