RestAPI 简介

1.前提工作 -- 创建索引库

2.RestClient的简单使用

创建索引库

删除索引库

索引库是否存在

创建文档

删除文档

修改文档

批量操作文档

match_all(全部查询)

match查询

term(精准查询)

range(范围查询)

geo(坐标查询)

functionScore(算分函数)

bool(布尔查询-多条件)

排序、分页

高亮

聚合

Bucket聚合(分组)

Metric聚合函数(min、max、avg)

词条(汉字 & 拼音)自动补全

SpringBoot 整合 EleasticSearch 8.0.X 弃用high-level-client

RestAPI 简介

ES官方提供了各种不同语言的客户端，用来操作ES。这些客户端的本质就是组装DSL语句，通过http请求发送给ES。官方文档地址：Elasticsearch Clients | Elastic

1.前提工作 -- 创建索引库

我们根据一个mysql的数据库表来创建对应的索引库

CREATE TABLE `tb_hotel` (`id` bigint(20) NOT NULL COMMENT '酒店id',`name` varchar(255) NOT NULL COMMENT '酒店名称；例：7天酒店',`address` varchar(255) NOT NULL COMMENT '酒店地址；例：航头路',`price` int(10) NOT NULL COMMENT '酒店价格；例：329',`score` int(2) NOT NULL COMMENT '酒店评分；例：45，就是4.5分',`brand` varchar(32) NOT NULL COMMENT '酒店品牌；例：如家',`city` varchar(32) NOT NULL COMMENT '所在城市；例：上海',`star_name` varchar(16) DEFAULT NULL COMMENT '酒店星级，从低到高分别是：1星到5星，1钻到5钻',`business` varchar(255) DEFAULT NULL COMMENT '商圈；例：虹桥',`latitude` varchar(32) NOT NULL COMMENT '纬度；例：31.2497',`longitude` varchar(32) NOT NULL COMMENT '经度；例：120.3925',`pic` varchar(255) DEFAULT NULL COMMENT '酒店图片；例:/img/1.jpg',PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;

创建的索引库如下:

PUT /hotel
{"mappings": {"properties": {"id": {"type": "keyword"},"name":{"type": "text","analyzer": "ik_max_word","copy_to": "all"},"address":{"type": "keyword","index": false},"price":{"type": "integer"},"score":{"type": "integer"},"brand":{"type": "keyword","copy_to": "all"},"city":{"type": "keyword","copy_to": "all"},"starName":{"type": "keyword"},"business":{"type": "keyword"},"location":{"type": "geo_point"},"pic":{"type": "keyword","index": false},"all":{"type": "text","analyzer": "ik_max_word"}}}
}

几个特殊字段说明：

location：地理坐标，里面包含精度、纬度
all：一个组合字段，其目的是将多字段的值利用copy_to合并，提供给用户搜索

地理坐标说明：

copy_to说明：

2.RestClient的简单使用

在elasticsearch提供的API中，与elasticsearch一切交互都封装在一个名为RestHighLevelClient的类中，必须先完成这个对象的初始化，建立与elasticsearch的连接。

分为三步：

1）引入es的RestHighLevelClient依赖：

<dependency><groupId>org.elasticsearch.client</groupId><artifactId>elasticsearch-rest-high-level-client</artifactId>
</dependency>

2）因为SpringBoot默认的ES版是7.6.2，所以我们需要覆盖默认的ES版本：

<properties><java.version>1.8</java.version><elasticsearch.version>7.12.1</elasticsearch.version>
</properties>

3）初始化RestHighLevelClient：

初始化的代码如下：

RestHighLevelClient client = new RestHighLevelClient(RestClient.builder(HttpHost.create("http://192.168.150.101:9200")
));

创建索引库

代码分为三步：

1）创建Request对象。因为是创建索引库的操作，因此Request是CreateIndexRequest。
2）添加请求参数，其实就是DSL的JSON参数部分。因为json字符串很长，这里是定义了静态字符串常量MAPPING_TEMPLATE，让代码看起来更加优雅。
3）发送请求，client.indices()方法的返回值是IndicesClient类型，封装了所有与索引库操作有关的方法。

code:

    @Testvoid createHotelIndex() throws IOException {CreateIndexRequest request = new CreateIndexRequest("hotel");request.source(MAPPING_TEMPLATE, XContentType.JSON);client.indices().create(request, RequestOptions.DEFAULT);}public class HotelConstants {public static final String MAPPING_TEMPLATE = "{\n" +"  \"mappings\": {\n" +"    \"properties\": {\n" +"      \"id\":{\n" +"        \"type\": \"keyword\"\n" +"      },\n" +"      \"name\":{\n" +"        \"type\": \"text\",\n" +"        \"analyzer\": \"ik_max_word\",\n" +"        \"copy_to\": \"all\"\n" +"      },\n" +"      \"address\":{\n" +"        \"type\": \"keyword\",\n" +"        \"index\": false\n" +"      },\n" +"      \"price\":{\n" +"        \"type\": \"integer\"\n" +"      },\n" +"      \"score\":{\n" +"        \"type\": \"integer\"\n" +"      },\n" +"      \"brand\":{\n" +"        \"type\": \"keyword\",\n" +"        \"copy_to\": \"all\"\n" +"      },\n" +"      \"city\":{\n" +"        \"type\": \"keyword\",\n" +"        \"copy_to\": \"all\"\n" +"      },\n" +"      \"starName\":{\n" +"        \"type\": \"keyword\"\n" +"      },\n" +"      \"business\":{\n" +"        \"type\": \"keyword\"\n" +"      },\n" +"      \"location\":{\n" +"        \"type\": \"geo_point\"\n" +"      },\n" +"      \"pic\":{\n" +"        \"type\": \"keyword\",\n" +"        \"index\": false\n" +"      },\n" +"      \"all\":{\n" +"        \"type\": \"text\",\n" +"        \"analyzer\": \"ik_max_word\"\n" +"      }\n" +"    }\n" +"  }\n" +"}";
}

删除索引库

删除索引库的DSL语句非常简单

DELETE /hotel

@Test
void testDeleteHotelIndex() throws IOException {// 1.创建Request对象DeleteIndexRequest request = new DeleteIndexRequest("hotel");// 2.发送请求client.indices().delete(request, RequestOptions.DEFAULT);
}

索引库是否存在

判断索引库是否存在，本质就是查询，对应的DSL是：

GET /hotel

@Test
void testExistsHotelIndex() throws IOException {// 1.创建Request对象GetIndexRequest request = new GetIndexRequest("hotel");// 2.发送请求boolean exists = client.indices().exists(request, RequestOptions.DEFAULT);// 3.输出System.err.println(exists ? "索引库已经存在！" : "索引库不存在！");
}

创建文档

创建文档的DSL语句如下：

POST /{索引库名}/_doc/1
{"name": "Jack","age": 21
}

code:

@Data
@NoArgsConstructor
public class HotelDoc {private Long id;private String name;private String address;private Integer price;private Integer score;private String brand;private String city;private String starName;private String business;private String location;private String pic;public HotelDoc(Hotel hotel) {this.id = hotel.getId();this.name = hotel.getName();this.address = hotel.getAddress();this.price = hotel.getPrice();this.score = hotel.getScore();this.brand = hotel.getBrand();this.city = hotel.getCity();this.starName = hotel.getStarName();this.business = hotel.getBusiness();this.location = hotel.getLatitude() + ", " + hotel.getLongitude();this.pic = hotel.getPic();}
}

    @Testvoid addHotelDoc() throws IOException {Hotel hotel = hotelService.getById(36934L);HotelDoc doc= new HotelDoc(hotel);String json = JSON.toJSONString(doc);IndexRequest request = new IndexRequest("hotel").id(hotel.getId().toString());request.source(json, XContentType.JSON);client.index(request, RequestOptions.DEFAULT);}

Hotel在数据库中经纬度字段是分开的，es中经纬度是一个字段location，geo_point，所以用HotelDoc和es的属性做对应

删除文档

删除的DSL为是这样的：

DELETE /hotel/_doc/{id}

@Test
void testDeleteDocument() throws IOException {// 1.准备RequestDeleteRequest request = new DeleteRequest("hotel", "61083");// 2.发送请求client.delete(request, RequestOptions.DEFAULT);
}

修改文档

修改我们讲过两种方式：

全量修改：本质是先根据id删除，再新增
增量修改：修改文档中的指定字段值

在RestClient的API中，全量修改与新增的API完全一致，判断依据是ID：

如果新增时，ID已经存在，则修改
如果新增时，ID不存在，则新增

这里是增量修改

    @Testvoid updateHotelDoc() throws IOException {UpdateRequest request = new UpdateRequest("hotel","36934");request.doc("price", 333,"starName","三钻");client.update(request, RequestOptions.DEFAULT);}

批量操作文档

批量处理BulkRequest，其本质就是将多个普通的CRUD请求组合在一起发送。

其中提供了一个add方法，用来添加其他请求：

可以看到，能添加的请求包括：

IndexRequest，也就是新增
UpdateRequest，也就是修改
DeleteRequest，也就是删除

因此Bulk中添加了多个IndexRequest，就是批量新增功能了。：

    @Testvoid bulkHotelDoc() throws IOException {List<Hotel> list = hotelService.list();BulkRequest request = new BulkRequest();for (Hotel hotel : list) {HotelDoc doc = new HotelDoc(hotel);request.add(new IndexRequest("hotel").id(doc.getId().toString()).source(JSON.toJSONString(doc), XContentType.JSON));}client.bulk(request, RequestOptions.DEFAULT);}

match_all(全部查询)

@Test
void testMatchAll() throws IOException {// 1.准备RequestSearchRequest request = new SearchRequest("hotel");// 2.准备DSLrequest.source().query(QueryBuilders.matchAllQuery());// 3.发送请求SearchResponse response = client.search(request, RequestOptions.DEFAULT);// 4.解析响应handleResponse(response);
}private void handleResponse(SearchResponse response) {// 4.解析响应SearchHits searchHits = response.getHits();// 4.1.获取总条数long total = searchHits.getTotalHits().value;System.out.println("共搜索到" + total + "条数据");// 4.2.文档数组SearchHit[] hits = searchHits.getHits();// 4.3.遍历for (SearchHit hit : hits) {// 获取文档sourceString json = hit.getSourceAsString();// 反序列化HotelDoc hotelDoc = JSON.parseObject(json, HotelDoc.class);System.out.println("hotelDoc = " + hotelDoc);}
}

match查询

    @Testvoid searchMatch() throws IOException {SearchRequest request = new SearchRequest("hotel");request.source().query(QueryBuilders.matchQuery("all", "如家"));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);printSearchResponse(searchResponse);}

    @Testvoid searchMatchMulti() throws IOException {SearchRequest request = new SearchRequest("hotel");request.source().query(QueryBuilders.multiMatchQuery("外滩", "business", "name"));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);printSearchResponse(searchResponse);}

term(精准查询)

term：词条精确匹配

    @Testvoid searchTerm() throws IOException {SearchRequest request = new SearchRequest("hotel");request.source().query(QueryBuilders.termQuery("city", "上海"));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);printSearchResponse(searchResponse);}

range(范围查询)

    @Testvoid searchRange() throws IOException {SearchRequest request = new SearchRequest("hotel");request.source().query(QueryBuilders.rangeQuery("price").lte(200));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);printSearchResponse(searchResponse);}

geo(坐标查询)

    @Testvoid searchGeo() throws IOException {SearchRequest request = new SearchRequest("hotel");request.source().query(QueryBuilders.geoDistanceQuery("location").point(31.21, 121.5).distance(15, DistanceUnit.KILOMETERS));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);printSearchResponse(searchResponse);}

functionScore(算分函数)

    @Testvoid searchFunctionScore() throws IOException {SearchRequest request = new SearchRequest("hotel");QueryBuilder queryBuilder = QueryBuilders.matchQuery("all", "外滩");FunctionScoreQueryBuilder.FilterFunctionBuilder[] filterFunctionBuilders = new FunctionScoreQueryBuilder.FilterFunctionBuilder[1];ScoreFunctionBuilder<WeightBuilder> scoreFunctionBuilder = new WeightBuilder();scoreFunctionBuilder.setWeight(10);QueryBuilder termQuery = QueryBuilders.termQuery("brand", "如家");FunctionScoreQueryBuilder.FilterFunctionBuilder category = new FunctionScoreQueryBuilder.FilterFunctionBuilder(termQuery, scoreFunctionBuilder);filterFunctionBuilders[0] = category;request.source().query(QueryBuilders.functionScoreQuery(queryBuilder,filterFunctionBuilders)
//                        .scoreMode(FunctionScoreQuery.ScoreMode.SUM).boostMode(CombineFunction.SUM));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);printSearchResponse(searchResponse);}

或者:

bool 查询搭配算分函数

 // 1.构建BooleanQueryBoolQueryBuilder boolQuery = QueryBuilders.boolQuery();// 关键字搜索String key = params.getKey();if (key == null || "".equals(key)) {boolQuery.must(QueryBuilders.matchAllQuery());} else {boolQuery.must(QueryBuilders.matchQuery("all", key));}// 城市条件if (params.getCity() != null && !params.getCity().equals("")) {boolQuery.filter(QueryBuilders.termQuery("city", params.getCity()));}// 品牌条件if (params.getBrand() != null && !params.getBrand().equals("")) {boolQuery.filter(QueryBuilders.termQuery("brand", params.getBrand()));}// 星级条件if (params.getStarName() != null && !params.getStarName().equals("")) {boolQuery.filter(QueryBuilders.termQuery("starName", params.getStarName()));}// 价格if (params.getMinPrice() != null && params.getMaxPrice() != null) {boolQuery.filter(QueryBuilders.rangeQuery("price").gte(params.getMinPrice()).lte(params.getMaxPrice()));}// 2.算分控制FunctionScoreQueryBuilder functionScoreQuery =QueryBuilders.functionScoreQuery(// 原始查询，相关性算分的查询boolQuery,// function score的数组new FunctionScoreQueryBuilder.FilterFunctionBuilder[]{// 其中的一个function score 元素new FunctionScoreQueryBuilder.FilterFunctionBuilder(// 过滤条件QueryBuilders.termQuery("isAD", true),// 算分函数ScoreFunctionBuilders.weightFactorFunction(10)).boostMode(CombineFunction.SUM)});request.source().query(functionScoreQuery);

bool(布尔查询-多条件)

布尔查询是用must、must_not、filter等方式组合其它查询，代码示例如下：

可以看到，API与其它查询的差别同样是在查询条件的构建，QueryBuilders，结果解析等其他代码完全不变。

    @Testvoid searchBool() throws IOException {SearchRequest request = new SearchRequest("hotel");QueryBuilder mustBuilder = QueryBuilders.matchQuery("name", "如家");QueryBuilder mustNotBuilder = QueryBuilders.rangeQuery("price").gt(400);QueryBuilder geoBuilder = QueryBuilders.geoDistanceQuery("location").point(31.21, 121.5).distance(10, DistanceUnit.KILOMETERS);request.source().query(QueryBuilders.boolQuery().must(mustBuilder).mustNot(mustNotBuilder).filter(geoBuilder));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);printSearchResponse(searchResponse);}

排序、分页

搜索结果的排序和分页是与query同级的参数，因此同样是使用request.source()来设置。

@Test
void testPageAndSort() throws IOException {// 页码，每页大小int page = 1, size = 5;// 1.准备RequestSearchRequest request = new SearchRequest("hotel");// 2.准备DSL// 2.1.queryrequest.source().query(QueryBuilders.matchAllQuery());// 2.2.排序 sortrequest.source().sort("price", SortOrder.ASC);// 2.3.分页 from、sizerequest.source().from((page - 1) * size).size(5);// 3.发送请求SearchResponse response = client.search(request, RequestOptions.DEFAULT);// 4.解析响应handleResponse(response);}

距离排序

 request.source().sort(SortBuilders.geoDistanceSort("location", new GeoPoint(location)).order(SortOrder.ASC).unit(DistanceUnit.KILOMETERS));

高亮

代码解读：

第一步：从结果中获取source。hit.getSourceAsString()，这部分是非高亮结果，json字符串。还需要反序列为HotelDoc对象
第二步：获取高亮结果。hit.getHighlightFields()，返回值是一个Map，key是高亮字段名称，值是HighlightField对象，代表高亮值
第三步：从map中根据高亮字段名称，获取高亮字段值对象HighlightField
第四步：从HighlightField中获取Fragments，并且转为字符串。这部分就是真正的高亮字符串了
第五步：用高亮的结果替换HotelDoc中的非高亮结果

private void handleResponse(SearchResponse response) {// 4.解析响应SearchHits searchHits = response.getHits();// 4.1.获取总条数long total = searchHits.getTotalHits().value;System.out.println("共搜索到" + total + "条数据");// 4.2.文档数组SearchHit[] hits = searchHits.getHits();// 4.3.遍历for (SearchHit hit : hits) {// 获取文档sourceString json = hit.getSourceAsString();// 反序列化HotelDoc hotelDoc = JSON.parseObject(json, HotelDoc.class);// 获取高亮结果Map<String, HighlightField> highlightFields = hit.getHighlightFields();if (!CollectionUtils.isEmpty(highlightFields)) {// 根据字段名获取高亮结果HighlightField highlightField = highlightFields.get("name");if (highlightField != null) {// 获取高亮值String name = highlightField.getFragments()[0].string();// 覆盖非高亮结果hotelDoc.setName(name);}}System.out.println("hotelDoc = " + hotelDoc);}
}

聚合

Bucket聚合(分组)

    void searchAggs() throws IOException {SearchRequest request = new SearchRequest("hotel");//不返回文档数据request.source().size(0);request.source().aggregation(AggregationBuilders.terms("brandAgg").field("brand").size(20).order(BucketOrder.aggregation("_count",true)));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);Aggregations aggregations = searchResponse.getAggregations();Terms brandTerms = aggregations.get("brandAgg");List<? extends Terms.Bucket> buckets = brandTerms.getBuckets();for (Terms.Bucket bucket : buckets) {System.out.println(bucket.getKeyAsString() + " : " + bucket.getDocCount());}
}

获取多个聚合结果:

GET /hotel/_search
{"size": 0,"aggs": {"brandAgg": {"terms": {"field": "brand","size": 2}},    "cityAgg": {"terms": {"field": "city","size": 2}}}
}

    request.source().aggregation(AggregationBuilders.terms("brandAgg").field("brand").size(2));request.source().aggregation(AggregationBuilders.terms("cityAgg").field("city").size(2));

Metric聚合函数(min、max、avg)

现在我们需要对桶内的酒店做运算，获取每个品牌的用户评分的min、max、avg等值。

        SearchRequest request = new SearchRequest("hotel");request.source().size(0);request.source().aggregation(AggregationBuilders.terms("brandAgg").field("brand").size(20).order(BucketOrder.aggregation("score_stats.avg",false)).subAggregation(AggregationBuilders.stats("score_stats").field("score")));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);

结果：

    @Testvoid searchAggsMetric() throws IOException {SearchRequest request = new SearchRequest("hotel");request.source().size(0);request.source().aggregation(AggregationBuilders.terms("brandAgg").field("brand").size(20).order(BucketOrder.aggregation("score_stats.avg",false)).subAggregation(AggregationBuilders.stats("score_stats").field("score")));SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);Aggregations aggregations = searchResponse.getAggregations();Terms brandTerms = aggregations.get("brandAgg");List<? extends Terms.Bucket> buckets = brandTerms.getBuckets();for (Terms.Bucket bucket : buckets) {Aggregations bucketAggs = bucket.getAggregations();Stats stats = bucketAggs.get("score_stats");System.out.println(bucket.getKeyAsString() +" : " + JSON.toJSONString(stats));}}

词条(汉字 & 拼音)自动补全

目标根据酒店品牌和商圈的关键字，或者拼音，补全关联到的内容。

此处以(keyword)精准词语为例

1. 支持拼音关联搜索，则需要安装拼音分词器。

拼音分词器的安装请参考:elasticsearch 安装(docker)_naki_bb的博客-CSDN博客

不需要拼音补全的课参考 elasticsearch DSL命令_naki_bb的博客-CSDN博客的自动补全

2.词条补全必须要有一个字段类型为suggestion。

索引库DSL语句如下

// 酒店数据索引库
PUT /hotel
{"settings": {"analysis": {"analyzer": {"text_anlyzer": {"tokenizer": "ik_max_word","filter": "py"},"completion_analyzer": {"tokenizer": "keyword","filter": "py"}},"filter": {"py": {"type": "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": {"id":{"type": "keyword"},"name":{"type": "text","analyzer": "text_anlyzer","search_analyzer": "ik_smart","copy_to": "all"},"address":{"type": "keyword","index": false},"price":{"type": "integer"},"score":{"type": "integer"},"brand":{"type": "keyword","copy_to": "all"},"city":{"type": "keyword"},"starName":{"type": "keyword"},"business":{"type": "keyword","copy_to": "all"},"location":{"type": "geo_point"},"pic":{"type": "keyword","index": false},"all":{"type": "text","analyzer": "text_anlyzer","search_analyzer": "ik_smart"},"suggestion":{"type": "completion","analyzer": "completion_analyzer"}}}
}

实体类:

HotelDoc中要添加一个字段，用来做自动补全，内容可以是酒店品牌、商圈等信息。按照自动补全字段的要求，最好是这些字段的数组。

import lombok.Data;
import lombok.NoArgsConstructor;import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;@Data
@NoArgsConstructor
public class HotelDoc {private Long id;private String name;private String address;private Integer price;private Integer score;private String brand;private String city;private String starName;private String business;private String location;private String pic;private Object distance;private Boolean isAD;private List<String> suggestion;public HotelDoc(Hotel hotel) {this.id = hotel.getId();this.name = hotel.getName();this.address = hotel.getAddress();this.price = hotel.getPrice();this.score = hotel.getScore();this.brand = hotel.getBrand();this.city = hotel.getCity();this.starName = hotel.getStarName();this.business = hotel.getBusiness();this.location = hotel.getLatitude() + ", " + hotel.getLongitude();this.pic = hotel.getPic();// 组装suggestionif(this.business.contains("/")){// business有多个值，需要切割String[] arr = this.business.split("/");// 添加元素this.suggestion = new ArrayList<>();this.suggestion.add(this.brand);Collections.addAll(this.suggestion, arr);}else {this.suggestion = Arrays.asList(this.brand, this.business);}}
}

重新导入数据功能，可以看到新的酒店数据中包含了suggestion：

API 示例

结果解析示例:

案例的实现如下:

@Override
public List<String> getSuggestions(String prefix) {try {// 1.准备RequestSearchRequest request = new SearchRequest("hotel");// 2.准备DSLrequest.source().suggest(new SuggestBuilder().addSuggestion("suggestions",SuggestBuilders.completionSuggestion("suggestion").prefix(prefix).skipDuplicates(true).size(10)));// 3.发起请求SearchResponse response = client.search(request, RequestOptions.DEFAULT);// 4.解析结果Suggest suggest = response.getSuggest();// 4.1.根据补全查询名称，获取补全结果CompletionSuggestion suggestions = suggest.getSuggestion("suggestions");// 4.2.获取optionsList<CompletionSuggestion.Entry.Option> options = suggestions.getOptions();// 4.3.遍历List<String> list = new ArrayList<>(options.size());for (CompletionSuggestion.Entry.Option option : options) {String text = option.getText().toString();list.add(text);}return list;} catch (IOException e) {throw new RuntimeException(e);}
}

SpringBoot 整合 EleasticSearch 8.0.X 弃用high-level-client

请参考

springboot集成elasticsearch 8.0以上版本_weixin_46501729的博客-CSDN博客

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elasticsearch 使用

RestAPI 简介

1.前提工作 -- 创建索引库

2.RestClient的简单使用

创建索引库

删除索引库

索引库是否存在

创建文档

删除文档

修改文档

批量操作文档

match_all(全部查询)

match查询

term(精准查询)

range(范围查询)

geo(坐标查询)

functionScore(算分函数)

bool(布尔查询-多条件)

排序、分页

高亮

聚合

Bucket聚合(分组)

Metric聚合函数(min、max、avg)

词条(汉字 & 拼音)自动补全

SpringBoot 整合 EleasticSearch 8.0.X 弃用high-level-client

elasticsearch 使用相关推荐

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