ES7基础篇-10-SpringBoot集成ES操作

0. 背景

下面会简单介绍一些关于es结合SpringBoot使用的案例，更多详情介绍应该去官网看看: https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-supported-apis.html

1. 配置环境依赖

1.1 查看一下当前使用的es版本

比如我演示使用的es版本为: 7.15.1

1.2 配置maven的依赖以及环境变量

  <dependency>
     <groupId>org.elasticsearch.client</groupId>
     <artifactId>elasticsearch-rest-high-level-client</artifactId>
     <version>7.15.1</version>
  </dependency>

配置属性:

1.3 配置yaml

spring:
  elasticsearch:
    rest:
      uris: http://192.168.72.143:9200

这里是因为我是单机模式，9200端口，但是如果是集群，就是9300端口，为了演示方便直接这样配置，但是一般公司使用的是集群，就需要配置集群的配置,或者在es的bean里面做文章都可以

2. 索引库操作

其实会使用kibana进行命令操作，在代码层面也是一样的，只不过命令换成了方法和一些类；下午结合kibana命令对应java代码进行展示

2.1 创建索引库

• 在kibana当中，创建索引库是这样操作

PUT wang_index_01
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1
  }
}

• 对应java代码:

注入依赖

  @Autowired
    private RestHighLevelClient client;

       // 创建索引
        CreateIndexRequest indexRequest = new CreateIndexRequest ("wang_index_01");
        //分片参数
        indexRequest.settings(Settings.builder()
                 //分片数
                .put("index.number_of_shards", 1)
                 // 副本数
                .put("index.number_of_replicas", 1)
        );
        // 创建索引操作客户端
        IndicesClient indices = client.indices();
        // 创建响应结果
        CreateIndexResponse createIndexResponse = indices.create(indexRequest, RequestOptions.DEFAULT);
        //获取响应值
        boolean acknowledged = createIndexResponse.isAcknowledged();
        System.out.println("acknowledged = " + acknowledged);

2.2 查询索引库

• 在kibana当中，查询索引库是这样操作

GET wang_index_01

• 对应java代码:

GetIndexRequest getIndexRequest = new GetIndexRequest();
getIndexRequest.indices("wang_index_01");
GetIndexResponse getIndexResponse = client.indices().get(getIndexRequest, RequestOptions.DEFAULT);
System.out.println("getIndexResponse = " + getIndexResponse);

2.3 删除索引库

• 在kibana当中，删除索引库是这样操作

DELETE wang_index_01

• 对应java代码:

DeleteIndexRequest deleteIndexRequest = new DeleteIndexRequest("wang_index_01");
AcknowledgedResponse delete = client.indices().delete(deleteIndexRequest, RequestOptions.DEFAULT);
boolean acknowledged = delete.isAcknowledged();
System.out.println("acknowledged = " + acknowledged);

2.4 总结

当使用es的客户端 RestHighLevelClient 时，对于索引库操作，不涉及映射，先获取他的索引库客户端

 // 创建索引操作客户端
  IndicesClient indices = client.indices();

然后借助idea的提示，会出现一系列API

每个API都可以同步或异步调用。 同步方法返回一个响应对象，而异步方法的名称以async后缀结尾，需要一个监听器参数，一旦收到响应或错误，就会被通知（由低级客户端管理的线程池）。

然后你就可以根据方法的提示，创建响应的api，做响应的操作，其他们都有一个共同的接口爸爸IndicesRequest ，有兴趣可以多了解一下

对于索引库的操作API：

• 创建索引库: CreateIndexRequest
• 查询索引库：GetIndexRequest
• 删除索引库：DeleteIndexRequest

对于索引的操作是基于***IndexRequest来进行操作的。
常见操作中还有校验索引是否存在：exists

参考腾讯客户端配置：腾讯Es客户端配置

3. 索引映射操作

3.1 创建映射

因为之前在2.1小章节，我们已经创建过索引库，所以这里就直接创建映射操作

• 在kibana当中，在已有索引库创建映射如下

PUT /wang_index_01/_mapping
{
  "properties": {
    "address": {
      "type": "text",
      "analyzer": "ik_max_word"
    },
    "userName": {
      "type": "keyword"
    },
    "userPhone": {
      "type": "text",
      "analyzer": "ik_max_word"
    }
  }
}

• 对应java代码如下:

PutMappingRequest putMappingRequest = new PutMappingRequest("wang_index_01");
        XContentBuilder builder = XContentFactory.jsonBuilder()
                .startObject()
                .startObject("properties")
                .startObject("address")
                .field("type", "text")
                .field("analyzer", "ik_max_word")
                .endObject()
                .startObject("userName")
                .field("type", "keyword")
                .endObject()
                .startObject("userPhone")
                .field("type", "text")
                .field("analyzer", "ik_max_word")
                .endObject()
                .endObject()
                .endObject();

        PutMappingRequest source = putMappingRequest.source(builder);

        AcknowledgedResponse acknowledgedResponse = client.indices().putMapping(source, RequestOptions.DEFAULT);

        boolean acknowledged = acknowledgedResponse.isAcknowledged();
        System.out.println("acknowledged = " + acknowledged);

其实代码根命令没啥区别，startObject你可以理解为是{ ,endObject可以理解为是} ,对应看kibana的命令你就特别熟悉了，简直一模一样；

也可以使用对象映射,比如：

• 自定义分词器枚举

@Getter
@SuppressWarnings("ALL")
public enum AnalyzerEnum {

    NO("不使用分词"),
    /**
     * 标准分词，默认分词器
     */
    STANDARD("standard"),

    /**
     * ik_smart：会做最粗粒度的拆分；已被分出的词语将不会再次被其它词语占有
     */
    IK_SMART("ik_smart"),

    /**
     * ik_max_word ：会将文本做最细粒度的拆分；尽可能多的拆分出词语
     */
    IK_MAX_WORD("ik_max_word");

    private String type;

    AnalyzerEnum(String type) {
        this.type = type;
    }
}

• 自定义类型枚举

@Getter
@SuppressWarnings("ALL")
public enum EsFieldEnum {

    TEXT("text"),

    KEYWORD("keyword"),

    INTEGER("integer"),

    LONG("long"),

    DOUBLE("double"),

    DATE("date"),

    /**
     * 单条数据
     */
    OBJECT("object"),

    /**
     * 嵌套数组
     */
    NESTED("nested"),
    ;

    EsFieldEnum (String type) {
        this.type = type;
    }
    private final String type;
}

• 自定义注解

@Retention(RetentionPolicy.RUNTIME)
@Target(ElementType.FIELD)
@Documented
@Inherited
public @interface EsField {

    EsFieldEnum type() default FieldTypeEnum.TEXT;

    /**
     * 指定分词器
     */
    AnalyzerEnum analyzer() default AnalyzerEnum.STANDARD;


}

• 实体类：

@Data
@Accessors(chain = true)
@SuppressWarnings("ALL")
public class EsStudent implements Serializable {
    private static final long serialVersionUID = 7100225268368014590L;

    @EsField(type = FieldTypeEnum.KEYWORD)
    private String name;
    @EsField(type = FieldTypeEnum.INTEGER)
    private int age;
}

• 实体类映射关系

private XContentBuilder generateBuilder(Class clazz) {
        XContentBuilder builder = null;
        try {
            builder = XContentFactory.jsonBuilder();
            builder.startObject();
            builder.startObject("properties");
            java.lang.reflect.Field[] declaredFields = clazz.getDeclaredFields();
            for (java.lang.reflect.Field f : declaredFields) {
                if (f.isAnnotationPresent(EsField.class)) {
                    // 获取注解
                    EsField declaredAnnotation = f.getDeclaredAnnotation(EsField.class);
                    if (declaredAnnotation.type() == FieldTypeEnum.OBJECT) {
                        // 获取当前类的对象-- Action
                        Class<?> type = f.getType();
                        java.lang.reflect.Field[] df2 = type.getDeclaredFields();
                        builder.startObject(f.getName());
                        builder.startObject("properties");
                        // 遍历该对象中的所有属性
                        for (java.lang.reflect.Field f2 : df2) {
                            if (f2.isAnnotationPresent(EsField.class)) {
                                // 获取注解
                                EsField declaredAnnotation2 = f2.getDeclaredAnnotation(EsField.class);
                                builder.startObject(f2.getName());
                                builder.field("type", declaredAnnotation2.type().getType());
                                // keyword不需要分词
                                if (declaredAnnotation2.type() == FieldTypeEnum.TEXT) {
                                    builder.field("analyzer", declaredAnnotation2.analyzer().getType());
                                }
                                builder.endObject();
                            }
                        }
                        builder.endObject();

                    } else {
                        builder.startObject(f.getName());
                        builder.field("type", declaredAnnotation.type().getType());
                        // keyword不需要分词
                        if (declaredAnnotation.type() == FieldTypeEnum.TEXT) {
                            builder.field("analyzer", declaredAnnotation.analyzer().getType());
                        }
                    }
                    builder.endObject();
                }
            }
            // 对应property
            builder.endObject();
            builder.endObject();
        } catch (IOException e) {
            log.error("【ES操作】 组装映射字段语句异常");
        }

        return builder;
    }

• 方法

    public boolean createIndex(String indexName, Class clazz) throws IOException {
        if (checkIndexExists(indexName)) {
            log.info("索引={}已存在", indexName);
            return false;
        }
        CreateIndexRequest request = new CreateIndexRequest(indexName);
        request.settings(Settings.builder()
                .put("index.number_of_shards", 3)
                .put("index.number_of_replicas", 3)
        );
        request.mapping(generateBuilder(clazz));
        CreateIndexResponse response = client.indices().create(request, RequestOptions.DEFAULT);
        boolean acknowledged = response.isAcknowledged();
        boolean shardsAcknowledged = response.isShardsAcknowledged();
        return acknowledged || shardsAcknowledged;
    }

3.2 查看映射

• 在kibana当中，查询映射:

GET wang_index_01/_mapping

• 对应java代码如下:

GetMappingsRequest getMappingsRequest = new GetMappingsRequest();
getMappingsRequest.indices("wang_index_01");
GetMappingsResponse mapping = client.indices().getMapping(getMappingsRequest, RequestOptions.DEFAULT);
Map<String, MappingMetadata> mappings = mapping.mappings();
MappingMetadata metadata = mappings.get("wang_index_01");
String s = metadata.getSourceAsMap().toString();
System.out.println("s = " + s);

3.3 总结

涉及到索引库，映射操作，其实在 client.indices() 都可以找到，见名思意就行；

• PutMappingRequest 新增映射
• GetMappingsRequest 查询映射

4. 文档操作

4.1 新增文档数据

• kibana当中新增数据如下

POST wang_index_01/_doc/1
{
  "address":"江西宜春上高泗溪镇",
  "userName":"张三",
  "userPhone":"15727538286"
}

• 对应代码：

        Map<String, Object> jsonMap = new HashMap<>();
        jsonMap.put("address", "江西宜春上高泗溪镇");
        jsonMap.put("userName", "张三");
        jsonMap.put("userPhone", "15727538286");
        IndexRequest indexRequest = new IndexRequest("wang_index_01")
                .id("1").source(jsonMap);

        Map<String, Object> jsonMap2 = new HashMap<>();
        jsonMap2.put("address", "江西宜春高安祥符镇");
        jsonMap2.put("userName", "李四");
        jsonMap2.put("userPhone", "15727538286");
        IndexRequest indexRequest2 = new IndexRequest("wang_index_01")
                .id("2").source(jsonMap2);


        BulkRequest request = new BulkRequest();
        request.add(indexRequest);
        request.add(indexRequest2);

        BulkResponse bulk = client.bulk(request, RequestOptions.DEFAULT);
        RestStatus status = bulk.status();
        System.out.println("status = " + status);

这里我采用批量新增的方式新增了2条记录
更多详细参考官网: https://www.elastic.co/guide/en/elasticsearch/client/java-rest/current/java-rest-high-document-index.html

4.2 删除文档数据

• kibana当中删除数据如下：

DELETE wang_index_01/_doc/1

• 对应java代码如下：

        DeleteRequest deleteRequest = new DeleteRequest("wang_index_01");
        deleteRequest.id("1");

        DeleteResponse delete = client.delete(deleteRequest, RequestOptions.DEFAULT);
        RestStatus status = delete.status();
        System.out.println("status = " + status);

4.3 查询文档数据

• kibana当中查询数据如下：

GET wang_index_01/_doc/1

• 对应java代码如下：

        GetRequest getRequest = new GetRequest("wang_index_01");
        getRequest.id("1");

        GetResponse documentFields = client.get(getRequest, RequestOptions.DEFAULT);
        String sourceAsString = documentFields.getSourceAsString();
        System.out.println("sourceAsString = " + sourceAsString);

4.4 修改文档数据

• kibana修改文档如下:

PUT wang_index_01/_doc/1
{
  
  "address":"江西宜春上高泗溪镇",
  "userName":"哈哈",
  "userPhone":"15727538288"
 
}

• 对应java代码如下:

        Map<String, Object> jsonMap = new HashMap<>();
        jsonMap.put("address", "江西宜春上高泗溪镇");
        jsonMap.put("userName", "哈哈");
        jsonMap.put("userPhone", "15727538287");

        UpdateRequest updateRequest = new UpdateRequest("wang_index_01","1");
        updateRequest.doc(jsonMap);
        UpdateResponse update = client.update(updateRequest, RequestOptions.DEFAULT);
        RestStatus status = update.status();
        System.out.println("status = " + status);

4.5 总结

• BulkRequest 新增数据
• DeleteRequest 删除数据
• UpdateRequest 修改数据
• GetRequest 查询数据

5. 搜索操作

ES最重要的环节就是搜索查询这一块，下面列举各种搜索操作

5.1 查询所有 match_all

• kibana查询所有：

GET wang_index_01/_search
{
  "query": {
    "match_all": {}
  }
}

• 对应代码:

        //创建搜索对象，入参可以为多个索引库参数
        SearchRequest searchRequest = new SearchRequest("wang_index_01");
        //创建查询构造器
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery());
        //设置查询构造器
        searchRequest.source(searchSourceBuilder);

        // 获取结果集
        SearchResponse search = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHit[] hits = search.getHits().getHits();
        //遍历每一条记录
        for (SearchHit hit : hits) {
            String sourceAsString = hit.getSourceAsString();
            System.out.println("sourceAsString = " + sourceAsString);
        }

其中，kibana命令的match_all 其实就是对应 QueryBuilders.matchAllQuery() ，点开查询条件的构造器会可以看到更多你想看到的
官方提供了QueryBuilders工厂帮我们构建各种实现类：

5.2 具体查询 match

• kibana

GET wang_index_01/_search
{
  "query": {
    "match": {
      "userName": "哈哈"
    }
  }
}

• java代码如下：

        //创建搜索对象，入参可以为多个索引库参数
        SearchRequest searchRequest = new SearchRequest("wang_index_01");
        //创建查询构造器
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchQuery("userName","哈哈"));
        //设置查询构造器
        searchRequest.source(searchSourceBuilder);

        // 获取结果集
        SearchResponse search = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHit[] hits = search.getHits().getHits();
        //遍历每一条记录
        for (SearchHit hit : hits) {
            String sourceAsString = hit.getSourceAsString();
            System.out.println("sourceAsString = " + sourceAsString);
        }

其实搜索类型的变化，仅仅是利用QueryBuilders构建的查询对象不同而已，其他代码基本一致：

• 组合查询，条件是并且的关系
• kibana

GET /wang_index_01/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "term": {
            "userName": "张三"
          }
        },
        {
          "term": {
            "passWord": "12345"
          }
        }
      ]
    }
  }
}

代码实现：

public SearchHit[] findByMap(String index, HashMap<String, Object> params, int size) {
        SearchRequest searchRequest = new SearchRequest(index);
        SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();
        sourceBuilder.timeout(new TimeValue(1, TimeUnit.SECONDS));
        sourceBuilder.size(size);

        BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery();
        if (CollUtil.isNotEmpty(params)) {
            for (String key : params.keySet()) {
                if (ObjectUtils.isEmpty(params.get(key))) {
                    continue;
                }
                if (params.get(key) instanceof List) {
                    List<Object> param = (List<Object>) params.get(key);
                    boolQueryBuilder.must(QueryBuilders.termsQuery(key, param));
                    continue;
                }
                boolQueryBuilder.must(QueryBuilders.matchQuery(key, params.get(key)));
            }
        }
        
        //设置查询构造器
        sourceBuilder.query(boolQueryBuilder);
        searchRequest.source(sourceBuilder);
        try {
            
            SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
            return searchResponse.getHits().getHits();
        } catch (Exception e) {
           e.printStackTrace();
        } 
        return new SearchHit[]{};
    }

5.3 范围查询 range

• kibana

GET wang_index_01/_search
{
  "query": {
   "range": {
     "userPhone": {
       "gte": 15727538288,
       "lte": 15727538289
     }
   }
  }
}

• java代码如下:

        //创建搜索对象，入参可以为多个索引库参数
        SearchRequest searchRequest = new SearchRequest("wang_index_01");
        //创建查询构造器
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.rangeQuery("userPhone").gt("15727538288").lt("15727538289"));
        //设置查询构造器
        searchRequest.source(searchSourceBuilder);

        // 获取结果集
        SearchResponse search = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHit[] hits = search.getHits().getHits();
        //遍历每一条记录
        for (SearchHit hit : hits) {
            String sourceAsString = hit.getSourceAsString();
            System.out.println("sourceAsString = " + sourceAsString);
        }

同理，查询构造器为 QueryBuilders.rangeQuery

5.4 过滤source

之前我们说过哈，每个字段都会保存在source下一份数据，所以默认store=false,
如果我们要做过滤操作，那肯定就从source入手;

• kibana命令如下：

GET wang_index_01/_search
{
  "_source": [
    "userPhone"
  ],
  "query": {
    "match_all": {}
  }
}

• java代码如下：

       //创建搜索对象，入参可以为多个索引库参数
        SearchRequest searchRequest = new SearchRequest("wang_index_01");
        //创建查询构造器
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery());
        searchSourceBuilder.fetchField("userPhone");

        //设置查询构造器
        searchRequest.source(searchSourceBuilder);



        // 获取结果集
        SearchResponse search = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHit[] hits = search.getHits().getHits();
        for (SearchHit hit : hits) {
            String sourceAsString = hit.getSourceAsString();
            System.out.println("sourceAsString = " + sourceAsString);
        }

5.5 排序 sort

• kibana排序如下:

GET wang_index_01/_search
{
  "query": {
    "match_all": {
      "boost": 1
    }
  },
  "fields": [
    {
      "field": "userPhone"
    }
  ],
  "sort": [
    {
      "userName": {
        "order": "asc"
      }
    }
  ]
}

注意，排序的字段一定不能是可分词的，不然会出现如下错误:
Text fields are not optimised for operations that require per-document field data like aggregations and sorting, so these operations are disabled by default. Please use a keyword field instead

• java代码如下所示:

        //创建搜索对象，入参可以为多个索引库参数
        SearchRequest searchRequest = new SearchRequest("wang_index_01");
        //创建查询构造器
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery());
        searchSourceBuilder.sort(new FieldSortBuilder("userName").order(SortOrder.ASC));
        searchSourceBuilder.fetchField("userPhone");
        //设置查询构造器
        searchRequest.source(searchSourceBuilder);
        
        // 获取结果集
        SearchResponse search = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHit[] hits = search.getHits().getHits();
        for (SearchHit hit : hits) {
            String sourceAsString = hit.getSourceAsString();
            System.out.println("sourceAsString = " + sourceAsString);
        }

5.6 分页 from size

• kibana命令如下：

GET wang_index_01/_search
{
  "query": {
    "match_all": {}
  },
  "from": 0,
  "size": 3
}

• java代码如下：

        SearchRequest searchRequest = new SearchRequest("wang_index_01");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery());
        searchRequest.source(searchSourceBuilder);

        // 添加分页
        int page = 1;
        int size = 3;
        int start = (page - 1) * size;
        // 配置分页
        searchSourceBuilder.from(start);
        searchSourceBuilder.size(3);

        // 获取结果集
        SearchResponse search = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHit[] hits = search.getHits().getHits();
        for (SearchHit hit : hits) {
            String sourceAsString = hit.getSourceAsString();
            System.out.println("sourceAsString = " + sourceAsString);
        }

5.7 聚合 aggs 之 度量（metrics）

aggregations实体包含了所有的聚合查询，如果是多个聚合查询可以用数组，如果只有一个聚合查询使用对象，aggregations也可以简写为aggs。

• kibana 聚合操作如下:

GET wang_index_01/_search
{
  "query": {
    "match_all": {}
  },
  "aggs": {
    "countUseName": {
      "value_count": {
        "field": "userName"
      }
    }
  }
}

• java代码如下:

        SearchRequest searchRequest = new SearchRequest("wang_index_01");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery());

        // count统计
        searchSourceBuilder.aggregation(AggregationBuilders.count("countUseName").field("userName"));


        searchRequest.source(searchSourceBuilder);



        // 获取结果集
        SearchResponse search = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHit[] hits = search.getHits().getHits();
        for (SearchHit hit : hits) {
            String sourceAsString = hit.getSourceAsString();
            System.out.println("sourceAsString = " + sourceAsString);
        }

其实度量等同于mysql里面的求最大值，最小值，平均值，求和，统计数量等，针对的是某个字段而言

5.8 聚合 aggs 之 桶（bucket）

Bucket 聚合不像metrics 那样基于某一个值去计算，每一个Bucket （桶）是按照我们定义的准则去判断数据是否会落入桶（bucket）中。一个单独的响应中，bucket（桶）的最大个数默认是10000，我们可以通过serarch.max_buckets去进行调整。
Bucket 聚合查询就像是数据库中的group by

• kibana举例如下:

GET wang_index/_search
{
  "query": {
    "match_all": {
      "boost": 1
    }
  },
  "aggregations": {
    "genderCount": {
      "terms": {
        "field": "gender",
        "size": 10,
        "min_doc_count": 1,
        "shard_min_doc_count": 0,
        "show_term_doc_count_error": false,
        "order": [
          {
            "_count": "desc"
          },
          {
            "_key": "asc"
          }
        ]
      }
    },
    "balanceAvg": {
      "avg": {
        "field": "balance"
      }
    }
  }
}

注意，只有不可分词才能参与聚合；

• 对应java代码如下:

       //1、创建查询请求，规定查询的索引
        SearchRequest request = new SearchRequest("wang_index");
        //2、创建条件构造
        SearchSourceBuilder builder = new SearchSourceBuilder();
        //3、构造条件
        MatchAllQueryBuilder matchAllQueryBuilder = QueryBuilders.matchAllQuery();
        builder.query(matchAllQueryBuilder);
        //聚合年龄分布
        TermsAggregationBuilder ageAgg = AggregationBuilders.terms("genderCount").field("gender");
        builder.aggregation(ageAgg);
        //聚合平均年龄
        AvgAggregationBuilder balanceAvg = AggregationBuilders.avg("balanceAvg").field("balance");
        builder.aggregation(balanceAvg);

        //4、将构造好的条件放入请求中
        request.source(builder);

        //5、开始执行发送request请求
        SearchResponse searchResponse = client.search(request, RequestOptions.DEFAULT);

        //6、开始处理返回的数据
        SearchHit[] hits = searchResponse.getHits().getHits();
        List<String> list = new ArrayList<String>();
        for (SearchHit hit : hits) {
            String hitString = hit.getSourceAsString();
            System.out.println(hitString);
            list.add(hitString);
        }

        Map<String, Aggregation> asMap = searchResponse.getAggregations().getAsMap();

        System.out.println("asMap = " + asMap);

5.8 高亮

• kibana举例如下：

GET wang_index_01/_search
{
  "query": {
    "match_all": {
      "boost": 1
    }
  },
  "highlight": {
    "pre_tags": [
      ""
    ],
    "post_tags": [
      ""
    ],
    "fields": {
      "userName": {}
    }
  }
}

• 对应java代码如下:

        SearchRequest searchRequest = new SearchRequest("wang_index_01");
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(QueryBuilders.matchAllQuery());
        
        HighlightBuilder highlightBuilder = new HighlightBuilder();
        highlightBuilder.field("userName")
                .preTags("\"\"")
                .postTags("");
        searchSourceBuilder.highlighter(highlightBuilder);
        
        searchRequest.source(searchSourceBuilder);

        
        // 获取结果集
        SearchResponse search = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHit[] hits = search.getHits().getHits();
        for (SearchHit hit : hits) {
            String sourceAsString = hit.getSourceAsString();
            System.out.println("sourceAsString = " + sourceAsString);
        }

高亮其实要配合前端做才好看；