Spring Data Elasticsearch实用小demo

Spring Data Elasticsearch

1. 概念 : 是Spring Data项目下的一个子模块,通过封装原始操作es的方法,从而简化开发人员的代码，提高开发效率。

入门前的准备工作

1. 新建maven工程es-demo
   

2. 导入依赖

    <parent>
       <groupId>org.springframework.boot</groupId>
       <artifactId>spring-boot-starter-parent</artifactId>
       <version>2.0.6.RELEASE</version>
       <relativePath/>
   </parent>
   
   <properties>
       <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
       <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
       <java.version>1.8</java.version>
   </properties>
   
   <dependencies>
       <dependency>
           <groupId>org.springframework.boot</groupId>
           <artifactId>spring-boot-starter-data-elasticsearch</artifactId>
       </dependency>
       <dependency>
           <groupId>org.springframework.boot</groupId>
           <artifactId>spring-boot-starter-test</artifactId>
           <scope>test</scope>
       </dependency>
       <dependency>
           <groupId>org.projectlombok</groupId>
           <artifactId>lombok</artifactId>
           <version>1.16.18</version>
           <scope>provided</scope>
       </dependency>
   </dependencies>
   

3. application.yml文件配置

   spring:
     data:
       elasticsearch:
         cluster-name: elasticsearch
         cluster-nodes: 192.168.0.155:9300
   

4. 创建启动类

   @SpringBootApplication
   public class ESApplication {
       public static void main(String[] args) {
           SpringApplication.run(ESApplication.class,args);
       }
   }
   

5. 模型类

   @AllArgsConstructor
   @NoArgsConstructor
   @Data
   @Document(indexName = "item", type = "docs", shards = 1, replicas = 1)
   public class Item implements Serializable {
   
       @Id
       private Long id;
   
       @Field(type = FieldType.Text, analyzer = "ik_max_word")
       private String title; //标题
   
       @Field(type = FieldType.Keyword)
       private String category;// 分类
   
       @Field(type = FieldType.Keyword)
       private String brand; // 品牌
   
       @Field(type = FieldType.Double)
       private Double price; // 价格
   
       @Field(type = FieldType.Keyword,index = false)
       private String images; // 图片地址
   }
   
   

   • @Document 作用在类，标记实体类为文档对象，一般有两个属性
     1. indexName：对应索引库名称
     2. type：对应在索引库中的类型
   2. shards：分片数量，默认5
   3. replicas：副本数量，默认1
   4. @Id 作用在成员变量，标记一个字段作为id主键
   5. @Field 作用在成员变量，标记为文档的字段，并指定字段映射属性：
      1. type：字段类型，取值是枚举：FieldType
      2. index：是否索引，布尔类型，默认是true
      3. store：是否存储，布尔类型，默认是false
      4. analyzer：分词器名称( ik_max_wprd或ik_smart )

入门开始

1. 创建索引和映射

   @RunWith(SpringRunner.class)
   @SpringBootTest
   public class EsTest {
       @Autowired
       private ElasticsearchTemplate elasticsearchTemplate;
   
       /**
        * 测试创建索引和映射
        */
       @Test
       public void addIndexTest(){
           elasticsearchTemplate.createIndex(Item.class);
           elasticsearchTemplate.putMapping(Item.class);
       }
   }
   
   

2. 删除索引

    @Test
       public void delIndexTest(){
           elasticsearchTemplate.deleteIndex(Item.class);
       }
   

3. 更简单的文档操作

   1. 定义接口继承

      public interface ItemMapper extends ElasticsearchRepository<Item,Long> {
      
      }
      

   2. 新增文档

       @Test
          public void addDocTest(){
              Item item = new Item(1L, "小米手机9", " 手机","小米", 3499.00, "http://image.es.com/13123.jpg");
              itemRepository.save(item);
      
          }
      

   3. 批量新增

      @Test
          public void addDocsTest(){
              List<Item> list = new ArrayList<>();
              list.add(new Item(2L, "坚果手机R1", " 手机", "锤子", 3689.00, "http://image.es.com/123123.jpg"));
              list.add(new Item(3L, "华为META20", " 手机", "华为", 4499.00, "http://image.es.com/333.jpg"));
              list.add(new Item(4L, "小米Mix2S", "手机", "小米", 4299.00, "http://image.es.com/131223.jpg"));
              list.add(new Item(5L, "荣耀V10", "手机", "华为", 2799.00, "http://image.es.com/131123.jpg"));
              list.add(new Item(6L, "Nova", "手机", "华为", 2999.00, "http://image.es.com/131123.jpg"));
              // 接收对象集合，实现批量新增
              itemRepository.saveAll(list);
      
          }
      

   4. 修改文档(和新增一样的接口,请求方式改为PUT)

   5. 基本查询

      1. 查询全部并根据价格升序

           @Test
             public void queryTest(){
                 Iterable<Item> items = itemRepository.findAll(Sort.by(Sort.Direction.DESC, "price"));
                 for (Item item : items) {
                     System.out.println(item);
                 }
             }
         	  /* output
         	    Item(id=3, title=华为META20, category= 手机, brand=华为, price=4499.0, images=http://image.es.com/333.jpg)
         		Item(id=4, title=小米Mix2S, category=手机, brand=小米, price=4299.0, images=http://image.es.com/131223.jpg)
         		Item(id=2, title=坚果手机R1, category= 手机, brand=锤子, price=3689.0, images=http://image.es.com/123123.jpg)
         		Item(id=1, title=小米手机8, category= 手机, brand=小米, price=3499.0, images=http://image.es.com/13123.jpg)
         		Item(id=6, title=Nova, category=手机, brand=华为, price=2999.0, images=http://image.es.com/131123.jpg)
         		*/
         

Es查询更多细节

1. 简单原生方式查询

      @Test
       public void queryTest2(){
           // 词条查询
           MatchQueryBuilder queryBuilder = QueryBuilders.matchQuery("title", "小米");
           // 执行查询
           Iterable<Item> items = itemRepository.search(queryBuilder);
           items.forEach(System.out::println);
       }
       /* output
   	Item(id=1, title=小米手机8, category= 手机, brand=小米, price=3499.0, images=http://image.es.com/13123.jpg)
   	Item(id=4, title=小米Mix2S, category=手机, brand=小米, price=4299.0, images=http://image.es.com/131223.jpg)
   	*/
   

2. 复杂原生方式查询

       @Test
       public void queryTest3(){
           //创建查询构建器
           NativeSearchQueryBuilder queryBuilder = new NativeSearchQueryBuilder();
           //结果过滤
           queryBuilder.withSourceFilter(new FetchSourceFilter(new String[]{"id","title","price"},null));
           //添加查询条件
           queryBuilder.withQuery(QueryBuilders.matchQuery("title","小米手机"));
           //排序
           queryBuilder.withSort(SortBuilders.fieldSort("price").order(SortOrder.DESC));
           //分页
           queryBuilder.withPageable(PageRequest.of(0,2));
           //查询
           Page<Item> result = itemRepository.search(queryBuilder.build());
   
           long totalElements = result.getTotalElements();
           System.out.println("总记录个数: "+totalElements);
           int totalPages = result.getTotalPages();
           System.out.println("总页数个数: "+totalPages);
           List<Item> itemList = result.getContent();
           itemList.forEach(System.out::println);
       }
       /* output
   		总记录个数: 3
   		总页数个数: 2
   		Item(id=4, title=小米Mix2S, category=null, brand=null, price=4299.0, images=null)
   		Item(id=2, title=坚果手机R1, category=null, brand=null, price=3689.0, images=null)
       */
   

3. 聚合为桶

   @Test
       public void queryTest4(){
           NativeSearchQueryBuilder queryBuilder = new NativeSearchQueryBuilder();
           // 1、添加一个新的聚合，聚合类型为terms，聚合名称为brands，聚合字段为brand
           queryBuilder.addAggregation(
                   AggregationBuilders.terms("brands").field("brand"));
           // 2、查询,需要把结果强转为AggregatedPage类型
           AggregatedPage<Item> aggPage = (AggregatedPage<Item>) itemRepository.search(queryBuilder.build());
           // 3、解析
           // 3.1、从结果中取出名为brands的那个聚合，
           // 因为是利用String类型字段来进行的term聚合，所以结果要强转为StringTerm类型
           StringTerms agg = (StringTerms) aggPage.getAggregation("brands");
           // 3.2、获取桶
           List<StringTerms.Bucket> buckets = agg.getBuckets();
           // 3.3、遍历
           for (StringTerms.Bucket bucket : buckets) {
           // 3.4、获取桶中的key，即品牌名称
               System.out.println("Key: "+bucket.getKeyAsString());
           // 3.5、获取桶中的文档数量
               System.out.println("Value: "+bucket.getDocCount());
           }
       }
       /* output
       	Key: 华为
   		Value: 3
   		Key: 小米
   		Value: 2
   		Key: 锤子
   		Value: 1
       */