06.ElasticSearch在Java中的使用

本文主要是介绍06.ElasticSearch在Java中的使用，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

ElasticSearch在Java中的使用

• • 引入依赖
  • 索引、类型和映射的操作
  • • 1.创建客户端操作对象
    • 2.创建索引
    • 3.删除索引
    • 4.创建索引、类型和映射
  • 文档的操作
  • • 1.准备工作
    • • 1. 引入依赖
      • 2.创建User实体类
      • 3.编写TestDocumentBase公用编码
    • 2.添加一个文档
    • • 1.自动生成id
      • 2. 手动指定id
    • 3.更新一条文档
    • 4.删除一条文档
    • 5.查询一条文档
    • 6.按关键字查询文档
    • 7.批量操作
    • 8.文档的高级查询
    • • 1.查询所有
      • 2.分页查询
      • 3.排序
      • 4.返回指定字段
      • 5.term查询
      • 6.range查询
      • 7.wildcard查询
      • 8.prefix查询
      • 9.ids查询
      • 10.fuzzy查询
      • 11.bool查询
      • 12.highLight查询
      • 13.filter查询

引入依赖

我们使用上次介绍ElasticSearch远程扩展词典时创建的springboot项目es_remote_dict来讲述06.ElasticSearch在Java中的使用，首先在pom.xml中引入以下依赖

<dependency>
  <groupId>org.elasticsearch</groupId>
  <artifactId>elasticsearch</artifactId>
  <version>6.8.0</version>
</dependency>

<dependency>
  <groupId>org.elasticsearch.client</groupId>
  <artifactId>transport</artifactId>
  <version>6.8.0</version>
</dependency>

<dependency>
  <groupId>org.elasticsearch.plugin</groupId>
  <artifactId>transport-netty4-client</artifactId>
  <version>6.8.0</version>
</dependency>

索引、类型和映射的操作

1.创建客户端操作对象

@Test
public void testCreateESClient() throws UnknownHostException {
		PreBuiltTransportClient preBuiltTransportClient = new PreBuiltTransportClient(Settings.EMPTY);
  	preBuiltTransportClient.addTransportAddress(new TransportAddress(
    InetAddress.getByName("192.168.8.101"),9300));
}

2.创建索引

/**
 * @Author Christy
 * @Date 2021/4/29 11:19
 **/
public class TestIndexTypeAndMapping {
    private TransportClient transportClient;

    @Before
    public void before() throws UnknownHostException {
        // 创建客户端
        this.transportClient = new PreBuiltTransportClient(Settings.EMPTY);
        // 设置ES服务地址和端口
        transportClient.addTransportAddresses(new TransportAddress(InetAddress.getByName("192.168.8.101"),9300))；
    }
    
    @After
    public void after(){
        transportClient.close();
    }
    
    // 创建索引
    @Test
    public void createIndex(){
        // 创建一个索引，前提是需要创建的索引不存在
        CreateIndexResponse createIndexResponse = transportClient.admin().indices().prepareCreate("tide").get();
        // 获取信息
        boolean acknowledged = createIndexResponse.isAcknowledged();
        System.out.println(acknowledged);

    }
}

运行createIndex()，我们可以看到索引创建成功

然后我们去kibana执行命令GET /_cat/indices?v查询一下索引是否存在

3.删除索引

// 删除索引
@Test
public void deleteIndex(){
  AcknowledgedResponse tide = transportClient.admin().indices().prepareDelete("tide").get();
  System.out.println("delete index tide is：" + tide.isAcknowledged());
}

再去kibana中查询看索引是否还在

4.创建索引、类型和映射

 // 创建索引 类型 映射
    @Test
    public void createIndexTypeMapping() throws ExecutionException, InterruptedException {
        // 创建一个索引请求对象
        CreateIndexRequest tide = new CreateIndexRequest("tide");
        // 索引设置类型
        /**
         * 参数一：type名字
         * 参数二：映射的JSON字符串
         * 参数三：映射格式类型
         */
        tide.mapping("user",
                "{\"properties\":{\"name\":{\"type\":\"text\",\"analyzer\":\"ik_max_word\",\"search_analyzer\":\"ik_max_word\"},\"age\":{\"type\":\"integer\"},\"bir\":{\"type\":\"date\"},\"introduce\":{\"type\":\"text\",\"analyzer\":\"ik_max_word\",\"search_analyzer\":\"ik_max_word\"},\"address\":{\"type\":\"keyword\"}}}",
                XContentType.JSON);

        // 创建索引
        CreateIndexResponse createIndexResponse = transportClient.admin().indices().create(tide).get();
        System.out.println("create index type and mapping is: " + createIndexResponse.isAcknowledged());
    }

执行结果显示成功

文档的操作

1.准备工作

1. 引入依赖

<dependency>
  <groupId>com.alibaba</groupId>
  <artifactId>fastjson</artifactId>
  <version>1.2.70</version>
</dependency>

2.创建User实体类

/**
 * @Author Christy
 * @Date 2021/4/29 12:40
 **/
@Data
@AllArgsConstructor
@NoArgsConstructor
@Accessors(chain = true)
public class User {
    private String id;
    private String name;
    private Integer age;
    private Date bir;
    private String introduce;
    private String address;
}

注意：User实体类中的属性必须跟ES中type的字段对应

3.编写TestDocumentBase公用编码

/**
 * 文档的基本操作
 * @author Christy
 * @date 2021/4/29 12:44
 * @return 
 */
public class TestDocumentBase {
    
    private TransportClient transportClient;
    
    @Before
    public void init() throws UnknownHostException {
        // 创建客户端
        this.transportClient = new PreBuiltTransportClient(Settings.EMPTY);
        // 设置es服务地址
        transportClient.addTransportAddresses(new TransportAddress(InetAddress.getByName("192.168.8.101"),9300));
    }
    
    @After
    public void after(){
        transportClient.close();
    }   
}

2.添加一个文档

1.自动生成id

/**
 * 添加一个文档自动生成一个id
 * @author Christy
 * @date 2021/4/29 12:48
 * @return
 */
public void createDocAutoId(){
  // 构建一个文档
  User user = new User(null, "孙悟空", 685, new Date(), "花果山水帘洞美猴王齐天大圣孙悟空", "花果山");
  // 转为JSON
  String userJSON = JSONObject.toJSONStringWithDateFormat(user, "yyyy-MM-dd");
  IndexResponse indexResponse = transportClient.prepareIndex("tide", "user").setSource(userJSON, XContentType.JSON).get();
  System.out.println("孙悟空出生了:" + indexResponse.status());
}

2. 手动指定id

/**
 * 添加一个文档指定一个id
 * @author Christy
 * @date 2021/4/29 12:53
 * @return
 */
public void createDocOptionId(){
  // 构建一个文档
  User user = new User("1", "猪八戒", 965, new Date(), "天蓬元帅猪刚鬣", "高老庄");
  // 转为JSON
  String userJSON = JSONObject.toJSONStringWithDateFormat(user, "yyyy-MM-dd");
  IndexResponse indexResponse = transportClient.prepareIndex("tide", "user",user.getId()).setSource(userJSON, XContentType.JSON).get();
  System.out.println("猪八戒投胎了:" + indexResponse.status());
}

我们根据id的生成方式不同添加了两条数据，现在我们去kibana中查看一下

3.更新一条文档

/**
 * 更新一条文档
 * @author Christy
 * @date 2021/4/29 13:41
 * @return
 */
@Test
public void updateDoc(){
  User user = new User();
  user.setIntroduce("天蓬元帅猪八戒掌管10万天河水军，威风凛凛！");
  String userJSON = JSONObject.toJSONString(user);
  UpdateResponse updateResponse = transportClient.prepareUpdate("tide", "user", "1").setDoc(userJSON, XContentType.JSON).get();
  System.out.println("猪八戒简介更新了：" + updateResponse.status());
}

4.删除一条文档

/**
 * 删除一条文档
 * @author Christy
 * @date 2021/4/29 13:57
 * @return
 */
@Test
public void deleteOne(){
  DeleteResponse deleteResponse = transportClient.prepareDelete("tide", "user", "roAjHHkBRz-Sn-2fB1gg").get();
  System.out.println("孙悟空被六耳猕猴打死了：" + deleteResponse.status());
}

我们再去kibana中查看一下数据，发现孙悟空真的被六耳猕猴打死了o(╥﹏╥)o

5.查询一条文档

/**
 * 查询一条文档
 * @author Christy
 * @date 2021/4/29 14:08
 * @param 
 * @return void
 */
@Test
public void queryOne() throws ParseException {
  GetResponse getResponse = transportClient.prepareGet("tide", "user", "1").get();
  System.out.println("提取猪八戒的档案成功：" + getResponse.getSourceAsString());

  Map<String, Object> sourceAsMap = getResponse.getSourceAsMap();
  User bajie = new User();
  bajie.setId(String.valueOf(sourceAsMap.get("id")));
  bajie.setName(String.valueOf(sourceAsMap.get("name")));
  bajie.setAge(Integer.valueOf(String.valueOf(sourceAsMap.get("age"))));
  bajie.setBir(new SimpleDateFormat("yyyy-MM-dd").parse(String.valueOf(sourceAsMap.get("bir"))));
  bajie.setIntroduce(String.valueOf(sourceAsMap.get("introduce")));
  bajie.setAddress(String.valueOf(sourceAsMap.get("address")));

  System.out.println("八戒你无处遁形了：" + bajie);
}

6.按关键字查询文档

/**
 * 查询所有符合条件的记录
 * @author Christy
 * @date 2021/4/29 14:34
 * @param 
 * @return void
 */
@Test
public void queryTerm(){
    TermQueryBuilder termQueryBuilder = QueryBuilders.termQuery("introduce", "最美");
    SearchResponse searchResponse = transportClient.prepareSearch("christy")
      .setTypes("user")
      .setQuery(termQueryBuilder)
      .get();

    System.out.println("符合条件的记录数：" + searchResponse.getHits().getTotalHits());
    System.out.println("符合条件的记录中的最大的分数：" + searchResponse.getHits().getMaxScore());
    SearchHit[] hits = searchResponse.getHits().getHits();
    for (SearchHit hit: hits) {
      System.out.println(hit.getSourceAsString());
    }
}

7.批量操作

/**
 * 批量操作
 * @author Christy
 * @date 2021/4/29 14:50
 * @param 
 * @return void
 */
@Test
public void bulk(){
  	User user = new User("10", "女娲", 100000, new Date(), "我是万物之主，整个宇宙都是我的", "九重天");
  	// 添加
  	IndexRequest indexRequest = new IndexRequest("christy", "user", user.getId())
    .source(JSONObject.toJSONStringWithDateFormat(user,"yyyy-MM-dd"), XContentType.JSON);

  	// 删除 我们将Elastic、TOM和Jerry删除掉
  	DeleteRequest deleteRequest01 = new DeleteRequest("christy", "user", "qYAtG3kBRz-Sn-2fMFjj");
  	DeleteRequest deleteRequest02 = new DeleteRequest("christy", "user", "qIAtG3kBRz-Sn-2fMFjj");
  	DeleteRequest deleteRequest03 = new DeleteRequest("christy", "user", "q4AtG3kBRz-Sn-2fMFjj");

  	// 更新
  	User user01 = new User();
  	user01.setIntroduce("我是一个干饭人，干饭人之歌说的就是我");
  	UpdateRequest updateRequest = new UpdateRequest("christy", "user", "CszCGHkB5KgTrUTeLyE_").doc(JSONObject.toJSONString(user01), XContentType.JSON);

  	BulkRequestBuilder bulkRequestBuilder = transportClient.prepareBulk();
  	BulkResponse bulkItemResponses = bulkRequestBuilder.add(indexRequest)
                  .add(deleteRequest01)
                  .add(deleteRequest02)
                  .add(deleteRequest03)
                  .add(updateRequest)
                  .get();

  	BulkItemResponse[] items = bulkItemResponses.getItems();
  	for (BulkItemResponse item : items) {
    	System.out.println(item.status());
  	}
}

我们再去kibana查询一下结果，本来我们有12条数据，删除三条，新增一条，目前总数应该在10条，而且我们修改了干饭人的简介

8.文档的高级查询

1.查询所有

@Test
public void testQuery(){
    MatchAllQueryBuilder matchAllQueryBuilder = QueryBuilders.matchAllQuery();

  	SearchResponse searchResponse = transportClient.prepareSearch("christy")
    	.setTypes("user")
    	.setQuery(matchAllQueryBuilder)
    	.get();

  	System.out.println("符合查询条件的文档总条数：" + searchResponse.getHits().getTotalHits());
  	System.out.println("符合查询条件的文档最大得分数：" + searchResponse.getHits().getMaxScore());
  	SearchHit[] hits = searchResponse.getHits().getHits();
  	for (SearchHit hit : hits) {
    	System.out.println("-------->" + hit.getSourceAsString());
  	}
}

2.分页查询

@Test
public void testQuery(){
    MatchAllQueryBuilder matchAllQueryBuilder = QueryBuilders.matchAllQuery();

    SearchResponse searchResponse = transportClient.prepareSearch("christy")
      .setTypes("user")
      .setQuery(matchAllQueryBuilder)
      .setFrom(0)     // 起始条数，默认从0开始 （当前页-1）*size
      .setSize(5)     // 设置每页展示的条数
      .get();

    System.out.println("符合查询条件的文档总条数：" + searchResponse.getHits().getTotalHits());
    System.out.println("符合查询条件的文档最大得分数：" + searchResponse.getHits().getMaxScore());
    SearchHit[] hits = searchResponse.getHits().getHits();
    for (SearchHit hit : hits) {
      System.out.println("-------->" + hit.getSourceAsString());
    }
}

3.排序

@Test
public void testQuery(){
    MatchAllQueryBuilder matchAllQueryBuilder = QueryBuilders.matchAllQuery();

    SearchResponse searchResponse = transportClient.prepareSearch("christy")
      .setTypes("user")
      .setQuery(matchAllQueryBuilder)
      .setFrom(0)     // 起始条数，默认从0开始 （当前页-1）*size
      .setSize(5)     // 设置每页展示的条数
      .addSort("age", SortOrder.DESC)     // 按年龄排序
      .get();

    System.out.println("符合查询条件的文档总条数：" + searchResponse.getHits().getTotalHits());
    System.out.println("符合查询条件的文档最大得分数：" + searchResponse.getHits().getMaxScore());
    SearchHit[] hits = searchResponse.getHits().getHits();
    for (SearchHit hit : hits) {
      System.out.println("-------->" + hit.getSourceAsString());
    }
}

上面的输出结果中最大的分数为NaN，是因为我们自定义了排序规则导致原先系统的按分数排序失效

4.返回指定字段

@Test
public void testQuery(){
    // 查询所有
    MatchAllQueryBuilder matchAllQueryBuilder = QueryBuilders.matchAllQuery();

    SearchResponse searchResponse = transportClient.prepareSearch("christy")
      .setTypes("user")
      .setQuery(matchAllQueryBuilder)
      .setFrom(0)     // 起始条数，默认从0开始 （当前页-1）*size
      .setSize(10)     // 设置每页展示的条数
      .addSort("age", SortOrder.DESC)     // 按年龄排序
      // 指定返回除bir以外的所有字段
      .setSource(SearchSourceBuilder.searchSource().fetchSource("*", "bir"))
      .get();

    System.out.println("符合查询条件的文档总条数：" + searchResponse.getHits().getTotalHits());
    System.out.println("符合查询条件的文档最大得分数：" + searchResponse.getHits().getMaxScore());
    SearchHit[] hits = searchResponse.getHits().getHits();
    for (SearchHit hit : hits) {
      System.out.println("-------->" + hit.getSourceAsString());
    }

    query(matchAllQueryBuilder);
}

5.term查询

通过以上例子我们发现除了查询条件不一样，其余的代码可以公用，我们先来封装一下代码

public void query(QueryBuilder queryBuilder){
    SearchResponse searchResponse = transportClient.prepareSearch("christy")
                .setTypes("user")
                .setQuery(queryBuilder)
                .setFrom(0)     // 起始条数，默认从0开始 （当前页-1）*size
                .setSize(10)     // 设置每页展示的条数
                .addSort("age", SortOrder.DESC)     // 按年龄排序
                .get();

    System.out.println("符合查询条件的文档总条数：" + searchResponse.getHits().getTotalHits());
    System.out.println("符合查询条件的文档最大得分数：" + searchResponse.getHits().getMaxScore());
    SearchHit[] hits = searchResponse.getHits().getHits();
    for (SearchHit hit : hits) {
        System.out.println("-------->" + hit.getSourceAsString());
    }
}

构造一个term查询的queryBuilder

@Test
public void testQuery(){
    // 查询所有
    MatchAllQueryBuilder matchAllQueryBuilder = QueryBuilders.matchAllQuery();
    // term查询
    TermQueryBuilder termQueryBuilder = QueryBuilders.termQuery("introduce", "最美");
    query(termQueryBuilder);
}

6.range查询

// range查询
RangeQueryBuilder rangeQueryBuilder = QueryBuilders.rangeQuery("age").gte(0).lte(20);
query(rangeQueryBuilder);

7.wildcard查询

// wildcardQuery 通配符查询 ？一个 * 0到多个
WildcardQueryBuilder wildcardQueryBuilder = QueryBuilders.wildcardQuery("introduce", "最*");
query(wildcardQueryBuilder);

8.prefix查询

//prefixQuery 前缀查询
PrefixQueryBuilder prefixQueryBuilder = QueryBuilders.prefixQuery("introduce", "最");
query(prefixQueryBuilder);

9.ids查询

// ids查询 我们把女娲和郝建查询出来
IdsQueryBuilder idsQueryBuilder = QueryBuilders.idsQuery().addIds("10").addIds("C8zCGHkB5KgTrUTeLyE_");
query(idsQueryBuilder);

10.fuzzy查询

// fuzzy查询 0-2不允许模糊 3-5 允许模糊一个 >5 最多允许模糊两个 这里只演示使用方法不再演示错误的做法
FuzzyQueryBuilder fuzzyQueryBuilder = QueryBuilders.fuzzyQuery("introduce", "普拉多");
query(fuzzyQueryBuilder);

11.bool查询

// bool查询 must     must not     should
// introduce 中包含最的
BoolQueryBuilder mustQueryBuilder = QueryBuilders.boolQuery().must(QueryBuilders.termQuery("introduce", "最"));
query(mustQueryBuilder);
// introduce 中不包含最的
BoolQueryBuilder mustNotQueryBuilder = QueryBuilders.boolQuery().mustNot(QueryBuilders.termQuery("introduce", "最"));
query(mustNotQueryBuilder);
// introduce中包含最或者年龄大于等于10岁的
BoolQueryBuilder shouldQueryBuilder = QueryBuilders.boolQuery().should(QueryBuilders.termQuery("introduce", "最"))
  .should(QueryBuilders.rangeQuery("age").gte(10));
query(shouldQueryBuilder);

12.highLight查询

我们先回忆一下高亮的查询语法和查询结果，然后按照高亮的语法和结果来写这个高亮查询

GET /christy/user/_search
{
  "query": {
    "term": {
      "introduce": {
        "value": "干饭"
      }
    }
  },
  "highlight": {
    "require_field_match": "false", 
    "fields": {
      "*":{}
    },
    "pre_tags": ["<span style='color:red;'>"],
    "post_tags": ["</span>"]
  }
}

那么我们的Java的代码应该像下面这这样

@Test
public void highLightQuery(){
    HighlightBuilder highlightBuilder = new HighlightBuilder()
      .field("*")
      .requireFieldMatch(false)
      .preTags("<span style='color:red'>")
      .postTags("</span>");

    SearchResponse searchResponse = transportClient.prepareSearch("christy")
      .setTypes("user")
      .setQuery(QueryBuilders.multiMatchQuery("干饭", "name", "introduce"))
      .highlighter(highlightBuilder)  // 高亮查询
      .get();

    List<User> userList = new ArrayList<>();
    SearchHit[] hits = searchResponse.getHits().getHits();
    for (SearchHit hit : hits) {
      User user = new User();

      Map<String, Object> sourceAsMap = hit.getSourceAsMap();
      user.setId(hit.getId());
      user.setName(sourceAsMap.get("name").toString());
      user.setAge(Integer.valueOf(sourceAsMap.get("age").toString()));
      //  user.setBir(new SimpleDateFormat("yyyy-MM-dd").parse(sourceAsMap.get("bir").toString());
      user.setIntroduce(sourceAsMap.get("introduce").toString());
      user.setAddress(sourceAsMap.get("address").toString());

      // 获取高亮字段
      Map<String, HighlightField> highlightFields = hit.getHighlightFields();
      if(highlightFields.containsKey("name")){
        String highLightName = highlightFields.get("name").fragments()[0].toString();
        user.setName(highLightName);
      }

      if(highlightFields.containsKey("introduce")){
        String highLightIntroduce = highlightFields.get("introduce").fragments()[0].toString();
        user.setIntroduce(highLightIntroduce);
      }

      userList.add(user);
    }

    // 遍历输出替换高亮后的结果
    userList.forEach(user -> System.out.println(user));
}

13.filter查询

/**
 * 过滤查询 主要用在查询执行之前对大量数据进行筛选
 * postFilter 用来过滤     * @author Christy
 * @date 2021/4/29 18:43
 * @return 
 */
@Test
public void filterQuery(){
    RangeQueryBuilder ageRangeQueryBuilder = QueryBuilders.rangeQuery("age").gte(0).lte(10);
    SearchResponse searchResponse = transportClient.prepareSearch("christy")
      .setTypes("user")
      .setPostFilter(ageRangeQueryBuilder) //过滤查询
      .setQuery(QueryBuilders.matchAllQuery())//查询
      .get();

    SearchHit[] hits = searchResponse.getHits().getHits();
    for (SearchHit hit : hits) {
      System.out.println(hit.getSourceAsString());
    }
}

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