数据聚合

聚合的分类

聚合(aggregations)可以实现对文档数据的统计、分析、运算。聚合常见的有三类:

桶(Bucket)聚合:用来对文档做分组
- TermAggregation:按照文档字段值分组
- Date Histogram:按照日期阶梯分组，例如一周为一组，或者一月为一组
度量（Metric）聚合:用以计算一些值，比如:最大值、最小值、平均值等
- Avg:求平均值
- Max:求最大值
- Min:求最小值
- Stats: 同时求max、min、avg、sum等
管道(pipeline)聚合:其它聚合的结果为基础做聚合

Bucket聚合

DSL实现

现在，我们要统计所有数据中的酒店品牌有几种，此时可以根据酒店品牌的名称做聚合。

类型为term类型，DSL示例:

GET/hotel/_search{
	"size": 0,//设置size为0，结果中不包含文档，只包含聚合结果
    "aggs " : {//定义聚合
        "brandAgg": {//给聚合起个名字
            "terms" : {//聚合的类型，按照品牌值聚合，所以选择term
                "field" : "brand",//参与聚合的字段
                "size" : 20//希望获取的聚合结果数量
            }   
        }   
    }    
}

聚合结果排序

默认情况下，Bucket聚合会统计Bucket内的文档数量，记为_count，并且按照_count降序排序。我们可以修改结果排序方式:

# 聚合
GET /hotel/_search
{
  "size": 0,
  "aggs": {
    "brandAgg": {
      "terms": {
        "field": "brand",
        "order": {
          "_count": "asc"
        }, 
        "size": 20
      }
    }
  }
}

限定聚合范围

默认情况下，Bucket聚合是对索引库的所有文档做聚合，我们可以限定要聚合的文档范围，只要添加query条件即可:

GET /hotel/_search
{
  "query": {
    "range": {
      "price": {
        "lte": 200 //只对价格低于200的做聚合
      }
    }
  }, 
  "size": 0,
  "aggs": {
    "brandAgg": {
      "terms": {
        "field": "brand",
        "size": 20
      }
    }
  }
}

Metrics聚合

DSL实现

例如，我们要求获取每个品牌的用户评分的min、max、avg等值.

我们可以利用stats聚合:

GET /hotel/_search
{
  "size": 0,
  "aggs": {
    "brandAgg": {
      "terms": {
        "field": "brand",
        "size": 20,
        "order": {
          "score_stats.avg": "desc"//根据平均评分降序排列
        }
      },
      "aggs": {//是brands聚合的子聚合，也就是分组后对每组分别计算
        "score_stats": {//聚合名称
          "stats": {//聚合类型，这里stats可以计算min、max、avg等
            "field": "score"//聚合字段，这里是score
          }
        }
      }
    }
  }
}

RestAPI实现聚合

我们以品牌聚合为例，演示下Java的RestClient使用，先看请求组装:

再看下聚合结果解析

@Test
void testAggregation() throws IOException {
    //1.准备Request
    SearchRequest request = new SearchRequest("hotel");
    //2.准备DSL
    //2.1设置size
    request.source().size(0);
    request.source().aggregation(AggregationBuilders
            .terms("brandAgg")
            .field("brand")
            .size(20)
    );
    //3.发出请求
    SearchResponse response = client.search(request, RequestOptions.DEFAULT);
    //4.解析结果
    Aggregations aggregations = response.getAggregations();
    Terms brandAgg = aggregations.get("brandAgg");
    List<? extends Terms.Bucket> buckets = brandAgg.getBuckets();
    for (Terms.Bucket bucket : buckets) {
        String key = bucket.getKeyAsString();
        long docCount = bucket.getDocCount();
        System.out.println(key + "：" + docCount);
    }
}

聚合案列

案例
在lUserService中定义方法，实现对品牌、城市、星级的聚合
需求:搜索页面的品牌、城市等信息不应该是在页面写死，而是通过聚合索引库中的酒店数据得来的:

在lUserService中定义一个方法，实现对品牌、城市、星级的聚合，方法声明如

/**
 * 查询城市、星级、品牌的聚合结果
 * @return 聚合结果，格式:["城市":["上海"，"北京""]，"品牌" :["如家"，"希尔顿"]}
 */
Map<String, List<String>> filters();

@Override
public Map<String, List<String>> filters() {
    try {
        //1.准备Request
        SearchRequest request = new SearchRequest("hotel");
        //2.准备DSL
        //2.1设置size
        request.source().size(0);
        //2.2聚合
        buildAggregation(request);
        //3.发出请求
        SearchResponse response = client.search(request, RequestOptions.DEFAULT);
        //4.解析结果
        Map<String, List<String>> results = new HashMap<>();
        Aggregations aggregations = response.getAggregations();
        //根据名称获取聚合结果
        List<String> brandList = getAggByName(aggregations, "brandAgg");
        List<String> cityList = getAggByName(aggregations, "cityAgg");
        List<String> starList = getAggByName(aggregations, "starAgg");
        //放入map
        results.put("品牌", brandList);
        results.put("城市", cityList);
        results.put("星级", starList);

        return results;
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
}

private static List<String> getAggByName(Aggregations aggregations, String name) {
    Terms brandAgg = aggregations.get(name);
    List<? extends Terms.Bucket> buckets = brandAgg.getBuckets();
    List<String> brandList = new ArrayList<>();
    for (Terms.Bucket bucket : buckets) {
        //获取key
        String key = bucket.getKeyAsString();
        brandList.add(key);
    }
    return brandList;
}

private void buildAggregation(SearchRequest request) {
    request.source().aggregation(AggregationBuilders
            .terms("brandAgg")
            .field("brand")
            .size(20)
    );
    request.source().aggregation(AggregationBuilders
            .terms("cityAgg")
            .field("city")
            .size(20)
    );
    request.source().aggregation(AggregationBuilders
            .terms("starAgg")
            .field("starName")
            .size(20)
    );
}

对接前端接口

前端页面会向服务端发起请求，查询品牌、城市、星级等字段的聚合结果:

3.1

3.2

可以看到请求参数与之前search时的RequestParam完全一致，这是在限定聚合时的文档范围。
例如:用户搜索“外滩”，价格在300~600，那聚合必须是在这个搜索条件基础上完成。

因此我们需要:

编写controller接口，接收该请求

@PostMapping("/filters")
public Map<String, List<String>> getFilters(@RequestBody RequestParams params) {
    return hotelService.filters(params);
}

修改IHotelServicet#getFilters()方法，添加RequestParam参数
```
Map<String, List<String>> filters(RequestParams params);
```

修改getFilters方法的业务，聚合时添加query条件

    @Override
    public Map<String, List<String>> filters(RequestParams params) {
        try {
            //1.准备Request
            SearchRequest request = new SearchRequest("hotel");
            //2.准备DSL
            //2.1query
            buildBasicQuery(params, request);
            //2.2设置size
            request.source().size(0);
            //2.3聚合
            buildAggregation(request);
            //3.发出请求
            SearchResponse response = client.search(request, RequestOptions.DEFAULT);
            //4.解析结果
            Map<String, List<String>> results = new HashMap<>();
            Aggregations aggregations = response.getAggregations();
            //根据名称获取聚合结果
            List<String> brandList = getAggByName(aggregations, "brandAgg");
            List<String> cityList = getAggByName(aggregations, "cityAgg");
            List<String> starList = getAggByName(aggregations, "starAgg");
            //放入map
            results.put("brand", brandList);
            results.put("city", cityList);
            results.put("starName", starList);

            return results;
        } catch (IOException e) {
            throw new RuntimeException(e);
        }
    }

    private static List<String> getAggByName(Aggregations aggregations, String name) {
        Terms brandAgg = aggregations.get(name);
        List<? extends Terms.Bucket> buckets = brandAgg.getBuckets();
        List<String> brandList = new ArrayList<>();
        for (Terms.Bucket bucket : buckets) {
            //获取key
            String key = bucket.getKeyAsString();
            brandList.add(key);
        }
        return brandList;
    }

    private void buildAggregation(SearchRequest request) {
        request.source().aggregation(AggregationBuilders
                .terms("brandAgg")
                .field("brand")
                .size(20)
        );
        request.source().aggregation(AggregationBuilders
                .terms("cityAgg")
                .field("city")
                .size(20)
        );
        request.source().aggregation(AggregationBuilders
                .terms("starAgg")
                .field("starName")
                .size(20)
        );
    }

    private void buildBasicQuery(RequestParams params, SearchRequest request) throws IOException {
        //1.构建BooleanQuery
        BoolQueryBuilder 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));
        }
        //city精确匹配
        String city = params.getCity();
        if (!(city == null || "".equals(city))) {
            boolQuery.filter(QueryBuilders.termQuery("city", city));
        }
        //brand精确匹配
        String brand = params.getBrand();
        if (!(brand == null || "".equals(brand))) {
            boolQuery.filter(QueryBuilders.termQuery("brand", brand));
        }
        //startName精确查询
        String startName = params.getStartName();
        if (!(startName == null || "".equals(startName))) {
            boolQuery.filter(QueryBuilders.termQuery("startName", startName));
        }
        //价格
        Integer minPrice = params.getMinPrice();
        Integer maxPrice = params.getMaxPrice();
        if (minPrice != null && maxPrice != null) {
            boolQuery.filter(QueryBuilders.rangeQuery("price").gte(minPrice).lte(maxPrice));
        }
        //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)
                                )
                        });
        request.source().query(functionScoreQuery);
    }
}

自动补全

拼音分词器

自动补全需求说明

当用户在搜索框输入字符时，我们应该提示出与该字符有关的搜索项，如图:

使用拼音分词

要实现根据字母做补全，就必须对文档按照拼音分词。在GitHub上恰好有elasticsearch的拼音分词插件。地址:https://github.com/medcl/elasticsearch-analysis-pinyin

安装方式与IK分词器一样，分三步:

解压
上传到虚拟机中，elasticsearch的plugin目录
重启elasticsearch
测试

# 测试拼音分词器
POST /_analyze
{
  "text": "如家酒店真不错",
  "analyzer": "pinyin"
}

{
  "tokens" : [
    {
      "token" : "ru",
      "start_offset" : 0,
      "end_offset" : 0,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "rjjdzbc",
      "start_offset" : 0,
      "end_offset" : 0,
      "type" : "word",
      "position" : 0
    },
    {
      "token" : "jia",
      "start_offset" : 0,
      "end_offset" : 0,
      "type" : "word",
      "position" : 1
    },
    {
      "token" : "jiu",
      "start_offset" : 0,
      "end_offset" : 0,
      "type" : "word",
      "position" : 2
    },
    {
      "token" : "dian",
      "start_offset" : 0,
      "end_offset" : 0,
      "type" : "word",
      "position" : 3
    },
    {
      "token" : "zhen",
      "start_offset" : 0,
      "end_offset" : 0,
      "type" : "word",
      "position" : 4
    },
    {
      "token" : "bu",
      "start_offset" : 0,
      "end_offset" : 0,
      "type" : "word",
      "position" : 5
    },
    {
      "token" : "cuo",
      "start_offset" : 0,
      "end_offset" : 0,
      "type" : "word",
      "position" : 6
    }
  ]
}

自定义分词器

elasticsearch中分词器(analyzer)的组成包含三部分:

character filters:在tokenizer之前对文本进行处理。例如删除字符、替换字符
tokenizer:将文本按照一定的规则切割成词条（term)。例如keyword，就是不分词;还有ik_smart
tokenizer filter:将tokenizer输出的词条做进一步处理。例如大小写转换、同义词处理、拼音处理等

我们可以在创建索引库时，通过settings来配置自定义的analyzer(分词器)∶

PUT /test{
	"settings": {
        "analysis" : {
            "analyzer" : { //自定义分词器
                "my_analyzer" : {//分词器名称
                    "tokenizer" : "ik_max_word",
                    "filter" : "pinyin"
                }
            }
        }
    }
}

此处的拼音分词器还需要进行进一步配置：

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
                }
            }
        }
    }
}

拼音分词器适合在创建倒排索引的时候使用，但不能在搜索的时候使用。

因此字段在创建倒排索引时应该用my_analyzer分词器;字段在搜索时应该使用ik_smart分词器;

PUT /test{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "ik_max_word",
          "filter": "py"
        }
      },
      "filter": {
        "py": {...}
      }
    }
  },
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "my_analyzer",
        "search_analyzer": "ik_smart"
      }
    }
  }
}

自动补全查询

completion suggester查询

elasticsearch提供了Completion Suggester查询来实现自动补全功能。这个查询会匹配以用户输入内容开头的词条并返回。为了提高补全查询的效率，对于文档中字段的类型有一些约束:

参与补全查询的字段必须是completion类型。

//创建索引库PUT test
{
  "mappings": {
    "properties": {
      "title": {
        "type": "completion"
      }
    }
  }
}

//示例数据
POST test/ _doc
{
  "title" : ["Sony","WH-1000XM3"]
}
POST test/_doc
{
  "title" : ["SK-II"，"PITERA"]
}
POST test/_doc
{
  "title" : ["Nintendo","switch"]
}

查询语法如下:

//自动补全查询
GET /test/_search
{
  "suggest": {
    "title_suggest": {
      "text": "s",//关键字
      "completion": {
        "field": "title",//补全查询的字段
        "skip_duplicates": true//跳过重复的
        "size": 10//获取前10条结果
      }
    }
  }
}

自动补全对字段的要求:

类型是completion类型
字段值是多词条的数组

实现酒店搜索框

案例
实现hotel索引库的自动补全、拼音搜索功能
实现思路如下:

修改hotel索引库结构，设置自定义拼音分词器
修改索引库的name、all字段，使用自定义分词器

索引库添加一个新字段suggestion，类型为completion类型，使用自定义的分词器

DELETE /hotel
# 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"
      }
    }
  }
}

给HotelDoc类添加suggestion字段，内容包含brand、business


@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 String 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();
        if (this.business.contains("/")) {
            // business有多个值，进行切割 like:江湾/五角场商业区
            String[] arr = this.business.split("/");
            // 添加元素
            this.suggestion = new ArrayList<>();
            this.suggestion.add(this.brand);
            Collections.addAll(this.suggestion, arr);
        } else if (this.business.contains("、")) {
            // business有多个值，进行切割 like:江湾、五角场商业区
            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);
        }
    }
}

重新导入数据到hotel库

运行ElasticsearchDocumentTest中的testBulkRequest

SDL测试

# 自动补全查询
POST /hotel/_search
{
  "suggest": {
    "title_suggest": {
      "text": "h", 
      "completion": {
        "field": "suggestion", 
        "skip_duplicates": true,
        "size": 10 
      }
    }
  }
}

RestAPI实现

发送请求

结果处理

@Test
void testSuggest() throws IOException {
    // 1.准备Request
    SearchRequest request = new SearchRequest("hotel");
    // 2.准备DSL
    request.source().suggest(new SuggestBuilder().addSuggestion(
            "suggestion",
            SuggestBuilders.completionSuggestion("suggestion")
                    .prefix("h")
                    .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根据名称获取补全结果
    CompletionSuggestion suggestion = suggest.getSuggestion("suggestion");
    // 4.2获取options并遍历
    for (Suggest.Suggestion.Entry.Option options : suggestion.getOptions()) {
        // 4.3获取一个option 中的text，也就是补全的词条
        String text = options.getText().string();
        System.out.println(text);
    }
}

运行结果：

案例
实现酒店搜索页面输入框的自动补全
查看前端页面，可以发现当我们在输入框键入时，前端会发起ajax请求:

实现案列

案例
实现酒店搜索页面输入框的自动补全

查看前端页面，可以发现当我们在输入框键入时，前端会发起ajax请求:

实现：

@GetMapping("/suggestion")
public List<String> getSuggestions(@RequestParam("key") String prefix){
    return hotelService.getSuggestions(prefix);
}

List<String> getSuggestions(String prefix);

@Override
public List<String> getSuggestions(String prefix) {
    try {
        // 1.准备Request
        SearchRequest request = new SearchRequest("hotel");
        // 2.准备DSL
        request.source().suggest(new SuggestBuilder().addSuggestion(
                "suggestion",
                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根据名称获取补全结果
        CompletionSuggestion suggestion = suggest.getSuggestion("suggestion");
        // 4.2获取options并遍历
        List<String> list = new ArrayList<>(suggestion.getOptions().size());
        for (Suggest.Suggestion.Entry.Option options : suggestion.getOptions()) {
            // 4.3获取一个option 中的text，也就是补全的词条
            String text = options.getText().string();
            list.add(text);
        }
        return list;
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
}

数据同步

数据同步问题分析

elasticsearch中的酒店数据来自于mysql数据库，因此mysql数据发生改变时，elasticsearch也必须跟着改变，这个就是elasticsearch与mysql之间的数据同步。

在微服务中，负责酒店管理（操作mysql ）的业务与负责酒店搜索（操作elasticsearch )的业务可能在两个不同的微服务上，数据同步该如何实现呢?

方案一:同步调用

优点:实现简单，粗暴
缺点:业务耦合度高

方案二:异步通知

优点:低耦合，实现难度一般
缺点:依赖mq的可靠性

方案三:监听binlog

优点:完全解除服务间耦合
缺点:开启binlog增加数据库负担、实现复杂度高

案例

案例利用MQ实现mysql与elasticsearch数据同步

利用课前资料提供的hotel-admin项目作为酒店管理的微服务。当酒店数据发生增、删、改时，要求对elasticsearch中数据也要完成相同操作。

步骤:

导入课前资料提供的hotel-admin项目，启动并测试酒店数据的CRUD

声明exchange、queue、RoutingKey

package cn.itcast.hotel.constants;

public class MqConstants {
    /**
     * 交换机
     */
    public static final String HOTEL_EXCHANGE = "hotel.topic";
    /**
     * 监听新增和修改的队列
     */
    public static final String HOTEL_INSERT_QUEUE = "hotel.insert.queue";
    /**
     * 监听删除的队列
     */
    public static final String HOTEL_DELETE_QUEUE = "hotel.delete.queue";
    /**
     * 新增或修改的RoutingKey
     */
    public static final String HOTEL_INSERT_KEY = "hotel.insert";
    /**
     * 删除的RoutingKey
     */
    public static final String HOTEL_DELETE_KEY = "hotel.delete";
}

package cn.itcast.hotel.config;

import cn.itcast.hotel.constants.MqConstants;
import org.springframework.amqp.core.Binding;
import org.springframework.amqp.core.BindingBuilder;
import org.springframework.amqp.core.Queue;
import org.springframework.amqp.core.TopicExchange;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class MqConfig {
    @Bean
    public TopicExchange topicExchange() {
        return new TopicExchange(MqConstants.HOTEL_EXCHANGE, true, false);
    }

    @Bean
    public Queue insertQueue() {
        return new Queue(MqConstants.HOTEL_INSERT_QUEUE, true);
    }

    @Bean
    public Queue deleteQueue() {
        return new Queue(MqConstants.HOTEL_DELETE_QUEUE, true);
    }

    @Bean
    public Binding insertQueueBinding() {
        return BindingBuilder.bind(insertQueue()).to(topicExchange()).with(MqConstants.HOTEL_INSERT_KEY);
    }

    @Bean
    public Binding deleteQueueBinding() {
        return BindingBuilder.bind(deleteQueue()).to(topicExchange()).with(MqConstants.HOTEL_DELETE_KEY);
    }

}

在hotel-admin中的增、删、改业务中完成消息发送

@PostMapping
public void saveHotel(@RequestBody Hotel hotel) {
    hotelService.save(hotel);
    //发送mq消息
    rabbitTemplate.convertAndSend(MqConstants.HOTEL_EXCHANGE, MqConstants.HOTEL_INSERT_KEY, hotel.getId());
}

@PutMapping()
public void updateById(@RequestBody Hotel hotel) {
    if (hotel.getId() == null) {
        throw new InvalidParameterException("id不能为空");
    }
    hotelService.updateById(hotel);
    //发送mq消息
    rabbitTemplate.convertAndSend(MqConstants.HOTEL_EXCHANGE, MqConstants.HOTEL_INSERT_KEY, hotel.getId());
}

@DeleteMapping("/{id}")
public void deleteById(@PathVariable("id") Long id) {
    hotelService.removeById(id);
    //发送mq消息
    rabbitTemplate.convertAndSend(MqConstants.HOTEL_EXCHANGE, MqConstants.HOTEL_DELETE_KEY, id);
}

在hotel-demo中完成消息监听，并更新elasticsearch中数据

package cn.itcast.hotel.mq;

import cn.itcast.hotel.constants.MqConstants;
import cn.itcast.hotel.service.IHotelService;
import org.springframework.amqp.rabbit.annotation.RabbitListener;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;

@Component
public class HotelListener {

    @Autowired
    private IHotelService hotelService;

    /**
     * 监听酒店新增或修改的业务
     * @param id 酒店id
     */
    @RabbitListener(queues = MqConstants.HOTEL_INSERT_QUEUE)
    private void listenHotelInsertOrUpdate(Long id) {
        hotelService.insertById(id);
    }

    /**
     * 监听酒店删除的业务
     * @param id 酒店id
     */
    @RabbitListener(queues = MqConstants.HOTEL_DELETE_QUEUE)
    private void listenHotelDelete(Long id) {
        hotelService.deleteById(id);
    }
}

void deleteById(Long id);

void insertById(Long id);

@Override
public void deleteById(Long id) {
    try {
        // 1.准备Request
        DeleteRequest request = new DeleteRequest("hotel", id.toString());
        // 2.发送请求
        client.delete(request, RequestOptions.DEFAULT);
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
}

@Override
public void insertById(Long id) {
    try {
        // 0.根据id查酒店数据
        Hotel hotel = getById(id);
        //转换为文档类型
        HotelDoc hotelDoc = new HotelDoc(hotel);

        //1.准备Request对象
        IndexRequest request = new IndexRequest("hotel").id(hotel.getId().toString());
        //2.准备Json文档
        request.source(JSON.toJSONString(hotelDoc), XContentType.JSON);
        //3.发送请求
        client.index(request, RequestOptions.DEFAULT);
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
}

启动并测试数据同步功能

elasticsearch集群

搭建ES集群

ES集群结构

单机的elasticsearch做数据存储，必然面临两个问题:海量数据存储问题、单点故障问题。

海量数据存储问题:将索引库从逻辑上拆分为N个分片(shard)，存储到多个节点
单点故障问题:将分片数据在不同节点备份（ replica )

搭建ES集群

我们计划利用3个docker容器模拟3个es的节点。

首先编写一个docker-compose文件，内容如下：

version: '2.2'
services:
  es01:
    image: elasticsearch:7.12.1
    container_name: es01
    environment:
      - node.name=es01
      - cluster.name=es-docker-cluster
      - discovery.seed_hosts=es02,es03
      - cluster.initial_master_nodes=es01,es02,es03
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
    volumes:
      - data01:/usr/share/elasticsearch/data
    ports:
      - 9200:9200
    networks:
      - elastic
  es02:
    image: elasticsearch:7.12.1
    container_name: es02
    environment:
      - node.name=es02
      - cluster.name=es-docker-cluster
      - discovery.seed_hosts=es01,es03
      - cluster.initial_master_nodes=es01,es02,es03
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
    volumes:
      - data02:/usr/share/elasticsearch/data
    ports:
      - 9201:9200
    networks:
      - elastic
  es03:
    image: elasticsearch:7.12.1
    container_name: es03
    environment:
      - node.name=es03
      - cluster.name=es-docker-cluster
      - discovery.seed_hosts=es01,es02
      - cluster.initial_master_nodes=es01,es02,es03
      - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
    volumes:
      - data03:/usr/share/elasticsearch/data
    networks:
      - elastic
    ports:
      - 9202:9200
volumes:
  data01:
    driver: local
  data02:
    driver: local
  data03:
    driver: local

networks:
  elastic:
    driver: bridge

es运行需要修改一些linux系统权限，修改/etc/sysctl.conf文件

vi /etc/sysctl.conf

添加下面的内容：

vm.max_map_count=262144

然后执行命令，让配置生效：

sysctl -p

通过docker-compose启动集群：

docker-compose up -d

集群状态监控

kibana可以监控es集群，不过新版本需要依赖es的x-pack 功能，配置比较复杂。

这里推荐使用cerebro来监控es集群状态，官方网址：https://github.com/lmenezes/cerebro

注意：在 Java 9 及以上版本中，模块化系统的安全性增强导致了 java.lang.ClassLoader.defineClass 方法默认不可访问。因此，在 Windows 系统中使用 .\cerebro.bat 启动 Cerebro 时，需要通过添加 --add-opens 参数来打开此方法的访问权限，才能避免 java.lang.reflect.InaccessibleObjectException 异常。所以此处采用jdk1.8运行

创建索引库

1）利用kibana的DevTools创建索引库

在DevTools中输入指令：

PUT /itcast
{
  "settings": {
    "number_of_shards": 3, // 分片数量
    "number_of_replicas": 1 // 副本数量
  },
  "mappings": {
    "properties": {
      // mapping映射定义 ...
    }
  }
}