ElasticSearch 数据聚合、自动补全（自定义分词器）、数据同步

数据聚合

一、聚合的种类

官方文档 => 聚合 https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations.html
聚合：对文档信息的统计、分类、运算。类似mysql sum、avg、count

• 桶（Bucket）聚合：用来对文档做分组
  • TermAggregation：按照文档字段值分组（相当于mysql group by）
  • Date Histogram：按照日期阶梯分组，例如一周一组，一月一组
• 度量（metric）聚合：用来计算一些值，最大值、平均值、最小值等。
  • Avg：平均值
  • Max：最大值
  • Min：最小值
  • Stats：同时求max、min、avg、sum等
• 管道（pipeline）聚合：以其他聚合结果为基础继续做集合

二、DSL实现聚合

1、Bucket（桶）聚合

_count：默认是按照文档数量的降序排序

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

上面使用的bucket聚合，会扫描索引库所有的文档进行聚合。可以限制扫描的范围：利用query条件即可。

GET /hotel/_search
{
  "query": {
    "range": {
      "price": {
        "lt": 200 # 只对价位低于200的聚合
      }
    }
  }, 
  "size": 0,
  "aggs": {
    "brandAgg": {
      "terms": {
        "field": "brand",
        "size": 20,
        "order": {
          "_count": "asc"
        }
      }
    }
  }
}

2、Metrics（度量）聚合

聚合的嵌套，先对外层进行聚合，在对内存进行聚合
注意嵌套查询：写在外层查询括号内，而非并立。

GET /hotel/_search
{
  "size": 0,
  "aggs": {
    "brandAgg": {
      "terms": {
        "field": "brand",
        "size": 10,
        "order": {
          "scoreAgg.avg": "asc"
        }
      },
      "aggs": {
        "scoreAgg": {
          "stats": {
            "field": "score"
          }
        }
      }
    }
  }
}

三、RestAPI实现聚合

bucket trem聚合（group by），实现品牌、星级、城市聚合的方法

    public Map<String, List<String>> filters(RequestParam requestParam) {
        String[] aggNames = new String[]{"brand","city","starName"};
        Map<String, List<String>> resultMap = new HashMap<>();

        SearchRequest searchRequest = new SearchRequest("hotel");

        // 限定聚合范围
        BoolQueryBuilder boolQueryBuilder = getBoolQueryBuilder(requestParam);
        searchRequest.source().query(boolQueryBuilder);

        // 聚合字段
        searchRequest.source().size(0);
        searchRequest.source().aggregation(AggregationBuilders.terms(aggNames[0]).field("brand").size(100));
        searchRequest.source().aggregation(AggregationBuilders.terms(aggNames[1]).field("city").size(100));
        searchRequest.source().aggregation(AggregationBuilders.terms(aggNames[2]).field("starName").size(100));

        try {
            SearchResponse searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
            Aggregations aggregations = searchResponse.getAggregations();

            for (String aggName : aggNames) {
                Terms terms =  aggregations.get(aggName);
                List<String> list = new ArrayList<>();
                for (Terms.Bucket bucket : terms.getBuckets()) {
                    list.add(bucket.getKeyAsString());
                }
                resultMap.put(aggName,list);
            }
            return resultMap;
        } catch (IOException e) {
            e.printStackTrace();
            return null;
        }
    }

自动补全

一、拼音分词器

下载拼音分词器：https://github.com/medcl/elasticsearch-analysis-pinyin/releases/tag/v8.6.0
解压放在plugins目录下（docker挂载的目录），然后重启es

二、自定义分词器

拼音分词器的过滤规则，参照上面下载的链接。

创建一个自定义分词器（text index库），分词器名：my_analyzer

// 自定义拼音分词器 + mapping约束
PUT /test
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "tokenizer": "ik_max_word",
          "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": {
      "name": {
        "type": "text",
        "analyzer": "my_analyzer",
        "search_analyzer": "ik_smart"
      }
    }
  }
}

三、自动补全查询

completion suggester查询：

• 字段类型必须是completion
• 字段值是多词条的数组才有意义

// 自动补全的索引库
PUT test2
{
  "mappings": {
    "properties": {
      "title":{
        "type": "completion"
      }
    }
  }
}
// 示例数据
POST test2/_doc
{
  "title": ["Sony", "WH-1000XM3"]
}
POST test2/_doc
{
  "title": ["SK-II", "PITERA"]
}
POST test2/_doc
{
  "title": ["Nintendo", "switch"]
}


// 自动补全查询
POST /test2/_search
{
  "suggest": {
    "title_suggest": {
      "text": "s", // 关键字
      "completion": {
        "field": "title", // 补全字段
        "skip_duplicates": true, // 跳过重复的
        "size": 10 // 获取前10条结果
      }
    }
  }
}

四、实现搜索款自动补全（例酒店信息）

在这里插入代码片

构建索引库

// 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"
      }
    }
  }
}

查询测试

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

GET /hotel/_search
{
  "suggest": {
    "YOUR_SUGGESTION": {
      "text": "s",
      "completion": {
        "field": "suggestion",
        "skip_duplicates": true // 跳过重复的
      }
    }
  }
}

    public List<String> getSuggestion(String prefix) {
        SearchRequest request = new SearchRequest("hotel");
        ArrayList<String> list = new ArrayList<>();
        try {

            request.source().suggest(new SuggestBuilder().addSuggestion(
                    "OneSuggestion",
                    SuggestBuilders
                            .completionSuggestion("suggestion")
                            .prefix(prefix)
                            .skipDuplicates(true)
                            .size(10)
            ));

            SearchResponse response = restHighLevelClient.search(request, RequestOptions.DEFAULT);
            Suggest suggest = response.getSuggest();
            CompletionSuggestion oneSuggestion = suggest.getSuggestion("OneSuggestion");
            List<CompletionSuggestion.Entry.Option> options = oneSuggestion.getOptions();
            for (CompletionSuggestion.Entry.Option option : options) {
                String text = option.getText().toString();
                list.add(text);
            }

        } catch (IOException e) {
            e.printStackTrace();
        }

        return list;
    }

数据同步

双写一致性

同步调用数据耦合，业务耦合

异步通知：增加实现难度

监听binlog（记录增删改操作）：增加mysql压力，中间价搭建