【ElasticSearch】大数据量情况下的前缀、中缀实时搜索方案

简述

业务开发中经常会遇到这样一种情况，用户在搜索框输入时要实时展示搜索相关的结果。要实现这个场景常用的方案有Completion Suggester、search_as_you_type。那么这两种方式有什么区别呢？一起来了解下。

环境说明：

数据量：9000w+

es版本：7.10.1

脚本执行工具：kibana

Completion Suggester和search_as_you_type的区别

1.Completion Suggester是基于前缀匹配、且数据结构存储在内存中，超级快，缺点是耗内存

2.search_as_you_type可以是前缀、中缀匹配，可以很快，但是要选好查询方式

3.Api调用方式不同，Completion Suggester是通过Suggest语句查询，search_as_you_type和常规查询方式一致

举个栗子

如何实现前缀匹配需求

使用Completion Suggester，示例如下：

1. 创建索引

PUT /es_demo
{
  "mappings": {
    "properties": {
      "title_comp": {
        "type": "completion",
        "analyzer": "standard"
      }
    }
  }
}

1. 初始化数据

POST _bulk
{"index":{"_index":"es_demo","_id":"1"}}
{"title_comp": "愤怒的小鸟"}
{"index":{"_index":"es_demo","_id":"2"}}
{"title_comp": "最后一只渡渡鸟"}
{"index":{"_index":"es_demo","_id":"3"}}
{"title_comp": "今天不加班啊"}
{"index":{"_index":"es_demo","_id":"4"}}
{"title_comp": "愤怒的青年"}
{"index":{"_index":"es_demo","_id":"5"}}
{"title_comp": "最后一只996程序猿"}
{"index":{"_index":"es_demo","_id":"6"}}
{"title_comp": "今日无事，勾栏听曲"}

1. 查询DSL
   
   通过前缀查询，查找以“愤怒”开头的字符串

GET /es_demo/_search
{
  "suggest": {
    "title_suggest": {
      "prefix": "愤怒",
      "completion": {
        "field": "title_comp"
      }
    }
  }
}

1. 查询代码demo

@SpringBootTest
public class SuggestTest {

    @Autowired
    private RestHighLevelClient restHighLevelClient;

    @Test
    public void testComp() {
        List<Map<String, Object>> list = suggestComplete("愤怒");
        list.forEach(m -> System.out.println("[" + m.get("title_comp") + "]"));
    }

    public List<Map<String, Object>> suggestComplete(String keyword) {
        CompletionSuggestionBuilder completionSuggestionBuilder = SuggestBuilders.completionSuggestion("title_comp");
        completionSuggestionBuilder.size(5)
                //跳过重复的
                .skipDuplicates(true);

        SuggestBuilder suggestBuilder = new SuggestBuilder();
        suggestBuilder.addSuggestion("suggest_title", completionSuggestionBuilder)
                .setGlobalText(keyword);

        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.suggest(suggestBuilder);

        SearchRequest searchRequest = new SearchRequest("es_demo").source(searchSourceBuilder);

        try {
            SearchResponse response = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
            CompletionSuggestion completionSuggestion = response.getSuggest().getSuggestion("suggest_title");

            List<Map<String, Object>> suggestList = new LinkedList<>();
            for (CompletionSuggestion.Entry.Option option : completionSuggestion.getOptions()) {
                Map<String, Object> map = new HashMap<>();
                map.put("title_comp", option.getHit().getSourceAsMap().get("title_comp"));
                suggestList.add(map);
            }

            return suggestList;
        } catch (IOException e) {
            throw new RuntimeException("ES查询出错");
        }
    }
}

查询结果：

[愤怒的小鸟]
[愤怒的青年]

如何实现中缀匹配需求

使用search_as_you_type，此处提供了hanlp_index和standard两种分词器的字段示例。示例如下：

1. 创建索引

PUT /es_search_as_you_type
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "fields": {
          "han": {
            "type": "search_as_you_type",
            "analyzer": "hanlp_index"
          },
          "stan": {
            "type": "search_as_you_type",
            "analyzer": "standard"
          }
        }
      }
    }
  }
}

1. 初始化数据

POST _bulk
{"index":{"_index":"es_search_as_you_type","_id":"1"}}
{"title": "愤怒的小鸟"}
{"index":{"_index":"es_search_as_you_type","_id":"2"}}
{"title": "最后一只渡渡鸟"}
{"index":{"_index":"es_search_as_you_type","_id":"3"}}
{"title": "今天不加班啊"}
{"index":{"_index":"es_search_as_you_type","_id":"4"}}
{"title": "愤怒的青年"}
{"index":{"_index":"es_search_as_you_type","_id":"5"}}
{"title": "最后一只996程序猿"}
{"index":{"_index":"es_search_as_you_type","_id":"6"}}
{"title": "今日无事，勾栏听曲"}

1. 查询DSL

GET /es_search_as_you_type/_search
{
  "query": {
    "match": {
      "title.stan": {
        "query": "的小",
        "operator": "and"
      }
    }
  }
}

1. 查询代码demo

@SpringBootTest
public class SuggestTest {

    @Autowired
    private RestHighLevelClient restHighLevelClient;

    @Test
    public void testSearchAsYouType() {
        List<Map<String, Object>> list = suggestSearchAsYouType("的小");
        list.forEach(m -> System.out.println("[" + m.get("title") + "]"));
    }

    public List<Map<String, Object>> suggestSearchAsYouType(String keyword) {
        //这里使用了search_as_you_type的2gram字段，可以根据自己需求调整配置
        MatchQueryBuilder matchQueryBuilder = matchQuery("title.stan._2gram", keyword).operator(Operator.AND);

        //需要返回的字段
        String[] includeFields = new String[]{"title"};
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder()
                .query(matchQueryBuilder).size(5)
                .fetchSource(includeFields, null)
                .trackTotalHits(false)
                .trackScores(true)
                .sort(SortBuilders.scoreSort());

        SearchRequest searchRequest = new SearchRequest("es_search_as_you_type").source(searchSourceBuilder);

        try {
            SearchResponse response = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
            org.elasticsearch.search.SearchHits hits = response.getHits();

            List<Map<String, Object>> suggestList = new LinkedList<>();
            for (org.elasticsearch.search.SearchHit hit : hits) {
                Map<String, Object> map = new HashMap<>();
                map.put("title", hit.getSourceAsMap().get("title").toString());
                suggestList.add(map);
            }
            return suggestList;
        } catch (IOException e) {
            throw new RuntimeException("ES查询出错");
        }
    }
}

查询结果：

[愤怒的小鸟]

分词器说明

查看分词结果的方式

第一种

指定分词器

GET _analyze
{
  "analyzer": "standard",
  "text": [
    "愤怒的小鸟"
  ]
}

第二种

指定使用某个字段的分词器

POST es_search_as_you_type/_analyze
{
  "field": "title.stan",
  "text": [
    "愤怒的青年"
  ]
}

hanlp_index和standard分词器的区别

standard分词器

• 默认会过滤掉符号
• 中文以单个字为最小单位，英文则会以空格符或其他符号或中文分隔作为一个单词

例：

GET _analyze
{
  "analyzer": "standard",
  "text": [
    "愤怒的小鸟"
  ]
}

分词结果：

{
  "tokens" : [
    {
      "token" : "愤",
      "start_offset" : 0,
      "end_offset" : 1,
      "type" : "<IDEOGRAPHIC>",
      "position" : 0
    },
    {
      "token" : "怒",
      "start_offset" : 1,
      "end_offset" : 2,
      "type" : "<IDEOGRAPHIC>",
      "position" : 1
    },
    {
      "token" : "的",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "<IDEOGRAPHIC>",
      "position" : 2
    },
    {
      "token" : "小",
      "start_offset" : 3,
      "end_offset" : 4,
      "type" : "<IDEOGRAPHIC>",
      "position" : 3
    },
    {
      "token" : "鸟",
      "start_offset" : 4,
      "end_offset" : 5,
      "type" : "<IDEOGRAPHIC>",
      "position" : 4
    }
  ]
}

hanlp_index分词器

• 默认不会过滤符号
• 通过语义等对字符串进行分词，会分出词语

例：

GET _analyze
{
  "analyzer": "hanlp_index",
  "text": [
    "愤怒的小鸟"
  ]
}

分词结果：

{
  "tokens" : [
    {
      "token" : "愤怒",
      "start_offset" : 0,
      "end_offset" : 2,
      "type" : "a",
      "position" : 0
    },
    {
      "token" : "的",
      "start_offset" : 2,
      "end_offset" : 3,
      "type" : "ude1",
      "position" : 1
    },
    {
      "token" : "小鸟",
      "start_offset" : 3,
      "end_offset" : 5,
      "type" : "n",
      "position" : 2
    }
  ]
}

生产实践中的查询情况

基本都是几百毫秒就解决。ps：如果一条数据字段很多，最好只返回几个需要的字段即可，否则数据传输就要占用较多时间。

总结

当然，无论是Completion Suggester还是search_as_you_type的查询配置方式都还有很多，例如Completion Suggester的Context Suggester，search_as_you_type的2gram、3gram，还有查询类型match_bool_prefix、match_phrase、match_phrase_prefix等等。各种组合起来都会产生不同的效果，笔者这里只是列举出一种还算可以的方式。关于其他的查询类型和配置如何使用以及分别是怎么工作的，下次有空再聊聊。

官方文档链接

https://www.elastic.co/guide/en/elasticsearch/reference/7.10/search-as-you-type.html