解析ElasticSearch ElasticSearch字段類型解析

ElasticSearch

一、字段的類型Field type的詳解

(資料圖片僅供參考)

下面就是所有的字段類型

字符串類型text,keyword整數類型integer,long,short,byte浮點類型double,float,half_float,scaled_float邏輯類型boolean日期類型date范圍類型range二進制類型binary復合類型數組類型array對象類型object嵌套類型nested地理類型地理坐標類型geo_point地理地圖geo_shape特殊類型IP類型ip范圍類型completion令牌計數類型token_count附件類型attachment抽取類型percolator

1、字符串類型

1.1、text

會被分詞器解析, 生成倒排索引, 支持模糊、精確查詢, 不用于排序, 很少用于聚合,常用于全文檢索.

1.2、keyword

不進行分詞，直接索引, 支持模糊、精確查詢, 支持聚合。如果需要為結構化內容, 比如 id、email、hostnames、狀態碼、標簽等進行索引，則推薦使用 keyword類型以進行完全匹配. 比如我們需要查詢"已發貨"的訂單, 標記為"已發布"的文章等.

ES會對"數值(numeric)"類型的字段(比如, integer, long)進行優化以支持范圍(range)查詢. 但是, 不是所有的數值類型的數據都需要使用"數值"類型, 比如產品id, 會員id, ISDN(出版社編號), 這些很少會被進行范圍查詢, 通常都是精確匹配(term query).

keyword類型的查詢通常比numeric的要快, 如果不需要范圍查詢, 則建議使用keyword類型.

如果不確定使用哪一種, 可以通過multi-field同時設置keyword和numeric類型.

2、數值類型(numeric)

類型說明取值范圍

byte8位有符號整數-128 ~ 127

short16位有符號整數-32768 ~ 32767

integer32位有符號整數-2,147,483,648 ~ 2,147,483,647 即:-2^31 ~ 2^32 - 1

long64位有符號整數-2^63 ~ 2^63 - 1

float32位單精度IEEE 754浮點類型, 有限值, 24bits2^-149 ~ (2 - 2^-23) · 2^127

double64位雙精度IEEE 754浮點類型, 有限值, 53bits2^-1074 ~ (2 - 2^-52) · 2^1023

half_float16位半精度IEEE 754浮點類型, 有限值, 11bits2^-24 ~ 65504

scaled_float帶有縮放因子scaling_factor的浮點數, 可以當整型看待

2.1、整型

byte, short, integer, long

2.2、浮點型

float, half_float, scaled_float, double 對于上面展示的3種浮點類型（float, half_float, scaled_float）來說， -0.00和+0.00是不同的值，使用term查詢-0.00時不會匹配+0.00, 反之亦然。對于范圍查詢(range query)來說也是如此：如果上邊界是-0.00則無法匹配+0.00，如果下邊界是+0.00則不會匹配-0.00

數值類型使用的注意事項:

在滿足業務需求的情況下, 盡量選擇范圍小的類型, 這與mysql等關系型數據庫的設計要求是一致的. 字段占用的空間越小, 搜索和索引的效率越高(這個有點廢話, 數據量越小肯定越快了).

如果是浮點數, 則也要優先考慮使用scaled_float類型。對于scaled_float類型, 在索引時會乘以scaling_factor并四舍五入到最接近的一個long類型的值.

如果我們存儲商品價格只需要精確到分, 兩位小數點, 把縮放因子設置為100, 那么浮點價格99.99存儲的是9999.

如果我們要存儲的值是2.34, 而因子是10, 那么會存儲為23, 在查詢/聚合/排序時會表現為這個值是2.3. 如果設置更大的因子(比如100), 可以提高精度, 但是會增加空間需求.

3、Object類型

JSON文檔天生具有層級關系: 文檔可能包含內部對象, 而這個對象可能本身又包含對象, 就是可以包含嵌套的對象.

4、Date

JSON沒有日期(date)類型, 在ES中date類型可以表現為:

字符串格式的日期, 比如: “2015-01-01”, “2015/01/01 12:10:30”long類型的自 epoch (1970-1-1) 以來的毫秒數integer類型的自 epoch (1970-1-1) 以來的秒數(時間戳, timestamp)

在ES內部, 日期被轉換為UTC格式(如果指定了時區), 并存儲為long類型的自 epoch (1970-1-1) 以來的毫秒數.

查詢時 , date類型會在內部轉換為long類型的范圍查詢，聚合和存儲字段的結果根據與字段關聯的日期格式轉換回字符串。

5、ip類型

ip類型的字段只能存儲ipv4或ipv6地址.

二、屬性的詳解

1、fielddata字段數據

參考鏈接：https://www.cnblogs.com/rickie/p/11665168.html

默認情況下是false

作用：把不能聚合、排序、通過腳本訪問的字段變為可以聚合、排序、通過腳本訪問的字段

text類型是不支持doc_value。所以要想對text類型的字段擁有聚合等功能，只能設置屬性的fielddata為true即可

缺點：構建和管理全部在內存，也就是在JVM的內存中。這意味著它本質上是不可擴展的。

fielddata可能會消耗大量的堆空間，尤其是在加載高基數（high cardinality）text字段時。一旦fielddata已加載到堆中，它將在該段的生命周期內保留。此外，加載fielddata是一個昂貴的過程，可能會導致用戶遇到延遲命中。這就是默認情況下禁用fielddata的原因。

如果需要對 text 類型字段進行排序、聚合、或者從腳本中訪問字段值，則會出現如下異常：

Fielddata is disabled on text fields by default. Set fielddata=true on [your_field_name] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory.

但是，在啟動fielddata 設置之前，需要考慮為什么針對text 類型字段進行排序、聚合、或腳本呢？通常情況下，這是不太合理的。

text字段在索引時，例如New York，這樣的詞會被分詞，會被拆成new、york 2個詞項，這樣當搜索new 或 york時，可以被搜索到。在此字段上面來一個terms的聚合會返回一個new的bucket和一個york的bucket，但是你可能想要的是一個單一new york的bucket。

怎么解決這一問題呢？

你可以使用 text 字段來實現全文本查詢，同時使用一個未分詞的 keyword 字段，且啟用doc_values，來處理聚合操作。

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)]

(1) 使用my_field 字段用于查詢；

(2) 使用my_field.keyword 字段用于聚合、排序、或腳本； 注意：但是我試了試，不行，我也不知道為啥

PUT /user3{"mappings": {"properties": {"name":{"type": "text",        "fields": {"keyword":{"type":"keyword"          }        }      }    }  }}POST /user3/_doc{"name":"hello word 的數據結構 你真棒"}GET /user3/_search{"query": {"match": {"name": "hello"    }  },   "aggs":{"testq":{"terms":{"field":"name.keyword"      }    }  }}結果輸出：{"took" : 1,  "timed_out" : false,  "_shards" : {"total" : 1,    "successful" : 1,    "skipped" : 0,    "failed" : 0  },  "hits" : {"total" : {"value" : 1,      "relation" : "eq"    },    "max_score" : 0.2876821,    "hits" : [      {"_index" : "user3",        "_type" : "_doc",        "_id" : "N_X1n34BgddXgx4111ZC",        "_score" : 0.2876821,        "_source" : {"name" : "hello word 的數據結構 你真棒"        }      }    ]  },  "aggregations" : {"testq" : {"doc_count_error_upper_bound" : 0,      "sum_other_doc_count" : 0,      "buckets" : [        {"key" : "hello word 的數據結構 你真棒",          "doc_count" : 1        }      ]    }  }}

也可以使用 PUT mapping API 在現有text 字段上啟用 fielddata，如下所示：

[外鏈圖片轉存失敗,源站可能有防盜鏈機制,建議將圖片保存下來直接上傳(img-C0iFF2N6-1644556258949)(data:image/png;base64,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)]

2、doc_value

doc_values

默認情況下，大部分字段是索引的，這樣讓這些字段可被搜索。倒排索引（inverted index）允許查詢請求在詞項列表中查找搜索項（search term），并立即獲得包含該詞項的文檔列表。

倒排索引（inverted index）：

[外鏈圖片轉存失敗,源站可能有防盜鏈機制,建議將圖片保存下來直接上傳(img-D0WU7j87-1644556258952)(data:image/png;base64,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)]

如果我們想要獲得所有包含 brown 的文檔的詞的完整列表，我們會創建如下查詢：

GET /my_index/_search{"query" : {"match" : {"body" : "brown"  } }, "aggs" : {"popular_terms": {"terms" : {"field" : "body"   }  } }}

倒排索引是根據詞項來排序的，所以我們首先在詞項列表中找到 brown，然后掃描所有列，找到包含 brown 的文檔。我們可以快速看到 Doc_1 和 Doc_2 包含 brown 這個 token。

然后，對于聚合部分，我們需要找到 Doc_1 和 Doc_2 里所有唯一的詞項。用倒排索引做這件事情代價很高： 我們會迭代索引里的每個詞項并收集 Doc_1 和 Doc_2 列里面 token。這很慢而且難以擴展：隨著詞項和文檔的數量增加，執行時間也會增加。

Doc values 通過轉置兩者間的關系來解決這個問題。倒排索引將詞項映射到包含它們的文檔，doc values 將文檔映射到它們包含的詞項：

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)]

當數據被轉置之后，想要收集到 Doc_1 和 Doc_2 的唯一 token 會非常容易。獲得每個文檔行，獲取所有的詞項，然后求兩個集合的并集。

Doc values 可以使聚合更快、更高效并且內存友好。Doc values 的存在是因為倒排索引只對某些操作是高效的。

倒排索引的優勢：在于查找包含某個項的文檔，而對于從另外一個方向的相反操作并不高效，即：確定哪些項是否存在單個文檔里，聚合需要這種訪問模式。

在 Elasticsearch 中，Doc Values 就是一種列式存儲結構，默認情況下每個字段的 Doc Values 都是激活的，Doc Values 是在索引時創建的。當字段索引時，Elasticsearch 為了能夠快速檢索，會把字段的值加入倒排索引中，同時它也會存儲該字段的 Doc Values。

Elasticsearch 中的 Doc Values 常被應用到以下場景：

對一個字段進行排序對一個字段進行聚合某些過濾，比如地理位置過濾某些與字段相關的腳本計算

因為文檔值（doc values）被序列化到磁盤，我們可以依靠操作系統的幫助來快速訪問。當 working set 遠小于節點的可用內存，系統會自動將所有的文檔值保存在內存中，使得其讀寫十分高速；當其遠大于可用內存，操作系統會自動把 Doc Values 加載到系統的頁緩存中，從而避免了 jvm 堆內存溢出異常。

因此，搜索和聚合是相互緊密纏繞的。搜索使用倒排索引查找文檔，聚合操作收集和聚合 doc values 里的數據。

doc values 支持大部分字段類型，但是text 字段類型不支持（因為analyzed）。

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)]

(1) status_code 字段默認啟動 doc_values 屬性；

(2) session_id 顯式設置 doc_values = false，但是仍然可以被查詢；

如果確信某字段不需要排序或者聚合，或者從腳本中訪問字段值，那么我們可以設置 doc_values = false，這樣可以節省磁盤空間。

3、executionHint

1、global ordinals

（1）what’s this?

當我們使用doc values或者fielddata存儲時，在磁盤中存儲的值不是真正的字段值，而是一個字典值(ordinal)。當我們進行聚合查詢的時候，es會把這個字典值跟真正字段值的映射字典加載到內存中，并對結果集做映射，轉化為真正的值。這份映射關系是shard級別的，為這個shard里面是所有segment服務，這也是global的體現。

（2）detail

字典關系是lazy init的，只有第一次使用的時候才會加載到內存中。在es的內存表現中提現成fielddata，這也是全keyword的index為什么也會有fielddata使用的原因。只會加載命中的segment的字典不會加載全部。字典關系在shard被觸發refresh以后就會失效。下次使用的時候需要再重新構建。所以可以提高refresh_interval的值，減少fresh頻率提高字典的生存時間。

2、eager_global_ordinals

（1）what’s this？

當在global ordinals的時候，refresh以后下一次查詢字典就需要重新構建，在追求查詢的場景下很影響查詢性能。可以使用eager_global_ordinals，即在每次refresh以后即可更新字典，字典常駐內存，減少了查詢的時候構建字典的耗時。

（2）使用場景

因為這份字典需要常駐內存，并且每次refresh以后就會重構，所以增大了內存以及cpu的消耗。推薦在低寫高查、數據量不大的index中使用。

（3）使用

PUT my_index/_mapping{"properties": {"tags": {"type": "keyword",      "eager_global_ordinals": true    }  }}

3、execution_hint

（1）what’ this?

上面介紹了global ordinal的使用場景，是doc_values以及fileddata的默認數據架構。除了這種模式，還可以選擇map模式。即不再使用字典而是直接把值加載到內存中計算，減去了構建字典的耗時。當查詢的結果集很小的情況下，可以使用map的模式不去構建字典。使用map還是global_ordinals的取決于構建字典的開銷與加載原始字典的開銷。當結果集大到一定程序，map的內存開銷的代價可能抵消了構建字典的開銷。

（2）how to use?

GET /_search{"aggs" : {"tags" : {"terms" : {"field" : "tags",                 "execution_hint": "map"              }         }    }}

4、Shard Size

為了提高該聚合的精確度，可以通過shard_size參數設置協調節點向各個分片請求的詞根個數，然后在協調節點進行聚合，最后只返回size個詞根給到客戶端，shard_size >= size，如果shard_size設置小于size，ES會自動將其設置為size，默認情況下shard_size建議設置為(1.5 * size + 10)。

三、java實現對Es的curd

//match查詢age是20的條件 QueryBuilders.matchQuery("age",20);  //term查詢age是20的條件 QueryBuilders.termQuery("age",20);  //terms查詢age是20或者200，或者50的條件 QueryBuilders.termsQuery("age",20,200,50);  //query_string全文檢索“xiumu” QueryBuilders.queryStringQuery("xiumu");  //query_string檢索username是“xiumu” QueryBuilders.queryStringQuery("xiumu").field("username");  //multi_match查詢字段username或者description的值是xiumu QueryBuilders.multiMatchQuery("xiumu","username","description");  //range查詢字段age的范圍是在[18-200]之間 QueryBuilders.rangeQuery("age").gte(18).lte(200);  //exits查詢字段age有值 QueryBuilders.existsQuery("age");  //wildcard查詢字段description是以“男人”結尾的 QueryBuilders.wildcardQuery("description","*男人");  //bool查詢 BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery(); //bool查詢里添加must多個條件 boolQueryBuilder.must(QueryBuilders.termQuery("age",20)); boolQueryBuilder.must(QueryBuilders.existsQuery("age")); boolQueryBuilder.must(QueryBuilders.wildcardQuery("description","*男人"));

（1）統計某個字段的數量  ValueCountBuilder vcb=  AggregationBuilders.count("count_uid").field("uid");（2）去重統計某個字段的數量（有少量誤差） CardinalityBuilder cb= AggregationBuilders.cardinality("distinct_count_uid").field("uid");（3）聚合過濾FilterAggregationBuilder fab= AggregationBuilders.filter("uid_filter").filter(QueryBuilders.queryStringQuery("uid:001"));（4）按某個字段分組TermsBuilder tb=  AggregationBuilders.terms("group_name").field("name");（5）求和SumBuilder  sumBuilder=AggregationBuilders.sum("sum_price").field("price");（6）求平均AvgBuilder ab= AggregationBuilders.avg("avg_price").field("price");（7）求最大值MaxBuilder mb= AggregationBuilders.max("max_price").field("price"); （8）求最小值MinBuilder min=AggregationBuilders.min("min_price").field("price");（9）按日期間隔分組DateHistogramBuilder dhb= AggregationBuilders.dateHistogram("dh").field("date");（10）獲取聚合里面的結果TopHitsBuilder thb=  AggregationBuilders.topHits("top_result");（11）嵌套的聚合NestedBuilder nb= AggregationBuilders.nested("negsted_path").path("quests");（12）反轉嵌套AggregationBuilders.reverseNested("res_negsted").path("kps ");

package com.robin.elasticsearch;import com.alibaba.fastjson.JSON;import com.robin.elasticsearch.common.DataUtil;import com.robin.elasticsearch.entity.User;import com.robin.elasticsearch.entity.User2;import com.robin.elasticsearch.entity.User3;import com.robin.elasticsearch.reponsity.UserMapper;import org.elasticsearch.action.admin.indices.delete.DeleteIndexRequest;import org.elasticsearch.action.bulk.BulkRequest;import org.elasticsearch.action.bulk.BulkResponse;import org.elasticsearch.action.delete.DeleteRequest;import org.elasticsearch.action.delete.DeleteResponse;import org.elasticsearch.action.get.GetRequest;import org.elasticsearch.action.get.GetResponse;import org.elasticsearch.action.index.IndexRequest;import org.elasticsearch.action.index.IndexResponse;import org.elasticsearch.action.search.SearchRequest;import org.elasticsearch.action.search.SearchResponse;import org.elasticsearch.action.support.master.AcknowledgedResponse;import org.elasticsearch.action.update.UpdateRequest;import org.elasticsearch.action.update.UpdateResponse;import org.elasticsearch.client.RequestOptions;import org.elasticsearch.client.RestHighLevelClient;import org.elasticsearch.client.indices.CreateIndexRequest;import org.elasticsearch.client.indices.CreateIndexResponse;import org.elasticsearch.client.indices.GetIndexRequest;import org.elasticsearch.common.xcontent.XContentType;import org.elasticsearch.index.query.*;import org.elasticsearch.rest.RestStatus;import org.elasticsearch.search.SearchHit;import org.elasticsearch.search.aggregations.Aggregation;import org.elasticsearch.search.aggregations.AggregationBuilders;import org.elasticsearch.search.aggregations.Aggregations;import org.elasticsearch.search.aggregations.BucketOrder;import org.elasticsearch.search.aggregations.bucket.filter.Filters;import org.elasticsearch.search.aggregations.bucket.filter.FiltersAggregationBuilder;import org.elasticsearch.search.aggregations.bucket.filter.FiltersAggregator;import org.elasticsearch.search.aggregations.bucket.histogram.DateHistogramAggregationBuilder;import org.elasticsearch.search.aggregations.bucket.histogram.DateHistogramInterval;import org.elasticsearch.search.aggregations.bucket.histogram.Histogram;import org.elasticsearch.search.aggregations.bucket.histogram.HistogramAggregationBuilder;import org.elasticsearch.search.aggregations.bucket.range.DateRangeAggregationBuilder;import org.elasticsearch.search.aggregations.bucket.range.IpRangeAggregationBuilder;import org.elasticsearch.search.aggregations.bucket.range.Range;import org.elasticsearch.search.aggregations.bucket.range.RangeAggregationBuilder;import org.elasticsearch.search.aggregations.bucket.terms.Terms;import org.elasticsearch.search.aggregations.bucket.terms.TermsAggregationBuilder;import org.elasticsearch.search.aggregations.metrics.*;import org.elasticsearch.search.builder.SearchSourceBuilder;import org.elasticsearch.search.fetch.subphase.highlight.HighlightBuilder;import org.elasticsearch.search.sort.FieldSortBuilder;import org.elasticsearch.search.sort.SortBuilders;import org.elasticsearch.search.sort.SortOrder;import org.junit.jupiter.api.Test;import org.springframework.beans.factory.annotation.Autowired;import org.springframework.boot.test.context.SpringBootTest;import org.springframework.data.domain.PageRequest;import org.springframework.data.elasticsearch.core.AggregationsContainer;import org.springframework.data.elasticsearch.core.ElasticsearchRestTemplate;import org.springframework.data.elasticsearch.core.SearchHits;import org.springframework.data.elasticsearch.core.clients.elasticsearch7.ElasticsearchAggregations;import org.springframework.data.elasticsearch.core.mapping.IndexCoordinates;import org.springframework.data.elasticsearch.core.query.NativeSearchQuery;import org.springframework.data.elasticsearch.core.query.NativeSearchQueryBuilder;import java.io.IOException;import java.util.ArrayList;import java.util.Iterator;import java.util.List;import java.util.Map;@SpringBootTestclass ESTests {@Autowired    UserMapper userMapper;    @Autowired    RestHighLevelClient restHighLevelClient;    @Autowired    private ElasticsearchRestTemplate elasticsearchRestTemplate;    //創建索引    @Test    void createIndex() throws IOException {CreateIndexRequest request = new CreateIndexRequest("zkwz");        CreateIndexResponse exists = restHighLevelClient.indices().create(request, RequestOptions.DEFAULT);    }    //刪除索引    @Test    void isExistIndex() throws IOException {GetIndexRequest request = new GetIndexRequest("zkwz");        boolean exists = restHighLevelClient.indices().exists(request, RequestOptions.DEFAULT);        System.out.println(exists);    }    //刪除索引    @Test    void deleteIndex() throws IOException {DeleteIndexRequest request = new DeleteIndexRequest("user");        AcknowledgedResponse delete = restHighLevelClient.indices().delete(request, RequestOptions.DEFAULT);        System.out.println(delete.isAcknowledged());    }    //創建文檔    @Test    void createDoc() throws IOException {IndexRequest request = new IndexRequest("user");        User user = new User("111", "cwx", 23);        IndexRequest source = request.id("123").source(JSON.toJSONString(user), XContentType.JSON);        String id = source.id();        IndexResponse index = restHighLevelClient.index(request, RequestOptions.DEFAULT);        System.out.println(index);    }    //獲取文檔的信息    @Test    void getDocInfo() throws IOException {//創建獲取文檔的請求        GetRequest request = new GetRequest("user", "123");        boolean exists = restHighLevelClient.exists(request, RequestOptions.DEFAULT);        System.out.println(exists);        GetResponse documentFields = restHighLevelClient.get(request, RequestOptions.DEFAULT);        String source = documentFields.getSourceAsString();        System.out.println(source);    }    //根據id更新文檔    @Test    void updateDocById() throws IOException {UpdateRequest request = new UpdateRequest("user", "123");        request.doc(JSON.toJSONString(new User("111", "cwx", 33)), XContentType.JSON);        UpdateResponse update = restHighLevelClient.update(request, RequestOptions.DEFAULT);        RestStatus status = update.status();        System.out.println(status);    }    //根據id刪除文檔    @Test    void deleteDocById() throws IOException {DeleteRequest request = new DeleteRequest("user", "123");        DeleteResponse delete = restHighLevelClient.delete(request, RequestOptions.DEFAULT);        RestStatus status = delete.status();        System.out.println(status);    }    //批量創建文檔    @Test    void createDocBulk() throws IOException {BulkRequest request = new BulkRequest();        User user1 = new User("777", "thegoodmen", 30);        User user2 = new User("888", "thegoodwomen", 50);        User user3 = new User("999", "thewellboy", 23);        User user4 = new User("100", "thewellgirl", 43);        User user5 = new User("121", "thegirlmen", 12);        ArrayListlist = new ArrayList<>();        list.add(user1);        list.add(user2);        list.add(user3);        list.add(user4);        list.add(user5);        for (User user : list) {request.add(new IndexRequest("user").source(JSON.toJSONString(user), XContentType.JSON));        }        BulkResponse bulk = restHighLevelClient.bulk(request, RequestOptions.DEFAULT);        boolean status = bulk.hasFailures();        System.out.println(status);    }    //基本查詢    @Test    void selectByAge() throws IOException {SearchRequest request = new SearchRequest();        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();        //matchQuery是分詞之后再查詢的、termQuery是直接進行匹配(但是這個查詢不到數據，換這個可以matchPhraseQuery)        MatchQueryBuilder builder = QueryBuilders.matchQuery("name", "good");        searchSourceBuilder.query(builder);        request.source(searchSourceBuilder);        SearchResponse search = restHighLevelClient.search(request, RequestOptions.DEFAULT);        Iteratoriterator = search.getHits().iterator();        while (iterator.hasNext()) {System.out.println(iterator.next());        }    }    //單條件查詢    @Test    void select1() throws IOException {//左右模糊查詢，相當于MySQL的%good%        SearchHitssearchHits = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.queryStringQuery("good").field("name")),                User.class,                IndexCoordinates.of("user"));        //精準匹配，相當于name="good"        SearchHitssearchHits1 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.termQuery("name", "good")),                User.class,                IndexCoordinates.of("user"));        //普通匹配，相當于name like "%good%"        SearchHitssearchHits2 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.matchQuery("name", "good")),                User.class,                IndexCoordinates.of("user"));        //模糊匹配        SearchHitssearchHits3 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.fuzzyQuery("name", "good")),                User.class,                IndexCoordinates.of("user"));        //前綴匹配        SearchHitssearchHits6 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.prefixQuery("name", "men")),                User.class,                IndexCoordinates.of("user"));        //多字段的模糊匹配        SearchHitssearchHits7 = elasticsearchRestTemplate.search(                    new NativeSearchQuery(QueryBuilders.multiMatchQuery("aaa", "name")),                User.class,                IndexCoordinates.of("user"));        //前綴匹配        SearchHitssearchHits8 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.wildcardQuery("name", "m*n")),                User.class,                IndexCoordinates.of("user"));        //多內容多字段的模糊匹配        String[] strings = {"good", "men"};        SearchHitssearchHits9 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.moreLikeThisQuery(new String[]{"aaa"}, MoreLikeThisQueryBuilder.Item.EMPTY_ARRAY)),                User.class,                IndexCoordinates.of("user"));        for (org.springframework.data.elasticsearch.core.SearchHite1 : searchHits9.getSearchHits()) {System.out.println(e1.getContent());        }    }    //多條件查詢    @Test    void select2() throws IOException {//數值型、日期、Ip的范圍查詢，相當于 between 80 and 90 ，80包含，90不包含        SearchHitssearchHits1 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.boolQuery().must(QueryBuilders.rangeQuery("age").from(80).to(90).includeLower(true).includeUpper(false))),                User.class,                IndexCoordinates.of("user"));        //范圍查詢、大于50小于等于90        SearchHitssearchHits2 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.boolQuery().must(QueryBuilders.rangeQuery("age").gt(50).lte(90))),                User.class,                IndexCoordinates.of("user"));        for (org.springframework.data.elasticsearch.core.SearchHite1 : searchHits1.getSearchHits()) {System.out.println(e1.getContent());        }    }    //多條件查詢    @Test    void select3() {// 排序        FieldSortBuilder price = SortBuilders.fieldSort("age").order(SortOrder.DESC);        // 高亮顯示        HighlightBuilder highlightBuilder = new HighlightBuilder().field("name");        // 分頁        PageRequest pageRequest = PageRequest.of(0, 2);        // 查詢條件        BoolQueryBuilder queryBuilder1 = new BoolQueryBuilder().must(new MatchQueryBuilder("name", "陳萬祥"));        BoolQueryBuilder queryBuilder2 = new BoolQueryBuilder().must(new MatchPhraseQueryBuilder("loc", "武威市"));        BoolQueryBuilder queryBuilder3 = new BoolQueryBuilder().must(QueryBuilders.queryStringQuery("蘭州市").field("name"));        //求和        SumAggregationBuilder field = AggregationBuilders.sum("sum").field("age");        //求平均值        AvgAggregationBuilder avgAggregationBuilder = AggregationBuilders.avg("avg").field("age");        ValueCountAggregationBuilder countAggregationBuilder = AggregationBuilders.count("count").field("name");        // 組裝上述查詢條件        NativeSearchQuery searchQuery = new NativeSearchQueryBuilder().withQuery(queryBuilder1)                .withSorts(price)                .withHighlightBuilder(highlightBuilder)                .withPageable(pageRequest)                .build();        NativeSearchQuery searchQuery2 = new NativeSearchQueryBuilder()                .withAggregations(field)                .withAggregations(avgAggregationBuilder)                .withAggregations(countAggregationBuilder)                .build();        SearchHitsuser = elasticsearchRestTemplate.search(searchQuery, User2.class, IndexCoordinates.of("user"));        for (org.springframework.data.elasticsearch.core.SearchHithit : user.getSearchHits()) {System.out.println(hit.getContent());            MaphighlightFields = hit.getHighlightFields();            System.out.println(highlightFields.toString());        }        AggregationsContainer aggregations = user.getAggregations();        ElasticsearchAggregations aggregations1 = (ElasticsearchAggregations) aggregations;        Aggregations aggregations2;        if (aggregations1 != null) {aggregations2 = aggregations1.aggregations();            Sum sum = aggregations2.get("sum");            Avg avg = aggregations2.get("avg");            ValueCount count = aggregations2.get("count");            System.out.println(DataUtil.rmZeroSuper(sum.getValue()));            System.out.println(DataUtil.rmZeroSuper(avg.getValue()));            System.out.println(DataUtil.rmZeroSuper(count.getValue()));        }    }    //最基礎的聚合查詢    @Test    void select4() throws IOException {SearchRequest request = new SearchRequest("user");        SearchSourceBuilder sourceBuilder = new SearchSourceBuilder();        sourceBuilder.query(QueryBuilders.matchQuery("name", "yyy"));        TermsAggregationBuilder termsAggregationBuilder = AggregationBuilders.terms("ageAvg").field("age").size(10);        sourceBuilder.aggregation(termsAggregationBuilder);        AvgAggregationBuilder sumAggregationBuilder = AggregationBuilders.avg("avg").field("age");        sourceBuilder.aggregation(sumAggregationBuilder);        request.source(sourceBuilder);        SearchResponse searchResponse = restHighLevelClient.search(request, RequestOptions.DEFAULT);        Aggregations aggregations = searchResponse.getAggregations();        Avg ageAvg = aggregations.get("avg");        System.out.println(ageAvg.getValue());        Terms terms = aggregations.get("ageAvg");        for (Terms.Bucket e1 : terms.getBuckets()) {System.out.println("年齡是" + e1.getKeyAsString() + "=====>" + "人數是" + e1.getDocCount());        }    }    //先分組再得到不同分組的指標查詢    @Test    void select5() {//先分組，并按指標排序        TermsAggregationBuilder termsAggregationBuilder1 = AggregationBuilders.terms("team_count").field("team").order(BucketOrder.aggregation("max_age", true));        termsAggregationBuilder1.subAggregation(AggregationBuilders.max("max_age").field("age"));        termsAggregationBuilder1.subAggregation(AggregationBuilders.avg("avg_age").field("age"));        termsAggregationBuilder1.subAggregation(AggregationBuilders.min("min_age").field("age"));        termsAggregationBuilder1.subAggregation(AggregationBuilders.sum("sum_age").field("age"));        NativeSearchQuery searchQuery = new NativeSearchQueryBuilder()                .withAggregations(termsAggregationBuilder1)                .build();        SearchHitsuser = elasticsearchRestTemplate.search(searchQuery, User3.class, IndexCoordinates.of("user1"));        for (org.springframework.data.elasticsearch.core.SearchHithit : user.getSearchHits()) {System.out.println(hit.getContent());        }        AggregationsContainer aggregations = user.getAggregations();        ElasticsearchAggregations aggregations1 = (ElasticsearchAggregations) aggregations;        Aggregations aggregations2;        if (aggregations1 != null) {aggregations2 = aggregations1.aggregations();            Terms terms = aggregations2.get("team_count");            //獲取聚合值的兩種方法            for (Terms.Bucket bucket : terms.getBuckets()) {Max max_age = bucket.getAggregations().get("max_age");                Min min_age = bucket.getAggregations().get("min_age");                Sum sum_age = bucket.getAggregations().get("sum_age");                Avg avg_age = bucket.getAggregations().get("avg_age");                System.out.println(bucket.getKeyAsString() + "的最大值是：" + max_age.getValue() + "的最小值是：" + min_age.getValue() + "的和是：" + sum_age.getValue() + "的平均值是：" + avg_age.getValue());            }            for (Terms.Bucket bucket : terms.getBuckets()) {MapasMap = bucket.getAggregations().asMap();                double max_age = ((ParsedMax) asMap.get("max_age")).getValue();                double min_age = ((ParsedMin) asMap.get("min_age")).getValue();                double sum_age = ((ParsedSum) asMap.get("sum_age")).getValue();                double avg_age = ((ParsedAvg) asMap.get("avg_age")).getValue();                System.out.println(bucket.getKeyAsString() + "的最大值是：" + max_age + "的最小值是：" + min_age + "的和是：" + sum_age + "的平均值是：" + avg_age);            }        }    }    //區間分組查詢    @Test    void select6() {HistogramAggregationBuilder histogramAggregationBuilder = AggregationBuilders.histogram("his_age").field("age").interval(10);        NativeSearchQuery searchQuery = new NativeSearchQueryBuilder()                .withAggregations(histogramAggregationBuilder)                .build();        SearchHitsuser = elasticsearchRestTemplate.search(searchQuery, User3.class, IndexCoordinates.of("user1"));        for (org.springframework.data.elasticsearch.core.SearchHithit : user.getSearchHits()) {System.out.println(hit.getContent());        }        AggregationsContainer aggregations = user.getAggregations();        ElasticsearchAggregations aggregations1 = (ElasticsearchAggregations) aggregations;        Aggregations aggregations2;        if (aggregations1 != null) {aggregations2 = aggregations1.aggregations();            Histogram terms = aggregations2.get("his_age");            for (Histogram.Bucket bucket : terms.getBuckets()) {System.out.println(bucket.getKeyAsString() + "的數量是：" + bucket.getDocCount());            }        }    }    //時間區間分組查詢    @Test    void select7() {/*          calendarInterval表示時間間隔是類型、minDocCount設置最小值、format設置輸出時間的格式化、          missing當那個統計字段為null的時候，填充某個值，然后按照填充的值，統計計算         */        DateHistogramAggregationBuilder aggregationBuilder = AggregationBuilders.dateHistogram("his_birth")                .field("birth")                .calendarInterval(DateHistogramInterval.MONTH)                .minDocCount(0)                .format("yyyy-MM-dd")                .missing("2021-07-27");        NativeSearchQuery searchQuery = new NativeSearchQueryBuilder()                .withAggregations(aggregationBuilder)                .build();        SearchHitsuser = elasticsearchRestTemplate.search(searchQuery, User3.class, IndexCoordinates.of("user1"));        for (org.springframework.data.elasticsearch.core.SearchHithit : user.getSearchHits()) {System.out.println(hit.getContent());        }        ElasticsearchAggregations aggregations = (ElasticsearchAggregations) user.getAggregations();        if (aggregations != null) {Histogram terms = aggregations.aggregations().get("his_birth");            for (Histogram.Bucket bucket : terms.getBuckets()) {System.out.println(bucket.getKeyAsString() + "的數量是：" + bucket.getDocCount());            }        }    }    //指定范圍區間分組查詢    @Test    void select8() {/*          calendarInterval表示時間間隔是類型、minDocCount設置最小值、format設置輸出時間的格式化、          missing當那個統計字段為null的時候，填充某個值，然后按照填充的值，統計計算         */        RangeAggregationBuilder aggregationBuilder = AggregationBuilders.range("range_age")                .field("age")                .addUnboundedTo(10)                .addRange(11, 30)                .addRange(31, 50)                .addUnboundedFrom(100);        NativeSearchQuery searchQuery = new NativeSearchQueryBuilder()                .withAggregations(aggregationBuilder)                .build();        SearchHitsuser = elasticsearchRestTemplate.search(searchQuery, User3.class, IndexCoordinates.of("user1"));        for (org.springframework.data.elasticsearch.core.SearchHithit : user.getSearchHits()) {System.out.println(hit.getContent());        }        ElasticsearchAggregations aggregations = (ElasticsearchAggregations) user.getAggregations();        if (aggregations != null) {Range terms = aggregations.aggregations().get("range_age");            for (Range.Bucket bucket : terms.getBuckets()) {System.out.println(bucket.getKeyAsString() + "的數量是：" + bucket.getDocCount());                //起始范圍值                Object from = bucket.getFrom();                //終止范圍值                Object to = bucket.getTo();                System.out.println(from + "\t" + to);            }        }    }    //指定時間范圍區間分組查詢    @Test    void select9() {DateRangeAggregationBuilder aggregationBuilder = AggregationBuilders.dateRange("data_range_birth")                .field("birth")                .format("yyyy")                .addUnboundedTo("2000")                .addRange("2001", "2010")                .addRange("2011", "2020")                .addUnboundedFrom("2021");        NativeSearchQuery searchQuery = new NativeSearchQueryBuilder()                .withAggregations(aggregationBuilder)                .build();        SearchHitsuser = elasticsearchRestTemplate.search(searchQuery, User3.class, IndexCoordinates.of("user1"));        for (org.springframework.data.elasticsearch.core.SearchHithit : user.getSearchHits()) {System.out.println(hit.getContent());        }        ElasticsearchAggregations aggregations = (ElasticsearchAggregations) user.getAggregations();        if (aggregations != null) {Range terms = aggregations.aggregations().get("data_range_birth");            for (Range.Bucket bucket : terms.getBuckets()) {System.out.println(bucket.getKeyAsString() + "的數量是：" + bucket.getDocCount());            }        }    }    //指定ip區間分組查詢    @Test    void select10() {//TODO 這個地方的ip類型不能用來聚合，加上.keyword也不行，不知如何解決        IpRangeAggregationBuilder aggregationBuilder = AggregationBuilders.ipRange("ip_range")                .field("ip")                .addUnboundedTo("192.168.0.0")                .addRange("192.168.0.1", "192.168.0.122")                .addRange("192.168.5.0", "192.168.7.134")                .addUnboundedFrom("192.168.12.122");        NativeSearchQuery searchQuery = new NativeSearchQueryBuilder()                .withAggregations(aggregationBuilder)                .build();        SearchHitsuser = elasticsearchRestTemplate.search(searchQuery, User3.class, IndexCoordinates.of("user1"));        for (org.springframework.data.elasticsearch.core.SearchHithit : user.getSearchHits()) {System.out.println(hit.getContent());        }        ElasticsearchAggregations aggregations = (ElasticsearchAggregations) user.getAggregations();        if (aggregations != null) {Range terms = aggregations.aggregations().get("ip_range");            for (Range.Bucket bucket : terms.getBuckets()) {System.out.println(bucket.getKeyAsString() + "的數量是：" + bucket.getDocCount());            }        }    }    //指定過濾條件分組查詢    @Test    void select11() {FiltersAggregationBuilder aggregationBuilder = AggregationBuilders.filters("filter",                new FiltersAggregator.KeyedFilter("men", QueryBuilders.termQuery("sex", "true")),                new FiltersAggregator.KeyedFilter("women", QueryBuilders.termQuery("sex", "false")));        // 高亮顯示        HighlightBuilder highlightBuilder = new HighlightBuilder().field("sex").preTags("").postTags("");        NativeSearchQuery searchQuery = new NativeSearchQueryBuilder()                .withAggregations(aggregationBuilder)                .withHighlightBuilder(highlightBuilder)                .build();        SearchHitsuser = elasticsearchRestTemplate.search(searchQuery, User3.class, IndexCoordinates.of("user1"));        for (org.springframework.data.elasticsearch.core.SearchHithit : user.getSearchHits()) {MaphighlightFields = hit.getHighlightFields();            System.out.println(hit.getContent());            System.out.println(highlightFields.toString());        }        ElasticsearchAggregations aggregations = (ElasticsearchAggregations) user.getAggregations();        if (aggregations != null) {Filters terms = aggregations.aggregations().get("filter");            for (Filters.Bucket bucket : terms.getBuckets()) {System.out.println(bucket.getKeyAsString() + "的數量是：" + bucket.getDocCount());            }        }    }}

四、命令的方式實現對Es的curd

DELETE /user3PUT /user{"settings": {"number_of_replicas": 2,    "number_of_shards": 3  },   "mappings": {"properties": {"name":{"type": "text",        "analyzer": "ik_max_word"      },      "sex":{"type": "boolean"      },      "age":{"type": "long"      },      "loc":{"type": "text",        "analyzer": "ik_max_word"      },      "team":{"type": "keyword"      },      "birth":{"type": "date"      },      "id":{"type": "ip"      },      "country":{"type": "keyword"      },      "habby":{"type": "text"      },      "balance":{"type": "float"      }    }  }}POST /user/_doc/{"name":"陳萬祥",  "age":12,  "sex":true,  "loc":"甘肅省武威市",  "team":"red",  "birth":"1998-01-10",  "ip":"192.168.0.3",  "country":"china",  "habby":["basketball","sing","dance"],  "balance":8700.12}POST /user/_doc/{"name":"陳萬祥",  "age":122,  "sex":true,  "loc":"甘肅省武威市",  "team":"blue",  "birth":"1998-01-10",  "ip":"192.168.0.",  "country":"france",  "habby":["basketball","sing"],  "balance":82111}POST /user/_doc/{"name":"勞霞明",  "age":21,  "sex":true,  "loc":"廣西欽州市",  "team":"red",  "birth":"1997-01-10",  "ip":"192.168.3.11",  "country":"china",  "habby":["swming","dance"],  "balance":800.12}POST /user/_doc/{"name":"汪明",  "age":24,  "sex":false,  "loc":"天津市",  "team":"green",  "birth":"1998-02-10",  "ip":"192.165.0.3",  "country":"china",  "habby":["sing","read"],  "balance":9000}POST /user/_doc/{"name":"蔡徐坤",  "age":12,  "sex":true,  "loc":"甘肅省蘭州市",  "team":"green",  "birth":"1998-03-10",  "ip":"192.167.0.0",  "country":"france",  "habby":["game","eat"],  "balance":2300}POST /user/_doc/{"name":"愛英斯坦",  "age":45,  "sex":true,  "loc":"甘肅省張掖市",  "team":"blue",  "birth":"2003-06-10",  "ip":"192.168.0.32",  "country":"england",  "habby":["basketball","sing","dance"],  "balance":6900}POST /user/_doc/{"name":"歐拉",  "age":65,  "sex":false,  "loc":"甘肅省天水市",  "team":"blue",  "birth":"2021-08-10",  "ip":"192.168.6.3",  "country":"china",  "habby":["basketball","dance"],  "balance":4300}POST /user/_doc/{"name":"上帝",  "age":22,  "sex":false,  "loc":"西方世界",  "team":"black",  "ip":"192.168.3.3",  "country":"england",  "habby":["eat","game","sing"],  "balance":12000.12}GET /user/_doc/_searchGET /user/_doc/_search{"query":{"term":{"name":"陳萬祥"    }  },  "from":0,  "size":10,  "sort":[{"age":"desc"  }],  "highlight":{"fields":{"name":{}    }  }}#求出user索引的和GET /user/_doc/_search{"aggs":{"user1_sum":{"sum":{"field":"age"      }    }  }}#求出user索引的最大值GET /user/_doc/_search{"size":0,  "aggs":{"user1_max":{"max":{"field":"age"      }    }  }}#根據team字段聚合分組、并得到每個分組的求和、最大值、最小值、平均值GET /user/_doc/_search{"size":0,  "aggs":{"user1_term":{"terms":{"field":"team"      },      "aggs":{"term_sum":{"sum":{"field":"age"        }        },        "term_max":{"max":{"field":"age"        }},        "term_min":{"min":{"field":"age"        }},        "term_avg":{"avg":{"field":"age"        }}      }    }  }}#查看工資范圍的百分比GET /user/_search{"size": 0,   "aggs": {"balance_a": {"percentile_ranks": {"field": "balance",        "values": [2000,4000,6000,8000,10000]      }    }  }}#查看一些計數、最小值、最大值、平均值、求和分別是多少GET /user/_search{"size": 0,   "aggs": {"balance_a": {"stats": {"field": "balance"      }    }  }}#查看一些其他的指標結果GET /user/_search{"size": 0,   "aggs": {"balance_a": {"extended_stats": {"field": "age"      }    }  }}#查看對字段sex的聚合GET /user/_search{"size": 0,   "aggs": {"sex_a": {"terms": {"field": "sex"      }    }  }}#聚合并排序GET /user/_search{"size": 0,   "aggs": {"sex_a": {"terms": {"field": "team",        "order": {"max_a": "desc"        }      },        "aggs":{"max_a":{"max":{"field": "age"            }          }        }          }  }}#聚合支持腳本GET /user/_search{"size": 0,   "aggs": {"sex_a": {"terms": {"script": "doc["team"].value"      }    }  }}#篩選出包含指定詞語和不包含的GET /user/_search{"size": 0,   "aggs": {"term_a": {"terms": {"field": "age",        "include": "*.市"      }    }  }}#------------------------------------------------DELETE user3#多字段聚合的實現方式：#1、通過腳本合并字段#2、使用copy_to方法合并兩個字段PUT /user3{"mappings": {"properties": {"name":{"type": "text",        "fielddata": true              }    }  }}PUT /user3{"mappings": {"properties": {"name":{"type": "text",        "fields": {"keyword":{"type":"keyword"          }        }      }    }  }}POST /user3/_doc{"name":"hello word"}GET /user3/_search{"query": {"match": {"name": "hello"    }  },   "aggs":{"testq":{"terms":{"field":"name.keyword"      }    }  }}PUT test3POST test3/stu{"name":"cwx"}POST test3/tes{"name":"lxm"}

五、集群

六、優化

1、聚合為什么慢？

大多數時候對單個字段的聚合查詢還是非?？斓模?但是當需要同時聚合多個字段時，就可能會產生大量的分組，最終結果就是占用 es 大量內存，從而導致 OOM 的情況發生。 實踐應用發現，以下情況都會比較慢： 1）待聚合文檔數比較多（千萬、億、十億甚至更多）； 2）聚合條件比較復雜（多重條件聚合）； 3）全量聚合（翻頁的場景用）。

2、聚合優化方案探討

參考鏈接：https://blog.csdn.net/laoyang360/article/details/79253294

優化方案一：默認深度優先聚合改為廣度優先聚合。

"collect_mode" : "breadth_first"

depth_first 直接進行子聚合的計算 breadth_first 先計算出當前聚合的結果，針對這個結果在對子聚合進行計算。 優化方案二： 每一層terms aggregation內部加一個 “execution_hint”: “map”。

"execution_hint": "map"

Map方式的結論可簡要概括如下： 1）查詢結果直接放入內存中構建map，在查詢結果集小的場景下，速度極快； 2）但如果待結果集合很大的情況，map方式不一定也快。

3、運用 shard_size 來提高 term aggregation 的精度

參考鏈接：https://blog.csdn.net/UbuntuTouch/article/details/104141398

請求的大?。╯ize）越大，結果將越準確，但計算最終結果的成本也將越高（這兩者都是由于在分片級別上管理的優先級隊列更大，并且節點和客戶端之間的數據傳輸也更大）。

shard_size 參數可用于最大程度地減少請求的大小帶來的額外工作。 定義后，它將確定協調節點將從每個分片請求多少個術語。 一旦所有分片都做出響應，協調節點便會將它們縮減為最終結果，該最終結果將基于size參數-這樣一來，可以提高返回條款的準確性，并避免流回大量存儲桶的開銷給客戶。

注意：shard_size 不能小于 size（因為意義不大）。 啟用時，Elasticsearch 將覆蓋它并將其重置為等于大小。

缺省 shard_size為（size* 1.5 + 10）。

Terms aggregation 對于大量數據來說通常是不精確的

我們先來看一下如下的一個圖：

從上面的圖中，在 shard_size 為3的情況下，我們想對 geoip.country_name 這個字段來進行 terms aggregation：

從 shard 0 中提取文檔數靠前的前三個，它們分別是 USA，India 及 France。它們的文檔數分別是5，4及4。 從 shard 1 中提取文檔數靠前的前單個，它們分別是 USA，India 及 Japan。它們的文檔數分別是4，5及3。 那么總的文檔數是：

USA為：5 + 4 = 9India為：4 + 5 = 9France為：4 + 0 = 4Japan為： 3 + 0 = 3

根據上面的計算，返回的結果將會是 USA，India 及 France。細心的開發者可能馬上可以看出來，在上面的統計中國其實是不精確的，這是因為在 shard 0 中，我們可以看見 Japan 有3個文檔沒有被統計進去。這個統計是基于我們對 shard_size 為3的情況。假如我們把 shard_size 提供到4，情況馬上就會不同，而且更加接近我們的實際的統計數據的結果。在這種情況下，Japan 將會有 3 + 6 共6很個文檔，應該是排名第3。

我們可以修改我們的請求如下：

GET logs_server*/_search{  "size": 0,  "aggs": {    "top_10_urls": {      "terms": {        "field": "geoip.country_name.keyword",        "size": 10,        "shard_size": 100      }    }  }}

我們可以通過增加 shard_size 來提高數據的精確性，但是必須注意的是這樣的代價是計算的成本增加，特別是針對大量數據而言。