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(shù)類型integer,long,short,byte浮點(diǎn)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(jìn)制類型binary復(fù)合類型數(shù)組類型array對(duì)象類型object嵌套類型nested地理類型地理坐標(biāo)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(jì)數(shù)類型token_count附件類型attachment抽取類型percolator

1、字符串類型

1.1、text

會(huì)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(jìn)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(jié)構(gòu)化內(nèi)容, 比如 id、email、hostnames、狀態(tài)碼、標(biāo)簽等進(jìn)行索引，則推薦使用 keyword類型以進(jìn)行完全匹配. 比如我們需要查詢"已發(fā)貨"的訂單, 標(biāo)記為"已發(fā)布"的文章等.

ES會(huì)對(duì)"數(shù)值(numeric)"類型的字段(比如, integer, long)進(jìn)行優(yōu)化以支持范圍(range)查詢. 但是, 不是所有的數(shù)值類型的數(shù)據(jù)都需要使用"數(shù)值"類型, 比如產(chǎn)品id, 會(huì)員id, ISDN(出版社編號(hào)), 這些很少會(huì)被進(jìn)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(guò)multi-field同時(shí)設(shè)置keyword和numeric類型.

2、數(shù)值類型(numeric)

類型說(shuō)明取值范圍

byte8位有符號(hào)整數(shù)-128 ~ 127

short16位有符號(hào)整數(shù)-32768 ~ 32767

integer32位有符號(hào)整數(shù)-2,147,483,648 ~ 2,147,483,647 即:-2^31 ~ 2^32 - 1

long64位有符號(hào)整數(shù)-2^63 ~ 2^63 - 1

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

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

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

scaled_float帶有縮放因子scaling_factor的浮點(diǎn)數(shù), 可以當(dāng)整型看待

2.1、整型

byte, short, integer, long

2.2、浮點(diǎn)型

float, half_float, scaled_float, double 對(duì)于上面展示的3種浮點(diǎn)類型（float, half_float, scaled_float）來(lái)說(shuō)， -0.00和+0.00是不同的值，使用term查詢-0.00時(shí)不會(huì)匹配+0.00, 反之亦然。對(duì)于范圍查詢(range query)來(lái)說(shuō)也是如此：如果上邊界是-0.00則無(wú)法匹配+0.00，如果下邊界是+0.00則不會(huì)匹配-0.00

數(shù)值類型使用的注意事項(xiàng):

在滿足業(yè)務(wù)需求的情況下, 盡量選擇范圍小的類型, 這與mysql等關(guān)系型數(shù)據(jù)庫(kù)的設(shè)計(jì)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(gè)有點(diǎn)廢話, 數(shù)據(jù)量越小肯定越快了).

如果是浮點(diǎn)數(shù), 則也要優(yōu)先考慮使用scaled_float類型。對(duì)于scaled_float類型, 在索引時(shí)會(huì)乘以scaling_factor并四舍五入到最接近的一個(gè)long類型的值.

如果我們存儲(chǔ)商品價(jià)格只需要精確到分, 兩位小數(shù)點(diǎn), 把縮放因子設(shè)置為100, 那么浮點(diǎn)價(jià)格99.99存儲(chǔ)的是9999.

如果我們要存儲(chǔ)的值是2.34, 而因子是10, 那么會(huì)存儲(chǔ)為23, 在查詢/聚合/排序時(shí)會(huì)表現(xiàn)為這個(gè)值是2.3. 如果設(shè)置更大的因子(比如100), 可以提高精度, 但是會(huì)增加空間需求.

3、Object類型

JSON文檔天生具有層級(jí)關(guān)系: 文檔可能包含內(nèi)部對(duì)象, 而這個(gè)對(duì)象可能本身又包含對(duì)象, 就是可以包含嵌套的對(duì)象.

4、Date

JSON沒(méi)有日期(date)類型, 在ES中date類型可以表現(xiàn)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(lái)的毫秒數(shù)integer類型的自 epoch (1970-1-1) 以來(lái)的秒數(shù)(時(shí)間戳, timestamp)

在ES內(nèi)部, 日期被轉(zhuǎn)換為UTC格式(如果指定了時(shí)區(qū)), 并存儲(chǔ)為long類型的自 epoch (1970-1-1) 以來(lái)的毫秒數(shù).

查詢時(shí) , date類型會(huì)在內(nèi)部轉(zhuǎn)換為long類型的范圍查詢，聚合和存儲(chǔ)字段的結(jié)果根據(jù)與字段關(guān)聯(lián)的日期格式轉(zhuǎn)換回字符串。

5、ip類型

ip類型的字段只能存儲(chǔ)ipv4或ipv6地址.

二、屬性的詳解

1、fielddata字段數(shù)據(jù)

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

默認(rèn)情況下是false

作用：把不能聚合、排序、通過(guò)腳本訪問(wèn)的字段變?yōu)榭梢跃酆稀⑴判颉⑼ㄟ^(guò)腳本訪問(wèn)的字段

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

缺點(diǎn)：構(gòu)建和管理全部在內(nèi)存，也就是在JVM的內(nèi)存中。這意味著它本質(zhì)上是不可擴(kuò)展的。

fielddata可能會(huì)消耗大量的堆空間，尤其是在加載高基數(shù)（high cardinality）text字段時(shí)。一旦fielddata已加載到堆中，它將在該段的生命周期內(nèi)保留。此外，加載fielddata是一個(gè)昂貴的過(guò)程，可能會(huì)導(dǎo)致用戶遇到延遲命中。這就是默認(rèn)情況下禁用fielddata的原因。

如果需要對(duì) text 類型字段進(jìn)行排序、聚合、或者從腳本中訪問(wèn)字段值，則會(huì)出現(xiàn)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(dòng)fielddata 設(shè)置之前，需要考慮為什么針對(duì)text 類型字段進(jìn)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(shí)，例如New York，這樣的詞會(huì)被分詞，會(huì)被拆成new、york 2個(gè)詞項(xiàng)，這樣當(dāng)搜索new 或 york時(shí)，可以被搜索到。在此字段上面來(lái)一個(gè)terms的聚合會(huì)返回一個(gè)new的bucket和一個(gè)york的bucket，但是你可能想要的是一個(gè)單一new york的bucket。

怎么解決這一問(wèn)題呢？

你可以使用 text 字段來(lái)實(shí)現(xiàn)全文本查詢，同時(shí)使用一個(gè)未分詞的 keyword 字段，且啟用doc_values，來(lái)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(shù)據(jù)結(jié)構(gòu) 你真棒"}GET /user3/_search{"query": {"match": {"name": "hello"    }  },   "aggs":{"testq":{"terms":{"field":"name.keyword"      }    }  }}結(jié)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(shù)據(jù)結(jié)構(gòu) 你真棒"        }      }    ]  },  "aggregations" : {"testq" : {"doc_count_error_upper_bound" : 0,      "sum_other_doc_count" : 0,      "buckets" : [        {"key" : "hello word 的數(shù)據(jù)結(jié)構(gòu) 你真棒",          "doc_count" : 1        }      ]    }  }}

也可以使用 PUT mapping API 在現(xiàn)有text 字段上啟用 fielddata，如下所示：

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)]

2、doc_value

doc_values

默認(rèn)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(qǐng)求在詞項(xiàng)列表中查找搜索項(xiàng)（search term），并立即獲得包含該詞項(xiàng)的文檔列表。

倒排索引（inverted index）：

[外鏈圖片轉(zhuǎn)存失敗,源站可能有防盜鏈機(jī)制,建議將圖片保存下來(lái)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(huì)創(chuàng)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(jù)詞項(xiàng)來(lái)排序的，所以我們首先在詞項(xiàng)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(gè) token。

然后，對(duì)于聚合部分，我們需要找到 Doc_1 和 Doc_2 里所有唯一的詞項(xiàng)。用倒排索引做這件事情代價(jià)很高： 我們會(huì)迭代索引里的每個(gè)詞項(xiàng)并收集 Doc_1 和 Doc_2 列里面 token。這很慢而且難以擴(kuò)展：隨著詞項(xiàng)和文檔的數(shù)量增加，執(zhí)行時(shí)間也會(huì)增加。

Doc values 通過(guò)轉(zhuǎn)置兩者間的關(guān)系來(lái)解決這個(gè)問(wèn)題。倒排索引將詞項(xiàng)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(xiàng)：

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)]

當(dāng)數(shù)據(jù)被轉(zhuǎn)置之后，想要收集到 Doc_1 和 Doc_2 的唯一 token 會(huì)非常容易。獲得每個(gè)文檔行，獲取所有的詞項(xiàng)，然后求兩個(gè)集合的并集。

Doc values 可以使聚合更快、更高效并且內(nèi)存友好。Doc values 的存在是因?yàn)榈古潘饕粚?duì)某些操作是高效的。

倒排索引的優(yōu)勢(shì)：在于查找包含某個(gè)項(xiàng)的文檔，而對(duì)于從另外一個(gè)方向的相反操作并不高效，即：確定哪些項(xiàng)是否存在單個(gè)文檔里，聚合需要這種訪問(wèn)模式。

在 Elasticsearch 中，Doc Values 就是一種列式存儲(chǔ)結(jié)構(gòu)，默認(rèn)情況下每個(gè)字段的 Doc Values 都是激活的，Doc Values 是在索引時(shí)創(chuàng)建的。當(dāng)字段索引時(shí)，Elasticsearch 為了能夠快速檢索，會(huì)把字段的值加入倒排索引中，同時(shí)它也會(huì)存儲(chǔ)該字段的 Doc Values。

Elasticsearch 中的 Doc Values 常被應(yīng)用到以下場(chǎng)景：

對(duì)一個(gè)字段進(jìn)行排序?qū)σ粋€(gè)字段進(jìn)行聚合某些過(guò)濾，比如地理位置過(guò)濾某些與字段相關(guān)的腳本計(jì)算

因?yàn)槲臋n值（doc values）被序列化到磁盤，我們可以依靠操作系統(tǒng)的幫助來(lái)快速訪問(wèn)。當(dāng) working set 遠(yuǎn)小于節(jié)點(diǎn)的可用內(nèi)存，系統(tǒng)會(huì)自動(dòng)將所有的文檔值保存在內(nèi)存中，使得其讀寫十分高速；當(dāng)其遠(yuǎn)大于可用內(nèi)存，操作系統(tǒng)會(huì)自動(dòng)把 Doc Values 加載到系統(tǒng)的頁(yè)緩存中，從而避免了 jvm 堆內(nèi)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(shù)據(jù)。

doc values 支持大部分字段類型，但是text 字段類型不支持（因?yàn)閍nalyzed）。

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)]

(1) status_code 字段默認(rèn)啟動(dòng) doc_values 屬性；

(2) session_id 顯式設(shè)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(wèn)字段值，那么我們可以設(shè)置 doc_values = false，這樣可以節(jié)省磁盤空間。

3、executionHint

1、global ordinals

（1）what’s this?

當(dāng)我們使用doc values或者fielddata存儲(chǔ)時(shí)，在磁盤中存儲(chǔ)的值不是真正的字段值，而是一個(gè)字典值(ordinal)。當(dāng)我們進(jìn)行聚合查詢的時(shí)候，es會(huì)把這個(gè)字典值跟真正字段值的映射字典加載到內(nèi)存中，并對(duì)結(jié)果集做映射，轉(zhuǎn)化為真正的值。這份映射關(guān)系是shard級(jí)別的，為這個(gè)shard里面是所有segment服務(wù)，這也是global的體現(xiàn)。

（2）detail

字典關(guān)系是lazy init的，只有第一次使用的時(shí)候才會(huì)加載到內(nèi)存中。在es的內(nèi)存表現(xiàn)中提現(xiàn)成fielddata，這也是全keyword的index為什么也會(huì)有fielddata使用的原因。只會(huì)加載命中的segment的字典不會(huì)加載全部。字典關(guān)系在shard被觸發(fā)refresh以后就會(huì)失效。下次使用的時(shí)候需要再重新構(gòu)建。所以可以提高refresh_interval的值，減少fresh頻率提高字典的生存時(shí)間。

2、eager_global_ordinals

（1）what’s this？

當(dāng)在global ordinals的時(shí)候，refresh以后下一次查詢字典就需要重新構(gòu)建，在追求查詢的場(chǎng)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(nèi)存，減少了查詢的時(shí)候構(gòu)建字典的耗時(shí)。

（2）使用場(chǎng)景

因?yàn)檫@份字典需要常駐內(nèi)存，并且每次refresh以后就會(huì)重構(gòu)，所以增大了內(nèi)存以及cpu的消耗。推薦在低寫高查、數(shù)據(jù)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(chǎng)景，是doc_values以及fileddata的默認(rèn)數(shù)據(jù)架構(gòu)。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(nèi)存中計(jì)算，減去了構(gòu)建字典的耗時(shí)。當(dāng)查詢的結(jié)果集很小的情況下，可以使用map的模式不去構(gòu)建字典。使用map還是global_ordinals的取決于構(gòu)建字典的開(kāi)銷與加載原始字典的開(kāi)銷。當(dāng)結(jié)果集大到一定程序，map的內(nèi)存開(kāi)銷的代價(jià)可能抵消了構(gòu)建字典的開(kāi)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(guò)shard_size參數(shù)設(shè)置協(xié)調(diào)節(jié)點(diǎn)向各個(gè)分片請(qǐng)求的詞根個(gè)數(shù)，然后在協(xié)調(diào)節(jié)點(diǎn)進(jìn)行聚合，最后只返回size個(gè)詞根給到客戶端，shard_size >= size，如果shard_size設(shè)置小于size，ES會(huì)自動(dòng)將其設(shè)置為size，默認(rèn)情況下shard_size建議設(shè)置為(1.5 * size + 10)。

三、java實(shí)現(xiàn)對(duì)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(jié)尾的 QueryBuilders.wildcardQuery("description","*男人");  //bool查詢 BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery(); //bool查詢里添加must多個(gè)條件 boolQueryBuilder.must(QueryBuilders.termQuery("age",20)); boolQueryBuilder.must(QueryBuilders.existsQuery("age")); boolQueryBuilder.must(QueryBuilders.wildcardQuery("description","*男人"));

（1）統(tǒng)計(jì)某個(gè)字段的數(shù)量  ValueCountBuilder vcb=  AggregationBuilders.count("count_uid").field("uid");（2）去重統(tǒng)計(jì)某個(gè)字段的數(shù)量（有少量誤差） CardinalityBuilder cb= AggregationBuilders.cardinality("distinct_count_uid").field("uid");（3）聚合過(guò)濾FilterAggregationBuilder fab= AggregationBuilders.filter("uid_filter").filter(QueryBuilders.queryStringQuery("uid:001"));（4）按某個(gè)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(jié)果TopHitsBuilder thb=  AggregationBuilders.topHits("top_result");（11）嵌套的聚合NestedBuilder nb= AggregationBuilders.nested("negsted_path").path("quests");（12）反轉(zhuǎn)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(chuàng)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(chuàng)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(chuàng)建獲取文檔的請(qǐng)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(jù)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(jù)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(chuàng)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(jìn)行匹配(但是這個(gè)查詢不到數(shù)據(jù)，換這個(gè)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(dāng)于MySQL的%good%        SearchHitssearchHits = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.queryStringQuery("good").field("name")),                User.class,                IndexCoordinates.of("user"));        //精準(zhǔn)匹配，相當(dāng)于name="good"        SearchHitssearchHits1 = elasticsearchRestTemplate.search(                new NativeSearchQuery(QueryBuilders.termQuery("name", "good")),                User.class,                IndexCoordinates.of("user"));        //普通匹配，相當(dāng)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(nèi)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(shù)值型、日期、Ip的范圍查詢，相當(dāng)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(yè)        PageRequest pageRequest = PageRequest.of(0, 2);        // 查詢條件        BoolQueryBuilder queryBuilder1 = new BoolQueryBuilder().must(new MatchQueryBuilder("name", "陳萬(wàn)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(chǔ)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(shù)是" + e1.getDocCount());        }    }    //先分組再得到不同分組的指標(biāo)查詢    @Test    void select5() {//先分組，并按指標(biāo)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(qū)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(shù)量是：" + bucket.getDocCount());            }        }    }    //時(shí)間區(qū)間分組查詢    @Test    void select7() {/*          calendarInterval表示時(shí)間間隔是類型、minDocCount設(shè)置最小值、format設(shè)置輸出時(shí)間的格式化、          missing當(dāng)那個(gè)統(tǒng)計(jì)字段為null的時(shí)候，填充某個(gè)值，然后按照填充的值，統(tǒng)計(jì)計(jì)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(shù)量是：" + bucket.getDocCount());            }        }    }    //指定范圍區(qū)間分組查詢    @Test    void select8() {/*          calendarInterval表示時(shí)間間隔是類型、minDocCount設(shè)置最小值、format設(shè)置輸出時(shí)間的格式化、          missing當(dāng)那個(gè)統(tǒng)計(jì)字段為null的時(shí)候，填充某個(gè)值，然后按照填充的值，統(tǒng)計(jì)計(jì)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(shù)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(shí)間范圍區(qū)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(shù)量是：" + bucket.getDocCount());            }        }    }    //指定ip區(qū)間分組查詢    @Test    void select10() {//TODO 這個(gè)地方的ip類型不能用來(lái)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(shù)量是：" + bucket.getDocCount());            }        }    }    //指定過(guò)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(shù)量是：" + bucket.getDocCount());            }        }    }}

四、命令的方式實(shí)現(xiàn)對(duì)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(wàn)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(wàn)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(ài)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(wàn)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(jù)team字段聚合分組、并得到每個(gè)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(jì)數(shù)、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(biāo)結(jié)果GET /user/_search{"size": 0,   "aggs": {"balance_a": {"extended_stats": {"field": "age"      }    }  }}#查看對(duì)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(yǔ)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(shí)現(xiàn)方式：#1、通過(guò)腳本合并字段#2、使用copy_to方法合并兩個(gè)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(yōu)化

1、聚合為什么慢？

大多數(shù)時(shí)候?qū)蝹€(gè)字段的聚合查詢還是非常快的， 但是當(dāng)需要同時(shí)聚合多個(gè)字段時(shí)，就可能會(huì)產(chǎn)生大量的分組，最終結(jié)果就是占用 es 大量?jī)?nèi)存，從而導(dǎo)致 OOM 的情況發(fā)生。 實(shí)踐應(yīng)用發(fā)現(xiàn)，以下情況都會(huì)比較慢： 1）待聚合文檔數(shù)比較多（千萬(wàn)、億、十億甚至更多）； 2）聚合條件比較復(fù)雜（多重條件聚合）； 3）全量聚合（翻頁(yè)的場(chǎng)景用）。

2、聚合優(yōu)化方案探討

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

優(yōu)化方案一：默認(rèn)深度優(yōu)先聚合改為廣度優(yōu)先聚合。

"collect_mode" : "breadth_first"

depth_first 直接進(jìn)行子聚合的計(jì)算 breadth_first 先計(jì)算出當(dāng)前聚合的結(jié)果，針對(duì)這個(gè)結(jié)果在對(duì)子聚合進(jìn)行計(jì)算。 優(yōu)化方案二： 每一層terms aggregation內(nèi)部加一個(gè) “execution_hint”: “map”。

"execution_hint": "map"

Map方式的結(jié)論可簡(jiǎn)要概括如下： 1）查詢結(jié)果直接放入內(nèi)存中構(gòu)建map，在查詢結(jié)果集小的場(chǎng)景下，速度極快； 2）但如果待結(jié)果集合很大的情況，map方式不一定也快。

3、運(yùn)用 shard_size 來(lái)提高 term aggregation 的精度

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

請(qǐng)求的大小（size）越大，結(jié)果將越準(zhǔn)確，但計(jì)算最終結(jié)果的成本也將越高（這兩者都是由于在分片級(jí)別上管理的優(yōu)先級(jí)隊(duì)列更大，并且節(jié)點(diǎn)和客戶端之間的數(shù)據(jù)傳輸也更大）。

shard_size 參數(shù)可用于最大程度地減少請(qǐng)求的大小帶來(lái)的額外工作。 定義后，它將確定協(xié)調(diào)節(jié)點(diǎn)將從每個(gè)分片請(qǐng)求多少個(gè)術(shù)語(yǔ)。 一旦所有分片都做出響應(yīng)，協(xié)調(diào)節(jié)點(diǎn)便會(huì)將它們縮減為最終結(jié)果，該最終結(jié)果將基于size參數(shù)-這樣一來(lái)，可以提高返回條款的準(zhǔn)確性，并避免流回大量存儲(chǔ)桶的開(kāi)銷給客戶。

注意：shard_size 不能小于 size（因?yàn)橐饬x不大）。 啟用時(shí)，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(duì)于大量數(shù)據(jù)來(lái)說(shuō)通常是不精確的

我們先來(lái)看一下如下的一個(gè)圖：

從上面的圖中，在 shard_size 為3的情況下，我們想對(duì) geoip.country_name 這個(gè)字段來(lái)進(jìn)行 terms aggregation：

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

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

根據(jù)上面的計(jì)算，返回的結(jié)果將會(huì)是 USA，India 及 France。細(xì)心的開(kāi)發(fā)者可能馬上可以看出來(lái)，在上面的統(tǒng)計(jì)中國(guó)其實(shí)是不精確的，這是因?yàn)樵? shard 0 中，我們可以看見(jiàn) Japan 有3個(gè)文檔沒(méi)有被統(tǒng)計(jì)進(jìn)去。這個(gè)統(tǒng)計(jì)是基于我們對(duì) shard_size 為3的情況。假如我們把 shard_size 提供到4，情況馬上就會(huì)不同，而且更加接近我們的實(shí)際的統(tǒng)計(jì)數(shù)據(jù)的結(jié)果。在這種情況下，Japan 將會(huì)有 3 + 6 共6很個(gè)文檔，應(yīng)該是排名第3。

我們可以修改我們的請(qǐng)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(guò)增加 shard_size 來(lái)提高數(shù)據(jù)的精確性，但是必須注意的是這樣的代價(jià)是計(jì)算的成本增加，特別是針對(duì)大量數(shù)據(jù)而言。