说明:以下全部均基于eslaticsearch 8.1 版本
官网文档地址:<https://www.elastic.co/guide/en/elasticsearch/reference/8.1/indices.html>
(相关资料图)
ElasticSearch | Mysql |
Index | Table |
Type废弃 | Table废弃 |
Document | Row |
Field | Column |
Mapping | Schema |
Everything is indexed | Index |
Query DSL | SQL |
GET http://... | select * from |
POST http://... | update table set ... |
Aggregations | group by\sum\sum |
cardinality | 去重 distinct |
reindex | 数据迁移 |
定义: 相同文档结构(Mapping)文档的结合 由唯一索引名称标定 一个集群中有多个索引 不同的索引代表不同的业务类型数据 注意事项: 索引名称不支持大写 索引名称最大支持255个字符长度 字段的名称,支持大写,不过建议全部统一小写
官网文档地址:<https://www.elastic.co/guide/en/elasticsearch/reference/8.1/index-modules.html>
注意: 静态参数索引创建后,不再可以修改,动态参数可以修改 思考: 一、为什么主分片创建后不可修改? A document is routed to a particular shard in an index using the following formula:
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/dynamic.html
自动检测字段类型后添加字段 也就是哪怕你没有在es的mapping中定义该字段,es也会动态的帮你检测字段类型
// 删除test01索引,保证这个索引现在是干净的DELETE test01// 不定义mapping,直接一条插入数据试试看,POST test01/_doc/1{ "name":"kangrui10"}// 然后我们查看test01该索引的mapping结构 看看name这个字段被定义成了什么类型// 由此可以看出,name一级为text类型,二级定义为keyword,但其实这并不是我们想要的结果,// 我们业务查询中name字段并不会被分词查询,一般都是全匹配(and name = xxx)// 以下的这种结果,我们想要实现全匹配 就需要 name.keyword = xxx 反而麻烦GET test01/_mapping{ "test01" : { "mappings" : { "properties" : { "name" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } } } } }}
可选值 | 说明 | 解释 |
true | New fields are added to the mapping (default). | 创建mapping时,如果不指定dynamic的值,默认true,即如果你的字段没有收到指定类型,就会es帮你动态匹配字段类型 |
false | New fields are ignored. These fields will not be indexed or searchable, but will still appear in the _source field of returned hits. These fields will not be added to the mapping, and new fields must be added explicitly. | 若设置为false,如果你的字段没有在es的mapping中创建,那么新的字段,一样可以写入,但是不能被查询,mapping中也不会有这个字段,也就是被写入的字段,不会被创建索引 |
strict | If new fields are detected, an exception is thrown and the document is rejected. New fields must be explicitly added to the mapping. | 若设置为strict,如果新的字段,没有在mapping中创建字段,添加会直接报错,生产环境推荐,更加严谨。示例如下,如要新增字段,就必须手动的新增字段 |
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/doc-values.html
DocValue其实是Lucene在构建倒排索引时,会额外建立一个有序的正排索引(基于document => field value的映射列表) DocValue本质上是一个序列化的 列式存储,这个结构非常适用于聚合(aggregations)、排序(Sorting)、脚本(scripts access to field)等操作。而且,这种存储方式也非常便于压缩,特别是数字类型。这样可以减少磁盘空间并且提高访问速度。 几乎所有字段类型都支持DocValue,除了text和annotated_text字段。
正排索引其实就是类似于数据库表,通过id和数据进行关联,通过搜索文档id,来获取对应的数据
// 创建一个索引,test03,字段满足以下条件// 1. speaker: keyword// 2. line_id: keyword and not aggregateable// 3. speech_number: integerPUT test03{ "mappings": { "properties": { "speaker": { "type": "keyword" }, "line_id":{ "type": "keyword", "doc_values": false }, "speech_number":{ "type": "integer" } } }}
<https://github.com/medcl/elasticsearch-analysis-ik>
官网地址: https://www.elastic.co/guide/en/elasticsearch/reference/8.1/analysis-analyzers.html
官网文档地址:<https://www.elastic.co/guide/en/elasticsearch/reference/8.1/analysis-charfilters.html> 可配置0个或多个
HTML Strip Character Filter:用途:删除HTML元素,如 ,并解 码HTML实体,如&amp
Mapping Character Filter:用途:替换指定字符
Pattern Replace Character Filter:用途:基于正则表达式替换指定字符
官网文档地址:<https://www.elastic.co/guide/en/elasticsearch/reference/8.1/analysis-tokenizers.html#_word_oriented_tokenizers> 只能配置一个 用分词器对文本进行分词
官网文档地址:<https://www.elastic.co/guide/en/elasticsearch/reference/8.1/analysis-tokenfilters.html> 可配置0个或多个 分词后再加工,比如转小写、删除某些特殊的停用词、增加同义词等
有一个文档,内容类似 dag & cat, 要求索引这个文档,并且使用match_parase_query, 查询dag & cat 或者 dag and cat,都能够查到 题目分析: 1.何为match_parase_query:match_phrase 会将检索关键词分词。match_phrase的分词结果必须在被检索字段的分词中都包含,而且顺序必须相同,而且默认必须都是连续的。 2.要实现 & 和 and 查询结果要等价,那么就需要自定义分词器来实现了,定制化的需求 3.如何自定义一个分词器:<https://www.elastic.co/guide/en/elasticsearch/reference/8.1/analysis-custom-analyzer.html> 4.解法1核心使用功能点,Mapping Character Filter 5.解法2核心使用功能点,<https://www.elastic.co/guide/en/elasticsearch/reference/8.1/analysis-synonym-tokenfilter.html>
# 新建索引PUT /test01{ "settings": { "analysis": { "analyzer": { "my_analyzer": { "char_filter": [ "my_mappings_char_filter" ], "tokenizer": "standard", } }, "char_filter": { "my_mappings_char_filter": { "type": "mapping", "mappings": [ "& => and" ] } } } }, "mappings": { "properties": { "content":{ "type": "text", "analyzer": "my_analyzer" } } }}// 说明// 三段论之Character filters,使用char_filter进行文本替换// 三段论之Token filters,使用默认分词器// 三段论之Token filters,未设定// 字段content 使用自定义分词器my_analyzer# 填充测试数据PUT test01/_bulk{"index":{"_id":1}}{"content":"doc & cat"}{"index":{"_id":2}}{"content":"doc and cat"}# 执行测试,doc & cat || oc and cat 结果输出都为两条POST test01/_search{ "query": { "bool": { "must": [ { "match_phrase": { "content": "doc & cat" } } ] } }}
# 解题思路,将& 和 and 设定为同义词,使用Token filters# 创建索引PUT /test02{ "settings": { "analysis": { "analyzer": { "my_synonym_analyzer": { "tokenizer": "whitespace", "filter": [ "my_synonym" ] } }, "filter": { "my_synonym": { "type": "synonym", "lenient": true, "synonyms": [ "& => and" ] } } } }, "mappings": { "properties": { "content": { "type": "text", "analyzer": "my_synonym_analyzer" } } }}// 说明// 三段论之Character filters,未设定// 三段论之Token filters,使用whitespace空格分词器,为什么不用默认分词器?因为默认分词器会把&分词后剔除了,就无法在去做分词后的过滤操作了// 三段论之Token filters,使用synony分词后过滤器,对&和and做同义词// 字段content 使用自定义分词器my_synonym_analyzer# 填充测试数据PUT test02/_bulk{"index":{"_id":1}}{"content":"doc & cat"}{"index":{"_id":2}}{"content":"doc and cat"}# 执行测试POST test02/_search{ "query": { "bool": { "must": [ { "match_phrase": { "content": "doc & cat" } } ] } }}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/multi-fields.html
// 单字段多类型,比如一个字段我想设置两种分词器PUT my-index-000001{ "mappings": { "properties": { "city": { "type": "text", "analyzer":"standard", "fields": { "fieldText": { "type": "text", "analyzer":"ik_smart", } } } } }}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/runtime.html
假如业务中需要根据某两个数字类型字段的差值来排序,也就是我需要一个不存在的字段, 那么此时应该怎么办? 当然你可以刷数,新增一个差值结果字段来实现,假如此时不允许你刷数新增字段怎么办?
# 假定有以下索引和数据PUT test03{ "mappings": { "properties": { "emotion": { "type": "integer" } } }}POST test03/_bulk{"index":{"_id":1}}{"emotion":2}{"index":{"_id":2}}{"emotion":5}{"index":{"_id":3}}{"emotion":10}{"index":{"_id":4}}{"emotion":3}# 要求:emotion > 5, 返回emotion_falg = "1", # 要求:emotion < 5, 返回emotion_falg = "-1", # 要求:emotion = 5, 返回emotion_falg = "0",
检索时指定运行时字段: <https://www.elastic.co/guide/en/elasticsearch/reference/8.1/runtime-search-request.html> 该字段本质上是不存在的,所以需要检索时要加上 fields *
GET test03/_search{ "fields": [ "*" ], "runtime_mappings": { "emotion_falg": { "type": "keyword", "script": { "source": """ if(doc["emotion"].value>5)emit("1"); if(doc["emotion"].value<5)emit("-1"); if(doc["emotion"].value==5)emit("0"); """ } } }}
创建索引时指定运行时字段:<https://www.elastic.co/guide/en/elasticsearch/reference/8.1/runtime-mapping-fields.html> 该方式支持通过运行时字段做检索
# 创建索引并指定运行时字段PUT test03_01{ "mappings": { "runtime": { "emotion_falg": { "type": "keyword", "script": { "source": """ if(doc["emotion"].value>5)emit("1"); if(doc["emotion"].value<5)emit("-1"); if(doc["emotion"].value==5)emit("0"); """ } } }, "properties": { "emotion": { "type": "integer" } } }}# 导入测试数据POST test03_01/_bulk{"index":{"_id":1}}{"emotion":2}{"index":{"_id":2}}{"emotion":5}{"index":{"_id":3}}{"emotion":10}{"index":{"_id":4}}{"emotion":3}# 查询测试GET test03_01/_search{ "fields": [ "*" ]}
# 有以下索引和数据PUT test04{ "mappings": { "properties": { "A":{ "type": "long" }, "B":{ "type": "long" } } }}PUT task04/_bulk{"index":{"_id":1}}{"A":100,"B":2}{"index":{"_id":2}}{"A":120,"B":2}{"index":{"_id":3}}{"A":120,"B":25}{"index":{"_id":4}}{"A":21,"B":25}# 需求:在task04索引里,创建一个runtime字段,其值是A-B,名称为A_B; 创建一个range聚合,分为三级:小于0,0-100,100以上;返回文档数// 使用知识点:// 1.检索时指定运行时字段: https://www.elastic.co/guide/en/elasticsearch/reference/8.1/runtime-search-request.html// 2.范围聚合 https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-bucket-range-aggregation.html
# 结果测试GET task04/_search{ "fields": [ "*" ], "size": 0, "runtime_mappings": { "A_B": { "type": "long", "script": { "source": """ emit(doc["A"].value - doc["B"].value); """ } } }, "aggs": { "price_ranges_A_B": { "range": { "field": "A_B", "ranges": [ { "to": 0 }, { "from": 0, "to": 100 }, { "from": 100 } ] } } }}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/highlighting.html
官网文档地址: https://www.elastic.co/guide/en/elasticsearch/reference/8.1/sort-search-results.html
// 注意:text类型默认是不能排或聚合的,如果非要排序或聚合,需要开启fielddataGET /kibana_sample_data_ecommerce/_search{ "query": { "match": { "customer_last_name": "wood" } }, "highlight": { "number_of_fragments": 3, "fragment_size": 150, "fields": { "customer_last_name": { "pre_tags": [ "" ], "post_tags": [ "" ] } } }, "sort": [ { "currency": { "order": "desc" }, "_score": { "order": "asc" } } ]}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/paginate-search-results.html
# 注意 from的起始值是 0 不是 1GET kibana_sample_data_ecommerce/_search{ "from": 5, "size": 20, "query": { "match": { "customer_last_name": "wood" } }}
# 题目In the spoken lines of the play, highlight the word Hamlet (int the text_entry field) startint the highlihnt with "#aaa#" and ending it with "#bbb#"return all of speech_number field lines in reverse order; "20" speech lines per page,starting from line "40"# highlight 处理 text_entry 字段 ; 关键词 Hamlet 高亮# page分页:from:40;size:20# speech_number:倒序POST test09/_search{ "from": 40, "size": 20, "query": { "bool": { "must": [ { "match": { "text_entry": "Hamlet" } } ] } }, "highlight": { "fields": { "text_entry": { "pre_tags": [ "#aaa#" ], "post_tags": [ "#bbb#" ] } } }, "sort": [ { "speech_number.keyword": { "order": "desc" } } ]}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/async-search.html
7.7.0
允许用户在异步搜索结果时可以检索,从而消除了仅在查询完成后才等待最终响应的情况
返回值 | 含义 |
id | 异步检索返回的唯一标识符 |
is_partial | 当查询不再运行时,指示再所有分片上搜索是成功还是失败。在执行查询时,is_partial=true |
is_running | 搜索是否仍然再执行 |
total | 将在多少分片上执行搜索 |
successful | 有多少分片已经成功完成搜索 |
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/aliases.html
在ES中,索引别名(index aliases)就像一个快捷方式或软连接,可以指向一个或多个索引。别名带给我们极大的灵活性,我们可以使用索引别名实现以下功能:
在一个运行中的ES集群中无缝的切换一个索引到另一个索引上(无需停机)分组多个索引,比如按月创建的索引,我们可以通过别名构造出一个最近3个月的索引查询一个索引里面的部分数据构成一个类似数据库的视图(views方式1:POST index_01,index_02.index_03/_search 方式2:POST index*/search
# 指定test05的别名为 test05_aliasesPUT test05{ "mappings": { "properties": { "name":{ "type": "keyword" } } }, "aliases": { "test05_aliases": {} }}使用索引模板的方式指定别名
PUT _index_template/template_1{ "index_patterns": ["te*", "bar*"], "template": { "settings": { "number_of_shards": 1 }, "mappings": { "_source": { "enabled": true }, "properties": { "host_name": { "type": "keyword" }, "created_at": { "type": "date", "format": "EEE MMM dd HH:mm:ss Z yyyy" } } }, "aliases": { "mydata": { } } }, "priority": 500, "composed_of": ["component_template1", "runtime_component_template"], "version": 3, "_meta": { "description": "my custom" }}对已有的索引创建别名
POST _aliases{ "actions": [ { "add": { "index": "logs-nginx.access-prod", "alias": "logs" } } ]}
POST _aliases{ "actions": [ { "remove": { "index": "logs-nginx.access-prod", "alias": "logs" } } ]}
# Define an index alias for "accounts-row" called "accounts-male": Apply a filter to only show the male account owners# 为"accounts-row"定义一个索引别名,称为"accounts-male":应用一个过滤器,只显示男性账户所有者POST _aliases{ "actions": [ { "add": { "index": "accounts-row", "alias": "accounts-male", "filter": { "bool": { "filter": [ { "term": { "gender.keyword": "male" } } ] } } } } ]}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-template.html
模板接受在运行时指定参数。搜索模板存储在服务器端,可以在不更改客户端代码的情况下进行修改。
# 创建检索模板PUT _scripts/my-search-template{ "script": { "lang": "mustache", "source": { "query": { "match": { "{{query_key}}": "{{query_value}}" } }, "from": "{{from}}", "size": "{{size}}" } }}# 使用检索模板查询GET my-index/_search/template{ "id": "my-search-template", "params": { "query_key": "your filed", "query_value": "your filed value", "from": 0, "size": 10 }}
PUT _scripts/my-search-template{ "script": { "lang": "mustache", "source": { "query": { "match": { "message": "{{query_string}}" } }, "from": "{{from}}", "size": "{{size}}" }, "params": { "query_string": "My query string" } }}
POST _render/template{ "id": "my-search-template", "params": { "query_string": "hello world", "from": 20, "size": 10 }}
GET my-index/_search/template{ "id": "my-search-template", "params": { "query_string": "hello world", "from": 0, "size": 10 }}
GET _cluster/state/metadata?pretty&filter_path=metadata.stored_scripts
DELETE _scripts/my-search-templateath=metadata.stored_scripts
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/query-dsl.html
// 一批数据里,有不同的标签,数据结构一致,不同的标签存储到不同的索引(A、B、C),最后要严格按照标签来分类展示的话,用什么查询比较好?// 要求:先展示A类,然后B类,然后C类# 测试数据如下put /index_a_123/_doc/1{ "title":"this is index_a..."}put /index_b_123/_doc/1{ "title":"this is index_b..."}put /index_c_123/_doc/1{ "title":"this is index_c..."}# 普通不指定的查询方式,该查询方式下,返回的三条结果数据评分是相同的POST index_*_123/_search{ "query": { "bool": { "must": [ { "match": { "title": "this" } } ] } }}官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-search.htmlindices_boost# 也就是索引层面提升权重POST index_*_123/_search{ "indices_boost": [ { "index_a_123": 10 }, { "index_b_123": 5 }, { "index_c_123": 1 } ], "query": { "bool": { "must": [ { "match": { "title": "this" } } ] } }}
某索引index_a有多个字段, 要求实现如下的查询:1)针对字段title,满足"ssas"或者"sasa’。2)针对字段tags(数组字段),如果tags字段包含"pingpang",则提升评分。要求:写出实现的DSL?# 测试数据如下put index_a/_bulk{"index":{"_id":1}}{"title":"ssas","tags":"basketball"}{"index":{"_id":2}}{"title":"sasa","tags":"pingpang; football"}# 解法1POST index_a/_search{ "query": { "bool": { "must": [ { "bool": { "should": [ { "match": { "title": "ssas" } }, { "match": { "title": "sasa" } } ] } } ], "should": [ { "match": { "tags": { "query": "pingpang", "boost": 1 } } } ] } }}# 解法2// https://www.elastic.co/guide/en/elasticsearch/reference/8.1/query-dsl-function-score-query.htmlPOST index_a/_search{ "query": { "bool": { "should": [ { "function_score": { "query": { "match": { "tags": { "query": "pingpang" } } }, "boost": 1 } } ], "must": [ { "bool": { "should": [ { "match": { "title": "ssas" } }, { "match": { "title": "sasa" } } ] } } ] } }}
对于某些结果不满意,但又不想通过 must_not 排除掉,可以考虑可以考虑boosting query的negative_boost。即:降低评分negative_boost(Required, float) Floating point number between 0 and 1.0 used to decrease the relevance scores of documents matching the negative query.官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/query-dsl-boosting-query.htmlPOST index_a/_search{ "query": { "boosting": { "positive": { "term": { "tags": "football" } }, "negative": { "term": { "tags": "pingpang" } }, "negative_boost": 0.5 } }}
如何同时根据 销量和浏览人数进行相关度提升?问题描述:针对商品,例如有想要有一个提升相关度的计算,同时针对销量和浏览人数?例如oldScore*(销量+浏览人数)************************** 商品 销量 浏览人数 A 10 10 B 20 20C 30 30************************** # 示例数据如下 put goods_index/_bulk{"index":{"_id":1}}{"name":"A","sales_count":10,"view_count":10}{"index":{"_id":2}}{"name":"B","sales_count":20,"view_count":20}{"index":{"_id":3}}{"name":"C","sales_count":30,"view_count":30}官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/query-dsl-function-score-query.html知识点:script_scorePOST goods_index/_search{ "query": { "function_score": { "query": { "match_all": {} }, "script_score": { "script": { "source": "_score * (doc["sales_count"].value+doc["view_count"].value)" } } } }}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/query-dsl-bool-query.html
写一个查询,要求某个关键字再文档的四个字段中至少包含两个以上功能点:bool 查询,should / minimum_should_match 1.检索的bool查询 2.细节点 minimum_should_match注意:minimum_should_match 当有其他子句的时候,默认值为0,当没有其他子句的时候默认值为1POST test_index/_search{ "query": { "bool": { "should": [ { "match": { "filed1": "kr" } }, { "match": { "filed2": "kr" } }, { "match": { "filed3": "kr" } }, { "match": { "filed4": "kr" } } ], "minimum_should_match": 2 } }}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations.html
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-bucket-terms-aggregation.html# 按照作者统计文档数POST bilili_elasticsearch/_search{ "size": 0, "aggs": { "agg_user": { "terms": { "field": "user", "size": 1 } } }}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-bucket-datehistogram-aggregation.html# 按照up_time 按月进行统计POST bilili_elasticsearch/_search{ "size": 0, "aggs": { "agg_up_time": { "date_histogram": { "field": "up_time", "calendar_interval": "month" } } }}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-metrics-max-aggregation.html# 获取up_time最大的POST bilili_elasticsearch/_search{ "size": 0, "aggs": { "agg_max_up_time": { "max": { "field": "up_time" } } }}
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-metrics-top-hits-aggregation.html# 根据user聚合只取一个聚合结果,并且获取命中数据的详情前3条,并按照指定字段排序POST bilili_elasticsearch/_search{ "size": 0, "aggs": { "terms_agg_user": { "terms": { "field": "user", "size": 1 }, "aggs": { "top_user_hits": { "top_hits": { "_source": { "includes": [ "video_time", "title", "see", "user", "up_time" ] }, "sort": [ { "see":{ "order": "desc" } } ], "size": 3 } } } } }}// 返回结果如下{ "took" : 91, "timed_out" : false, "_shards" : { "total" : 1, "successful" : 1, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : { "value" : 1000, "relation" : "eq" }, "max_score" : null, "hits" : [ ] }, "aggregations" : { "terms_agg_user" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 975, "buckets" : [ { "key" : "Elastic搜索", "doc_count" : 25, "top_user_hits" : { "hits" : { "total" : { "value" : 25, "relation" : "eq" }, "max_score" : null, "hits" : [ { "_index" : "bilili_elasticsearch", "_id" : "5ccCVoQBUyqsIDX6wIcm", "_score" : null, "_source" : { "video_time" : "03:45", "see" : "92", "up_time" : "2021-03-19", "title" : "Elastic 社区大会2021: 用加 Gatling 进行Elasticsearch的负载测试,寓教于乐。", "user" : "Elastic搜索" }, "sort" : [ "92" ] }, { "_index" : "bilili_elasticsearch", "_id" : "8scCVoQBUyqsIDX6wIgn", "_score" : null, "_source" : { "video_time" : "10:18", "see" : "79", "up_time" : "2020-10-20", "title" : "为Elasticsearch启动htpps访问", "user" : "Elastic搜索" }, "sort" : [ "79" ] }, { "_index" : "bilili_elasticsearch", "_id" : "7scCVoQBUyqsIDX6wIcm", "_score" : null, "_source" : { "video_time" : "04:41", "see" : "71", "up_time" : "2021-03-19", "title" : "Elastic 社区大会2021: Elasticsearch作为一个地理空间的数据库", "user" : "Elastic搜索" }, "sort" : [ "71" ] } ] } } } ] } }}
Pipeline:基于聚合的聚合 官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-pipeline.html
官网文档地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-pipeline-bucket-selector-aggregation.html
# 根据order_date按月分组,并且求销售总额大于1000POST kibana_sample_data_ecommerce/_search{ "size": 0, "aggs": { "date_his_aggs": { "date_histogram": { "field": "order_date", "calendar_interval": "month" }, "aggs": { "sum_aggs": { "sum": { "field": "total_unique_products" } }, "sales_bucket_filter": { "bucket_selector": { "buckets_path": { "totalSales": "sum_aggs" }, "script": "params.totalSales > 1000" } } } } }}
earthquakes索引中包含了过去30个月的地震信息,请通过一句查询,获取以下信息l 过去30个月,每个月的平均 magl 过去30个月里,平均mag最高的一个月及其平均magl 搜索不能返回任何文档 max_bucket 官网地址:https://www.elastic.co/guide/en/elasticsearch/reference/8.1/search-aggregations-pipeline-max-bucket-aggregation.htmlPOST earthquakes/_search{ "size": 0, "query": { "range": { "time": { "gte": "now-30M/d", "lte": "now" } } }, "aggs": { "agg_time_his": { "date_histogram": { "field": "time", "calendar_interval": "month" }, "aggs": { "avg_aggs": { "avg": { "field": "mag" } } } }, "max_mag_sales": { "max_bucket": { "buckets_path": "agg_time_his>avg_aggs" } } }}