## 一、 document數據格式 ### 面向文檔的搜索分析引擎： 1. 應用系統的數據結構都是面向對象的，復雜的 2. 對象數據存儲到數據庫中，只能拆解開來，變為扁平的多張表，每次查詢的時候還得還原回對象格式，相當麻煩 3. ES是面向文檔的，文檔中存儲的數據結構，與面向對象的數據結構是一樣的，基于這種文檔數據結構，es可以提供復雜的索引，全文檢索，分析聚合等功能 4. es的document用json數據格式來表達 ~~~ public class Employee { private String email; private String firstName; private String lastName; private EmployeeInfo info; private Date joinDate; } private class EmployeeInfo { private String bio; // 性格 private Integer age; private String[] interests; // 興趣愛好 } EmployeeInfo info = new EmployeeInfo(); info.setBio("curious and modest"); info.setAge(30); info.setInterests(new String[]{"bike", "climb"}); Employee employee = new Employee(); employee.setEmail("zhangsan@sina.com"); employee.setFirstName("san"); employee.setLastName("zhang"); employee.setInfo(info); employee.setJoinDate(new Date()); employee對象：里面包含了Employee類自己的屬性，還有一個EmployeeInfo對象 兩張表：employee表，employee_info表，將employee對象的數據重新拆開來，變成Employee數據和EmployeeInfo數據 employee表：email，first_name，last_name，join_date，4個字段 employee_info表：bio，age，interests，3個字段；此外還有一個外鍵字段，比如employee_id，關聯著employee表 { "email": "zhangsan@sina.com", "first_name": "san", "last_name": "zhang", "info": { "bio": "curious and modest", "age": 30, "interests": [ "bike", "climb" ] }, "join_date": "2020/12/01" } 我們就明白了es的document數據格式和數據庫的關系型數據格式的區別 復制代碼 ~~~ ## 二、簡單的集群管理 ### 1、快速檢查集群的健康狀況 ~~~ es提供了一套api，叫做cat api，可以查看es中各種各樣的數據 GET /_cat/health?v epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent 1488006741 15:12:21 elasticsearch yellow 1 1 1 1 0 0 1 0 - 50.0% epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent 1488007113 15:18:33 elasticsearch green 2 2 2 1 0 0 0 0 - 100.0% epoch timestamp cluster status node.total node.data shards pri relo init unassign pending_tasks max_task_wait_time active_shards_percent 1488007216 15:20:16 elasticsearch yellow 1 1 1 1 0 0 1 0 - 50.0% 復制代碼 ~~~ ### 2、集群的健康狀況？green、yellow、red？ 1. **green**：每個索引的primary shard和replica shard都是active狀態的 2. **yellow**：每個索引的primary shard都是active狀態的，但是部分replica shard不是active狀態，處于不可用的狀態 3. **red**：不是所有索引的primary shard都是active狀態的，部分索引有數據丟失了 ### 3、快速查看集群中有哪些索引？ ~~~ GET /_cat/indices?v health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open .kibana rUm9n9wMRQCCrRDEhqneBg 1 1 1 0 3.1kb 3.1kb 復制代碼 ~~~ ### 4、 簡單的索引操作 ~~~ 創建索引：PUT /test_index?pretty health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open test_index XmS9DTAtSkSZSwWhhGEKkQ 5 1 0 0 650b 650b yellow open .kibana rUm9n9wMRQCCrRDEhqneBg 1 1 1 0 3.1kb 3.1kb 刪除索引：DELETE /test_index?pretty health status index uuid pri rep docs.count docs.deleted store.size pri.store.size yellow open .kibana rUm9n9wMRQCCrRDEhqneBg 1 1 1 0 3.1kb 3.1kb 復制代碼 ~~~ ## 三、面向document的CRUD ### 1、新增商品：新增文檔，建立索引 ~~~ PUT /index/type/id { "json數據" } PUT /ecommerce/product/1 { "name" : "gaolujie yagao", "desc" : "gaoxiao meibai", "price" : 30, "producer" : "gaolujie producer", "tags": [ "meibai", "fangzhu" ] } { "_index": "ecommerce", "_type": "product", "_id": "1", "_version": 1, "result": "created", "_shards": { "total": 2, "successful": 1, "failed": 0 }, "created": true } PUT /ecommerce/product/2 { "name" : "jiajieshi yagao", "desc" : "youxiao fangzhu", "price" : 25, "producer" : "jiajieshi producer", "tags": [ "fangzhu" ] } PUT /ecommerce/product/3 { "name" : "zhonghua yagao", "desc" : "caoben zhiwu", "price" : 40, "producer" : "zhonghua producer", "tags": [ "qingxin" ] } es會自動建立index和type，不需要提前創建，而且es默認會對document每個field都建立倒排索引，讓其可以被搜索 復制代碼 ~~~ ### 2、查詢商品：檢索文檔 ~~~ GET /index/type/id GET /ecommerce/product/1 { "_index": "ecommerce", "_type": "product", "_id": "1", "_version": 1, "found": true, "_source": { "name": "gaolujie yagao", "desc": "gaoxiao meibai", "price": 30, "producer": "gaolujie producer", "tags": [ "meibai", "fangzhu" ] } } 復制代碼 ~~~ ### 3、 修改商品：替換文檔 ~~~ PUT /ecommerce/product/1 { "name" : "jiaqiangban gaolujie yagao", "desc" : "gaoxiao meibai", "price" : 30, "producer" : "gaolujie producer", "tags": [ "meibai", "fangzhu" ] } { "_index": "ecommerce", "_type": "product", "_id": "1", "_version": 1, "result": "created", "_shards": { "total": 2, "successful": 1, "failed": 0 }, "created": true } { "_index": "ecommerce", "_type": "product", "_id": "1", "_version": 2, "result": "updated", "_shards": { "total": 2, "successful": 1, "failed": 0 }, "created": false } PUT /ecommerce/product/1 { "name" : "jiaqiangban gaolujie yagao" } 替換方式有一個不好，即使必須帶上所有的field，才能去進行信息的修改 復制代碼 ~~~ ### 4、修改商品：更新文檔 ~~~ POST /ecommerce/product/1/_update { "doc": { "name": "jiaqiangban gaolujie yagao" } } { "_index": "ecommerce", "_type": "product", "_id": "1", "_version": 8, "result": "updated", "_shards": { "total": 2, "successful": 1, "failed": 0 } } 復制代碼 ~~~ ### 5、 刪除商品：刪除文檔 ~~~ DELETE /ecommerce/product/1 { "found": true, "_index": "ecommerce", "_type": "product", "_id": "1", "_version": 9, "result": "deleted", "_shards": { "total": 2, "successful": 1, "failed": 0 } } { "_index": "ecommerce", "_type": "product", "_id": "1", "found": false } 復制代碼 ~~~ ## 四、多種搜索方式 ### 1、query string search ~~~ 搜索全部商品：GET /ecommerce/product/_search took：耗費了幾毫秒 timed_out：是否超時，這里是沒有 _shards：數據拆成了5個分片，所以對于搜索請求，會打到所有的primary shard（或者是它的某個replica shard也可以） hits.total：查詢結果的數量，3個document hits.max_score：score的含義，就是document對于一個search的相關度的匹配分數，越相關，就越匹配，分數也高 hits.hits：包含了匹配搜索的document的詳細數據 { "took": 2, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 3, "max_score": 1, "hits": [ { "_index": "ecommerce", "_type": "product", "_id": "2", "_score": 1, "_source": { "name": "jiajieshi yagao", "desc": "youxiao fangzhu", "price": 25, "producer": "jiajieshi producer", "tags": [ "fangzhu" ] } }, { "_index": "ecommerce", "_type": "product", "_id": "1", "_score": 1, "_source": { "name": "gaolujie yagao", "desc": "gaoxiao meibai", "price": 30, "producer": "gaolujie producer", "tags": [ "meibai", "fangzhu" ] } }, { "_index": "ecommerce", "_type": "product", "_id": "3", "_score": 1, "_source": { "name": "zhonghua yagao", "desc": "caoben zhiwu", "price": 40, "producer": "zhonghua producer", "tags": [ "qingxin" ] } } ] } } query string search的由來，因為search參數都是以http請求的query string來附帶的 搜索商品名稱中包含yagao的商品，而且按照售價降序排序：GET /ecommerce/product/_search?q=name:yagao&sort=price:desc 適用于臨時的在命令行使用一些工具，比如curl，快速的發出請求，來檢索想要的信息；但是如果查詢請求很復雜，是很難去構建的 在生產環境中，幾乎很少使用query string search 復制代碼 ~~~ ### 2、query DSL ~~~ DSL：Domain Specified Language，特定領域的語言 http request body：請求體，可以用json的格式來構建查詢語法，比較方便，可以構建各種復雜的語法，比query string search肯定強大多了 查詢所有的商品 GET /ecommerce/product/_search { "query": { "match_all": {} } } 查詢名稱包含yagao的商品，同時按照價格降序排序 GET /ecommerce/product/_search { "query" : { "match" : { "name" : "yagao" } }, "sort": [ { "price": "desc" } ] } 分頁查詢商品，總共3條商品，假設每頁就顯示1條商品，現在顯示第2頁，所以就查出來第2個商品 GET /ecommerce/product/_search { "query": { "match_all": {} }, "from": 1, "size": 1 } 指定要查詢出來商品的名稱和價格就可以 GET /ecommerce/product/_search { "query": { "match_all": {} }, "_source": ["name", "price"] } 更加適合生產環境的使用，可以構建復雜的查詢 復制代碼 ~~~ ### 3、query filter ~~~ 搜索商品名稱包含yagao，而且售價大于25元的商品 GET /ecommerce/product/_search { "query" : { "bool" : { "must" : { "match" : { "name" : "yagao" } }, "filter" : { "range" : { "price" : { "gt" : 25 } } } } } } 復制代碼 ~~~ ### 4、full-text search（全文檢索） ~~~ GET /ecommerce/product/_search { "query" : { "match" : { "producer" : "yagao producer" } } } { "took": 4, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 4, "max_score": 0.70293105, "hits": [ { "_index": "ecommerce", "_type": "product", "_id": "4", "_score": 0.70293105, "_source": { "name": "special yagao", "desc": "special meibai", "price": 50, "producer": "special yagao producer", "tags": [ "meibai" ] } }, { "_index": "ecommerce", "_type": "product", "_id": "1", "_score": 0.25811607, "_source": { "name": "gaolujie yagao", "desc": "gaoxiao meibai", "price": 30, "producer": "gaolujie producer", "tags": [ "meibai", "fangzhu" ] } }, { "_index": "ecommerce", "_type": "product", "_id": "3", "_score": 0.25811607, "_source": { "name": "zhonghua yagao", "desc": "caoben zhiwu", "price": 40, "producer": "zhonghua producer", "tags": [ "qingxin" ] } }, { "_index": "ecommerce", "_type": "product", "_id": "2", "_score": 0.1805489, "_source": { "name": "jiajieshi yagao", "desc": "youxiao fangzhu", "price": 25, "producer": "jiajieshi producer", "tags": [ "fangzhu" ] } } ] } } 復制代碼 ~~~ ### 5、phrase search（短語搜索） ~~~ 跟全文檢索相對應，相反，全文檢索會將輸入的搜索串拆解開來，去倒排索引里面去一一匹配，只要能匹配上任意一個拆解后的單詞，就可以作為結果返回 phrase search，要求輸入的搜索串，必須在指定的字段文本中，完全包含一模一樣的，才可以算匹配，才能作為結果返回 GET /ecommerce/product/_search { "query" : { "match_phrase" : { "producer" : "yagao producer" } } } { "took": 11, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 1, "max_score": 0.70293105, "hits": [ { "_index": "ecommerce", "_type": "product", "_id": "4", "_score": 0.70293105, "_source": { "name": "special yagao", "desc": "special meibai", "price": 50, "producer": "special yagao producer", "tags": [ "meibai" ] } } ] } } 復制代碼 ~~~ ### 6、highlight search（高亮搜索結果） ~~~ GET /ecommerce/product/_search { "query" : { "match" : { "producer" : "producer" } }, "highlight": { "fields" : { "producer" : {} } } } 復制代碼 ~~~ ## 五、聚合分析 ### 1、計算每個tag下的商品數量 ~~~ GET /ecommerce/product/_search { "size": 0, "aggs": { "all_tags": { "terms": { "field": "tags" } } } } { "took": 20, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 4, "max_score": 0, "hits": [] }, "aggregations": { "group_by_tags": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "fangzhu", "doc_count": 2 }, { "key": "meibai", "doc_count": 2 }, { "key": "qingxin", "doc_count": 1 } ] } } } 復制代碼 ~~~ ### 3、對名稱中包含yagao的商品，計算每個tag下的商品數量 ~~~ GET /ecommerce/product/_search { "size": 0, "query": { "match": { "name": "yagao" } }, "aggs": { "all_tags": { "terms": { "field": "tags" } } } } 復制代碼 ~~~ ### 4、先分組，再算每組的平均值，計算每個tag下的商品的平均價格 ~~~ GET /ecommerce/product/_search { "size": 0, "aggs" : { "group_by_tags" : { "terms" : { "field" : "tags" }, "aggs" : { "avg_price" : { "avg" : { "field" : "price" } } } } } } { "took": 8, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 4, "max_score": 0, "hits": [] }, "aggregations": { "group_by_tags": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "fangzhu", "doc_count": 2, "avg_price": { "value": 27.5 } }, { "key": "meibai", "doc_count": 2, "avg_price": { "value": 40 } }, { "key": "qingxin", "doc_count": 1, "avg_price": { "value": 40 } } ] } } } 復制代碼 ~~~ ### 5、計算每個tag下的商品的平均價格，并且按照平均價格降序排序 ~~~ GET /ecommerce/product/_search { "size": 0, "aggs" : { "all_tags" : { "terms" : { "field" : "tags", "order": { "avg_price": "desc" } }, "aggs" : { "avg_price" : { "avg" : { "field" : "price" } } } } } } 復制代碼 ~~~ ### 6、按照指定的價格范圍區間進行分組，然后在每組內再按照tag進行分組，最后再計算每組的平均價格 ~~~ GET /ecommerce/product/_search { "size": 0, "aggs": { "group_by_price": { "range": { "field": "price", "ranges": [ { "from": 0, "to": 20 }, { "from": 20, "to": 40 }, { "from": 40, "to": 50 } ] }, "aggs": { "group_by_tags": { "terms": { "field": "tags" }, "aggs": { "average_price": { "avg": { "field": "price" } } } } } } } } ~~~ 作者：Leo\_CX330 鏈接：https://juejin.cn/post/6908348672308838408 來源：掘金 著作權歸作者所有。商業轉載請聯系作者獲得授權，非商業轉載請注明出處。