基于Elasticsearch 為電商提供商品資料大資料查詢
前言
對于現代電商的產品，維度的多員花，與一套強大的搜索引擎，那是非常必要的，今天我們主要是描述我們在從事電商搜索引擎程序中的遇到的一些問題和經驗分享，
程序
資料準備
1、我們準備為我們需要做查找的資料做好一張視圖，方便我們分析資料查找維度，與查找場景需求，附加代碼，對于Mysql 創建視圖不清楚的，可以自行查找具體的檔案了解，在我們完成視圖創建后，我們就已經有了一張視圖表，供我們資料使用，
select `g`.`goods_id` AS `goods_id`,`g`.`publisher_sn` AS `publisher_sn`,`g`.`add_time` AS `add_time`,`g`.`last_update` AS `last_update`,`g`.`goods_name` AS `goods_name`,`g`.`fineness` AS `fineness`,`g`.`look` AS `look`,`g`.`cat_path` AS `cat_path`,`g`.`goods_number` AS `goods_number`,`g`.`shop_price` AS `shop_price`,`g`.`goods_weight` AS `weight`,`g`.`keywords` AS `keywords`,`g`.`goods_desc` AS `goods_desc`,`g`.`isbn` AS `isbn`,`a`.`attr_value` AS `author`,`b`.`attr_value` AS `publisher`,`c`.`attr_value` AS `yiname`,`m`.`age` AS `age`,`m`.`press_intro` AS `press_intro`,`m`.`author_info` AS `author_info`,`m`.`media_intro` AS `media_intro`,`m`.`catalog` AS `catalog`,`m`.`prologue` AS `prologue`,`m`.`selling_point_1` AS `selling_point_1`,`m`.`selling_point_2` AS `selling_point_2`,`m`.`selling_point_3` AS `selling_point_3`,`m`.`detail_intro_1` AS `detail_intro_1`,`m`.`detail_intro_2` AS `detail_intro_2`,`m`.`detail_intro_3` AS `detail_intro_3`,`m`.`wtao_intro` AS `wtao_intro`,`m`.`video_intro` AS `video_intro`,`co`.`positive` AS `positive`,`co`.`negative` AS `negative`,`s`.`name` AS `series_name`,`s`.`name_cn` AS `series_name_cn`,`v`.`title` AS `v_title`,`v`.`article` AS `v_article`,`k`.`bunch_no` AS `bunch_no` from ((((((((`sd_goods` `g` left join `sd_goods_attr` `c` on((`g`.`goods_id` = `c`.`goods_id`))) left join `sd_goods_attr` `a` on((`g`.`goods_id` = `a`.`goods_id`))) left join `sd_goods_attr` `b` on((`g`.`goods_id` = `b`.`goods_id`))) left join `sd_goods_more` `m` on((`g`.`goods_id` = `m`.`goods_id`))) left join `sd_cover_text` `co` on((`g`.`isbn` = `co`.`isbn`))) left join `sd_series_name` `s` on((`g`.`isbn` = `s`.`isbn`))) left join `nosql`.`video_words_result` `v` on((`g`.`isbn` = `v`.`isbn`))) left join `sd_bunch` `k` on((`g`.`isbn` = `k`.`isbn`))) where ((`c`.`attr_id` = 1) and (`a`.`attr_id` = 2) and (`b`.`attr_id` = 3))
2、創建查詢索引，在創建這塊的時候，需要主要創建程序中的型別的選擇，方便您在查詢程序中可以應用的更準確與方便，
PUT /products
{
"settings": {
"number_of_shards": 5,
"number_of_replicas": 1
},
"mappings": {
"properties": {
"goods_id":{
"type": "text"
},
"publisher_sn":{
"type": "text"
},
"goods_name": {
"type": "text",
"analyzer": "ik_smart"
},
"keywords": {
"type": "text",
"analyzer": "ik_smart"
},
"weight":{
"type":"keyword"
},
"goods_desc": {
"type": "text",
"analyzer": "ik_smart"
},
"author": {
"type": "text",
"analyzer": "ik_smart"
},
"publisher": {
"type": "text",
"analyzer": "ik_smart"
},
"yiname": {
"type": "text",
"analyzer": "ik_smart"
},
"fineness":{
"type": "text"
},
"look":{
"type": "text"
},
"isbn":{
"type": "text"
},
"age":{
"type": "text"
},
"press_intro": {
"type": "text",
"analyzer": "ik_smart"
},
"author_info": {
"type": "text",
"analyzer": "ik_smart"
},
"media_intro": {
"type": "text",
"analyzer": "ik_smart"
},
"positive": {
"type": "text",
"analyzer": "ik_smart"
},
"negative": {
"type": "text",
"analyzer": "ik_smart"
},
"series_name": {
"type": "text",
"analyzer": "ik_smart"
},
"series_name_cn": {
"type": "text",
"analyzer": "ik_smart"
},
"v_title":{
"type": "text",
"analyzer": "ik_smart"
},
"v_article":{
"type": "text",
"analyzer": "ik_smart"
}
}
}
}
3、索引資料的添加，資料的添加方式更多的看具體的團隊的情況，我們這邊主要是使用Canal 來幫助我們完成資料的添加與新增資料的添加，在使用Canal的時候，需要有JAVA經驗，會更好的解決一些同步程序中的問題，
4、對于未使用現成資料同步工具的，自己也是可以根據具體場景寫Hook 來完成資料的添加，對于有不清楚的地方，可以聯系我們了解，
5、對于 Elasticsearch 的部署搭建不熟悉的同步，可以參考我們的 Docker-composer 快速部署方式，
資料使用
資料查找應用，基于 SDK 查找, 對于 Query DSL 不熟悉的同步可以基于我們前面的 How to build a OR condition in Elasticsearch Query DSL 了解更多
本文來自 PMOSH，作者：PMOSH，轉載請注明原文鏈接：https://www.cnblogs.com/pmo-sh/p/e-commerce-product-use-by-elasticsearch.html
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標籤：大數據
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