基于Elasticsearch 为电商提供商品数据大数据查询

前言

对于现代电商的产品，维度的多员花，与一套强大的搜索引擎，那是非常必要的。今天我们主要是描述我们在从事电商搜索引擎过程中的遇到的一些问题和经验分享。

过程

数据准备

1、我们准备为我们需要做查找的数据做好一张视图，方便我们分析数据查找维度，与查找场景需求。附加代码，对于Mysql 创建视图不清楚的，可以自行查找具体的文档了解，在我们完成视图创建后，我们就已经有了一张视图表，供我们数据使用。

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、创建查询索引，在创建这块的时候，需要主要创建过程中的类型的选择，方便您在查询过程中可以应用的更准确与方便。

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"
       }
     }
 }
}