ElasticSearch分页查询的实现
1、设置mapping
PUT /t_order { "settings": { "number_of_shards": 1, "number_of_replicas": 1 }, "mappings" : { "properties" : { "cancel_reason" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "cancel_time" : { "type" : "date" }, "create_time" : { "type" : "date" }, "create_user" : { "type" : "long" }, "delivery_type" : { "type" : "byte" }, "discount_amount" : { "type" : "integer" }, "expired_time" : { "type" : "date" }, "id" : { "type" : "long" }, "is_deleted" : { "type" : "byte" }, "is_pay" : { "type" : "byte" }, "is_postsale" : { "type" : "byte" }, "order_amount" : { "type" : "integer" }, "order_code" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "order_remark" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "order_status" : { "type" : "byte" }, "pay_amount" : { "type" : "integer" }, "pay_time" : { "type" : "date" }, "pay_type" : { "type" : "byte" }, "postage" : { "type" : "integer" }, "product_amount" : { "type" : "integer" }, "serial_code" : { "type" : "text", "fields" : { "keyword" : { "type" : "keyword", "ignore_above" : 256 } } }, "shop_id" : { "type" : "long" }, "update_time" : { "type" : "date" }, "update_user" : { "type" : "long" }, "user_id" : { "type" : "long" } } } POST /t_order/_search { "query": { "match_all": {} } }
2、添加测试数据
POST /t_order/_bulk
{
"id": 202208780570360889300,
"order_code": "20222379790329301675",
"order_amount": 1,
"pay_amount": 1,
"discount_amount": 0,
"product_amount": 1,
"order_status": 0,
"is_deleted": 0,
"user_id": 202208761977967681500,
"shop_id": 117979,
"expired_time": 1648558094000,
"postage": 0,
"cancel_time": 1648556595000,
"cancel_reason": "订单逾期未支付系统自动取消订单",
"order_remark": "",
"delivery_type": 2,
"pay_time": null,
"pay_type": 1,
"is_pay": 0,
"is_postsale": 0,
"create_time": 1648556294000,
"create_user": 1508761977967681500,
"update_time": 1648556595000,
"update_user": 999999
}
3、演示(show me the code):
### from + size [深度翻页不推荐使用 From + size]
#from + size 两个参数定义了结果页面显示数据的内容。
#from:未指定,默认值是 0,注意不是1,代表当前页返回数据的起始值。
#size:未指定,默认值是 10,代表当前页返回数据的条数。
POST /ds-trade_t_order/_search { "from": 0, "size": 20, "query": { "match_all": {} } }
### searchAfter [官方文档强调:不再建议使用scroll API进行深度分页。如果要分页检索超过 Top 10,000+ 结果时,推荐使用:PIT + search_after。]
#part1:创建 PIT 视图,这是前置条件不能省。
POST /ds-trade_t_order/_pit?keep_alive=5m
#part2:创建基础查询语句,这里要设置翻页的条件。
POST /_search { "size": 20, "track_total_hits": true, "query": { "match_all": {} }, "pit": { "id": "l9G1AwEQZHMtdHJhZGVfdF9vcmRlchY2M3VFTm9uZ1RrT1ltbWx5RDZvQllnABZaeDFQbHhSMVJGNktBZm5kakxqYTZBAAAAAAAAIhFCFml1Uy1Kb21pU2Zxdlc4OHhfWE1aSkEAARY2M3VFTm9uZ1RrT1ltbWx5RDZvQllnAAA=" }, "sort": [ { "create_time": { "order": "desc" } } ] }
#part3:实现后续翻页:后续翻页都需要借助 search_after 指定前一页的最后一个文档的 sort 字段值。
POST /_search { "size": 20, "track_total_hits": true, "query": { "match_all": {} }, "pit": { "id": "l9G1AwEQZHMtdHJhZGVfdF9vcmRlchY2M3VFTm9uZ1RrT1ltbWx5RDZvQllnABZaeDFQbHhSMVJGNktBZm5kakxqYTZBAAAAAAAAIhFCFml1Uy1Kb21pU2Zxdlc4OHhfWE1aSkEAARY2M3VFTm9uZ1RrT1ltbWx5RDZvQllnAAA=" }, "sort": [ { "create_time": { "order": "desc" } } ], "search_after": [ 1648557674000, 7 ] }
###scroll [全量或数据量很大时遍历结果数据,而非分页查询。]
#part1:指定检索语句同时设置 scroll 上下文保留时间
POST /t_order/_search?scroll=3m { "size": 20, "query": { "match_all": {} } , "sort": [ { "create_time": { "order": "desc" } } ] }
#part2:指定检索语句同时设置 scroll 上下文保留时间
POST /_search/scroll { "scroll":"3m", "scroll_id":"FGluY2x1ZGVfY29udGV4dF91dWlkDXF1ZXJ5QW5kRmV0Y2gBFml1Uy1Kb21pU2Zxdlc4OHhfWE1aSkEAAAAAACIXbhZaeDFQbHhSMVJGNktBZm5kakxqYTZB" }
总结:
From+ size:需要随机跳转不同分页(类似主流搜索引擎)、Top 10000 条数据之内分页显示场景。
search_after:仅需要向后翻页的场景及超过Top 10000 数据需要分页场景。
Scroll:需要遍历全量数据场景 。而非翻页的场景(翻页场景scrol id 最多打开500个)。
max_result_window:调大治标不治本,不建议调过大。
PIT:本质是视图。
另外:根据实际经验得出一些参考意见:
1、search_after pit 不适用于商品列表分页查询(类似京猫这种商品列表),因为用户从商品列表进入商品详情,长时间停留在详情页查看后,返回商品列表继续翻页,此时
keep_alive 已经过期,出现无法翻页的错误。
2、Scroll 也会有上述问题。同时scroll也会有连接过多的问题,不适用于分页场景。
To prevent against issues caused by having too many scrolls open, the user is not allowed to open scrolls past a certain limit. By default, the maximum number of open scrolls is 500. This limit can be updated with the search.max_open_scroll_context cluster setting.
参考资料————————————————
版权声明:本文为CSDN博主「铭毅天下」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
原文链接:https://blog.csdn.net/laoyang360/article/details/116472697