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4.8.0 单表列表 性能优化前后对比 #315

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4.8.0 单表列表 性能优化前后对比#315
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@TommyLemon
TommyLemon
opened on Sep 26, 2021

经过 ed036ef 这次优化，
4.8.0 对比 4.7.2 在 Log.DEBUG = true(开启日志)的情况下：

TestRecord[] 耗时降低至原来 73%，性能提升 27% 至原来 1.3 倍；
Moment[] 耗时降低至原来 80%，性能提升 20% 至原来 1.2 倍；
朋友圈列表耗时降低至原来 81%，性能提升 19% 至原来 1.2 倍。

其中每个数组都按 100 条来测试，如果每页数量更大或每项数据量更大，则提升会更加明显。

Activity

  1. TommyLemon
    closed this as completedon Sep 26, 2021

  2. TommyLemon commented on Sep 27, 2021

    @TommyLemon
    TommyLemon
    on Sep 27, 2021
    MemberAuthor

    主要因为省掉了 AbstractObjectParser.onSQLExecute 878 行 for 循环 put 100 条数据（有日志） 3ms-8ms （MacBook Pro（13 英寸，2015 年初期）2.9 GHz Intel Core i5 16 GB 1867 MHz DDR3 MacOS 10.11.6 ）
    https://github.com/Tencent/APIJSON/blob/master/APIJSONORM/src/main/java/apijson/orm/AbstractObjectParser.java

    以及 AbstractParser.onArrayParse 中用 1 次 addAll 替代 100 次 add 减少不必要的 ArrayList 频繁校验和扩容
    https://github.com/Tencent/APIJSON/blob/master/APIJSONORM/src/main/java/apijson/orm/AbstractParser.java

  3. TommyLemon commented on Nov 22, 2021

    @TommyLemon
    TommyLemon
    on Nov 22, 2021
    MemberAuthor

    腾讯 CSIG 某项目性能测试结果

    1. 测试环境

    1.1 机器配置

    腾讯云tke docker pod， 4 核 / 16G。

    1.2 db机器配置

    腾讯云8核32000MB内存，805GB存储空间

    1.3 测试表建表DML、数据量（mysql 5.7）

    CREATE TABLE `t_xxxx_xxxx` (
    `x_id` bigint(11) unsigned NOT NULL AUTO_INCREMENT,
    `x_xxxx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xID',
    `x_xid` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xxID',
    `x_xx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xID',
    `x_xxxx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xxxID',
    `x_xxxxx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xxID',
    `x_xxxx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xxID',
    `x_uin` bigint(20) unsigned NOT NULL DEFAULT '0' COMMENT 'xxuin',
    `x_send_time` datetime DEFAULT NULL COMMENT '推送消息时间',
    `x_xxxx_result` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xx结果',
    `x_xxx_xxxx_result` varchar(255) DEFAULT '' COMMENT 'xx结果',
    `x_result` tinyint(4) unsigned NOT NULL DEFAULT '0' COMMENT '0错误 1正确 2未设置',
    `x_create_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间, 落地时间',
    `x_credit` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xx数量',
    `x_xxxxxx_xxx_id` varchar(32) NOT NULL COMMENT '公共参数, 上报应用',
    `x_xxxxxx_source` varchar(32) NOT NULL COMMENT '公共参数, 上报服务名',
    `x_xxxxxx_server` varchar(32) NOT NULL COMMENT '公共参数, 上报服务端ip',
    `x_xxxxxx_event_time` datetime NOT NULL COMMENT '公共参数, 上报时间',
    `x_xxxxxx_client` varchar(32) NOT NULL COMMENT '公共参数, 客户端ip',
    `x_xxxxxx_trace_id` varchar(64) NOT NULL COMMENT '公共参数',
    `x_xxxxxx_sdk` varchar(16) NOT NULL COMMENT '公共参数, sdk版本',
    PRIMARY KEY (`x_id`, `x_uin`),
    UNIQUE KEY `udx_uid_xxxxid` (`x_uin`, `x_xxxx_id`),
    KEY `idx_xid` (`x_xid`)
  ) ENGINE = InnoDB DEFAULT CHARSET = utf8mb4  COMMENT = 'xx事件表';

    • 数据量：18558903

    • mysql版本：5.7

    • 数据分布：使用group by 统计，基于其中x_xid来group by，得到以下表格：

    select x_xid, count(x_id) counter from t_xxxx_xxxx group by x_xid order by counter desc limit 10;

    x_xid counter
    xxxx36 696376
    xxxx38 418576
    xxxx63 384503
    xxxx40 372080
    xxxx41 301364
    xxxx08 248243
    xxxx46 223820
    xxxx07 220234
    xxxx44 207721
    xxxx02 152795

    1.4 日志打印设置

    Log.DEBUG = false;

AbstractParser.IS_PRINT_REQUEST_STRING_LOG = false;
AbstractParser.IS_PRINT_REQUEST_ENDTIME_LOG = false;
AbstractParser.IS_PRINT_BIG_LOG = false;

    2. 测试脚本 (使用Table[]: {Table: {}}格式)

    脚本统计方式：

    • 基于linux time命令输出的realtime来统计。

    • 分2个场景测试：一个不带where条件、一个带x_xid in (xxxx36,xxxx38)的条件，该条件能匹配出100W+数据，方便覆盖10W-100W之间的任何数据量场景，这里事先用select x_xid, count(x_id) c from t_xxxx_xxxx group by x_xid order by c desc;这样的语句对表做了统计，x_xid=xxxx36有696376条记录，x_xid=xxxx38有418576条记录。

    脚本：apitest.sh

    #!/bin/bash
printf -- '--------------------------\n开始不带where条件的情况测试\n'
time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":100000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 10w_no_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":200000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 20w_no_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":500000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 50w_no_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":800000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 80w_no_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":1000000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 100w_no_where.log



printf -- '--------------------------\n开始带where条件的情况测试\n'
time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":100000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 10w_with_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":200000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 20w_with_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":500000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 50w_with_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":800000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 80w_with_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":1000000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 100w_with_where.log

    也就是 MySQL 5.7 共 1.9KW 记录的大表，统计 CRUL 10-20M/s 网速从发起请求到接收完回包的总时长

    数量级 4.7.0(5次取平均值) 4.8.0(5次取平均值) 是否正常回包 where条件 性能提升
    10W 1.739s 1.159s 是 无 50%。即((1/1.159-1/1.739)/(1/1.739))*100%
    20W 3.518s 2.676s 是 无 31.5%
    50W 9.257s 6.952s 是 无 33.2%
    80W 16.236s 10.697s -Xmx=3192M时无法正常回包，OOM错误，调大-Xmx参数后ok。 无 51.8%
    100W 19.748s 14.466s -Xmx=3192M时无法正常回包，OOM错误，调大-Xmx参数后ok 无 36.5%
    10W 1.928s 1.392s 是 "x_xid{}":[xxxx36,xxxx38]，覆盖数据超过100W数据。 38.5%
    20W 4.149s 2.852s 是 "x_xid{}":[xxxx36,xxxx38] 45.5%
    50W 10.652s 7.231s 是 "x_xid{}":[xxxx36,xxxx38] 47.3%
    80W 16.975s 12.465s 调整了-Xmx后正常回包 "x_xid{}":[xxxx36,xxxx38] 36.2%
    100W 20.632s 16.481s 调整了-Xmx后正常回包 "x_xid{}":[xxxx36,xxxx38] 25.2%
  4. TommyLemon
    reopened this on Nov 22, 2021

  5. TommyLemon commented on Nov 22, 2021

    @TommyLemon
    TommyLemon
    on Nov 22, 2021
    MemberAuthor

    腾讯 CSIG 某项目性能测试结果

    1. 测试环境

    1.1 机器配置

    腾讯云tke docker pod， 4 核 / 16G。

    1.2 db机器配置

    腾讯云8核32000MB内存，805GB存储空间

    1.3 测试表建表DML、数据量（mysql 5.7）

    CREATE TABLE `t_xxxx_xxxx` (
    `x_id` bigint(11) unsigned NOT NULL AUTO_INCREMENT,
    `x_xxxx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xID',
    `x_xid` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xxID',
    `x_xx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xID',
    `x_xxxx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xxxID',
    `x_xxxxx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xxID',
    `x_xxxx_id` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xxID',
    `x_uin` bigint(20) unsigned NOT NULL DEFAULT '0' COMMENT 'xxuin',
    `x_send_time` datetime DEFAULT NULL COMMENT '推送消息时间',
    `x_xxxx_result` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xx结果',
    `x_xxx_xxxx_result` varchar(255) DEFAULT '' COMMENT 'xx结果',
    `x_result` tinyint(4) unsigned NOT NULL DEFAULT '0' COMMENT '0错误 1正确 2未设置',
    `x_create_time` datetime NOT NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间, 落地时间',
    `x_credit` int(11) unsigned NOT NULL DEFAULT '0' COMMENT 'xx数量',
    `x_xxxxxx_xxx_id` varchar(32) NOT NULL COMMENT '公共参数, 上报应用',
    `x_xxxxxx_source` varchar(32) NOT NULL COMMENT '公共参数, 上报服务名',
    `x_xxxxxx_server` varchar(32) NOT NULL COMMENT '公共参数, 上报服务端ip',
    `x_xxxxxx_event_time` datetime NOT NULL COMMENT '公共参数, 上报时间',
    `x_xxxxxx_client` varchar(32) NOT NULL COMMENT '公共参数, 客户端ip',
    `x_xxxxxx_trace_id` varchar(64) NOT NULL COMMENT '公共参数',
    `x_xxxxxx_sdk` varchar(16) NOT NULL COMMENT '公共参数, sdk版本',
    PRIMARY KEY (`x_id`, `x_uin`),
    UNIQUE KEY `udx_uid_xxxxid` (`x_uin`, `x_xxxx_id`),
    KEY `idx_xid` (`x_xid`)
  ) ENGINE = InnoDB DEFAULT CHARSET = utf8mb4  COMMENT = 'xx事件表';
    • 数据量：18558903
    • mysql版本：5.7
    • 数据分布：使用group by 统计，基于其中x_xid来group by，得到以下表格：

    select x_xid, count(x_id) counter from t_xxxx_xxxx group by x_xid order by counter desc limit 10;

    x_xid counter
    xxxx36 696376
    xxxx38 418576
    xxxx63 384503
    xxxx40 372080
    xxxx41 301364
    xxxx08 248243
    xxxx46 223820
    xxxx07 220234
    xxxx44 207721
    xxxx02 152795

    1.4 日志打印设置

    Log.DEBUG = false;

AbstractParser.IS_PRINT_REQUEST_STRING_LOG = false;
AbstractParser.IS_PRINT_REQUEST_ENDTIME_LOG = false;
AbstractParser.IS_PRINT_BIG_LOG = false;

    2. 测试脚本 (使用Table[]: {Table: {}}格式)

    脚本统计方式：

    • 基于linux time命令输出的realtime来统计。
    • 分2个场景测试：一个不带where条件、一个带x_xid in (xxxx36,xxxx38)的条件，该条件能匹配出100W+数据，方便覆盖10W-100W之间的任何数据量场景，这里事先用select x_xid, count(x_id) c from t_xxxx_xxxx group by x_xid order by c desc;这样的语句对表做了统计，x_xid=xxxx36有696376条记录，x_xid=xxxx38有418576条记录。

    脚本：apitest.sh

    #!/bin/bash
printf -- '--------------------------\n开始不带where条件的情况测试\n'
time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":100000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 10w_no_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":200000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 20w_no_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":500000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 50w_no_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":800000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 80w_no_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":1000000, "T_xxxx_xxxx":{"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 100w_no_where.log



printf -- '--------------------------\n开始带where条件的情况测试\n'
time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":100000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 10w_with_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":200000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 20w_with_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":500000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 50w_with_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":800000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 80w_with_where.log

time curl  -X POST -H 'Content-Type:application/json' 'http://x.xxx.xx.xxx:xxxx/get' -d '{"T_xxxx_xxxx[]":{"count":1000000, "T_xxxx_xxxx":{"x_xid{}":[xxxx36,xxxx38],"@column":"x_uin,x_send_time,x_xxxx_id,x_xid,x_xx_id,x_xxxxx_id,x_xxxx_result,x_result,x_credit"}}}' > 100w_with_where.log

    也就是 MySQL 5.7 共 1.9KW 记录的大表，统计 CRUL 10-20M/s 网速从发起请求到接收完回包的总时长

    数量级 4.7.0(5次取平均值) 4.8.0(5次取平均值) 是否正常回包 where条件 性能提升
    10W 1.739s 1.159s 是 无 50%。即((1/1.159-1/1.739)/(1/1.739))*100%
    20W 3.518s 2.676s 是 无 31.5%
    50W 9.257s 6.952s 是 无 33.2%
    80W 16.236s 10.697s -Xmx=3192M时无法正常回包，OOM错误，调大-Xmx参数后ok。 无 51.8%
    100W 19.748s 14.466s -Xmx=3192M时无法正常回包，OOM错误，调大-Xmx参数后ok 无 36.5%
    10W 1.928s 1.392s 是 "x_xid{}":[xxxx36,xxxx38]，覆盖数据超过100W数据。 38.5%
    20W 4.149s 2.852s 是 "x_xid{}":[xxxx36,xxxx38] 45.5%
    50W 10.652s 7.231s 是 "x_xid{}":[xxxx36,xxxx38] 47.3%
    80W 16.975s 12.465s 调整了-Xmx后正常回包 "x_xid{}":[xxxx36,xxxx38] 36.2%
    100W 20.632s 16.481s 调整了-Xmx后正常回包 "x_xid{}":[xxxx36,xxxx38] 25.2%

    以之前的 APIJSON 4.6.0 连接 2.3KW 大表带条件查出 12W+ 数据来估计：
    https://github.com/Tencent/APIJSON/releases/tag/4.6.0

    ---------- | ---------- | ---------- | ---------- | -------------
    Total | Received | Time Total | Time Spent | Current Speed
    72.5M | 72.5M | 0:00:05 | 0:00:05 | 20.0M

    /get >> http请求结束:5624

    4.6.0-4.7.2 都没有明显的性能优化，所以 4.7.0 只花了约 2s 应该是因为换了张表，平均每行数据量减少了约 65% 为原来的 35%。
    APIJSON 4.6.0 查原来 2.3KW 大表中 100W 数据按新旧表数据量比例估计耗时 = 20.632s / 65% = 31.7s；
    APIJSON 4.6.0 查原来 2.3KW 大表中 100W 数据按同表查出数据量比例估计耗时 = 100W*72.5M/(12-13W)/(20M/s) = 27.9s-30.2s。

    两种方式估算结果基本一致，也可以按这个 35% 新旧表平均每行数据量比例估算排除网络耗时后的整个服务耗时：

    APIJSON 4.6.0 查原来 2.3KW 大表中 12W+ 数据量服务耗时 = 总耗时 5.624s - 数据 72.5M/下载速度 20.0Mbps = 2.00s；
    APIJSON 4.7.0 查现在 1.9KW 大表中 10W 数据量服务耗时 = 总耗时 1.928s - 数据 72.5M*35%(10/12)/(下载速度 10-20.0Mbps) = 0.00-0.87s；
    APIJSON 4.8.0 查现在 1.9KW 大表中 10W 数据量服务耗时 = 总耗时 1.392s - 数据 72.5M*35%    (10/12)/(下载速度 10-20.0Mbps) = 0.00-0.33s，降低 62%；
    APIJSON 4.7.0 查现在 1.9KW 大表中 100W 数据量服务耗时 = 总耗时 20.632s - 数据 725M*35%(10/12)/(下载速度 10-20.0Mbps) = 0.00-10.06s；
    APIJSON 4.8.0 查现在 1.9KW 大表中 100W 数据量服务耗时 = 总耗时 16.481s - 数据 725M*35%    (10/12)/(下载速度 10-20.0Mbps) = 0.00-5.91s，降低 41%。

  6. TommyLemon
    added
    Performance 性能
    on Nov 26, 2021

  7. TommyLemon commented on Jan 9, 2022

    @TommyLemon
    TommyLemon
    on Jan 9, 2022
    MemberAuthor

    腾讯业务百万数据 6s 响应，APIJSON 性能优化背后的故事
    https://my.oschina.net/tommylemon/blog/5375645
    image

  8. TommyLemon
    changed the title [-]单表列表 性能优化前后对比[/-] [+]4.8.0 单表列表 性能优化前后对比[/+] on Jan 9, 2022
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