From bf63d7ef7f5736a31447c62af23c183544d98447 Mon Sep 17 00:00:00 2001 From: yupi <592789970@qq.com> Date: Sat, 14 May 2022 17:00:16 +0800 Subject: [PATCH] =?UTF-8?q?add=20=E6=94=AF=E6=8C=81=E5=AF=BC=E5=85=A5?= =?UTF-8?q?=E5=A4=8D=E6=9D=82=E7=94=A8=E4=BE=8B=20update=20=E8=A1=A5?= =?UTF-8?q?=E5=85=85=20README.md=20=E7=9A=84=E5=A4=8D=E6=9D=82=E4=BE=8B?= =?UTF-8?q?=E5=AD=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 68 ++++++++++++++++++++++++++ doc/assets/complex-example-result.png | Bin 0 -> 563669 bytes doc/assets/complex-example.png | Bin 0 -> 84554 bytes src/App.vue | 10 +++- src/examples/complex.json | 31 ++++++++++++ src/examples/index.ts | 2 + src/generator/index.ts | 3 ++ 7 files changed, 113 insertions(+), 1 deletion(-) create mode 100644 doc/assets/complex-example-result.png create mode 100644 doc/assets/complex-example.png create mode 100644 src/examples/complex.json diff --git a/README.md b/README.md index e07c7a9..adeb856 100644 --- a/README.md +++ b/README.md @@ -58,6 +58,8 @@ from 当然,以上只是一个示例,真实大数据离线分析的场景下,SQL 可比这复杂 N 倍! +如果感兴趣的话,欢迎往下看文档,还有更复杂的示例~ + ## 优势 1. 支持在线编辑 JSON 和 SQL,支持代码高亮、语法校验、一键格式化、查找和替换、代码块折叠等,体验良好 @@ -99,6 +101,72 @@ from `@xxx(yy = 1 ||| zz = #{变量})`:引用其他 SQL,可传参,参数可再用变量来表示,使用 |||(三个竖线)来分隔参数。 +## 复杂示例 + +需求:用一句 SQL 查询出以下表格 + +![](./doc/assets/complex-example.png) + +这个表格的难点在哪? + +1. 查汇总和查明细的粒度不同,不能用 group by 区分,只能用 union(红色) +2. 分类列中不同行的数据有交叉,不能用 group by 区分,只能用 union( +3. 每一列由多张表共同 join 而成,且不同分类可关联的表不同,须进行区分(灰色表示无法关联),并将缺失的字段补齐(否则无法 union) +4. 不同行的同一列计算公式可能不同(蓝色) +5. 不同列的过滤条件不同(比如最后两列墨绿色是要查全校,其余列只查 1 年级) +6. 要查询同环比,只能用 2 份完整的数据去 join 然后错位计算来得出 + +显然,这个表中很多查询逻辑是重复但又不同的。 + +这么算下来,最后这个 SQL 中到底会包含多少个基础表的 select 呢?每个基础表查询要重复编写多少遍呢? + +然而,这个表格也只是鱼皮对实际需求简化后才得来的,实际需求比这还复杂几倍! + +可想而知,人工写有多恶心?! + +但是使用本工具,只需编写如下结构化的 JSON: + +```json +{ + "main": "select (a / b - 1) from (@查整体(date = 今天)) a, (@查整体(date = 昨天)) b", + "查整体": "@查年级() union @查1班() union @查2班() where date = #{date}", + "查年级": "@查汇总_性别汇总() union @查汇总_性别分组() union @查汇总_爱好汇总() union @查汇总_爱好分组() union @查汇总_电脑类别汇总() union @查汇总_电脑类别分组()", + "查汇总_性别汇总": "@查除电脑关联表()", + "查汇总_性别分组": "@查除电脑关联表() group by 性别", + "查汇总_爱好汇总": "@查除电脑关联表()", + "查汇总_爱好分组": "@查除电脑关联表() where 爱好 in (xx) group by 爱好", + "查汇总_电脑类别汇总": "@查除三连和学习表()", + "查汇总_电脑类别分组": "@查除三连和学习表() group by 电脑类别", + + "查1班": "@查1班_性别汇总() union @查1班_性别分组() union @查1班_爱好汇总() union @查1班_爱好分组() union @查1班_电脑类别汇总() union @查汇总_电脑类别分组()", + "查1班_性别汇总": "@查除电脑关联表() where 1班", + "查1班_性别分组": "@查除电脑关联表() where 1班 group by 性别", + "查1班_爱好汇总": "@查除电脑关联表() where 1班", + "查1班_爱好分组": "@查除电脑关联表() where 1班 and 爱好 in (xx) group by 爱好", + "查1班_电脑类别汇总": "@查除三连和学习表() where 1班", + "查1班_电脑类别分组": "@查除三连和学习表() where 1班 group by 电脑类别", + + "查2班": "@查2班_性别汇总() union @查2班_性别分组() union @查2班_电脑类别汇总() union @查2班_电脑类别分组()", + "查2班_性别汇总": "@查除电脑关联表() where 2班", + "查2班_性别分组": "@查除电脑关联表() where 2班 group by 性别", + "查2班_电脑类别汇总": "@查除三连和学习表() where 2班", + "查2班_电脑类别分组": "@查除三连和学习表() where 2班 group by 电脑类别", + + "查所有关联表": "@查信息表() left join (@查三连表()) left join (@查学习表()) left join (@查电脑表()) left join (@查全校信息())", + "查除电脑关联表": "@查信息表() left join (@查三连表()) left join (@查学习表()) left join (@查全校信息())", + "查除三连和学习表": "@查信息表() left join (@查电脑表()) left join (@查全校信息())", + "查信息表": "select 字段 from 信息表 where 年级 = 1", + "查三连表": "select 字段 from 三连表 where 年级 = 1", + "查学习表": "select 字段 from 学习表 where 年级 = 1", + "查电脑表": "select 字段 from 电脑表 where 年级 = 1", + "查全校信息": "select 字段 from 信息表" +} +``` + +就能自动生成 SQL 了,还可以查看调用关系,非常清晰: + +![](./doc/assets/complex-example-result.png) + ## 实现 diff --git a/doc/assets/complex-example-result.png b/doc/assets/complex-example-result.png new file mode 100644 index 0000000000000000000000000000000000000000..935a6a187a64b1c1458ebdfbba21c74dd46fc591 GIT binary patch literal 563669 zcmZs=2UJsSvj(an7F0lrAXTMzrS~YkNDxAi8l_6_9TEir=}HlV01843C`gk|M0yti z>4X+~3pF9_hVMJ)zvrBLcNS!=4e#ukXUa1(JMIZk`vwgg&AD^uZs_W07*oE0=gwWu 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