TERADATA SQL学习随笔<一>

此博客内容简介及目录

http://www.cnblogs.com/weibaar/p/6644261.html

最近在TERADATA环境学习SQL。在这里记录一下学习中查过的知识点，作为备案。

目录：

1. 关于SQL学习及所用在线数据库
2. 表联合 （join）
3. SQL子查询
4. 在select时创建新字段 (as, case when)
5. 数据分组 （group by + 聚合函数count, sum, avg等）
6. 利用over (partition by)进行数据分组并创建新字段
7. 样本选择

1、关于SQL学习及所用在线数据库

之前有看过一些SQL学习的书。但如果从学习效率来说，跟着书学习SQL，不如直接看生产环境的工作代码，遇到语句不懂时搜索引擎查找相应语句，效率会更高（例如本文就是此类的集中体现）。

当然，除了零星的知识点以外，网上还存在一些在线练习SQL取数的网站，在这里列举如下：

1）基础SQL测试： http://www.w3school.com.cn/quiz/quiz.asp?quiz=sql

　　　　　　　　   http://sqlzoo.net/

2）基本语法练习-CodeCamdy： https://www.codecademy.com/zh/learn/learn-sql

3）在线直连数据库练习 http://www.sqlcourse.com

　　　　　　　　　　　https://livesql.oracle.com/apex/livesql/file/index.html

　　　　　　　　　　　http://www.sql-ex.ru/

当然，我们也可以去下一些基础练习数据库，安装mySql等进行练习。不过一般入门，学语法比学怎么装数据库更重要。见仁见智吧。

另外，相比于简单易懂的SQL语法学习，如何优化SQL语句效率，如何理解数据库结构等等更加重要。这也是下一步我学习的重点。 具体参考这个链接 https://www.zhihu.com/question/20116482

那么接下来，我选用oracle数据库来练习最近查阅的知识点。

在这个网站注册　https://livesql.oracle.com/apex/livesql/file/index.html 后，在code library选择【EMP and DEPT 】数据源导入，会在我们自己账户里创建以下表： dept, emp。以下代码均以此为实例改写。下图为两个表概览

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" 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xw7lop++3Xe2MGrBvuLMqDhfGQomwFfMfgZ8hcyp1LWL/fKVDkO+Eqp6IYtUF20+vdeHvS9kSmfxFdLQmte4GjHOhoSmEax2iSFWvo6kVO0Wsb7iKcoYRud0xIR/8OUd0Cf3laXkBqyv3Nwsp+5+IPtuJcF+xTmnE6ywPYseqHhDvmKofchXqJxKXbso0lfCDy/4LJKJ7xTjvCFYxcNTelNfIS9XLB3U/zD3Xs5mrZ3dQtMYVrvEeF8RUrVjRPoKzb67QKIF6LVRR6epfWWgcgq+0k1fa/aax9nDF4SjEegrlnvYM5SIxoK34BGu/GJ8hXoZn1OxayI5gBVhM18ZsQceUKbbSZ9iT7jhc4yf0l1rpK/4HbmLE/+Ur+feiaX9utAUzWrLGO0rUqp2jChfoQbMUIa0A3WlaJaieoUBs1/pzpGl/cpwMRyH0N+DufIyC3vElVXTO7XkDfKVsHOwpgNv5cl17SLSV5zUpT4l2MhXyDLAEOazKO9XLAbkeSqdneEpncxXbjxfIJbi5j7Eu2ISmjZgZcFZNiWv3rH7FTJVO0aMrzDl1XPSMVOpb8pwby+wEX3lpj7Klu5XtuorTNbMGNxQw5fDVJJC71fYCzOChZNTqWsXxe1Xxl3YCVkk1EEfOYcvZ0OmdGJfMbYZzLq89jfydl5oime1VcTdr2Be3I/3FUpffjQx5hF3uxL+ezDqrbKv1NO3XwTRcSL5ihtpbl+xngryFTenwstclOYrIT+dcPthsujJwxmL5p/hthI0pdP5ivm6gatExjuEpmhW28V4X5FStVuM9hVaSs4BzMAKjZ1jcVvLEX+/4r1gwFdubpbTyXRq/5UAe8mYAJv7CnH3wpR6r2qMPQdzl0thvuLllH2Zi7J8JfzC3nyAy6Ldmz8S3SFO6BtDpnQqX6F+bMLswK1X2u8XmqJYbRsRviKmapcY6SsDJ0rk4aN9gMtdG29QnUNrsVUGnWPlnoE7Jf2j5Zzn9Ql8xRok/yrdmYfkwBBP0WkjrlricnpToK8Q6eCqYBCGsmi9j1n6jTcySiDE5Bl94mT1wbElG40nSaOT+Ayx2iWkP4TidZSoiCZG9N+vAGG/2MooKZbkwFQmICUcYCZHWeWG+goWSoolOTCVCUgJB5jJUVa5ob6ChZJiSQ5MZQJSwgFmcpRVbqivYKGkWJIDU5mAlHCAmRxllRvqK1goKZbkwFQmICUcYCZHWeWG+goWSoolOTCVCUgJB5jJUVa5ob6ChZJiSQ5MZQJSwgFmcpRVbqivYKGkWJIDU5mAlHCAmRxllRvqK1goKZbkwFQmICUcYCZHWeWG+goWSoolOTCVCUgJB5jJUVa5ob6ChZJiSQ5MZQJSwgFmcpRVbqivYKGkWJIDU5mAlHCAmRxllRvqK1goKZbkwFQmICUcYCZHWeUG6ysKhUKhUMSB9pW3+4P9YiujpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUbI3zlcnFS+ThZXBrfWc3oBu5zu6mqZqu4GOgGq2v31ZeLE48L8bDNaDWzeqm/03zFfZsf6gaxrGZ2f/VgmC9ohseL0Y7Bi5oM02pyRkYI02mKCF9GrDKtdpOVTdjNgSBahxVP2Xy1n+P6DaF5qvsyerHkZ77ODZ2MLOC1bOLMVnHOSsmxend6CRwyl5ac7iBWu0NgHfPiCtTpVrHJfuVycWJGuZrRg2l97XJxYkZv12muh+EY6Aax9wFfWc2qk8Vi5vCxenQ6dDwnDqKv9N37JnK5OKlmCyemOtsnJ3zUdJikjxn2yYS5mrn+k1bnUcq0Veroz4A9mpJofVYBNCi11AM20lc8h3dWDa4IfGeRw6HZOw81/1oMTlg+OaZYHEbSm1e+VknCYi3ZM18x4cZlTsfAgd0GNvAVq8A4HuN8zVO1UaPMtrjSHOgrhFz5dzXMBIIe15y+0haMpv/LxclstZo5U/FkcXlJ1BTjOS/q4TA9hPqKIIlYxCjTHWaelSRMKRaOkvdMT+tycVK3jKoE7Rqg43iyWLUdmbESawd+LcdCSFynvOGFYGgFDx4y1leEdEez2i4iVvNujOnnXCSifSW0SPuR8rU5MitjfMWrxPyTs9VbT8Zdj4Ros/vK6rIxiNXMNYMuGD9G4zkvajpMeRjgfYVY7TLrO/+53reljarHiuRBmbXbyXhfuawH/209ptZwMiJ4a9X/4FEJSVxaX6F3mMQhQdD6VTpj3HdfER0YwlhifYXY1c9W1EGfr2M3MZVR26IKc5ivuFNY8hVDo7Zc6x5X5NzM7ytNMWkqi8HMKYl+8lczo6xQc9QMUw4k0Ffog/3NEKNMq8AIpPyDL1a0PivqY6Lk+7qJ8JV6LJulAlNkPLbde8aIdDhxyXyFOB5n3mzfNTDLRC7dI1ltGSGsbGV6B891DQY4Cov0FWqjwRyJUmenlMnGpyP43j60Unj10bu0ph70L7QjqioTS8vocnFyMpudOHsMzyws226/3B28tK1MmNaAGUEZDsSEOXC/uDEilOkyky49nYsKUbQ2K5lIl2farkJ13w3dalbNZjN3Z8KKwHzTifxbFToKfjQT+QrpWvKbveeC0j2K1dYhsSJl2gfYD/4++4o3YAPnBp1zVO1q396v9CpIWYvdHDvq5X2FeMxeym9/v9JbgP1f/ZLO3VO55znNYteImg2TOeMzfGXQj6P3niIilGlzkc5G3CLJi9ZnxVFxnvMlR/uKY93eptT+L7KIMmMoG6vTPpy4JL5C8AodMq+yDqR7BCvuZT1yyDvgW2bO+y0KvYXZHWJ8hVkM+QsxOj57ne0fWYzOSuj9in/WQUrDX5BXXlGl6sF2fKVHG4Ajd1P0ZsDugTwfJjF2Y30ldihlRCiT1oDAP+jqyGXFkhm6SY7ar1C8BBGExMDQH0rc5r5C2caIITOaQtIdymo3GP8bB+bwZy99hRxl8qaQHFP3IMY/Bs7rK4OrGdE1jUbyHD+Tr5Bcmw8ZF/B3MpeLE2NBJ4VJbz/H+cq4q4MwRChzYCdtgy280l1czO/BRMocSHbNh5II5A44BCVuQ1+hjxfHDBm9Nx96qgBfYSYjiq2M9xVGSvYE4fTmHo84/45c4445B2Mu+uxA3I97XlaPBPsd+ApRLbpIvcORk9ms3+TwYXqLvwhfyXBzH6NM4vSTX/Dw92bCwMp/oiFv4FL5iiQCuQMWIYnbyFfYvIYOGXmlH7Rf3mNfoW7+rFsFgMuVt6N9RWJuH0hfkp/7A20fx0RVofh7e/8UiDb8TqROUbUWXP7BUsQQj/SVajajynw7TG403anDQJjGl73kCWG6hSv1WVikMu2Rdpc19GjJonVZ8Y3GK7g5Efoe1lfqv09kRSB3ICEwcZLYg+em3Qf7ZqvBfyWT7mBWO4V8oMrGNUKq20P0368AYb/YyigpluTAVCYgJRxgJkdZ5Yb6ChZKiiU5MJUJSAkHmMlRVrmhvoKFkmJJDkxlAlLCAWZylFVuqK9goaRYkgNTmYCUcICZHGWVG+orWCgpluTAVCYgJRxgJkdZ5Yb6ChZKiiU5MJUJSAkHmMlRVrmhvoKFkmJJDkxlAlLCAWZylFVuqK9goaRYkgNTmYCUcICZHGWVG+orWCgpluTAVCYgJRxgJkdZ5Yb6ChZKiiU5MJUJSAkHmMlRVrmhvoKFkmJJDkxlAlLCAWZylFVuqK9goaRYkgNTmYCUcICZHGWVG6yvKBQKhUIRB/UVhUKhUKQE7Ss3+4P9YiujpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6M8JWL+aTyMZlfiE01llPj8+nS6NVsMR4YGYPUzLzhYj4RX9g8ZpH1AjVal1MiYvvpDWJZTn32dsIaalZIzWcGCy9qMkyryYnU+tROqtMUOZw8IpTpt5usbMJuDoxWKRRRfuarnf4DuxcDtUbF7r/TY/0eN+wgZbKJM1vFnqTkWL2TvRCa9uRHCZ7vcZjV7hBUxwipRAgpOzbZr1zMJ9zoWU12xV1O+zG3qpzZMDYGrokox+0bBnxlOa0m87k7Aa2HahGTUSaPxRGVP+Eu5pNqOndiuphPqmoymfRM3KjpMKXEOf7pPmT7T1qdRynTVmmdEYqVPWTWU/xDDasAGnb/I7rnYMc14CuTycRZ8wyLU0pc86/5oMz55JgkmAy0BTPQE/l0h7PaJeRc1bPby5I5HeOElAUb+MpAgbFqrDnA7HOCTw3FwDNkO5R9pSHtcnceMkMx/lvIzBCYWFrXaHq9mE+my+XUWZJP5hcOvzqj/XNkAESYUgChvhI9nDxilOkOM8/KbKHGnYuFo+Q909Ma0z2Hcb4yn0+lLzP9M4nrlDe8fAqt4ISq6o+W/kJoePi4Lsey2i6kub+8IW3DjTH9nItEtK+Ebla82AcmdkQ9ltb4fHeir3TqdWQc4isbuMqAti4ag1hOXTPoeNkEm1xf9MZit9NhyuKE9xVinc0fVzrP9b5NHNeOLFKUWdedjOqew0hfuVj2xhLsK0OJS+srpNUSG2z6jXy641ltF0Os/PpIej+EscT6iqAoWiJGAZPKUsL9ykBdk3zF0KhfK+3DLutf9fJqk3GV1yy1QTQVwmDmlET7uKL5bv1VLwAiTLnIBfqKfUiYBnHKNAqMQMo/meIPQD1W1MfeQPTiGNU9h7G+ctOvLgLvV4YTl8xX3JLZB0f5CnW5IqQ7mtWWMd5XvIPn6XKjVW06RPpK6BlYh+5CTdwixGUkua949dFe/dK3ZNRh8HgwsbSMLuaTyXQ6cfYYnllYB67tl71pyoRpydeYxoYD2bCvVELuTeOTE6nMjhldGr02ZwpfeL+SsFnJRLo8d0ke0z2H0b7SG0ugr7S98KOZyFc81zIGUXJc8zkp3XGsto7RvkK57z77SvgZWP+ZMdTEs1Ezy4hhJMu2mX6hPTTeQrZuYuZCpv1KXwns/+oXk+S5o38mbkTNhsmcdRu+MujH8YsECXHKNLhIZyNukTSXEP4xv82Ko+I8Z0pO6t6x7pCZFuIr3Um9t8ylXzacuCS+UvfNnbjJOzm3srLpHs2KexmzqkyB2P0KsWtJSywCMb4ybrNC3qex52SRMYzleSOozl+Q9+S8ymAXg+7wPPX9CqWWlosjd1P0Zu7r1aq9sKHDJIrzWF+JP9SUEKNM9yNpeIKvjlxWLJnQm+SIahDhK9TNHIuQxG3uK75hOeoL8BVTz2HpLmS/QizHUS7ux/uKMDHJJm90ieX1ZqlI+3swX43OGZGzriRuJTaoqXQsJNfmQ8YF/J3MxXxiLOikMOl75XG+sqG/kohQ5qgrcrbeisUt4vdgY7rn4PsKc6VrNS2n7gI3qHuG/oa+Qp1SkKskdn9A781ZvkGsdobxvkKbP4CtjPcVQUpMk3MmYn0rye3uwB8QOPeB/pGW9whljeQtohmaO33jiup4XyGKRCdAz8In02m/yeHD9Mw+wlcy3NzHKNNhwXZSWqYAABBdSURBVLsd2zI0lkN/djCwgRsjleWUNRLiHpA775xOQ/YrIYnbyFfCAhc81xVYULqHWO0QEb5if5ZhKReLkb4ydrPSgLnQpdcmoyuRPB7WS2yLId5MG34nUlflzj7c6370II/0lWo6pcp8S8uNpjt1GAjT+LI3LP75meWm4onnhthEmZQE7FCIE4YgQYryM17h6SZG731/fqxWnOR9m/GtkLcGJo7jc3MzdJYQ0IcjfW42+71KU2+/fIUsk/RFIMJW5ebmRv//YGgoKZbkwFQmICUcYCZHWeWG+goWSoolOTCVCUgJB5jJUVa5ob6ChZJiSQ5MZQJSwgFmcpRVbqivYKGkWJIDU5mAlHCAmRxllRvqK1goKZbkwFQmICUcYCZHWeWG+goWSoolOTCVCUgJB5jJUVa5ob6ChZJiSQ5MZQJSwgFmcpRVbqivYKGkWJIDU5mAlHCAmRxllRvqK1goKZbkwFQmICUcYCZHWeWG+goWSoolOTCVCUgJB5jJUVa5ob6ChZJiSQ5MZQJSwgFmcpRVbqivYKGkWJIDU5mAlHCAmRxllRvqK1goKZbkwFQmICUcYCZHWeUG6ysKhUKhUMSB9pW3+4P9YiujpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUbI3zlcnFS+ThZXNKNsxX/uNMmNAXGwLSsZiRZqrl7b0PG4HG5OOmedHu0+zRbnXdtGMtqZndZszTf0fC2XivHYlKm0m7F2n1ByIDTFJcBAVHKdNu58fJzYLRKoYjlgJd2vFbIIROk7qYnfIqxiTNbxe6k5Fi9h5UEX3zGF7xGNq2YFVxgJYolUKdbxSb7lcvFSTemXr3yv0k3C02hCGO7mvmTzS6W9b9qQicnvZpdXwkL5HJxktAja+103fsmcrk4qWYLZxTkWJp+TxaLmTevCR8zZi8zXquZm9K0Oo9WprNqIFkZEnCfkjXKU7I6sfq3hsF+8yDoIbOHpdYHNTetFhFS4pp/LQaZy2s+JsFWLHJ2zK+ufBmPZrVLcKxEsZgJSFBLU2EDX7FGX/KVgUq0cSIifMWeM9Yn9X8sFidkbCxhv8coMLG0rtHvDmer1cwpJCeLS2cY5FjedlPRnZHSsIT6SqqMGIhRpitMnpXZ4iZEikUqB2FZHZWpwCEz+iTGPGDOCYnrlDfsiKEV3CBFbqmFtZy1kinRV2x4i5UwdW8Z0b7iRCD4ijDc4UqQMN5XqPS3ETRtl30xDveVzdcKkq90nFYzt7J0FO1xkGMx8m8NhCxOeF8h1tnM+s5/zjzVFbYDLiuSB1X56U7GaIceMoJg/0G8rwwlLq2v9K5FnOZyB7X+IikAe+8rjAWHnExuB7G+4irKOcP1TvRX1Hmp0DQyhoBvuTtmd4606u1UXFfvt4G+0h9zbjSqoq+8rQ2iIWZMI6ck+tsyMhazB3NOyoUn0FfCz1vCEaVMs54LpPyDL287O85XvIFolE68fcRBGDNk4fuVEceTw4lL5itmsfQFRhuG58b2/Ur0ldjOEF7HSAnU/0hyArQ5In1luPQ4N932GtlYmTBNI2MI+NaQr7SD1E/IthjL9/besnYTi+RiaQV0uTg5mc1OnAWrVzj8fRkXC1FJrfnqByRkQL4G3xzxyuyY0aWRXAx51xXjfKUn0uWZXniPuYhihuytG7fVp73mi/qNADOaiXzFcS2n15qC9xZ5Oywva/bXV/yBdab1PvvK8AmHEZ2wrQ3f8Q7FEPCt0fuV7hw5cL/i9hU1vgO+4v5Xv5gkV65iLHYo9ldd6s4yeXC/MqZWhiNOmQYX/ijqrVfOzGpcbzVi9ivWc8Smh3Qsx7q5TYn/swDaPfrXUtWWfVlA4pL4CuUbJql6De6+ZXAixlyJyXDOZHLIO4SAd/JH7FrSEotAjK8E1FbpuLT/RGgaGUPAt+h1vN9uttXnTuN9pf1mMo+k1GJdCLkw6ggTi7/vaPkS2RnrK9F7TxExyiTPhwT+4y8J5Z/wuSdymxyH80Mmcjf1G36VE5K4zX1FdHrjzZQdy3mTIt3H/QolFn9VgnJxP95XQpTpraTMSJ2VMdM0LoaAb1nzRLiYdQ/a7TXnbnyFPN1vPmRcYCgWPxB7V+1nZ5yvpPohg4kIZY66gWfNQ7hdif89GHO+w0MaslBfCdZlUOI29BXxeNF6CbUFHYhBmqn75yuMWEjzB7CV8b4SMKTuIYj1iD35haZRMdANl4sZayR2HPTZUd1yMpsF+Mpq5p/oJYtF8hVi2nUv52OhevRuvuwDmJG+kuHmPkaZDgt+XNiWoTM9+U80mA1cRHLkIQv2ldAzypDEbeQrITzINAXMrYH07pmvDP3cZPxmNDtG+oo8+ZytOdNMrT3YBwNj4JqMMyKZFHNq3X2Lv7e390B5YmF9pZrNqHrSDhMbi3CDzNzCcwf5lbNK4L17c0QpU7i3tkPx1wVhIykWKeMVRv/k0WXIuQ4/ZOG+El5+AhNHpK8DmxzyTM+4+eN75VRlsZXD2ytfGRLLCKluD9F/vwKE/WIro6RYkgNTmYCUcICZHGWVG+orWCgpluTAVCYgJRxgJkdZ5Yb6ChZKiiU5MJUJSAkHmMlRVrmhvoKFkmJJDkxlAlLCAWZylFVuqK9goaRYkgNTmYCUcICZHGWVG+orWCgpluTAVCYgJRxgJkdZ5Yb6ChZKiiU5MJUJSAkHmMlRVrmhvoKFkmJJDkxlAlLCAWZylFVuqK9goaRYkgNTmYCUcICZHGWVG+orWCgpluTAVCYgJRxgJkdZ5Yb6ChZKiiU5MJUJSAkHmMlRVrmhvoKFkmJJDkxlAlLCAWZylFVusL6iUCgUCkUc1FcUCoVCkRK0r9zsD/aLrYySYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuTHCVy7mk8rHZH4hNtVYTo3Pp0uzW6EpPAa+0ezeIESEZL3bfKxrsKjarIWmdLF0hK1A6lebHy2ndDQOmYv5xE9J8wqX93JqvIPMKa0BIunxiFAm1ejGZn6DSNHQUA6VA398zM8jckSOpitAo0s3A+GatJ4cGGk6Bj45di/e00J2NhwvkdUuIbASq1i0kDJik/3KxXzCjZ7VtJyaAS+nVVDTqBgEgvYE63v36udyalYi20yoMssQFZoCIGf+Yj6ppnPHDpbTqppMJo6LmxSW02oyn3u8aF+hnMVIpJhTud+NEaVMmY0bTliTy4prqrUz9zuyJBb6or5TYjTtmWQNoZUBetlAwZ4GEkmhEIwYL0fA7T+dNq+yOBTppihW2wbHyho/Ny4hVbvEBr5iK5lvIqpc2yg0jYuB/FyQuzAE/lNEP7vxlUZgTqWskzaf2lboSK/ZUnlx0dm2difWsEg5He53Q0QpU2ITKuEhVuTnF/NJnSlfa+5QhGTVeXZo6hhdknoZjMzNG89R6jC0gpt9DITChC00RbPaLiJW80KqdotoXwndrHixm61C07gYeIrCWpR8E9Xil6ad+EpHw+bTJHE5tdRm53+6JJgNVVxqLocsiXbsK84ICmzSjOIgJS9nvsbCXYwbTa8Hu+LG+Yr4AjYYE2N8hdlgNmdbpISNM0ahKZrVdjF+byelareI9RVhm+k1ucLw9utk06gYJJYUUX5uUS10Hdi2rxgi8nbGk/nFzcW8PQuzKBj/cKiJ9b+Tr5sQNqdh/W6CKGU6twFe/EvqSF5ooljJjAJ8JThl7GiG7ldGzDKjMvOnZwNn12G1kj/fqP9hSNzdiC6bOIWmWFbbRniuSAnYqdoxIn1l9AFCN7tHNQXHMEDVqw3jfIW+CN+yr5BeYv13ZywmBfO/w1fy3bcnE+o7ZE5ppimxoTJvyJ8fkNd7QhPFSqbk7/GoO4OglPGjSdxUWsfukVe73UgLapd6HDzX9Vj1YfU6at/Sv61LRPt1oWkkq50hhJU/sEyqdowoXwk/A+s/M4bavWqjm0bFMPylpntjRb9f+xX+cNE+8JguvUUtc+k5XP/roiJvTIhKtUNfGT5JJRPnPS00Uaxk2uTZoVns66W14wrUFksYTecZd+ti3EEQV4X0fs6oYcyh0nC6Ayu4maJ+3e3tuft9iHd9JjRFs6JIxhp0AMKE5B5pMqnaMWJ8ZdxmhbxPYxYUA5tqPoawLwpv5uneUFFt3VccTZvKdvYu1p2uUzTs0jFc/wNiIYZsd74SsFhzNiX+sHaH9UwTxUqmHXAnFSYaaTSF0M3xCLkf61/mHayF5qRD8Ny0197cmZ6/0rGGLPTifh/3K9S6W0rVbjHeV4Zuw92mRKtCOYawLxo1hY8i6FJ1275CJIaugcupuYDxmZsdleYrob8pIM8InUahiWAl0x7kFZgwcTQDfSV47TY8DYIsapSvMPtI56qI8Q6hKZ7VViGxYo4OhFTtFqN9RZAl0+TcFLr3TuMlT8TAUJUOtb2R6r5u0wi7cAlqCgATC9Epc7F+MZ9Mp9axgW/z1H1uYCxyTvsPd+ArAepx9GY/YsciNPmsZNpyAXY5id3woxnsK6EvdM7MvCDCJmtgBbc7s95FTVvr8M+aqUxTHKstQz4DF45Xxm9Gs2Okr4zdrDSwN/DeKohpGhED08L/Foh4uXcgZ502+Q9u0VfIotEm3Gns98v04sU5DKSPyIRYhnK6I19h5ceOsd9MRB8gzIFyQGbYbAoUy8BohvtKePkRLvxD+whNjqQyt80YF9Jn+Wk7yGqnkM4qGB257QhblZubG/3/g6GhpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUb6itYKCmW5MBUJiAlHGAmR1nlhvoKFkqKJTkwlQlICQeYyVFWuaG+goWSYkkOTGUCUsIBZnKUVW6or2ChpFiSA1OZgJRwgJkcZZUbjq/8J/D+D0ChB03YAAAAAElFTkSuQmCC" alt="" width="362" height="150" />

2、表联合

select job,loc,dname from emp, dept where emp.deptno=dept.deptno

select job,loc,dname from emp join dept on dept.deptno = emp.deptno

如上，上面两个表都是用deptno联合的，但一个是选择了所有表，再筛选，另一个是做一个join

最常用的还是join

3、SQL的子查询

select  loc, dname from
 (select job,loc,dname from emp
        join dept on dept.deptno = emp.deptno
        where loc = 'DALLAS' OR loc = 'New York'    )

查询套查询，在实际工作环境中挺常见的。不是很难，就是用（）把子查询括起来。

可以把代码拷贝到如notepad++等代码编辑器里，看到具体查询嵌套关系，再一层一层反推取数逻辑。

4、在select里创建新字段

select创建新字段，可以直接用...as 创建字段

select ename, loc, 'DALLAS & NY' as city from emp
        join dept on dept.deptno = emp.deptno
        where loc = 'DALLAS' OR loc = 'New York'

同理，也可以用case when创建有条件判断的字段，只需要加上括号即可。这个貌似还挺常见的

select ename, job,
(case when sal >2000 then 'high'
                        when sal <1000 then 'low'
                        else 'middle'
                    End) as salary_level
    from emp

当然，case when还可以用于分组统计时创建字段，与group by联合使用。

---按job统计薪水大于1500的有多少人
select job,
sum(case when sal >1500 then 1

                        else 0
                    End) as salary_gt1500,
count(*) as people_vol
    from emp
    group by job

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" alt="" width="239" height="102" />

如要具体了解case when其他用法，还可再查看以下博文：

http://blog.sina.com.cn/s/blog_4c538f6c01012mzt.html

http://www.cnblogs.com/cyrix/articles/1750184.html

5、数据分组并统计

一般数据分组用group by分组，基本与聚合函数配合使用。

原则是select后面的所有列，如果没有用聚合函数，那么必须在group by里重写一遍。

举例如下面两段代码，第一段会报错

---错误：使用聚合函数count但没有group by，或只group by一列
select mgr, job, count(*) as people_vol from emp
group by mgr
---正确：group by后引用完整列
select mgr, job, count(*) as people_vol from emp
group by mgr, job

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VWm0qzq/9RHHCdLJcjkwAOrJFlO2C8GFl23kXfC5lDgtoJrfdCi4uURzd/WNiBYnMheYDsfRAcWBM8xwENtfcoT+X8z4U6hblndtIi6wHrce86dr5wbg0mcGCNbJgDZjdYtQPaXoUcSCWI4cD62DgHHGsFT7f0vuPUNgYcSKdeBRbgRkPcbXW7cGBtbJ4D2rM59J1YX2JYR3CuboPLofB3Yu1znL0Kr+SA+fnaPvwmw8UBgHCowAE+IBwacIAPCIcGHOADwqEBB/iAcGjAAT4gHBpwgA8IhwYc4APCoQEH+IBwaMABPiAcGnCADwiHBhzgA8KhAQf4gHBoLHAAgA3B68BN/69jTWpYUn1AODTgAB8QDg3bgf8De3zDJYmhNoMAAAAASUVORK5CYII=" alt="" width="131" height="97" />

具体可参阅以下博文： http://www.cnblogs.com/gaiyang/archive/2011/04/01/2002452.html

6、利用over (partition by)分组并算相应值

之前的group by主要用于分组统计，而如sum, count等则是与group by组合输出同组的一行数据。

如果我们要对每一行数据都输出统计量，我们可以用over (partition by)进行分组并输出。

这个可以用于：分组排序，或分组聚合等等。

举例可看以下代码

---按job分组并给每个emp排序
select job, ename, sal,
row_number() over (Partition by job order by sal desc) as ranking
from emp
---分组并按job求各组平均值
select job, ename, sal,
avg(sal) over (Partition by job) as average_salary
from emp

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" alt="" width="225" height="120" />

具体用法，建议参阅以下博客：

http://www.cnblogs.com/lanzi/archive/2010/10/26/1861338.html

http://www.cnblogs.com/fxgachiever/archive/2010/09/15/1826792.html

7、数据分组并筛选：where与having

用where进行数据筛选在SQL里最常见。其工作环境中，主要嵌套子查询、或者多条件（and or联合）使用。注意结构就可以。

另外还有一种数据筛选是在分组以后进行，即group by .... having.... 之所以引用having，是因为where语句无法对聚合函数进行筛选

典型示例如下：

SELECT A COUNT(B) FROM TABLE GROUP BY A HAVING COUNT(B)>2

where 子句的作用是在对查询结果进行分组前，将不符合where条件的行去掉，即在分组之前过滤数据，条件中不能包含聚组函数，使用where条件显示特定的行。

having 子句的作用是筛选满足条件的组，即在分组之后过滤数据，条件中经常包含聚组函数，使用having 条件显示特定的组，也可以使用多个分组标准进行分组。

——引用自 http://www.cnblogs.com/gaiyang/archive/2011/04/01/2002452.html

我们可以用以下示例尝试

---按mgr, job分组，并选出职工数>1的组 
---（由于select是sql运行最后一步，比group by晚执行。所以having里不能直接用empl_vol）
select mgr, job, count(*) as empl_vol
from emp
group by mgr, job
having count(*)  > 1

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8、数据分组并排序并筛选选出最近一批的数据

Group BY XXXX
HAVING
SUM(filteraaa) > 3
QUALIFY RANK () OVER(PARTITION BY aa_ID ORDER BY MONTH_ID) =1;

Qualify rank 优化。报表执行顺序，确保每一步过滤掉足够多的信息

http://blog.sina.com.cn/s/blog_4d281a0301016jw2.html

http://community.teradata.com/t5/Database/qualify-rank-over-partition-question/td-p/47965

http://blog.sina.com.cn/s/blog_62d120530101h7vi.html

关于QUALIFY RANK() over组合应用

查阅搜索引擎发现，qualify rank() over用法似乎是teradata独特的用法之一。

类似于之前查阅的row_number() over….只不过这里qualify …可以直接=1或=2获得首位排序账户

sql 语句执行顺序

http://www.cnblogs.com/summer_adai/archive/2011/10/28/2227605.html

补充

show select * from TABLEAAA

可以批量看有哪些字段