参考官方文档地址:

http://dev.mysql.com/doc/refman/5.7/en/explain.html

为什么用explain . 如果你的页面返回结果很慢,你就需要使用explain去分析你的sql是否需要优化了.

1/ 官方定义

The EXPLAIN statement provides information about how MySQL executes statements: 

explain 语句提供 mysql 语句执行信息.

2/ 注意点

1) explain 能分析的语句包括  'SELECT, DELETE, INSERT, REPLACE, and UPDATE

2) explain 可以分析某个mysql的connection Id  

3) 使用explain 查看索引的使用 和 表的连接顺序 ,以提高查询速度

4) 如果你有索引,但是没有使用上,你需要ANALYZE TABLE. 

补充mysql索引失效的情况.
1 where 条件中有or
2 多列索引不是第一部分
3 like查询以%开头
4 字段类型是字符串,而where条件是数字
5 mysql自己估计全表扫描比索引快的时候(假设数据结果数量已知,可通过索引的count()获取结果集数量,因为索引是根据位置去0(1)读取,所以结果集数量为T则读取T次,全表扫描读取数据Block,假设数据量紧凑存储在N个Block上,全表扫描读取N次,一般在T>N,且达到某个比例的时候,此比例是否可设置有待研究,mysql不使用索引) SHOW SESSION STATUS LIKE 'Handler_read%'
handler_read_key:这个值越高越好,越高表示使用索引查询到的次数
handler_read_rnd_next:这个值越高,说明查询低效

3/ 输出格式

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5f0b91u2O6OfeJvJamFmanKuXy/IVf7Hw+vtutF8IuRxn/3fVRAbvA7bgrkP439bFVl2H31srz7XRek5yabVaJlH92/eRPabJpJWuE8es6x77b+Us0I+/tpKjNy9+Hb91uRzf+LJDXbN7jTb9Fpbid8rD5T+0BtxOW+Ka8twPAAbczfGrPsxBpZlKfCdSqotvZCVxlaYO8SHnB/7q+p+tRuBH+dCbTbnj40UpYkylGbV7+NrNTX7udiermPcx8rVP4kIfP/rbGUi5vjFEefn6I9ARuE5JseiCc3XyJazLv+KiAXeB2VyF4R5LjTyRSubKw/xQIa1EmN/9Ze0BztNub4HYr0hobeaFdOS4pbA1A2A1/ZAe3A0dptzfB7SoCwhYCwtppycDZ4HbgGO32pjxuF76HuCdf3ox2H6PKgbAA5KLd3oSxXUVA2EJAWABy0W5vgttVBIQtBIQFIBft9qaTbgcAAOCeZPeh34CxXUVA2EJAWABy0W5vgttVBIQtBIQFIBft9ia4XUVA2EJAWABy0W5vgttVBIQtBIQFIBft9ia4XUVA2EJAWABy0W5vgttVBIQtBIQFIBft9ia4XUVA2EIkC1vuZ7S0mtPPaDMSfJmNAD8VBr6i3TAFt6sICFuIQFiWy4bktDnrBPO4Z0Hfup2Qqi4d1rxfud2+JiUPB8VoN0z9G7eb+r8/JRmqTdn1u0Q71QBhC6ELG81vl0yQgzAkg9ud9ijevB+5XYImBQ8H5YDbGcNTM+8SzxsX5jS/AxC2EMfcbrYDP7prtglPuVcIKU9dUVdYS5pKvSf9FOQMLIXvOK+77Nbw2BPX6O8lu7mA69WIJx3nObz8xMOpFN41hq1iw2CW+zjMdwSOc3u3C/Iuu/BMEsfxxN/dc7JH+RFYCMr0FD/JNvd76hd2Kdv3UvL3sD1L6a7rl5Jrge2QH97QI27HDIoG0G2zd4iRMqR+iNcshT+vQQzy1u3ip6CuSEvSAL6YRBjNveadv0bppKJdKSfdPpO6Eg/n9fCE8mGryMHzOIzj4GsNvuH2bmeM0YYg0zS5Al6i1C3wvumedUMkh/j72V3S8JoQ9v3srGOx2sT22NJTv/z73i7mlzf0kNu5IGs/BZvDsZcyZGBmRsrY824Fdk4RMQI75xdzCM94Dl+jetJ0t/MGkXZkdtjthMZ7rbL1zONjnJfx4JevPoEFbmeMFpT56IQGZVr6zQ/2g3IwxPn7OzC51wpNCMsqnHpuWUEt72e32NjUL5vWw397Q0++t7PhkU5NCvNrfhjla2C4q/mHbNt3TkGO98ddgXH6RI0n8RrVk55zO3rWo25Hyu9Jsbof/wd8CdzOGDkoT/0fi4ckKPtRNxaUzdRfcTDn0YSwitsp7dHc7rc39Fu325kC46GZvRw6MraLzrJF5wjt2O4Lt4s3QD0pF5DN0OoDSs0s1cP3Gh98ZIO65RPMLhNwO2NonN2mrVywM3wKbJlwY7t2JtxYNL0oTQgru53WHtXtfnpDv3U7/n4oIHA79ipJeG/H35aF7+3kcyS8t/vC7RKuUXtZSC8r4nbK5accrjRebdVyF4Zh+7h8wku7LMDtjDF0nYKLh2TKquv7btm3LJRfPvLFFGSpQ1AVn/y60UxmPcLak26v4tw5hPY8SentbyPWdS7TsfN+ydduZ7zpST8Qb9OA9v2QLTeP1szERYn0jLFThC0LlkfqozO/eaeuUT4p3Ty8PtvliicVlqQmHq7IsNcqtnQ09l0CHABuBzIAYQsBYf81WA55HdrtTXC7ioCwhYCw/xq43XVotzfB7SoCwhYCwv5r4HbXod3elMft/FdCvyKHAhVRy2Mk/HnAQqvvSmsRFoD2abc3YWxXERC2EBAWgFy025vgdhUBYQsBYQHIRbu96aTbAQAAuCfZfeg3YGxXERC2EBAWgFy025vgdhUBYQsBYQHIRbu9CW5XERC2EBAWgFy025vgdhUBYQsBYQHIRbu9CW5XERC2EBAWgFy025vgdhUBYQuRLGxtP/lRW3sAaDhMwe0qAsIWIhCW51t1P8z/c3eZx2jONa09LLnNr2FtJi3RtoNr0W6YasztjiZFaysrHoQthC5sNONPefYyjNY4ttPajGypNwFulxMpVXfO8tUCYQtxzO1mmnGNlhS2bkeNsx0uDq+PERLZuU1rDWIyOVJqeH3U9tAcsOTUXh5wCmu0lmwvbORW2K/OHra0JLwW6tPeRUmieanoVKnBv+fubvfs/v7+uufTZgwl3/tJGlE6Glh+eNim93QpuZXfIbZ72JDiaHlyalp8TTHaC0lTWfmucxm6CwBhC3HE7ViYp6m0t83eIf5R8/jgH2xq7g9J2urylXuJTXnVkfaQlOhCAdJIYcylZRJXG8nTz4qZ0GPbtaTnQstDdUFl3N3tzBq5tlj2fnb2/9NEc2SzaLfE6yXMTb3dFRlSvJ+dFGQPlH92JExP/d8WZN/PjsZoW4b+f+r//n4elCHs9xxyOxey7adgc+hJihuI03uKQ3h+EWnPbg2siWEbvCsQHSZiY0fczr8o25iI1LC7qoHb+QH3bSMlHyV4QVl891MwKL/9srYJbA9rGBlDFU55A2ELcfK9nQ3B4TQesw92lOZ2fGGM5BCC6SjtYR4jFuBju3i1vF6pkfoXgl23889uP2std62A6dUJ3M4o0Y+tZvBDYhtBmZX+F2M7CPst37rdzmKRBLdjSxQPje3OuV30PaN/pk0EtZFfuJ06Moy4nXjxoBbgdubZscmoNcS9n+59DJ3SWlCD8rZ56r0jlKB8oDzfMPVkwk0Myqz81BcdhEDYQnzrdjvza2lux15epb+3O+d2cadQ3tupjfzC7WLv7fbdDqs8KwRuZ55d1/duao2+nrHblgLb0EReBGHYTrvZL8+OOFRemgC02/rJHbpcAl+t8U9mMiHst3ztdsab5ONBOGUmkxw+zvPI1rQ8xDWZ45xpbBcO7z6vYXi9wjWZYiMD42RtJnu17bRaptPepDFGdlUCt/Mn3MAJIGwh2u2f51CXhrj9cBJwknZ7U8a/QFi/t+do1U2BsIVot3+eQjA7uB3IRbu9qca/Lr8tELYQ9xOWryLF+g+Qj3Z7Ux6381/lXJ0cyicJC7IAYQHIRbu9CWO7ioCwhYCwAOSi3d4Et6sICFsICAtALtrtTSfdDgAAwD3J7kO/AWO7ioCwhYCwAOSi3d4Et6sICFsICAtALtrtTXC7ioCwhYCwAOSi3d4Et6sICFsICAtALtrtTXC7ioCwhYCwAOSi3d4Et6sICFuIZGHxIyMA7NBumILbVQSELUQgLM9a6n5f65zbBb9DebgAK4ssN6Bm2g1TcLuKgLCF0IWNZvz5F/jpCgCojHbDVE63i6S6Bim0+xgtVPsAHHO72Q786K5ILnDqkeTnmIVcbp/XMM4fsX4TpNDzRoQ2LernNSw7w598jiUsByAL7YapTG7HM3n+2eyc6/Y1Q5v99OyWf2xmUJLCjVZ1szQ38mNE8qd6me4moh9Ngydv14UleYW6juVJl7cL1P0AHHE7ZxQk6zZNwO0dYo7k6dbqZw0QcsGyPUrm8Z1GApCH27udMUb7aj/1NEbbMs/uj6e9tttd8fezu5XhKW43WVWdTIulsbThq27adk3YN9kx9X+72yNU+wAccjtnNvZTsJk7ybZBTaLK3E6on1VFN7nmkZN659/K7DQSgDzA7YzRJ7LcdhL4aOA2xryXQsEQ4e+v0smxEsjCck2olFIdyvaYsGTwyNTWtqtU+wCcfG9n/YNniwsyxm1H+QMq+5m6nVQ/q4ptIkZqD/SMbDtkp5EA5AFuZ0zktc0W42gB+hXe2GCnBvFbIAk79X9srHPS7RKFnXp5LKVt51T7AHzrdjsDJWVsx+YeT7rdegQ7Thn/YTQHfgLczhgav+hs2rqrn/wZLTY5ZuMgi+43Q34halV6Pzs+IiNCOcXTtjuY60y9PYG2PUK1D8C3bie+YjPhUSnv7Q66nTHzOIyjf9jeezsASgG3M8bQqa8gNArhr+t7uhyBx0HMZG6Qub1VMasIFYrKlLL9z70nk9U+cxdqfQC+djsTLJiklXjTjPE1mXG3cxOSbK1KOLv5EtdkRhoJQB7gdnvw7/UmmMgCpuXHaJ9/+gAUFrbsHKIyuVnqdADEaTdMFXc7cakDGa7c9y1dSLuPUYQaHoDCwhZd7h/+DgvcDvxL2g1T+C2VioCwhSgn7DrvWGTScJ2WjP4xOwC/pt0wlcft/gqT40oboPbHSPjzAG/MVim1CwtAO7TbmzC2qwgIWwgIC0Au2u1NcLuKgLCFgLAA5KLd3nTS7QAAANyT7D70GzC2qwgIWwgIC0Au2u1NcLuKgLCFgLAA5KLd3gS3qwgIWwgIC0Au2u1NcLuKgLCFgLAA5KLd3gS3qwgIWwgIC0Au2u1NcLuKgLCFSBa2wp8p0ZpUpqkVCiDQRiuvSrthCm5XERC2EIGwLFcAyRlQTxi1P4/5pduFP7MZ5TcCzON3P7VWz226I+2Gqcbczkt+NvV/f0o2NPsrV1/+0v6z+76OVCBsIXRhoxl/quCCY7t5/PKHRSu8TTcCbvfPiCeOeefIK/Oz3DQQthDH3G62Az+6a7YJ5aSfaR7nWUw457bSw8jWdXOwhafEE7LZ0Ygfb5utZ5w/O5c2vqiP+NXO44OnkbW1CA0Ir9Ed5l+MrBK/TbRA0m3iVaXeiKiYwJjKwtQh8rjd+j39aZO7sChGfkvYy+dpi3cdS2MtbNeGFM+ue072CD96CkGZ/rRxWrqZpQ5yXD9p9awb13PaT/HrtUDYnwm7EbqdC3JKWvAwv09SMnE3pRhWIKQMYvnQnce5kmLCdLFtpJ6dS6Nl5Gq3ZHs06Z5UMpYEyUvYpyV8lwsE7UwQPP1G7IgJjIHbmTXibDH3/exIamwXFqf+jwRZu33q/3a3h3vJebdt5Lx6ebfh/exS4jIJyjS1t1IPT1v67NZD4te1AGFj9eQW1hgjup0LtvZTsDkIvt7wYhue+eM8W1sYvWMuRWqxDd4KpLYt6dJY9XK1n9fwGAYvqXtQUrgidrVCSzyVlOKxGmKCp9+IiJjAGLidMcYLh+8lGr1tUFohIUtM8xnZvlYbBmXhvFp5IWfNfraa9RgaRqP1uCawAB27rgUI+0thjTE77+1s7KMTXt6EYniUPcwfG9DPYe46bwufyRT8eNue2jb90kQBItV64y+1ZCQ/Hzk+ppK8IfVa1IGmfiN2xATGwO1M8A4mISizrfJQINgeH1KY3aBspv54suxn99f3ve8ckXq2i/QvnhQ4MraDsHRnNmGNMalut/MlXxkfCTN2wiSdMqY7MrbbaVvU7faqD5jHx/B6cb+IjoCk4aY+tgtVksd2Cdeyuz2sEaO5BOB25tmxL+k2HvGo6GIZ2z71NuJp2xfkCTf2dmdnws1bfJjCWsfE4nK8nmfXT/7MW+y6FiDsbj0ZhTXGpLpddGLu8Hs770g5tsvv7Xjl4Xs7sW1RtwsbKTU+1IsIF29AeI1Z3tulXUv8vZ3Qpp1rAQZuZ4x5dl3f0+UF9N2LMNPEZ6zobJW4nc5WuV3LQnlyXnE2L/EUMvb1la3RniNWz9SrK0cOTrhB2ELCGmOS3c7YKS9pfuujLJvkR0VWKwpLA6lLiQs+aVP32hZzO3b6cZ5HV8qr9kPXhjBnDBugrMj0TsjWd2qFPcnYV4HobWJVpd2IHTGBMXA788PV5G0gTyzuAGH3ySdsPjD/BW7E3d2OLAE//PLmYuwvmdCBsBHyCpsVuB24EXd3O5AFCFsIuB0AuWg3TOVxO/9VDkhjV9i2Ef6cYOHo2OxbriYsAP+OdnsTxnYVAWELAWEByEW7vQluVxEQthAQFoBctNub4HYVAWELAWEByEW7vemk2wEAALgn2X3oN2BsVxEQthAQFoBctNub4HYVAWELAWEByEW7vQluVxEQthAQFoBctNub4HYVAWELAWEByEW7vQluVxEQthAQFoBctObAMm0AABe3SURBVNub4HYVAWELkSzsT34DrPYfGtMSyZ6oAVyQdsMU3K4iIGwhAmFZVheS3eVcmBYzpun8xgvm8Wy2GrgdiNFumMqazfVwOk/AgLCF0PtnNL9dIX5yknk8nZsNbgdiwO2MQSa2r4GwhTjmdrMd+NFdLvVnEMh55tXPzuHji3qBX+08+llOvdMpeVLJ5mEc/Xyk/DKJGUm1eW7njVzF7N707K9PmoxK6x9h00jDNHnB74DbGbMFZfLD9/1k+A/h2yRt68Y1iNtP567hMkDYQhxxO56Q22USp1HYi7PUHnYPp2Xkardc3CwnNznbh2Qsd+flbfKOVd1OqY3/h44ThUbJZ9/Tgcz/horKymjVgp8CtzOGBWWX0oWOS97PzkVenoT62f06C0yFQNhCHHI7F8vtp2BzENxdON49nEdvsdrPa3gMg+h1lO0IxYGS3E6+Cu8/zIklY4qeXdPBuuieWcvXqXwZAKWB2xljE23TkYSQ4YzG6+3/PEDfFghbiJPv7Ww8pRNtZH4wOEo/XDxJpFp94QtfYLONvXYsSHU7oTbpcojfSLOYwtkTdGCf14asn2VllGrBb4HbGWPMs/vr+57n6pz6yDTaFosx/liAsIX41u12Vl3suV1kbCdXO4+P4fUKXcRzwbNjO/tJrE28nOW/YoPlsyfoINnVeuTO6FM9HPwAuJ0xdm5tYnF56mMLCp9dP2H8sQFhC/Gt2+28JNpzu/B9lfQeK2yTMGqim1xFu+/t6Efy9kutTbiceRzGUXRn8expOgRuRd9Z6quBpEsEvwJu5xbKT71dOfEmu4QJN2P8CH5zIGwhvnY748358Si763aGrcmc59GV8qplCzGYM4blWUVk7s8u/aBNdccNr489TqxNuZzYXxV6Z0+TUWr7Q1UmLi/4HXC7s2D8QYCwhWi3f9YDzAUstNub/pnb2ZFKMCq5LxC2EO32z2o4+Hsx4Lq025v+9dgOECBsISDsF/DVkuD2tNub8ridvxj+LDmuqGHafYwqB8ICkIt2exPGdhUBYQsBYQHIRbu9CW5XERC2EBAWgFy025tOuh0AAIB7kt2HfgPGdhUBYQsBYQHIRbu9CW5XERC2EBAWgFy025vgdhUBYQsBYQHIRbu9CW5XERC2EBAWgFy025vgdhUBYQsBYQHIRbu9CW5XERC2EMnC7qT2yXpU6ZrtryprP/iVq9nlLr/O896ddsMU3K4iIGwhAmF5ElP3O/sVuN088kw9J2uWf8Q5U+XBqeB2N6LdMJU/408hbLLuC/+0/32EXU76sx+t1vtnNONPKjnD7jw+crmdmDkPbge+BG5njE06WpL3D87x77iqsGIK9fcPE6sfc7uZpoKjJYWt7qggVxtJ+73mrfOPpnndHo/HMI5iRje5PULDaJJYL9udmi7u8MWGZ0yqR7vubfMR3ULZ51GvysveEE/MC/aB2xmzBeW3yzHaT8bQz3/dcy26blwDrP20e2ohKEv1r0OHvmctqZ7GhSXpZp/92og3zzhLDng/u/5p95a9QUfcjoVbKcl4mFGcpj8Ni2l1knrcmEvIOS4dywqI2cClgU9i5bsXKyWY3amHtE3IyH5IN3rUZmc8B6xfFR3SIk3f18DtjGFB2QUvGkXfz85FXp5uNPGLfhiUtfppM1oZETYt7LMjW/m8qDa2o65c9AYdcjsXC+2nYHMQ7ofXRyum18lylC9FBEMSjlX2e7aZ4naHL1Zwi6R6XNtku0zWjR02vF47N9DzQ8x+fg/czpj1DRMfSQRf7Xm83v6fnGjbD4t6/WyirJFE3g0L+352pHlebfszmYVv0Mn3djZ0elOOD2+dIwmqQjGlTm9sR8ZD6qu1MOL746R4WE+rPH6x8shMqUdpm58z75hu7HIewzDQBiqNJw4Ks/sWuJ0xy7f7vvdntyLTaFuMS3+DEwwC1Pov5na1C8vdLmV82ZLb7QTJeCzVo7bwduyg2+mjtfNuF79YbYwl1qOPJNmRR3XzCuxN4LrtMLscwO2MsXNfE4vLUx9b7Pfs+ulIpAunvLT6r+V2DQhLK3h23kzm9olcQktut7O0QXxvl1KnuPLiiNt9+97u3MWGE5H79XhrRXjZQ7oJBUjlauPncRhHmF0G4HZuoTxZq/Amu4QJN2P8CK5jaxUn7vztdh6un9yh9Rte28KSyc+u4945+atafnyDvnY7YyffhIlMdlRYLGVsJ6wo3DmWnT0YImqDnNTKIxfL270z5Si1TVqReUw34RLn8cEMT2i86LfgOHC7szQy6voNlxG2tmVBFfZPfX0JKIL8VQEcp8LelMg/czs6pmjj7wPK07qwZHC3/zcPv6S+/imYHWJxSSBwNurrTan867EdIEDYQlQpLF8+iEhcCr7+E3xNlb0piTxu57/6qZUcihWk1GMk/DnBwl0G1e32TwBqo93ehLFdRUDYQkBYAHLRbm+C21UEhC0EhAUgF+32ppNuBwAA4J5k96HfgLFdRUDYQkBYAHLRbm+C21UEhC0EhAUgF+32JrhdRUDYQkBYAHLRbm+C21UEhC0EhAUgF+32JrhdRUDYQkBYAHLRbm+C21UEhC1EsrClU8Ig5UwC+UX6iey3ubfthim4XUVA2EIEwrKfySc/3gW3qwBRpHk89ttqrPw52Q/+tOZt7m27Yeofux1+G5oCYQuhCxvN+JOf20TEb5BEOpoigpfH2C4ncLtTeAmvbw+ELcQxt5vtwI/uEhKN83q83eEWQyIiP68LlJ/XMM525Dm8PqQekqiVlCH1yJnjDrcwrQHhZrkBn9cwzusee+1hI0l1L882xHx1YuO18uduK03SKwuuN9uvlubfWxrYrjPC7dZsL93zvf0E8TaioD9JTEKwn0UU0RnCFuOI27E4/XAmRNOtepFKTOctpu1OcTsvsSv94HKCC40M23WyhWkNoKHb5bL9fPyti0uw/K6BknSjaAVh1vV4VnSh/KnbSi5h53BaRq6WJmpvOuMF3M4YswXaJbxO/RKVaV7P97NjwRdDEA6ELcQht3ORyH4KNgdjjyCZuZyqNcntmDOwgZMzG6WRit0da2FKA7zxTXheZhVCa2khrzFCfbFKpGlOwe0O39bdS9CarVb7eQ2PYWjc6+B2K2G+bCHXDHmLhKDMgbCFOPnezoYtnoqOz95J0dnfQgPhrtuxWcV9swnsSZiUO9PC5AaQUnzxj1ChrOT+y9OgIXLjlfLnbitzO+VwsdmRai+RUxZuZ4yRgrKZ+kjYRVDmQNhCfOt28QUIuyMnZgeS27Hprm/cTryMcy2MN0Aa1LBQrluFOA78ZmwnKJDmdvHbuut2kbGdXO08PobXKzLh3AZwO2OMGJTN1P+FG1cQlDkQthDful1sllDcG3krFs52ea988rhdMEw52sJ4A6T3dlTLeZTHdrKSQWPyv7c7d1ujbqc2W67WyhN7wdoCcDt/bQRd9c4n3dY9/kwclslD2GJ87XbGm6ULZqPI7BVd7uEv4vNe4tjd8/i920VXZJ5pYbwBr3BNJqlwnLdLCkc6opJ0iadVQ2g++4qgXqxX/txt3XW7SLO9aukXGiMbejvA7UAGIGwhIGxWdmYAwbVptzfB7SoCwhYCwmYFbndr2u1NcLuKgLCFgLBZgdvdmnZ7Ux638xfDnyXHFTVMu49R5UBYAHLRbm/C2K4iIGwhICwAuWi3N8HtKgLCFgLCApCLdnsT3K4iIGwhICwAuWi3N510OwAAAPckuw/9BoztKgLCFgLCApCLdnsT3K4iIGwhICwAuWi3N8HtKgLCFgLCApCLdnsT3K4iIGwhICwAuWi3N8HtKgLCFgLCApCLdnsT3K4iIGwhriis/ZX9arODXvoHxi59cXHa7U1wu4qAsIUQhfVSTKuxax55ys6v7YVVeBIpq115jrW8WkPIcROrvbjytBum4HYVAWELobldSshLLJZOlgr/SbA92PJLG8KlLy5Ou2EKblcRELYQ6W7HU6LOQapPmuFzTdK5DQznML1pkGB1J5GpFD9JHSQ1tpSA9PMaxnktb7NkC8ey5gUpwJWksDstl5quJX09cMnkMoRM71JTU+6LchNZW8QWkqpecLv2yOZ2S8rs7vl+r8mzXc5skk2b59feiqz5ubunX77rum3jHYCwhUh1Oy+TuTHBlCENlC4QzuODf7Bx9fNx1bgDeYX0SCHNd+BHrB1eYS+TeHgsOf08DuM4OBtcrUEN4pGWi5ODn9fAnZ84btol82PG2W+DIu/efZFvIjmx2Aa60ctFfi/gdsaYLbguYXTql5D77P6659sVWPa+n50L2suetdT72dnyU+8i9R2AsIVIeG9nh15C8NXcjhmLKyQOGuUDw0/s9P7bOVez5nZaze5Ye+Q8PsZ5GQ9yN9GieFr9aus32zhyyWTv5zU8hkF7W3novuzdC7ENytXcELidMYZEVosefJcdz24Ju1Pvgu86IPHGMXcAwhbiyHu7dWKLBlnF7Vy009yOTf+JYztvqUw44cdCKhvrSG7HZ93kY90obp24JP9ICjBlWMuVtsntsYfvXrK2Nxg/KvLu3hf5JrrSYhu868N7uwb5Z2439d3zPfXdc+q75+QXs6UxBDEGwn7LiVUqWzT7wu1YdNbHE5GgKcwdxsd2UuwOPrJB3fJJ0iI4RXRsJywS1cd28UuW987jY3i9iOWo8n7tdmIbMLbbgNsZY6SgbAyZPzNsqDH1fd93z7eZ+q5zMZmVn/pbDUIgbCHOuZ3wouio27HQvPveTm7Fkfd2/lhKtoZ5fAzDQJaaDMN+eP/yvZ3yVmznki1WSyeqKu/Xbie3IbheuF1r5HI7OkvGZ8reZC2Fv32Jv1NP3kCx0rcKyRC2GKl/byeuR9w2BmsyU6KqmwqbR7Z24uGtMpRm9fy9PDLvuZ16rL/GgkZuZUWmJ5nQciH0f17DOAsrVY9c8uOxLpoUmivKm8XttBbSi3H3827A7UAGIGwhICwAuWi3N8HtKgLCFgLCApCLdnsT3K4iIGwhICwAuWi3N+Vxu7/C5LjSBmj3MaocCAtALtrtTRjbVQSELQSEBSAX7fYmuF1FQNhCQFgActFubzrpdgAAAO5Jdh/6DRjbVQSELQSEBSAX7fYmuF1FQNhCQFgActFub4LbVQSELQSEBSAX7fYmuF1FQNhCQFgActFub4LbVQSELQSEBSAX7fYmuF1FQNhC3F5Y+xvHkawPvzlp4bxwQvXlzqjV/I9y3/3qtO32JrhdRUDYQojCCrnLf8Y8/tp2fnm+4KRyYr4SImR0u/3mVet2JP8SuwopL9Nx2g1Tmd1OS4/Ncs8YY0i+mjBz26+Y+r+/qpKaQthCaG7Hc4j/zhDiqfWy80+ir3JSFpJrdruE5lXrdo4SIsPtjDHGvJ/dwRj35jlJF5R82yUgSVArAMIWYt/tgtygfMS3ZPpettusol4h5SiXKM1lc/USp8lJ5YT8e+JZvGvydmuZ5FKuiI0JWf7S7cuBkg7PP2kwttvJHpciOzkdSTv3Utzu5aXZ877deOlbE++RWLPhtnP0foVbjPIgRa5dE1luWNqJHHC7IOvoFuziQw0/KPMMpX9a8lISSZet3fO97d+OUMrT7f3UQFCGsN9zZGxHA5+1wCUAsFSfPBLoR5HQKLqIMcZ8Ph+/GuK+rqHiWcQ2pCQ6V6/IHksaOo/DOPJoKjSBn8DulGYyI5nQU2RnZajGQZNYCnWWBJ1V7A2BUu6RUjO/xsT7FW4hdzD2IEnXHhE5bFjKiRhwO2NMbAgiDjW07eIQhBZcsnO7fYshLFumfjlYKU9n/tqYcDMGwn7L/ns7KTAbFx6CrV5wPHZU5D0aOZSN8nzzMV4RoVZmkqLb6VdEzki8YRlqudJaOAxOuu92x2Vfy3gtFwxYKyG4k36UeGVazWrzo/crcgcVHdRr33O7oyfiwO2MMSWDcjA0YW+vpt6vRCvvlTw+Q1gUCFuIhJlMstVjjet85syLjilH6W7HppzEsV0w1+hPngmt2o3mkSvyA/eqFf+Htj00vWNulyh7WMZruXCp3iZ2G8JvFmExdp3sHmk1b9uP3a/9u+CfR732Pbc7eiIO3M4YU3QIEp0ZC4OyVr7BoGwMhP2WA24nhr7dKHDsqDDQS698xNdY+syhNDUYH9vpV8RmMMmgbvkkCRee4vDYLkXAXVc6MLbbKlRPvXuPEsZ26fcr3BL6src919hu90QcuJ0xJmNQ3rZNvR1qhIsPHUJQ1spPPRm7TH0DE27GQNhvOeB28vSc8C0+8sZFPiriduwVijhuiJxF3pvw3k69Ihbb5/ExDANZSTIMCUOho253Unap5dH3dl4V7G1k5ILUeyTWzK4x8X7J1yJ+XeEPknTte253+EQMuJ1RVkH4ayy2Hdp27yA2FuGnkOuhB0jlvbX8fVVvmCBsIY64nfFmrYLFbPZob3pq7ygaO7aj7Vd0e9g8hmO7h/deUZwY8/fuDIrEwZhw7LLZbaB+oqzIDE8gjzOZCImyi5dP1iUS/cgh40yL0J2RvzzZv0dazf6l6/cr+hS5C4mYkHjtmshKw5JO5IDbgQxA2EI0J6wfZ378B3q3QYnnIEZzvckCt6sICFuI1oQVzA5BuQDQ9Qyt9SYH3K4iIGwhGhSWr+dDSM7MR1tKCnZpsDet5HE7/1VRa+RQMgPtPkaVA2EByEW7vQlju4qAsIWAsADkot3eBLerCAhbCAgLQC7a7U0n3Q4AAMA9ye5DvwFju4qAsIWAsADkot3eBLerCAhbCAgLQC7a7U1wu4qAsIWAsADkot3eBLerCAhbCAgLQC7a7U1wu4qAsIWAsADkot3eBLerCAhbiPPCaklb4slc8nPufPaXf/FbLCAb7YYpuF1FiMKGSUx+HWnbJ/LExpOyJLgd+a3FeeTZxjKajJyEIV7/T3/yuOC1g7poN/7ncTsho3U12V4aQnE7PwUX3O4oev/U8rVtHBnblUxUcOae//I5QZKG+3B3tzNe+tC01NVSKu1bo7nd62OTRxvjRzHy88EsT9U4uwxWNOiFKbGvj9o/lyDth2qSM+xFtRa32wTYkdxsx+6RlDZOzPRGsqz59YTt0duw7toeM3vo8PoYIXtb0Lyc1w7qB25n3W7qqc9tg75+stk+u+dbGAxa31t2dM83PdQYlixUzbXdONpM5jhvcckYw2JJJAcxi5QkqTHNxXmXKKP0TysktTsqEZUxsv10nmjxHhnz+ZCMm1oqc1a/WE/qc8KvnedqpR/s8yc2L9O1g+qB2xH/8kZ172dHHI6nwFbGdouxLfVM/Zase5rcgX+XHBXG3I743YeOK+Skn3yH/RRsvkmQkfsndwMSh/kbKDGeu68Ke2539B7JTdxxO7Gew88J/8j3iVOVRa8dVArcThnb2X3SkCzidsLojQ8Hb+h2dJjnVkiwGEhjM9lhowlPmsbmpy6NKCybgLPDGE9SOvyT353uRfyj98hvWIrbqfUce074R83tlObluXZQP3A7/t7OY5uE9CzqiNtN/R97LXhLt1s/qN/Z3UclmtxnNMeRhPUGK8pwJPvYbvcesfWMiWO7PbdLbEOK26nNy3HtoAXgdr7bWT8i85dTz8d37oipZ1YYuh1d9hLMiF6Gfbfzl2hG3ouI0eSmr0gEYYOZuU2kQFLhvZ23/fS7K8XthNN853aJbUh0O7l5Oa4dtMDd3U74C4S/fiLbySoVYnhu4Yk1L7IWhe8gp+j6vrvkZGaK2wV/gEcmllik0qIJm4i6SYwJhJX+IMxFZroQcR6dkOJ2IvW2PxhHH7pHpLA7zZdul9aGpJlMsXm5rh00wN3dDmQBwhYCwgKQi3Z7E9yuIiBsISAsALlotzfB7SoCwhYCwgKQi3Z7Ux63C17a/YgcClREu49R5UBYAHLRbm/C2K4iIGwhICwAuWi3N8HtKgLCFgLCApCLdnvTSbcDAABwT7L70G+42qsvAAAAIARuBwAA4PrA7QAAAFwfuB0AAIDrA7cDAABwfeB2AAAArg/cDgAAwPWB2wEAALg+cDsAAADXB24HAADg+sDtAAAAXB+4HQAAgOsDtwMAAHB94HYAAACuD9wOAADA9YHbAQAAuD5wOwAAANcHbgcAAOD6/A+T2rBthh2ZMwAAAABJRU5ErkJggg==" alt="" />  

重点关注:

1  key 是否使用索引

2  rows 查询返回的结果集数量

3  filtered 过滤的结果.

rows * filtered 得出将要关联的数据条目数量.所以filtered 越小越好,rows 也是越小越好

***

explain connection Id 会分析链接最近一次执行的sql语句. 结果会变动,甚至如果语句不是insert ,select 等操作会报错. show warnings

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