mysql 优化

1、存储过程造数据

CREATE DEFINER=`root`@`localhost` PROCEDURE `generate_test_data`(`n` int)
begin
declare i int;
DECLARE max_id bigint;
set i=1;
select max(dmId) into @max_id from wt_wallet ;
set autocommit = 0;
while i<=n do
#生成 供应商 应收 对账 提现 钱包
INSERT INTO `ppcs_test_v1`.`wt_wallet` (`dmId`, `owner_type`, `owner_id`, `owner_name`, `item_id`, `item_name`, `item_amount`, `payer_type`, `payer_id`, `create_time`, `create_by`, `update_time`, `update_by`)
VALUES (@max_id+(i-1)*3+1 , '1', 1718683099023360 + i, concat('供应商', i), '1', '应收', RAND()*10000, '2', 1707710161422336, now()*1000, NULL, NULL, NULL);
INSERT INTO `ppcs_test_v1`.`wt_wallet` (`dmId`, `owner_type`, `owner_id`, `owner_name`, `item_id`, `item_name`, `item_amount`, `payer_type`, `payer_id`, `create_time`, `create_by`, `update_time`, `update_by`)
VALUES (@max_id+(i-1)*3+2, '1', 1718683099023360 + i, concat('供应商', i), '0', '对账中', RAND()*10000, '2', 1707710161422336, now()*1000, NULL, NULL, NULL);
INSERT INTO `ppcs_test_v1`.`wt_wallet` (`dmId`, `owner_type`, `owner_id`, `owner_name`, `item_id`, `item_name`, `item_amount`, `payer_type`, `payer_id`, `create_time`, `create_by`, `update_time`, `update_by`)
VALUES (@max_id+i*3, '1', 1718683099023360 + i, concat('供应商', i), '4', '提现', RAND()*10000, '2', 1707710161422336, now()*1000, NULL, NULL, NULL);
#RAND()*10000生成10000以内的随机数
if i%1000 = 0 then
COMMIT;
end if;
set i=i+1;
end while;
set autocommit = 1;
end

2、碎片整理:optimize table table_name;

3、分析表 analyze table table_name;

4、explain select sql 语句

5、procedure analyse() 优化表结构

6、 delete update 后面可以加上 limit 限制,防止 批量删除出错 或 误删除。

而且数据量 大的时候可能 导致 未走索引, 网友说是因为“MYSQL会自动判断最优的执行计划,可能你不加limit的时候数据量增到导致MYSQL判断你索引不是最优计划,从而不走,FORCE INDEX 可以走你的想法”

EXPLAIN update wt_bldgl_income t force index(idx_wt_bldgl_income_rel_time) set t.stat = 3 ,t.update_time = 1467365030000 where t.release_time < 1467540146000 and t.stat = 1 ;

EXPLAIN update wt_bldgl_income t set t.stat = 3 ,t.update_time = 1467365030000 where t.release_time < 1467540146000 and t.stat = 1;

EXPLAIN update wt_bldgl_income t set t.stat = 3 ,t.update_time = 1467365030000 where t.release_time < 1467540146000 and t.stat = 1 limit 1000  ;
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bqqVZ48zR+FwfFNW1yePbvKdCPofWCLMvHtMPeYNBIMeg1t/fkd3kYxWm06xzKLR/XnbzW83zYvk07iJaHMrE0eRarcehT3kk+9M4taW8pd6gn1umse846vhGe9B4E2/Rt4uct3FrivT+mudDO11nL80yW9xVZnYJctK98nv8t661du3Oo5BXW7qLa+wqcYxW+3dm2I7v0Let5HvNZ0HZn+T0ut/tkW6PxOu58fC2FAxlyknhPgefUqVOp7S5P7ykYud5lIndkeN4LVSzka5GefE9iwegSafdsmJ3pVb5VgfZSEaiw5Snk8/Q1533OZCJxH00TAICCibR7lk91vIyfPHny6aefnjt3rnVPgWTRWVCozOt6l0n3IRERzzjjfXJIEfJ5tOEt3qDj5BlApOrvwmkvlSQW8rVIGb1SAapYyNewa0lhOnBooQAAlFyGnoIyGVMAAAAAAACKI9FTUGO5mG6CkYqFfB5LJXqJRbnlB6NDvupVudXPcsuPnULns1LKARgJ6jlQfLQ7oBIwpgAAAAAAADCmAAAAAAAAWLHuKchmTEGRMwoAAAAAAIqAMQUAAAAAAMAw5pxzzvmXP3z87xouMIdmM6ZgzJgxpco0AAAAAADIr+Hh4bfffvvyyy+vWbhw4SzZFI4mLWZMAQAAAAAA1alGRBo+7ft9NKmrgPcUAAAAAABQnfT3FPz+v8yhjCkAAAAAAKA68dsHAAAAAADAoPcUXPCX5lDGFAAAAAAAUJ1qRCT6xsAFDQ3mUMYUAAAAAABQnWpWr169SWY1JXUUMKYAAAAAAIAqVbtw4cL00GzGFNBZAAAAAADA6GP9RkO6CQAAAAAAqE789gEAAAAAADAwpgAAAAAAABgYUwAAAAAAAAyMKQAAAAAAAAbGFAAAAAAAAANjCgAAAAAAgKHmR2/8Pj00yzEFA90yfb7xb0a3JALbnhcROfS8vmhZoXZgZSETL4RIu8fj8YViFotiIZ/H47FdbFrbeRNZpGOdtKv4GMUOvTtres/E6T0TZ2zsLnVespFVuyiOsmpHA/0Tp/dMbHt3v9XCLSt7Js7o35IaPLRIO/Q9iwZERKR340Q1RP1nTs2pnqSlI8e62/REjMCUdJLzY5d+5vgjC8+eeo1buTO3tctabteRUqmidmffjuzSt6vnecmnQ/qFa3dZqrj7w/xyPL4AkJ0nXzt8yoqiKM7/JwxtUlo3mQOU7V1K61Jlu/rHYaV1qVJQK7JIf10iP4UR9AbCLiJ7g1HbpVHnxUo44A1ms5VM6VQkV+WcA7f1JF/5cZtOQcth+7YJ05/rUj8PvXNL6zv7ck6q0MfLJKt2UarjVRJD79wyfdsrysG7LI/guucmrHinq3XbK6mrrL1re2pMI8RcHxzqiWU6ytHUzWkO3pVIZ/u2CSsOKhnSt4mft3CXhjYpK97Mcd0RKkI9LKvrSMnbXTToFcmQh0K3O9t2ZJe+TT3PVz5L1e6ylM394ai8jjsfXwBw9uGHHz7++OMffPBBod5TMONqWZrdw0jzMxnzeITeZTJjvvQmRiXMl94j+jpHpE0fyJD0MGenMcBBTSQRufuQLElfxZROxo5n7flKe0T9M9KuPW1Rn7x07uhu8mj0KI6i+vOabB6EJB7upCau5cLj8YVC9mMVsk7evLoa2N7ebrnlxIY9vlDIZ6xnZFSNr6bi87WrIdpqelKm/cr8QDincu5dZhzftsQhtjzuDvUkf/kxdtjn86m77JCOUcqmQ+Nyu+YytkjNim/a/q23BjLvjLOc8qnGUfdbzaRagdTqnZp3u3bhttzyVA8t2lH+6n8Ox3HcZZu2TptpuWigf+LWCVsXj08OPdZ974FZW1tW+ZKD59xqhGw/MGXeZRPVz7b1xCYdOwMHdrddq6Xju3zxvgNbnNO3i5+vcEvqmWSGflFTL1XG2eM32illxnwxnvHaXF/UpNqe1659xipurkc5nH/UGuQLxWIpFzNX9d8hvmW7M23a4zGd99xut0TtLiUDDX3NitLld4xY6HbnwDJ9u3qer3wWtN2JWN/XafeN3cb9oWW7yzzYZ/Rexx2Ob/I1FgAcFWhMwbrD2v/ZjClIeibzppHauqXK9HmmsQn65xXzlHWHtTgr5inTlxrrJnq71y1Nes5j+ax43VIjnaFNpnQsRVP7f8MB41mLyzEFIqKvGw16k59RpD3DCQcSkZVwQET0Z6fGmtGg10jRLp0M0uObn5+Yl4YDxmMV85aDXlMewgEtn4lU9NUSBWneYDTolXw/EzbXzO1dxvF1OO4FHVOQWoam/bVOJxwOGxGSaknhn629smLthOvWGg91c+O2fIzYptYV9EpyhVM/27YLt+WW33qY2o7yWv9zkfZMSXvWpKQ9nDx4V+u2u1rV4772lnVH05KyeJhpVU/s0jnapQZet3aC+cnk9m3JcZKqnEX6dvHzFW7tsNKavFy7wKnnkHnGZ/P1y/b68qZyXeLK9aZ2qXJ3PVIUJYfzQDggiZYSDmRTD9OvC7bxbdqd3Xmvgtpd0iU7S4VrdzbtyC79DPV8xPksbLtTbO/r1Ha3Xf+caHe294f2Ru913OL4qnnSTgTlMloIQNlJjCmotew+yGZMQcaXGvpulbWPyZw7RtSRcf3XRevAvkiWfUF+vFN8Ivu+IIsv0iIsXin7HtNjXyxr58sS/a8bnJM+IlsPSWyxJIY+eEQGRGyfg9V3NA+2R8S/x+fplKAyMHnDYHNXfU57JRIID3TUa8n2BH2rIuK3e04R2TAY7NE35O+KBgdbREQkFuqT4IA/kUhfS455cc6n9vykfnKj7DEypGgblvqOAaVDz09jYq9E/F3hDb5QrKNDRLzBLr9IRCQw2y+i9WLHQn07duxo8HQaW2uMiDg/rnFl3C0yab5Mf1ZExCNy39Mi4v645099x5JGT2KHA2Gt4Ow17FnuaWrS/8rtKX8s5Gvo3JES6A1GjSNlbebilv2LRQb6J7a9e/2aLB5nFVSiHhrtRWzahbgst8LXw7zU/1yPo4Who4PK/tbp+tzVtvO2qsf30NHdsf2D9VdsffWyiXKsu21b95zkR/qHDm6aNGFTcmIW9cQ2nXMDa1r0BIcWzejfYv/Iyyn90rhIZvxaBkSGlskaka33Sv+vjcvQ9V+XOReJiIy7WOQ3IpL5PFP/BVlzi4iIXCmLs4ifL0ad8Xd1ifv67xDfpt1Zn/cqqt15g1FlINeLvS5v7c6mHdmlX7R8For9fV3i/tBodzvt7w/zpKKu43b8XYrSldOaAKqNdU9Bfn774CJZdrWs/NWI8jfhYheRVy6WGStlzUUiIoeelx87x75IJo2TNfe6SH9y4+CeWGSXBMONfaFI82DjZBcrJ/FObch1VRumb/IVon5yozc4MJDpKjsyi5+WxeqnnTJjmWy9N5fjnj+my3Ok3eMLKU67H2lv6GvWb1BjId+qnDZpdOXkxDftif6NL8tlRbkjtOWqvbgst6LUw5Fvd4TH0cw3bf+r00RE5Fh32zt15q8TnvMXa3+eG5ghs3qPBeaca6y4/cCUabfapZlUT5zTERG59Kbr394rovYUDB44JpKIcF5dpvTt4ucr3Nqll8jQETkgct8l0rtT9l3iGNvteaZU5yW39d82vkO7szrvVVS7C/k8Psn5W5mmEO3O3I7s03dXz93ns7Dtzt19XTFU3nUcAHJWqPcUqMbdIvKs7M0i5j79OUzbw0nha0z9wdt/IdOuFLlSJj1rzEkb2Kinf0T2jdMe7MgRuffZ1E0Mqa852GlMaZt2iel1Blnwz27ctWrDYPPN/tmNfcv7Gmebn0MM7lGfWUTas5jptqOzxZir2dKZnFL6VjtXGVPeVukdy/UdzdJnvCBgedHeUe+f3dhpmt+m73B9R/PgctO0t8iGwWbHO6uUdLLkopx7l5nebSEi+p2983FPryc55SfSnjb30PxCBxGRpJ6mtHRiewa9egHGQi2pzxNc1Td3ejdOXDmkfR7ob305052cng3LjLgoHyN2LORrMtdnU3sReUFteXbtwnW5Fbwe2shtuwU07rJNbbJSP/RbDsispK/3x7q3nneT+RG3XT2xS6d3o/EW7kPvBhPxfZcvlqNa+MA7KydNmOmcvl38fIXb8X1KDmyUfVeL71OydYNM+pRzdNfXF7fxRcRt+7Lith7axLdvdzbnvUpqdx0DSnNfQ4HmdefQ7izbkR239dxtPgvb7jLd16Wyuz/MYBRex53oryko2gYBVLAHVE++lsN7CrZ3KdfNM/5N70oK1KaNvZnFPLHDSquawjxl3SZj3XVLlRWmTRhvQ3gzaaOJqWhDm/TAedqK5tcfJBaZJ6KvW2pKal6mOerRxEx88xxpRVH0aV8iGV+MrM6oDgS8evzkudZJ9KTMSwIBY0p2NGik4k3MnbNPxyFHafETaetTPZPyamzYnHrUHJyWijaXPBo1zTtPWiFT0bkqZ8Xx4Nousqkn7vOjLkgKdNpZq3TMh1etMOZp/C4KzTVtcnj27ynQj631gizLx7TD3mDQSDHoNbeX5HdhGMVptIscyi0f9dCqHeW9/ruxfZt2EC0PZWJp8ixW49CnvJN86J1b0t5S7lBPrNNZ95x1fCM86T0ItunbxM9buLXDSqs+BXqF6cUE6hljxZvG2cP8qoL088wK00Uz6SLl9nqkuGpfqfUzeb50+hKn64hlvbVrdw6VvMLaXVR7X4FzrMK3O9t2ZJe+ZT3PYz4L2u4s7+uc2p3N/aGT0Xgddzi+4UBBtghgNEm8p8Bz6tSp1Otsnt5TMHK9y0Tu0LuT4SgW8rVIzwiHRwK5Cfk8fc0jHZ1rn7iPqg0AwIhF2j3Lpxbqcg1glDh58uTTTz89d+7cQr6nYGR6l0n3IZHFsmacbC3NrPIKEPJ5tOFs3uDIX7kE5CIW6tsRWFKY6qfV8AZPpzfoOB0UAAA483cp+XxzKIDRrazHFAAAAAAAgOJIjCmwfqMh3QQjFQv5PJaK+NKacs4PUM5oL0Dx0e4AACgnjCkAAAAAAACMKQAAAAAAAFasewqyGVNQ5IwCAAAAAIAiYEwBAAAAAAAwjDnnnHNiNZMvv2SsOTSbMQVjxowpVaYBAAAAAEB+DQ8Pv/3225dffnnNwoWzLhjoCUeTFjOmAAAAAACA6lQj0vBp34UpobynAAAAAACA6lQjEn1jz+SmhqRQxhQAAAAAAFCdan60epNc8+mUUMYUAAAAAABQnWontyz89AWpoYwpAAAAAACgOtWkdxMIYwoAAAAAAKhWY84555x/+Zd/+cPH/67B1GUQj8cZUwAAAAAAQPVQfyXxsssuq124cGH64mzGFNBZAAAAAADAaHL++ecfPXrUc+rUKcvFdBMAAAAAAFA9Tp48+dZbbx08eNC6p4AxBQAAAAAAVJWTJ0++//77tbW1NZaL6SYAAAAAAKA6WfcU8NsHAAAAAABUJ8YUAAAAAAAAA2MKAAAAAACAgTEFAAAAAADAULN69erVP3ojJTTLMQUD3TJ9vvFvRrckAtueFxE59Ly+aFmhdmBlIRMvhEi7x+PxhWIWi2Ihn8fjsV1sWtt5E1mkY520q/gYxQ69O2t6z8TpPRNnbOwudV6ykVW7KI6yakcD/ROn90xse3e/1cItK3smzujfkho8tEg79D2LBkREpHfjRDVE/WdOzamepKUjx7rb9ESMwJR0kvNjl37m+CMLz556jVu5M7e1y1pu15FSqaJ2Z9+O7NK3q+d5yadD+oVrd1mquPvD/HI8vqkSt+vq3XtqUt3W4babdhnfOhGb/JdtfQNGp1OnTh1+7cknXzt8KpmiKM7/JwxtUlo3mQOU7V1K61Jlu/rHYaV1qVJQK7JIf10iP4UR9AbCLiJ7g1HbpVHnxUo44A1ms5VM6VQkV+WcA7f1JF/5cZtOQcth+7YJ05/rUj8PvXNL6zv7ck6q0MfLJKt2UarjVRJD79wyfdsrysG7LI/guucmrHinq3XbK6mrrL1re2pMI8RcHxzqiWU6ytHUzWkO3pVIZ/u2CSsOKhnSt4mft3CXhjYpK97Mcd0RKkI9LD5bHUQAACAASURBVKvrSMnbXTToFcmQh0K3O9t2ZJe+TT3PVz5L1e6ylM394ai8jjsfX9u10u7n88jV/ZVt/su7vgGjxocffnjo0KHDhw8X6j0FM66Wpdk9jDQ/kzGPR+hdJjPmS29iVMJ86T2ir3NE2vSBDEkPc3YaAxyM7swj0jZfug/JkvRVTOlk7HjWnq+0R9Q/I+3a0xb1yUvnju4mj0aP4iiqP6/J5kFI4uFOauJaLjweXyhkP1Yh6+TNq6uB7e3tlltObNjjC4V8xnpGRtX4aio+X7saoq2mJ2Xar8wPhHMq595lxvFtSxxiy+PuUE/ylx9jh30+n7rLDukYpWw6NC63ay5ji9Ss+Kbt33prIPPOOMspn2ocdb/VTKoVSK3eqXm3axduyy1P9dCiHeWv/udwHMddtmnrtJmWiwb6J26dsHXx+OTQY933Hpi1tWWVLzl4zq1GyPYDU+ZdNlH9bFtPbNKxM3Bgd9u1Wjq+yxfvO7DFOX27+PkKt6SeSWboFzX1UmWcPX6jnVJmzBfjGa/N9UVNqu157dpnrOLmepTD+UetQb5QLJZyMXNV/x3iW7Y706Y9HtN5z+12S9TuUjLQ0NesKF1+x4iFbncOLNO3q+f5ymdB252I9X2ddt/YbdwfWra7zIN9Ru913OH4umIx1uCItM2XGctkpVryO5MOgfXYBLf3V/b5L3h9c2K+NwGqxgMPPPDAk6+dSqOMbEzBusPa/9mMKUh6JvOmkdq6pcr0eaaxCfrnFfOUdYe1OCvmKdOXGusmOizXLU16zmPZl7luqZHO0CZTOpaiqf2/4YDxrMXlmAIR0deNBr3JzyjSnuGEA4nISjggIvqzU2PNaNBrpGiXTgbp8c3PT8xLwwHjsYp5y0GvKQ/hgJbPRCr6aomCNG8wGvRKvp8Jm2vm9i7j+Doc94KOKUgtQ9P+WqcTDoeNCEm1pPDP1l5ZsXbCdWuNh7q5cVs+RmxT6wp6JbnCqZ9t24XbcstvPUxtR3mt/7lIeyajPatR0h5OHryrddtdrepxX3vLuqNpSVk8zLSqJ3bpHO1SA69bO8H8ZHL7tuQ4SVXOIn27+PkKt3ZYaU1erl3g1HPIPOOz+fple315U7kuceV6U7tUubseKYqSw3kgHJBESwkHsqmH6dcF2/g27c7uvFdB7S7pkp2lwrU7m3Zkl36Gej7ifBa23Sm293Vqu9uuf060O9v7Q3uj9zpucXyd2Y0pSA0/rLTOU1a8qShvakchZWiVZTq5jO1Ny3/B65sT/QRaLqOsgAJKjCmoXbhwYTS8+kdv1H/50xckug+yGVOQ8aWGvltl7WMy544RdWRc/3XROrAvkmVfkB/vFJ/Ivi/I4ou0CItXyr7H9NgXy9r5skT/6wbnpI/I1kMSWyyJoQ8ekQER2+dg9R3Ng+0R8e/xeTolqAxM3jDY3FWf016JBMIDHfVasj1B36qI+O2eU0Q2DAZ79A35u6LBwRYREYmF+iS4o8mj5j8a9P7nfr/HExMRRVFyzJZFPrXnJ/WTG2WPkSFlQMtufceA0qHnpzGxVyL+rvAGXyjW0SEi3mCXXyQiEpjtF9F6Y2Ohvh07djR4Oo2tNUZEnB/XuDLuFpk0X6Y/KyLiEbnvaRFxf9zzp75jSaMnscOBsFZw9hr2LPc0Nel/5faUPxbyNXTuSAn0BqPGkbI2c3HL/sUiA/0T2969fk0Wj7MKKlEPjfYiNu1CXJZb4ethXup/rsfRwtDRQWV/63R97mfbeVvV43vo6O7Y/sH6K7a+etlEOdbdtq17TvIj/UMHN02asCk5MYt6YpvOuYE1LXqCQ4tm9G/J4pFX2dTDi2TGr2VAZGiZrBHZeq/0/9q4DF3/dZlzkYjIuItFfiMimc8z9V+QNbeIiMiVsjiL+Pli1Bl/V5e4r/8O8W3anfV5r6LanTcYVQZyvdjr8tbubNqRXfpFy2eh2N/XJe4PjXa30/7+ME8q6jpeSONk8ZUiO0X+Tnwih0qWj2LydylKV6kzARRZrYg0NM2K/igmpp6C/Pz2wUWy7GpZ+asR5W/CxS4ir1wsM1bKmotERA49Lz92jn2RTBona+51kf7kxsE9scguCYYb+0KR5sHGyS5WTuKd2pDrqskSnQKepm7vjTMUpXSXDbfqJzd6gwMDma6yI7P4aVmsftopM5bJ1ntzOe75Y7rMRNo9vpDitPuR9oa+Zv0GNRbyrcppk0ZXTk58057o3/iyXFaUO0JbrtqLy3IrSj0c+XZHeBzNfNP2vzpNRESOdbe9U2f+OuE5f7H257mBGTKr91hgzrnGitsPTJl2q12aSfXEOR0RkUtvuv7tvSJqT8HggWMiiQjn1WVK3y5+vsKtXXqJDB2RAyL3XSK9O2XfJY6x3Z5nSnVeclv/beM7tDur815FtbuQz+OTkX4rK0S7M7cj+/Td1XP3+Sxsu3N3X1cMlXcdH10KW98ApKgRkWh40+4L/tIcOvL3FKjG3SLyrOzNIuY+/TlM28NJ4WtM/cHbfyHTrhS5UiY9a8xJG9iop39E9o3THuzIEbn32dRNDKmvOdhpzKeadom7t7P6ZzfuWrVhsPlm/+zGvuV9jbPNzyEG96jPLCLtWcx029HZYszVbOlMTil9q52rjClvq/SO5fqOZulLTJfqWFK073L+2Y2dpnla+g7XdzQPLjdN34psGGx2vLNKSSdLLsq5d5np3RYiot/ZOx/39HqSU34i7WlzD80vdBARSeppSksntmfQqxdgLNSS+jzBVX1zp3fjxJVD2ueB/taXs7myWuyuykX5GLFjIV+T+TUnpvYi8oLa8uzahetyK3g9tJHbdgto3GWb2mSlfui3HJBZSV/vj3VvPe8m8yNuu3pil07vRuMt1ofeDSbi+y5fLEe18IF3Vk6aMNM5fbv4+Qq34/uUHNgo+64W36dk6waZ9Cnn6K6vL27ji4jb9mXFbT20iW/f7mzOe5XU7joGlOa+hgLNT86h3Vm2Iztu67nbfBa23WW6r0tld3+YwSi8jpcnl/dXFgp9nneiv6Yg9xSASjSS9xRs71Kum2f8m96VFKhNUnozi3lih5VWNYV5yrpNxrrrliorTJswZj29mbTRxFS0oU164DxtRfPrDxKLzBOl1i01JTUv0xyqaGImvnmOtKIo+vQlkYwvRlZnVAcCXj1+8lzrJHpS5iWBgDElOxpMpDIwMKAnZJ+OQ47S4ifS1qd6JuXV2LA59ag5OC0VbS55NGqad560Qqaic1XOiuPBtV1kU0/c50ddkBTotLNW6ZiKU6sw5mn8LgrNNW1yePbvKdCPrfWCLMvHtMPeYNBIMeg1t5fkd2EYxWm0ixzKLR/10Kod5b3+u7F9m3YQLQ9lYmnyLFDj0Ke8I3ronVvS3hrtUE+s01n3nHV8IzzpPQi26dvEz1u4tcNKqz4FeoXpxQTqGUOdsptyqbI8z6wwXTSTLlJur0eKq/aVWj+T50unL3G6jljWW7t251DJK6zdRbX3FTjHKny7s21Hdulb1vM85rOg7c7yvs6p3dncHzoZjddx5+ObLuW8ZHe+mj5P2Z64Y+/SSrt1k3ZEVrxpm46iuLu/csh/Yc/z9sKBghwpoDwl3lPgOXXqVOp1Nk/vKRi53mUid+jdyXDU0Lnjryb+VSknraGKhXyevuZCzZkM+XzSQ9UGAAAlEWn3LJ9a0ldDAMV08uTJ999/v7a21rqnQLLoLCh0FnuXSfchERHPONlamlnlFSDk86jD2ZSBDvEGS50dVKVYyNewa0mmHw/LTaKGizfoOB0UAAAAwEhl6CkokzEFyJ7H41Hy95MHAAAAAIBqk+gpqLFcTDfBSMVCPo+lEr20ptzyA5Qz2gtQfLQ7AADKCWMKRgnGFAAAAAAARoIxBQAAAAAAwIJ1T0E2YwqKnFEAAAAAAFAEjCkAAAAAAACGmvDq1avD0ZRQxhQAAAAAAFCdasTnuzAtlDEFAAAAAABUp5qmeotQxhQAAAAAAFCdai1DGVNQKTwej/pBURTz59LlCAAAAABQ2WpW9wy8v3vTj974vTmUMQWVQtGlfAYAAAAAIDeeU4df+9Ebf/nlpoaUBYwpAAAAAACgepw8efL999+vra21/pVExhQAAAAAAFCdxpxz4f84/rs9sZrJl18yNhEaj8cZUwAAAAAAQPUYHh4+fvx4TU2N59SpU+mLsxlTQGcBAAAAAACjRobZB3QTAAAAAABQnXhPAQAAAAAA0Hg8HsYUAAAAAAAAzZgxYxhTAAAAAAAANLY9BYwpAAAAAACgCtXU1DCmAAAAAAAAaGx7ChhTAAAAAABAFaqpqalZvXr16tXhaPKCLMcUDHTL9PnGvxndkghse15E5NDz+qJl+cx3Ill1K0UWC/naIy7im0tp5c6CZSvfIu0ej8cXilksioV8Ho/HdrFpbedNZJGOddKu4pdKuR33ge5c2svKPDXekC/1oDkex6zqT0FR/4smt3Kwk4fyiYV8I6576kWqHBp+Rvkt/0Irq/o/0D9xes/Etnf3Wy3csrJn4oz+LanBQ4um90yc3jNxRs+iARER6d04UQ1R/xmpHetu0wMTkZ3TP/TuLC1+cnhe8umQvtvwvMvXdapMOB6vVM73w26v+7ndJ6QmYpP/ktcTAG4dP37c8+GHH5Y6GwAAAAAAoMQS/QO1v/3tb4uwvaGhoaGhoT/84Q8i4vF4EuGKoqSEAAAAAACgfltMOP/88y+99NJLL720VPmpdNl87/6Lv/gL9UNtbW1tgfMjIjI0NHTttdeed955KQcbAAAAAICM/vCHP7z22muTJk0qdUYqVUpPQU1NTU1NzZgxY8aMGaN+Pn78eGJpbXHeTXjs2LHzzz8/HldExNxXwJgCAAAAAEC6xGNmRVHi8eGPfezP//SnP/Fy/exZftFOBKrvKlL7CNJj1o4ZM6bgGRTxeDyKIiJKPB5PH1bAOAMAQBWqqRmj/ghRPB5P/xCPD5c4fwAAlId4XDl9Ol5T41GfgZc6O5XK4/HU1tbW1NTE4/HTp097dJaRizH1QKUoyvBwXFW0jQIAULbOOGPMjTdc/8lPfvL7D602f7jxxhs7v7Hw9Gl6CgAAEBGJx5Xh+LAIQ9FHqqam5jOf+cz27dtra2uHh53uNIrXUyCixBWFngIAAFSKokyZMsXn86V8uOyyyxRF4XIJAIBK/SI5pobRBHnwwQcfzJ8//+mnnxYRh5uNYo4pEK2fgFsfAABEFCV+4YUXnnf++Skf/uLcc9Wu9VJnEACAshCPx5V4XFFqSp2RUWLs2LH33HPP9773PY/HY3e/UbyeghMnTpw4ceLUqY9Onz5dtI0CAFC2/uIv/lz98Kc/fZD+4Y9//O/SZAsAgDITj8c/On167NizSp2R0WDMmDEXX3zx3/7t30YiEb/fX/qeAvU9BcPDw0y8BABARIaH4w4fuFwCAKCKx+PDp0/Hh3kTfh58/OMfb2xsrKur+8QnPlEmsw8UtZuAMQUAAIjI8PDw3/zN33zsYx9L+XD22WcPD3O5BABAMxyPD58eVhTm5eXB1KlTL7744ilTpgwPD8fj8XL57YPTp09z6wMAgIjE4/Hx48f/+Z//ecqHsWPHqj9fVOoMAgBQFtTLYjzOmII8mDx58lVXXRWPx4eHh+26CaSYPQWnh4dPnDhx4sSHdAUBACAi//3ff5ryiUa7D6dOnSpx/gAAKA/xuDI8PBzni+SIxePxr371q2rPi0M3gRSzp+DUyVN//ON/x+PDcYWuIAAA5MOTJ0udBQAAyp4iiopfBRqx7Ecs1tbUFOOnJj7+8Y//13/9/rzzzh0eZnoJAAAAAMCdDz44fuaZZxbnC2w18JikL611HnKQL/X19fv37z969GgRtgUAAAAAGGXOOOOMv/7rvy7OF9jRIWNZJboJ0mN6fvvb3xYqX6bNF3oTAAAAAIBqoDCfPTs5fBM/44wz1A+1Z555Zr7zAwAAAAAAKhVzPAAAAAAAgIGeAgAAAAAAYPDkfY7Hgw89lt8E8+hb37yj1FkAAAAAAKCs1RYi0fL8Ql7OXRgAAAAAAJSJ2qr6/lxVO4u8KM9uLwAAAAAonNpvdrbnN8WHgl35TTCP8r6zuVGLqEwyI+QnU04AAAAAoKrUHj9+vNR5KJ5y2NmxY8cmPpOfdOWTH3NOAAAAAKB68NsHAAAAAADAUKPkm+PmIu2e9ojp71jIl/S3SCzk89jwhWIj3Nu872xuyiozCvmxN8L6BgAAAACVyNRT8FLH9Y/tLeiXq1ho+WBwkT9TnrzBqJpUNOg1f06KFGn3eDwpvQwZjXzvRo78OCuf/LirWwAAAAAwWtQoL3Wep/r7tb9YfOV5Jp0v5ff7VWRVZ+OSjnrzuIGGzh3dTaljBnZ0NiSWmj/r6cRCPo9nuQS8tluyk5bZlzqT93LfY9en7PW+x64/z8b1j+0bYfmUe37U7Bib7OzsTNqckV3LbOWUIcfySSmR9OJJzX9uZZKeEwAAAACoHjVxRZGW9b9Ps75FFCWeA7stxULLJdzll1jI1yI92pexaNAbCOvfzAY66kUk85iC+o4BRRlYNNX93qZkde9jq967f8FMc5CStteKXHX/r9Qy+dX9V5k/i2IkdMP5ms6XMpSPYhpdX6j8xF/qzDY7TvmJK+rm1U2ubxHF/5BaN666/1e/f7F9/MyH1ivNNzy2NzlbphqUaeMu86PuvylcEUXZ+9gNNru5N/peTllIzQkAAAAAVJVaRVGk57YLeiyWzfXn87vSqs4d3eLpFm8wOnDzCz6PMUbA0y0iIoGw0uUXUccUdCbWMz57gyPNQ/L+vPKD737im7+boCh7f+i/eskvE+F6YVy1/BeRr4oiv1zyqQu+m1hqfL5qufpl8pWFX1Me/d3vJonIvh/6r/7Gy79b/dlS5mffD/0v+H/3u4dEXln48du+4c86O0n5eWXhx7/0jIj0XLDrqqt++ctPXSAic/0vf+MbsrpJEUX7Hj3zoV9E/T94uX2BIiLJ360VI1auzGvv+6H/6j3f/N0CRYwH/nFFFGVCe+QXP/Rf8A3ld6s/q2faxFxWMvcn2RcGAAAAAFSpWkVE5v7k/YdmpizY8s0Lw5LPnoIuRVkUCkU7Ovwi0jGgdFhHqzctUocf6EMN8iDpm2fXQ/KT8Exl7w+bFsgj778/SQ1serQ+bBSGouwTuWr5G+H2SerSBfKI6bP6lXXmQ+GZWtITr//cVc/F9iozJ5UwPxPbww+pSc9smisPZZ+dpPzMfOj9NyY3fXrPN9+/M9a04HNvhOsfvTAci/2b1CtaVDWuurV9MXOYOcF89RRMbP/m3Asf+mHT50zhek4mtoffF1EUZeZD77//kLbCvq6mBXs+8YnJdz7UPsk6SQAAAACAhdo/fvCRPPOlC5+xWDbn2j/+8Y9521JIHUbQ2SkSCEenLje9eEBE1CEFDSFfarCYxxeYBh7kxLw/wSW/7JULn5Erl2795+nP33jhfTv1JVphzHly7/Lr5IOTw7+879MXLkmsZ3y+cukHaQU0+N4v6z/7cadyO+ecc9QPp0+fLnR+fv7cM1fetNUxO7b5ETn4/M9+KTu/9I/jlw4Pywd//OAj+ejkyfrxf/Wv7w3WT0hJNC1baobmua9B9vm5at7S4buee0/k5A9u/FJa8ciVS7f+87zxet7X/v2M2J17l193cO3f3/iD7xsLcskJAAAAAFSV2riiyJeeiN53bcqCbUsbXlHi9q8dcC1pGEEstDzpO38s5FslSQMKYiFfy65GGZTGxqmLuvI0rMC8P/dGo19Zu/bAvHnXSlzmro/OtY4+zrTo4NrmRbKqz/StM6WAti39yu7vvnJv1sVWqPwcXNv82fvfEvnSE9H116ZmMsv8yMEtUeUK+dKdM6OtvTtlRp2IfOlL0Z/85L5eEZG6Xr3abFvavL91lShXfPeVO/d+9pXPRlv3Ny+SVX0Tn2jeHx9ZDUpZe9zc9esPrm1+YrxeBtuWNjxa94pRAPF4XNt5+e4T3939lS8+9UrfvPUPrm2uq5PvvtKXS3cBAAAAAFSdWkUU+Ulrw08slt322XwP1Y5pYwYCQYdXDqiRvMHowKIXfC2yaJG0eDw7RjaYQGPen57bJt//lsj994vc9sTL9Y9ef/9bSXFve2LPfeN7bksNls823J8Sa5r6sX/p5FZ5Yk/LpdmXWqHyc2nL+j0tItK/tKHh0e++vL4ly6/I5vwcfDVW1zRZYjKt9btX/Ls8qCVy7733Huy57YmJ6++bpsbvf/nfm1ovvXT8+vUi/UuvqBuvKPtFRFGm3bt+2ggH+6et3b/0s/e/ddsTWvjB/f8ub/37qwda9P3rXzq5df0V3315z57xItLysty2aO1161ta1u9pOdhzW0PDW1e4KAwAAAAAqFK18c/cs3v3PTZL8zmmoN3j6fYGo4pSLyKxkK+zSXuToSYQFom0e5qMSKL+aKI60CDS7vE0pc1RaPJ0u5mRYN6fL/fu/nLij4PPPHpb9+57phl/z3kqHo+PMyIdfGbOt/dOlj0yeXLd7ffMTX2M33/3lIB0777nMxmLLOXd/gXKj+oz93TfNuXxV/d/ea79t2O7/OxX6qZN2Ht/LB6PKyIS//nS2w7c3jt3vPQ/cf9b0h3/+dIpW2buvv3Ao/K13nFaIby8fvLMe7T4udYdu/yI9N895fG67u9csUVLu/+J+yd3b657/Imff1krKb0uG8u/tlvLW6Loss8XrzQAAAAAUJ1q7p5yd7/03z3nmYNy8Jk5U6ZMmfPMQREROfjMHP1jXnSZfgdRRMT4dUTjNxD9qZEM/i5FURSly6/9SqJJ7mMN1D2eMuXubRkjfVse6L29TqTu9tvl21OmTLm7PynC43WbTV/sS5qfg8/M0f84+Mzj66/wX5vTQ/Rpc5O6F7ZtWf/Wihvv7u+/e0vdd64QEZEr6sb3PxXx3z4tsbE937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" 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" alt="" />

7、查看表索引信息

show INDEX FROM table_name;

8、 清空 二进制log

  reset MASTER;

9、字段类型不匹配导致 不走索引

表结构如下

CREATE TABLE `tb_apps` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`app_id` varchar(128) DEFAULT '',
`app_name` varchar(255) DEFAULT NULL,
`created_at` datetime DEFAULT NULL COMMENT '创建时间',
`updated_at` datetime DEFAULT NULL,
PRIMARY KEY (`id`),
KEY `idx_app_update_time` (`updated_at`),
KEY `idx_app_aid_country_ctime` (`app_id`) USING BTREE
) ENGINE=InnoDB AUTO_INCREMENT=0 DEFAULT CHARSET=utf8 ROW_FORMAT=COMPACT;

查询语句:

select app_id, app_name from tb_apps t where t.app_id=895670960;

解释执行后发现未走索引

mysql 优化

sql改成

EXPLAIN  select app_id, app_name from tb_apps t where t.app_id='';

这次走索引了

mysql 优化

参考资料:

1、MySQL数据表碎片整理 http://www.365mini.com/page/mysql-optimize-table.htm

2、MySQL定期分析检查与优化表 http://www.cnblogs.com/littlehb/archive/2013/05/08/3067175.html

3、 mysql优化Analyze Table http://blog.****.net/alongken2005/article/details/6394016

4、MYSQL explain详解 http://blog.****.net/zhuxineli/article/details/14455029

5、mysql删除大表更快的drop table办法

6、mysql 性能优化方向 http://www.cnblogs.com/AloneSword/p/3207697.html

7、MySQL缓存的查询和清除命令使用详解 http://www.jb51.net/article/75955.htm

8、MySQL的优化点总结---通过计算多种状态的百分比看MySQL的性能情况 http://www.tuicool.com/articles/zAnaIvM

9、 mysql的缓存机制 http://blog.itpub.net/15480802/viewspace-755582/

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