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django的jsCalendar的widget
看见在插入数据量近6000W的时候,还能达到1200条/秒左右
插入的文章见:
http://hanyh.iteye.com/admin/blogs/434077
1,机器为4核16G内存,脚本和数据库在同一机器上跑,插入的速度还能快更多。
2,mysql为5.1.30
回复要先做题,太麻烦了,直接改原贴。
对1楼的回复,需要说明几个问题
1,oracle版本和基本配置(oracle所用的内存,缓冲是多大?)
2,所在的硬件环境
---------------
3,我的相关文章注明了,插入脚本和数据库是在同一个机器上,为了模拟真实的负载,100个并发连接全是单独直接连接数据库,并没有把全部硬件资源用在数据库上,mysqld的配置也有限
4,即使oracle在大数据量快一点也是正常的,但是这足够证明mysql不慢!!!单表这个插入速度的量对绝大部分应用足够了。
10W条插入所耗时间,插入条数记数(原来表中有1000W记录) 12.2486999035,100000 12.2773001194,200000 12.2486000061,300000 12.071199894,400000 12.0887999535,500000 12.263600111,600000 12.8111000061,700000 12.8373999596,800000 12.901900053,900000 14.3822999001,1000000 14.4759001732,1100000 12.5479998589,1200000 12.6721999645,1300000 12.7108001709,1400000 12.6102998257,1500000 12.7771999836,1600000 13.2545001507,1700000 13.3555998802,1800000 13.4908001423,1900000 14.0559999943,2000000 14.8170998096,2100000 13.132600069,2200000 13.0569000244,2300000 13.0878999233,2400000 13.361700058,2500000 13.535599947,2600000 13.8598001003,2700000 14.0957000256,2800000 14.1975998878,2900000 14.082100153,3000000 14.7009999752,3100000 13.8789999485,3200000 14.0099000931,3300000 14.3070998192,3400000 14.5513000488,3500000 14.6245000362,3600000 14.6152999401,3700000 14.8347001076,3800000 14.7455999851,3900000 14.8991999626,4000000 15.0450000763,4100000 14.9217998981,4200000 14.9628000259,4300000 15.2028999329,4400000 15.2783000469,4500000 15.5104000568,4600000 15.4739000797,4700000 15.6339998245,4800000 15.7406001091,4900000 15.9275999069,5000000 16.4046001434,5100000 16.3574998379,5200000 16.1486001015,5300000 16.4531998634,5400000 16.5682001114,5500000 16.6905999184,5600000 16.6999001503,5700000 16.958799839,5800000 16.8580000401,5900000 16.9960999489,6000000 17.1826000214,6100000 17.203799963,6200000 17.4459002018,6300000 17.2922999859,6400000 17.5569000244,6500000 17.807199955,6600000 17.8497998714,6700000 17.9196000099,6800000 18.1556999683,6900000 18.4064002037,7000000 18.320499897,7100000 18.4403998852,7200000 18.6754000187,7300000 18.9267001152,7400000 19.0785999298,7500000 19.0410001278,7600000 19.2544999123,7700000 19.1266999245,7800000 19.5298001766,7900000 19.4387998581,8000000 19.4728000164,8100000 19.7027001381,8200000 20.0515999794,8300000 19.959800005,8400000 20.5871999264,8500000 20.2063000202,8600000 20.3708999157,8700000 20.3410999775,8800000 20.4716000557,8900000 20.5167999268,9000000 20.4343001842,9100000 20.7989997864,9200000 21.1456000805,9300000 20.8066999912,9400000 21.0187001228,9500000 21.2816998959,9600000 21.2311000824,9700000 21.4526998997,9800000 21.5680000782,9900000 21.656899929,10000000 21.6686999798,10100000 22.111000061,10200000 22.0908999443,10300000 21.929899931,10400000 22.1587002277,10500000 22.3671998978,10600000 22.6442000866,10700000 22.5406999588,10800000 22.2986998558,10900000 22.7825000286,11000000 22.7992999554,11100000 23.0840001106,11200000 23.0336999893,11300000 23.1243000031,11400000 23.0193998814,11500000 23.2432000637,11600000 23.2276000977,11700000 23.3639998436,11800000 23.7211000919,11900000 23.9835999012,12000000 23.7651000023,12100000 23.7512001991,12200000 23.8873000145,12300000 24.027299881,12400000 24.192800045,12500000 23.9718000889,12600000 24.4296000004,12700000 24.4260997772,12800000 24.546900034,12900000 24.5542001724,13000000 24.7776999474,13100000 24.6698999405,13200000 24.4569001198,13300000 24.6951999664,13400000 25.339099884,13500000 25.0239999294,13600000 25.0064001083,13700000 25.3452999592,13800000 25.204100132,13900000 25.6594998837,14000000 25.1654000282,14100000 25.9886000156,14200000 25.7527999878,14300000 26.1656000614,14400000 25.9951999187,14500000 25.7889001369,14600000 26.6344997883,14700000 26.6328001022,14800000 26.4196000099,14900000 26.6807999611,15000000 27.2302000523,15100000 26.7781000137,15200000 26.5967998505,15300000 26.8879001141,15400000 27.6101000309,15500000 27.2957000732,15600000 27.0910999775,15700000 27.6517000198,15800000 26.7947998047,15900000 28.0234999657,16000000 27.4553000927,16100000 28.0745000839,16200000 28.2358000278,16300000 27.3213000298,16400000 28.6037998199,16500000 28.0062000751,16600000 28.3991000652,16700000 28.1582999229,16800000 32.2086000443,16900000 28.7537000179,17000000 29.0751998425,17100000 29.1440000534,17200000 29.3770999908,17300000 29.4169001579,17400000 29.6337997913,17500000 30.0696001053,17600000 30.6554000378,17700000 29.4310998917,17800000 30.3331000805,17900000 30.3801000118,18000000 30.7383999825,18100000 30.2995998859,18200000 33.7615001202,18300000 31.6524000168,18400000 30.5258998871,18500000 34.0257999897,18600000 34.9193999767,18700000 33.0338001251,18800000 34.0789000988,18900000 33.1794998646,19000000 31.6475000381,19100000 32.1933999062,19200000 33.4735000134,19300000 31.7174999714,19400000 33.2877001762,19500000 32.1487998962,19600000 33.2555000782,19700000 32.7623000145,19800000 33.1905999184,19900000 34.0055000782,20000000 33.1561999321,20100000 35.1417000294,20200000 33.367399931,20300000 35.7843999863,20400000 36.7400000095,20500000 33.1805000305,20600000 36.9372000694,20700000 34.1338000298,20800000 35.1993999481,20900000 35.8675999641,21000000 36.9663999081,21100000 34.8279001713,21200000 35.6563999653,21300000 35.8894000053,21400000 36.3938999176,21500000 38.5266001225,21600000 36.6399998665,21700000 37.6226000786,21800000 38.1543998718,21900000 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74.0174000263,43500000 73.7207999229,43600000 71.5483000278,43700000 74.5278000832,43800000 70.5735998154,43900000 72.9801001549,44000000 70.3861000538,44100000 76.1578998566,44200000 74.9391000271,44300000 74.238699913,44400000 76.2427999973,44500000 74.2488000393,44600000 73.1296000481,44700000 75.1103000641,44800000 76.4396998882,44900000 75.4574000835,45000000 76.2295000553,45100000 75.5018999577,45200000 78.0773999691,45300000 73.2374000549,45400000 75.1116998196,45500000 78.1565001011,45600000 75.5194001198,45700000 74.2967998981,45800000 77.8838999271,45900000 73.4882001877,46000000 76.2270998955,46100000 77.8396000862,46200000 76.1662998199,46300000 76.1633000374,46400000 78.557500124,46500000 74.4247000217,46600000 76.2005999088,46700000 78.2168998718,46800000 76.527200222,46900000 77.2237999439,47000000 78.0637998581,47100000 78.5652000904,47200000 75.3833999634,47300000 75.9161999226,47400000 79.5389001369,47500000 77.780600071,47600000 80.0218999386,47700000 75.6284000874,47800000 79.8923997879,47900000 77.7213001251,48000000
插入的文章见:
http://hanyh.iteye.com/admin/blogs/434077
1,机器为4核16G内存,脚本和数据库在同一机器上跑,插入的速度还能快更多。
2,mysql为5.1.30
回复要先做题,太麻烦了,直接改原贴。
对1楼的回复,需要说明几个问题
1,oracle版本和基本配置(oracle所用的内存,缓冲是多大?)
2,所在的硬件环境
---------------
3,我的相关文章注明了,插入脚本和数据库是在同一个机器上,为了模拟真实的负载,100个并发连接全是单独直接连接数据库,并没有把全部硬件资源用在数据库上,mysqld的配置也有限
4,即使oracle在大数据量快一点也是正常的,但是这足够证明mysql不慢!!!单表这个插入速度的量对绝大部分应用足够了。
评论
2 楼
若水上善
2009-09-21
mysql插入响应速度是很快的,但是并发查询似乎不是很快。如果有这么多条的话,还是建议用oracle或者mysql集群,否则硬盘的速度跟不上的。
1 楼
seujacky
2009-07-29
看见在插入数据量近6000W的时候,还能达到1200条/秒左右
这个也叫快?前天我插入的速度是1万条每秒。数据库是oracle的。java程序。数据还是从文件中解析出来的文件有300多个数据量是6千多万条。
这个也叫快?前天我插入的速度是1万条每秒。数据库是oracle的。java程序。数据还是从文件中解析出来的文件有300多个数据量是6千多万条。
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在智慧城市建设的大潮中,智慧园区作为其中的璀璨明珠,正以其独特的魅力引领着产业园区的新一轮变革。想象一下,一个集绿色、高端、智能、创新于一体的未来园区,它不仅融合了科技研发、商业居住、办公文创等多种功能,更通过深度应用信息技术,实现了从传统到智慧的华丽转身。 智慧园区通过“四化”建设——即园区运营精细化、园区体验智能化、园区服务专业化和园区设施信息化,彻底颠覆了传统园区的管理模式。在这里,基础设施的数据收集与分析让管理变得更加主动和高效,从温湿度监控到烟雾报警,从消防水箱液位监测到消防栓防盗水装置,每一处细节都彰显着智能的力量。而远程抄表、空调和变配电的智能化管控,更是在节能降耗的同时,极大地提升了园区的运维效率。更令人兴奋的是,通过智慧监控、人流统计和自动访客系统等高科技手段,园区的安全防范能力得到了质的飞跃,让每一位入驻企业和个人都能享受到“拎包入住”般的便捷与安心。 更令人瞩目的是,智慧园区还构建了集信息服务、企业服务、物业服务于一体的综合服务体系。无论是通过园区门户进行信息查询、投诉反馈,还是享受便捷的电商服务、法律咨询和融资支持,亦或是利用云ERP和云OA系统提升企业的管理水平和运营效率,智慧园区都以其全面、专业、高效的服务,为企业的发展插上了腾飞的翅膀。而这一切的背后,是大数据、云计算、人工智能等前沿技术的深度融合与应用,它们如同智慧的大脑,让园区的管理和服务变得更加聪明、更加贴心。走进智慧园区,就像踏入了一个充满无限可能的未来世界,这里不仅有科技的魅力,更有生活的温度,让人不禁对未来充满了无限的憧憬与期待。
1.版本:matlab2014/2019a/2024a 2.附赠案例数据可直接运行matlab程序。 3.代码特点:参数化编程、参数可方便更改、代码编程思路清晰、注释明细。 4.适用对象:计算机,电子信息工程、数学等专业的大学生课程设计、期末大作业和毕业设计。
内容概要:本文介绍了使用 Matlab 实现基于 BO(贝叶斯优化)的 Transformer 结合 GRU 门控循环单元时间序列预测的具体项目案例。文章首先介绍了时间序列预测的重要性及其现有方法存在的限制,随后深入阐述了该项目的目标、挑战与特色。重点描述了项目中采用的技术手段——结合 Transformer 和 GRU 模型的优点,通过贝叶斯优化进行超参数调整。文中给出了模型的具体实现步骤、代码示例以及完整的项目流程。同时强调了数据预处理、特征提取、窗口化分割、超参数搜索等关键技术点,并讨论了系统的设计部署细节、可视化界面制作等内容。 适合人群:具有一定机器学习基础,尤其是熟悉时间序列预测与深度学习的科研工作者或从业者。 使用场景及目标:适用于金融、医疗、能源等多个行业的高精度时间序列预测。该模型可通过捕捉长时间跨度下的复杂模式,提供更为精准的趋势预判,辅助相关机构作出合理的前瞻规划。 其他说明:此项目还涵盖了从数据采集到模型发布的全流程讲解,以及GUI图形用户界面的设计实现,有助于用户友好性提升和技术应用落地。此外,文档包含了详尽的操作指南和丰富的附录资料,包括完整的程序清单、性能评价指标等,便于读者动手实践。
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