我写了一个jmeter脚本，其中线程数为1000，加速时间为1，循环为永久，持续时间为一小时。
当我运行这个脚本时，我看到jmeter开始创建线程，并且当我将循环设置为永久时，所以不应该在持续时间结束之前完成非线程。
但问题是jmeter无法同时创建这些线程。我在摘要中添加了简短的日志详细信息。
在1分钟内创建了76个主题。 在10分钟内创建了264个线程。 在59分钟内创建了590个线程。
然后它开始结束现有线程，因为它已经是一个小时，然后开始创建其他510个线程。最后，它显示创建了1000个线程。
但实际上它必须同时创建它们并且可能只需要3-5分钟，否则它必须失败。
我在AWS中使用m3.medium实例进行了这些测试。
在此之前，我在一小时内使用了带有100个线程的t2.micro服务器。一切都很好，我的意思是所有的线程都是在几分钟内完成的，并且一直活跃到一小时之内。
我在摘要中添加了最后一行。
summary + 840 in 00:00:25 = 33.1/s Avg: 6989 Min: 19 Max: 69102 Err: 3 (0.36%) Active: 591 Started: 591 Finished: 0
summary = 424179 in 00:59:03 = 119.7/s Avg: 2963 Min: 19 Max: 137203 Err: 498 (0.12%)
summary + 1542 in 00:01:31 = 17.0/s Avg: 36302 Min: 19 Max: 136434 Err: 388 (25.16%) Active: 4 Started: 683 Finished: 679
summary = 425721 in 01:00:33 = 117.2/s Avg: 3084 Min: 19 Max: 137203 Err: 886 (0.21%)
summary + 171 in 00:00:30 = 5.7/s Avg: 367 Min: 178 Max: 920 Err: 0 (0.00%) Active: 8 Started: 858 Finished: 850
summary = 425892 in 01:01:03 = 116.3/s Avg: 3082 Min: 19 Max: 137203 Err: 886 (0.21%)
summary + 149 in 00:00:24 = 6.1/s Avg: 352 Min: 162 Max: 992 Err: 0 (0.00%) Active: 0 Started: 1000 Finished: 1000
summary = 426041 in 01:01:28 = 115.5/s Avg: 3081 Min: 19 Max: 137203 Err: 886 (0.21%)
那么有人可以为这种情况建议解决方案吗？
summary + 1507 in 00:01:30 = 16.7/s Avg: 34686 Min: 23 Max: 116663 Err: 258 (17.12%) Active: 588 Started: 588 Finished: 0
summary = 404415 in 00:57:31 = 117.2/s Avg: 3111 Min: 19 Max: 116663 Err: 674 (0.17%)
summary + 378 in 00:00:27 = 14.0/s Avg: 31968 Min: 33 Max: 112930 Err: 54 (14.29%) Active: 589 Started: 589 Finished: 0
summary = 404793 in 00:57:58 = 116.4/s Avg: 3138 Min: 19 Max: 116663 Err: 728 (0.18%)
summary + 1868 in 00:01:30 = 20.7/s Avg: 30655 Min: 19 Max: 116585 Err: 126 (6.75%) Active: 592 Started: 592 Finished: 0
summary = 406661 in 00:59:28 = 114.0/s Avg: 3265 Min: 19 Max: 116663 Err: 854 (0.21%)
summary + 896 in 00:01:06 = 13.7/s Avg: 21775 Min: 19 Max: 90675 Err: 8 (0.89%) Active: 476 Started: 593 Finished: 117
summary = 407557 in 01:00:33 = 112.2/s Avg: 3305 Min: 19 Max: 116663 Err: 862 (0.21%)
summary + 537 in 00:00:24 = 22.2/s Avg: 30182 Min: 171 Max: 88596 Err: 85 (15.83%) Active: 2 Started: 701 Finished: 699
summary = 408094 in 01:00:58 = 111.6/s Avg: 3341 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 165 in 00:00:30 = 5.5/s Avg: 288 Min: 167 Max: 803 Err: 0 (0.00%) Active: 4 Started: 868 Finished: 864
...
...
summary = 412193 in 01:12:28 = 94.8/s Avg: 3311 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 178 in 00:00:30 = 5.9/s Avg: 334 Min: 169 Max: 959 Err: 0 (0.00%) Active: 3 Started: 4979 Finished: 4976
summary = 412371 in 01:12:58 = 94.2/s Avg: 3309 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 178 in 00:00:30 = 6.0/s Avg: 326 Min: 172 Max: 1072 Err: 0 (0.00%) Active: 2 Started: 5156 Finished: 5154
summary = 412549 in 01:13:28 = 93.6/s Avg: 3308 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 175 in 00:00:30 = 5.8/s Avg: 343 Min: 173 Max: 1066 Err: 0 (0.00%) Active: 3 Started: 5332 Finished: 5329
summary = 412724 in 01:13:58 = 93.0/s Avg: 3307 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 168 in 00:00:30 = 5.6/s Avg: 352 Min: 175 Max: 1057 Err: 0 (0.00%) Active: 4 Started: 5501 Finished: 5497
summary = 412892 in 01:14:28 = 92.4/s Avg: 3306 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 179 in 00:00:30 = 6.0/s Avg: 335 Min: 168 Max: 914 Err: 0 (0.00%) Active: 1 Started: 5677 Finished: 5676
summary = 413071 in 01:14:58 = 91.8/s Avg: 3304 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 178 in 00:00:30 = 5.9/s Avg: 306 Min: 169 Max: 984 Err: 0 (0.00%) Active: 2 Started: 5856 Finished: 5854
...
...
summary = 416548 in 01:24:58 = 81.7/s Avg: 3279 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 175 in 00:00:30 = 5.9/s Avg: 322 Min: 172 Max: 935 Err: 0 (0.00%) Active: 3 Started: 9331 Finished: 9328
summary = 416723 in 01:25:28 = 81.3/s Avg: 3278 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 172 in 00:00:30 = 5.7/s Avg: 320 Min: 163 Max: 902 Err: 0 (0.00%) Active: 6 Started: 9506 Finished: 9500
summary = 416895 in 01:25:58 = 80.8/s Avg: 3277 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 175 in 00:00:30 = 5.8/s Avg: 319 Min: 168 Max: 908 Err: 0 (0.00%) Active: 3 Started: 9678 Finished: 9675
summary = 417070 in 01:26:28 = 80.4/s Avg: 3276 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 170 in 00:00:30 = 5.7/s Avg: 321 Min: 165 Max: 1287 Err: 0 (0.00%) Active: 4 Started: 9849 Finished: 9845
summary = 417240 in 01:26:58 = 80.0/s Avg: 3275 Min: 19 Max: 116663 Err: 947 (0.23%)
summary + 154 in 00:00:26 = 5.9/s Avg: 325 Min: 165 Max: 979 Err: 0 (0.00%) Active: 0 Started: 10000 Finished: 10000
summary = 417394 in 01:27:24 = 79.6/s Avg: 3273 Min: 19 Max: 116663 Err: 947 (0.23%)
Tidying up ... @ Sun Jul 30 09:16:26 UTC 2017 (1501406186446)
... end of run
答案 0 :(得分：1)
放入Amazon instances specifications：
Model vCPU Mem (GiB) SSD Storage (GB)
m3.medium 1 3.75 1 x 4
我强烈怀疑您将能够在1秒内启动1000个线程，因为您的实例很可能会耗尽资源，请使用ie Amazon CloudWatch或JMeter's PerfMon Plugin
因此，请确保您的JMeter负载生成器有足够的备用资源，就像JMeter实例将被重载一样，它将无法足够快地发送请求，因此您将看到非常大的响应时间，尽管您的测试应用程序可能表现良好。
我建议在distributed mode中添加另一个实例并运行JMeter，这样你就应该处于更安全的位置。