Java ScheduledExecutorService BAD Precision [英] Java ScheduledExecutorService BAD Precision
本文介绍了Java ScheduledExecutorService BAD Precision的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
您好我已经编写了一个简单的程序来测试ScduledExecutorService.schedule（）函数的精度。
Hi there i've written a simple program to test the precision of the ScduledExecutorService.schedule() function.
测试设置延迟并检查所需延迟与有效结果之间的有效距离。
The test sets a delay and check the effective distance between the required delay and the effective result.
测试已在运行Linux 3.8 x86_64的i7机器上执行，包括OpenJDK 1.7和Oracle JDK 1.7
The test have been performed on a i7 machine running Linux 3.8 x86_64, with both OpenJDK 1.7 and Oracle JDK 1.7
测试结果非常糟糕，这里有一个列表向您显示推定和有效延迟之间的平均增量：
The result of the test is really bad, here there is a list to show you the average delta between presumed and effective delay:
图例：
- 睡眠（ms）：所需的延迟（以毫秒为单位）
- deltaAVG（ms）：以毫秒为单位获得的所需延迟和有效延迟之间的平均差异
- deltaAVG_PERC ：所需的错误百分比/有效
- delta MIN / MAX ：获得的所需延迟和有效延迟之间的最小/最大差异
- Sleep(ms): the delay required in milliseconds
- deltaAVG(ms): the average difference between the required and effective delay obtained in milliseconds
- deltaAVG_PERC: the error percentage required/effective
- delta MIN/MAX: the minimum / maximum difference between required and effective delay obtained
Sleep(ms): 0.010 deltaAVG(ms): 0.029 deltaAVG_PERC: 0289.6 % delta MIN/MAX (ms): 0.029/0.029
Sleep(ms): 0.020 deltaAVG(ms): 0.061 deltaAVG_PERC: 0299.3 % delta MIN/MAX (ms): 0.006/4.578
Sleep(ms): 0.030 deltaAVG(ms): 0.066 deltaAVG_PERC: 0221.1 % delta MIN/MAX (ms): 0.009/1.639
Sleep(ms): 0.040 deltaAVG(ms): 0.066 deltaAVG_PERC: 0165.3 % delta MIN/MAX (ms): 0.010/0.445
Sleep(ms): 0.050 deltaAVG(ms): 0.069 deltaAVG_PERC: 0138.0 % delta MIN/MAX (ms): 0.006/0.370
Sleep(ms): 0.060 deltaAVG(ms): 0.067 deltaAVG_PERC: 0111.8 % delta MIN/MAX (ms): 0.052/0.700
Sleep(ms): 0.070 deltaAVG(ms): 0.067 deltaAVG_PERC: 0096.0 % delta MIN/MAX (ms): 0.053/5.597
Sleep(ms): 0.080 deltaAVG(ms): 0.067 deltaAVG_PERC: 0083.6 % delta MIN/MAX (ms): 0.054/1.978
Sleep(ms): 0.090 deltaAVG(ms): 0.065 deltaAVG_PERC: 0072.8 % delta MIN/MAX (ms): 0.034/1.548
Sleep(ms): 0.100 deltaAVG(ms): 0.066 deltaAVG_PERC: 0066.3 % delta MIN/MAX (ms): 0.026/1.969
Sleep(ms): 0.110 deltaAVG(ms): 0.065 deltaAVG_PERC: 0058.7 % delta MIN/MAX (ms): 0.036/1.075
Sleep(ms): 0.120 deltaAVG(ms): 0.064 deltaAVG_PERC: 0053.5 % delta MIN/MAX (ms): 0.012/0.454
Sleep(ms): 0.130 deltaAVG(ms): 0.067 deltaAVG_PERC: 0051.6 % delta MIN/MAX (ms): 0.038/1.523
Sleep(ms): 0.140 deltaAVG(ms): 0.066 deltaAVG_PERC: 0047.0 % delta MIN/MAX (ms): 0.038/0.544
Sleep(ms): 0.150 deltaAVG(ms): 0.066 deltaAVG_PERC: 0044.0 % delta MIN/MAX (ms): 0.031/0.498
Sleep(ms): 0.160 deltaAVG(ms): 0.065 deltaAVG_PERC: 0040.4 % delta MIN/MAX (ms): 0.022/0.432
Sleep(ms): 0.170 deltaAVG(ms): 0.067 deltaAVG_PERC: 0039.6 % delta MIN/MAX (ms): 0.031/0.229
Sleep(ms): 0.180 deltaAVG(ms): 0.071 deltaAVG_PERC: 0039.3 % delta MIN/MAX (ms): 0.046/0.291
Sleep(ms): 0.190 deltaAVG(ms): 0.074 deltaAVG_PERC: 0039.1 % delta MIN/MAX (ms): 0.042/1.069
Sleep(ms): 0.200 deltaAVG(ms): 0.071 deltaAVG_PERC: 0035.5 % delta MIN/MAX (ms): 0.031/0.293
Sleep(ms): 0.210 deltaAVG(ms): 0.072 deltaAVG_PERC: 0034.3 % delta MIN/MAX (ms): 0.028/1.058
Sleep(ms): 0.220 deltaAVG(ms): 0.075 deltaAVG_PERC: 0034.0 % delta MIN/MAX (ms): 0.055/1.879
Sleep(ms): 0.230 deltaAVG(ms): 0.075 deltaAVG_PERC: 0032.5 % delta MIN/MAX (ms): 0.040/0.514
Sleep(ms): 0.240 deltaAVG(ms): 0.075 deltaAVG_PERC: 0031.4 % delta MIN/MAX (ms): 0.055/1.715
Sleep(ms): 0.250 deltaAVG(ms): 0.075 deltaAVG_PERC: 0030.2 % delta MIN/MAX (ms): 0.044/1.025
Sleep(ms): 0.260 deltaAVG(ms): 0.076 deltaAVG_PERC: 0029.2 % delta MIN/MAX (ms): 0.038/1.561
Sleep(ms): 0.270 deltaAVG(ms): 0.076 deltaAVG_PERC: 0028.1 % delta MIN/MAX (ms): 0.050/0.697
Sleep(ms): 0.280 deltaAVG(ms): 0.075 deltaAVG_PERC: 0026.8 % delta MIN/MAX (ms): 0.039/0.996
Sleep(ms): 0.290 deltaAVG(ms): 0.076 deltaAVG_PERC: 0026.3 % delta MIN/MAX (ms): 0.032/0.475
Sleep(ms): 0.300 deltaAVG(ms): 0.077 deltaAVG_PERC: 0025.6 % delta MIN/MAX (ms): 0.055/2.136
Sleep(ms): 0.310 deltaAVG(ms): 0.077 deltaAVG_PERC: 0024.9 % delta MIN/MAX (ms): 0.042/0.373
Sleep(ms): 0.320 deltaAVG(ms): 0.079 deltaAVG_PERC: 0024.6 % delta MIN/MAX (ms): 0.036/2.622
Sleep(ms): 0.330 deltaAVG(ms): 0.080 deltaAVG_PERC: 0024.3 % delta MIN/MAX (ms): 0.038/1.367
Sleep(ms): 0.340 deltaAVG(ms): 0.080 deltaAVG_PERC: 0023.5 % delta MIN/MAX (ms): 0.028/0.308
Sleep(ms): 0.350 deltaAVG(ms): 0.079 deltaAVG_PERC: 0022.7 % delta MIN/MAX (ms): 0.055/1.885
Sleep(ms): 0.360 deltaAVG(ms): 0.076 deltaAVG_PERC: 0021.1 % delta MIN/MAX (ms): 0.053/0.403
Sleep(ms): 0.370 deltaAVG(ms): 0.079 deltaAVG_PERC: 0021.3 % delta MIN/MAX (ms): 0.056/0.390
Sleep(ms): 0.380 deltaAVG(ms): 0.079 deltaAVG_PERC: 0020.9 % delta MIN/MAX (ms): 0.055/3.777
Sleep(ms): 0.390 deltaAVG(ms): 0.081 deltaAVG_PERC: 0020.9 % delta MIN/MAX (ms): 0.058/0.320
Sleep(ms): 0.400 deltaAVG(ms): 0.080 deltaAVG_PERC: 0019.9 % delta MIN/MAX (ms): 0.056/0.203
Sleep(ms): 0.410 deltaAVG(ms): 0.082 deltaAVG_PERC: 0019.9 % delta MIN/MAX (ms): 0.051/0.562
Sleep(ms): 0.420 deltaAVG(ms): 0.082 deltaAVG_PERC: 0019.6 % delta MIN/MAX (ms): 0.056/0.913
Sleep(ms): 0.430 deltaAVG(ms): 0.080 deltaAVG_PERC: 0018.6 % delta MIN/MAX (ms): 0.053/0.938
Sleep(ms): 0.440 deltaAVG(ms): 0.085 deltaAVG_PERC: 0019.4 % delta MIN/MAX (ms): 0.055/0.582
Sleep(ms): 0.450 deltaAVG(ms): 0.086 deltaAVG_PERC: 0019.1 % delta MIN/MAX (ms): 0.041/0.179
Sleep(ms): 0.460 deltaAVG(ms): 0.083 deltaAVG_PERC: 0018.0 % delta MIN/MAX (ms): 0.032/0.235
Sleep(ms): 0.470 deltaAVG(ms): 0.088 deltaAVG_PERC: 0018.6 % delta MIN/MAX (ms): 0.042/0.581
Sleep(ms): 0.480 deltaAVG(ms): 0.088 deltaAVG_PERC: 0018.3 % delta MIN/MAX (ms): 0.040/0.477
Sleep(ms): 0.490 deltaAVG(ms): 0.086 deltaAVG_PERC: 0017.5 % delta MIN/MAX (ms): 0.032/0.931
Sleep(ms): 0.500 deltaAVG(ms): 0.088 deltaAVG_PERC: 0017.5 % delta MIN/MAX (ms): 0.055/0.521
Sleep(ms): 0.510 deltaAVG(ms): 0.081 deltaAVG_PERC: 0016.0 % delta MIN/MAX (ms): 0.056/0.225
Sleep(ms): 0.520 deltaAVG(ms): 0.088 deltaAVG_PERC: 0016.9 % delta MIN/MAX (ms): 0.055/0.344
Sleep(ms): 0.530 deltaAVG(ms): 0.085 deltaAVG_PERC: 0016.0 % delta MIN/MAX (ms): 0.035/0.819
Sleep(ms): 0.540 deltaAVG(ms): 0.084 deltaAVG_PERC: 0015.6 % delta MIN/MAX (ms): 0.026/0.961
Sleep(ms): 0.550 deltaAVG(ms): 0.093 deltaAVG_PERC: 0016.9 % delta MIN/MAX (ms): 0.058/0.570
Sleep(ms): 0.560 deltaAVG(ms): 0.085 deltaAVG_PERC: 0015.3 % delta MIN/MAX (ms): 0.033/0.176
Sleep(ms): 0.570 deltaAVG(ms): 0.090 deltaAVG_PERC: 0015.8 % delta MIN/MAX (ms): 0.043/0.289
Sleep(ms): 0.580 deltaAVG(ms): 0.087 deltaAVG_PERC: 0014.9 % delta MIN/MAX (ms): 0.041/0.258
Sleep(ms): 0.590 deltaAVG(ms): 0.082 deltaAVG_PERC: 0013.9 % delta MIN/MAX (ms): 0.057/0.352
Sleep(ms): 0.600 deltaAVG(ms): 0.083 deltaAVG_PERC: 0013.9 % delta MIN/MAX (ms): 0.060/0.393
Sleep(ms): 0.610 deltaAVG(ms): 0.084 deltaAVG_PERC: 0013.8 % delta MIN/MAX (ms): 0.059/0.177
Sleep(ms): 0.620 deltaAVG(ms): 0.095 deltaAVG_PERC: 0015.3 % delta MIN/MAX (ms): 0.041/0.273
Sleep(ms): 0.630 deltaAVG(ms): 0.080 deltaAVG_PERC: 0012.6 % delta MIN/MAX (ms): 0.059/0.253
Sleep(ms): 0.640 deltaAVG(ms): 0.085 deltaAVG_PERC: 0013.3 % delta MIN/MAX (ms): 0.060/0.422
Sleep(ms): 0.650 deltaAVG(ms): 0.100 deltaAVG_PERC: 0015.4 % delta MIN/MAX (ms): 0.050/0.641
Sleep(ms): 0.660 deltaAVG(ms): 0.090 deltaAVG_PERC: 0013.7 % delta MIN/MAX (ms): 0.058/0.170
Sleep(ms): 0.670 deltaAVG(ms): 0.097 deltaAVG_PERC: 0014.5 % delta MIN/MAX (ms): 0.055/0.578
Sleep(ms): 0.680 deltaAVG(ms): 0.094 deltaAVG_PERC: 0013.8 % delta MIN/MAX (ms): 0.060/3.560
Sleep(ms): 0.690 deltaAVG(ms): 0.092 deltaAVG_PERC: 0013.3 % delta MIN/MAX (ms): 0.059/0.178
Sleep(ms): 0.700 deltaAVG(ms): 0.094 deltaAVG_PERC: 0013.4 % delta MIN/MAX (ms): 0.060/0.202
Sleep(ms): 0.710 deltaAVG(ms): 0.102 deltaAVG_PERC: 0014.3 % delta MIN/MAX (ms): 0.056/0.227
Sleep(ms): 0.720 deltaAVG(ms): 0.084 deltaAVG_PERC: 0011.7 % delta MIN/MAX (ms): 0.060/0.177
Sleep(ms): 0.730 deltaAVG(ms): 0.099 deltaAVG_PERC: 0013.5 % delta MIN/MAX (ms): 0.046/0.723
Sleep(ms): 0.740 deltaAVG(ms): 0.098 deltaAVG_PERC: 0013.2 % delta MIN/MAX (ms): 0.058/0.203
Sleep(ms): 0.750 deltaAVG(ms): 0.104 deltaAVG_PERC: 0013.9 % delta MIN/MAX (ms): 0.059/0.274
Sleep(ms): 0.760 deltaAVG(ms): 0.105 deltaAVG_PERC: 0013.8 % delta MIN/MAX (ms): 0.056/0.274
Sleep(ms): 0.770 deltaAVG(ms): 0.104 deltaAVG_PERC: 0013.5 % delta MIN/MAX (ms): 0.056/0.631
Sleep(ms): 0.780 deltaAVG(ms): 0.099 deltaAVG_PERC: 0012.7 % delta MIN/MAX (ms): 0.044/0.191
Sleep(ms): 0.790 deltaAVG(ms): 0.099 deltaAVG_PERC: 0012.5 % delta MIN/MAX (ms): 0.041/0.167
Sleep(ms): 0.800 deltaAVG(ms): 0.104 deltaAVG_PERC: 0013.0 % delta MIN/MAX (ms): 0.044/0.223
Sleep(ms): 0.810 deltaAVG(ms): 0.095 deltaAVG_PERC: 0011.7 % delta MIN/MAX (ms): 0.060/0.761
Sleep(ms): 0.820 deltaAVG(ms): 0.101 deltaAVG_PERC: 0012.3 % delta MIN/MAX (ms): 0.058/0.231
Sleep(ms): 0.830 deltaAVG(ms): 0.102 deltaAVG_PERC: 0012.3 % delta MIN/MAX (ms): 0.060/0.552
Sleep(ms): 0.840 deltaAVG(ms): 0.106 deltaAVG_PERC: 0012.6 % delta MIN/MAX (ms): 0.060/0.517
Sleep(ms): 0.850 deltaAVG(ms): 0.109 deltaAVG_PERC: 0012.9 % delta MIN/MAX (ms): 0.061/0.204
Sleep(ms): 0.860 deltaAVG(ms): 0.107 deltaAVG_PERC: 0012.5 % delta MIN/MAX (ms): 0.062/0.532
Sleep(ms): 0.870 deltaAVG(ms): 0.109 deltaAVG_PERC: 0012.5 % delta MIN/MAX (ms): 0.061/0.266
Sleep(ms): 0.880 deltaAVG(ms): 0.108 deltaAVG_PERC: 0012.3 % delta MIN/MAX (ms): 0.057/0.753
Sleep(ms): 0.890 deltaAVG(ms): 0.108 deltaAVG_PERC: 0012.2 % delta MIN/MAX (ms): 0.060/0.553
Sleep(ms): 0.900 deltaAVG(ms): 0.108 deltaAVG_PERC: 0011.9 % delta MIN/MAX (ms): 0.056/0.369
Sleep(ms): 0.910 deltaAVG(ms): 0.106 deltaAVG_PERC: 0011.6 % delta MIN/MAX (ms): 0.057/0.213
Sleep(ms): 0.920 deltaAVG(ms): 0.107 deltaAVG_PERC: 0011.6 % delta MIN/MAX (ms): 0.057/0.185
Sleep(ms): 0.930 deltaAVG(ms): 0.107 deltaAVG_PERC: 0011.5 % delta MIN/MAX (ms): 0.044/0.842
Sleep(ms): 0.940 deltaAVG(ms): 0.111 deltaAVG_PERC: 0011.8 % delta MIN/MAX (ms): 0.064/0.395
Sleep(ms): 0.950 deltaAVG(ms): 0.108 deltaAVG_PERC: 0011.4 % delta MIN/MAX (ms): 0.061/0.207
Sleep(ms): 0.960 deltaAVG(ms): 0.110 deltaAVG_PERC: 0011.5 % delta MIN/MAX (ms): 0.042/0.215
Sleep(ms): 0.970 deltaAVG(ms): 0.107 deltaAVG_PERC: 0011.0 % delta MIN/MAX (ms): 0.049/0.646
Sleep(ms): 0.980 deltaAVG(ms): 0.110 deltaAVG_PERC: 0011.2 % delta MIN/MAX (ms): 0.059/0.317
Sleep(ms): 0.990 deltaAVG(ms): 0.109 deltaAVG_PERC: 0011.0 % delta MIN/MAX (ms): 0.061/0.205
Sleep(ms): 1.000 deltaAVG(ms): 0.103 deltaAVG_PERC: 0010.3 % delta MIN/MAX (ms): 0.052/0.283
Sleep(ms): 1.010 deltaAVG(ms): 0.109 deltaAVG_PERC: 0010.8 % delta MIN/MAX (ms): 0.058/0.295
Sleep(ms): 1.020 deltaAVG(ms): 0.107 deltaAVG_PERC: 0010.5 % delta MIN/MAX (ms): 0.063/0.562
Sleep(ms): 1.030 deltaAVG(ms): 0.105 deltaAVG_PERC: 0010.2 % delta MIN/MAX (ms): 0.060/0.256
Sleep(ms): 1.040 deltaAVG(ms): 0.110 deltaAVG_PERC: 0010.6 % delta MIN/MAX (ms): 0.059/0.231
Sleep(ms): 1.050 deltaAVG(ms): 0.110 deltaAVG_PERC: 0010.5 % delta MIN/MAX (ms): 0.059/0.570
Sleep(ms): 1.060 deltaAVG(ms): 0.109 deltaAVG_PERC: 0010.2 % delta MIN/MAX (ms): 0.059/0.210
Sleep(ms): 1.070 deltaAVG(ms): 0.110 deltaAVG_PERC: 0010.3 % delta MIN/MAX (ms): 0.035/0.460
Sleep(ms): 1.080 deltaAVG(ms): 0.110 deltaAVG_PERC: 0010.2 % delta MIN/MAX (ms): 0.062/0.189
Sleep(ms): 1.090 deltaAVG(ms): 0.110 deltaAVG_PERC: 0010.1 % delta MIN/MAX (ms): 0.058/0.228
Sleep(ms): 1.100 deltaAVG(ms): 0.111 deltaAVG_PERC: 0010.0 % delta MIN/MAX (ms): 0.061/0.513
Sleep(ms): 1.110 deltaAVG(ms): 0.110 deltaAVG_PERC: 0009.9 % delta MIN/MAX (ms): 0.052/0.200
Sleep(ms): 1.120 deltaAVG(ms): 0.110 deltaAVG_PERC: 0009.9 % delta MIN/MAX (ms): 0.048/0.248
Sleep(ms): 1.130 deltaAVG(ms): 0.108 deltaAVG_PERC: 0009.6 % delta MIN/MAX (ms): 0.061/0.570
Sleep(ms): 1.140 deltaAVG(ms): 0.111 deltaAVG_PERC: 0009.7 % delta MIN/MAX (ms): 0.065/0.184
Sleep(ms): 1.150 deltaAVG(ms): 0.112 deltaAVG_PERC: 0009.7 % delta MIN/MAX (ms): 0.063/0.449
Sleep(ms): 1.160 deltaAVG(ms): 0.109 deltaAVG_PERC: 0009.4 % delta MIN/MAX (ms): 0.049/0.298
Sleep(ms): 1.170 deltaAVG(ms): 0.107 deltaAVG_PERC: 0009.1 % delta MIN/MAX (ms): 0.059/0.212
Sleep(ms): 1.180 deltaAVG(ms): 0.107 deltaAVG_PERC: 0009.1 % delta MIN/MAX (ms): 0.060/0.224
Sleep(ms): 1.190 deltaAVG(ms): 0.114 deltaAVG_PERC: 0009.6 % delta MIN/MAX (ms): 0.061/0.217
Sleep(ms): 1.200 deltaAVG(ms): 0.109 deltaAVG_PERC: 0009.1 % delta MIN/MAX (ms): 0.058/0.231
Sleep(ms): 1.210 deltaAVG(ms): 0.115 deltaAVG_PERC: 0009.5 % delta MIN/MAX (ms): 0.061/0.237
Sleep(ms): 1.220 deltaAVG(ms): 0.108 deltaAVG_PERC: 0008.8 % delta MIN/MAX (ms): 0.063/0.207
Sleep(ms): 1.230 deltaAVG(ms): 0.107 deltaAVG_PERC: 0008.7 % delta MIN/MAX (ms): 0.059/0.355
Sleep(ms): 1.240 deltaAVG(ms): 0.113 deltaAVG_PERC: 0009.1 % delta MIN/MAX (ms): 0.059/0.197
Sleep(ms): 1.250 deltaAVG(ms): 0.114 deltaAVG_PERC: 0009.1 % delta MIN/MAX (ms): 0.059/0.235
Sleep(ms): 1.260 deltaAVG(ms): 0.113 deltaAVG_PERC: 0009.0 % delta MIN/MAX (ms): 0.061/0.219
Sleep(ms): 1.270 deltaAVG(ms): 0.113 deltaAVG_PERC: 0008.9 % delta MIN/MAX (ms): 0.060/0.284
Sleep(ms): 1.280 deltaAVG(ms): 0.112 deltaAVG_PERC: 0008.8 % delta MIN/MAX (ms): 0.060/0.222
Sleep(ms): 1.290 deltaAVG(ms): 0.114 deltaAVG_PERC: 0008.9 % delta MIN/MAX (ms): 0.063/0.182
Sleep(ms): 1.300 deltaAVG(ms): 0.112 deltaAVG_PERC: 0008.6 % delta MIN/MAX (ms): 0.058/0.209
正如您所看到的，名为deltaAVG的平均误差相对于延迟而言正在增加。
As you can see the average Error called deltaAVG is increasing relatively to the delay.
如何才能在延迟时获得更好的结果？我的意思是i7机器上10微秒的300％错误率太高了。
How is possible to get a better result in delay? I mean 300% error rate on 10 microsecond on i7 machine is too much.
这里是我用来测试的代码：
here is the code i used for testing:
package threadexecutor_perftest;
import java.text.DecimalFormat;
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.ThreadFactory;
import java.util.concurrent.TimeUnit;
import statistica.timedValuesAverage;
/**
*
* @author salvatore novelli salvatore.novelli domain gmail.com
*/
public class ThreadExecutor_PerfTest implements Runnable, ThreadFactory {
private final ScheduledExecutorService executor;
private long start;
private long stop;
private long delay_nano = 10000;
private final int averageTimeLen_ms = 2000;
private TimedValuesAverage<Double> deltaAVG = new TimedValuesAverage<>(averageTimeLen_ms);
DecimalFormat int3 = new DecimalFormat("0.000");
DecimalFormat int4 = new DecimalFormat("0000.0");
/**
* @param args the command line arguments
*/
public static void main(String[] args) {
ThreadExecutor_PerfTest test = new ThreadExecutor_PerfTest();
test.start();
}
public ThreadExecutor_PerfTest() {
executor = Executors.newSingleThreadScheduledExecutor(this);
}
public boolean start() {
executor.schedule(this, 0L, TimeUnit.NANOSECONDS);
return true;
}
private long DBG_lastReport;
@Override
public void run() {
stop = System.nanoTime();
if (start > 0) {
long deltaT = (stop - start) - delay_nano;
deltaAVG.put((double) deltaT);
//report status every averageTimeLen_ms
if ((System.currentTimeMillis() - DBG_lastReport) > averageTimeLen_ms) {
System.out.println(" Sleep(ms): " + int3.format(delay_nano / 1000000.0)
+ " deltaAVG(ms): " + int3.format(deltaAVG.getAverage() / 1000000.0)
+ " deltaAVG_PERC: " + int4.format((deltaAVG.getAverage() / delay_nano) * 100)+" %"
+ " delta MIN/MAX (ms): " + int3.format(deltaAVG.getSmallestEver() / 1000000.0) + "/" + int3.format(deltaAVG.getGreatestEver() / 1000000.0));
//increase delay by 10 micro seconds (1000 nano seconds)
delay_nano += 10000;
deltaAVG = new TimedValuesAverage<>(averageTimeLen_ms);
DBG_lastReport = System.currentTimeMillis();
}
}
start = System.nanoTime();
executor.schedule(this, delay_nano, TimeUnit.NANOSECONDS);
}
@Override
public Thread newThread(Runnable r) {
Thread t = new Thread(r, "Exec-test");
t.setPriority(Thread.MAX_PRIORITY);
return t;
}
}
推荐答案
Executors.newSingleThreadScheduledExecutor（this）; 在引擎盖下使用 ScheduledThreadPoolExecutor 。在该类的JavaDocs中，它指出：
Executors.newSingleThreadScheduledExecutor(this); uses a ScheduledThreadPoolExecutor under the hood. In the JavaDocs for that class it states:
延迟任务在启用之前执行，但没有任何
实时 - 时间保证在启用后，他们将开始
。计划执行时间完全相同的任务是以先进先出（FIFO）提交顺序启用
。
Delayed tasks execute no sooner than they are enabled, but without any real-time guarantees about when, after they are enabled, they will commence. Tasks scheduled for exactly the same execution time are enabled in first-in-first-out (FIFO) order of submission.
<正如n1ckolas所指出的那样，你将难以在纯Java中获得这种精确度。
As n1ckolas has pointed out, you are going to have difficulty trying to get this kind of precision in pure Java.
然而，有 some 事物，它们可能比ScheduledThreadPoolExecutor更准确，尽管它们的准确性将取决于操作系统，硬件等。
However, there are some things you can try which might be more accurate than a ScheduledThreadPoolExecutor although their accuracy will be dependent on the OS, the hardware, etc.
这篇关于Java ScheduledExecutorService BAD Precision的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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