java 多线程压测_［转］比较Jmeter、Grinder和JAVA多线程本身压力测试所带来的性能开销...
1. 测试环境
jmeter版本 ：jmeter 2.4
grinder的版本 ： Grinder 3
JAVA的版本：JDK 1.6
2. 测试代码
Jmeter测试代码
public class Sampler {
public void test() {
return;
}
}
public class JmeterTest extends AbstractJavaSamplerClient {
Sampler sampler;
@Override
public SampleResult runTest(JavaSamplerContext context) {
SampleResult results = new SampleResult();
results.sampleStart();
sampler.test();
results.sampleEnd();
results.setSuccessful(true);
return results;
}
@Override
public void setupTest(JavaSamplerContext arg0) {
sampler = new Sampler();
}
}
grinder测试代码
public class Sampler {
public void test() {
return;
}
}
# Test.py
#
# A minimal script that tests The Grinder logging facility.
#
# This script shows the recommended style for scripts, with a
# TestRunner class. The script is executed just once by each worker
# process and defines the TestRunner class. The Grinder creates an
# instance of TestRunner for each worker thread, and repeatedly calls
# the instance for each run of that thread.
from net.grinder.script.Grinder import grinder
from net.grinder.script import Test
from sampler import Sampler
test = Test(1, "Sample")
class TestRunner:
# This method is called for every run.
def __call__(self):
mySampler = test.wrap(Sampler())
mySampler.test()
Java本身多线程
public static void test(int numOfThreads, int times) throws InterruptedException, ExecutionException {
ExecutorService executor = Executors.newFixedThreadPool(numOfThreads);
final Sampler sampler = new Sampler();
List> results = new ArrayList>();
for (int i = 0; i
results.add(executor.submit(new Callable() {
@Override
public Long call() throws Exception {
long begin = System.currentTimeMillis();
sampler.test();
long end = System.currentTimeMillis();
return end - begin;
}
}));
}
executor.shutdown();
while(!executor.awaitTermination(1, TimeUnit.SECONDS));
long sum = 0;
for (Future result : results) {
sum += result.get();
}
System.out.println("---------------------------------");
System.out.println("number of threads :" + numOfThreads + " times:" + times);
System.out.println("running time: " + sum + "ms");
System.out.println("TPS: " + (double)(100000 * 1000) / (double)(sum));
System.out.println();
}
3. 测试结果
10个线程 100000次运行
--
TPS
Jmeter
50426.10
Grinder
290275.76
Java threads
2.5E7
20个线程 100000次运行
--
TPS
Jmeter
49215.02
Grinder
225402.91
Java threads
2.5E7
50个线程 100000次运行
--
TPS
Jmeter
29312.61
Grinder
212242.13
Java threads
2.5E7
100个线程 100000次运行
--
TPS
Jmeter
29031.03
Grinder
245507.22
Java threads
2.5E7
200个线程 10000次运行(这里减少了一个0)
--
TPS
Jmeter
28039.87
Grinder
232801.77
Java threads
2.5E7
300个线程 10000次运行
--
TPS
Jmeter
27208.16
Grinder
236537.10
Java threads
1818181.81
1000个线程 10000次运行
--
TPS
Jmeter
27208.16
Grinder
236537.10
Java threads
2.5E7
4. 结论
可以看出Jmeter的本身性能开销是很大的，只适合一般应用的性能测试
Grinder在测试的时候发现上下文切换比较严重，而可能是因为内部机制导致的开销较大的，当然如果测试memcache肯定是不适合的，但一般的应用测试基本上没有问题
JAVA多线程本身并发框架性能开销也是有的，但是比较低，适合要求较高的性能测试，如对redis和memcache构建的应用进行压测
转：http://blog.csdn.net/techq/article/details/6628533