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<Future<Long>> results = new ArrayList<Future<Long>>();
- for (int i = 0; i < times; i++) {
- results.add(executor.submit(new Callable<Long>() {
- @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<Long> 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