我正在尝试对我们的客户端代码进行基准测试。因此，我决定编写一个多线程程序来对我的客户端代码进行基准测试。我试图测量以下方法将需要多少时间（95百分位）-
1）首先，使用20个线程和运行15分钟的多线程代码。我得到95百分位为37ms。我正在使用 -
以下是实现Runnable接口的类 -
attributes = deClient.getDEAttributes(columnsList);
下面是我编写的用于在上述方法上进行基准测试的多线程代码。我在两种情况下看到了很多变化 -1）首先，使用20个线程和运行15分钟的多线程代码。我得到95百分位为37ms。我正在使用 -
ExecutorService service = Executors.newFixedThreadPool(20);
2) 但是，如果我使用以下代码运行同样的程序 15分钟:
ExecutorService service = Executors.newSingleThreadExecutor();
而不是
ExecutorService service = Executors.newFixedThreadPool(20);
当使用newSingleThreadExecutor运行代码时，95％分位数仅为7ms，这远低于使用newFixedThreadPool（20）时的数字。
有人能告诉我使用以下两种方法出现高性能问题的原因是什么-
newSingleThreadExecutor vs newFixedThreadPool(20)
无论哪种方式，我都要运行我的程序15分钟。
以下是我的代码-
public static void main(String[] args) {
try {
// create thread pool with given size
//ExecutorService service = Executors.newFixedThreadPool(20);
ExecutorService service = Executors.newSingleThreadExecutor();
long startTime = System.currentTimeMillis();
long endTime = startTime + (15 * 60 * 1000);//Running for 15 minutes
for (int i = 0; i < threads; i++) {
service.submit(new ServiceTask(endTime, serviceList));
}
// wait for termination
service.shutdown();
service.awaitTermination(Long.MAX_VALUE, TimeUnit.DAYS);
} catch (InterruptedException e) {
} catch (Exception e) {
}
}
以下是实现Runnable接口的类 -
class ServiceTask implements Runnable {
private static final Logger LOG = Logger.getLogger(ServiceTask.class.getName());
private static Random random = new SecureRandom();
public static volatile AtomicInteger countSize = new AtomicInteger();
private final long endTime;
private final LinkedHashMap<String, ServiceInfo> tableLists;
public static ConcurrentHashMap<Long, Long> selectHistogram = new ConcurrentHashMap<Long, Long>();
public ServiceTask(long endTime, LinkedHashMap<String, ServiceInfo> tableList) {
this.endTime = endTime;
this.tableLists = tableList;
}
@Override
public void run() {
try {
while (System.currentTimeMillis() <= endTime) {
double randomNumber = random.nextDouble() * 100.0;
ServiceInfo service = selectRandomService(randomNumber);
final String id = generateRandomId(random);
final List<String> columnsList = getColumns(service.getColumns());
List<DEAttribute<?>> attributes = null;
DEKey bk = new DEKey(service.getKeys(), id);
List<DEKey> list = new ArrayList<DEKey>();
list.add(bk);
Client deClient = new Client(list);
final long start = System.nanoTime();
attributes = deClient.getDEAttributes(columnsList);
final long end = System.nanoTime() - start;
final long key = end / 1000000L;
boolean done = false;
while(!done) {
Long oldValue = selectHistogram.putIfAbsent(key, 1L);
if(oldValue != null) {
done = selectHistogram.replace(key, oldValue, oldValue + 1);
} else {
done = true;
}
}
countSize.getAndAdd(attributes.size());
handleDEAttribute(attributes);
if (BEServiceLnP.sleepTime > 0L) {
Thread.sleep(BEServiceLnP.sleepTime);
}
}
} catch (Exception e) {
}
}
}
更新：-
这是我的处理器规格- 我正在Linux机器上运行程序，定义为2个处理器：
vendor_id : GenuineIntel
cpu family : 6
model : 45
model name : Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz
stepping : 7
cpu MHz : 2599.999
cache size : 20480 KB
fpu : yes
fpu_exception : yes
cpuid level : 13
wp : yes
flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss syscall nx rdtscp lm constant_tsc arch_perfmon pebs bts rep_good xtopology tsc_reliable nonstop_tsc aperfmperf pni pclmulqdq ssse3 cx16 sse4_1 sse4_2 popcnt aes hypervisor lahf_lm arat pln pts
bogomips : 5199.99
clflush size : 64
cache_alignment : 64
address sizes : 40 bits physical, 48 bits virtual
power management: